Failaka Island is located 20 kilometers east of the Kuwait mainland. The island includes archaeological sites dating back to the Bronze, Hellenistic, Christian, and Islamic ages. To develop the island as a tourist attraction the state is pursuing a new urban plan based on the island's environmental potential. This study is the basis of the urban plan depends on environmental criteria from the view of Geoarchaeology. The study analysis the land-use and land-cover changes of Failaka Island between 1958- 2018. It provides a topographic survey of the island's coastline and a classification of its geomorphological features and a state of the art identification of its archaeological sites by using a drone to make a terrain model. The study used a medium to high-resolution image analysis of the land-use and land-cover changes: WorldView2-50cm 2010 and 2018; Landsat 8; aerial photography; Drone images and a digital elevation model (DEM), to analysis the expected sea level changes by the end of this century. This study also created a geodatabase of the island that can be adapted for future studies. The results emphasize the importance of preserving the historical and ecological features of the island while developing its infrastructure.

Occupational incidents in construction workplaces present a persistent challenge, with severe consequences for both workers and project outcomes. This study aims to examine the specific causal factors contributing to these incidents, focusing on the interaction between worker-related, environmental, and procedural factors. The objective is to identify key contributors to workplace accidents and develop a unified predictive model to mitigate future risks. To achieve this, we employed a machine learning approach on a comprehensive accidentology dataset collected from contractors across multiple construction sites. The dataset includes variables such as worker ex-perience, environmental conditions, adherence to safety protocols, and more. By analyzing direct, indirect, and root causes, the methodology uncovers hidden patterns and interdependencies that traditional analysis might overlook. The study’s findings indicate that worker experience and environmental factors are the most significant contributors to incident occurrence, with a clear interaction effect between these variables. The results not only confirm previous research but also offer enhanced predictive capabilities for future safety measures. This research demonstrates the value of machine learning in generating data-driven insights, ultimately aiding in the develop-ment of targeted interventions to improve safety standards in the construction industry.

This paper aims to investigate carbon monoxide (CO) concentrations on roadways of Karachi, potential blood levels carboxy-hemoglobin (COHb) in Karachi. Geographical information system (GIS) was used for spatial analysis of diseases potentiality while an interpolation technique has been applied for surface generation with town boundaries and later evaluates risk areas.The higher concentration of carbon monoxide in the ambient is mainly due to automobile emissions. The City center and CBD areas are more perilous.

Improved oil recovery from tight carbonate formations may provide the world with a major source of lower-rate power over several decades. Here we provide an overview of the Arab D formation in the largest oil field on earth, the Ghawar. We investigate the occurrence of microporosity of different origins and sizes using scanning electron microscopy (SEM) and pore casting techniques. Then, we present a robust calculation of the probability of invasion and oil saturation distribution in the nested micropores using mercury injection capillary pressure data available in the literature. We show that large portions of the micropores in Arab D formation would have been bypassed during primary drainage unless the invading crude oil ganglia were sufficiently long. Considering the asphaltenic nature of oil in the Ghawar, we expect the invaded portions of the pores to turn mixed-wet, thus becoming inaccessible to waterflooding until further measures are taken to modify the system’s chemistry.

Embracing Fourth Industrial Revolution(4IR) techniques is a need for a long-term sustainability and to do automation in the processes of Industrial and education sector. Rapid advancement in the information technology and other engineering technologies demand more proactive steps in the education institutes. In the post pandemic world, there is an increasing demand of changes in teaching and learning methodologies with the support of modern digital technologies. So, all of the teaching institutes need to have a significant step change in the areas of learning and teaching. The impact of 4 IR is more on the education sectors compared to previous generation’s industrial revolution. Keeping track of key technologies can be quite a challenge as they keep evolving at a very fast pace. Fourth Industrial revolution is the current technological digital revolution in which major focus is on cyber physical and biological systems such as Artificial Intelligence, Robotics, Internet on Things, Virtual Reality etc., The educators and institutions should establish the pedagogies and teaching with the aid of Industry 4.0 techniques to evolve education 4.0. So, it is very important to understand the challenges and competencies required about the implementation of 4IR by the faculty members and students who are currently studying in Higher Education Institutes (HEIs). This paper aim is to do a systematic review about industry 4.0 techniques and its application in relate to the online learning and teaching, change in pedagogies, enhancement of data management in HEIs, digital and virtual laboratories etc.

Flash flooding is one of the most devastating natural events that leads to enormous and recurring loss of life. Kuwait was subjected to severe rainstorms in the winter of 2018 and 2020 followed by an extreme violent flood that had not been known in Kuwait since 1976. It resulted in several geomorphological and environmental impacts in urban and desert areas. This produced some positive results, such as geomorphological activity in landforms, the flow of some valleys and the prosperity of wildlife in the Kuwaiti desert. Negative results included some problems in the metropolitan area and destruction of some road networks that intersect the main valleys, and which were not equipped with crossings for avoiding floods. There was also the emergence of some problems in the infrastructure. Study of flash floods requires the involvement of all scientific and executive bodies to avoid environmental risk. The study aims to: 1- Monitor geomorphological and environmental changes. 2- Assess the impact of floods in the urban areas and on infrastructure. 3- Modeling the impact. 4- Creating solutions and adaptions to the flash flood. The study uses several methods such as remote sensing (RS), geographic information systems (GIS), hydrologic modeling and fieldwork to evaluate the impact of flash flood hazards on the sustainable urban development of Kuwait state. This approach is rarely used in Kuwait. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban developments in Kuwait, and help them to manage flood water, by identifying the most appropriate places for storage to exploit water in agriculture and drinking.

Influence of the volute design parameters on the stable operating range and the performance of a centrifugal compressor was investigated experimentally for the following design conditions. The volute casing surface roughness was changed from 0 to 200 µm in five steps and tested. The axial distance between the casing and the diffuser cascades (clearance) was varied in five steps as C/b₂ = 1.2, 2.4%, 3.6%, 4.8%, and 6% relative to the impeller exit width. The radial distance from the impeller blade exit to the diffuser vane inlet in (vaneless space radius) in six steps as C/r₂ = 1.03, 1.05, 1.07, 1.09, 1.11, and 1. 13 relatives to the rotor outlet radius. The volute outlet geometry of a symmetric and tangent with different area ratios from 0.4 to 0.9. Finally, the compressor has been tested experimentally with different pinched vaned diffuser distances relative to the impeller exit width of bp/b2 =1, 0.98, 0.96, 0.94, 0.92, and 0.9. These testes were carried at different compressor operating conditions, and the time variations of the static pressure were recorded using five high-frequency response pressure transducers. A hot-wire anemometer is used to measure the compressor flow rate. The rotating stall and surge at the vaneless zone after the impeller exit can be detected by analyzing the fluctuations of pressure signals and the power spectrum density by using the Fast Fourier Transformation analysis. The experimental results indicated that the compressor with volute casing surface roughness of 100mm gives about 10.9% enhancements in the stable operating and 1% in pressure rise coefficient. The best axial distance between the diffuser vanes and the casing is 2.4% of the impeller blade height and the compressor in this design gives about 12.4% improvements in the stable working range. The compressor with radial vaneless distance of 1.056 gives improvements of about 16.6% in the surge margin, 2% in the pressure recovery and 4% in pressure rise coefficients. The compressor with a pinched diffuser of b/bp of 0.98 gives an improvement of 20.8% in surge point.

Recently, most countries suffering from global warming due to the waste gases coming out of the combustion engines used to generate electric power by traditional methods. Therefore, many countries are currently trying to find alternative solutions to this problem by using renewable energies such as solar energy, wind energy, and energy generated from the waves of the ocean and sea to overcome pollution problems. The waves of the seas and oceans have enormous and largely untapped energy, so this work presents an efficient way to use the energy of sea waves to generate electricity. And also, trying to be a way to produce electricity from wave energy in the future. The best suitable places along the shores of Jizan city were inspected to install the buoy system to revenue advantage of the wave supreme height to achieve the height amount of electrical energy. A mathematical model was made to analyze the wave energy and convert it into energy extracted by mechanical force. The mathematical analyzes used the data collected from satellite maps of the numerous severe waves in the Red sea of the Jazan area, and the data published in previous research along that area. It was found that the beach of the Al Shuqaiq area is the greatest for installing the buoy and obtaining the highest electrical energy due to the presence of the highest wave intensity, followed by the beach of the Baysh and Al Morgan area. Also, one of the objectives of this research is to study the design of a device powered by a buoy to use the waves of the Red Sea to generate electric power. The influence of the buoy system design parameters such as the buoy diameter, length of the cylinder, and the length of the connecting rod on the electrical energy generation from wave energy was investigated. The current device is designed with a gearbox to produce continuous power with a single electric generator. A floating mooring device uses the rise and fall of bulges to convert sea wave energy into electrical energy. The device consists of a float, arm, two wheels of different diameters, a gear set, and an electric generator. The effect of the design of several factors on the performance of the device for converting the sea waves energy into electrical energy, including the length of the buoy arm, the wave height by changing the cam diameter, and the conversion ratios between the gear set, to optimize the output power of the wave energy.

A lot of research studies have been surveyed the completed genomes of prokaryotic and eukaryotic and focused on the correlation between the percentage of microsatellite sequences in completed genomes and the whole size of the organism genomes. There are fewer studies made in repetitive sequences otherwise simple sequence repeats or long tandem repeats of virus genomes. simple sequence repeats (SSRs) are the most important regions for recombination and moving repeats blocks from site to another site in the genomes. A tool was programmed and designed by visual basic 6.0 to find the long tandem repeats in DNA sequences of the small genomes. The tool named “Repeater Finder Regular Expression”, (RFRE) Version 1.0, 2016. The tool was utilized to discover different pattern of long tandem repeats (LTR) motifs on the completed genomes of human corona virus strains by using a joined regular expression language. In this study, a twenty-nine accession numbers of human coronavirus completed genomes, (hku1) strains were retrieved from the Genbank. The researcher can write a different regular expression patterns and joined regular expression patterns through the designed tool to search and find a specific motif of nucleotide sequences inside the complete genomes. The RFRE tool searched and found three different total lengths of a perfect long tandem repeats (240bp, 300bp and 480bp). A Dot plot gave a picture view for the long tandem repeat sequences in the completed genome sequence (KF430201.1) of human coronavirus. The genomic dot plot tool YASS was used as a genomic similarity searching tool to check for the uninterrupted repeats and confirm the sensitivity of the (RFRE) tool. To identify the recombination site in the genomes of human coronavirus the RAT tool was applied to find the recombination sites between the completed genomes of human corona viruses .The RAT tool recognized the recombination site in the nucleotide position (3012) and at the same time this recombination site position (3012) was also recognized as a beginning position of a long tandem repeat. A precise motif was predicted from the translated repeats of Human Corona Virus which found by PRATT tool. There was a relationship between the total length of long tandem repeats and genome size of Human Corona Virus and the correlation value R² was equal to (0,451). In conclusion, this study presented the importance of finding the long tandem repeats of human coronavirus and gives a relationship between the completed genome size of human coronavirus types and long tandem repeats. The nucleotide position (3012) was a hot spot site for a recombination among the complete genomes of human coronavirus and also identified as a repetitive site in the genomes of human corona virus (hku1). The repeats in human coronavirus (hku1) were predicated to be a main major role of virus evolution.

Background/Objectives: This study aimed to identify various risk factors associated with breast cancer and explore different treatment strategies based on socio-economic, hormonal, and clinical perspectives.; Methods: We analyzed socio-economic, hormonal, genetic, and clinical variables using data from 356 questionnaires obtained from breast cancer patients at Hassan II Regional Hospital in Agadir, Morocco ; Results:The study reveals that the average age of breast cancer patients is 50 ± 10.72 years, with a mode of 47 years and an age range from 29 to 94 years. Notably, women aged 38 are most frequently diagnosed with triple-negative breast cancer. Significant socio-economic trends were observed among the participants, with the majority being housewives (91%) and nearly half residing in rural areas (48.60%). High rates of illiteracy (74.72%) and financial difficulties (54.2%) pose challenges in accessing healthcare services. Psychological distress is prevalent, with 40.7% experiencing anxiety and 30.1% experiencing sadness. The use of oral contraceptives varies, with implications for breast cancer risk. Additionally, statistics on menarche and menopause suggest hormonal influences on breast cancer development. Histological analysis indicates a predominance of grade II invasive ductal carcinoma and left breast localization. Metastases are present in nearly 38.20% of cases. Notably, surgery as the primary treatment effectively treats medium-sized tumors while limiting metastasis. Furthermore, a comparison of treatment strategies reveals a preference for letrozole among postmenopausal patients and tamoxifen among premenopausal patients; Conclusions:These findings highlight the complexity of breast cancer risk factors and therapeutic approaches, emphasizing the importance of personalized care and targeted interventions.

The main objective of the paper is to provide a thorough analysis of currently used modulation control schemes for single phase bidirectional dual active bridge DC/DC converter. In this article, it will be shown that single phase shift, extended phase shift and Dual phase shift modulation schemes are special cases of triple phase shift (TPS) modulation scheme. The article aims to highlight six TPS switching modes and their complements with the operational constraints. Unlike previous studies that regarded TPS as a complex scheme, this paper simplifies the analysis of each mode and aims to standardize the understanding of TPS modulation in DAB converters. Power equations, range of power transferred and zero voltage switching (ZVS) are derived for all the modes under TPS without assuming fundamental component analysis. Additionally, a generic optimization algorithm is developed to show the advantages of TPS modulation and thus, detailed analysis presented in this paper provides valuable insights for single phase DAB converter designers in identifying a wide range of optimization algorithms to achieve higher efficiency under TPS modulation. This analysis contributes to the advancement of bidirectional DAB converter technology and facilitates its application in various power electronic systems.

