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.

Steel fibre reinforced concrete (SFRC) consists of short hooked steel fibres randomly distributed and oriented within the cementitious matrix. The contribution of fibre in bridging cracks to provide the stress transfer required relies upon the orientation of the fibre in the concrete. Bridging fibre aligned with a crack are less effective than those inclined to it. Therefore, understanding the pull-out behaviour of misaligned fibre is a key factor to quantify and optimize the design of SFRC in the structural applications. In the laboratory a single oriented fibre embedded in a solid cylinder of concrete is subjected to a pull-out test where the axis of a tensile force is aligned with the axis of the cylinder. Based upon the observed behaviour this paper presents a new analytical bounding approach to capture the pull-out response of misaligned hooked-end steel fibre embedded in a concrete matrix. The analysis is based upon a transversely isotropic condition assumed for the plasticity that occurs in the cold drawn fibre. The model accounts for the influence of fibre diameter and the combined strengths of the steel and concrete. The model predictions have also been compared with other experimental results published in the literature. The results showed that the model can predict pull-out loads reasonably for different fibre orientations between bounds of failure derived from the strengths of the two constituents. A critical orientation is observed at maximum strength. Therein, an understanding of the pull-out behaviour of misaligned, hooked-end fibres is necessary to optimize the strength of concrete from this method of reinforcement.

IoT system becomes hot topic nowadays for smart home, IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circular polarized array rectenna system to harvest enough power from any direction to increase life time of the batteries used in IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node and low power microcontroller are used to operate in lower power modes. The proposed system has an 84.6 days lifetime which is approximately 10 times the lifetime for similar system. On the other hand, the proposed power management circuit operated at 0.3 V DC to boost the voltage to ~3.7V from radio frequency energy harvesting and manage battery level to increase the batteries lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a non-static plan, approve energy consumption and avoiding failure of sensor nodes in smart home.

The success rate of extracorporeal shock wave lithotripsy (ESWL) is influenced by various factors, including stone density, which is determined through computed tomography scans in Hounsfield Units (HU). Materials and method: This retrospective single-center study was conducted in King Fahad hospital. Sixty-seven adult patients with renal and ureteric stones were selected randomly and enrolled in the study. Their age ranged from 20 to 69 years. The patients were examined with non-contrast enhancement (NCCT) to assess the HU of stones, consequently, treated with ESWL. Results: of the 67 patients, 37.3% of the stones were completely fragmented, while 62.7% were partially fragmented. HU, location of the stone, a multiplicity of the stone, and patient age were significant factors contributing to stone fragility (p-values< 0.05). The HU has a positive significant linear correlation with serum calcium (r= .28, p-value=.036), while serum acid has a negative correlation (r=-.55, p-value= < .001). Thus, the probability of calcium-containing stones formation increased with increased HU. In contrast, uric acid stone formation is likely to develop with decreasing HU with serum uric acid. Renal stones in patients with diabetes mellitus and hypertension were not completely fragmented compared to those without clinical history. Conclusion: Mean HU, location of the stone, laterality, status of stone, and the number of ESWL sessions were the most significant factors affecting stone fragility. CT attenuation values can predict the composition of stones from serum calcium and uric acid examinations. Hypertension and Diabetes mellitus are risk factors for renal stone fragmentation.

Multi-criteria decision analysis (MCDA), one of the prevalent branches of operations research, aims to design mathematical and computational tools for selecting the best alternative among several choices with respect to specific criteria. In the cloud, MCDA based online brokers uses customer specified criteria to rank different service providers. However, subjected to limited domain knowledge, the customer may exclude relevant or include irrelevant criterion, which could result in suboptimal ranking of service providers. To deal with such misspecification, this research proposes a model, which uses notion of factor analysis from the domain of unsupervised machine learning. The model is evaluated using two quality-of-service (QoS) based datasets. The first dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The second dataset i.e., feedback from servers, was generated from cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The simulation runs in a stable cloud environment i.e. when uncertainty is low, shows that online broker (equipped with the proposed model) produces optimized ranking of service providers as compared to other brokers. This is due the fact that proposed model assigns priorities to criteria objectively (using machine learning) rather than using priorities based on subjective judgments of the customer. This research will benefit potential cloud customers that view insufficient domain knowledge as a limiting factor for acquisition of web services in the cloud.

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.

As more end devices are getting connected, the Internet will become more congested. A variety of congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims at mitigating the congestion on the network layer by active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.

Keywords: Utilizing; HOMER; techno-economic; sustainable; urban (List three to ten pertinent keywords specific to the article yet reasonably common within the subject discipline.)

This study investigates the stochastic dynamics of hepatitis B virus (HBV) infection using a newly proposed stochastic model. Contrary to deterministic models that fail to encapsulate the inherent randomness and fluctuations in biological processes, our stochastic model provides a more realistic representation of HBV infection dynamics. It incorporates random variability, thereby acknowledging the changes in viral and cellular populations and uncertainties in parameters such as infection rates and immune responses. We examine the solution's existence, uniqueness, and positivity for the proposed model, followed by a comprehensive stability analysis. We provide the necessary and sufficient conditions for local and global stability, offering deep insight into the infection dynamics. Furthermore, we utilize numerical simulations to corroborate our theoretical results. This research provides a robust tool for understanding the complex behavior of HBV dynamics, contributing significantly to the ongoing quest for more effective HBV control and prevention strategies.

An algebraic system is introduced, which is very useful for doing scattering calculations in quantum field theory. It is the set of all real numbers greater than or equal to -m^2 with parity designation and a special rule for addition and subtraction, where m is the rest mass of the scattered particle

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.

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.

The review emphasizes the importance of the petroleum refining industry in Iraq, highlighting the environmental obstacles it presents. The industry's significant role in the country's economy necessitates a crucial evaluation and improvement of the environmental impact of oil refining in Iraq. The study scrutinizes the progression of crude oil refining techniques, from basic distillation to advanced technologies like vacuum distillation, thermal cracking, and catalytic cracking, all of which are associated with environmental damage. The refining processes produce hazardous gases such as carbon dioxide, carbon monoxide, sulfur oxides, and nitrogen oxides. Refineries also consume substantial amounts of water and discharge effluent, causing environmental issues. It highlights that significant solid waste is produced throughout the refining process, contributing to the environmental difficulties caused by the petroleum industry in Iraq. The paper proposes several approaches to enhancing the environmental performance of Iraq's petroleum refining processes through a comprehensive environmental impact assessment, focusing on identifying problematic areas, measuring environmental damage, and proposing solutions. It provides insights into the types of waste generated in Iraqi refineries, such as polluted hydrocarbon liquids, and highlights the environmental impact of leaks and spills from oil and byproducts, affecting groundwater and soil quality. The paper suggests reusing consumable auxiliary agents and mineral elements in other industrial fields to minimize waste generation, as well as employing advanced technologies like artificial intelligence and machine learning for efficient refining processes and reduced effluents. By offering suggestions to policymakers, industry stakeholders, and environmental regulators in Iraq, the study aims to inform sustainable practices and environmental management strategies within the petroleum refining sector, contributing to improved environmental outcomes and sustainable development in the industry.

