Antimicrobial resistance (AMR) is a recurring global problem, which constantly demands new antimicrobial compounds to challenge the resistance. It is well known that essential oils (EOs) have been known for biological activities including antimicrobial properties. In this study, EOs from seven aromatic plants of Asir region of southwestern Saudi Arabia were tested for their antimicrobial efficacy against four drug resistant pathogenic bacterial isolates (Staphylococcus aureus, Streptococcus pyogenes, Escherichia coli and Streptococcus typhimurium) and one fungal isolate (Candida albicans). Chemical compositions of EOs were determined by Gas chromatography-Mass Spectrometry (GC-MS). The results revealed that EOs from Mentha cervina, Ocimum basilicum and Origanum vulgare proved most active against all isolates with inhibitory zone range between17 to 45 mm. The lowest minimum inhibitory concentration (MIC) of 0.025mg/ml was observed for Staph. aureus and Streptococcus pyogenes with EO of Origanum vulgare. All the three EOs showed significant anti candida activity. Together form the results the EOs from Mentha cervina, Ocimum basilicum and Origanum vulgare demonstrated a significant antimicrobial efficacy against drug resistant microorganisms.

: Early and accurate detection of infectious diseases is a key step for surveillance, epidemiology, and control, notably timely disease diagnosis, patient management and follow-up. In this study, we aimed to develop hand-held ultra-fast duplex PCR assays coupled to amplicon detection by lateral flow (LF) immunoassay to deliver a rapid, and simple molecular diagnostic test for con-comitant detection and identification of the main Leishmania parasites encountered in Tunisia and the Old World. We selected two DNA targets to amplify L. major/L. tropica and L. infantum/L. tropica group of species DNAs, respectively. We optimized the experimental conditions of a du-plex ultra-fast PCR. The amplification is performed by a portable Palm convection PCR machine within 18 min and the products are detected by a LF cassette within 10 minutes. The test allows the identification of the infecting species according to the position and number of test lines re-vealed. Tested on a selection of DNAs of representative Leishmania strains of the three studied species (N=37), the ultra-fast duplex PCR-LF showed consistent, stable, and reproducible results. The analytical limit of detection of the test was 0.4pg for L. major, 4pg for L. infantum and 40pg for L. tropica.

Zwitterionic polymers emerge as very useful materials for applications in antifouling surfaces. The properties of these polymers can be tuned by variation of the chemical structure of the corresponding monomer. In this study, two zwitterionic ammonium sulfonate monomers bearing an hydroxyl function and a styrenic polymerizable groups were prepared in two steps. The two monomers were obtained using 4-vinylbenzyl chloride as the key precursor. The zwitterionic monomers were characterized by 1H NMR, 13C NMR, IR spectroscopy and high-resolution mass spectrometry (HRMS). These two monomers enable the preparation of novel zwitterionic polymers with enhanced hydrophilicity due to the presence of a hydroxyl group.

Hybrid materials derived adsorbents have showed a great applicable efficiency in various fields including industrial uses and environmental remediation. Herein, the zinc oxide nanoparticles anchored carbon (ZnO-C) is fabricated and utilized for wastewater treatment by adsorption of Zn(II), Cd(II), Co(II) and Mn(II). The surface and structural characteristics are examined by TEM, SEM, XRD, FTIR, EDS and BET surface area. The Kinetics and equilibrium investigations are applied to optimize the adsorptive removal of Zn(II), Cd(II), Co(II) and Mn(II) onto ZnO-C. Results indicated formation of ZnO-C in crystalline spherical granules with nano-size between 29.3 and 48.8 nm. In addition, the spherical granules are gathered to form clusters. The FTIR indicated that the ZnO-C surface is rich with OH groups and ZnO. The adsorption capacity is reported as 212, 209, 200 and 230 mg/L for Zn(II), Cd(II), Co(II) and Mn(II) respectively. The optimum condition for maximum adsorption were pH between 5 and 6, contact time of 180 min and adsorbent dose of 0.1 g/L. The adsorptive removal data modeling for uptake of Zn(II), Cd(II), Co(II) and Mn(II) onto ZnO-C showed agreement with the assumption of pseudo 2nd order kinetic model and Freundlich isotherm suggesting fast adsorption rate and multilayer mechanism. The achieved adsorption capacity using the prepared ZnO-C is more effective compared to ZnO, carbon, Fe3O4 and Fe3O4-C. Real wastewater samples are applied including valley water, industrial wastewater and rain wastewater and evaluated for applicable uptake of Zn(II), Cd(II), Co(II) and Mn(II) using ZnO-C and Fe3O4-C with high efficiency.

The realization of a BRST cohomology of the 4D topological gauge-affine gravity is established in terms of a superconnection formalism. Identification of fields in the quantized theory is occurred directly as usual in terms of superconnection and its supercurvature components with the double covering of the general affine group GA¯(4,R). Then, by means of an appropriate decomposition of the metalinear double-covering group SL¯(5,R) with respect to the general linear double-covering group GL¯(4,R) one can easily obtain the enlargements of the fields while remaining consistent with the BRST algebra. This leads to the descent equations allowing us to build the observables of the theory by means of the BRST algebra constructed using a sa¯(5,R) algebra-valued superconnection. In particular, we discuss the construction of topological invariants with torsion.

water resources in Morocco have been severely influenced by climate change (CC) and pro-longed drought, particularly, in the pre-Saharan zone. The Tafilalet region faces increasing pres-sure due to high demographic growth, increased demand for water, excessive groundwater con-sumption, and investment in agriculture. Using the Water Evaluation and Planning System (WEAP), this study assessed land cover and elevation and created future scenarios using the Standard Precipitation Index (SPI) and Standard Groundwater Level Index (SGI) to monitor hy-droclimate drought conditions in the Ziz watershed from 1989 to 2022. Results show decreasing precipitation, particularly from 2010-2020, with the last four years being the most challenging (SPI<-2). SPI and SGI have a high correlation, suggesting precipitation impacts groundwater in-dicators. Future scenarios predict a high risk of CC, with a 30% decrease in annual precipitation accumulation by 2100 under the optimistic scenario SSP126. Minimum temperature is expected to increase by 1.08°C (SSP126) and 2.61°C (SSP585), and maximum temperature by 1.05°C (SSP126) and 2.93°C (SSP585). Keywords: Climate Change, Water Resources, SPI, SGI, WEAP, Ziz watershed, Drought

Multilayered metallic sheets fabricated by hot metal forming processes are attractive structural components in the aerospace and automobile industry. The strength of the mechanical bond between the different layers is critical to the applicability of such components in load bearing applications. While it is well understood that the process conditions such as temperature, deformation and rate of deformation influence the bond strength, a clear functional relationship between these conditions and the bond strength has yet to be established. The objective of the current study is to investigate the joining of Aluminum-6061 alloy specimens by hot compression bonding. Hot compression tests of beam specimen pairs were conducted at different temperatures (300-500℃), with different strains and strain rates (10-2-10-1 [1sec]). Coupled thermo-mechanical finite element models were utilized to compute the time dependent thermo-mechanical fields that develop along the specimen interface. The computed values were used to map the spatial distribution of a scalar parameter J which is hypothesized to govern bond formation. Mechanical tensile tests were used to determine the bond strength and expose the bonded surfaces. It is demonstrated that the computed J distribution can be correlated well with SEM observations of the debonded surface. It is also shown that the actual bonded area can be predicted from the computations for a certain range of critical J values.

Fault detection is an important step for subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complicated faulting structures of seismic images, manual seismic interpretation is time taking and need intensive work. To overcome this problem, geoscientists are coming up with productive computer-aided techniques for assisting in interpreter science for many years. However, in this paper, we used a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field of Browse Basin, Australia. Basically, this field is highly structured with complex normal faulting throughout the targeted Plover Formations. So, our motivation for this work in this field is to compare machine learning-based fault prediction to user interpreted faults identification with supported by seismic variance attribute. We found very satisfying result using DL with having an improved fault probability volume that outperform variance technology. Therefore, we propose that this workflow could reduce time and able to predict fault quite accurately in any field area.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

Abstract Several studies have been conducted to strengthen weak road layers, regardless of whether they are un-der construction or are undergoing maintenance. Using substrates filled with different materials has proven effective for increasing the strength of these layers. In this study, I evaluated the coefficient of lateral soil pressure under various conditions when using reinforced and unreinforced piles filled with in-expensive material and the extent of their contribution to strengthening road layers. The results con-firmed that there was a difference in the coefficient of lateral soil pressure when the sand-filled piles were reinforced. The sand-filled piles were divided into several parts by arming. The maximum coeffi-cient of lateral soil pressure was 0.38 in the case of sand (1 mm) with reinforcement, whereas it was 0.29 in the same case without reinforcement as shown in Figure 1. The maximum coefficient of lateral soil pressure was 0.27 in the case of granite grain (2 mm) with reinforcement, whereas it was 0.17 in the same case without reinforcement. Moreover, the maximum lateral pressure coefficient was 0.26 in the case of granite grain (3 mm) with reinforcement, and it was 0.30 in the same case without reinforce-ment.

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

The aim of this research work is to study the influence of temperature, solar irradiation and wind dust on the production of solar energy. In addition, it aims to study the assessment of water quality, in particular groundwater used for cooling and cleaning photovoltaic panels (quality analysis). it's an important source, stable and sustainable can be used entirely for cooling and cleaning PV modules from the sales dust, in order to improve their performance. To date, all research studies carried out in this context of cooling and cleaning using water have not taken account of the nature of this water. We have taken a sample of water from a well in a rural area in the Darra Tafilalet region of Morocco. This region has an arid and semi-arid climate, with very high temperatures and dust-laden winds, particularly in summer. These meteorological characteristics influence the efficiency of cells, particularly photovoltaic (PV) panels, reducing their performance. Next, the water sample was analysed in the laboratory. The results obtained show that the groundwater in this area is very sensitive to cooling and cleaning photovoltaic panels. Moreover, these results indicate that the temperature, solar irradiation and wind-blown dust have a significant influence on energy production.

Construction of Pontoons is based on multiple elements, dimensions and weight. The study has addressed about how the industry of the floating reinforcement concrete precast (pontoons) installs in the factory with the combinations of utility, electricity services, and Internet service. The pontoon bridges are successfully installed in the road for transport or sea for shops. The installation process for pontoons is successfully attempted in a balanced situation above surface of the sea to the resistant of floating precast (pontoons) to any ambient effects such as weather conditions, the movement of the waves or any others effects. The findings elaborate that it is not just a military solution. Pontoon installation can significantly serve for civil purposes.

