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.

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.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

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 use of artificial intelligence (AI) is becoming more prevalent across industries as diverse as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large data sets and requires a continuous supply of high-quality data. However, using data for AI is not without its challenges. This paper comprehensively reviews and critically examine the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concern, and technical expertise and skills. This paper examines e these challenges in details and offers advices on how companies can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain a competitive advantage in the digital age.

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).

Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.

The integration of automation and process optimization within the context of AI-powered digital transformation has emerged as a pivotal strategy for organizations aiming to enhance efficiency, foster innovation, and competitiveness. This paper is devoted to present innovative contribution by providing a comprehensive structured roadmap that outlines the foundational principles necessary for the successful integration of automation and optimizing processes within the con-text of emerging AI technologies. The paper introduce a cohesive framework consisting of es-sential key pillars: Data-Driven Insights, Seamless Automation, Adaptive Learning and Contin-uous Improvement, Human-Centric Collaboration, Ethical and Responsible AI, Strategic Align-ment, Scalability, and Innovation. These pillars function as guiding principles to navigate the in-tricate landscape of automation-driven initiatives within AI-powered digital transformation. By embracing these pillars, organizations can embark on a transformative journey that maximizes the potential of automation, fosters innovation, and positions them as leaders in the ever-evolving landscape of AI-driven business operations.

Keywords: AI Readiness Assessment; Business Processes; and Data Infrastructure.

High-density polyethylene (HDPE) waste poses a significant environmental challenge due to its non-biodegradable nature and the vast quantities generated annually. However, conventional recycling methods are energy-intensive and often yield low-quality products. Herein, HDPE waste is upcycled into anti-aging superhydrophobic thin films suitable for outdoor applications. A two-layer spin-casting method combined with heating-induced crosslinking is utilized to produce an exceptionally rough superhydrophobic surface, featuring a root mean square (RMS) roughness of 50 nm, an average crest height of 222 nm, an average trough depth of –264 nm, and a contact angle (CA) of 148°. To assess durability, weathering tests were conducted, revealing the films' susceptibility to degradation under harsh conditions. The films' resistance to environmental factors is improved by incorporating a UV absorber, maintaining their hydrophobic properties and mechanical strength. Our research demonstrates a sustainable method for upcycling waste into high-performance, weather-resistant superhydrophobic films.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

Background:Fontan completion is the last stage of univentricular repair, which drains the inferior vena cava against gravity to a higher-pressure pulmonary arterial circulation. The passivity and anti-gravity nature of this circulation render a failing Fontan, not an uncommon encounter, twenty years ago, Rodefeld and colleagues, decided to explore the feasibility of vascular pumps to support the passive nature of Fontan circulation, and since then, trials are still ongoing, and we aimed through this review to wrap up the results of these trials. Methods:A literature review has been conducted, to identify any in vivo or in vitro trial of a Fontan assist device. Several outcome parameters determined whether the trial will be included or not in this review, including, the flow rate of a pump if applicable, the number of compressions per minute if a compression device has been used, the resultant flow rate, the inferior and superior vena cava pressures, associated with each setting of the respective device, and any evidence of hemolysis or clotting within the circuit. Results:A total of ten trials have been included, with 23 different settings. Three designs have been identified, out of which two fall within the pump design, namely the connecting chamber and the intravascular pump designs; only one trial explored the possible use of a compression device. The commonest design encountered in in-vivo trials was the intravascular pump (67%). No reports of significant hemolysis or thrombosis were found. An increasing flow rate beyond 5L/min was associated with a negative outcome in the connecting chamber design, increasing upstream pressure in the SVC, while intravascular pumps did not display this limitation. Conclusion:Univentricular heart patients are a relatively small population, with very extensive needs, despite twenty years of trials, no study has yet concluded the optimal design or settings for a Fontan assist device. In this review, we identified that the intravascular pump remains the most practical design, due to its easy mode of insertion, and that it is not faced with the limitation or risk of increasing upstream resistance. There is still a long way to conclude its use as other designs such as compression devices have not yet been fully studied.

Purpose: When it comes to improving citizen service delivery standard and processes, or broadly, management in government (MIG), many reform programs and improvement efforts are ongoing across different ministries, departments, and agencies. This paper is part of a larger empirical research where we are interested to know what those programs and projects are, and how they are doing, and what the gaps are. Design/methodology/approach: Analyzing the publicly available data on the government portal and donors’ website, this article provides an overall idea of current reforms in various places of government. In parallel, field research was conducted through visiting agency offices, observing office environment and mechanisms, and interviewing public managers who are working at district and sub-district level offices of directorates or agencies.Findings: As a trend, state-led development process and the traditional mode of administration have moved to New Public Management based quality and performance approach. All donors have their own strategy framework document like ‘Country Assistance strategy’, ‘Country Operations and Business Plan’, Logical Framework Document, Focus or Priority Streams, etc. Many of the projects are sectoral and agency or ministry specific and have an inherent limitations, due to ‘cadre’ based organizational structural arrangement. So, it is common ‘not to own the projects’, rather ‘using the fund as much as possible anyway within the given project duration’. When the fund ends, implementing units, either government or NGOs, abandon the projects. That is why many projects are actually half done without significant and sustained direction.Originality/value: We can conclude that the typical ‘top-down’ or ‘trickle down’ conception is still dominant in design and implementation of reforms programs and service quality efforts. So, how to reach systemically at the bottom where public managers provide services to citizens, needs even more deliberation.

We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.

Cell aging is associated with the increased production of mitochondrial reactive oxygen species (mtROS) due to mitochondrial dysfunction, and various phytonutrients and estrogens have been shown to improve skin health. Thus, the aim of the current study was to examine damage to dermal fibroblasts by chemically-induced mitochondrial dysfunction and to study the mechanism of the protective effects of carotenoids, polyphenols, and estradiol. Rotenone, a Complex I inhibitor, caused mitochondrial dysfunction in human dermal fibroblasts, substantially reducing respiration and ATP levels, followed by increased mitochondrial and cytosolic ROS, which resulted in apoptotic cell death, increased matrix metalloproteinase-1 (MMP1) secretion, and decreased collagen secretion. Pre-treatment with carotenoid-rich tomato extracts, rosemary extract, and estradiol reversed these effects. These protective effects can be partially explained by a cooperative activation of antioxidant response element (ARE/Nrf2) transcriptional activity by the protective compounds and rotenone, which led to upregulation of antioxidant proteins such as NQO1. To determine if ARE/Nrf2 activity is crucial for cell protection, we inhibited it using the Nrf2 inhibitors ML385 and ochratoxin A. This inhibition markedly reduced the protective effects of the test compounds by diminishing their effect to reduce cytosolic ROS. Our study results indicate that phytonutrients and estradiol protect skin cells from damage caused by mitochondria-generated ROS and, thus, may delay skin aging and improve skin health.

The coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with CGAN based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for covid-19 especially in chest CT images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the coronavirus infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The Outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium that remains a prevalent clinical and environmental challenge. Quorum sensing (QS) molecules are effective biomarkers in pinpointing the presence of P. aeruginosa. This study aimed to develop a convenient-to-use whole-cell biosensor using P. aeruginosa reporters individually encapsulated within alginate-poly-L-lysine (alginate-PLL) microbeads to specifically detect the presence of bacterial autoinducers. The PLL-reinforced microbeads were prepared using a two-step method involving ionic crosslinking and subsequent coating with thin layers of PLL. The alginate-PLL beads showed good stability in the presence of a known cation scavenger (sodium citrate), which typically limits the widespread applications of calcium alginate. In media containing synthetic autoinducers, such as N-(3-oxo dodecanoyl) homoserine lactone (3-oxo-C12-HSL) and N-butanoyl-L-homoserine lactone (C4-HSL) or cell-free supernatants of planktonic or flow cell biofilm effluent of wild P. aeruginosa (PAO1), the encapsulated bacteria enabled a dose-dependent detection of the presence of these QS molecules. The prepared bioreporter beads remained stable during prolonged storage at 4 and -80 °C and were ready for on-the-spot sensing without the need for recovery. This whole-cell biosensor demonstrates the practicality of the encapsulated bioreporter for bacterial detection based on specific QS molecules.