In this research, we investigate the bending of photon light and/or time-like geodesics within the context of four-dimensional space-time geometry, specifically in the presence of topological defects. These topological defects manifest as cosmic string space-time configurations, encompassing screw dislocation, spiral dislocation, and spacelike dislocation. Our primary objective is to determine the extent of deflection experienced by light-like and time-like geodesics and analyze how this deflection is affected by the cosmic string parameter, screw dislocation parameter, and spacelike dislocation parameter. To accomplish this, we employ a derivation technique that centers on the observation that photon rays (light-like geodesics) and/or time-like geodesics follow paths parallel to the z-axis while maintaining a constant radius along the cylinder.

Abstract: To overcome the environmental impact of cement production in cocnrete , the construction industry is adopting eco-friendly approaches, such as incorporating alternative and recycled materials, minimizing carbon emissions in concrete production. One such material that has gained prominence is Ground Granulated Blast Furnace Slag (GGBFS),. This study focuses on investigating the compressive strength of concrete at 28 days of age by examining the influences of several factors, such as temperature, water-to-binder ratio (w/b), GGBFS-to-binder ratio (GGBFS/b), fine aggregate, coarse aggregate, and superplasticizer. A statistical modeling approach was employed to comprehensively analyze these parameters and assess their impact on the compressive strength. To accomplish this, the study collected and analyzed data from the literature, resulting in a dataset of 210 observations. The dataset was divided into training and testing groups, and statistical analyses were performed to assess the relationships between the input parameters and compressive strength. The correlation analysis revealed insignificant relationships between the input parameters and compressive strength, indicating that multiple factors affect the strength. Different models, such as linear regression, nonlinear regression, quadratic, full quadratic models, and artificial neural networks (ANN) were employed to predict the compressive strength. The findings of this study contribute to a better understanding of the factors that influence the compressive strength of concrete containing GGBFS. The results underscore the importance of considering multiple parameters to predict strength accurately.

Choosing the optimal location for a city based on sound environmental geomorphology planning is of the utmost importance to achieving environmental sustainability, as it can spare the State and other decision-making entities a great deal of stress in the long run. GIS offers great potential for environmental planners to choose the most appropriate places for the cities of the future, especially when coupled with environmental geomorphological analyses. The State of Kuwait seeks sustainable development through the implementation of clear and specific urban plans, some of which suffer from a severe lack of geomorphological and spatially based environmental planning. This study aims to: 1) Conduct suitability modelling for establishing new cities in Kuwait, 2) Assess the current 2005-2030 urban plan, and 3) Propose possible recommendations and solutions for potential urban problems. The study relies on integrating several methods to devise a framework that will aid researchers and decision-makers in selecting optimal locations for built structures based on analysis and modelling (e.g., digital elevation model, geologic mapping, geomorphology, natural hazards, heritage/archaeological sites, military areas, oil fields, soils). Using this methodology in choosing city sites contributes to achieving sustainable development, reducing city problems, saving countries’ budgets, and saving lives. Results from this study enhance understanding of how environmental geomorphology, when combined with GIS, can be harnessed to achieve sustainable urban development in the Arabian Gulf countries and other desert countries.

Optimizing the nitrogen (N) timings and rate can improve nutrient uptake, and nutrient-efficiencies, especially of N in wheat under the changing climate scenario. Climatic stress in the form of high temperature and drought resulted in the decreased crop physiology and, ultimately, grain yield. Taking the example of rainfed wheat, we quantified the impact of N application rates as full and split-dose at three variable sites of rainfed Pothwar, Pakistan by conducting field experiments for two years (2013-14 and 201-15). Treatments include, T1 = Control (No fertilizer applied), full dose of N applied at the time of crop sowing, i.e. T2 = 50 kg N ha^-1, T3 = 100 kg N ha^-1 and T4 = 150 kg N ha^-1 and split application of N at different timings during different stages of the crop called as split application of N, i.e. T5: Application of 50 kg N ha^-1 (15 kg N ha^-1 (Sowing) : 20 kg N ha^-1 (Tillering) :15 kg N ha^-1 (Anthesis), T6: Application of 100 kg N ha^-1 (30 kg N ha^-1 (Sowing): 40 kg N ha^-1 (Tillering) : 30 kg N ha^-1 (Anthesis) and T7: Application of 150 kg N ha^-1(45 kg N ha^-1 (Sowing) : 60 kg N ha^-1 (Tillering) : 45 kg N ha^-1 (Anthesis). Three study sites include viz. Islamabad (High rainfall with optimum temperature), University Research Farm (URF)-Koont (Medium rainfall with moderate temperature), and Talagang (low rainfall with high temperature). Results showed that the highest stomatal conductance (0.80 mole m^-2 sec^-1), net photosynthetic rate (20.07 μmole m^-2s^-1), transpiration rate (9.58 mmole m^-2s^-1), intercellular CO2 concentration (329.25 μmole CO₂ mol^-1 air), SPAD values (58.86 %) and proline contents (35.42 μg g^-1) were obtained for split application of N (T6 = Split N100) compared to control and full dose of N treatments. Among sites, these physiological traits remained highest at Islamabad and lowest at Talagang, while among years, maximum values of the measured parameters were obtained in 2013-14. A similar trend was observed for crop total N, N efficiencies, and agronomic traits of the crop. Our results suggest that optimum N application rate and its suitable timings can help to harvest real benefits of N as in our findings, split dose resulted in the maximum performance of the crop from physiological parameters to the agronomic traits of the rainfed wheat crop.

This study involved the fabrication of a set of Aluminum ion-grafted SBA-15 utilizing ethylene-diamine and trimethylamine ionic liquids. The primary objective was to examine the impact of the fabrication environment on the physicochemical characteristics of the catalysts. Comprehensive characterization of the Al-SBA-15 catalysts was conducted using various techniques, including XRD, FTIR, surface area, pyridine FTIR, 27Al-NMR, TGA, HRTEM, and FESEM, to analyze their physicochemical characteristics. Furthermore, the acidic characteristics were examined by con-ducting potentiometric titration in a nonaqueous solvent and employing FTIR spectroscopy to an-alyze the chemisorbed pyridine. The effectiveness of the fabricated acid materials was evaluated by testing their performance in the acetic acid esterification with butanol. The findings obtained reveal that the mesostructured of SBA-15 remains intact following the successful inclusion of Al3+ ions into the silica frameworks. Additionally, a remarkable enhancement in the existence of both Bronsted and Lewis acid centers is noted due to the grafting process of Al3+ ions. At temperatures of 80°C and 100°C, the reaction in Al-SBA-15(T-120) proceeds swiftly, reaching approximately 32% and 38% conversion, respectively, within a span of 110 minutes. The excellent catalytic performance observed in the esterification reaction can be attributed to two factors: the homogeneous distribution of Al3+ ions within the SBA-15 frameworks and the acidic character of Al-SBA-15. The findings further indicate that the grafting process for incorporating Al3+ ions into the silica matrix is more efficient.

Breast cancer is the most widespread malignancy in women from corner to corner of the world. Nanostructured lipid carrier (NLC) proved its efficacy in the treatment of cancer. NLC loaded with Imatinib (IMA) (NANIMA) was prepared and evaluated for in vitro efficacy in MCF-7 breast cancer cells. Hot homogenization method was used for the preparation of NANIMAs. Aqueous solution of surfactants (hot) was mixed with molten mixture of stearic acid and sesame oil (hot) under homogenization. The prepared NANIMAs were characterized and evaluated for size, polydispersity index, zeta potential, encapsulation efficiency, release studies, stability studies and MTT assay (cytotoxicity studies). The optimized NANIMA revealed particle size of 104.63 ± 9.55 d.nm, PdI of 0.227 ± 0.06 and EE of 99.79 ± 0.03. All NANIMAs revealed slow and sustained release behaviour. The surfactants used in the preparation of NANIMAs exhibited their effects on particle size, zeta potential, encapsulation efficiency, stability studies and release studies. The cytotoxicity studies unveiled 8.75 times increase in cytotoxicity for optimized NANIMA (IC50 = 6 µM) when compared with IMA alone (IC50 = 52.5 µM) on MCF-7 breast cancer cells. After this, NLC containing IMA possibly will be employed for the cure of breast cancer. A lesser amount of IMA loaded NLC will be sufficient than IMA alone for the treatment of breast cancer. Moreover, the patient will exhibit not as much of adverse effects as in case of IMA alone. Consequently, NANIMA could prove to be useful for effective therapeutic outcome in breast cancer treatment.

A new green-validated and highly sensitive electrochemical method for the determination of molnupiavir (MOV) has been developed using cyclic voltammetry. The proposed analytical platform involves the use of disposable screen-printed reduced graphene oxide 2.5% electrode (SPrGOE 2.5%) for the first time to measure MOV with high specificity. The surface morphology of the sensor was investigated by using a scanning electron microscope armed with an energy-dispersive X-ray probe. The fabricated sensor attains improved sensitivity when sodium dodecyl sulfate (SDS) surfactant was added to the supporting electrolyte solution. Thus, various factors were investigated in order to select the best supporting electrolyte for voltammetric determination of MOV, and the optimum conditions were achieved by employing 0.04 M Britton-Robinson at pH 2, 10-4 M SDS, and SPrGOE 2.5%. Well-defined MOV oxidation peaks were obtained with no reduction peaks in cyclic voltammetry using diffusion-controlled pathways. The method was validated using differential pulse voltammetry according to ICH guidelines, providing it to be precise, accurate, specific, and robust with linearity over a concentration range of 50-6017 ng/mL. The incorporation of the reduced graphene nanoparticles supported the in situ interaction between electrode surface and MOV, improved the selectivity, and reduced the detection limit to the concentration level of 15.98 ng/mL. The stability investigation demonstrated that SPrGOE 2.5% can provide high-stability behavior throughout a three-month period under refrigeration. The fabricated SPrGOE 2.5% was successfully employed for the measurement of MOV in pharmaceutical capsules and human bio-fluids without the interference of endogenous matrix components as well as the commonly used excipient.

At secondary healthcare, Carbapenem-Resistant Enterobacterales (CREs) such as observed in Klebsiella pneumoniae are a global public health priority with significant clinical outcomes. In this study, we describe clinical, phenotypic, and genotypic characteristics of three pan-drug resistant (PDR) isolates that demonstrated extended resistance to conventional and novel antimicrobials. All patients had risk factors for acquisition of multidrug resistant organisms while microbiological susceptibility testing showed resistance to all conventional antimicrobials. Advanced susceptibility testing demonstrated resistance to broad agents such as ceftazidime-avibactam, ceftolozane-tazobactam, and meropenem-vaborbactam. Nevertheless, all isolates were susceptible to cefiderocol, one of the novel antimicrobials that demonstrated potent in vitro activity against resistant Gram-negative bacteria including CREs pointing towards potential therapeutic role for PDR pathogens. Expanded genomic studies revealed multiple antimicrobial resistant genes (ARGs), including blaNMD-5 and blaOXA derivative types as well as a mutated outer membrane porin protein (OmpK37).

Some contaminants of emerging concern (CECs) are known to survive conventional wastewater treatment plants, which introduce them back to the environment and can potentially cycle up in drinking water supplies. This is especially concerning because of the inherent ability of some CECs to induce physiological effects in humans at very low doses. Advanced oxidation processes (AOPs) such as TiO2 based photocatalysis are of prominent interest for addressing CECs in aqueous environments. Natural water resources often contain dissolved metal cations concentrations in excess of targeted CEC concentrations. These cations may significantly, adversely impact degradation of CECs by scavenging TiO2 surface generated electrons. Consequently, simple pseudo first order or Langmuir-Hinshelwood kinetics are not sufficient for reactor design and process analysis in some scenarios. Rhodamine B dye and dissolved copper cations were studied as reaction surrogates to demonstrate that TiO₂ catalyzed degradation for very dilute solutions is very nearly completely due to homogeneous reaction with hydroxyl radicals and that in this scenario the hole trapping pathway has negligible impact. Chemical reaction kinetic studies were then carried out to develop a robust model for RB/metal reactions that is exact in the electron pathways for hydroxyl radical production and metal scavenging.

The State of Kuwait seeks sustainable development through implementation of clear and specific urban plans, some of which suffer from a severe lack of geomorphological and spatially-based environmental planning, such as the use of geographic information systems (GIS). Choosing the optimal location for a city based on sound environmental planning is of the utmost importance to achieving environmental sustainability, as it can spare the State and other decision-making entities a great deal of stress in the long run. GIS, in particular, offers great potential for the environmental planner and decision-maker to choose the most appropriate places for the cities of the future, especially when coupled with geomorphological analyses. To achieve the Vision of Kuwait 2035, one of the State’s planning objectives focuses on Urban Planning for the Establishment of Environmental Cities that Achieve (UPEECA) environmental sustainability criteria. To gain a more holistic analysis related to the Vision, this study aims to: 1) Conduct suitability modelling for establishing new cities in Kuwait, 2) Assess the current 2005-2030 urban plan, and 3) Propose possible recommendations and solutions for potential urban problems. The study relies on integrating several methods to devise a framework that will aid researchers and decision-makers in selecting optimal locations for built structures based on analysis and modelling (e.g., digital elevation model, geologic mapping, geomorphology, natural hazards, heritage/archaeological sites, military areas, oil fields, soils)

Failaka Island, located in the far east of Kuwait Bay, is about 20 km from the State of Kuwait’s coast, and represents a focal point for regional geography and history, with pristine beaches and archaeological sites dating to the Bronze, Iron, Hellenistic, Christian and Islamic periods. According to environmental data and in coordination with local authorities to develop an urban plan, the island is set to become the first tourist destination for the State of Kuwait. To achieve the Vision of Kuwait 2035, one of the planning objectives focuses around Urban Planning for the Establishment of Environmental Cities that Achieve (UPEECA) environmental sustainability criteria. The paper then, aims to propose the environmental urban plan for Failaka Island. Based around Environmental Analytical Hierarchical Processes (EAHP), and using the Field Calculator and ModelBuilder functions in ArcGIS, this research centres on the feasibility of carrying out an urban plan using suitability modelling that includes four factors and 13 criteria covering the island’s ecological and human composition. This study utilizes both remote sensing (UAVs for 3D imaging) and field study (ground truthing) to identify changes in land use and land cover – such as using sample analysis of the historical sites and soils for tracing evidence and creating/updating a soil map – and create the first GIS database for the island that can lead to generating a suitability model.