Fractal analysis of time series aims to extract the hidden or complex structures issued from the associated real data, such as scaling laws, non-stationary, dynamics, and so on. In the present work, the air quality index and its components are considered as time series owing fractal structures. To understand their underlying dynamics, wavelet multifractal approach is applied starting with a wavelet multifractal spectrum test to confirm the multifractality of the underlying data. Next, a new model is introduced improving the classical minmax one, provided with a statistical measure for the adequacy of the new model. The theoretical findings are applied to the north west Tabuk region of Saudi Arabia for environmental and climate data related to pollutants and factors involved in the estimation of the air quality index.

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.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed nonalcoholic fatty liver disease (NAFLD), is a widespread global health concern that affects around 25% of the global population. Its influence is expanding, and it is anticipated to overtake alcohol as the leading cause of liver failure and liver-related death worldwide. Unfortunately, there are no approved therapies for MASLD; as such, national and international regulatory health agencies undertook strategies and action plans designed to expedite the development of drugs for treatment of MASLD. A sedentary lifestyle and an unhealthy diet intake are important risk factors. Western countries have a greater estimated prevalence of MASLD partly due to lifestyle habits. Mitochondrial dysfunction is strongly linked to the development of MASLD. Further, it has been speculated that mitophagy, a type of mitochondrial quality control, may be impaired in MASLD. Thyroid hormone (TH) coordinates signals from the nuclear and mitochondrial genomes to control mitochondrial biogenesis and function in hepatocytes. Mitochondria are known TH targets and preclinical and clinical studies suggest that TH, thyroid receptor β (TR-β) analogs, and synthetic analogs specific to liver could be of therapeutic benefit in treating MASLD. In this review, we highlight how mitochondrial dysfunction contributes to development of MASLD, and how understanding the role of TH in improving mitochondrial function paved the way for innovative drug development programs of TH-based therapies targeting MASLD.

Patients satisfaction with fast and high-quality services is the most important in the healthcare settings and specially in outpatient departments and private clinics. High quality services on health sector are indicated by continuous quality improvement (CQI). CQI is a progressive incremental process focused on safety of all participants, outcomes, systematic process, regulated and improved working environment at the later than earlier stages. Among the various suggested strategies, we adopted the patents feedback to improve the quality of services our clinic. All of the patient visited hospital were sent a massage on mobile and requested to provide their feedback on our services. Roughly around 5% of all visitors responded and filled up questionnaire. Majority of them were satisfied with the services in various department. However, they provided us some suggestions to identify the further gaps and improve the services in improving patients experience at our clinic. We considered their feedback, identified problems, redesigned the policies and implemented. After implementation of new strategies, we preliminary again evaluated the patients’ feedback on our services. Patients feedback explores that optimized methods of services for the has considerably increased the patients’ satisfaction. Taken together, our this shows that patients’ feedback is very important factor to improve patients’ experience at outpatient clinics using continuous quality improvement tools. For that reason, this study would serve as reference for public health stack holders, administrators and researchers.

In the post antimicrobial era, increasing attention is paid towards using bacteriophage (phage in short) therapy to control antibiotic-resistant bacteria. The first step in phage therapy applications is isolating highly efficient lytic phages or phage cocktails from various sources. When a double-layer- agar with around 0.7% agar in top agar is employed, it results in a low number of phage isolation with a poor resolution, and in many cases, you miss the phage. To address this problem, a low concentration of agar in top agar is examined for better phage isolation. Here, our results proved the efficiency of isolating phage upon formulating a double-layer agar with 0.3% agar in top agar. A sewage sample was collected then phages were isolated, purified, and spotted on a top layer agar with 0.3% agar. The results showed the possibility of isolating a higher number of phages on 0.3% top agar than 0.7%. The finding advocates using 0.3% top agar for the double-layer agar, as it will provide fast, better, and easy phage screening and isolation.

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.

The semi-logarithmic plot of the cumulative number of cases of epidemics resembles the response of a first-order systems for a step load. This similarity was utilized to develop a first order model that can be used for extracting information about the dynamics of infectious disease epidemics. The developed model was validated using COVID-19 data of China. It was also heuristically fitted to other 13 countries. Obtained results indicated that the model can reliably forecasts the number of infected person, epidemic growth speed towards steady-state condition (process time constant, T), and time to reach steady-state condition (4T). The developed model will help public health authorities in developing more effective control strategies of epidemics.

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.

Based on the definition of double fuzzy relations, two types of new rough set models are constructed, which are multi-granulation double fuzzy rough sets called optimistic and pessimistic multi-granulation double fuzzy rough sets, and some of their properties were studied. In addition, we study the relationship between optimistic and pessimistic multi-granulation double fuzzy rough sets and double fuzzy rough sets.

This paper evaluates an architectural design using an Energy Consumption consideration of Natural Ventilation in a hot dry climate (Khartoum State), at dwellings design applied. Method was used analysis of Autodesk IEVS software natural ventilation and energy consumption simulation. It resulted that to cooling, by natural ventilation in dwelling design used to get indoor temperature at the comfortable level during the summer. The Best-Case Situation of natural ventilation with consider of energy saving Based on the CFD simulation analysis, the performance of internal air velocity is approximately 0.7m/s. The wind velocity starts to slow down towards the rear of the apartment spaces. Approximately 95% of the internal areas have average air velocity between 0.43 m/s to 0.9 m/s. Internal Air flow pattern single floor house plan at Khartoum (Alazhari City); showing the internal air velocity of 0.7m/s near the window opening positions with the wind directions in the details and pressure of the air as worst-case situation, with velocity and temperature of the air with average 22o as a best-case situation. during a year. Architectural design process in the urban area of Alazhari City for dwelling towards to saving energy was applied and determined into an urban planning neighbourhood at Khartoum.

: Pearl millet (Pennisetum glaucum L) R. Br) is a subtropical grain and forage crop. It is privileged with several desirable forage attributes. Nevertheless, research on pearl millet is limited especially as a forage crop in the developing countries. Therefore, the objectives of this study were to investigate the field performance and stability of pearl millet genotypes for forage yield across seven environments. The study was conducted in seven environments (combination of locations and seasons) during 2016/2017-2018/2019 seasons. Twenty-five pearl millet genotypes, selected based on forage yield from a core collection of 200 accessions, were arranged in alpha lattice design with three replications. The parameters measured were fresh forage yield, days to flowering, plant height, number of culms m-2, leaf to stem ratio, and stem girth. The combined analysis revealed that environments, genotypes and their interaction had significant effects on all traits studied except the genotypic effect on stem girth. Across the seven environments, four genotypes (G14, G01, G12, and G22) out-yielded the check genotype in fresh matter yield by 20.7, 16.5, 11.0 and 9.8%, respectively. The additive main effects and multiplicative interaction (AMMI) analysis showed that the genotype, environment and their interaction were highly significant (P≤0.001) for fresh matter yield. The results of AMMI Stability Values (ASV) and the genotype selection index (GSI) combined with the AMMI estimate-based selection showed that genotypes G14, G22 and G01 were the most stable and adapted genotypes and were superior to the check genotype. These results indicate that forage pearl millet varieties could be developed directly through evaluation of the wealth available collections or indirectly through hybridization in crop breeding programs.