Modernizing the retail sector is essential to societal progress toward creating a smart city. The retail sector is crucial to the development of a smart city because of its adaptability and capacity for innovation. However, due to their perishability, supermarkets throw away a lot of fruits and vegetables. This article discusses the design of a sub-optimal process for processing sorted fruits using a reverse logistics approach, demonstrating how productivity can be increased despite constraints on processing capacity. This paper presents the results of a case study conducted in a Qatari supermarket that offers a variety of regular fruit processing techniques. In order to maximize output and cut down on waste, this project aimed to develop a reverse logistics system for imperfect fruit.

Currently, many manufacturing industries use machine learning, artificial intelligence, robotics, and blockchain technologies as process-streamlining instruments of industry 4.0. Before implementing industry 4.0 tools in any process of a manufacturing industry's supply chain, it is essential to evaluate their adaptability. In this paper, the adaptability of industry 4.0 procurement tools to an oil and gas-related manufacturing company's procurement process is evaluated to determine the company's level of maturity. The active participation of participants is evaluated to determine the level of maturity of the main procurement process areas. The results identified the areas of the procurement process that can be improved by implementing the suggested industry 4.0 tools and techniques.

Abstract: Obstructive sleep apnea (OSA) is well known to often improve with non-supine positioning as opposed to su¬pine positioning. Emerging research supports a role for sleep position management in patients with central sleep apnea (CSA) as well. We report a case of de novo Complex Sleep apnea Syndrome (CompSAS) in a 78-year-old female, who presented after a car accident due to unclear syncope. Diagnostic polysomnography (PSG) showed a moderate OSA. A CompSAS developed under Automatic positive airway pressure (APAP), while download data of 4 years showed a good adherence. No significant benefit was reported under Adaptive Servo Ventilation (ASV) and BiPAP-ST, while a reduction of CSA in non-supine position was noticed. Oxygen and sleep positional therapy (SPT) were considered resulting in a significant improvement of CSA and sleep quality. Further research on the prevalence of positional CSA is needed.

Whether it’s cellphones, personal computers, or gaming consoles, technology is a part of everyone’s everyday lives, and storage is frequently a problem. One of the solutions we have for this difficulty is the on-demand accessibility of computer framework assets, which enables cloud storage and makes it accessible in any format and from any device. It is a major benefit of cloud computing. Amazon Web Administrations (or basically AWS) may be a secure cloud services stage advertising about everything businesses ought to construct advanced applications with adaptability, versatility, and unwavering quality. Another advantage is that it is significantly less expensive than purchasing items with comparable functionality, such as an SSD storage device. For many enterprises, it is also preferable to host their servers in the cloud using services like Google Cloud and Oracle Cloud. Our main topic of the paper is to compare three different major cloud computing services—AWS, Google, and Azure. Since there are different types of cloud computing services available, we would compare them to determine which is best for usage by individuals or organizations. We can also look at the services’ shared features and unique aspects that they provide to consumers.

Lap joints were friction stir welded in AA 7075-T6 at 1320 rpm and using two welding speeds: 70 mm/min and 120 mm/min. The temperature profiles of the weld zone were registered during the process; furthermore, the microhardness, tensile strength, and microstructure of the joints produced were examined. This study brings new insights into the thermomechanical behavior of the material during the process. The peak temperature obtained with the lower welding speed (70 mm/min) is 10% higher than that obtained with the higher speed (120 mm/min). Moreover, a 5% increase in microhardness and a 6% increase in tensile strength were were observed increasing the welding speed. Addition-ally, the microstructure examination demonstrated that, by decreasing the welding speed, the larger interaction between the tool and the material results in a deeper stir zone due to the increased heat diffusion downwards to the material. This heat flow affects the thermal profile and influences the resulting mechanical properties of the welded joints.

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.

Segmentation is an important step in medical imaging. In particular, machine learning, especially deep learning, has been widely used to efficiently improve and speed up the segmentation process in clinical practice. Despite the acceptable segmentation results of multi-stage models, little attention was paid to the use of deep learning algorithms for brain image segmentation, which could be due to the lack of training data. Therefore, in this paper, we propose a Generative Adversarial Network (GAN) model that performs transfer learning to segment MRI brain images.Our model enables the generation of more labeled brain images from existing labeled and unlabeled images. Our segmentation targets brain tissue images, including white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). We evaluate the performance of our GAN model using a commonly used evaluation metric, which is Dice Coefficient (DC). Our experimental results reveal that our proposed model significantly improves segmentation results compared to the standard GAN model. We observe that our model is 2.1–10.83 minutes faster than stat-of-the-art-models.

Pharmacogenomics (PGx) is the study of how variations of the patient genetic makeup could influence the selection and dosing of medications, including urate-lowering therapies (ULTs). Patients with gout present with several comorbidities, warranting the use of several long-term medications that increase their pill burden and the risk of adverse drug events. PGx testing can identify individuals who are more or less likely to benefit from a given treatment and ultimately improve adherence and reduce polypharmacy. Gout management involves using various ULTs associated with specific genetic variations that could modulate medication safety and efficacy. Individuals carrying the HLA-B*58:01 variant are at a higher risk of serious and life-threatening skin reactions from taking allopurinol. Besides, racial disparities in HLA-B distribution warrant testing for its status in high-risk populations, specifically some Asian subgroups and African Americans. G6PD-deficient individuals are prone to develop hemolytic anemia and methemoglobinemia with pegloticase and probenecid use. Patients with the less active form of the drug-metabolizing CYP2C9 are at higher risk for NSAID-related upper gastrointestinal (GI) bleeding. The drug-gene pair interactions among various gout therapies were also reviewed. Allopurinol-related genes included AOX, ABCG2, and SLC22A12. Febuxostat-related genes included UGT1A1. Benzbromarone- and probenecid- related genes included SLC22A12 and ABCB1. Colchicine-related genes included CYP2D6, ABCB1, rs6916345 G>A, and SEPHS1. Finally, the corticosteroid-related gene included HCG22; and the anakinra-related gene included IL1RA. This review synthesizes the most contemporary level of evidence for using PGx in gout pharmacotherapy, as referenced in the CPIC guidelines, FDA, PharmGKB, and current literature.

A relevant aspect in the epidemiology of Tegumentary Leishmaniasis (TL) is the Leishmania parasites carrying a viral endosymbiont Leishmania RNA Virus 1 (LRV1), a dsRNA virus. Leishmania parasites carrying LRV1 are prone to causing more severe TL symptoms, increasing the likelihood of unfavorable clinical outcomes. LRV1 has been observed in cultured strains of five L. (Viannia) species, and host specificity was suggested when studying LRV1 from L. braziliensis and L. guyanensis strains. The coevolution hypothesis of LRV1 and Leishmania was based on phylogenetic analyses, implying an association between LRV1 genotypes, Leishmania species, and their geographic origins. This study aimed to investigate LRV1 specificity to Leishmania (Viannia) species hosts by analyzing LRV1 from L. (Viannia) species. To this end, LRV1 was screened in L. (Viannia) species other than L. braziliensis or L. guyanensis, and it was detected in 11 out of 15 L. naiffi and in 2 out of 4 L. shawi. Phylogenetic analyses based on partial LRV1 genomic sequencing supported the hypothesis of host specificity, as LRV1 clustered according to their respective Leishmania species hosts. These findings underscore the importance of investigating Leishmania and LRV1 coevolution and its impact on Leishmania (Viannia) species dispersion and pathogenesis in the American Continent.