Tropical forests harbor complex communities that are linked together by biotic relationships. Asian forests in particular have lost many apex predators due to habitat loss. We studied a small forest patch in northeastern Bangladesh, Satchari National Park, to determine density and diversity of nocturnal mammals and evaluate their relationships. Transects were walked from February 2015 to July 2016 and density was estimated using distance sampling. Nine species of mammals (5 arboreal and 4 ground-dwelling) were encountered. Densities of the common palm civets, Paradoxurus hermaphrodites, Bengal slow loris, Nycticebus bengalensis, were the highest (19.48 and 15.03 individuals/km2). Density of small Indian civets, large Indian civets and Indian mongoose were lower (2.31-5.55 individuals/km2). Unexpectedly, a wide range of nocturnal mammals co-existed in this forest patch, in spite of fragmentation and severe disturbance. We did not find any significant association between any of the species studied, although this could be an artifact of low sample size. Conservation in Bangladesh remains a challenge due to high human population density. Thus, strict conservation measures are needed to permit the long-term survival of these species.

The requirement for developing tools capable of detecting and monitoring heavy metals (HMs) has recently taken on more significance due to worries about their toxic effect on human health and aquatic habitats. In this study, a simple electrochemical sensing carbon paste electrode (CPE) based on composite of Zinc/Iron layered double hydroxide (LDH) and polyaniline (PANI) was developed for the determination of lead ions in aquatic solutions. For this purpose, Zn-Fe LDH/PANI was prepared and characterized by Fourier transform infrared (FTIR), X ray diffraction (XRD), and scanning electron microscopy (SEM). Modified carbon electrode based Zn-Fe LDH/PANI was electrochemically characterized compared with unmodified electrode in FCN as a redox probe using cyclic voltammetry (CV). Thus, Zn-Fe LDH/PANI was utilized as sensing material for the electrochemical determination of lead ions using differential pulse voltammetry (DPV). Zn-Fe LDH/PANI/CPE show limits of detection (LOD) and quantification (LOQ) of 167.8 nM and 559.4 nM, respectively. Zn/Mg/Fe LDH was prepared using co-precipitation method and characterized by XRD, FTIR, SEM and BET, used for removal of Pb+2 ions. Removal process was investigated at different conditions as pH, metal ion concentrations, starting adsorbent dose at first and contact time. Zn/Mg/Fe LDH was evaluated as an effective adsorbent material for lead ions elimination in aquatic solutions, with capacity of adsorption 700 mg/g. The elimination of lead ions on Zn/Mg/Fe LDH fitted the pseudo first-order kinetics model and the isotherm was matched with Langmuir model. Studies revealed that the ideal removal conditions were pH = 5.0, an adsorbent mass of 0.05 g, and 20 ml of 50 ppm metal ion concentration at 60 min. The Zn-Fe LDH/PANI composite also showed potential anti-cancer properties against lung cancer cells (A549) while maintaining safety for normal lung fibroblast cells (WI-38). Collectively, advancements in electrochemical sensors offer promising solutions for lead ion detection, with wide-reaching applications in wastewater treatment and cytotoxicity assessment. These innovations have the potential to enhance environmental monitoring and public health safety.

Purpose: To evaluate the efficacy and survival rates of same session ab interno trabeculectomy with the trabectome and Ahmed glaucoma valve implant (AT) in comparison to the Ahmed glaucoma valve alone (A). Method: A total of 107 eyes undergoing primary glaucoma surgery were enrolled in this retrospective comparative case series, including 48 eyes which underwent AT and 59 eyes which received A alone. Participants were identified using the procedural terminology codes, and their medical records were reviewed. The primary outcome measure was surgical success, defined as intraocular pressure (IOP) > 5 mmHg, ≤ 21 mmHg, and IOP reduction ≥ 20% from baseline at two consecutive visits after three months, no reoperation for glaucoma. Secondary outcome measures were IOP, the number of glaucoma medications, incidence of a hypertensive phase, and best corrected visual acuity (BCVA). Results: The cumulative probability of success at one year was 70% in AT, and 65% in A (p=0.85). IOP decreased significantly from 26.6 ± 10.1 mmHg at baseline to 14.7 ± 3.3 mmHg at the final follow-up in AT (p= 0.001). The corresponding numbers for A were 28.8 ± 10.2 and 16.7 ± 4.9, respectively (p= 0.001). The final IOP was significantly lower in AT (p= 0.022). The number of medications at baseline was comparable in both groups (2.6 ± 1.2 in AT and 2.5 ± 1.3 in A, p=0.851). Corresponding number at 1 year visit was 1.2±2 in AT and 2.8±1.8 in A (p=0.001). The incidence of a hypertensive phase was 18.7% in AT and 35.5% in A (p=0.05). HP resolved in only 30% of eyes. The criteria for HP resolution were fulfilled in 9 eyes (30%). There was no difference in the rate of resolution of the hypertensive phase between AT and A (33.3% and 28.5%, respectively, p=0.67). Conclusion: Ahmed glaucoma valve implant with same session trabectome surgery significantly decreased the rate of the hypertensive phase and postoperative IOP as well as the number of glaucoma medications.

Background: We aimed to study the frequency, management, and outcomes of patients with blunt pancreatic trauma. Methods: We reviewed the medical records for all patients admitted with pancreatic injuries between 2011 and 2017 at the only level 1 trauma center in the country. Results: There were 71 patients admitted with pancreatic trauma (0.6% of trauma admissions and 3.4% of abdominal injury admissions) with a mean age of 31 years. Sixty-two patients had pancreatic injury grade I-II and 9 had grade III-IV. Thirty-eight percent had GCS <9 and 73% had ISS >16. The level of pancreatic enzymes was significantly proportional to the grade of injury. Over half of patients required a laparotomy, of them 12 patients had an intervention on the pancreas. Eight patients developed complications related to pancreatic injuries ranging from pancreatitis to pancreatico-cutaneous fistula while 35% developed hemorrhagic shock. Mortality was 31% and regardless of the grade of injury, the mortality was associated with high ISS, low GCS and presence of hemorrhagic shock. Conclusion: Pancreatic injuries following blunt trauma are rare and the injured subjects are usually young male. However, most injuries are of low-grade severity. This study shows that shock, higher ISS and lower GCS are associated with worse in-hospital out-comes. Non-operative management may suffice in patients with lower grade injuries, which may not be the case in patients with higher grade injuries unless carefully selected

Advances in early detection of Breast cancer and treatment improvements have significant-ly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study a novel methodology for developing an interpretable computer-aided diagnosis (CAD) system for breast cancer detection, leveraging explaina-ble Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combine the continuous and binary representations of PSO and SMO to effectively manage trade-offs between Accuracy, feature selection and hyperparameter tuning. We evaluate several CAD models and investigate the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor filters, and edge detection. We further shedding light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. Also with HSMO our models achieved a high accuracy of 98.27% and F1- score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

This study introduces an integrated method for managing process risks in a Business Process Reengineering (BPR) project, using Robust Data Envelopment Analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA: Severity, Occurrence, and Detection (S-O-D), and incorporating two additional factors (Breakdown Cost and Breakdown Duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an au-tomotive sector company, and the results showed the impact of uncertainty on values by com-paring different approaches such as RPN, PFMEA-DEA, and PFMEA-RDEA. A new reduced risk categorization was achieved, who allowed for decision-makers to focus on necessary actions for reengineering.

The present study deals with the transplantation of mature Argan trees (Argania spinosa L. Skeels) and focuses on optimizing survival rates depending on the seasons, the type of shoot pruning applied to the trees and preconditioning treatments consisting of root pruningand watering for a few months before transplanting. So far, the transplantation of this species at the adult stage has not received enough attention in spite of its ecological and socio-economic importance for the regions where it thrives. The results obtained show that transplanting in summer, autumn and winter resulted in survival rates exceeding 50%. The Argan trees transplanted in spring hardly achieved 10% survival. However, the total cutback of the crown induced survival improvement that reached a rate greater than 70% for summer, autumn and winter, and 50% for spring. Watering alone or combined with root pruning for 6 months helped improve the recovery rate but only when carried out in autumn with transplantation in spring. On the other hand, when these pre-treatments were applied in the spring to transplant the trees in summer, the recovery rate was not improved.

Automotive aerodynamics comprises of the study of aerodynamics of road vehicles. Its main goals are reducing drag, minimizing noise emission, improving fuel economy, preventing undesired lift forces and minimizing other causes of aerodynamic instability at high speeds. The Ahmed body has the form of a highly simplified car, consisting of a blunt nose with rounded edges fixed onto a box-like middle section and a rear end that has an upper slanted surface, the angle of which can be varied. It retains vital features of real vehicles in order to study the flow fields around it and the related turbulence models which characterizes the actual flow at elevated Reynolds number. In the present study, the aerodynamic behavior of this body is investigated numerically by the aid of commercial CFD tool: Ansys Fluent. The results of the simulation are validated with available experimental data and results of the simulations from other literatures. The numerical data were obtained for a fixed free stream velocity of 25 m/s at the inlet. The simulations were performed at a fixed slant angle of 25 degree and zero yaw angle. The present study focuses on how local refinement of mesh inside the concerned body and the outside, helps affect the results and for which grid dependency test is the primary objective of this paper. The present study also helps demonstrate how the drag of the body behaves, which is mainly the effect of pressure drag force generated at the rear portion of the body. The study also focuses on important properties like the velocity magnitude at different locations for different meshing cases, and to capture the flow pattern in the front or near the wake region. The study can be further helpful to future researchers in determining resistance, fuel efficiency etc. helping designers to optimize in specialized areas for better efficiency.

Maldivian coral reefs have been experiencing significant degradation due to a combination of global climate change and local anthropogenic pressures. To enforce the conservation of coral reefs worldwide, coral restoration is becoming a popular tool to restore ecosystems actively. In the Maldives, restoration interventions are performed only around touristic islands, where there are economic resources available to support these projects. Unfortunately, on local islands, coral restoration does not benefit from the same support and is rarely boosted. A challenging coral restoration intervention has been performed, for the first time, a on a local island of the Maldives affected by intense human pressures that caused the degradation of its reefs. A total of 242 coral fragments were collected from impacted colonies and transferred to the coral nursery of the island. Survival and growth rates of the fragments were monitored for 12 months. After one year, a survival rate of 70.2% was recorded. Although this rate might appear lower when compared to other restoration experiences, it is very promising considering the origin of the fragments and the poor quality of the environment where they have been transplanted. Some potential threats to the success of this restoration have also been identified, i.e., water temperature anomaly, diseases and parasites, the latter being the leading causes of coral mortality. The procedure presented here is comparatively less expensive than the typical relocation of entire coral colonies from donor healthy reefs to degraded reefs, thus providing an opportunity and a viable option also for local islands to restore their reefs and preserve local biodiversity.

Hadhramout Initiative Against Corona (HIAC ) formed a working group of clinicians relevant to the management of COVID-19 to formulate clinical practice guidelines (CPG) for clinicians caring for the patients infected with COVID-19 in Yemen. The regional guidelines on the management of COVID-19 were thoroughly reviewed and its applicability was assessed for Yemen. HIAC’s recommendations covered (2) sections: the first one is the diagnosis, including case definition, risk stratification of the affected cases, investigations (prioritization of reverse transcription-polymerase chain reaction (RT-PCR) testing, D-dimer, chest x-ray, and chest computed tomography, while the second part covers the treatments (mainly Favipiravir, Remdesivir, Hydroxychloroquine, and Glucocorticoid, Anticoagulants, and Supportive measures). In conclusion, the adoption of cost-effective and evidence-based guidelines in Yemen will standardize the management of the patients infected with COVID-19 and protect both the patients and the health care workers from malpractice.

Global health is significantly impacted by antimicrobial resistance. Antibiotic misuse and overuse have resulted in the emergence of drug-resistant bacteria, viruses, parasites and fungi, which makes ordinary infections more difficult to cure and accelerates the spread of disease. The World Health Organisation has declared that antimicrobial resistance could result in up to 10 million deaths yearly by 2050, making it a worldwide health emergency. One of the greatest challenges to global health, food security, and development today, according to the World Health Organisation is the emergence of antibiotic resistance. Even though antibiotic resistance can arise naturally, improper usage of these medications and many other factors are accelerating its global spread. Antimicrobial resistance is frequently referred to as the "Silent Pandemic" and is a problem that requires current action and should be managed more effectively rather than considered as a future situation. This paper address a number of antimicrobial classes, global health, Onehealth and how drug resistance affects human health, and related risk factors. This review will also provide background information on the causes and processes of antimicrobial resistance in brief by identifying these characteristics, targeted interventions can be developed to lower the risk of illnesses that are resistant to antibiotics. It will also offer insights into new and emerging solutions that are being studied and may be created in the future to address the advancement of antimicrobial resistance.