Important first phases in the process of implementing CO2 subsurface and ocean storage projects include selecting of best possible location(s) for CO2 storage, site selection evaluation. Sites must fulfil a number of criteria that boil down to the following basics: they must be able to accept the desired volume of CO2 at the rate at which it is supplied from the CO2 source(s); they must as well be safe and reliable; and must comply with regulatory and other societal requirements. They also must have at least public acceptance and be based on sound financial analysis. Site geology; hydrogeological, pressure, and geothermal regimes; land features; location, climate, and access, etc. can all be refined from these basic criteria. In addition to aiding in site selection, site characterization is essential for other purposes, such as foreseeing the fate and impacts of the injected CO2, and informing subsequent phases of site development, including design, permitting, operation, monitoring, and eventual abandonment. According to data from the IEA, in 2022, emissions from Africa and Asia's emerging markets and developing economies, excluding China's, increased by 4.2%, which is equivalent to 206 million tonnes of CO2 and were higher than those from developed economies. Coal-fired power generation was responsible for more than half of the rise in emissions that were recorded in the region. The difficulty of achieving sustainable socio-economic progress in the developing countries is entwined with the work of reducing CO2 emissions, which is a demanding project for the economy. Organisations from developing countries, such as Bangladesh, Cameroon, India, and Nigeria, have formed partnerships with organisations in other countries for lessons learn and investment within the climate change arena. The basaltic rocks, coal seams, depleted oil and gas reservoirs, soils, deep saline aquifers, and sedimentary basins that developing countries (Bangladesh, Cameroon, India, and Nigeria etc) possesses all contribute to the individual country's significant geological sequestration potential. There are limited or no carbon capture and storage or clean development mechanism projects running in these countries at this time. The site selection and characterization procedure are not complete without an estimate of the storage capacity of a storage location. Estimating storage capacity relies on volumetric estimates because a site must accept the planned volume of CO2 during the active injection period. As more and more applications make use of site characterization, so too does the body of written material on the topic. As the science of CO2 storage develops, regulatory requirements are implemented, field experience grows, and the economics of CO2 capture and storage improve, so too will site selection and characterisation change.

Inorganic nanoparticles based on magnetite improve the mechanical, thermal, and magnetic properties of microporous cryogel polymer composites. Here we report the synthesis of microporous cryogel based on the crosslinked sodium vinyl sulfonate (Na-VS) and 2-acrylamido-2-methylpropane sulfonic acid sodium salt (Na-AMPS). The magnetite nanoparticles were inserted into Na-VS/Na-AMPS cryogel either during its crosslinking polymerization or by in-situ technique after its crosslinking. The morphology, particle sizes, thermal stability and magnetite contents of Na-VS/Na-AMPS cryogel and its magnetite composites were investigated. The prepared Na-VS/Na-AMPS cryogel and its magnetite composites were used as adsorbents for methylene blue (MB) cationic dye using optimum conditions. The magnetite Na-VS/Na-AMPS cryogel composite prepared by in-situ technique achieved the best adsorption MB removal capacity for 7 cycles among the other adsorbents via chemical adsorption mechanism at room temperature.

This review is dedicated to sustainable practices in liquid chromatography. HPLC and UHPLC methods contribute significantly to routine analytical techniques. Therefore, the transfer of classical liquid chromatographic methods into sustainable ones is of utmost importance in moving toward sustainable development goals. Among other principles to render a liquid chromatographic method green, substitution of the organic solvent component in the mobile phase with a greener one received great attention. This review concentrates on choosing the best alternative green organic solvent to replace the classical solvent in the mobile phase for easy rapid transfer to a more sustainable normal phase or reversed-phase liquid chromatography. The main focus of this review will be on describing the transfer of non-green to green and white chromatographic methods in an effort to elevate the sustainability best practice in analytical chemistry. The greenness properties and greenness ranking in addition to the chromatographic suitability of seventeen organic solvents for liquid chromatography are mentioned to have a clear insight into the issue of rapidly choosing the appropriate solvent to transfer a classical HPLC or UHPLC method into a more sustainable one. A simple guide is proposed for making the liquid chromatographic method more sustainable.

This study focusses on the geological formations of the Alfallé massif located approximately 75km east of the town of N'Goura, in the Hadjer-Lamis Province of Chad (13°N, 17°E), and their ability to provide good quality aggregates in an area with poor hard rock ressources. We investigated the petrographic and mechanical characteristics of these formations in a resource perspective. The massif is made up of three lithologies: alkali feldspar megacrystalline granite, biotite-rich fine grained quartz monzonite and biotite-rich fine grained gneiss. Alkali feldspar megacrystalline granite is composed of quartz (30%), orthoclase (30%), plagioclase (20%), biotite (15%) and opaque minerals (≈5%). Micro-monzonite is composed of quartz (15-20%), orthoclase (20-25%), plagioclase (18-25%), biotite (20-25%) and opaque minerals (≈4%). The biotite gneiss is composed of quartz (25-30%), alkali feldspars (25-27%), plagioclase (17%), biotite (25-27%) and opaque minerals (≈5%). The mechanical characteristics of the aggregates show that the aggregates studied have specific density values of between 2.58 and 2.75g/cm3 with an average value of 2.66g/cm3, a Los-Angeles coefficient of 11.2% to 28.9% with an average value of 20.1% and a wet Micro-Deval coefficient of 5.2% to 10.8% with an average value of 7.6%. The values obtained indicate that the materials studied have excellent to satisfactory properties, and that Alfallé massif provide the best characteristics among six different quarrying area in Central Chad. We propose that such rare residual reliefs within the mega-Chad paleolake area reach optimal mechanical properties through a natural selection process, specific of the paleolake erosional conditions.

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

The transition to sustainable energy systems in the Middle East and North Africa (MENA) region addresses critical challenges of environmental protection, energy security, and socio-economic resilience. Using neo-institutional theory, this paper examines the intricate relationship between formal and informal institutions and their significant impact on the region’s energy transition. Despite the MENA region’s abundant renewable energy resources, numerous institutional barriers, such as governance inefficiencies, regulatory inconsistencies, and entrenched socio-cultural norms, significantly hinder this transition. Through a review of existing literature, this study presents a comprehensive analytical framework designed to overcome these barriers, with a focus on the Just and Sustainable Energy Transition model. This strategic model emphasizes equity, sustainability, and inclusive governance as pillars for reform. The study offers detailed recommendations for institutional change and underscores the importance of collaborative efforts among governments, industry stakeholders, and international partners to unlock the MENA region’s renewable energy potential, advance global sustainability goals, and ensure a more secure and resilient energy future.

Extracellular vesicles (EVs), including exosomes, microvesicles, and other lipid vesicles derived from cells, play a pivotal role in intercellular communication by transferring information between cells. EVs secreted by progenitor and stem cells have been associated with the therapeutic effects observed in cell-based therapies, and they also contribute to tissue regeneration following injury, such as in orthopedic surgery cases. This review briefly explores the involvement of EVs in tissue repair and nerve regeneration, their potential as drug carriers, and their significance in stem cell research and cell-based therapies. It underscores the importance for bioengineers to comprehend and manipulate EV activity to optimize the efficacy of tissue engineering and regenerative therapies.

By shifting eating hours from daytime to nighttime, Ramadan intermittent fasting (RIF) illustrates how religious beliefs influence human dietary behavior and subsequent anthropometric and metabolic measures. This study aimed to investigate socioeconomic characteristics and examine how observing RIF affects dietary intake, anthropometric indices, and metabolic markers in healthy premenopausal (PRE-M) and post-menopausal (POST-M) Saudi women. Before starting the study, the authors hypothesized that the RIF improves some nutritional aspects and health in respondents, but not after breaking the fast.. The study includes 62 women (31 PRE-M, 21-43 years, and 31 POST-M, 44-68 years). A structured questionnaire was used to collect socioeconomic data. Physical activity, anthropometric, dietary, and biochemical assessments were undertaken before RIF and at the end of the third week of Ramadan fasting month. Socioeconomic data varied among respondents. Observing RIF was associated with significantly (at either p ≤ 0.01 or p ≤ 0.05) lower intake of calories, macronutrients, minerals (except for Na), and vitamins than before RIF for both groups. In terms of body mass index (BMI), and waist-to-height ratio (WHtR) for PRE-M, the percentage of overweight decreased significantly (p≤0.01) while obesity remained unchanged. In contrast, for POST-M, the percentage of overweight increased significantly (p≤0.05), but obesity decreased after RIF. Both groups' waist-to-hip ratio (WHR), body fat (BF), and fat mass (FM) decreased after RIF. High-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), triglycerides (TG), and blood pressure (SBP, DBP) were generally maintained at acceptable normal levels in most responders before and after RIF. However, low-density lipoprotein cholesterol (LDL-C) was significantly decreased during RIF than before, especially for POST-M. Age, occupation, and monthly income were the most important factors influencing women’s nutritional status and body fat. In conclusion, observing RIF by PRE- and POST-M Saudi women was associated with significant improvements in variable health indicators, with a few exceptions, particularly POST-M, and may help lower risk factors for chronic diseases.

Capturing and storing CO2 (CCS) was once regarded as a significant, urgent, and required option for reducing the emissions of CO2 from coal and oil and gas industries and mitigating the serious impacts of CO2 on the atmosphere and the environment. This recognition came about as a result of extensive research conducted in the past. The CCS cycle comes to a close with the last phase of CO2 storage, which is accomplished primarily by the adsorption of CO2 in the ocean and injection of CO2 subsurface reservoir formation, in addition to the formation of limestone by the process of CO2 reactivity with reservoir formation minerals through injectivities. CCS is the last stage in the carbon capture and storage (CCS) cycle and is accomplished chiefly via oceanic and subterranean geological sequestration, as well as mineral carbonation. The injection of supercritical CO2 into geological formations disrupts the sub-surface's existing physical and chemical conditions; changes can occur in the pore fluid pressure, temperature state, chemical reactivity, and stress distribution of the reservoir rock. This paper aims at advancing our current knowledge in CO2 injection and storage systems particularly CO2 storage methods and the challenges encountered during the implementation of each method and analyses on how key uncertainties in CCS can be reduced. CCS sites are essentially unified systems; yet, given the scientific context, these storage systems are typically split during scientific investigations based on the physics and spatial scales involved. Separating the physics by using the chosen system as a boundary condition is a strategy that works effectively for a wide variety of physical applications. Unfortunately, the separation technique does not accurately capture the behaviour of the larger important system in the case of water and gas flow in porous media. This is due to the complexity of geological subsurface systems, which prevents the approach from being able to effectively capture the behaviour of the larger relevant system. This consequently gives rise to different CCS technology with different applications, costs and social and environmental impacts. The findings of this study can help improve understanding of selecting a suitable CCS application method and can further improve the efficiency of greenhouse gas emissions and their environmental impact, promoting the process sustainability and helping to tackle some of the most important issues that human being is currently accounting global climate change. Though this technology has already had large-scale development for the last decade, some issues and uncertainties are identified. Special attention was focused on the basic findings achieved in CO2 storage operational projects to date. The study has demonstrated that though a number of CCS technology has been researched and implemented to date, choosing a suitable and acceptable CCS technology is still daunting in terms of its technological application, cost-effectiveness and socio-environmental acceptance.