The main objective of the present study was to investigate arsenate [As (V)] resistance genes in rice cultivars grown in arsenic contaminated Egyptian soil in order to genetically induce resistance against arsenic in the local rice varieties as well as defining contaminated rice grains and/or soil. Three local rice cultivars; Sakha 102-104 were cultivated on modified Murashige and Skoog Basal Medium (MS medium) containing elevated concentrations of arsenate (0.1, 1 and 10 mg/l). The three varieties showed different resistant attitudes against arsenate with Sakha 104 being the most resistant. Extracted messenger RNA (mRNA) from treated and untreated Sakha 104 plantlets was scanned using differential display to demonstrate the arsenate resistant genes using three different arbitrary primers. About 100 different RNAs with (1500 bp - 50 bp) were obtained from which seven were up-regulated genes, subjected to DNA cloning using TOPO TA system and the selected clones were sequenced. The sequence analysis described four genes out of the seven namely disease resistance protein RPM1, Epstein-Barr virus EBNA-1-like, CwfJ family protein and outer membrane lipoprotein OmlA while the other three genes were hypothetical proteins. It is concluded the four induced genes in the resistant rice cultivar considered as a direct response to arsenic soil pollution. Genes detected in the present study can be used as geno-sensors for rice grains and soil contamination with As (V). Moreover, local rice cultivars may be genetically modified with such genes to induce high resistance and to overcome arsenic soil pollution.

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.

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño-Southern Oscillation (ENSO) is considered a significant signal of global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, which month the low-resolution sea surface temperature data is from. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

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.

Urbanization influences food culture, particularly in low- and middle-income countries, where there is an increasing consumption of processed and pre-packaged foods. This shift is contributing to a rise in non-communicable diseases. Food labeling standards are crucial for regulating manufacturing practices and aiding consumers in making healthy food choices. We aimed to assess the compliance of local and imported pre-packaged snacks with Tanzanian and international labeling standards in Dar es Salaam, Tanzania. A cross-sectional study was conducted on 180 snack products. A checklist based on Tanzanian and Codex labeling standards was used to evaluate adherence. We also examined factors influencing adherence, such as product origin, price, category, purchase location, and package size. The majority of the snacks demonstrated partial adherence to labeling standards. Imported products showed significantly better full adherence to both Tanzanian and International standards. Notably, over half of the products used English for labeling, and infrequently used the recommended World Health Organization Front-of-Pack nutrition Labeling. Product category, origin, and package size were significantly associated with higher levels of International standard adherence (p<0.05). The inadequate adherence to mandatory labeling standards, scarce use of Swahili and FoPL highlight the need to strengthen labeling practices and potential challenges faced by consumers in understanding nutritional information. Thus, strengthening and emphasizing good labeling practices is of urgency as we seek to address diet-related Non-communicable diseases.

As abiotic stress, drought limits plant growth and minimizes productivity. The increased request for valuable essential oil extracted from geranium (Pelargonium graveolens L.) is mainly associated with plant growth, which is adversely affected by drought. Melatonin (MT) has been used to enhance plant growth under abiotic stress, however, its impact to overcome drought stress of aromatic plants including geranium is poorly investigated. In the current investigation, MT application at 100 µM was applied under 100 % (well-watered) or 50 % (drought stress) of FC to verify this role. Drought stress markedly reduced growth parameters, herb yield, and total chlorophyll; however, MT alleviated these effects. In contrast, drought enhanced the essential oil percentage in geranium leaves. Despite the reduction in oil yield caused by drought, MT application mitigated this reduction and improved both oil yield and oil components. Besides, MT treatment enhanced the accumulation of total phenols, glutathione, and proline and improved the activity of ascorbate peroxidase, catalase, and glutathione reductase with possible alleviation of drought-induced oxidative damage. Therefore, it reduced both H2O2 and malondialdehyde accumulation, and finally maintained membrane integrity. Overall, this is the first report that reveals that MT application can improve geranium resistance to drought by enhancing the antioxidant potential and protecting the cell membrane from oxidative damage.

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

The work reports a facile synthesis of high thermally-stable nanocrystalline anatase TiO2 nanoparticles (NPs) doped with different atomic concentrations (0.5, 1.0, 3.0, and 6.0%) of Gd3+ and Nd3+ ions by a template-free and one-step solvothermal process, using titanium(IV) butoxide as titanium precursor and dimethyl sulfoxide (DMSO) as solvent. The structure and morphology of the Gd3+, Nd3+, and 0.5%Gd3+-0.5%Nd3+/doped TiO2 NPs have been characterized by using various analytical techniques. The Gd3+/ and Nd3+/TiO2 molar ratios were found to have a pronounced impact on the crystalline structure, size, and morphology of TiO2 NPs. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) studies revealed the proper substitution of Ti4+ by Gd3+ and Nd3+ ions in the TiO2 host lattice. The as-prepared Gdx/TiO2, Ndx/TiO2, and Gd1.0/Ndx/TiO2 bimetallic NPs, x = 0.5, 1.0, 3.0, and 6%, have been investigated as electrocatalysts for hydrogen evolution reaction (HER) in 1.0 M KOH solution using a variety of electrochemical techniques. At any doping percentage, the Gd1.0/Ndx/TiO2 bimetallic NPs showed higher HER catalytic performance than their corresponding counterparts i.e. Gdx/TiO2 and Ndx/TiO2. Upon increasing the Nd content from 0.5 to 6.0%, the HER catalytic performance of the Gd1.0/Ndx/TiO2 bimetallic NPs was generally enhanced. Among the studied materials, the bimetallic Gd1.0/Nd6.0/TiO2 NPs emerged as the most promising catalyst with an onset potential of -22 mV vs. RHE, a Tafel slope of 109 mV dec-1, and an exchange current density of 0.72 mA cm-2. Such HER electrochemical kinetic parameters are close to those recorded by the commercial Pt/C (onset potential: -15 mV, Tafel slope: 106 mV dec-1, and exchange current density: 0.80 mA cm-2), and also comparable with those measured by the most active electrocatalysts reported in the literature. The synergistic interaction of Gd and Nd is thought to be the major cause of the bimetallic catalyst's activity.

Improving the quality of petroleum products and refining processes through the use of artificial intelligence and machine learning techniques is the topic of this article. It shows that expert knowledge and conventional empirical models can't get you where you want to go in a refining process. To effectively capture complex interactions and forecast fuel qualities, machine learning techniques such as principal component analysis (PCA), support vector machines (SVM), artificial neural networks (ANN), and partial least squares (PLS) are suggested. Gasoline and other petroleum products, as well as property prediction, process control, product quality, and operational efficiency in refineries, can all be improved with the help of machine learning applied to spectral or distillation curve data. An exciting new direction in optimizing operations, meeting environmental norms, and precisely estimating gasoline quality is offered by advanced machine learning algorithms.

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.

This review provides an understanding of the teaching and practising of forensic accounting across the globe. It includes a review of published forensic accounting studies that conclude that forensic accounting is of benefit to both students and professionals. Despite this, there are areas of concern; namely, the insufficiency of an appropriate structure of regulation and a lack of control over the academic qualifications to enter the profession. This review of both the teaching and practising of forensic accounting provides an overview of the vital issues and concerns that must be understood in order to advance the essential application of forensic accounting. It is expected that this review will influence future policy that will advance business and limit episodes of fraud. The amount of published literature on the subject of forensic accounting is widespread and diverse. However, there is a lack of comprehensive understanding regarding the strengths and weaknesses of forensic accounting; this review will address this and provide policymakers with a thorough explanation of the subject.