Background: Migraine is one of the most common health conditions in the reproductive male and female population. Premenstrual syndrome (PMS) is a common condition that affects many women during their menstrual cycle. around 50 to 60% have menstrual headaches among the migraineur women. The symptoms in the majority were mild, but 5–8% may suffer moderate to severe symptoms, accompanied by considerable distress or functional dysfunction. Premenstrual migraine (PMM) refers to migraines that occur with the menstrual cycle. It affects approximately 50% of women with migraine and can be debilitating in terms of both frequency and severity of symptoms. This information is important for medical professionals in Saudi Arabia to be aware of so, as to provide better medical care and support which can significantly improve the person's quality of life of women who suffer from PMS and menstrual migraines. Objectives: To estimate the prevalence of menstrual migraines in women, to evaluate the severity and frequency of menstrual migraines in women with PMS, and to identify potential risk factors aggravating menstrual migraines in women with PMS. Methodology: A cross-sectional community-based study was conducted on reproductive age (18-50Years) women who have regular menstrual cycles with experience migraine headaches and are diagnosed with PMS and menstrual migraine, using a self-administered questionnaire between December 2022 to May 2023 in Saudi Arabia. Results: Out of 2130 women participants 397 were suffering from migraine which is 18.6%. Of the total 397(18.6%) Migraineurs individuals 230 (57.9%) were suffering from menstrual migraine and 167 (42.1%) were non-menstrual migraineurs, in general, reproductive women menstrual migraine is 10.7% and non-menstrual migraine is 7.8%. The BMI is increasing the incidence of menstrual migraines is increasing. Migraine Disability Around one-third of the participants were suffering from III–moderate disability 134 (33.8) Most of the menstrual migraine victims at least 3 days in the last three months were absent from their duties due to their menstrual migraines. Conclusions: Migraine attacks during the menstrual cycle cause the impairment of their social and physical ability to perform their routine household, and academic activities and unable to fulfill their professional commitments. To better understand the Menstrual Migraine and Non-menstrual migraine large prospective studies are required to plan the best management methods.

Three-dimensional (3D) facies and petrophysical models were generated from previously published data of carbonate strata in the Dam Formation (eastern Saudi Arabia) to quantitatively investigate, describe, understand, model, and predict the permeability anisotropy of tidal flat carbonate within a sequence stratigraphic framework. The resulting 3D models were used to conduct fluid flow simulations to demonstrate how permeability anisotropy influences the production of hydrocarbons and ultimately affects decisions concerning future drilling in the exploration and development of carbonate reservoirs that have tidal flat strata. The constructed 3D facies model consists of four lithofacies associations, two of which were grain-dominated associations and two of which were mud-dominated associations. These lithofacies associations varied spatially in four reservoir zones (zones 1 to 4), which represent two fourth-order sequences in the uppermost part of the Dam Formation. Zones 1 and 3 consist of transgressive parasequences, and zones 2 and 4 consist of the regressive parasequences of these sequences. The 3D porosity and permeability models have a coherent match with the distribution of the lithofacies and the stratigraphic framework of the Dam Formation. The results suggested that the permeability anisotropy in zones 1 and 3 is controlled by the occurrence of the grain-dominated lithofacies associated with tidal flat channels. This lithofacies association overlies the sequence boundaries of sequences 1 and 3, forms reservoir bodies with relatively high permeability values, and is elongated perpendicular to the shoreline of the depositional environments. In contrast, permeability anisotropy in zones 2 and 4 is thought to be controlled by the occurrence of the grain-dominated lithofacies associated with the oolitic shoal. This lithofacies association overlies the maximum flooding surface of sequences 2 and 4, forms reservoir bodies with relatively high permeability values, and is elongated parallel to the shoreline of the depositional environments. Fluid flow simulation results suggested that the trend in hydrocarbon production from the constructed 3D models depends on permeability anisotropy in each reservoir zone. Thus, recognizing trends in permeability anisotropy, which might be predicted using sequences stratigraphy, could help to identify potential areas for future drilling.

Many redox diseases, including diabetes, are associated with an imbalance of calcium homeostasis, regulated mainly by a Ca2+-ATPase pump (SERCA). Dysfunction of this enzyme may be prevented by natural polyphenolic compounds, representing a possible supporting treatment. Compounds that increase SERCA activity/expression may be useful for the treatment of diabetic complications. Stimulation of SERCA1 activity was analyzed experimentally and by molecular modeling. SERCA1 activity under methylglyoxal- and palmitate-induced oxidative stress was evaluated. The viability of INS-1E cells and insulin secretion were determined. [6]-Gingerol, resveratrol, and ellagic acid increased SERCA1 activity and exerted a protective effect under oxidative stress in the noncellular system. We found for the first time that the binding of polyphenols ([6]-gingerol, resveratrol, ellagic acid) to Glu439 in the SERCA1 P-domain may be critical for the stimulation of its activity. Moreover, this binding may also be important in the protective effects against oxidative stress. Direct stimulation of SERCA1 activity by ellagic acid was observed for the first time. In INS-1E cells, these compounds increased insulin secretion.

Introduction: Hypertension is a serious public health problem in Morocco. The objective of this pilot study was to estimate the salt intake of Moroccan adults by measuring 24-hour urinary sodium excretion. Methods: 132 participants were recruited from the central region of Morocco. Sodium, potassium and creatinine levels were measured using the 24-hour urine samples. Additional data included anthropometric measurements and socio-demographic questionnaire. Results: The daily sodium, potassium and creatinine excretions were 2779.1±1334.9 mg, 1350.0±642.8 mg and 820.3 ± 381.2 mg, respectively. Data analysis revealed that 71.2% of the participants had a daily sodium intake more than 2000 mg (5g of salt) recommended by the WHO. The mean of potassium excretion was 1350.0±642.8 mg /day, this average was lower than 3.51g per day recommended by the WHO. Conclusion: Public health measures to reduce sodium and increase potassium consumption in order to decrease the population’s risk of high blood pressure and heart disease are recommended.

Nutrients management is a mainspring in agricultural systems for sustained productivity, economy sustainability and environmental quality. Excessive and lesser nutrient application caused environmental pollution and reduced production, respectively impacting socio-economics of the entire ecosystems. Sustainable agricultural production thus demands supplementation of nutrients either through natural processes, application of animal by-products, and mineral fertilizers to crop fields. Technology application for treating useless agricultural wastes into useful source is a management strategy that prevents environment pollution. Crude animal manures triggered soil degradation, attenuation air and water quality, and resulted in higher concentrations of heavy metals. Primary nutrients, i.e., nitrogen, phosphorus and potassium are used globally and are non-renewable. Augmented upsurge in prices of inorganic fertilizers and required discharge restrictions on nutrient has stimulated the technological developments including biochar, composting, vermicomposting, composting with biochar, pyrolysis, forward osmosis, and electro-dialysis to recover nutrients. Therefore, outlining the research gaps considering the present and imminent potential of these technologies for adaptation of nutrient recycling is of great importance. Thus, it’s need of an hour to fix our environment and for that scientists are trying to introduce and renovate the technologies which have immense potential to mitigate the negative effects of technology adversities on our environment.

Mandibular third molar is the most frequently impacted tooth. Mandibular third molar impaction bears consequences on the adjacent mandibular second molar (MSM) like bone loss. It can be identified initially using orthopantomography (OPG). The objective is to compare the changes in the bone level distal to MSM between patients with extracted and non-extracted impacted man-dibular third molars (IMTM) using OPG. In this retrospective case-control study, 160 archived (OPGs) of 80 patients visiting the Dental Hospital of the University of Barcelona (HOUB) were randomly selected. The participants were stratified into two groups. The study group involved measuring the bone level distal to the mandibular second molar before and after the extraction of the IMTM. The control group, on the other hand, had bone levels measured twice within 3–6-month period without the extraction of the IMTM. The mean ages of patients in study and control groups were 35.5 ± 15.45 and 33 ± 16.49, respectively. Study group demonstrated signifi-cant (p < 0.05) bone gain after extraction, whereas control group demonstrated significant bone loss (p < 0.0001) after follow-up. Bone improvements were observed distal to the mandibular second molar after the extraction of the IMTM compared to those in control group.

Exotic plants are usually problematic for the native species where they coexist. This study evaluated the effect of naturalised alien Cenchrus echinatus L. on native plants in the urban vegetation. A field trial was conducted to assess the effect of this species on cover and diversity of the native vegetation. The allelopathic potential of such species was examined. Plots comprising C. echinatus had lower cover of some native species. Lower floristic diversity was observed at more dense cover of this plant. The soil under Cenchrus attained lower N, P and K contents. This soil had no effect on germination and growth of the native species. It also comprised germinable seeds of some species which were absent from the standing vegetation. Exotic C. echinatus may exert a negative effect on the native vegetation of the urban plant communities. Dense cover of Cenchrus may inhibit germination of native species, leading to reduction in their cover. The reduction in cover and diversity of native species was not attributed to allelopathy. These results suggest that naturalised C. echinatus may be more competitor than the native ones, particularly at higher densities. Furthermore, it may represent a threat for the native plants in the urban vegetation.

Natural product especially secondary metabolites that produced by plants under the stressed condition shown to have a different pharmacological impact. Aeluropus lagopoides is one of the typical halophyte plants survivals under stressed conditions. It has been used for wound healing and as a painkiller. The bioactivity and the chemical composition of this plant have poorly investigated. Consequently, chemical components of A. lagopoides leaves were extracted using hexane (nonpolar), ethyl acetate (semi-polar), n-butanol (polar) to extract the most extensive variety of metabolites. The cytotoxicity and anticancer impact of extracted secondary metabolites evaluated against breast (MCF-7), colon (HCT-116), and liver (HepG2) cancer cell lines using SRB test. The mechanism of action verified by observing the appearance of apoptotic bodies using the fluorescent microscope while their antiproliferative impact had been evaluated using flow cytometer. Results revealed that secondary metabolites extracted using hexane and ethyl acetate were having the highest cytotoxicity and thus anticancer activity effect on HepG2 with IC₅₀ (24.29 ± 0.85, 11.22 ± 0.679 µg/mL) respectively. Where apoptotic bodies observed, flow cytometer results exhibited that secondary metabolites can inhibit cell cycle in G0/G1 phase. Accordingly, A. lagopoides hexane and ethyl acetate extracts may consider as a candidate anti-cancer drug.

Introduction: Hospitalization in a COVID-19 unit represents a significant source of stress and psychological distress for patients. Our study aims to determine the factors of psychological distress in the therapeutic and psychosocial management of patients hospitalized with a COVID-19 infection. Methods: This cross-sectional study was carried out over 8 months at the COVID-19 zone of the CHU HASSAN II of FES. 99 patients were evaluated within days of diagnosis by psychometric scales of anxiety, depression, and post-traumatic stress. In the course of this follow-up, we found the presence of psychological impacts in these patients, and we tried to find correlating factors between the occurrence of psychiatric disorders and the set of factors related to sociodemographic data, hospitalization course and treatments taken, as well as family support. Results: 35% of patients had scores in favor of post-traumatic stress disorder, 37% of the patients had definite depression and 50% had anxiety. Post-traumatic stress, depression, and anxiety were correlated with sociodemographic factors, hospitalization, treatments received, and length of stay. Conclusion: The prevalence of anxiety-depressive disorders and post-traumatic stress disorder was high in patients with COVID-19, hence the interest in providing psychological and psychosocial care in the emergency department.

Alzheimer's disease is a multifactorial neurodegenerative disorder, with Acetylcholinesterase, Butyrylcholinesterase, Beta secretase, Glycogen Synthase Kinase beta and Monoamine oxidase B playing central role in it's pathogenesis. Therefore, this research aims to discover potential curative multitarget drugs derived from African natural products capable of addressing the multifactorial characteristics of the disease. In silico approaches were used to filter a 880 african natural compounds library based on selected pharmacokinetic properties. Molecular docking against the five aforementioned proteins, followed by Molecular Mechanics with Generalised Born and Surface Area Solvation scoring for the top compounds were used to assess binding. Density Functional Theory studies were used to assess electronic transitions. Pharmacokinetic filters resulted in 200 compounds, of which only five were selected after molecular docking. Density Functional Theory and Molecular Mechanics with Generalised Born and Surface Area Solvation scoring resulted in 3 potential multitarget compounds. Compound 157 (ZINC000095485950) showed triple-target inhibitory activity against Butyrylcholinesterase, Glycogen Synthase Kinase beta and Beta-secretase, while compounds 159 (ZINC000095485952) and 696 (ZINC000039144622) showed dual-target inhibitory activity against Butyrylcholinesterase and Glycogen Synthase Kinase beta. Molecular dynamics simulation and further experimental assessments are suggested.

This study aims to evaluate the effect of different drying methodologies (room, microwave, convective, oven and freeze-drying) on the chemical composition, the microbiological quality of extracts and the biological activities namely clotting milk and antioxidant activities for both spontaneous and cultivated Onopordum nervosum ssp. platylepis. The results showed that the drying methodology has significantly affected the phenolic composition. Freeze dried flowers showed the best amounts of total phenols, flavonoids and condensed tannins followed by the microwave dried flowers. However, the latest presented the lowest protein content. Finally, the biological activities were significantly dependent on the used drying process. The effective concentration EC50 values let to classify the samples according to their decreasing power to inhibit DPPH free radicals: dryer<oven<room <microwave<freeze-drying. The drying process significantly affected the clotting milk activity, freeze dried flowers showed the highest activity. All the obtained results do not reveal a significant difference between cultivated and spontaneous plants.

Organizational climate is the ascribed psychological meanings and significance associated with the procedures, policies and practices that are recognized and rewarded in the workplace, and hence mediates the effects of environmental stimuli on individuals’ response. Safety climate is a specific organizational climate, i.e., organizational climate for safety. Previous research claimed that organizational climate provides foundation for safety climate, but without elaboration on the foundational mechanisms. This paper attempts to fill up this knowledge gap. As organizational climate is a multi-dimensional phenomenon, this paper chooses two dimensions, i.e., perceived organizational support (POS) and participative decision-making (PaDM), for illustrative purposes. Drawing on an interactive approach to forming climate perceptions, this paper introduces two interactive constructs, i.e., leader-member exchange (LMX) and team member exchange (TMX), and establishes a multiple mediation model depicting the foundational effect of organizational climate on safety climate. A random sample of Hong Kong based construction personnel is used to validate the model. The results show that both POS and PaDM are positively associated with perceived safety climate, both LMX and TMX fully mediate the effect of PaDM on safety climate, and only LMX partially mediates the effect of POS on safety climate. This study sheds light on the foundational effects of organizational climate on safety climate. POS can improve the quality of reciprocal exchange about safety matters between construction personnel and their supervisors, and hence raise construction personnel’s awareness of the priority of safety. PaDM can improve the quality of reciprocal exchange about safety matters vertically and horizontally, and hence have construction personnel aware the importance of safety. In practice, this paper suggests that project managers timely recognize and reward construction personnel’s contribution, genuinely cares about their well-being, and take their suggestions seriously in making decisions. In this way, the quality of both vertical and horizontal exchange about safety matters improves, and a sound and positive safety climate ensues.