Carbon subsurface storage (CSS) has already been recognised as a critical, urgent, and essential method for reducing anthropogenic CO2 emissions and mitigating the severe effects of climate change. CSS is the last stage in the carbon capture and storage (CCS) cycle and is accomplished chiefly via oceanic and subterranean geological sequestration, as well as mineral carbonation. The injection of supercritical CO2 into geological formations causes a disruption in the subsurface's existing physical and chemical conditions; changes can occur in the pore fluid pressure, temperature state, chemical reactivity, and stress distribution of the reservoir rock. This paper aims to present a comprehensive review on the current advances in CSS, particularly CO2 storage methods and the challenges encountered during the implementation of each methods and analyses on how key uncertainties in CSS can be reduce. CSS sites are essentially unified systems; yet, given the scientific context, these systems are typically split during scientific investigations based on the physics and temporal/spatial scales involved. Separating the physics by using the system as a boundary condition is a strategy that works effectively for a wide variety of physical applications. Unfortunately, the separation technique does not accurately capture the behaviour of the larger important system in the case of water and gas flow in porous media. This is due to the complexity of geological subsurface systems, which prevents the approach from being able to effectively capture the behaviour of the larger relevant system. This consequently give rise to different CSS methods. A good CSS application method can further improve the efficiency of greenhouse gas emission and their environmental impact, promoting the process sustainability and helping to tackle some of the most important issues that human being is currently accounting, global climate change. Though this technology is already large-scale development for the last decade, some issues and uncertainties are identified. Special attention was focused on the basic findings achieved in CO2 storage operational projects to date.

Despite its remote discovery (1977) and numerous reports of its presence in vari-ous plant species in many countries, the available molecular information for the artichoke Italian latent virus (AILV) is still limited to a single complete genome sequence (RNA1 and 2) isolate from grapevine (AILV-V) and to a partial sequence portion from RNA2 of an isolate of unknow origin and host, both reported in the GenBank. Here we report the results of molecular analyses conducted on RNA2 of AILV isolates, sequenced for the first time in this study, together with the first-time finding of AILV in a new host plant species, i.e., chard (Beta vulgaris subsp. vulgaris). The different AILV isolates sequenced are from artichoke (AILV-C), gladiolus (AILV-G), sonchus (AILV-S) and chard (AILV-B). At the bio-logical level, the AILV-B was found to be involved with vein clearing and mott-ling symptoms on chard leaves. At the molecular level, sequencing results of RNA2 segments showed that AILV-C, AILV-G, AILV-S and AILV-B were 4,629 nt in length (excluding the 3’ terminal polyA tail), hence one nt shorter than that of AILV-V reported in the GenBank. The comparative sequence analysis between the CR of RNA2 of all isolates showed that AILV-V was the most variable isolate, with the lowest sequence identities of 83.2% and 84.7% at the nucleotides and amino acids levels, respectively. Putative intra-species sequence recombination sites were predicted among the AILV isolates, of which the AILV-V, AILV-C and AILV-B genomes were found to be mostly involved. This study adds further mo-lecular information on the previously unrecorded high genomic variability of the Nepoviruses of subgroup B, together with information on recombinations that may have conditioned the infection and transmission of AILV in nature; thus learning the way forward to design a new integrated pest management of chard plantation.

Flexible polymer-based dye-sensitized solar cells (DSSCs) offer promising potential for lightweight, cost-effective and versatile photovoltaic applications. However, the critical challenge in their widespread applications is the weak thermal stability of the majority of polymeric substrates, which can only withstand a maximum temperature processing of 150°C. In this study, we propose a facile and low-cost strategy to develop at low temperature DSSC flexible photoanode based on a polymeric matrix. Highly porous nanocomposites fibrous mats composed of polyethylene terephthalate (PET) and titanium dioxide (TiO2) nanobars were prepared through electrospinning process using different configurations (uniaxial electrospinning, coaxial electrospinning, and electrospray-assisted electrospinning). These techniques enabled precise control of the microstructure and the positioning of TiO2 within the composite nanofibers. Therefore, the as-produced photoanodes were loaded with N719 dye and tested in DSSC prototype using iodide-triiodide electrolyte and platinum (Pt) coated counter electrode. The results show that incorporating TiO2 on the fiber surface through the electrospray-assisted electrospinning enhanced the performance of the nanofiber composite, leading to improved dye loading capacity, electron transfer efficiency and photovoltaic performance.

To map Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV 2) spreading and evaluate immune responses variations against this virus, it was essential to locally set up efficient serological tests. The SARS-CoV2 immunogenic proteins were very expensive and not affordable mainly for Low and Middle Income Countries (LMICs). For this purpose, the commonly used antigen, Re-ceptor-Binding Domain (RBD) of Spike S1 protein (S1RBD), was produced using the the Baculovirus Expression Vector System (BEVS) . During the current study, the expression of S1RBD was monitored using western blot under different culture conditions. Different parameters were studied: Multiplicity Of Infection (MOI), cell density at infection and harvest time. Hence, optimal conditions for efficient S1RBD production were identified: MOI 3; cell density at infection 2-3x 106 cells/mL and time post-infection (tpi or harvest time) of 72h and 72-96 h successively for expression in shake-flasks and 7L-bioreactor. A high production yield of S1RBD varying between 4mg and 70 mg per liter of crude cell culture supernatant was achieved, respectively, in shake-flasks and in 7L-bioreactor. Moreover, the produced S1RBD showed an excellent antigenicity potential against COVID-19 patient sera evaluated by western blot. Thus, additional serological assays, such as ELISA, were developed using the purified S1RDB (published by other research groups).

During the 1970s, Tunisian durum wheat landraces were replaced progressively by modern cultivars. These landraces are nowadays maintained by smallholder farmers in some ecological niches and are threatened gradually by extinction resulting on the narrowing of the genetic diversity. This study aims to investigate patterns of phenotypic variability using twelve quantitative traits in a panel of 189 durum wheat landraces and seven checks, based on farmer’s population name attribution and genetic structure. Our results showed high phenotypic variability among and within landraces and checks for the majority of the studied traits. The principal components analysis showed similar grouping using farmers name attribution and genetic structure using K = 6, which confirmed the identification of a new gene pool in the oases of Tunisia, represented by the sub-population Jenah Zarzoura and the robustness and high relationships between phenotypic and genome-wide genetic structure using DArTseq method. These findings will enhance the conservation efforts of these landraces and their use in breeding efforts at national and international levels to adapt to dry conditions.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the Morlet 1-D decomposition is applied. The generalized Gaussian distribution (GGD) statistical model is fitted to the resulting wavelet coefficients. A k-nearest neighbors (k-NN) self-supervised classifier is trained using the GGD parameters to detect the spike-and-wave pattern. Experiments were conducted using 106 spike-and-wave signals and 106 non-spike-and-wave signals for training and another 96 annotated EEG segments from six human subjects for testing. The proposed SWD classification methodology achieved 95 % sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These results set the path to new research to study causes underlying the so-called absence epilepsy in long-term EEG recordings.

Arbuscular mycorrhizal fungi (AMF) are soil microorganisms living in symbiosis with most terrestrial plants. They are known to improve plant tolerance to numerous abiotic and biotic stresses through the systemic induction of resistance mechanisms. With the aim of developing a more sustainable agriculture, reducing the use of chemical inputs is becoming of a major concern. After providing an overview on the AMF history, phylogeny, development cycle and the symbiosis benefits, the current review aims at exploring the potential of AMF as biostimulants and/or biocontrol agents. Nowadays, AMF inoculums are already increasingly used as biostimulants, improving mineral nutrient plant acquisition. However, their role as promising tool into the biocontrol market, as alternative to chemical phytosanitary products, is underexplored and discussed. Thus, in the current review, we will address the mechanisms of mycorrhized plant resistance to biotic stresses induced by AMF, and highlight the various factors in favor of inoculum application, but also the challenges that remain to be overcame.

Periapical diseases are a frequent evolution of pulp necrosis. They appear without any painful episode. The incidence splits between 6% and 55%. Initially we can treat inflammatory periapical injury by conservative approach and studies reported 85% of success rate after endodontic disin-fection. Several techniques can be described in the nonsurgical management of peri-radicular le-sions: the root canal treatment, decompression procedure, decompression technique, calcium hydroxide, Apexum procedure. Unfortunately, periapical lesion recuperating is an unpredictable procedure. In some cases surgical approach is preffered to manage large periradicular wound including periapical cysts and abscess. periodic examination is primordial to evaluate efficacy of the therapy. In this report we describe an inflammatory radicular cyst of the anterior maxilla with which was successfully treated by conventional root canal retreatment and endodontic surgery

Self-compacting concrete (SCC) production is a complex operation that requires finding a good combination and a suitable dosage of each of its constituents. Several formulation methods have been developed to meet the workability requirements of SCC. In this paper, the relationships between the fresh properties of SCC based on local metakaolin (MK) and the spreading of its mortar was investigated. The results showed the existence of good correlations between the spreading of the mortar and the fresh state properties of the related SCC and that the MK substitution rate did not affect these correlations. The choice of the mortar flow rate should target appropriate fresh properties of the SCC and a viscosity enhancing agent (VEA) could be used if the mix is too fluid and present an instability.

Medical implants made of biodegradable materials could be of advantage for temporary applications such mechanical support during bone-healing or as vascular stents to keep blood vessels open. After completion of the healing process the implant would disappear, avoiding long-term side effects or the need for surgical removal. Various corrodible metal alloys based on magnesium, iron or zinc have been proposed as sturdier and potentially less inflammatory alternative to degradable organic polymers, in particular for load-bearing applications. Despite the recent introduction of magnesium-based screws the remaining hurdles to routine clinical applications are still challenging, such as limiting mechanical material characteristics or unsuitable corrosion characteristics. Here, salient features and clinical prospects of currently investigated biodegradable implant materials are summarized with a main focus on magnesium alloys. A mechanism of action for the stimulation of bone growth due to the exertion of mechanical force by magnesium corrosion products is discussed. To explain divergent in vitro and in vivo effects of magnesium a novel model for bacterial biofilm infections is proposed which predicts crucial consequences antibacterial implant strategies.

Introduction the outbreak of the Israel-Gaza conflict, initiated by a major attack on the civilian population in Israel provided a tragic opportunity to study the community wide association between severe emotional stressors related to acts of war and the incidence and clinical characteristics of Takotsubo cardiomyopathy (TCM) in this scenario. Methods Eight consecutive cases of TCM diagnosed during the first 50 days of the Israel-Gaza conflict were retrospectively collected from two southern medical centers who provided care to most of the war victims throughout the period of monitoring. The medical records were thoroughly reviewed and analyzed. Results During the monitoring period, the incidence of TCM increased substantially compared to the control period. Eight patients were included in the analysis. All patients were female and 100% of patients presented within the first 3 weeks of conflict whereas after this initial phase, despite the continuing war, the incidence of TCM reverted to pre-conflict levels. Two patients (25%) were under 50 years of age. All patients had hazardous clinical characteristics including severe left ventricular (LV) dysfunction (≤35%) and prolonged QTc interval (>500 ms). Five patients (62.5%) experienced complications. Nevertheless, no in-hospital mortality was reported. Conclusions Repeated exposure to severe emotional stress, on both personal and national level, due to mass crisis such as war is associated with an increased incidence of TCM potentially with high-risk phenotype in comparison to TCM following other, less potent, psychological insults.