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.

Recent years have seen dramatic growth in the number of scholarly scientific works dedicated to the topic of Artificial Intelligence (AI), banking, and finance. With the development of scientometric tools, it is now possible to map, visualize, analyze, and assess scientific activities in many fields of engineering and social sciences including Islamic banking and finance. Based on data retrieved from the Scopus database and using a qualitative method, this study investigates, evaluates, and identifies significant development, trends, and players in the application of AI in Islamic banking and finance using the Visualization Of Similarities Method (VOS) between objects in VOSviewer. From 2006 to 2022, 387 documents were retrieved from the Scopus database; Results revealed that the top five most active countries in terms of publications are Malaysia (117 documents), Indonesia (89 documents), the United Kingdom (36 documents), the USA (24 documents), and Saudi Arabia and Bahrain (24 documents each). The International Islamic University Malaysia and Universitas Indonesia came out on top of active institutions while the top funding source came from the Ministry of Higher Education, Malaysia. The comprehensive findings and analysis reported in this study serve as a roadmap for future academics to create theory and practice for applying AI in Islamic banking and finance.

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.

Late onset Pompe disease is a recessive lysosomal storage disease. Clinical features include skeletal muscles deficiency and diaphragm weakness. Clinical management relies on supportive treatment and mechanical ventilation in patents with chronic respiratory failure. M mode ultrasound and tissue Doppler imaging can be used to assess and to follow up diaphragm function.

Rivaroxaban and aspirin are commonly used antithrombotic agents that are used in combination for the prevention of coronary artery disease (CAD) and atherothrombotic events in adult patients after an acute coronary syndrome (ACS) with elevated cardiac biomarkers, or with coronary artery disease (CAD) or symptomatic peripheral artery disease (PAD) in high-risk patients. The recommended dosage is 2.5 mg Rivaroxaban twice daily with 75–100 mg aspirin daily. This study aimed to develop a fixed-dose combination tablet of rivaroxaban (2.5 mg) and aspirin (50 mg) to decrease the patient's pill burden and enhance medication adherence. The product formula was developed based on compatibility studies among the active ingredients and the active ingredients' compatibility studies. The formula and the manufacturing procedure were chosen based on the risk assessment for each active substance, wet granulation with both actives intragranular was found to have faster dissolution than direct mix formulae. Furthermore, a validated reverse-phase HPLC stability indicator method was developed to detect APIs and their possible degradants in the formula.

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.

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 research work focuses on the potential application of an organic compound, santalol obtained from santalum album in the inhibition of the enzyme tyrosinase which is actively involved in the biosynthesis of the melanin pigment. Over-production of melanin causes undesirable pigmentation in humans as well as other organisms that significantly downgrade their aesthetic value. The study is designed to explain the purification of tyrosinase from the mushroom Agaricus bisporus, followed by activity assay and enzyme kinetic to give insight into the santalol modulated tyrosinase inhibition in a dose dependent manner. The multi-spectroscopic techniques like UV-vis, fluorescence, and isothermal calorimetry are employed to deduce the efficiency of santalol as potential candidate against the tyrosinase enzyme activity. Experimental results are further verified by molecular docking. Santalol derived from the essential oils of santalum album, is widely used as remedy for skin disorders and potion for fair complexion since ancient times. Based on enzyme kinetics and biophysical characterization, this is the first scientific evidence where santalol inhibits tyrosinase, which may be employed in agriculture, food, and cosmetic industries by prevent excess melanin formation or browning.

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.

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.

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.

Climate change has become a major agenda item in international relations and in national energy policy-making circles around the world. This article studies the surprising evolution of the energy policy, and more particularly the energy transition, currently happening in the Arabian Gulf region, which features some of the world’s largest exporters of oil and gas. Qatar, Saudi Arabia and other neighboring energy exporters, plan to export blue and green hydrogen across Asia as well as towards Europe in the years and decades to come. Although poorly known and understood abroad, this recent strategy doesn’t threaten the current exports of oil and gas (still needed for a few decades) but prepares the evolution of their national energy industries towards the future decarbonized energy demand of their main customers in East and South Asia, and beyond. The world’s largest exporters of Liquefied Natural Gas, Qatar, has established industrial policies and projects to upscale CCUS, which can enable blue hydrogen production, as well as natural carbon sinks domestically via afforestation projects.

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.

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.

Reactive powder concrete (RPC) is a modern form of cementitious material. Major components include cement with a very low water-to-binder (w/b) ratio, steel fibres, superplasticizers, and very fine additives used in place of coarse aggregate, and very little water. The primary goal of this study is to examine the impact of immersion in sulfuric acid on compressive strength and to evaluate the impact of curing procedures on the preparation of RPC with high characteristics. The experimental work includes 4 mixes, which are prepared using 30 mm hooked-end steel fibers with a ratio of 0.4 by volume, silica fume and metakaolin with a ratio of 25% of cement weight. Compressive and indirect tensile strengths, ultrasonic velocity, and bulk density tests were conducted on the RPC that was prepared to evaluate its qualities. The results showed that heat curing resulted in a greater increase in strength than water curing did, and that mechanical properties improved with increasing SF more than MK additions. Heat-cured RPC demonstrated greater resistance to sulfuric acid immersion tests on compressive strength compared to water-cured RPC.

Hand gesture recognition has many valuable applications in engineering and health care. This study proposes a novel model which can accurately distinguish hand gestures using forearm muscles' surface electromyogram (sEMG) signals. A deep learning algorithm with hyper parameters impacting the final model’s accuracy and a convolutional neural network (CNN) were employed in the recognition stage. The number of convolutional layers, kernels per layer, and neurons in the dense layer were selected for optimization, while the remaining parameters, such as the learning rate, batch size, and number of epochs, were chosen based on trial and error and prior knowledge. The optimal values for the selected hyperparameters were obtained using a genetic algorithm to achieve maximum recognition accuracy. The UC2018 Dual-Myo database was used for training and testing the model based on EMG signals characterizing the activity of eight different hand gestures. The final structure of the model consisted of two convolutional layers with 131 and 28 kernels, a dense layer with 111 neurons, and a softmax layer with eight neurons. Upon optimizing the hyperparameters using the genetic algorithm, the accuracy of the proposed model increased from 91.86% to 96.4% at best and 95.3% on average in real-time applications and 99.6% in an offline mode. Future work is warranted towards improving the architecture and the computational cost.

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.

5-(4-aminophenyl )-2-amino -1,3,4-thiadiazole was prepared by reaction of Thiosemicarbazide with 4-amino benzoic acid under reflux condition for 7 hours. The compound which has been synthesized successfully was subjected to addition reaction with 4-(Dimethylamino) benzaldehyde under reflux condition for 6 hours to synthesize Schiff bases. These compounds was characterized by using FTIR) and evaluated for their anticancer activity. The effect of (1, 3, 4-thiadiazole derivative) on the activity of malignant cells was studied by using different types of cell lines [Breast cancer, and human prostate cancer]. And was used the Electron microscope to show that the effect of the derivative on the cancer cells before and after 3 days of the injection time. It was found that the Schiff base of thiadiazole-1,3,4-(Dimethylamino)benzylidineamino]- [4-2-phenyl]amino was effective in reducing the size and density of malignant cells. That of 46.7 while in breast ) 145(DUprostate for growth inhibition produce of equal 85.9 µg/ml.