Background/Objectives Gastroparesis, a chronic digestive disorder characterized by delayed gastric emptying, often results from diabetes, post-surgical complications, autoimmune diseases, and neurological disorders. In approximately 50% of cases, the cause is idiopathic gastroparesis (IGD). Recent studies suggest a link between chronic enteroviral infections and persistent gas-trointestinal symptoms, including delayed gastric emptying. This study investigates the effects of a silydianin-rich extract from Silybum marianum seeds on enteroviral infections in vitro and the mitigation of delayed gastric emptying in mice. Silydianin, a key bioactive compound known for its liver-protective and antioxidant properties, has not been extensively studied for its impact on enteroviral infections and gastroparesis; Methods NMR spectroscopy (¹H, ¹³C, and DEPT) identi-fied silydianin as the primary compound, with minor flavonolignans and trace amounts of an oxidation product. The study assessed the cytotoxicity and antiviral activity of the extract at various stages of the viral life cycle, including virucidal activity, cell protection, and post-infection effects, using neutral red assays in RD cells, with results confirmed by real-time PCR. The viruses studied included Coxsackievirus B2, Coxsackievirus A10, Poliovirus SL-1, and Enterovirus EV71. The impact on delayed gastric emptying was evaluated in a mouse model of gastroparesis using doses of 100 and 200 mg/kg compared to a control; Results: The silydianin-rich extract exhibited con-sistent antiviral activity, with the highest selectivity index (SI) for EV71 (4.08) during virucidal activity. It provided moderate cell protection, with EC50 values ranging from 120.88 to 186.10 µg/mL and SI values from 2.20 to 3.39. Post-infection treatment showed varying efficacy, with Coxsackie A10 demonstrating the highest SI (3.90). In vivo, the extract at 200 mg/kg significantly improved gastric emptying to 96.47% and slightly increased gastrointestinal transit from 50.33% to 61.46%; Conclusions These results suggest that silydianin may be effective in treating enteroviral infections and enhancing intestinal function, making it a promising candidate for gastroparesis treatment and warranting further research.

(1) Background: Factors reported in literature associated with inappropriate prescribing of antimicrobials, such as physicians with less experience, uncertain diagnosis, and patient caregiver influences on physicians' decisions. Monitoring antimicrobial resistance is critical for identifying emerging resistance patterns, developing, and assessing the effectiveness of mitigation strategies. Improvement in prescribing `antimicrobials would minimize the risk of resistance and, consequently, improve patients' clinical and health outcomes. The purpose of the study is to delineate factors associated with antimicrobial resistance, describe the factors influencing prescriber’s choice during prescribing of antimicrobial, and examine factors related to consequences of inappropriate prescribing of antimicrobial. (2) Methods: Cross-sectional study conducted among healthcare providers (190) in six tertiary hospitals in the Eastern province of Saudi Arabia. The research panel has developed validated and piloted survey specific with closed-ended questions. A value of P &lt;0.05 was considered for statistical significance. All data analysis was performed using Statistical Package for Social Sciences (IBM SPSS version 23.0). (3) Results: 72.7% of the respondents have agreed that poor skills and knowledge are key factors that contribute to the inappropriate prescribing of antimicrobials. All the respondents acknowledged effectiveness and previous experience with the antimicrobial, and reading scientific materials (such as books, articles, and the internet) were key factors influencing physicians’ choice during antimicrobial prescribing. (4) Conclusion: The current study has identified comprehensive education and training needs for healthcare providers about antimicrobial resistance. Using antimicrobials unnecessarily, insufficient duration of antimicrobial use, and using broad spectrum antimicrobials, were reported to be common practices. Further, poor skills and knowledge were a key factor that contributed to the inappropriate use and overuse of antimicrobials and using antimicrobials without physician prescription (self-medication) were the key factors which contribute to AMR from participants’ perspectives. Furthermore, internal policy and guidelines are needed to ensure that the antimicrobials are prescribed in accordance with standard protocols and clinical guidelines.

Background: Convectional methods for drug delivery often faces setbacks due to systemic distribution, short half-life and degradation of therapeutics and therefore reduce concentrations of drug available to target tissue. Nanotheranostic provide a novel method for treating and diagnosing diseases Methodology: collection and review of relevant literatureResult: while nanotheranostic offer advantage of personalized medicine and often combines diagnosis and therapy using single molecular approach, nuclear medicine relies on radioactive isotopes to diagnosed and destroys cancer cells. In both cases, nanocarriers such as lipid-based, polymer-based, drug-conjugate, inorganic nanoparticles are used to deliver drugs/probes/isotopes to target site, generating images and thereafter chemotherapy/radiotherapy begins.Conclusion: Nanotheranostic plays important role in diseases diagnostic, therapy, imaging, monitoring of disease progression / response through the use of nanocarriers. This is made possible through nanoparticles/nanocarriers that delivers drug to the target tissues/cells.

Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.

Background: As the impacts of diabetes-induced reproductive damage are now evident in young people, we are now in an urgent need to devise new ways to protect and enhance the reproductive health of diabetic people. The purpose of this study was to assess the protective impact of enalapril (an ACE inhibitor) and paricalcitol (a vitamin D analogue) alone and in combination, on streptozotocin (STZ) diabetes-induced testicular dysfunction in rats. Material and methods: This study was carried on 50 male Sprague-Dawley rats; 10 normal rats were allocated as non-diabetic control group. 40 rats developed diabetes after receiving a single dose of STZ, then the diabetic rats were divided into four groups of equivalent number assigned as diabetic control, enalapril treated, paricalcitol treated, in addition to combined enalapril and paricalcitol treated groups. The effects of mono and combined therapy with paricalcitol and enalapril on testicular functions, sperm activity, glycemic state oxidative stress and inflammatory parameters as well as histopathological examination were assessed when compared to the normal and diabetic control rats. Results: As a result of diabetes induction, epididymal sperm count, sperm motility, serum levels of testosterone, follicle stimulating hormone (FSH) as well as luteinizing hormone (LH) and the antioxidant enzyme activities were significantly decreased, while abnormal sperm (%), insulin resistance, nitric oxide (NO), malondialdehyde (MDA), interleukin-6 (IL-6) as well as tumor necrosis factor-α (TNF-α) were significantly increased, along with sever distortion of testicular structure. Interestingly, treatment with paricalcitol and enalapril either alone or in combination significantly improved the sperm parameters, increased antioxidant enzyme activities in addition to serum levels of testosterone, FSH and LH, reduced insulin resistance, IL-6 and TNF-α levels and finally ameliorated the diabetes-induced testicular oxidative stress and histopathological damage with somewhat superior effect for paricalcitol monotherapy and combined therapy with both drugs than monotherapy with enalapril alone. Conclusion: Monotherapy with paricalcitol and its combination therapy with enalapril has a somewhat superior effect in improving the diabetes-induced testicular dysfunction (most probably as a result of their hypoglycemic, antioxidant, anti-inflammatory and anti-apoptotic properties), than monotherapy with enalapril alone in male rats, recommending a synergistic impact of both drugs.

This paper presents preliminary operational performance results of a pilot grid-connected photovoltaic (PV) system designed and installed on the rooftop of the Ministry of Petroleum, Energy and Mining headquarter in Nouakchott (latitude of 18.1°N and the longitude of 16.0°W), Mauritania. The aim is for the government to demonstrate the relevance of using solar energy and to encourage the uptake of solar PV technology for commercial and residential building applications in Mauritania.. In this study, the grid-connected PV system has a peak power of 48 kW and the performance monitoring was carried out during one year, with a system that allow to measure DC power, inverter and system conversion efficiency, energy generated by the PV arrays, solar radiation in the inclination plane of panels, ambient temperature and module temperature. During this period, the PV plant was found to supply 65,668 kWh to the grid. The final yield ranged from 3.91 to 5.09 kWh/kWp/day. The performance ratio was found to vary from 69.69% to 89.35% and the annual capacity factor was found to be 19%. Finally, performance parameters were compared with other PV plants installed in the same region of Northern Africa. The outcome of this work is deemed important in assisting accurate PV system design and decision-making.

{\bf Abstract.} \\ {\bf Background:} Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 burden among the Rohingya refugees of Bangladesh to keep up with the disease outbreak's pace, health needs, and disaster preparedness. \noindent{\bf Methodology and Findings:} To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious Recovered (SEIR) transmission model. All the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and good fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be good control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number ($ \mathcal{R}_{0} $) has been estimated to be 0.7563. \noindent{\bf Conclusion:} Our analysis suggests that, due to the extensive precautions from the Bangladesh government as well as other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. Although detailed and pragmatic preparedness should be adopted for the worst scenario.

as the growth and popularity of technology has become simultaneous ascend in both impacts and numbers of cyber criminals thanks to the web. For many years, the organization has strived in ways of preventing any attacks from cyber-criminal with advanced techniques. Cybercriminals and intruders are developing a more advanced way to breach the security surface of an organization. Advanced Persistent Threats are also known as APT are new and a lot more sophisticated version for multistep attack scenarios that are known and are targeted just to achieve a goal most commonly undercover activities. this report, there will cover everything I know that tells us about APT with more word and brief explanations

Peste des Petits Ruminants (PPR), a highly contagious viral disease, causes significant economic losses in sheep and goats. Laboratory diagnosis is crucial to disease control and eradication. Since PPRVs have recently adopted new hosts, genetic diversity within isolates of the same lineage is more likely than if they were host-specific, sheep and goats most often. ELISA detects antibodies and antigens in serology. However, mishandling, environmental conditions (temperature and humidity), storage, and sensitivity issues of commercially available ELISA kits prevent rapid PPR virus detection in third-world countries like Pakistan. In the 2020-21 outbreaks in Pakistan, 325 blood samples, 19 swabs, and 6 virus tissue samples of sheep and goats were collected from Gilgit, Islamabad, and Fateh Jang. These virus isolates were submitted to NCBI for partial N gene sequencing. Antigen was prepared from indigenous virus Using semi-purified antigen from PPRV, an indirect ELISA for the detection of PPR antibodies in goat and sheep serum was developed using MW600922 grown in Vero cells. Dilutions of 1:200 serums and 1:32 antigens improve antibody detection. The N-gene-based analysis revealed a better understanding of PPRV genetic characterization. This study develops an RT-PCR assay to detect PPRV by targeting the N-protein gene. This assay offers an accurate and affordable diagnostic tool for PPRV and partial genome sequencing. Results described that PPRV isolates from the current study have 99.73% and 99.4% similarity to previously published Lahore and Faisalabad isolates from Pakistan, respectively. Compared to previous isolates from NCBI data, the virus showed high divergence rates with the Turkish strain. The comparative analysis between Commercial kit (c-ELISA) and I-ELISA revealed that the former had a high sensitivity of 90.60% and specificity of 85.23% compared with the cELISA kit. By comparing I-ELISA and VNT, we find that the current assay is 100% specific and 82.14 % sensitive. Based on these results, serological surveys for PPR antibodies, on a larger scale, in small ruminants can be conducted with indirect ELISA rather than competitive ELISA. Furthermore, the cost and storage efficiency was highly significant because the currently developed method has a low production cost, which is much lesser than the commercially available kit. Our findings demonstrated a significant breakthrough in terms of cost-effectiveness and storage efficiency, and they are highly recommended for developing countries.

Direct Energy Deposition (DED) is a versatile and efficient method in metal additive manufacturing. However, humping, caused by abnormal dynamics in the melt pool (MP), poses a significant threat to the geometric integrity of manufactured products. Current state-of-the-art (SOTA) methods primarily detect humping by analyzing late-stage spatial abnormalities, such as MP detachment. This approach is fundamentally reactive, leading to a tendency to miss early humping spatiotemporal dynamics, like cyclic elongation of the MP. This study introduces a novel, proactive indicator named VIMPS (Variability of Instantaneous MP Solidification-Front Speed), a physics-based tool designed to quantify early abnormal fluctuations in MP solidification speed. The experiments demonstrate VIMPS correlation with humping-induced geometric inaccuracies. By capturing early spatiotemporal dynamics of the MP, VIMPS reduces detection latency by 30 seconds compared to existing SOTAs that focus solely on spatial abnormalities. This significant improvement transforms detection from reactive to proactive, providing the time needed for corrective actions to enhance the overall productivity and quality of the DED process.

Overuse and misuse of antibiotics can pose the risks of spreading mutant strains that show antimicrobial resistance (AMR), with negative impact on management of bacterial infections and economic implications on healthcare systems. Research and development of natural antibacterial agents could be a priority in the next years to improve number of effective antibacterial molecules and to reduce AMR phenomenon and its development. The present study identified the most effective concentration and contact time of Thymus vulgaris L. essential oil (TEO) to obtain bactericidal effect in vitro against different Gram + and Gram – bacterial strains. Six clinically isolated (wild types) bacterial strains, (Citrobacter freundii, Enterococcus feciorum, Proteus mirabilis, Acinetobacter cioffi, Pasteurella putrefaciens, Klebsiella pneumoniae) and two ATCC (Staphylococcus aureus and Streptococcus mutans) were tested after 1min, 3min and 5min of contact with TEO. Preliminary results on S. aureus after 24h incubation revealed a TEO concentration of 9.28mg/mL (w/v) that completely inhibited bacteria growth keeping cell viability. Total suppression of bacterial growth at all tested contact times was observed for all tested bacterial strains and the results were confirmed after 48h incubation. Bacterial growth suppression was confirmed even with presence of organic component. These preliminary results showed in vitro antimicrobial efficacy of TEO against different Gram+ and Gram- bacterial strains. Future studies are necessary to confirm the reproducibility of these results even on other strains and to define exactly the molecular mechanisms of EOs, in order to consider TEO as a valid alternative of classic antibiotic therapies and subsequently to reduce the occurrence of AMR.