This is a case study in the domain of public sector management or management in government, with particular reference to education as a sector as a whole. Bangladesh has shown persistent upward trends in development indicators during the last four decades. In the education sector also, many indicators improved - literacy, the female proportion of literacy, number of educational institutions, etc. Quantity wise investment and indicators increased and improved at all levels and dimensions like primary, secondary, tertiary, technical, and so on. But qualitative improvement remained mysteriously low, despite government intention to increase quality of teaching, learning, and administration. This paper aimed to draw an overall picture of qualitative scenario of the education sector. Analyzing secondary sources like public media, and primary sources like citizen interactions, this qualitative study consolidated the problems of the education sector in a coherent whole. It argues that many problems of education sector are not education problems or budget problems, rather purely and simply ‘management’ problems, and so, can effectively be solved with basic but thorough understanding of the basic technicalities of management as a discipline.

This paper deals with experimental study of the influence of conductive (Fe₃O₄) and insulating (Al₂O₃) nanostructured particles at various concentrations on the dielectric strength of transformer mineral oil. The method of preparation and characterization of these nanofluids (NFs) through the measurements of zeta potential, the real and imaginary parts of dielectric constant as well as the concentration and size of nanoparticles using Scanning Electron Microscope (SEM) images of nanoparticles powders and Dispersive x-ray Spectroscopy (EDS) analysis are presented. Experimental findings reveal that these two types of nanoparticles materials significantly improve AC breakdown voltage and the magnitude of this improvement depends on the concentration, size and nature (material) of nanoparticles. For a given type of nanoparticles, the effect is more marked with the smallest nanoparticles. The conductive nanoparticles offer higher enhancement of dielectric strength compared with insulating nanoparticles based nanofluids. With Fe₃O₄, the breakdown voltage (BDV) can exceed twice that of mineral oil and it increases by more than 76% with Al₂O₃. The physicochemical mechanisms implicated in this improvement are discussed.

Mycoviruses are widespread across the major taxa of fungi including yeasts. Mycoviruses in the yeast fungus Geotrichum candidum are not well studied with three G. candidum-associated viral species characterized to date, all of which belong to the Totiviridae genus Totivirus. In this study, we report the molecular characteristics of another two totiviruses co-infecting isolate Gc6 of G. candidum. The two totiviruses were tentatively named Geotrichum candidum totivirus 2 (GcTV2-Gc6) and GcTV4-Gc6). Both viruses have typical genome organization of totiviruses comprising two ORFs encoding capsid protein (CP) and RNA-dependent RNA polymerase (RdRp) at the N and C termini, respectively. Genomes of GcTV2-Gc6 and GcTV4-Gc6 are 4592 and 4530 bp long, respectively. Both viruses contain the -1 frameshifting elements and their proteins could be expressed as a single fusion protein. GcTV2-Gc6 is closely related to a totivirus isolated from the same host whereas GcTV4-Gc6 is related to insect-associated totiviruses. The phylogenetic analysis indicated that GcTV2-Gc6 and GcTV4-Gc6 belong to two different sister clades, I-A and I-B, respectively. It is interesting that all viruses identified from G. candidum belong to the genus Totivirus. It is possible that the RNA interference (RNAi) mechanism is not active to suppress totivirus accumulation in isolate Gc6.

Amidst escalating environmental concerns, short natural fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% about the impact load, absorbed energy, and tensile strength, respectively.

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

The main purpose of our research is the evaluation of the efficacity of solvent polarity on phenolic composition, as well as the antibacterial, and antioxidant activities of extract of Moroccan Mentha aquatica L., leaves. To attempt these objectives, the HPLC-DAD was used to identify the biomolecules. The total amount of phenolic and flavonoid content was determined using the Folin-Ciocalteu and aluminum trichloride processes as well as the DPPH, RP, and TAC tests were used to evaluate the antioxidant capabilities. In addition, by employing both in vitro and in silico analysis through the microdilution method, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the extracts were assessed against multidrug-resistant bacteria. Furthermore, the High-Performance Liquid Chromatography (HPLC) analysis indicated that all extracts of M. aquatica were abundant in noteworthy phytochemical compounds, especially gallic acid, vanillic acid, chlorobenzoic acid, quercetin, and kaempferol. Furthermore, the results indicated that the total phenolic content was higher in hydroethanolic extract (62.19 ± 1.19 mg GAE/g DW) in contrast to the acetonic and aqueous extracts (22.22 ± 0.64, and 22.44± 0.57 mg GAE/g DW) respectively. Similarly, the aqueous ethanol extract contained the greatest flavonoid concentration, with values of 29.15± 0.09 mg QE/g DW when compared with the other extracts. The seam extract poses excellent antioxidant power with an IC50 of 0.06 ± 0.00 mg/mL for the DPPH test, and lower RP with EC50 of 80± 0.00 µg/ml when compared with acetonic and aqueous extract (370± 0.00 and 460± 0.01 µg/mL) respectively. Moreover, the hydroethanolic extract has a high level of TAC 75.09± 2.00 mg EAA/g DW, followed by aqueous extract carrying a value of 28.53 ± 1.36 mg EAA/g DW. The antibacterial capacity is ranged between 0.78 ± 0.05 mg/mL and 12.60 ± 0.00 mg/L. The results of the in-silico study demonstrated that quercetin, vanillic acid, and naringenin components contribute to the antioxidant capability. While gallic acid, kaempferol, and quercetin components are attributed to the antibacterial capacity.

The coronavirus (covid-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest x-ray images is presented. The lack of benchmark datasets for covid-19 especially in chest x-rays images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of the virus from the available x-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the covid-19, normal, pneumonia bacterial, and pneumonia virus. The dataset is divided into 90% for the GAN and the training and the validation phase, while 10% used in the testing phase. The GAN helps in generating more images from the original dataset to be 30 times larger than the originally collected dataset. The GAN also help in overcoming the overfitting problem and made the proposed model more robust. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models prove it is efficiency according to validation, testing accuracy, and performance measurements such as precision, recall, and F1 score. Three case scenarios are tested through the paper, the first scenario which includes 4 classes from the dataset, while the second scenario includes 3 classes and the third scenario includes 2 classes. All the scenarios include the covid-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes 2 classes(covid-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthen the obtained results through the research. Finally, this research may be considered one of the first trails to use GAN and deep transfer models together to help in detecting coronaviruses (covid-19) within the absence of a benchmark dataset around the world, especially in x-rays chest images.

In real-life scenario, there are many mathematical tools to handle the incomplete and imprecise data. One of them is the fuzzy approach. This article aims to contribute to the literature of fuzzy multi-objective dynamic programming issues involving the fuzzy objective functions. The piecewise quadratic fuzzy numbers characterize these fuzzy parameters. Some basic notions in the problem under the α-pareto optimal solution concept is redefined and analyzed to study the stability of the problem. Furthermore, a technique, named as enhancing decomposition approach, is presented for achieving a subset for the parametric space that contains the sameα-pareto optimal solution. For a better understanding and comprehension of the suggested concept, a numerical example is provided.