Cyrene was evaluated for the first time as a potential sustainable mobile phase solvent in re-versed-phase chromatography. As a benign biodegradable solvent, Cyrene is an attractive re-placement to classical non-green organic chromatographic solvents such as acetonitrile and a modifier, co-eluent to known green solvents such as ethanol. Compared to ethanol, Cyrene is less toxic, non-flammable, bio-based, biodegradable and cheaper solvent. A fire safety spider Chart was generated to compare the properties of Cyrene to ethanol and show its superiority as a greener solvent. Cyrene's behaviour, advantages, and drawbacks in reversed-phase chromatography, including cut-off value, elution power, selectivity, and effect on the column, were investigated using a model drug mixture of moxifloxacin and metronidazole. A monolithic C18 (10 x 4.6 mm) column was used as a stationary phase. Different ratios of Cyrene: ethanol with an aqueous portion of sodium acetate buffer mobile phases were tested. A mobile phase consisting of Cyrene: ethanol: 0.1 M sodium acetate buffer pH 4.25 (8:13:79, v/v) was selected as most suitable mobile phase system for separating and simultaneously determining metronidazole and moxifloxacin. The greenness and whiteness of the method were evaluated using the qualitative green assessment tool AGREE and the white analytical chemistry assessment tool RGB12. Further potentials of Cyrene as a solvent or modifier in normal phase chromatography, liquid chromatography-mass spectrometry, and supercritical fluid chromatography are discussed.

A sustainable reversed-phase chromatographic method has been developed and validated for the simultaneous determination of three active pharmaceutical ingredients, dorzolamide, brinzolamide, and timolol, used to treat glaucoma. The eco-friendly solvent isopropanol has been used as an organic mobile phase constituent. According to the Hansen space green solvent selection tool, isopropanol has a G score of 6.5, comparable to ethanol, which has a G score of 6.6. The mobile phase consists of isopropanol: aqueous sodium acetate buffer (0.1 M, pH 4.25) in the ratio of 10:90 (v/v). The flow rate was maintained at 1 mL/min. Dorzolamide and brinzolamide were detected at 254 nm, and timolol was detected at 295 nm. A high-purity silica with a polymeric C18 modification column (5 µm,150 x 4.6 mm) was used for this separation. The three compounds were eluted within 8 min. The method was validated according to ICH guidelines. The calibration curves were linear in the range of 20-70 µg/mL, 40-140 µg/mL, and 20-70 µg/mL for dorzolamide, brinzolamide, and timolol, respectively. The LODs were found to be 1.61 µg/mL, 1.60 µg/mL, and 3.16 µg/mL for dorzolamide, brinzolamide, and timolol, respectively. Good accuracy and precision were obtained for the three compounds. The greenness and whiteness of the method were indicated using the AGREE and RGB12 tools, respectively.

This qualitative study aims at identifying the essence of personal experiences of accounting partners in Saudi Arabia. For this phenomenology approach employs to understand the lived experience of accounting partners and how would such experience affect their career. It mainly aims to explore the different experiences of fifteen (15) Saudi partners working in various accounting companies in Saudi Arabia. The interviews conducted to explore the most important challenges and difficulties for accountants to become a partner, and the most important job skills that the partners must have. This study found that there is urgent need for a number of things, including the required number of years of experience to become a partner, and to make sure that the partners are having the right education and professionally qualified. In addition, other different skills that the partner must have include responsible leadership, professional communication, good marketing skills and excellent command of English. Moreover, this research is vital for the concerned parties to identify the current challenges in the accounting profession in Kingdom of Saudi Arabia.

Despite of being an exceptionally biodiversity rich country, the forest coverage of Bangladesh is declining at an alarming rate. Declaration and management of protected areas in this regard is one of the efforts from government side to tackle the loss of biodiversity. The limited numbers of forest-protected areas (FPA), established to conserve the dwindling forest biodiversity of the country with high pressure on them for timber, non-timber forest products, and fuelwood - makes their management challenging. Moreover, most of the FPAs of the country declared only in the recent decades with very limited infrastructure, manpower and policy support for monitoring and governance. Some people-centred approaches for the management of FPAs and alternative livelihood and income generation subsidies although made available through a few project interventions, their number are still inadequate and performance remains less than satisfactory. This chapter provides a critical review of the FPAs of Bangladesh looking at their role in biodiversity conservation, management challenges, and key lessons from previous management interventions with recommendations for the future. It has been revealed that the FPA system of Bangladesh still poorly represents the diverse forest ecosystems with relatively small forest size and lack of corridors for the movement of wildlife. There are ample opportunities to render co-management of FPAs an effective strategy to minimize the conflicts in FPAs management in the country. It is, however, important to ensure the access of local forest-dependent people to different alternative income generating options that may adequately support their livelihoods.

Currently, with a large number of fatality rates, coronavirus disease-2019 (COVID-19) has emerged as a potential threat to human health worldwide. It has been well-known that severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is responsible for COVID-19 and World Health Organization (WHO) proclaimed the contagious disease as a global pandemic. Researchers from different parts of the world amalgamate together inquest of remedies for this deadly virus. Recently, it has been demonstrated that the spike glycoprotein (SGP) of SARS-CoV-2 is the mediator behind the entrance into the host cells. Our group has comprehensibly analyzed the SGP of SARS-CoV-2 through multiple sequence analysis along with the phylogenetic analysis. Further, this research work predicted the most immunogenic epitopes for both B-cell and T-cell. Notably, we focused mainly on major histocompatibility complex (MHC) class I potential peptides and predicted two epitopes; WTAGAAAYY and GAAAYYVGY, that bind with the MHC class I alleles which are further validated by molecular docking analysis. Furthermore, this study also proposed that the selected epitopes were shown availability in a greater range of the population. Hence, our study comes up with a strong base for the implementation of designing novel vaccine candidates against SARS-CoV-2, however adequate laboratory works will need to be conducted for the appropriate application.

Marketing is the manner of how to make our sales the best in the market, our prices the most accessible, our clients satisfactory, and thus our brand is the largest distributed. This needs sophisticated and advanced understanding of the total network related. Indeed, marketing data may be seen in different forms such as qualitative and quantitative. However, in the literature, it is easily noticed that large bibliography may be collected about qualitative studies, against few studies on the quantitative point of view. This is a major drawback that makes the marketing science still focusing on the design, although the market is strongly depending on quantities such as money and time. Indeed, marketing data may be a time series such as brand sales per specified periods, brand related-prices over specified periods, market shares, ..., etc. The purpose of the present work is to investigate some marketing models based on time series due to brands. We will precisely study the effect of the time scale on the persistence of brands sales in the market and on the forecasting of such a persistence according to the characteristics of the brand and the related market competition or competitors. Our study is acted on a sample of Saudi brands during the period November 22, 2017 to December 30, 2021.

The reliability of Underwater Wireless Sensor Networks (UWSNs) is measured in terms of energy consumption (EC), end-to-end delay(E2E) and packet delivery ratio (PDR). The adverse effects of channel may cause data loss. Reducing delay up to possible extend improves the reliability of the network, also increasing the number of nodes in a particular network increase reliability. Besides, increasing the number of nodes improve reliability but also increase power consumption. In order to overcome these shortcomings, the two routing protocols are proposed in this paper, namely Delay and Reliability Aware Routing (DRAR) protocol and Cooperative Delay and Reliability Aware Routing (Co-DRAR) protocol for UWSNs. In DRAR protocol, network is divided into two equal regions where two sink nodes(SNs) are positioned at upper region of the network and two SNs are placed at the mid region of the network. The protocol choose the relay node based on residual energy (RE), distance and Bit Error Rate (BER). These parameters protect the data packets from corruption and also provide stable path (where nodes remain active for longer period and do not die quickly). The protocol uses single link and may get worse sometimes while changing channel circumstances. To address this problem, cooperative routing scheme is added to DRAR protocol in order to developed its enhanced version known as Co-DRAR protocol. The protocol works by allowing the destination to receive multiple copies of data packets in order to decide the quality of packets. The proposed protocols DRAR and Co-DRAR perform routing irrespective to geographical position of sensors nodes conversely to the some of conventional routing protocols that's way our propose protocol better perform than the well-known protocol i.e. Depth base routing (DBR) in terms of EC, E2E, PDR, dead nodes, packet drop ratio and number of alive nodes (ANs).

In the present paper, we investigate the impact of the timescale factor on the quality of life index behavior on specific time intervals characterized by the presence of socio-economic, political, and/or health severe movements such as pandemics and crises. We essentially aim to show that effectively the quality of life measuring based on a single index in the existing studies may be described more adequately by a variable index due to the social, political, economic, and also healthy environment. The variability discovered is expressed by the existence and the estimation of a multi-index instead of a single one relatively to many factors. Our focus is mainly on the effect of the COVID-19 pandemic on the quality of life. Our model is applied empirically to a sample corresponding to Saudi Arabia as a case of study during the period from January 1990 to December 2021 as the main period affected by the COVID-19 pandemic. The sample is based on social media conversations and texts discussing and describing the satisfaction with the quality of life. The study confirms effectively that the role of the timescale factor is more described when considering a multi-index rather than measurement on the whole time interval.

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

For 80 years high-intensity focused ultrasound (HIFU) has been a significant area for medical research. It is a non-invasive procedure which causes cell death in a highly localised area at a defined distance from the transducer. Since 2000, the clinical indications have broadened considerable as the technology has improved. This review looks back at the history of HIFU and the current clinical indications.

Ni-Al alloys have good corrosion-resistant properties making them preferable as a material of choice compared to ordinary metals. A good understanding of the phase diagram and thermodynamic properties of the Ni-Al alloy binary system is essential in determining relevant industrial applications of the system. The Ni-Al phase diagram is being reassessed since those found in the literatures are based on very old experimental studies. The phase diagram and thermodynamic properties of the Ni-Al alloy is determined using the Thermo-Calc 2022b software over a temperature range of 200 K to 2000 K and 1 atm of pressure. A total of nineteen fields (including elemental Al and Ni) comprising of five single solid phases, one liquid phase, and twelve double regions was obtained from the equilibrium calculation. The thermodynamic calculation has six invariant reactions comprising of two eutectic reactions (914.83 ± 0.025 K and 1641.80 ± 0.025 K), three peritectic reactions (1123.55 ± 0.025 K, 1400.72 ± 0.025 K and 1642.65 ± 0.025 K), and one peritectoid reaction (913.96 ± 0.025 K). The solubility of Al in Ni is also determined. The Thermo-Calc temperature – composition of the Ni-Al alloy is compared with the existing experimental studies to identify the physical properties of the different thermodynamic properties of the Ni-Al alloy binary system.

Nanopeening treatment was applied to the AISI 420 steel to decrease its sensitivity to the tribocorrosion damage. Microstructural investigation highlighted that the Nanopeening treatment led to a high plastic deformation and nanostructured surface layer with 110 µm depth. . In order to study the combined effect of corrosion and mechanical wear, tribocorrosion tests were performed on non–treated and nanopeened samples in boric acid and lithium hydroxide solution, considering both continuous and intermittent sliding. It was found that the AISI 420 steel is sensitive the synergism between mechanical friction and electrochemical corrosion with a domination of abrasive wear. Adhesive wear was also detected in the wear track. Indeed, the mechanical wear was pronounced under intermittent sliding because of hard wear debris generation from the repassivated layer during rotating time. The Nanopeening treatment led to enhance mechanical performance and corrosion properties. Such improvement could be explained by the high plastic deformation resulting in the nano-structuration of grains and increasing hardness of AISI 420 steel.