Optimizing the nitrogen (N) timings and rate can improve nutrient uptake, and nutrient-efficiencies, especially of N in wheat under the changing climate scenario. Climatic stress in the form of high temperature and drought resulted in the decreased crop physiology and, ultimately, grain yield. Taking the example of rainfed wheat, we quantified the impact of N application rates as full and split-dose at three variable sites of rainfed Pothwar, Pakistan by conducting field experiments for two years (2013-14 and 201-15). Treatments include, T₁ = Control (No fertilizer applied), full dose of N applied at the time of crop sowing, i.e. T₂ = 50 kg N ha^-1, T₃ = 100 kg N ha^-1 and T₄ = 150 kg N ha^-1 and split application of N at different timings during different stages of the crop called as split application of N, i.e. T₅: Application of 50 kg N ha^-1 (15 kg N ha^-1 (Sowing) : 20 kg N ha^-1 (Tillering) :15 kg N ha^-1 (Anthesis), T₆: Application of 100 kg N ha^-1 (30 kg N ha^-1 (Sowing): 40 kg N ha^-1 (Tillering) : 30 kg N ha^-1 (Anthesis) and T₇: Application of 150 kg N ha^-1(45 kg N ha^-1 (Sowing) : 60 kg N ha^-1 (Tillering) : 45 kg N ha^-1 (Anthesis). Three study sites include viz. Islamabad (High rainfall with optimum temperature), University Research Farm (URF)-Koont (Medium rainfall with moderate temperature), and Talagang (low rainfall with high temperature). Results showed that the highest stomatal conductance (0.80 mole m^-2 sec^-1), net photosynthetic rate (20.07 μmole m^-2s^-1), transpiration rate (9.58 mmole m^-2s^-1), intercellular CO₂ concentration (329.25 μmole CO₂ mol^-1 air), SPAD values (58.86 %) and proline contents (35.42 μg g^-1) were obtained for split application of N (T₆ = Split N₁₀₀) compared to control and full dose of N treatments. Among sites, these physiological traits remained highest at Islamabad and lowest at Talagang, while among years, maximum values of the measured parameters were obtained in 2013-14. A similar trend was observed for crop total N, N efficiencies, and agronomic traits of the crop. Our results suggest that optimum N application rate and its suitable timings can help to harvest real benefits of N as in our findings, split dose resulted in the maximum performance of the crop from physiological parameters to the agronomic traits of the rainfed wheat crop.

The capacitated team orienteering problem with time windows (CTOPTW) is a NP-hard combinatorial optimization problem. In the CTOPTW, a set of customers is given each with a profit, a demand, a service time and a time window. A homogeneous fleet of vehicles is available for serving customers and collecting their associated profits. Each vehicle is constrained by a maximum tour duration and a limited capacity. The CTOPTW is concerned with the determination of a preset number of vehicle tours that begin and end at a depot, visit each customer no more than once while satisfying the time duration, time window and vehicle capacity constraints on each tour. The objective is to maximize the total profit collected. In this study we propose an iterated local search (ILS) algorithm to deal with the CTOPTW. ILS is a single solution based meta-heuristic that successively invokes a local search procedure to explore the solution space. A perturbation operator is used to modify the current local optimum solution in order to provide a starting solution for the local search procedure. As different problems and instances have different characteristics, the success of the ILS is highly dependent on the local search procedure, the perturbation operator(s) and the perturbation strength. The basic ILS uses a single perturbation operator and the perturbation strength remains the same during the optimization process. To address these issues, we use three different perturbation operators and a varying perturbation strength which changes as the algorithm progresses. The idea is to assign a larger perturbation strength in the early stages of the search in order to focus on exploring the search space. The perturbation strength is gradually decreased so that we focus more on exploitation. The computational results show that the proposed ILS algorithm is able to generate high quality solutions on the CTOPTW benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both the solution quality and computational time. Moreover, the proposed ILS produces 21 best known results and 5 new best solutions.

Background: Aberrations in endothelial cells, immune and oxidative pathways are associated with atherosclerosis (ATS) and unstable angina (UA). The role of trace elements, minerals, and the endogenous opioid system (EOS) in UA are less well established. Methods: We measured lipid, insulin resistance (IR), and immune, trace element (copper and zinc), mineral (magnesium, calcium), EOS (β-endorphin and mu-opioid receptor (MOR)) and antioxidant (vitamin D3) biomarkers in patients with ATS (n=60) and UA (n=60) and healthy controls (n=58). Results: ATS patients showed increased atherogenic and IR indices, IL-6, IL-10, β-endorphin, copper and magnesium, and lower zinc than healthy controls. Logistic regression showed that UA was significantly discriminated from ATS without UA with an accuracy of 85.5% using calcium, IL-10, β-endorphin, MOR, triglycerides, IR (all positively), and copper and vitamin D3 (inversely). Neural networks showed that UA was discriminated from ATS without UA with an area under the ROC curve of 0.942 using MOR, β-endorphin, calcium, insulin resistance, vitamin D3 and copper as input variables. We found that 50.0% of the variance in IR was explained by the regression on copper, IL-10, IL-6 (all positively), and zinc (inversely), while 32.9% of the variance in the atherogenic index of plasma was explained by copper, IL-10 (both positively), and magnesium (inversely). Conclusion: UA is not only mediated by insulin resistance, atherogenicity, and immune disorders, but also by aberrations in the endogenous opioid system and trace elements as well as lowered antioxidant levels. Copper appears to play a key role in IR and atherogenicity.

The classification of kidney tumors has undergone a remarkable evolution since the early 19th century, reflecting advances in medical science and technology. This historical overview traces the development of renal tumor classification, beginning with early clinical observations and macroscopic descriptions to the integration of molecular diagnostics in contemporary classifications. The establishment of international organizations like the World Health Organization (WHO) facilitated collaboration and standardization in tumor classification, leading to refined classifications and standardized nomenclature. Advancements in histological and molecular studies, particularly genetic analyses, revolutionized the understanding of renal cell carcinomas, enabling precise categorization based on molecular characteristics. The journey from rudimentary classifications to sophisticated systems based on detailed histological, immunohistochemical, and molecular criteria underscores the continuous evolution of kidney tumor classification. This article provides a comprehensive overview of this evolution, highlighting the contributions of scholars and the impact of technological advancements on our understanding and management of kidney tumors.

Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to create high-resolution images from low-resolution ones. It has important applications in fields 2 such as medical imaging and security surveillance. While traditional methods such as interpolation 3 and reconstruction-based models have been used in the past, deep learning techniques have recently 4 gained attention due to their superior performance and computational efficiency. This article proposes 5 an Autoencoder based Deep Learning Model for SSIR, in particular, a light model that uses fewer 6 parameters without compromising performance. The down-sampling part of the Autoencoder 7 mainly uses 3 by 3 convolution and has no subsampling layers. The up-sampling part uses transpose 8 convolution and residual connections from the down sampling part. The model is trained using a 9 subset of the VILRC ImageNet database. The model is evaluated using quantitative metrics PSNR, 10 SSIM as well as qualitative measures such as perceptual quality. PSNR and SSIM figures as high as 11 76.06 and 0.93 are reported.

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide and a major global health burden. The treatment of HCC has experienced a paradigm shift in the last few years, following the discovery of immunotherapy. However, despite the profound success of immunotherapy in other cancer types, their response in HCC remains unsatisfactory. Some of the reasons that can explain this poor response include the immunosuppressive microenvironment of HCC, the absence of reliable biomarkers for response, and drug resistance. Previous preclinical and clinical studies have highlighted the role of combination therapy in overcoming some of these hurdles. In this article, we discuss the benefit of combining immune checkpoint inhibitors (ICIs) with stereotactic body radiation therapy (SBRT) in HCC and shed light on promising clinical trials.

With the emergence of the Internet and WWW has become a comprehensive source of information for the extraction of meaningful information has become a significant challenge in the past decade. Furthermore, the available information is classified in unstructured, structured, and semi-structured forms. Numerous amount of information on the web is also characterized in an unstructured or semi-structured format. This usually extracts the potential useful information from these multiple forms and has been considered a leading area of research in this modern era. The authors presented web content mining as a subcategory of web mining that focuses on important extracting patterns from the contexts available in the documents on the web. Hence, this paper focuses on the multiple content mining concepts, tools, and techniques implemented on the documents available on WWW.

The border between Niger and Libya, often described as an "ungoverned space," epitomizes the complex interplay of economic, political, and security challenges within the Sahel region. Authorities and international actors have made various efforts to establish control, but tribal structures predominantly manage governance in these areas. These structures lack formal institutions and rely heavily on external security interventions to combat terrorism and irregular migration. The increasing militarization of the region raises significant concerns about the long-term efficacy of these approaches in achieving sustainable security. This study argues that enduring stability in such contexts requires a nuanced balance of economic, political, and social factors. The research methodology includes a comprehensive literature review of security issues, governance, state fragility, and counter-terrorism, utilizing scientific journals, books, reports, and newspapers. The study investigated the dual nature of ungoverned spaces, examining their role in facilitating terrorism and organized crime while highlighting the critical need for governance to enhance citizen welfare and support state functions. Recent developments in Libya and the Sahel under-score the urgency of addressing security and developmental challenges. The study concludes by advocating for sustainable political and development initiatives over militarization, emphasizing the necessity of a new social contract to address the security challenges in the Sahel region effectively.

Synthesis of a novel series of chromen-3-yl-pyridine moieties. IR, NMR, and MS spectroscopy were used to confirm the structure of these novel compounds and study of antitumor activity of these compounds. The structure activity relationship investigation demonstrated that 2,4-diamino-5-(3-methoxyphenyl)-7-(2-oxo-2H-chromen-3-yl)-1,8-naphthyridine-3-carbonitrile (16), was more effective and naphthyridine-3-carbonitrile derivatives 17, 18 and pyrido[2,3-d]pyrimidine derivative 12, while compounds 5a,b, 9c, 11, 13 and 14 were moderate activity for antitumor activities.

The flavonoid silymarin (SMN) has been widely used in preclinical and clinical studies to treat various types of cancer, either alone or in combination with chemotherapy agents. Herein, we investigated the therapeutic efficacy of SMN and its combination with capecitabine (CAP) and irinotecan (IRI) in a mouse model of colon cancer. A modified diet supplemented with SMN (2500 ppm) with mono- and combined therapy of CAP and IRI were used following 1, 2 dimethylhydrazine (DMH)-induced colon cancer. The lipid profile, liver function tests, and cytokine levels were measured. Additionally, the lipid peroxidation and inflammation indices as well as the antioxidant activities were examined. Eventually, western blotting analysis for colonic BAX and Bcl-2 levels and histopathological examination of colon sections were carried out. SMN alone and in combination with the chemotherapeutic agents could attenuate the DMH-induced hyperlipidemia and elevated liver function enzyme levels. SMN supplementation with chemotherapy agents could also improve antioxidant activity and reverse the elevated levels of lipid peroxidation and inflammatory markers. Furthermore, significant upregulation of BAX and downregulation of Bcl-2 were observed. Besides, carcinogen-induced polyp multiplicity, dysplastic, and inflammatory changes were ameliorated considerably by treatment regimens. Our results showed that the potential anticancer properties of SMN could enhance the therapeutic effects of chemotherapy agents and reduce hepatotoxicity.

Avian Metapneumovirus (aMPV) is a significant poultry pathogen with a global presence, primarily causing respiratory issues in turkeys. It also affects chickens, although the severity of its impact is often lessened in this species. In Morocco, aMPV has been detected in broiler flocks, prompting the need to deeply analyze circulating strains to understand better the epidemiology and develop control measures accordingly. This research focuses on the sequencing and molecular characterization of aMPV in these flocks. Additionally, aMPV isolated from turkeys displaying Turkey Rhinotracheitis (TRT) signs, were included in the study to compare the findings. RNA extracted from positive swabs was subjected to nested PCR, targeting the attachment protein of the G gene, followed by gel electrophoresis. Amplicons were purified and sequenced using the Sanger method. Bioinformatics tools facilitated sequence analyses, including BLAST for similarity searches and Mega® for phylogenetic analysis using the maximum likelihood method with 1000 bootstrap replicates. The investigation unveiled the existence of three distinct clades of the same aMPV/B strain, two of which originated from used vaccines and one from field recombination, all circulating in broilers and turkeys and indicating potential virus transmission between both poultry species. This article presents the first-ever molecular characterization of aMPV isolated from Moroccan broilers and turkeys, encompassing comprehensive investigations on its presence and subtype, and genetic characterization.

This paper identifies a criterion for choosing the largest set of rejected hypotheses in high-dimensional data analysis where Multiple Hypothesis testing is used in exploratory research to identify significant associations among many variables. The method neither requires predetermined thresholds for level of significance, nor uses presumed thresholds for false discovery rate. The upper limit for number of rejected hypotheses is determined by finding maximum difference between expected true hypotheses and false hypotheses among all possible sets of rejected hypotheses. Methods of choosing a reasonable number of rejected hypotheses and application to non-parametric analysis of ordinal survey data are presented.