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

Gold nanoparticles are currently used for the treatment of cancer through a myriad of modalities and delivery approaches. Conjugation of tumor imaging Single Photon Emitting Computed Tomographic (SPECT) radiopharmaceutical to gold nanoparticles will allow systemic targeting and imaging of cancer tissues simultaneously. In this study, gold nanoparticles (AuNPs) were prepared using Epigallocatechingallate (EGCG), loaded with doxorubicin (Dox), and characterized before and after doxorubicin conjugation. Cytotoxicity of EGCG-AuNPs and Dox-EGCG-AuNPs were evaluated against breast carcinoma (MCF-7) and hepatocellular carcinoma (HepG-2) cell lines demonstrating high cytotoxic effects of Dox-EGCG-AuNPs against both cell lines. Doxorubicin was radiolabeled with ^99mTc and our new approach has optimized various labeling conditions resulting in a radiochemical yield of 93.5 ± 2.04%. Biodistribution of ^99mTc-Dox-EGCG-AuNPs was studied in normal and tumor bearing mice following I.V. and intratumoral injections at different time intervals. Results showed high uptake of the intravenously injected ^99mTc-Dox-EGCG-AuNPs in tumor tissue (22.45 %ID/g at 2 h). In addition, localized intratumoral injection of ^99mTc-Dox-EGCG-AuNPs showed extremely high levels of uptake in tumor (80.22 %ID/g at 15 min) with high retention for extended periods post injection. Our results present prospects for the utility of ^99mTc-Dox-EGCG-AuNPs as a multiplexed theranostic agent through SPECT imaging of tumor tissue and therapy through photothermal destruction of cancer tissue through the application of exogenous laser lights as well as through tyrosine phosphatases inhibitor (through EGCG), and topoisomerase II inhibitor (through doxorubicin) effects.

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.

In this article we investigate the generalized Kawahara-KdV system. A new topological approach is applied to prove the existence of at least one classical solution and at least two nonnegative classical solutions. The arguments are based upon recent theoretical results.

As a result of rapid population growth, an exponentially growing human population, and industrial expansion, it has become increasingly difficult to manage municipal solid wastes throughout the world. Decentralized waste management systems have created difficult situations in developing countries such as Malaysia. Wastes generated in the country, due to various cultural, social, and religious activities, organic and contributing to environmental pollution (air, water, and soil) and human health troubles. A questionnaire survey was participated by 220 construction professionals in Malaysia using structured and semi-structured methods. The framework was assessed using A partial least square structural equation modeling (PLS-SEM) to target sustainable development goals (SDG). Statistical analysis results indicate a significant effect between SCW management, since(r(270)=.687, P&lt;0.001). Improving factors has strong relationship with SCW management, since(r(270)=.723, P&lt;0.001). The mediation results also suggested a significant indirect positive effect of improving factors drivers on SCW management through policy-related factors sinceβ=0.688, t=8.254, P&lt;0.001, 95% CI for β=0.536,0.866. Finally, policy-related factors construct has a strong relationship with SCWM) management, since(r(270)=.811, P&lt;0.001) With the R Square of 0.787 and 0.785. The developed framework can improve construction waste management in the construction industry and enhance construction waste management to achieve global sustainable development goals. The findings show that one of the most critical issues of enhancing profitability is using preventive policies to reduce construction waste. This study could guide construction industry stakeholders in identifying the different waste management features during a building project's construction and design stage

Reading is a complex cognitive task involving processes from different systems. The present work aims to identify some points of divergence reported in the reading literature and discuss them in a new experimental paradigm framework. Inspired by the paradigms of perceptual identification and rapid parallel presentation (RPVP), we emphasize that the originality of our experimental paradigm lies in the recruitment of multi-stable Arabic percepts within the region where low-level processing occurs (i.e., the visual span area). With good flexibility, the current paradigm has reached higher-order processing levels. In agreement with previous works highlighting the parafoveal-on-foveal effect, results suggest parallel word processing. Furthermore, they suggest a rapid extraction of syntactic and semantic information from words in sentences while attributing an advantage to semantic processing in the emergence of the sentence superiority effect.

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. In this paper, we propose (PDTS) a punctuation detection approach for segmenting Arabic text, built on top of a multilingual BERT-based model and some generic linguistic rules. Furthermore, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost.

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.

Domain-specific systems-on-chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, while hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29x speedup and 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in execution time and EDP, respectively.

On March 11, 2020, the World Health Organization declared the coronavirus outbreak a pandemic. Since December 2019, the world has experienced an outbreak of coronavirus disease 2019 (COVID-19). Epidemiology, risk factors, and clinical characteristics of patients with COVID-19 have been reported but the factors affecting the immune system against COVID-19 have not been well described. In this article, we provide a novel hypothesis to describe how an increase in cellular adenosine triphosphate (c-ATP) can potentially improve the efficiency of innate and adaptive immune systems to either prevent and fight off COVID-19.

The contemporary landscape of eHealth is characterized by major concerns related to electronic medical records (EMRs). In an EMR, critical patient health information gets its own repository, which is the core of the eHealth system. Notably, the EMR not only retrieves health information generated in ambulatory clinical settings but also includes court-generated records. These overlapping records assume the role of a foundational data source for a comprehensive electronic health record (EHR). This scholarly review sheds light on recent trends in medical records and associated challenges, while at the same time providing insights into Saudi Arabia’s targeted approaches to healthcare delivery. Additionally, the talk expands to include a description of blockchain technology and its underlying conceptual framework. This demonstration positions blockchain technology as a promising approach that requires consideration for future use in the healthcare system.

The global demand for rubber is on a steady rise, which is driven by the increasing production of automobiles and the growing need for industrial, medical, and household products. This surge in demand has led to a significant increase in rubber waste, posing a major global environmental challenge. End-of-life tire (ELT) is a primary source of rubber waste, which possesses significant environmental hazards due to its massive stockpiles. While landfilling is a low-cost and easy-to-implement solution, it is now largely prohibited due to environmental concerns. Recently, ELT rubber waste has garnered considerable attention for its potential applications in civil engineering and construction. These applications not only enhance sustainability but also foster a circular economy between ELT rubber waste and the civil engineering and construction sectors. This review article concentrates on the recent research progress and challenges in the civil engineering applications of ELT rubber waste. It also discusses commercially available recycled rubber-based construction materials, their properties, testing standards, and certification. To the best of the authors' knowledge, this is the first time such a discussion on commercial products has been presented.