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

Aim: This research is aimed at examining the cost-effectiveness of olanzapine versus risperidone in dementia patients with behavioural and psychological symptoms in Thailand. Methods: An existing Markov model based on a critical review through the comprehensive literature search and a justification for the most appropriate model for a Thai setting was adapted to simulate the disease progression of patients with dementia with behavioural disturbances until their need for full-time care (FTC). The time to the FTC state was estimated by a predictive equation developed by Rive et al. (2010). The model was conducted to assess the expected costs and outcomes associated with olanzapine compared with risperidone for Thai patients with BPSD aged 60 years and above. This model performed over a five-year time horizon with a one-month cycle length based on a societal perspective. The incremental cost-effectiveness ratio was used as the estimated outcome. Sensitivity analyses were also conducted to demonstrate the robustness of the results. Results: Over 5 years, olanzapine was found to be a cost-effective therapeutic option for the treatment of behaviourally disturbed patients with dementia compared with risperidone, in Thailand from a societal perspective (ICER < THB 160,000). The model underwent extensive sensitivity analyses, which also confirmed that olanzapine was the dominant strategy following the base-case findings. Conclusions: By comparison with risperidone, the model suggests that olanzapine can be regarded as a cost-effective therapeutic strategy for the management of patients with behavioural and psychological symptoms in Thailand.

In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.

Low Back Pain (LBP) is one of the most common problems among adults. The usual physiotherapy treatment is to perform physical exercises. However, some LBP patients have false beliefs regarding their pain and they tend to avoid physical movements which might increase their pain and disability. Virtual Reality (VR) has shown to be an effective intervention in improving motor functions and reducing pain perception. Existing VR interventions for LBP rehabilitation were based on a non-immersive VR, whereas to effectively reduce the pain intensity, we need an immersive VR. In this paper, we introduce the development and evaluation of a serious game called RabbitRun with an immersive experience to engage the patients in a virtual environment and distract them from the pain while performing LBP exercises. The initial usability evaluation results suggest that RabbitRun game is enjoyable and acceptable. The game is easy to play and learn and most of the participants are willing to play the game at home. This solution will enhance the rehabilitation outcome since the patients who are suffering from LBP can use the system at their home and train more for long period of time using a smartphone and low-cost virtual reality device such as Google Cardboard.

There is a need for strategic healthcare workforce planning to address shortages and ensure universal health coverage, aiming to optimize work-force per population ratio, distribution, and competencies. Amidst rising demands exacerbated by aging populations and events like the COVID-19 pandemic, strategic workforce planning becomes crucial for addressing healthcare inequalities by forecasting needs, bridging skill gaps, and adapting to changing demographics and care models. Israel has invested in strategic planning to tackle healthcare workforce challenges; however, globally there is insufficient planning informed by health labor market analysis, which hinders efforts to address healthcare inequalities. The paper reviews national policies in Israel, Australia, Germany, and Norway, ex-amining statistics and government publications to highlight strategic plan-ning considerations and address challenges in healthcare workforce plan-ning. This paper focuses both on the critical role of physicians in healthcare systems, and on the changing borders between healthcare professions and different national contexts which demand more context dependent strategic workforce planning. We highlight common themes among national policies, including the need for data collection, increasing the capacity of domestic training programs, and the need to address geographic maldistribution of physicians. Ultimately, we aim to guide policy in the development of a re-silient and sustainable healthcare workforce capable of meeting diverse community needs and promoting equity in healthcare services.

When focusing on materials science in civil engineering, the current trend is to investigate the use of innovative solutions in order to enhance thermal and energy performances. This trend is amplified with the need for a sustainable development strategy for the construction sector. This paper assesses the integration of a Phase Change Material (PCM) in cement intended for building construction. The key characteristic of PCMs is their capacity to absorb energy and restore it. In building construction, this feature could be harnessed to save energy by incorporating PCMs in the materials used. In this study, passive integration of PCM in cement was tested and thermal properties of such an integration was assessed. The results provide insights into how PCMs affect cement as part of the concrete mixture, thus identifying the contribution of PCM-based cements in concrete mixtures.

Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior.

Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus 13 voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ 14 constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources 15 on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar 16 PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed 17 using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of 18 simulation and statistical results of EO with other optimization techniques showed that EO is more effective 19 and superior.

The prevalence of autism in children in the world is estimated as one per 62 children, higher levels reported in some countries. These children experience significant problems with the development of social, behavioural and verbal and non-verbal communication skills. The skills impairment levels varies from an individual to another and that made teaching autistics a challenge for caregivers such as teachers and relatives. Hence, there are quite a number of frameworks of a software learning systems which focus on gaining the children’s attention using representational visual illustration as a learning method instead of the textual form. However, majority of these tools are lacking the personalisation ability to suite everyone in the spectrum. Assistive technology offers an alternative way to attract children with autism. Therefore, this research is proposing an Adaptive Content Management Learning System (ACMLS) model to assist caregivers to produce, design and fine-tune or customise the learning materials appropriately so that the system interface and the materials are suitable for every individual in the spectrum according to each child personal profile aiming to make learning attractive and to contribute in improving their social, communication and behavioural skills and nonetheless, their attention level to the delivered educational topics. The ACMLS model design adopts four main components which are: (1) Design component: which covers the visual design, design principles and the mental model of the children with autism. (2) Technology component: which covers the assistive technology tools and the architecture of the ACMLS system. (3) Education component: Which covers the learning objectives, styles, strategies, methods and the cognitive model. (4) Participants component: which covers the main participants who’re playing a role in the ACMLS model such as: caregivers and children with autism.

The prevalence of autism in children in the world is estimated as one per 62 children, higher levels reported in some countries. These children experience significant problems with the development of social, behavioural and verbal and non-verbal communication skills. The skills impairment levels varies from an individual to another and that made teaching autistics a challenge for caregivers such as teachers and relatives. Hence, there are quite a number of frameworks of a software learning systems which focus on gaining the children’s attention using representational visual illustration as a learning method instead of the textual form. However, majority of these tools are lacking the personalisation ability to suite everyone in the spectrum. Assistive technology offers an alternative way to attract children with autism to use. Therefore, this research is proposing Autism Content Management Learning System (ACMLS) Framework to assist caregivers to produce, design and fine-tune or customise the learning materials appropriately so that the system interface and the materials are suitable for every individual in the spectrum according to each child personal profile aiming to make learning attractive and to contribute in improving their social, communication and behavioural skills and nonetheless, their attention level to the delivered educational topics. The ACMLS framework design adopts four main components which are: (1) Design component: which covers the visual design, design principles and the mental model of the children with autism. (2) Technology component: which covers the assistive technology tools and the architecture of the ACMLS system. (3) Education component: Which covers the learning objectives, styles, strategies, methods and the cognitive model. (4) Participants component: which covers the main participants who’re playing a role in the ACMLS framework such as: caregivers and children with autism.

In Bangladesh, family businesses play a significant role in the country's business landscape because so few private companies are set up as large-scale, non-family ownership firms. Thus, it is vital to comprehend family businesses in order to comprehend Bangladeshi company dynamics. In this paper, authors attempted to survey the existing research in the area of family business in Bangladesh. It was found that the area as a research is totally an open field and a lot of new research can be done in the said area. Although a good number of studies can be classified as focused on the SME (small and medium enterprise) domain, a major shortcoming of those SME studies is that the SMEs themselves vary greatly in terms of size, scope, and technology. Whereas most of the family businesses can be predominantly put in the SME category, the particular issue of family business and family dynamics is not very common in those studies. This paper suggests that a good amount of research or studies can be done focusing on family business constitution, family members' participation norm, recruitment and promotion norm, family business succession rules and succession planning, training for family business leadership and management, training for board and directorial supervision, and so on.