Annually, more than 1.5 million preventable new hepatitis B (HBV) infections continue to occur, with an estimated global burden of 296 million individuals living with chronic hepatitis B infection. This substantial health challenge results in over 820,000 annual deaths attributed to complications such as liver cirrhosis and hepatocellular carcinoma (HCC). HBV vaccination remains the cornerstone of public health policy to prevent chronic hepatitis B and its related complications. It serves as a crucial element in the global effort to eliminate HBV, as established by the World Health Organization (WHO), with an ambitious 90% vaccination target by 2030. However, reports on global birth dose coverage reveal substantial variability, with an overall coverage rate of only 46%. This comprehensive review thoroughly examines global trends in HBV vaccination coverage, investigating the profound impact of vaccination on HBV prevalence and its consequences across diverse populations, including both high-risk and general demographics. Additionally, the review addresses the formidable challenges and facilitating factors essential for achieving WHO's HBV vaccination coverage objectives and elimination strategies in the coming decade and beyond.

Keywords: Green human resource management1; Green supply chain management2; Regulation and legislation3; Sustainable development goals4.

Three different fields intersect in search of an understanding of the point-of-origin of modern-age diseases: 1) D-amino acids and their role under stress conditions; 2) evolutionary origin of the mitochondrion organelle in the eukaryotic cell; and 3) gut microbiota and human healthHere it is first suggested that D-amino acids function as universal signaling agents, after having evolved as prokaryotic communication, part of an organic communication process that governs the basic activities of all the cells and coordinates cell action.Mitochondria (symbiotic prokaryotic organelles), are creative source of D-amino acids as signaling agents in the central nervous system and in the neuroendocrine systems.Amino acids racemases catalyzes the conversion between the L-enantiomers ( protein building blocks) into D-enantiomers (signaling agents).It is suggested that hectic modern life may affect human health by causing stress to the gut microbiota. These affected, gut microbiota then secrete D-amino acids that enter the blood stream, as signaling agents, causing communication errors in the central nervous system, and in the neuroendocrine systems due to excessive quantity of D-amino acids.Treating gut microbiota with inhibitors of amino acids racemases or finding D-amino acid scavengers may be used in developing novel therapeutic strategies for diseases related to the central nervous system and neuroendocrine systems caused by stressed gut microbiota.

There is a growing body of evidence supporting the significant role of bacterial biofilms in the pathogenesis of various human diseases including cancer. Biofilms are polymicrobial communities enclosed within an extracellular matrix composed of polysaccharides, proteins, extracellular DNA, and lipids. This complex matrix provides protection against antibiotics and host immune responses, enabling the microorganisms to establish persistent infections. Moreover, biofilms induce anti-inflammatory responses and metabolic changes in the host, further facilitating their survival. Many of these changes are comparable to those observed in cancer cells. This review will cover recent research on the role of bacterial biofilms in carcinogenesis, especially in colorectal (CRC) and gastric cancers, emphasizing the shared physical and chemical characteristics of biofilms and cancer. This review will also discuss the interactions between bacteria and the tumor microenvironment, which can facilitate oncogene expression and cancer progression. This information will provide insight into developing new therapies to identify and treat biofilm-associated cancers, such as utilizing bacteria as delivery vectors, using bacteria to upregulate immune function, or more selectively targeting biofilms and cancer for their shared traits.

This study delves into the dynamic field of medical education research, emphasizing the integration of research training within medical curricula. It seeks to understand the impact of such integration on the competencies of future medical professionals. The primary aim is to systematically categorize and analyze the current trends and future directions in medical education research. This involves assessing the influence of research training on medical students' skills and the methodologies used in such research. Methods: The research employs an extensive bibliographic literature review across multiple databases. It classifies studies into categories like experiential or case studies, editorials, and original research articles. This classification is based on criteria such as geographical location, research objectives, theoretical frameworks, and methodologies. Results: Findings reveal a diverse landscape in medical education research, with a significant emphasis on research training. The research showcases varying methodologies and approaches used globally, highlighting the thematic focus and geographical distribution of these studies. Conclusion: Medical education research is a globally expansive and evolving field. It underscores the importance of continuous investigation, particularly focusing on integrating research elements at curricular levels and exploring innovative educational strategies. The study also points out potential gaps in research, especially in underrepresented regions, indicating directions for future research efforts.

Policing duties may inherently be dangerous due to stab, blunt trauma and ballistic threats. The addition of individual light armour vests (ILAVs) has been suggested as a means to protect officers. However, the addition of the extra load of the ILAV may affect officer ability to conduct occupational tasks. The purpose of this study was to determine if wearing any of 3 different ILAVs (ILAV A, ILAV B, & ILAV C) affected occupational task performance when compared to that in normal station wear. A prospective, within-subjects repeated measures design was employed, using a counterbalanced randomization in which each ILAV was worn for an entire day while officers completed a variety of occupationally relevant tasks. These tasks included a victim drag, car exit and 5 meter sprint, step down and marksmanship task. Results showed that performance in each task did not vary between any of the ILAV or normal station wear conditions. There was less variability in the marksmanship task with ILAV B, however. The results suggest that none of the ILAVs used in this study were heavy enough to significantly affect task performance in the assessed tasks when compared to wearing normal station wear.

E-commerce is a rapidly evolving global trend that is having a significant impact on consumer behavior and business strategies. Despite its growing impact, e-commerce adoption by Moroccan firms remains low and research on this topic in this context is scarce. This study aims to fill this gap by investigating the main determinants influencing e-commerce adoption by Moroccan firms. We use logit, probit, and conditional mixed process-probit models to identify the critical factors driving e-commerce adoption. Our results reveal five key findings. First, newer firms that are more open to innovation and change are more likely to adopt e-commerce. Second, firms with a higher proportion of highly educated employees are more likely to adopt e-commerce. Third, the digital skills of new employees do not directly influence the likelihood of e-commerce adoption. Fourth, being listed on digital platforms increases the likelihood of e-commerce adoption. Finally, there is a positive relationship between firms engaged in innovation activities and e-commerce adoption. These findings highlight the need for additional investment in promoting modern organizational practices, reskilling workers, and implementing advanced technologies to facilitate effective e-commerce integration among Moroccan SMEs. By addressing these challenges, Moroccan firms can harness the full potential of e-commerce and contribute to the country’s economic growth and digital transformation.

In response to growing industrial demands, necessitated by larger ships and increased cargo volumes, many port authorities worldwide are undertaking a significant expansion of their infrastructure to increase their capacity. Significant expansion raises challenges such as potential congestion and environmental concerns, especially for port located in urban area as the case study explored in this paper. To address these issues, the study employs a combination of scenario analysis developed in collaboration with the port authority and logistic system simulation. This approach assesses the capacity of existing access gates to handle the increased trucking traffic and optimizes the unloading process of construction trucks at a new terminal. Key findings suggest that while the current gate capacities are adequate, optimizing unloading times at the new terminal is critical to prevent road congestion in urban area before the gate and reduce negative impacts such as sound and atmospheric pollution. The study advocates for an appointment system during peak hours to manage traffic flow effectively and underscores the importance of collaboration between the city and port authorities in managing truck traffic to mitigate congestion on the city side. Nonetheless, the study recognizes certain constraints, including the incomplete evaluation of the influence of intermodal transport on truck traffic. Furthermore, the study emphasizes the need for future research to consider the correlation between vehicle numbers and service levels, aiming to effectively adjust the time in the system.

In this study, we aim to evaluate how cyclodextrin molecules affect the hydrophilicity and selec-tivity of PVDF membrane. Herein, we prepared PVDF-PVP(1-x) β-CDx membranes, with x rang-ing from 0 to 1 wt%. The membranes were deeply characterized by means of X-ray diffraction (XRD), attenuated total reflectance infrared (ATR-IR), Raman spectroscopies, Thermal gravimet-ric analysis (TGA), Contact angle measurement (CA), scanning electron microscopy (SEM) and atomic force microscopy (AFM). The study unequivocally demonstrates the effectiveness of the addition of β-CD molecules on membranes' structural, morphological, and dielectric properties. Adding 0.25wt% β-CD enhanced hydrophilicity, with CA decreasing from 72.6° to 56° and roughness decreasing from 81 to 29 nm. The PVDF-PVP(1-x) β-CDx membranes were then fully characterized using electrochemical impedance spectroscopy (EIS) to determine their dielectric properties, which clearly showed that the presence of β-CD induced polarization of the mem-brane. The separation of various dyes with different sizes and charges was utilized. These dyes included methylene blue (MB, positive, 0.504 nm), methyl red (MR, neutral, 0.487 nm), rose Bengal (RB, negative, 0.588 nm), and brilliant blue (BB, negative, 0.798 nm). Based on our re-search, we found that the membrane's selectivity is determined by both exclusion factors (such as size and dielectric properties) and inclusion inside the β-CD cage. We found that using alpha-CD instead of beta-CD led to the disappearance of dye inclusion. These findings strongly sug-gest that β-CD molecules can be a viable solution for dye removal from wastewater.

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.

Dictionary Learning (DL) and Sparse Representation (SR) based Classifier have impacted greatly on the classification performance and has had good recognition rate on image data. In Video Semantic Analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called Sparsity based Locality-Sensitive Discriminative Dictionary Learning(SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

Abstract: The VSI represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with Irritable Bowel Syndrome (IBS) and other Chronic Inflammatory Bowel Diseases (IBD). However, an Italian version of the instrument is not yet available for the reference population. This study utilized a national sample of 500 individuals, including healthy controls and patients with Ulcerative Colitis and Crohn's Disease, to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, the research applied a questionnaire and the VSI through an online format to 500 participants. Data analysis revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach’s α = 0.95) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with other measures of anxiety and the severity of gastrointestinal symptoms, confirming its role in enhancing the understanding and management of IBS and IBD in Italy.

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works including Feedback-based Optimized Fuzzy Scheduling Approach (FOFSA) algorithm, Adaptive Task Allocation Technique (ATAT) and Osmosis Load Balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision-making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based Fuzzy Healthcare System (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for efficient Industrial Internet of Healthcare Things (IIoHT) of the future.

In the quest for robust and efficient digital communication, this paper introduces cutting-edge key exchange protocols leveraging tropical semirings’ computational prowess and block matrices’ structural resilience. Moving away from the conventional use of finite fields, these protocols deliver markedly faster processing speeds and heightened security. We present two implementations of our concept, each utilizing a different platform for the set of commuting matrices: one employing tropical polynomials of matrices and the other employing Linde-de la Puente matrices. The inherent simplicity of tropical semirings leads to a decrease in operational complexity, and using block matrices enhances our protocols’ security profile. The security of these protocols relies on the Matrix Decomposition Problem. We also provide a comparative analysis of our protocols against existing matrix block-based protocols in finite fields. This research marks a significant shift in cryptographic protocol design, specifically tailored for demanding engineering applications, and sets a new standard in secure and efficient digital communication.