Recently, microfluidics deformability cytometry has emerged as a powerful tool for high throughput mechanical phenotyping of large populations of cells. These methods characterize cells by their mechanical fingerprints through exerting hydrodynamic forces and monitoring the resulting deformation. These devices have shown great promise for label free cytometry, yet there is a critical need to improve their accuracy and reconcile any discrepancies with other methods, such as atomic force microscopy. In this study, we employ computational fluid dynamics simulations and uncover how the elasticity of frequently used carrier fluids such as methyl-cellulose dissolved in phosphate-buffered saline is significantly influential to the resulting cellular deformation. We conducted CFD simulations that are conventionally used within the deformability cytometry field, which neglect fluid elasticity. Subsequently, we incorporated a more comprehensive model that simulates the viscoelastic nature of the carrier fluid. A comparison of the predicted stresses between these two approaches underscores the significance of the emerging elastic stresses in addition to the well-recognized viscous stresses along the channel. Furthermore, we utilize a two-phase flow model to predict the deformation of a promyelocyte (i.e. HL-60 cell type) within a hydrodynamic constriction channel. The obtained results highlight a substantial impact of the elasticity of carrier fluid on cellular deformation and raise questions about the accuracy of mechanical property estimates derived by neglecting elastic stresses.

Hepatocellular carcinoma (HCC) early diagnosis is challenging. Moreover, for patients with al-pha-fetoprotein (AFP)-negative HCC, this challenge is augmented. MicroRNAs (miRs) profile may serve as potential HCC molecular markers. We aimed to assess plasma homo sabines (hsa)-miR-21-5p, hsa-miR-155-5p, hsa-miR-192-5p, hsa-miR-199a-5p expression levels, as panel of biomarkers for HCC in chronic hepatitis C virus (CHCV) patients with liver cirrhosis (LC), es-pecially AFP-negative HCC cases, a step toward non-protein coding (nc) RNA precision medicine. Subjects and Methods: 79 patients enrolled with CHCV infection with LC, subclassified into LC group without HCC (n=40) and LC with HCC (n=39) in comparison with 15 apparently healthy control subjects. Real-time quantitative PCR was used to measure plasma hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-192-5p, and hsa-miR-199a-5p. Results: Plasma hsa-miR-21-5p and hsa-miR-155-5p demonstrated significant upregulation, while hsa-miR-199a-5p demonstrated significant downregulation in the HCC group (n=39) when compared to LC group (n=40). Hsa-miR-21-5p expression was positively correlated with serum AFP, insulin, and insulin re-sistance (r=0.5, p<0.001, r=0.334, p=0.01, and r=0.303, p=0.02, respectively). According to the ROC curves, for differentiating HCC from LC, combining AFP with each of hsa-miR-21-5p, hsa-miR-155-5p, and miR199a-5p improved the diagnostic sensitivity to 87%, 82%, and 84%, re-spectively, vs 69% for AFP alone, with an acceptable specificity of 77.5%, 77.5%, and 80%, respec-tively, and AUC= 0.89, 0.85, and 0.90, respectively vs. 0.85 for AFP alone. Hsa-miR-21-5p/hsa-miR-199a-5p and hsa-miR-155-5p/hsa-miR-199a-5p ratios discriminated HCC from LC at AUC=0.76 and 0.71, respectively, with sensitivities=94% and 92%, and specifici-ties=48% and 53%, respectively. Upregulation of plasma hsa-miR-21-5p was considered as an independent risk factor for HCC development [OR=1.198(1.063–1.329), p=0.002]. Conclusion: Combining each of hsa-miR-21-5p, hsa-miR-155-5p, and hsa-miR-199a-5p with AFP made possi-ble to identify HCC development in LC patients’ cohort with higher sensitivity than using AFP alone. Hsa-miR-21-5p/hsa-miR-199a-5p and hsa-miR-155-5p/hsa-miR-199a-5p ratios are poten-tial HCC molecular markers for AFP-negative HCC patients. Hsa-miR-21-5p was linked to insu-lin metabolism in HCC patients’ group as well as being an upregulated independent risk factor for the emergence of HCC from LC in CHCV patients.

The paper examines the impact of inward remittances flows on per capita GDP growth in Bangladesh during 1976-2012. We find that the growth effect of remittances is negative at first but becomes positive at a later stage, an evidence of a non-linear. Unproductive use of remittances was rampant in the beginning when they were received by migrant families but better social and economic investments led to more productive utilisation of remittances receipts at later periods. This was the possible mechanism behind the U-shaped relationship. Unlike what is suggested in the literature that the effect of remittances is more pronounced in a less financially developed economy, our evidence do not show that the effect of remittances on per capita GDP growth in Bangladesh is conditional on the level of financial development.

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

This paper explores the influence of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on the start-up intention through the mediating roles of entrepreneurship educational outcomes; entrepreneurship cognition, entrepreneurship competencies, and innovation spirit of students in higher education institutions in the Middle East. The proposed conceptual framework was empirically tested with partial least squares structural equation modeling using 166 survey responses from fourth-year undergraduate students enrolled in business administration programs in four higher education institutions in the Middle East region. The findings reveal that ChatGPT significantly promotes start-up intention, while ChatGPT has insignificant influence on entrepreneurship educational outcomes. Conversely, the SimVenture platform negatively influences start-up intention. Additionally, the SimVenture platform significantly improves entrepreneurship educational outcomes; thereby entrepreneurship educational outcomes directly influence start-up intention. Furthermore, the mediation paths show ChatGPT has an insignificant indirect influence on start-up intention through the mediating roles of entrepreneurship educational outcomes, while the SimVenture platform exhibits a notable indirect influence on startup intentions through improved entrepreneurship educational outcomes. These findings seek to provide insights for higher education institutions in the Middle East region in leveraging GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) to enhance entrepreneurial knowledge, awareness, competencies, and the spirit of innovation, which leads to promoting students’ intentions to start their businesses. Theoretically, the study contributes to filling the gap in the existing literature between AI tools and entrepreneurship education. This study is among the first to empirically explore the influences of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on entrepreneurship educational outcomes and the intention of students to embark on entrepreneurial ventures. It highlights the need for understanding AI technologies as distinct pedagogical tools.

The architecture, Engineering, and Construction (AEC) industry is dealing with serious challenges on a global scale, i.e., project delays, budget overruns, and insufficient stakeholder collaboration. In this context, Value Engineering (VE) and Building Information Modeling (BIM) have been used recently to address these challenges and cope with large-scale and complex construction projects. An extensive body of literature highlights significant advantages of integrated VE within BIM technology. Despite this, it is full practical potential has not yet been realized. For this reason, this paper aims to investigate the integration of Value Engineering (VE) and Building Information Modeling (BIM) within a Common Data Environment (CDE) for the Architecture, Engineering, and Construction (AEC) industry. In doing this research, a comprehensive framework was developed and systematically implemented into practice through the use of four interconnected modules: (M. 1); Creating CDE, (M. 2); Design BIM Model Development, (M. 3); Value Analysis / Engineering Model, and (M. 4); Value Engineering Study. Introducing a new term the ""VEIDEA"" databank based on the OmniClass classification system provides a structured database utilized to store, manage, and accumulate VE ideas. Moreover, the study incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase. Such a framework seeks to assist designers and VE team members make informed decisions when choosing appropriate design alternatives. Lastly, a case study method was adopted to examine the proposed framework for an office building. Quantitative data showed a significant link between cost savings and VR integration with BIM technology. For instance, cost of the RC slabs dropped by 20%, while flooring materials cost dropped by a 39%. Empirically, evidence from the case study supports literature regarding t the potential for significant enhancements in both cost-effectiveness and overall project performance through this innovative integration. In the context of knowledge contribution, this study provides an innovative way to assist designers and VE team members in making well-informed decisions when choosing appropriate design alternatives.

Enhanced oil recovery (EOR) refers to the various techniques used to increase the amount of crude oil that can be extracted from an oil field. These techniques are used after the primary and secondary recovery methods have been depleted. There are three main types of EOR: thermal, gas injection, and chemical. EOR techniques can be expensive and complex, but they can help to recover additional oil that would otherwise be left in the reservoir. EOR can also extend the life of an oil field and increase the overall production from a given field. The parameters affecting oil recovery are a major concern in EOR systems, and these parameters need more consideration with the factors affecting them. In this study, the influence of permeability variations on fluid flow in a sandstone reservoir was explored, as well as an up-to-date description of three states of EOR with thorough explanations of the methods utilized and the mechanisms driving their oil recovery application. The difficulties encountered in the use of the different standard EOR Mechanisms were noted, and solutions to these difficulties were proposed. Furthermore, the contemporary trend of adding nanotechnology and its synergistic effects on the stability and efficiency of traditional chemicals for EOR were examined and analysed. Finally, laboratory findings and field initiatives were discussed. The review went into detail about the transport of nanoparticles across reservoirs, as well as the evaluation of EOR, mobility ratio, and fluid displacement efficiency. This review provides thorough information on the uses of improved oil recovery mechanisms for sustainable energy production.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

Fusion protein therapeutics engineering is advancing to meet the need for novel medicine. Herein, we further characterize the development of novel RTA & PAP-S1 antiviral fusion proteins. In brief, RTA/PAP-S1 and PAP-S1/RTA fusion proteins were produced in both cell free and E. coli in vivo expression systems, purified by His-tag affinity chromatography, and protein synthesis inhibitory activity assayed by comparison to the production of a control protein, CalmL3. Results showed that the RTA/PAP-S1 fusion protein is amenable to standardized production and purification and has both increased potency and less toxicity compared to either RTA or PAP-S1 alone. Thus, this research highlights the developmental potential of novel fusion proteins with reduced cytotoxic risk and increased potency.

Emotions play a vital role in understanding human behavior and interpersonal relationships. The ability to recognize emotions through Electroencephalogram (EEG) signals offers an alternative to traditional methods, such as questionnaires, enabling the identification of emotional states in a non-intrusive manner. Automatic emotion recognition holds great potential, eliminating the need for clinical examinations or physical visits, thereby contributing significantly to the advancement of Brain-Computer Interface (BCI) technology. However, one of the key challenges lies in effectively selecting and extracting relevant features from the EEG signal to establish meaningful distinctions between different emotional states. The process of feature selection is often time-consuming and demanding. In this research, we propose a groundbreaking approach for automatically identifying three emotional states (positive, negative, and neutral) by leveraging auditory stimulation of EEG signals. Our novel method directly applies the raw EEG signal to a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) architecture, bypassing the conventional feature extraction and selection steps. This unconventional approach offers a significant departure from existing literature. Our proposed network architecture comprises ten convolutional layers, followed by three LSTM layers and two fully connected layers. Through extensive simulations and evaluations on 12 active channels, our algorithm demonstrates exceptional performance, achieving an accuracy of 97.42\% and 95.23\% for the binary classification of negative and positive emotions, as well as a Cohen's Kappa coefficient of 0.96 and 0.93 for the three-class classification (negative, neutral, and positive), respectively. These promising results highlight the efficacy of our novel methodology and its potential implications in advancing emotion recognition using EEG signals.

In this digital era, online learning and teaching is an important platform for college and university learners which required many technical facilities and in depth knowledge to have learning process effectively. Students and teachers in Higher Education Institutions (HEIs) were forced to grapple with the inevitability of shifting to online classes due to the advantages of effective distance learning or for large learning group. In the case of Oman, there have been some efforts to integrate alternative learning mechanisms such as online and mobile education in the past but not followed the scale in the post pandemic world. This study seeks to utilize the principles of Total Quality Management (TQM) through the implementation of Quality Function Deployment (QFD) as a way to better understand the experiences of Omani teachers and students from HEIs. A voice of customer from teachers and students have been collected and recorded. In this regard, 165 staff and 355 students from HEIs of Engineering participated in the survey. After the survey, a house of quality analysis was done to analyze the requirements based on how strongly they are related to the standard learning outcomes expected from HEIs in Oman. The results show that more attention is required in online education in the Sultanate of Oman before it is implemented as a major part in learning and teaching.

The study is based on the notion that big data predictive analytics is important for developing strategic alliances performance of companies. this study investigates the relationship between big data predictive analytics, big data culture, and competitive strategies 'techniques were adopted, such as descriptive statistics, correlation,regression, etc. using SPSS and SmartPLS statistical software. Hypotheses were tested with bootstrapped analysis using SEM (through SmartPLS). The study developed a structural equation model by using the SEM analysis. The results of the SEM analysis suggested the hypothesized model of the study was valid. The results supported all the hypotheses of the study.Through empirical analysis, demonstrated the conclusion is that the big data predictive analytics has a positive and significant relationship with strategic alliance performance

This study proposes a structure for companies to use when implementing human resource practices in open innovation. Despite the fact that open innovation has received a lot of attention in the innovation management field as companies open their doors to information exchange in an effort to spur creative thinking, there are very few empirical articles that connect this trend to the human resource management literature. Our findings are the result of an extensive qualitative investigation into Julphar Gulf Pharmaceutical Industries Manufacturers in the United Arab Emirates (UAE) and its open innovation program. Internal, external, and combined are the three primary pillars of human resource management. We also demonstrate how the evolution of the open innovation initiative is linked to the state of the art in HRM and open innovation literature. The framework identifies HRM practices for both internal and external participants in the open innovation effort. Much of this HRM is done off the books, in a setting separate from the host company. By providing actual evidence of how firms use HRM to manage open innovation projects, our research adds to the scant and mostly theoretical literature linking open innovation and HRM.

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

In January 2020, the World Health Organization declared a public health emergency and announced a new coronavirus disease (COVID-19), which would later go on to be declared as a pandemic, changing the global sphere and placing the economies of almost all countries under heavy stress. The airline industry, that had just begun recovering after facing crises one after another in the last two decades, from early 2000 due to 9/11, to the global financial crisis later, is now oce again facing an enormous challenge of closed borders and greater lockdowns due to the pandemic. Borders are closed, with very few planes are in the air, while the rest are grounded. The purpose of this paper is to give a conceptual understanding of the current pandemic situation and its consequences on the airline industry. The paper takes a unique perspective of human resource management (HRM) that is rarely used in the airline industry.