The methylene blue (MB) adsorption was performed on a natural material powder of Cynarascolymus as identified by Cs. To analyze the Cs material, FTIR, SEM, isoelectric point (pHpzc) analysis, TGA, and DRX were used. The maximum experimental adsorption capacity of the Cs material was 203.333, 192.187, and 179,380 mg•g-1 at 298, 303, and 313 K, respectively. The correlation coefficients (R2) and average percentage errors APE(%) values for the kinetic and isotherms models indicated that the adsorption kinetics fol-lowed a pseudo-nth order model and that the Traditional isotherm model Red-lich-Peterson (R-P) correctly described the experimental data obtained at 298, 303, and 313 K, respectively. The steric, energetic, and thermodynamic characteristics of the most rele-vant advanced model (double-energy single-layer model (AM 2)) were analyzed in detail. The number of active sites for the first receptors (n1) was determined to be 0.129, 0.610, and 6.833; whereas the number of second active sites (n2) was determined to be 1.444, 1.675, and 2.036 at 298, 303, and 313 K, respectively. This indicated the presence of both multi–docking and multimolecular modes for the first style of MB ions (n1); while only a multi-molecular mode for the second style of MB ions (n2). Thermodynamic characteristics demonstrated that MB adsorption onto the Cs adsorbent is spontaneous and feasible.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

In this paper we designed system for smart university building based on artificial intelligence (AI) and internet of things (IOT). Our idea can be summarized in smart security system that has different sensors to detect the surrounding environment of the class room in campus which keep everyone and everything on campus safer. By using (IOT), (AI) technologies and applications and by using microcontroller programming we can make the university building safer, secure and more energy saves.

Batch and transport experiments were used to investigate the remediation of a loamy sand soil contaminated with Cr(VI) using zero valent iron nanoparticles (nZVI) stabilized by the carboxy methyl cellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (Two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at larger particle concentration. Batch experiments showed that removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5 % at pH 5 and 7, respectively. The Two-site sorption model described quite well (r2 = 0.96-0.98) the transport of Cr(VI) in soil as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and shorter time to reach maximum relative concentration at lower pH. The Hydrus-1D model has proven to be an efficient tool for assessing the removal of Cr(VI) pollutants, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

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.

Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.

This paper investigates the influence of magnetic field for a two dimensional deformations on a two temperature problem at the free surface of a semi-infinite semiconducting medium under the effect of mechanical force during a photothermal theory and the effect of hydrostatic initial stress on the medium. The Harmonic Wave Method (Normal Mode Analysis) has been used to obtain the equations of elastic waves, heat conduction equation, quasi-static electric field, carrier density, two temperature coefficient, ratios, and constitutive relationships for the thermo-magnetic-electric medium. The effects of several parameters as thermoelastic and thermoelectric coupling parameters and two temperature parameter of this force on the displacement component, force stress, carrier density and temperature distribution has been depicted graphically.

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.

The present article investigates the threshold analysis of yielding in a variable thickness rotating disk based on the Tresca criterion. The analysis is conducted on a Functionally Graded Material (FGM) constructed from tailored materials, following the theory of small deformations and for the case of plane stress. The elasticity modulus, density, and yield stress are assumed to be power functions of the radial coordinate. Poisson’s ratio is considered constant due to small variations in different materials. Additionally, the governing equation for the rotating disk is solved analytically. Besides the material type, another factor affecting the distribution of effective stress magnitudes is the cross-sectional shape (profile) of the disk. The thickness of the cross-sectional disk varies radially as a power function. In the current analysis, different conditions for the onset of yielding and the evolution of plastic flow are considered. For evaluation and validation, the obtained results are compared and validated against similar results for specific cases (homogeneous disk and constant thickness graded disk) available in previous references. The results indicate that considering variable thickness for the disk section significantly affects the stresses and the location of the initiation of yielding

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.

Data dealing with the assessment of heavy metal pollution in road dusts in Jeddah, Saudi Arabia and its implication to human health risk of human exposure to heavy metals, are scarce. Road dusts were collected from five different functional areas (traffic areas TA, parking areas PA, residential areas RA, mixed residential commercial areas MCRA and suburban areas SA) in Jeddah and one in rural area (RUA) in Hada Al Sham. We aimed to measure the pollution levels of heavy metals and estimate their health risk of human exposure applying risk assessment models described by USEPA. Using geo-accumulation index (I_geo), the pollution level of heavy metals in urban road dusts was in the following order Cd > As > Pb > Zn > Cu > Ni > Cr > V > Mn > Co > Fe. Urban road dust was found to be moderately to heavily contaminated with As, Pb and Zn, and heavily to extremely contaminated with Cd. Calculation of enrichment factor (EF) revealed that heavy metals in TA had the highest values compared to that of the other functional areas. Cd, As, Pb, Zn and Cu were severely enriched, while Mn, V, Co, Ni and Cr were moderately enriched. Fe was consider as a natural element and consequently excluded. The concentrations of heavy metals in road dusts of functional areas were in the following order: TA > PA > MCRA > SA > RA > RUA. The study revealed that both children and adults in all studied areas having health quotient (HQ) < 1 are at negligible non-carcinogenic risk. The only exception was for children exposed to As in TA. They had an ingestion health quotient (HQ_ing) 1.18 and a health index (HI) 1.19. The most prominent exposure route was ingestion. The cancer risk for children and adults from exposure to Pb, Cd, Co, Ni, and Cr was found to be negligible (< 1 x 10^-6).

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.

Aflatoxin M1 (AFM1), a carcinogenic toxin, mainly contaminates cow milk and poses significant challenges to the dairy industry and, thereby, public health in most tropical countries, including Ethiopia. This study aimed to investigate the prevalence and level of AFM1 in raw cow milk collected from Specialized Dairy Farms and Milk Vendors in three Urban Centers: Chiro town, Dire Dawa, and Harar cities in Eastern Ethiopia. 180 milk samples were collected from two major Milk Sources: Specialized Dairy Farms (N=90) and Milk Vendors (N=90) using a simple random sampling technique. AFM1 was analyzed using high-performance liquid chromatography. The study revealed that, 63.9% (115/810) prevalence and 0.179±0.48µg/L mean of AFM1 level in milk, which 39.13% and 26.08% exceed the tolerable limits of EU and FDA respectively. A 40.0% prevalence and 0.344±0.72µg/L average of AFM1 in the milk from Dire Dawa city was significantly higher than the samples from other Urban Centers. However, 30.43% and 29.57% prevalence as well as 0.055±±0.13µg/L and 0.140±0.33µg/L average of AFM1 in milk from Chiro town and Harar city, respectively were not significant. Furthermore, milk samples from Dairy Farms revealed a higher prevalence (57.39%) and level (0.252±0.64µg/L) of AFM1 than Milk Vendors, with a prevalence of 42.61% and an average of 0.107±0.21µg/L. Therefore, this study concludes that substantial milk samples were contaminated by AFM1 and urges the need to enhance farmers' awareness on mitigation.

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.

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.