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, whose accuracy was sufficient to determine flow patterns and slopes, as well as to approximate the subsurface stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff 5-10 minutes. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

Fossil fuels and carbon origin resources are affecting our environment. Therefore, alternative energy sources have to be established to co-produce energy along with fossil fuels and carbon origin resources until it is the right time to replace them. Microbial Fuel Cell (MFC) is a promising technology in the field of energy production. Compared to the conventional power sources it is more efficient and not controlled by the Carnot cycle. Its high efficiencies, low noise, and less pollutant output could make it revolutionize in the power generation industry with a shift from centrally located generating stations and long-distance transmission lines to dispersed power generation at load sites. In this review, several characteristics of the MFC technology will be highlighted. First, a brief history of abiotic to biological fuel cells and subsequently, microbial fuel cells is presented. Second, the focus is then shifted to elements responsible for the making MFC working with efficiency. Setup of the MFC system for every element and their assembly is then introduced, followed by an explanation of the working machinery principle. Finally, microbial fuel cell designs and types of main configurations used are presented along with scalability of the technology for the proper application.

3D printing is commonly used to create complex objects at a low cost. It has also been applied in the fabrication of metamaterials and metasurfaces, which alter the propagation path of electromagnetic waves. These structures have diverse applications, including enhancing beam direction, improving antenna gain and signal-to-noise ratio, and creating filters and reflectors. Metasurfaces are cheap to fabricate and their use is expected to boost antenna efficiency in 5G telecommunications and beyond. However, accurately characterizing their profile remains a significant challenge. This paper presents a low-cost fully automated 4-axis instrument for microwave metasurface charac-terization, overcoming limitations of manual methods and offering an efficient and accurate approach. This research represents a significant advancement in microwave metasurface characterization, enabling sophisticated techniques to advance wireless communication technolo-gies, as it is the case of the future 5G and millimeter-wave communication systems. The equipment has an approximate cost of $1550 USD and was able to characterize a metalens with a focal distance of 20 cm designed for signals at 30 GHz. It was possible to create 2D and 3D profiles of the intensity distribution focused by the metalens and extract parameters such as the gain (8.05 dB), 3dB depth of focus (11 cm) and full width at half maximum (2.17 cm). The comparison of the measurements with the diffraction calculations of the metalens showed a very good agreement between the in-tensity distributions and the parameters of the metalens.

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

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

To face the high tolerance of biofilms to antibiotics, it is urgent to develop new strategies to fight against these bacterial consortia. We describe here an innova-tive antibiofilm nano vector, consisting of a Dispersin B-permethylated-β-cyclodextrin/ciprofloxacin adamantly (DspB-β-CD/CIP-Ad). For this purpose, complexation assays between CIP-Ad and (i) unmodified β-CD and (ii) different derivatives of β-CD, that are 2,3-O-dimethyl-β-CD, 2,6-O-dimethyl-β-CD, and 2,3,6-O-trimethyl-β-CD were tested. A stoichiometry of 1/1 was obtained for the β -CD/CIP-Ad complex by NMR analysis. ITC experiments were carried out to determine Ka, ΔH, ΔS thermodynamic parameters of the complex between β-CD or its different derivatives in the presence of CIP-Ad. A stoichiometry 1/1 of β-CD/CIP-Ad complexes was confirmed with variable affinity according to the type of methylation. A phase solubility study showed increased CIP-Ad solubili-ty with CDs concentration, pointing out complex formation. The evaluation of the antibacterial activity of CIP-Ad and the 2,3-O-dimethyl-β-CD/CIP-Ad or 2,3,6-O-trimethyl-β-CD/CIP-Ad complexes was performed on S.epidermidis strains. MIC studies showed that the complex of CIP-Ad and 2,3-O-dimethyl-β-CD exhibited similar antimicrobial activity to CIP-Ad alone, while the interaction with 2,3,6-O-trimethyl-β-CD increased MIC values. Antimicrobial assays on S. epidermidis biofilms demonstrated that the synergistic effect observed with the DspB/CIP association was partly maintained with the 2,3-O-dimethyl-β-CDs/CIP-Ad complex. To obtain this "all in one" nano vector, able to destroy the biofilm matrix and release the antibiotic simultaneously, we covalently grafted DspB on three carboxylic permethylated CD derivatives with different length spacer arms. The strategy was validated by demonstrating that a DspB-permethylated-β-CD/ciprofloxacin-Ad nanovector exhibited efficient antibiofilm activity.

This study examines the role of using algorithmic HRM to make strategic decisions pertaining to HR activities in organizations. The study develops a scale to measure algorithmic HRM usage in its first phase, and in the second phase, it finds that algorithmic HRM having significant impact on the strategic HR decision making that helps and enables organization in creating the competitive advantage. Utilizing the author’s LinkedIn profile, 234 final participants considered for this research. Collected data analyzed by applying PLS SEM. It evaluates the mediation role of the HR strategic decision making, and the HR digital maturity as a moderator in seeing the impact of algorithmic HRM on the competitive advantage of the firm. The study finds a strong relationship between the algorithmic HRM usage and competitive advantage, a significant relationship between algorithmic HRM usage and the strategic HR decision making, and lastly, a significant relationship between strategic HR decision making and the competitive advantage. The moderating role of HR digital maturity was found to be insignificant in this research which paves the way for further research. This research, the model, and its findings contribute to the theory and implicate the practicality of algorithmic HRM. It's one of few papers that address the algorithmic HRM usage in the transitioning economy like Saudi Arabia.

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

Osteoarthritis is a painful, disabling condition which is increasing in prevalence as a result of an ageing population. With no recognised disease limiting therapeutics, arthroplasty of the hip and knee is the most common and effective treatment for lower limb osteoarthritis, however lower limb arthroplasty has a finite life-span and a proportion of patients will require revision arthroplasty. With increasing life expectancy and an increasing proportion of younger (<65 years) patients undergoing arthroplasty, the demand for revision arthroplasty after implant failure is also set to increase. Statins are cholesterol modulating drugs widely used for cardiovascular risk reduction which have been noted to have pleiotropic effects including potentially influencing arthroplasty survival. In vitro studies have demonstrated pleiotropic effects in human bone cells, including enhancement of osteoblastogenesis following simvastatin exposure, and in vivo studies have demonstrated that intraperitoneal simvastatin can increase peri-implant bone growth in rats following titanium tibial implant insertion. There is evidence also that statins may also influence osseointegration, enhancing bone growth at the bone-implant interface, subsequently improving the functional survival of implants. Data from the Danish Hip Arthroplasty Registry and Clinical Practice Research Datalink in the UK suggest a reduction in the risk of lower limb revision arthroplasty in statin ever-users vs never users, and a time dependent effect of statin administration on reduction in risk of revision. In this article we review the clinical and scientific evidence linking statins and risk of revision arthroplasty.

COVID-19 has substantially influenced healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. Healthcare practitioners have used AI systems for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methodology: A cross-sectional study was conducted the approval from the Research Ethics Committee of King Abdulaziz University (KAU), Saudi Arabia. The study used sequential sampling approaches to include 50 Real-Time Polymerase Chain Reaction (RT-PCR) positive COVID-19 patients from KAU's coronavirus isolation wards. A pre-designed form was used to collect each patient's demographic information, including age and gender, signs and symptoms, illness severity (mild, moderate, severe), and laboratory findings. Furthermore, the length of the hospital stay and the result, whether the patient recovered or died, were reported. Results: The study involved 50 patients with varying degrees of disease severity, most of whom suffered from fever, fatigue, cough, sore throat, and diarrhoea. Laboratory analysis of COVID-19 patients showed increased white blood cell and platelet counts, with C-reactive protein levels above normal. Elevated LDH levels indicated possible tissue damage, while ferritin levels were elevated. Other enzymes were in the normal range, but bilirubin levels were slightly elevated. Overall, the patient's lab results indicated inflammation and possible blood clot formation. The study evaluated the predictive accuracy for outcomes and mortality of COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-dimers, and hospital stay (p-value ≤0.05). The predictive accuracy mortality of patients with COVID-19 using AI showed Hospital stay, D-Dimers ALP, Bilirubin, LDH, CRP, and Ferritin significantly affected hospital mortality. (p ≤ 0.0001). Conclusion: Artificial Intelligence is crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. AI can track the crisis at various scales, facilitate research, and aid in developing treatment regimens, prevention strategies, and drugs and vaccines. It also aids in monitoring health and facilitating research on the virus.

Soil erosion seriously affects vineyards. In this study, the influence of two plant covers on soil moisture and the effect of different physiographic conditions on runoff and sediment yields were evaluated in a rainfed vineyard formed by four fields (NE Spain) during 15 months. One field had spontaneous vegetation as plant cover and three fields had a cover crop of common sainfoin. The vineyards’ rows were dry and stable, whereas the inter-row areas were wet although very variable, and the corridors were wet and very stable. Soil moisture in the inter-row areas with Common sainfoin was much higher than in the rows (62% - 70%) whereas this difference was lower with spontaneous vegetation (40%). Two runoff and sediment traps (STs) were installed in two ephemeral gullies, and 26 time-integrated surveys (TIS) done. The mean and maximum runoff yields were 9.8 and 30.7 l TIS^–1 in ST2 and 13.5 and 30.2 l TIS^–1 in ST3. The mean turbidity was 333 and 19 g l^–1, and the maximum sediment yields were 41,260 and 2,778 g TIS^–1 in ST2 and ST3. Changes in the canopy covers (grapevines and plant covers) and rainfall parameters explained the runoff and sediment dynamics.

Establishment of protected areas (PAs) is one of the key global conservation strategies that currently cover approximately 15% of the earth’s land surface. Globally, PA networks are designed to curb the growing anthropogenic pressures in areas with high biological diversity. Despite the importance of PAs in conserving the vanishing biodiversity and unique habitats, many of them are in critical condition due to poor governance thus functioning below the expected level. Moreover, in many developing countries, the PA coverage is below the global standard. Recognizing their contemporary role in conservation, governments have recently agreed to expand the global PA coverage to 17% by the year 2020 (Aichi target 11). This book with eight chapters from different regions of the world provides an overview of the PAs governance, institutional mechanisms, conservation benefits, limitations and challenges associated with their respective policy discourse, integrated management, and functional attributes. Protected areas expect to to play an important role in the long rn in conservation and protection of biodiversity and ecosystems particularly in countries where population pressure and habitat loss are high. Regular intervention, political commitment, and effective governance are essential for the sustainability of PAs across the world. Here, we also attempted to shed some light on future development clues for the sustainable management and monitoring of PAs worldwide.

Neuronal timing is critical for many brain functions such as perception and learning. At the level of the chemical synapse, the synaptic delay is determined by the presynaptic release probability (Pr) and the waveform of the presynaptic action potential (AP). Depolarization-induced analogue-digital facilitation (d-ADF) is a form of context-dependent synaptic facilitation that is induced by constant depolarization of the presynaptic neuron and is mediated by voltage inactivation of presynaptic Kv1 channels. Whether synaptic delay is modulated during d-ADF remains unclear. We show here that despite Pr is elevated during d-ADF at L5-L5 synapses, the synaptic delay is surprisingly unchanged, suggesting that both Pr- and AP-dependent changes in synaptic delay compensate for each other. We conclude that in contrast with other short- or long-term modulation of presynaptic release, timing is not affected during d-ADF because of the opposite interaction of Pr- and AP-dependent modulations of synaptic delay.