In this study, we introduce the dynamics of a Hepatitis B virus (HBV) model with the class of asymptomatic carriers and conduct a comprehensive analysis to explore its theoretical aspects and examine the crossover effect within the HBV model. To investigate the crossover behavior of the operators, we divide the study interval into two subintervals. In the first interval, the classical derivative is employed to study the qualitative properties of the proposed system, while in the second interval, we utilize the ABC fractional differential operator. Consequently, the study is initiated using the piecewise Atangana-Baleanu derivative framework for the systems. The HBV model is then analyzed to determine the existence, Hyers-Ulam (HU) stability, and disease-free equilibrium point of the model. Moreover, we showcase the application of an Adams-type predictor-corrector (PC) technique for Atangana-Baleanu derivatives and an extended Adams-Bashforth-Moulton (ABM) method for Caputo derivatives through numerical results. Subsequently, we employ computational methods to numerically solve the models and visually present the obtained outcomes using different fractional-order values. This network is designed to provide more precise information for disease modeling, considering that communities often interact with one another, and the rate of disease spread is influenced by this factor.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Microfiltration membranes derived from semi-crystalline polymers face various challenges when synthesized through the extrusion–casting technique, including the use of large quantities of polymer, long casting times, and the generation of substantial waste. This study focuses on synthesizing these membranes using spin-casting followed by stretch-induced pore formation. Recycled high-density polyethylene (HDPE) and virgin polyethylene powder, combined with a calcium carbonate filler, were used as the source materials for the membranes. The influence of the polymer-filler ratio with and without stretching on the morphology, tensile strength, and water flow rate was investigated. Optimal conditions were determined, emphasizing a balance between pore structure and mechanical integrity. The permeable membrane possessed a mean pore diameter of 500 nm, exhibited a tensile strength measuring 32 MPa, allowed water to flow at a rate of 19 ml/min, and had a water contact angle of 126°. These membranes effectively eliminated suspended particles from water, with their performance evaluated against that of commercially available membranes. This research outlines a synthesis route for microfiltration membranes tailored to semi-crystalline polymers and their plastic forms, utilizing the spin-casting technique.

The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.

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.

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.

In the Lemongrass (Cymbopogon citratus), the investigations showed a wide range of active molecules their biological properties. However, most of these studies were conducted on the wid varieties, while cultivated plan addressed. This study aimed to characterize the biomolecules and biological activities of Lemongrass growing us African conditions sons in in Marocco. Marocco. Phenolic compound profiles of aqueous (AE) E) ethanol ethanol (EE), (EE), and methanol (ME) extract: fractions were obtained with steric exclusion chromatography on Sephadex 050 gel and identified by LC-MS/MS polyphenols (TPC), flavonoids (TFC), and antioxidant activities (FRAP: scavenging value and TAC: Total Antioxidant C the fraction were evaluated as well as the antimicrobial activity Obtained results showed that the ME contained e compounds (ie. Apigenine-7-0-rutinoside and myricitine-3-0-rutinoside). The AE showed the presence of five mole kaemiferol-3-0-glucuronide), while EE showed the presence of three molecules (Le. Quercetine-3-0-rutinoside). Reg chemical characterization, the rization, e highest value of Total phenolic content (TPC) was obtained in conten btained in AE ΑΕ (25) (4.60-0.29 mg highest value of Total flavonoids content (TFC) was obtained Ined in ME (29) (0.7±0.08 mg/g). Concerning the antioxide the highest FRAP was obtained in ME (29) (97.89%) and the highest total antioxidant capacity (TAC) was obtained (89.89%) Correlation between FRAP, TPC, and TFC was noted poly in fractions of AE and ME. All tested extracts of and their fractions showed a significant antimicrobial effect. The lowest minimum inhibitory concentration (MIC) wa for ME against E. col. Extracts biological activities and their fractions were governed by their active molecules. Ther new and clarify a novel aspect of bioactive molecules in the extracts of cultivated C. citratus Equaly, throughout this we clarified the relationship between identified molecules and their biological properties including antioxidant and comparative studies new activities, which is new for the study area. This study is suggested to be a reference. assays of other biological activities for the study plant

Analytical instruments used for scientific research and education are often expensive. This limitation poses a challenge for schools and educators in low-income countries to provide practical science training to students. To address this problem, the improvisation of analytical instruments and instructional materials has emerged as a viable solution. By incorporating practical experiments into science education, students have the opportunity to engage directly with scientific instruments, conduct investigations, and collect data. This hands-on approach fosters a deeper understanding of scientific principles, enhances critical thinking skills, and cultivates scientific mindsets among students. Herein, we review and discuss, step-by-step, the approach of using a smartphone for quantitative analysis, referred to as smartphone digital image colorimetry (SDIC). SDIC, with its practicality and accessibility, utilizes smartphone digital cameras for image acquisition and the freely available image processing programs, such as ImageJ and smartphone Apps, for image quantification. Using SDIC can enable students in low-income countries to develop essential scientific skills, including critical thinking, data interpretation, and an evidence-based approach to scientific research. By leveraging the ubiquity and affordability of smartphone technology, SDIC offers an accessible and cost-effective approach to practical science education, bridging the gap between theory and practice in resource-constrained settings.

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.

OCT (Optical coherent tomography) is an imaging method used to generate a picture of the back of the eyes and retinas. By using an interferometer, part of the light is directed to the sample and another portion is sent to a reference arm with a well-known length [1]. Interferometers merge two or more sources of light to create interference patterns. We split the light into two beams that travel different optical paths and are combined to produce interference. There are two types of OCT: one is time domain (TD-OCT) and the other Fourier domain (FDOCT). TD-OCT is a system which depends on a mechanical cycle time of the moving reference mirror. In FD-OCT the reference mirror is fixed, and assists in sampling multiple points from the ocular structures simultaneously. Optical coherent tomography (OCT) generates sub-surface images of translucent or opaque materials at a resolution equivalent to a low power microscope. OCT build up clear 3D images of thick samples by rejecting background signals whilst collecting light directly reflected from a surface of interest. OCT systems use broadband sources and split light in a fiber coupler, first on a direct reference mirror, and as the sample is being measured. The noise types in OCT systems are receiver noise shot noise, intensity noise, and beat noise. In OCT systems there is balanced detection and unbalanced detection. In balanced detection, the total photo current variance or noise is σ2 i = σ2 re + σ2 sh + σ2 be. In contrast, OCT unbalanced systems contain Michelson interferometer. A balanced OCT system is an extended version of the unbalanced variant with two more beam splitters added for double detection. Balanced OCT systems can include additional attenuators at the detector arm to improve the detection efficiency. OCT balanced systems with four optical couplers and attenuator gives graphs of noise [V 2] as a function of typical values of IDC (detector photo current or mean photo current) for different attenuation values, and SNR as a function of mean photo current (IDC) for different attenuation values and other OCT system parameters. OCT SNR graphs for different K1, K2, K3 and K4 splitt

In contemporary times, the landscape of malware analysis has advanced into an era of sophisticated threat detection. Today's malware sandboxes not only conduct rudimentary analyses but have evolved to incorporate cutting-edge artificial intelligence and machine learning capabilities. These advancements empower them to discern subtle anomalies and recognize emerging threats with a heightened level of accuracy. Moreover, malware sandboxes have adeptly adapted to counteract evasion tactics, creating a more realistic and challenging environment for malicious entities attempting to detect and evade analysis. This paper delves into the maturation of malware sandbox technology, tracing its progression from basic analysis to the intricate realm of advanced threat hunting. At the core of this evolution is the instrumental role played by malware sandboxes in providing a secure and dynamic environment for the in-depth examination of malicious code, contributing significantly to the ongoing battle against evolving cyber threats. In addressing the ongoing challenges of evasive malware detection, the focus lies on advancing detection mechanisms, leveraging machine learning models, and evolving malware sandboxes to create adaptive environments. Future efforts should prioritize the creation of comprehensive datasets, distinguish between legitimate and malicious evasion techniques, enhance detection of unknown tactics, optimize execution environments, and enable adaptability to zero-day malware through efficient learning mechanisms, thereby fortifying cybersecurity defences against emerging threats.

Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal dimensions. A set of synthetic experiments were designed in this study to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM as well as streamflow. Results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but not so much in the bottom soil layer. The updating of model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments.

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.

Water is essential for all living things however its pain has become serious. Many industrial activities cause its pollution by the release of polluting byproduct. Waste water treatment is hence necessary. In this context, the waste water of the textile industry containing Red Acid 52 was treated by the solid waste of the washed natural phosphate byproduct. Natural phosphate was also studied. The solid materials were first characterized by chemical analysis, Fourier Transform Infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The phosphate materials were after that, tested in the adsorption of the Red Acid 52. The experimental data indicated that the phosphate waste rock allowed the removal of Red Acid 52. Its maximum retention capacity attained 18.4 mg.g-1. Calcinations of materials inhibits the removal capacity found reduced by 60 to 70%. The adsorption kinetics of the Red Acid 52 on the material is well described by the pseudo second order model while the adsorption isotherms are identified by the Langmuir model. Hereafter, the thermodynamic study revealed that the adsorption process is spontaneous and exothermic. Keywords: Waste water, Phosphate co-product, Adsorption, Red Acid 52.

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.

Learning dental procedures is a complex task involving the development of fine motor skills. The reported use of theories and/ or evidence for designing learning activities to develop the fine motor skills needed for dental practice is limited. The aim of this review is to explore the available body of knowledge related to learning fine motor skills in dentistry. Evidence from studies investigating motor skill learning highlights the negative impact of self-focus and self-regulation on learning outcomes, particularly during the early stages of learning. The development of activities and schedules that enable novices to demonstrate characteristics similar to experts, without the reported long period of ‘deliberate practice’, is clearly of value. Outcomes of learning implicitly are important in dentistry because working under stressful conditions is common, either during undergraduate study or in practice. It is suggested that learning implicitly in the simulation stage can reduce disrupted performance when transitioning to clinical settings. Therefore, further investigation of effective methods for learning dental fine motor skills is indicated, using approaches that result in robust performance, even under stressful conditions.

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime’s understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an open- accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

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.

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

Background/Purpose: Over the last two decades, malaria has remained a major worldwide public health concern, especially in the developing countries leading to high morbidity and mortality among children. Nigeria is the world most burdened malaria endemic nation, contributing more than a quarter of global malaria cases. This study determined the prevalence of malaria among children 6-59 months in Nigeria, and the effects of individual and contextual factors. Methods: The study utilized data from 2018 Nigeria Demographic and Health Survey (NDHS) involving a weighted sample size of 10,185 children who were tested for malaria using rapid diagnostic test (RDT). Given the hierarchical structure of the data set, such that children at level-1 are nested in community at level-2, and nested in states and Federal Capital Territory (FCT) at level-3, multilevel mixed effect logistic regression models were used for the analysis. Results: The proportion of children 6-59 months of age in Nigeria that had malaria fever positive as assessed by RDTs was 35.5% (3,418/10185), (CI: 33.9-37.1). Kebbi State had the highest proportion of children 6-59 months who were malaria positive, 77.6%, (CI: 70.2-83.5), followed by Katsina State, 55.5%, (CI: 47.7-63.1). The Federal Capital Territory (FCT), Abuja had the proportion of 29.6%, (CI: 21.6-39.0), malaria positive children of 6-59 months of age. Children between the age of 48 and 59 months were 2.68 times more likely to have malaria fever than children 6-11 months of age (AOR=2.68, 95% CI: 2.03-3.54). Also, children from the rural area (AOR= 2.12, 95% CI: 1.75-2.57), were more likely to suffer from malaria infection compare with children from urban area. Conclusion: The study identified some individual and contextual predictors of malaria among children in Nigeria. These factors are areas that need to be considered for policy designs and implementations toward control and total elimination of malaria-related morbidity and mortality among children in Nigeria.

Aim: This study measures and evaluates the socioeconomic burden of people living with keratoconus in Saudi Arabia. Methods: The study employed a cross-sectional design, a Keratoconus Economic Burden Questionnaire, and a convenient sample of 89 keratoconus patients (58.4% male) drawn from multiple regions in Saudi Arabia. It was conducted using online surveys and the data was analysed using appropriate quantitative techniques. Results: The mean age and annual income of participants were 33.24 years and Saudi Riyal (SAR) 33,505.6180 (SD=62,215.29), respectively, with only 37% being employed for wages. Up to 94.4% needed glasses or contact lenses at least once a week and 73.0% received care from optometrists. The condition forced 45.9% of the respondents to change careers or leisure activities, with a further 51.3% having to take time off work. The mean annual out-of-pocket expenses for buying and maintaining glasses or contact lenses as well as traveling and accommodation for keratoconus-related treatment were SAR 8,673.19 (SD=11,307.73), with 48.32 incurring upwards of SAR 12,000 over the period. The treatment costs increased with disease duration, r(89) = .216, p < .05. Regression results show that the existence of comorbid eye disease, changing glasses at least once a year, and wearing either glasses or contact lenses at least once a week individually have statistically significant, negative effects on the total annual keratoconus treatment costs, while disease duration, utilisation of optometrists, and taking time off had a statistically significant increase on the total cost (p<.05). Conclusion: With a prevalence rate of 1 in 375, progressive debilitation, and the lifetime nature of the disease, keratoconus is a critical public health concern in Saudi Arabia. The resulting visual impairment and discomfort as well as both direct and indirect economic burdens have considerable impacts on the patient's quality of life.