A new approach for predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called group method of data handling (GMDH) which is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in literature. The model was then evaluated using statistical indices as well as compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate a high accuracy of the proposed model with values of 0.9719 and 0.9513 for training and testing for the dimensionless of the distance from the nozzle to the return point xr/D, and 0.9454 and 0.9565 for training and testing for the dimensionless of the terminal rise height yt/D . Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of estimate (SEE) was applied to demonstrate which model performed the best in terms of getting closer to the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and was the best model with approximately 10% error, which was the lowest value of error among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

The epidermal growth factor receptor (EGFR) exon-19 deletion is one of the most common mutations detected in lung cancer patients. Although exon-19 deletion is frequently detected in adenocarcinoma, observing this mutation in germline cells is very rare. Besides, the co-occurrence of homozygous and heterozygous mutations in dual primary cancers in a person is very uncommon. This article presents a 53-year-old Iranian woman with no history of smoking who was diagnose with two primary cancers; invasive ductal carcinoma, and primary pulmonary lung adenocarcinoma. The case reported a history of breast cancer in her sister and a history of lung cancer in her father. To select the best choice of treatment the EGFR gene was analyzed with Sanger’s sequencing method from DNA extracted from the patient’s lung tissue sample. Observing two primary cancers in this patient and considering her family pedigree, germline cells were also analyzed using samples recruited from the patient’s peripheral blood to investigate any EGFR mutations in her germline cells. The obtained data revealed that the lung tissue of the patient carried a homozygous form of EGFR exon-19 deletion while her peripheral blood contained a heterozygous form of this mutation, which is exceptionally rare.

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 modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

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).

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.

Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.

Computing in the cloud-edge continuum, as opposed to cloud computing, relies on high performance processing on the extreme edge of the IoT hierarchy. Hardware acceleration is a mandatory solution to achieve the performance requirements, yet it can be tightly tied to particular computation kernels, even within the same application. Vector-oriented hardware acceleration has gained renewed interest to support AI applications like convolutional networks or classification algorithms. We present a comprehensive investigation of the performance and power efficiency achievable by configurable vector acceleration subsystems, obtaining evidence of both the high potential of the proposed microarchitecture and the advantage of hardware customization in total transparency to the software program.

Extensive genome structure variation, such as copy number variations (CNVs) and presence/absence variations, is the basis for the remarkable maize genetic diversity; however, the effect of CNVs on maize herbivory defense remains largely underexplored. Here, we report that the naturally occurring duplication of the maize 9-lipoxygenase gene, ZmLOX5, leads to increased resistance of maize to herbivory by fall armyworms (FAW). Previously, we showed that ZmLOX5-derived oxylipins are required for defense against chewing insect herbivores and identified several inbred lines, including Yu796, that contained duplicated CNVs of ZmLOX5, referred to as Yu796-2×LOX5. To test whether introgression of the Yu796-2×LOX5 locus into the herbivore-susceptible B73 background that contains a single ZmLOX5 gene is a feasible approach to increase resistance, we generated a series of near-isogenic lines that contained either 2, 1 or 0 copies of the Yu796-2×LOX5 locus in the B73 background by six back crosses (BC6). Droplet digital PCR (ddPCR) confirmed the successful introgression of the Yu796-2×LOX5 locus in B73. The resulting B73-2×LOX5 inbred line displayed increased resistance against FAW associated with increased expression of ZmLOX5, increased wound-induced production of its primary oxylipin product, the α-ketol, 9-hydroxy-10-oxo-12(Z),15(Z)-octadecadienoic acid (9,10-KODA), and the downstream defense hormones regulated by this molecule, 12-oxo-phytodienoic acid (12-OPDA) and abscisic acid (ABA). Surprisingly, wound-induced JA-Ile production was not increased in B73-2×LOX5, resulting from the increased JA catabolism. Furthermore, B73-2×LOX5 displayed enhanced drought tolerance likely due to increased ABA and 12-OPDA content. Taken together, this study revealed that the duplicated CNV of ZmLOX5 quantitively contributes to maize antiherbivore defense and presents proof-of-concept evidence that the introgression of naturally occurring duplicated CNVs of a defensive gene into the productive, but susceptible, crop varieties is a feasible breeding approach for enhancing plant resistance to herbivory and tolerance to abiotic stress.

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.

The present work describes the optimization and validation of a highly selective and sensitive analytical method using solid phase extraction and liquid chromatography tandem mass spectrometry (SPE LC-MS/MS) for the determination of some frequently prescribed pharmaceuticals in urban wastewater received and treated by Sharjah sewage treatment plant (STP). The extraction efficiency of different SPE cartridges was tested and the simultaneous extraction of pharmaceuticals was successfully accomplished using hydrophilic-lipophilic-balanced reversed phase Waters^® Oasis HLB cartridge (200 mg/ 6 mL) at pH 3. The analytes were separated on an Aquity BEH C₁₈ column (1.7 µm, 2.1 mm x 150 mm) using gradient elution and the mass spectrometric analysis were performed in multiple reactions monitoring (MRM) selecting two precursor ions to produce ion transition for each pharmaceutical using positive electrospray ionization (+ESI) mode. The correlation coefficient values in the linear calibration plot for each target compound exceeded 0.99 and the recovery percentages of the investigated pharmaceuticals were more than 84%. Limit of detection (LOD) varied between 0.1-1.5 ng/L and limit of quantification (LOQ) was 0.3-5 ng/L for all analytes. The precision of the method was calculated as the relative standard deviation (RSD%) of replicate measurements and was found to be in the ranges of 2.2% to 7.7% and 2.2% to 8.6% for inter and intra-day analysis, respectively. All of the obtained validation parameters satisfied the requirements and guidelines of analytical method validation.

Wheeled mobile robot dynamics and suitable controller design are challenging but rewarding fields of study. By understanding the dynamics of wheeled mobile robots, it could be possible to design suitable hybrid control schemes for wheeled mobile robots. Since hybrid control schemes involve combining individual control methods to create a more effective overall control strategy. This can be done in a variety of ways, such as using fuzzy linear quadratic Gaussian control. We were therefore inspired to create dynamic models and their controller designs for the wheeled mobile robot by examining the dynamics of the wheeled mobile robot. The novelty of the current paper is to hybridize the different control schemes for wheeled mobile robots in order to get better performance. Entire systems models were simulated in a MATLAB/SIMULINK environment. The results obtained for the settling time response by FLQG were 87.1% over LQG; this study compared the effectiveness of current and previous FLQG controllers in external disturbances and found peak amplitude improvements of 71.25%. Therefore, the proposed controller is suitable for use with the wheeled mobile robot.

Although laccase has been recognized as a wonder molecule, and green enzyme, the use of low yielding fungal strains, poor production, purification, and low enzyme kinetics have hampered its larger-scale applications. Hence the present research was aimed to select high yielding fungal strains and to optimize the production, purification, and kinetics of laccase of Aspergillus sp. HB_RZ4. Aspergillus sp. HB_RZ4 produced a copious amount of laccase on under meso-acidophillic shaking conditions in a medium containing glucose and yeast extract. A 25 µM of CuSO4 enhanced the enzyme yield. The enzyme was best purified on Sephadex G-100 column. Purified enzyme resembled with the laccase of A. flavus. Kinetics of purified enzyme revealed the high substrate specificity and good velocity of reaction with ABTS as substrate. The enzyme was stable over a wide range of pH and temperature. The peptide structure of the purified enzyme resembled with the laccase of A. kawachii IFO 4308. The fungus decolorized various dyes independent of the requirement of a laccase mediator system (LMS). Aspergillus sp. HB_RZ4 came out as a potent natural producer of laccase, it decolorized the dyes even in absence of LMS and thus can be used for bioremediation.