In addition to identifying and prosecuting cyber attackers, attack attribution activities can provide valuable information guiding the defenders’ security procedures and giving them greater confidence in incident response and remediation. However, technical analysis involved in cyberattack attribution requires high skills, experience, access to up-to-date Cyber Threat Intelligence, and significant investigators’ effort. Attribution results are not always reliable, and skilful attackers often work hard to cover their traces and mislead or confuse investigators. In this article, we present a tool designed to support technical attack attribution and implemented as a machine learning model extending the OpenCTI platform. We also discuss the tool’s performance in the investigation of a recent cyberattack.

Almost all modern inorganic light-emitting diode (LED) designs are based on double heterojunctions (DHJs) whose structure and current injection principle have remained essentially unchanged for decades. Although highly efficient devices based on the DHJ design have been developed and commercialized for energy-efficient general lighting, the conventional DHJ design requires burying the active region (AR) inside a pn-junction. This has hindered the development of emitters utilizing nanostructured ARs located close to device surfaces such as nanowires or surface quantum wells. Modern DHJ III-N LEDs also exhibit resistive losses which arise from the DHJ device geometry. The recently introduced diffusion-driven charge transport (DDCT) emitter design offers a novel way to transport charge carriers to unconventionally placed ARs. In a DDCT device, the AR is located apart from the pn-junction and the charge carriers are injected into the AR by bipolar diffusion. This device design allows the integration of surface ARs to semiconductor LEDs and offers a promising method to reduce resistive losses in high power devices. In this work, we present a review of the recent progress in gallium nitride (GaN) based DDCT devices, and an outlook of potential DDCT has for opto- and microelectronics.

All living cells maintain a charge distribution across their cell membrane (membrane potential) by carefully controlled ion fluxes. These bioelectric signals regulate cell behavior (such as migration, proliferation, differentiation) as well as higher-level tissue and organ patterning. Thus, voltage gradients represent an important parameter for diagnostics as well as a promising target for therapeutic interventions in birth defects, injury, and cancer. However, despite much progress in cell and molecular biology, little is known about bioelectric states in human stem cells. Here, we present simple methods to simultaneously track ion dynamics, membrane voltage, cell morphology, and cell activity (pH and ROS), using fluorescent reporter dyes in living human neurons derived from induced neural stem cells (hiNSC). We developed and tested functional protocols for manipulating ion fluxes, membrane potential, and cell activity, and tracking neural responses to wounding and re-innervation in vitro. Finally, using morphology sensor, we tested and quantified the ability of physiological actuators (neurotransmitters and pH) to manipulate nerve wound re-innervation. These methods are not specific to a particular cell type and should be broadly applicable to the study of bioelectrical controls across a wide range of combinations of models and endpoints.

Polymer electrolyte was used as a medium for testing the performance of microband electrodes under conditions of linear diffusion. Cyclic voltammetry and chronoamperometry experiments were performed in such viscous medium, where diffusion rates are much slower than in fluid solutions. Confirmation of the existence of such conditions from the log i vs. log v or log i vs. log t relationships in the current equation always yielded values lower than the expected value of 0.5. This could indicate impure linear diffusion profile, i. e. some contribution from radial diffusion (edge effect). However, performing these tests in monomeric solvents of similar viscosities, such as glycerol or propylene glycol, gave the desired value of 0.5. These results lead to the conclusion that current equations which are based on Fick’s laws may not be applicable for polymer electrolytes, where various obstruction to diffusion make it a more complicated process than in monomeric solvents.

This paper presents a novel method for automatic modulation classification (AMC) for cognitive radio (CR) networks based on a simple classifier that is trained with high-order cumulant. The proposed method focuses on the statistical behavior of modulated signals and includes analog modulation and digital schemes, which received less attention in the literature. The effectiveness of the proposed method is demonstrated through simulation results using high-quality generated signals under different signal-to-noise ratios (SNRs) and channel conditions. The classification performance achieved by the proposed method is superior to that of the more complex deep learning methods, making it well-suited for deployment in end units of CR networks, particularly in military and emergency service applications. The proposed method offers a cost-effective, high-quality solution for AMC that meets the stringent requirements of these critical applications.

Recently we have seen a relaxation of the historic restrictions on the use and subsequent research on the Cannabis plants, generally classified as Cannabis sativa and Cannabis indica. What research has been performed to date has centered on chemical analysis of plant flower products, namely cannabinoids and various terpenes that directly contribute to phenotypic characteristics of the female flowers. In addition, we have seen many groups recently completing genetic profiles of various plants of commercial value. To date, no comprehensive attempt has been made to profile the proteomes of these plants. We report herein our progress on constructing a comprehensive draft map of the Cannabis proteome. To date we have identified over 17,000 potential protein sequences. Unfortunately, no annotated genome of Cannabis plants currently exists. We present a method by which “next generation” DNA sequencing output and shotgun proteomics data can be combined to produce annotated FASTA files, bypassing the need for annotated genetic information altogether in traditional proteomics workflows. The resulting material represents the first comprehensive annotated protein FASTA for any Cannabis plant. Using this annotated database as reference we can refine our protein identifications, resulting in the confident identification of 13,000 proteins with putative function. Furthermore, we demonstrate that post-translational modifications play an important role in the proteomes of Cannabis flower, particularly lysine acetylation and protein glycosylation. To facilitate the evolution of analytical investigations into these plant materials, we have created a portal to host resources we have developed from proteomic and metabolomic analysis of Cannabis plant material as well as our results integrating these resources. All data for this project is available to view or download at www.CannabisDraftMap.Org

Several factors influence traffic congestion and overall traffic dynamics. Simulation modeling has been utilized to understand the traffic performance parameters during traffic congestions. This paper focuses on driver behavior of route selection by differentiating three distinguishable decisions, which are shortest distance routing, shortest time routing and less crowded road routing. This research generated 864 different scenarios to capture various traffic dynamics under collective driving behavior of route selection. Factors such as vehicle arrival rate, behaviors at system boundary and traffic light phasing were considered. The simulation results revealed that shortest time routing scenario offered the best solution considering all forms of interactions among the factors. Overall, this routing behavior reduces traffic wait time and total time (by 69.5% and 65.72%) compared to shortest distance routing.

Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. A null model was created using frequencies of random SNPs. Results: Polygenic selection signal of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications. A polygenic score comprising 9 SNPs predicts population IQ (r=0.88), outperforming 99% of the polygenic scores obtained from sets of random SNPs (Monte Carlo p= 0.011). Its predictive power remains unaffected after controlling for spatial autocorrelation (Beta= 0.83). The largest polygenic score (161 SNPs) exhibits similar predictive power (Beta=0.8). Random polygenic scores are moderate predictors of population IQ (thanks to spatial autocorrelation), and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise. Conclusion: This study provides guidance for using GWAS hits together with random SNPs for testing polygenic selection using Monte Carlo simulations.

Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

This study delves into the intricate interplay of economic growth components, specifically focusing on Consumption and investment in Saudi Arabia from 2000 to 2022. Employing vector error correction models and cointegration techniques, we analyze the short- and long-term dynamics within the relationship of Consumption, investment, and economic growth. Granger causality analysis is also used to discern these pivotal variables' causal connections. Our empirical analysis reveals a persistent long-term cointegration relationship among the variables, underscoring the enduring nature of their interdependency. Furthermore, our findings highlight Consumption and investment's statistically significant positive impact on economic growth. Notably, the short-term analysis unveils a stable model characterized by an annual adjustment to equilibrium of 100%.Moreover, the Granger causality study demonstrates unidirectional causal linkages between Consumption, investment, and economic growth. These findings hold substantial implications for policy formulation in Saudi Arabia. Policymakers must grasp the ramifications of burgeoning prosperity and evolving private consumption patterns on future environmental outcomes. Achieving sustainable long-term results necessitates equal emphasis on bolstering private Consumption and fostering other facets of economic growth.

The Capital Asset Pricing Model is a widely applied model to describe risky markets and to deduce their systematic risk. Its estimation, therefore, remains an important task in Econo-financial studies. Empirically, it focuses on the impact of return interval on the betas. Existing studies somehow turn around the same idea of measuring the value of the beta according to the uniform intervals of time during a fixed period. However, it is noticed easily, and especially in the last decade that many factors such as socio-political, and Econo-environmental ones have led to a perturbation in the timeline of the worldwide development, and especially in countries and regions having political changes. This led us to introduce a new idea of risk estimation taking into account the non-uniform changes in markets by introducing a non-uniform wavelet analysis. We aim to explain the Econo-political situation of Arab spring countries and the effect of the revolutions on the market beta. The main novelty is firstly the construction of a dynamic backward-forward model for missing data, and next the application of random non-uniform wavelets. The proposed procedure will be acted empirically on a sample corresponding to TUNINDEX stock as a representative index of the Tunisian market actively traded over the period January 14, 2016, to January 13, 2021. The chosen 5-years period is important as it constitutes the first 5-years-after the revolution and depends strongly on the Socio-Econo-political stability in the revolutionary countries.

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

Streptococcus mutans is a key bacterium in dental caries- one of the most prevalent chronic infectious diseases. Conventional treatment both fails to specifically target the pathogenic bacteria and at-tempts to eradicate commensal bacteria as well. Thus, caries remains one of most common and challenging diseases. The use of bacterial viruses as anti-bacterial agents, is gaining interest worldwide. Hardly any phages were described against S. mutans. The objective of this study was to isolate anti-S. mutans phages and to characterize their antimi-crobial properties. Human saliva samples were filtered and screened for potential phages. Standard double-layered agar method was used for isolation. Whole genome sequence analysis and morphology visualization by TEM, were used for anti-S. mutans phage identification. Antibacterial properties were evaluated using clinical strains and ATCC strains of S. mutans in various states. Antibacterial effect was also tested on human cariogenic dentin. One phage against S. mutans was isolated and termed SMHBZ8. This phage showed effective lytic activity in vitro against both planktonic and biofilm S. mutans cultures. Moreover, the phage showed antibacterial effect when used on cariogenic dentin. The isolation and characterization of SMHBZ8 may be the first step in developing a potential phage therapy for dental caries.

The intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance underscores the need for rigorous dataset evaluation and selection protocols to ensure the reliability and generalizability of classification outcomes. This study involved a thorough examination of selected publicly available plant diseases datasets, with an emphasis on how well they performed as measured by GLCM metrics. After first classifying the datasets according to their GLCM metrics, dataset_2 (D2) and dataset_5 (D5) were found, respectively, to be the best-performing dataset in all GLCM analyses. The same datasets were then used to train deep learning models, and their classification performances were assessed. A noteworthy association was observed between the results of training deep learning models and the performance ratings derived from GLCM studies. More specifically, dataset_2 (D2) performed best in both GLCM analysis and deep learning model performance, indicating a strong correlation between the accuracy of classification and the textural qualities that GLCM captured. In the context of plant disease identification, in particular, these results highlight the significance of clearly defined dataset selection criteria in deep learning applications. Scholars can improve the accuracy and dependability of deep learning models for diagnosing plant diseases by giving preference to datasets with favorable GLCM metrics. The research also emphasizes the importance of texture features being taken into account in addition to conventional image features, highlighting the necessity of transparency and rigor in dataset selection procedures.