Seawater contamination is a global challenge due to its hazardous effects on marine organisms and human health. Twenty-three surface seawater samples were collected from Al Lith intertidal area along the Saudi Red Sea coast to evaluate the ecological risks and document the potential sources of Cr, Cd, Fe, Zn, and Pb. Contamination factor (CF), contamination degree (Cd), water quality index (WQI), and heavy metal pollution index (HPI), as well as multivariate tools were applied. The averages of HMs (μg/L) had the following order: Zn (6.616) &gt; Pb (0.284) &gt; Cd and Cr (0.268) &gt; Fe (0.197). Results of CF showed moderate contamination of seawater with Cd and low contamination for Cr, Fe, Zn, and Pb. However, 26.09% of the samples showed considerable contamination for Cd. Average Cd values revealed low contamination for HMs, while 17.39% of the samples showed moderate contamination. HPI average values indicated medium pollution for Al Lith seawater, meanwhile 13 samples reported high pollution. The higher HPI values were reported in samples characterized by higher concentration of HMs, particularly Cd and Zn. Correlation matrix and principal component analysis suggested anthropogenic sources for Pb and Zn, mostly from industrial and agricultural effluents, landfilling and domestic wastewaters, apart from their natural sources.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

: Plant-derived terpenes have aroused considerable interest as chemotherapeutic agents for a variety of diseases. This study aimed at the isolation and purification of the scarce terpenes psiadin, plectranthone and saudinolide from their respective plants, followed by the determination of antiproliferative activity, against hepatic cancer cell lines (HepG2, Hep3B), and the potential molecular mechanisms. Time- and dose-dependent cytotoxicity, evaluated using MTT and colony-forming assays, were exhibited by psiadin and plectranthone against the cancer cells. Flow cytometry showed that these two terpenes blocked cell cycle progression and induced mitochondrial-mediated apoptosis, particularly through increased cytochrome c and disruption of mitochondrial membrane potential. Additionally, they initiated the generation of reactive oxygen species as well as inhibiting NF-B. Psiadin lowered several essential cyclins and cyclin-dependent kinases and reduced RB activation. It was concluded that psiadin, in particular, has a significant therapeutic potential with the biggest advantage of differentiating between cancer and normal cells which is acutely lacking in current cytotoxic drugs. Its precise mode of action needs further investigation but appears predominantly to cause cell cycle arrest by interfering with cyclin production. It will be important to determine, in future studies, whether these terpenes will similarly inhibit other cancer cell lines and retain its activity against tumors in vivo.

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.

Soil salinity is seen as a major restriction for crop production, and with water scarcity this problem becomes more complicated. Mulching is crucial to salinity dynamics management by decreasing evaporation with improving the soil’s characteristics. Using chitosan as an eco-friendly biostimulant can enhance plant defense genes during different abiotic stresses. Recently, agricultural research has recognized nanoparticles as a pioneer material due to their distinctive physicochemical features. Therefore, a lysimeter experiment was conducted to investigate the interactive effects of mulching (UNM: un-mulched, WPM: white plastic, RSM: rice straw and SDM: sawdust) and chitosan foliar application (Ch0: control, Ch1: 250 mg chitosan L-1, Ch2: 125 mg nano chitosan L-1, and Ch3: 62.5 mg nano chitosan L-1) on the biochemical soil characteristics and common beans productivity under salt affected soil conditions. Organic mulching (RSM and SDM) treatments significantly improved the soil's organic carbon, available nutrient content, and total count of bacteria. WPM treatment lowered soil EC to 6.63 dS m-1 and increased soil water content to 34.13%. The application of Ch3 caused considerable increases in the plant height, shoot dry weight, root dry weight, grain yield, and nutrient content in the seed. The total fungi count in the soil and Na% in the seed was significantly decreased due to chitosan foliar applications. Moreover, the interactive effect of different mulch materials plus foliar by chitosan applications gave a statistically similar seed yield in both seasons. Overall, this study revealed the potential of the mulching treatments and foliar application of nano chitosan in improved biochemical soil characteristics and common bean productivity under saline soil conditions.

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.

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.

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.

In 2023, residential and commercial sectors together consumed approximately 27.6% of total U.S. energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States (U.S.) residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified, and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data was analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using RECS data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives.

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.

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.

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.

Background: Respiratory syncytial virus (RSV) is a significant cause of illness in adults, especially older adults and those with underlying conditions. This study aimed to assess the incidence of RSV hospitalizations in adults and identify risk factors for hospitalization and poor outcomes. Methods: A retrospective cohort study was conducted using data from two hospitals in southern Israel from 2016-2022. We calculated incidence rates of RSV and influenza hospitalizations. Risk factors for hospitalization were analyzed using Poisson regression. We evaluated poor outcomes (death, ICU admission, or mechanical ventilation) among RSV-hospitalized patients. Results: The median annual incidence of RSV hospitalization was 28.2/100,000 population, increasing with age to 199/100,000 in those ≥75 years. Significant risk factors for RSV hospitalization included pulmonary diseases (RR 4.2, 95% CI 3.4-5.2), cardiovascular diseases (RR 3.3, 95% CI 2.6-4.2), and chronic renal failure (RR 2.9, 95% CI 2.3-3.7). Among hospitalized RSV patients, 13.9% had poor outcomes. Renal failure (RR 1.81, 95% CI 1.23-2.66), neutropenia (RR 2.53, 95% CI 1.19-5.35), neutrophilia (RR 1.66, 95% CI 1.81-2.34), and lymphopenia (RR 2.03, 95% CI 1.37-3.0) were associated with poor outcomes. Conclusions: RSV causes a substantial burden of hospitalizations in adults, particularly among older adults and those with comorbidities. Identifying high-risk groups can help target prevention and treatment strategies, including vaccination.

Optimization techniques are pivotal across various scientific domains, typically involving solving problems among feasible alternatives based on specific goals, alternatives, or constraints. In mathematical optimization, traditional methods like Simulated Annealing (SA) do not guarantee finding global minima/optima, especially in cases that contain high dimensions in objective functions. This paper introduces a novel approach where a free-energy driven self-adaptive SA algorithm is designed to handle such cases by incorporating free-energy costs within the Metropolis-Hastings framework. This algorithm not only dynamically adjusts to the changing dimensions of the objective function, but it also enhances a faster optimization process. Furthermore, as examples we demonstrate its capability on a convex unimodal function and the non-convex Rastrigin function, revealing faster convergence to search global minima compared to standard SA algorithm. At last, parameter estimation of noisy exponential data has been executed by our Simulated Annealing driven by the Free-Energy (SAFE) algorithm. Our results suggest that this novel approach may significantly improve optimization speed and accuracy, providing a robust tool for complex multidimensional optimization problems.

Excess hepatic lipid accumulation is the hallmark of non-alcoholic fatty liver disease (NAFLD), for which no medication is currently approved. However, Glucagon-Like Peptide-1 Receptor Ag-onists (GLP-1RAs), already approved for treating type 2 diabetes, have lately emerged as possible treatments. Herein we aimed to investigate how the GLP-1RA Exendin-4 (Ex-4) affects the mi-croRNA (miRNAs) expression profile using an in vitro model of steatosis. Total RNA, including miRNAs, was isolated from untreated, steatotic, and Ex-4-treated steatotic cells and used for probing a panel of 799 highly curated miRNAs using NanoString technology. Enrichment path-way analysis was used to find the signaling pathways and cellular functions associated with the differentially expressed miRNAs. Our data shows that Ex-4 reversed the expression of a set of miRNAs. Functional enrichment analysis highlighted many relevant signaling pathways and cellular functions enriched in the differentially expressed miRNAs, including hepatic fibrosis, in-sulin receptor, PPAR, Wnt/β-Catenin, VEGF, and mTOR receptor signaling pathways, fibrosis of the liver, cirrhosis of the liver, proliferation of hepatic stellate cells, diabetes mellitus, glucose metabolism disorder and proliferation of liver cells. Our findings suggest that miRNAs may play essential roles in the processes driving steatosis reduction in response to GLP-1R agonists, which warrants further functional investigations.