A thin-shell wormhole is crafted by the cut-and-paste method of two Bardeen de-Sitter black holes using Darmois-Israel formalism. Energy conditions are considered for different values of magnetic charge while both mass and cosmological constant are fixed. The attractive and repulsive characteristics of the throat of the thin-shell wormhole are also examined through the radial acceleration. Dynamics and stability of the wormhole are studied around the static solutions of the linearized radial perturbations at the throat of the wormhole. The regions of stability are determined by checking out the condition of concavity of the potential as a function in the throat radius for different values of magnetic charges.

Purpose: To compare the efficacy and safety of graft-free short tunnel small flap (STSF) technique with that of scleral patch graft (SPG) in Ahmed glaucoma valve (AGV) implantation. Design: Randomized clinical trial. Participants: Eighty-eyes of eighty patients with medically uncontrolled glaucoma including 41 in STSF and 39 eyes in SPG. Methods: Patients were enrolled and assigned randomly to STSF or SPG. Main Outcome Measures: tube exposure, Intraocular pressure (IOP), number of glaucoma medications, best corrected visual acuity (BCVA), surgical complications, and success rate ( defined as intraocular pressure (IOP) >5 mmHg, ≤21 mmHg, and IOP reduction ≥20% from baseline at two consecutive visits after three months, no reoperation for glaucoma). Results: only one case in SPG developed tube exposure at 1-year follow-up. The cumulative probability of success during the first year of follow-up was 70% in the STSF and 65% in SPG (P = 0.36). IOP decreased significantly from 29.6 ± 8.6 mmHg at baseline to 16.4 ± 3.6 mmHg at the final follow-up in STSF (p = 0.001). The corresponding numbers for SPG were 30.9 ± 11.2 and 15.8 ± 4.7, respectively (p = 0.001). The final IOP was comparable between both groups (p = 0.65). Mean ± standard deviation of the number of glaucoma medications was 1.8 ± 0.9 in STSF and 1.6 ± 0.9 in SPG at final follow-up (P = 0.32). Postoperative complications developed in 8 patients (19%) in STSF and 9 patients (23%) in SPG (P = 0.81). Conclusions: STSF and SPG techniques had comparable complication rate at one-year follow-up. Both techniques were comparable in terms of success rate, postoperative IOP, and glaucoma medications.

A precise understanding of the genes and associated genomes of SARS-CoV2 is important for various reasons such as discovering origin of the virus and virulence and so on. A thorough descriptive understanding of the SARS-CoV2 genomes and other coronavirus of the beta-coronavirus genus is primarily important. In this article, a set of ten genomes of four CoVs and their associated genes are considered for this present study. A spatial representations of nucleotide bases including purine-pyrimidine representations of the different genes of the corresponding genomes are quantified using Hurst exponent, Shannon entropy and density estimation of different nucleotides including GC content, in order to draw a comparison and contrast among the ten genomes of different types of CoVs which include MERS, SARS-CoV, HKU1 (Human Coronavirus) and associated their genes.

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

Nonsteroidal anti-inflammatory drugs (NSAIDs), which block the activity of cyclooxygenase (COX) isoenzymes and inhibit the synthesis of prostaglandin, have been used for pain relief. We have developed a method to separate a mixture of three NSAIDs, such as aspirin, paracetamol, and naproxen, using reverse-phase high-performance liquid chromatography (RP-HPLC). An isocratic mobile phase consisting of acidic water and acetonitrile was selected to run at a low flow rate, such as 0.8 mL/min. The mixture of three NSAIDs was injected at a low volume into a C18 column that was 150 mm in length and characterized using a UV detector at 230 nm. We identified three peaks in the chromatogram indicating the three compounds. The elution time of the peaks was less than 10 minutes. To identify multiple peaks on the isocratic flow using a short column, further studies are required regarding the proposed method to generate microfluidic devices for nanoLC.

This paper relies on accounting-based measures of country risk to investigate U.S. global banks' exposure to foreign country risk over the 2017 fiscal year as measured by the sum of cross-border risk, foreign office risk, and derivative risk claims. We achieve this using panel linear modeling methods with country level heterogeneity and time fixed effects, along with a constructed panel data of 284 observations on 71 countries distributed across 6 world regional blocks, and observed over 4 consecutive quarters starting from 4th quarter 2016 and ending with 3rd quarter 2017. The results show that on average, over the four quarters, a 1% increase in foreign banking sector's claims significantly increases U.S. global banks cross border risk exposure by 0.34%, while reducing derivative risk exposure by 0.22%, but have no significant impact on foreign office risk exposure. Similar results are observed with public sector claims which significantly increase banks' exposure to cross border risk by 0.21%, while reducing derivative risk exposure by 0.19%. Conversely however, non-bank financial sector claims are found to have no significant affect on cross-border risk exposure, but significantly reduce foreign office risk exposure by 0.09%, while increasing derivative risk exposure by 0.06%. These results indicate the presence of sectoral heterogeneities in U.S. banks' exposure to foreign counterparties' risk, and also that overall, over the course of 2017 the level of U.S. global banks' cross-border risk exposure increased, while their level of derivative risk exposure decreased, and the level of foreign office risk exposure remained relatively unchanged.

The Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) first emerged in 2019 in China and has resulted in millions of human morbidities and mortalities across the globe. It has been shown that this novel virus originated in animals, mutated, and made the cross-species jump to humans. At the time of this communication, the Coronavirus disease (COVID-19) may be on way to an endemic form, however the threat of the virus is more for susceptible (older people and immunocompromised) people. The human body has more bacterial cells than its own, thus making us more bacterial than humans. As a consequence, the bacteriomes in the human body heavily influence human health and disease. The bacteriomes in the body and the immune system seem to be in constant association during bacterial and viral infections. In this review, we identify various bacterial spp. in major bacteriomes (oral, nasal, lung, and gut) of the body in healthy humans and compare them with dysbiotic bacteriomes of COVID-19 patients. We try to identify key bacterial spp. that have a positive effect on the functionality of the immune system and human health. These select bacterial spp. could be used as potential probiotics to counter or prevent COVID-19 infections. In addition, we try to identify key metabolites produced by probiotic bacterial spp. that could have potential anti-viral effects against SARS-CoV-2. These metabolites could be subject to future therapeutic trials to determine their anti-viral efficacy.

Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

This paper presents a cooperative parallel tabu search meta-heuristic for the vehicle routing problem with time windows. It is based on the scheme in which several search threads cooperate by asynchronously exchanging information on the best solutions identified. The exchanges are performed through a mechanism called adaptive memory which holds and manages a pool of solutions. This enforces the asynchronous strategy of information exchanges and ensures the independence of the individual search threads. Each of these independent threads implements a tabu search meta-heuristic. Comprehensive computational experiments and comparisons to best known solutions show that the proposed cooperative parallel tabu search algorithm is able to achieve 48 new best solutions on VRPTW benchmark instances.

A second order rational difference equation $$x_{n+1}=px_{n}+\frac{q}{x_{n-1}^2}$$ with the parameters $p$ and $q$ which lies in $(0,1)$, is studied. The dynamics of the equilibrium is characterized through the trichotomy of the parameter $p<\frac{1}{2}$, $p=\frac{1}{2}$ and $p>\frac{1}{2}$. It is found that there is no periodic solution of period $2$ and $3$ but there exists periodic solutions with only periodic solution $5$ and $10$ are achieved computationally.

Seasonal snowpacks, characterized by their snow water equivalent (SWE), play a major role in the hydrological cycle, snow melt contributions to floods and the subsequent availability of water resources downstream. Accurately estimating SWE and understanding its spatial and temporal variations presents a considerable challenge, particularly within mountainous regions characterized by complex terrain and limited observational data. Seeking to enhance the performance of the widely used Soil and Water Assessment Tool (SWAT), we report a new approach characterising snowpack behaviour incorporating both modelled and remotely sensed derived SWE calibration data. We focus on the Chenab River Basin (CRB) a headwater catchment of the Indus Basin, globally significant in terms of human inhabitants and intensifying flood risk due to climate change. We conducted a thorough assessment of five satellite-derived and reanalysis-based precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE, and CPC UPP. This reveals significant levels of uncertainty in global precipitation products when compared to reference data from observed stations as well as in the resulting simulated streamflow from the SWAT model. Subsequently, we expanded the scope of the SWAT model to encompass the spatial and temporal simulation of SWE. This was achieved by incorporating information from remotely sensed and modelled SWE products, manually adjusting snow parameters in R-SWAT for both the main basin and at sub-basin scales. Integrating SWE from reference snow products into the calibration process, alongside streamflow data, substantially enhanced modelling accuracy to simulate SWE compared to the conventional auto-calibration and single-variable approaches reliant solely on streamflow data. This approach results in considerable improvement in SWE predictions and to some extent in streamflow simulation in catchments dominated by snow. This research highlights the potential of remote sensing and modelled SWE parameterisation in the absence of in-situ snowpack data in high-altitude environments. An improved understanding of SWE behaviour is vital for predicting hydrological responses spanning hazards to water resources in the populous downstream regions of the Indus Basin, especially in the face of climate change.

Successful strategies that can contribute to poverty reduction and improve the livelihoods of the poor, particularly in Sub-Saharan Africa (SSA), are critically needed to address the technology adoption constraints. The objectives of this study were to assess the adoption rate of improved technologies and management practices and to identify the key factors influencing their adoption in the major wheat-producing areas of Sudan's irrigated, dry, and heat-prone environments. A farm survey was conducted in 2021 using a structured questionnaire that included all recommended technological options for optimum and sustainable wheat production. A total of 300 farmers, 93, 101, and 106 from Northern (NS), Kassala (KS), and Gezira (GS) states, respectively, were selected and interviewed. Besides descriptive statistics, a binary logistic model was used to identify the socioeconomic and production factors affecting farmers' perceptions of improved and recommended technological options. The study found a wide range of adoption rates depending on the specific technology practice and the area surveyed. The lowest adoption rate was observed for land preparation (6.5%) in NS. Adoption rates ranging from 26-100% were observed for technologies such as sowing date, seed rate, seed treatment, awareness of released varieties, nitrogen and phosphorus fertilizers application, and chemical weed control. The difference in the productivity of technological options adopters was significant (P = 0.015) compared to non-adopters. The binary logistic regression results showed that five out of seven explanatory variables hypothesized to influence wheat farmers' perceptions on the decision to adopt improved and recommended technologies significantly influenced farmers' decision to adopt the technologies. In particular, access to quality seed, financial credit, and extension services were found to be the most critical determinants of adopting improved technologies. Approaches that bring together all stakeholders along the crop value chain, including policymakers, to jointly analyze, identify, and prioritize challenges, develop and apply solutions and work plans using feedback and learning mechanisms are expected to increase farmer awareness and adoption of improved technologies, ultimately leading to sustainable wheat production.

In recent years there has been a clear increase in bottled water consumption, globally which translates to significant health and environmental concerns. This cross-sectional study aims to understand the attitude and preferences of university students in Qatar towards use of bottled and tap water for drinking purposes using an online survey questionnaire. The number of students who responded to the online survey was 14% of the entire student population within the university (n = 688). Overall, majority of the students reported plastic bottled water as the main source of drinking water they preferred to use on campus (n=468; 68.02%), with a higher proportion of females (72.08%) preferring the use of plastic bottled water. Out of the 468 students who preferred plastic bottled water, safety was the most important factor (43.80%) followed by convenience (16.88%) and taste (15.60%). Cost (15.17%), personal/family habits (5.13%), Environmental concerns (2.14%) and minerals (1.28%) were found to be the least important factors. Among the 45 students who preferred tap water over plastic bottled water, cost (46.67%) was the dominant factor followed by convenience (20.00%) and environmental concerns and safety (13.33% each), taste (4.44%), and personal/family habits (2.22%). This study recommends campaigns to focus on attitude and behavior change and not solely emphasize knowledge; 71.7% believed that plastic water bottles are more harmful to the environment, yet the greater majority still resorted to this source. There is an immediate need to further educate students through environmental and health literacy programs on water consumption and quality. Enabling the population to understand the positive and negative aspects of their choices, may be an effective remedy to ensuring a healthy population and healthy environment.

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

Traditional metals such as stainless steel, titanium and cobalt chrome are used in biomedical applications (implants, scaffolds etc.) but suffer from issues such as osseointegration and compatibility with existing bone. One way to improve traditional biomaterials is to incorporate ceramics with these metals so that their mechanical properties can be similar to cortical bones. Tricalcium phosphate is such a ceramic with properties so that it can be used in human body. This research explores the use of binder jetting based additive manufacturing process to create a novel biocomposite made of cobalt chrome and tricalcium phosphate. Experiments were conducted and processing parameters were varied to study their effect on the printing of this biocomposite. Layer thickness, binder saturation and drying time affected the dimensional tolerance and the density of the green samples. This effect is important to understand so that the material can be optimized for use in specific applications.

One of the most prevalent illnesses, diabetes does not directly result in patient mortality. But, it increases the risk of death. Any disease that may be predicted in its early stages can lessen its fatal effects while also enhancing the quality of the healthcare system. For the early-stage prediction of diabetes or such types of non-communicable diseases, we need the proper set of influential features. This research has developed a machine learning-based disease prediction model to identify the influential features for diabetes prediction and give a near-perfect classification accuracy. This model includes Min-Max normalization for data normalization, Isolation Forest (iForest) for outlier removal, Synthetic Minority Oversampling Technique (SMOTE) for oversampling, Random Forest based Recursive Feature Elimination (RFE-RF) test, Chi-Square test, and Minimum Redundancy Maximum Relevancy (mRMR) test based feature selection methods for identifying the influential features, and Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naive Bayes (NB) for the classification. The results clarify that the proposed model outperforms the previous models and studies. The SVM has attained an accuracy of 99.58% in classification using the five features chosen from the Chi-Square test. Lastly, SHAP, an explainable AI model, has been used to assess the classifier model’s performance. These selected features and the classifier model can be used for early-stage diabetes prediction.