Micro-gyroscopes based on the Coriolis principle are widely employed in inertial navigation, motion control, and vibration analysis applications. This paper presents our main contributions which include a novel dual-frame optomechanical gyroscope, a unique photonic crystal cavity design, and advanced numerical simulation and optimization methods. The proposed design utilizes an optical cavity formed between dual oscillating frames, whereby input rotation induces a measurable phase shift via optomechanical coupling. Actuation of the frames is achieved electrostatically via an interdigitated comb-drive design. Through theoretical modeling based on cavity optomechanics and finite element simulation, the operating principle and performance parameters are evaluated in detail. Results indicate an expected angular rate sensitivity of 22.8 mV/°/s and angle random walk of 7.1×10-5 °/h1/2, representing superior precision than to existing micro-electromechanical systems gyroscopes of comparable scale. Detailed analysis of the optomechanical transduction mechanism suggests this dual-frame approach could enable angular vibration detection with resolution exceeding state-of-the-art solutions.

We studied the influence of lava meal, zeolite and top layer of sandy soil as bedding additives on gaseous C and N losses from a sloping-floor barn of naturally ventilated animal housing. We selected four barn units where eight young bulls’ group was reared in each barn. Chopped straw of wheat and barley applied daily at the rate of 5 kg per livestock unit (LU) in bedding areas where one LU consisted of 500 kg body mass of live bulls. Zeolite, lava meal and sandy soil (18% clay + silt) applied in barn at the rate of 10, 20 and 30% of straw dose, respectively. Static flux chamber was used to measure gases emissions from the barn unit and mass balance calculation was used to calculate straw manure total N (TN) losses during housing phase. On an average, all bedding additives decreased 85% of the NH₃ emission compared to control; however, they did not influence CH₄ emission. Zeolite decreased CO₂ (35%) and N₂O (37%) emission rates. Subsequently, lava meal, sandy soil and zeolite decreased 23, 37, and 50% of TN losses from barn manure, respectively. Overall, measured N emissions through NH₃-N and N₂O-N from the barns was 11% of calculated TN losses while remainder 89% was most probably attributed to di-nitrogen (N₂), a harmless gas. Hence, in straw-based cattle housings, zeolite could be a promising additive for reduction of CO₂, N₂O and NH₃ emissions and sandy soil can be considered as cheap and readily available resource for reducing NH₃ emission.

Soil-borne Trichoderma spp. have been extensively studied for their biocontrol activities against pathogens and growth promotion ability with plants. However, the beneficial effect of Trichoderma on inducing resistance against insect herbivores has been considered only recently. Among diverse Trichoderma species, consistent with previous reports, we showed that root colonization by T. virens triggered induced systemic resistance (ISR) to leaf-infecting fungal pathogens. Whether T. virens induces ISR to insect pests has not been tested before. In this study, we investigated whether T. virens affects jasmonic acid (JA) biosynthesis and defense against fall armyworm (FAW) and western corn rootworm (WCR). Unexpectedly, the results showed that T. virens colonization of maize seedlings grown in autoclaved soil suppressed wound-induced production of JA, resulting in reduced resistance to FAW. Similarly, the bacterial endophyte Pseudomonas chlororaphis 30-84 suppressed systemic resistance to FAW due to reduced JA. Further comparative analyses of the systemic effects of these endophytes when applied into sterile or non-sterile field soil showed that both T. virens and P. chlororaphis 30-84 triggered ISR against hemibiotrophic fungal pathogen Colletotrichum graminicola in both soil conditions, but only suppressed resistance to FAW in sterile soil. In contrast to the effect on FAW defense, T. virens colonization of maize roots suppressed WCR larvae survival and weight gain. This is the first report suggesting the potential role of T. virens as a biocontrol agent against WCR.

This paper investigates an innovative photovoltaic cell configuration, denoted as “Au/CuSCN/RbGeI3/CdTe/ZnO/TiO2/FTO", using SCAPS-1D simulation. The research focuses on optimizing device performance by analyzing key components, including ZnO as the electron transport layer (ETL), RbGeI3 as the perovskite absorber layer, CuSCN as the hole transport layer (HTL), and CdTe as the active buffer layer. A comparison is made with the standard “Au/CuSCN/CH3NH3SnI3/CdTe/ZnO/TiO2/FTO" configuration under dark and illuminated conditions. Through an in-depth investigation involving temperature effects, generation rate analysis, RbGeI3 absorber layer thickness, Nd level, and interface defect levels, an optimized design for a unit area is achieved. The proposed solar cell demonstrates outstanding performance metrics, including an open circuit voltage (Voc) of 0.94V, a short circuit current density (Jsc) of 34.52mA/cm2, a fill factor (FF) of 77.77%, and an energy conversion efficiency (η) of 25.40%. These values significantly outperform the reference models with energy conversion efficiencies of 10.11% and 21.09%. Real-world applicability is validated by implementing these optimized characteristics in a photovoltaic system for Pakistan’s geographical location using PVSyst software. The suggested solar cell achieves a remarkable performance ratio (PR) of 85.6%, surpassing the PR of the reference basic solar cell model at 83%. The findings of this research offer a promising and superior solar cell design, incorporating innovative materials and optimization techniques to achieve exceptional energy conversion efficiency and overall performance. This study’s outcomes have the potential to advance photovoltaic technology and contribute to sustainable energy solutions worldwide.

Like living organisms, cancer cells require energy to survive and interact with their environment. Mitochondria are the main organelles for energy production and cellular metabolism. Recently, investigators demonstrated that cancer cells can hijack mitochondria from immune cells. This behavior sheds light on a pivotal piece in the cancer puzzle, the ‘dependence’ on the normal cells. This article illustrates the benefits of new, functional mitochondria for cancer cells that urge them to hijack mitochondria. It describes how functional mitochondria help cancer cells’ survival in the harsh tumor microenvironment, immune evasion, progression, and treatment resistance. Recent evidence has put forward the pivotal role of mitochondria in cancer stem cells’ metabolism. This theory highlights the mitochondria in cancer biology and explains how targeted anti-mitochondrial treatments can improve oncological outcomes.

Thermal plasma technique is becoming prominent in the treatment of variety of waste ranging from municipal solid waste, incinerator residue, hospital waste, electronics waste and industrial sludge. Application of the new treatment technology to petroleum sludge requires information on the nature and characteristics of the sludge that will be use to optimize the treatment system. In this investigation, petroleum sludge obtained from Petronas Melaka was characterized for its physical and chemical features. Proximate and ultimate analysis as well as determination of elemental composition were carried out. The sludge was found to contain high moisture (78.91%), low ash (5.06%), low volatiles (5.52%) and high fixed carbon (10.51%). The sludge has a TOC of 54.48% and HHV of 23.599MJ/kg. Despite the high moisture content, the higher heating value (HHV) is high when compared to literature values. The high value of HHV may be associated with the high fixed carbon, low ash content and high value of TOC. The apparent density of the sludge is slightly lower. Fourteen heavy metals are detected in significant quantities. Proper waste management that will safely dispose the sludge is required. The waste disposal technique should take into cognizant the possibility of leaching of heavy metals into ground water on one hand and the gasification of lighter ones on the other.