Various wood fibres, including pine and flax, are used in wood–plastic composites (WPCs). This paper studies the effect of the lignin content on the morphological and thermal degradation and damage due to the ultraviolet (UV) aging of polypropylene/flax (PP-flax) and polypropylene/pine (PP-pine) fibres composites. Flax and pine fibres exhibited distinct densities of 1.51 and 1.47 g/cm³, respectively, potentially influenced by growth factors and varying compositions of light substances, such as hemicellulose, lignin, and impurities. The thermal decomposition mass loss increased proportionally with the percentage of lignin in the fibre. From 238°C to 390°C, the pine fibre exhibited a 72% mass loss compared to flax fibre, which showed a 54% mass loss, owing to the higher lignin content in pine fibres. Based on differential scanning calorimetry (DSC), the fibre composition affected the material melting temperature. Moreover, DSC curves revealed a higher degree of crystallinity in the case of the PP-flax biocomposite (12%) compared to pure polypropylene (9%) and PP-pine (6%). This result can be attributed to the high content of crystalline components in flax fibres, such as cellulose. Acoustic emissions analysis confirmed that the high lignin content delays degradation and mitigates the appearance of microcracks on the surface of the PP-pine biocomposite. Overall, the study provides valuable data for understanding the UV degradation phenomenon in biocomposites and highlights the influence of fibre composition on material performance.

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.

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.

Coronavirus disease 2019 (COVID-19) was recently appeared all over the world. The viral main protease (3-chymotrypsin-like cysteine enzyme) controls COVID-19 duplication and manages its life cycle, making it a drug discovery target. Therefore, herein, we analyzed the theoretical approaches of 10 structurally different hydrolysable tannins as natural anti-COVID-19 through binding with the main protease of 2019-nCoV using molecular docking modelling via Molecular Operating Environment (MOE v2009) software. Our results revealed that there are top three hits may serve as potential anti-COVID-19 lead molecules for further optimization and drug development to control COVID-19. Pedunculagin, tercatain, and punicalin were found to faithfully interact with the receptor binding site and catalytic dyad (Cys145 and His41) of COVID-19 main protease, showing their successfully inhibit the protease enzyme of 2019-nCoV. We anticipated that this study would pave way for tannins based novel small molecules as more efficacious and selective anti-COVID-19 therapeutic compounds.

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.

Several stochastic H∞ filters for estimating the attitude of a rigid body from line-of-sight measurements and rate gyro readings are developed. The measurements are corrupted by white noise with unknown variances. Our approach consists of estimating the quaternion while attenuating the transmission gain from the unknown variances and initial errors to the current estimation error. The time-varying H∞ gains are computed through differential and algebraic linear matrix inequalities whose parameters are independent of the state. The case of a gyro drift is addressed, too. Extensive Monte Carlo simulations show that the proposed stochastic H∞ quaternion filters perform well for a wide range of noise variances. The actual attenuation, which improves with the noise level and is worst in the noise-free case, is better than the guaranteed attenuation by one order of magnitude. The proposed stochastic H∞ filter produces smaller biases than a standard quaternion Kalman filter and similar standard deviations at large noise levels. An essential advantage of this H∞ filter is that the gains are independent of the quaternion, which makes it insensitive to modeling errors. This desired feature is illustrated by comparing its performances against those of unmatched Kalman filters. When provided with too high or too low noise variances the Kalman filter is outperformed by the $H_\infty$ filter, which essentially delivers identical error magnitudes.

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and the most common form of head and neck cancer, posing significant challenges for disease management. Single-cell RNA sequencing (sc-RNAseq) has emerged as a valuable tool for addressing these challenges by enabling detailed characterization of the tumor microenvironment (TME) at the cellular level. This review assesses the utility of sc-RNAseq in HNSCC research, compiling current strategies employing single-cell technologies. For HNSCC etiology, sc-RNAseq allows for the construction of cellular atlases, characterization of different cell types, and investigation of key genes and processes involved in cancer initiation, development, and progression within the TME. In terms of HNSCC diagnosis and prognosis, the high resolution offered by sc-RNAseq enables the identification of cell type-specific signatures, enhancing prognostic models and disease stratifiers for more accurate patient outcome assessments. Regarding HNSCC treatment, sc-RNAseq provides insights into cellular responses to various treatments, including radiotherapy, chemotherapy, and immunotherapy, contributing to improved patient stratification and personalized treatment strategies. This review highlights the unique contributions of sc-RNAseq to HNSCC research, addressing its cellular and biological complexity, and emphasizes its potential for advancing research and clinical practice in other cancer types.

Pre-diabetes is a serious health condition where blood sugar levels are higher than normal, but not high enough yet to be diagnosed as type 2 diabetes. Pre-diabetes puts one at an increased risk of developing type 2 diabetes and heart disease Methodology: A cross-sectional study was carried out on 384 patients aged 20-70 years old, attending the dental clinics to assess the risk for diabetes, using the FINDRISC questionnaire, HbA1c blood test and a periodontal examination. Results: The mean age of participants was 38.90±10.74. 32.3% were categorized as no risk, 46.6% low risk, while 19% and 2.1% moderate and high risk of developing diabetes respectively. Tests for serum HbA1c Level showed 46.1 % had normal HbA1c followed by 18.0% and 3.6 % were pre-diabetic and diabetic respectively. 19.3% of participants had periodontal pockets measuring more than 4mm and 15.9% measuring more than 6mm. Conclusion: The study has proven to be useful in identifying patients at high-risk of developing diabetes. Controlling and managing periodontal disease could be a new aspect to include in the standards for diabetes care. Dental settings could be a successful platform to carry out the screening and risk stratification of pre-diabetic patients.

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.

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.

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.

Triple negative breast cancer (TNBC) is the most aggressive breast cancer accounting for around 15% of identified breast cancer cases. TNBC, by lacking estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), is unresponsive to current targeted therapies. Existing treatment relies on chemotherapeutic treatment but, despite an initial response to chemotherapy, the inception of resistance and relapse is unfortunately common. Dasatinib is an approved second-generation inhibitor of multiple tyrosine kinases and literature data strongly support its use in the management of TNBC. However, dasatinib binds to plasma proteins and undergoes extensive metabolism through oxidation and conjugation. To protect dasatinib from fast pharmacokinetic degradation and to prolong its activity, it was encapsulated on poly(styrene-co-maleic acid) (SMA) micelles. The obtained SMA-dasatinib nanoparticles (NPs) were evaluated for their physicochemical properties, in vitro antiproliferative activity in different TNBC cell lines, and in vivo anticancer activity in a syngeneic model of breast cancer. Obtained results showed that SMA-dasatinib is more potent against 4T1 TNBC tumor growth in vivo compared to free drug. This enhanced effect was ascribed to the encapsulation of the drug protecting it from a rapid metabolism. Our finding highlights the often-overlooked value of nanoformulations in protecting its cargo from degradation. Overall, results may provide an alternative therapeutic strategy for TNBC management.

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.

Cooperative non-orthogonal multiple access (CNOMA) has been recently adapted with energy harvesting (EH) to increase energy efficiency and extend the lifetime of energy-constrained wireless networks. In EH, two protocols are usually used for CNOMA, namely, power splitting (PS) and time switching (TS). In order to improve the existing EH protocols, this paper investigates a hybrid EH protocol-assisted CNOMA, which is a combination of the two aforementioned main existing EH protocols. The end-to-end bit error rate (BER) and outage probability (OP) expressions of users in our scheme are derived over Nakagami-m fading channels. For the sake of comparison and to highlight the achievable gains, the performance of the hybrid EH (HEH) protocol is compared with the benchmark schemes (i.e., existing EH protocols and no-EH). Based on extensive simulations, it is shown that the analytical results match perfectly with simulations which demonstrates the correctness of the derivations. Numerical results clearly indicate the superiority of the proposed scheme over all the considered benchmarks. In addition, we discuss the optimum value of EH factors to minimize the error and outage in the proposed scheme and show that an optimum value can be obtained according to channel parameters.

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

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.