Colorectal cancer (CRC) is a weighty cause of mortality worldwide, with growing incidence in the Middle East. Understanding the level of awareness about CRC screening among the Syrian pop-ulation is crucial public health interventions. A cross-sectional study was conducted from Sep-tember 22, 2023, to October 15, 2023, involving 928 participants from six major governorates in Syria. An online questionnaire containing socio-demographic, knowledge, and attitude sections was used to collect data. Descriptive statistics and chi-square tests were employed for data analysis using (SPSS) v28.0. The survey revealed a lack of awareness regarding CRC screening among the Syrian population, with only 499(53%) of the proportion demonstrating adequate knowledge about the disease and its screening methods. Significant associations were observed between the level of knowledge and socio-demographic factors including gender, work status, and economic status (p < 0.05). The findings emphasize the need for public health initiatives to enhance awareness and knowledge about CRC and its early screening methods in Syria. Education is essential for improving early detection and reducing the burden of colorectal cancer in the region. Efforts to increase public understanding of CRC screening are critical for reducing the incidence and mortality.

Saudi Arabia is an oil-reliant nation as a large percentage of its GDP comes from oil resources. Oil dependency leaves a county at the mercy of the international crude market, and a decrease in the price of crude can seriously destabilize the economy of such nations. An example is the case of Venezuela whose dependence on oil caused a national disaster (McCarthy, 2017). As such, the nation’s exports, GDP, and government revenue are primarily dependent on oil revenue, and the recent decrease in the oil prices has decreased Venezuela’s national revenue resulting in economic collapse as well as inflation. A shift from a resource based economy to a knowledge based economy will help Saudi Arabia become less reliant on its oil revenues for its economic stability and growth (Nurunnabi, 2017).

Ocular drug delivery is one of the most challenging administration routes due to the very low drug bioavailability. In this work, we produce and characterize mucoadhesive mixed polymeric micelles (PMs) made of chitosan and poly(vinyl alcohol) backbones graft-hydrophobized with short poly(methyl methacrylate) blocks and use them to encapsulate cannabidiol (CBD), an anti-inflammatory cannabinoid. CBD-loaded mixed PMs are physically stabilized by ionotropic crosslinking of the CS domains with sodium tripolyphoshate and spray-drying. These mixed PMs display CBD loading capacity of 20% w/w and sizes of 100-200 nm, and spherical morphology (cryogenic-transmission electron microscopy). The good compatibility of the unloaded and CBD-loaded PMs is assessed in a human corneal epithelial cell line. Then, we confirm the permeability of CBD-free PMs and nanoencapsulated CBD in cornea cell monolayers under liquid-liquid and air-liquid conditions. Overall, our results highlight the potential of these polymeric nanocarriers for ocular drug delivery.

This paper presents a new method for approximating the classical arcsine function. The proposed approximating methodology is simpler in its approach than other classical approaches and undeniably innovative. It is based on matrix representation besides the basic interpolation to approximate the inverse trigonometric function. It provides an efficient model which allows for reliable and precise calculations. The results are as per our knowledge unseen results in the previous literature.

The present paper is concerned with the study of the quality of life index. Such an index has become an important index for measuring the well-being of individuals. However, the quality of life index is always a subjective, intangible, and often hard to quantify with precision due to the lack of quantitative models dealing with. The main goal of the present paper is thus to propose a mathematical, quantitative model for the measurement of a quality of life index. The main novelty is firstly the construction of a wavelet dynamic multiscale model to quantify and investigate the effect of time scale on the quality of life index measuring. The proposed procedure is acted empirically on a sample corresponding to Saudi Arabia as a case of study during the period from 2003 to 2020 as part of the 2030-vision plan. Saudi Arabia has implemented the so-called 2030-vision plan where the quality of life improvement is one of the main goals to be attempted. The findings show that wavelets are capable to localize the time-wise behavior of the index contrarily to classical studies which estimate a global view of the index. Moreover, the study shows the link between the quality of life behavior and many other indices.

The relative operating characteristic (ROC) curve is a popular diagnostic tool in forecast verification, with the area under the ROC curve (AUC) used as a verification metric measuring the discrimination ability of a forecast. Along with calibration, discrimination is deemed as a fundamental probabilistic forecast attribute. In particular, in ensemble forecast verification, AUC provides a basis for the comparison of potential predictive skill of competing forecasts. While this approach is straightforward when dealing with forecasts of common events (e.g. probability of precipitation), the AUC interpretation can turn out to be oversimplistic or misleading when focusing on rare events (e.g. precipitation exceeding some warning criterion). How should we interpret AUC of ensemble forecasts when focusing on rare events? How can changes in the way probability forecasts are derived from the ensemble forecast affect AUC results? How can we detect a genuine improvement in terms of predictive skill? Based on verification experiments, a critical eye is cast on the AUC interpretation to answer these questions. As well as the traditional trapezoidal approximation and the well-known bi-normal fitting model, we discuss a new approach which embraces the concept of imprecise probabilities and relies on the subdivision of the lowest ensemble probability category.

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.

In this study, the olive pomace was considered because of its importance in the environment. So, this study carried out to estimate the total nitrogen content and the fatty acids profile of olive pomace. The olive pomace samples were collected from Irbid, Jarash, Ajloun and Mafraq during the harvesting season 2014 to study the biochemical characteristics of fatty acid composition and the total nitrogen (TN) content. The total nitrogen content was determined by standard Kjeldahl method and the fatty acid profile was detected by gas chromatography (GC). The results show that the total nitrogen content ranged from (0.39 ± 0.0) to (0.62 ± 0.02) with statistically significant difference suggesting that the composition of matrices and their percentage may be responsible for composition of amendments. The calculated total protein percentage ranged between (2.43 ± 0.00) to (3.87 ± 0.17). Also, 13 different fatty acids were quantitatively profiled and quantitated. Oleic acid (C 18:1) was found to be the highest percentage of all other fatty acids and ranged between (59.03%) and (63.81%), moreover the C 18:1/C 18:2 (oil quality) was calculated and C.V% showed variation meaning that nutritional implication could affect the oxidative stability of oils. In conclusion, OP by-product could give a sustainable and alternative-cheap source for fertilizers, pharmaceutical industries, cosmetics and other industries.

Sulfated polysaccharides from seaweed are highly active natural substances having valuable applications. In the present paper, attempts have been made to discuss the physicochemical and structural features of polysaccharides isolated from red marine alga Alsidium corallinum (ACPs) and its protective effect in hepatic impairments induced by tebuconazole (TBZ) in rats. Structural features were determined using High performance liquid chromatography, Fourier transformed infrared, and solid state ¹H and ¹³C-Nuclear magnetic resonance analysis. ACPs are found to be hetero-sulfated-anionic polysaccharides that contained carbohydrates, sulfate groups and uronic acids. In vitro biological activities suggested the effective antioxidant and antimicrobial capacities of ACPs. For antioxidant testing in vivo, the biochemical analysis and plasma profiles displayed that oral administration of ACPs could mitigate blood lipid indicators, including total cholesterol, triglyceride, low and high density lipoprotein cholesterol, bilirubin, liver function indexes involving alanine aminotransferase and aspartate aminotransferase, showed that ACPs possessed obvious antioxidant activities. Besides, the intervention of ACPs potentially inhibited lipid peroxidation, protein oxidation, key enzymes of lipid metabolism (<0.001) and increased levels and antioxidant status (<0.05). Histomorphological observation confirmed that ACPs intervention could partially repair liver injuries caused by TBZ. The computational results showed that A. corallinum monosaccharides bound 1JIJ, 1HD2 and 1WL4 receptors with acceptable affinities, which together with deep embedding and molecular interactions support the antioxidant, antimicrobial and hypolipidemic outlined effects in the in vitro and in vivo findings. In view of their prominent antioxidant effects, ACPs stand for promising candidates in liver diseases and need be considered in pharmaceutical applications.

From the hospital wastewater, a novel bacteriophage was isolated and characterized. According to characterization properties, this bacteriophage belongs to the Siphoviridae family, the maximum bacteriophage titer was recorded at 37°C and a pH of 7.2, had a 44,789 bp linear double-strand DNA genome, and within the genome sequence, there are 61 genes, all of which are encoded into proteins. Although this bacteriophage does not have any virulence factors or antimicrobial resistance genes and had specific lytic activity against some antimicrobial resistance S. aureus clinical isolates.

This paper presents a concise mathematical framework for investigating both feed-forward and backward process, during the training to learn model weights, of an artificial neural network (ANN). Inspired from the idea of the two-step rule for backpropagation, we define a notion of F_adjoint which is aimed at a better description of the backpropagation algorithm. In particular, by introducing the notions of F-propagation and F-adjoint through a deep neural network architecture, the backpropagation associated to a cost/loss function is proven to be completely characterized by the F-adjoint of the corresponding F-propagation relatively to the partial derivative, with respect to the inputs, of the cost function.

Micellar Casein Concentrate (MCC) is manufactured from microfiltration (MF) of skim milk utilizing ceramic or polymeric membrane filtration. While ceramic filtration has higher efficiency, use of polymeric is cost effective and the process is familiar to several US dairy processors. The aim of the present study was to develop an optimized membrane filtration process to produce MCC using spiral wound polymeric membrane filtration (SW MF) system by systematic selection of transmembrane pressure (TMP) and level of diafiltration (DF). Using skim milk as feed material, preliminary lab-scale MF experiments were conducted using 0.5 µm polyvinylidene fluoride (PVDF) membrane. Three TMP (34.5, 62.1, and 103.4 kPa) and three levels of DF (70, 100, and 150%) along with a process without DF as control were used in the study. Effect of TMP and effectiveness of DF on flux rates, SP removal, casein to total protein (CN/TKN) ratio, casein to true protein (CN/TP) ratio, rejection of casein (rej CN) and SP (rej SP) were evaluated. At all TMP values used in the study, the overall flux (O Flux) increased with the level of DF. Highest O Flux of 30.77 liter per meter square per hour (LMH) was obtained with 34.5 kPa pressure and 150% DF. The impact of DF was more pronounced at lower pressures than at the higher pressures used in the study. With controlled DF, instantaneous flux was maintained within 80% of initial flux for the entire process run. For all the experiments, casein has a rejection of 0.97 to 1.0, while serum protein has the lowest rejection of 0.10 at 34.5 kPa pressure and 150% DF level. Use of 34.5 kPa and DF level of 150 % contributed to 81.45% SP removal, and casein to true protein ratio of 0.96. SP removal data from the lab-scale experiments were fitted into a mathematical model using DF and square of TMP as factors. The model predicts SP removal within 90-95% of actual SP removal got from the pilot plant experiments.

We seek to examine effective collaboration when engaging in value cocreation with the customer for mobile financial services in EMs. Our study is a motivation for multi-stakeholder engagement in value cocreation with financial inclusion of all consumers including marginalized populations. Furthermore, our work discovers that social sustainability is a product of social creativity and results in financial inclusion. We apply a qualitative descriptive methodology utilizing exemplars from extant research and phenomenology to analyze the data from the contexts of Ghana and Kenya. The results show that value is co-created for mobile financial services in networks in emerging markets when actors integrate resources for social and environmental sustainability as essential elements for mobile financial inclusion.