Background: Alzheimer’s disease affecting about 24 million people world-wide. The socio-economic burden on world-economies costing more than 172 billion US $ annually for the US alone. Objectives: To prepare aqueous extract of T. foenum graecum seeds (FSE) to explore the possible treatment for cognitive deficit in experimental animals. Materials and methods: FSE was subjected to preliminary phytochemical evaluation and antioxidant effect using free radical scavenging method (DPPH). All the animal behavior was video recorded with no human intervention during observation and animal groupings were blinded to avoid investigator bias. Different doses of FSE (5%, 10% and 20%), control, standard (Piracetam, 200 mg/kg, IP.) were given for male albino mice a period of 15 days followed by cognitive assessment in elevated plus maze and novel objection recognition tests. Ttransfer latencies and time exploring novel and familiar objects were recorded in respective tests. Retention of this learned-task was examined again 24 h later and inflexion ratio (IR) and discriminative index (DI) were calculated respectively. Next in the second set of experiment same groups and treatments were continued but scopolamine was administered to all the groups except normal control one hour after the last dose and examined similarly. Results: FSE showed potential antioxidant effect and a dose dependent increase in transfer latency and improved DI indicating a nootropic effect. FSE at 20% showed significant reversal of scopolamine induced dementia in the second set of experiment. Conclusion: FSE improved memory as well as reversed the chemically induced memory deficits in experimental mice.

Background: Patients with mild cognitive impairment end up progressing to Alzheimer’s disease (AD) leading to straining burden on public health. R. officinalis long been known as the herb of remembrance and can be a potential cognition enhancer for AD. The aims of the review were to summarize the qualitative and quantitative aspects of R.O and its active constituents in enhancing the cognition. MATERIALS AND METHOD Google scholar and PubMed structured search to find relevant studies that assessed the effect of R.O extract or any of its active constituents on cognitive performance in animals. Data extraction: Following information from each included study was extracted: (1) article information (2) characteristics of study animals (3) type of intervention; type, dose, duration, and frequency of administration of R.O (4) type of outcome measure. Data synthesis: Data were analyzed using Review Manager (RevMan 5.3, 2014] and meta-analysis was performed for the outcome measures on all relevant tasks within the included papers by computing the standardized mean difference ps. RESULTS. 23 studies for qualitative and fifteen for meta-analysis were selected. From fifteen included papers, 22 studies with 35 comparisons were meta-analyzed. Effect sizes for intact animals and impaired animals respectively was (mean g and 95% CI 1.19 [0.74, 1.64; 0.57 [0.19,0.96]. The R. officinalis had positive effect on both groups of animals. The subgroup analyses exhibited substantial unexplained heterogeneity between studies. Mechanisms of R.O was anticholinesterase, procholinergic, antioxidant, anti-amyloid, neuroprotective and anti-inflammatory agent CONCLUSIONS: R.O improves cognitive function. Limitations: Considerable heterogeneity between studies.

We have conducted a comprehensive year-long sampling campaign for particulate matter (PM) pollutants at a site near a major highway, following standard protocols. Total mass, and elemental and chemical composition of the fine fractions (PM2.5) of traffic-related pollutants are determined utilizing several complementary techniques. These complementary techniques included gravimetric analysis, X-ray fluorescence (XRF), scanning electron microscopy and energy dispersive spectroscopy, X-ray diffraction (XRD) and black carbon multi-wavelength absorption. The results show that the PM2.5 mass concentrations are within or slightly above international standards and include natural dust and anthropogenic pollutants such as black carbon, sulfates and other traffic-related elements. Anthropogenic sources include the secondary pollutants Mascagnite and Koktaite, traffic emissions such as exhaust emissions and tires, brakes and road erosions. It was found that the sulfates are the highest contributors to PM2.5 (~40%), as evident from XRD results and the S content in the XRF analysis.

Unmanned Aerial Vehicles (UAV) are increasingly being used in a variety of domains and precision agriculture is no exception. Precision agriculture is the future of agriculture and will play a key role in long-term sustainability of agricultural practices. This paper presents a survey of how image data collected using UAVs has been used in conjunction with ma-chine learning techniques to support precision agriculture. Numerous agricultural applications including classification of crop types and trees, crops detection, weed detection, cropland cover, and segmentation of farming fields are discussed. A variety of supervised, semi-supervised and unsupervised machine learning techniques for image-based preci-sion agriculture are compared. The survey showed that for traditional machine learning approaches, Random Forests performed better than Support Vector Machines (SVM) and K-Nearest Neighbor Algorithm (KNN) for crop/weed classification. And, while Convolutional Neural Networks (CNN) have been used extensively, the U-Net-based models out-performed conventional CNN models for classification and segmentation tasks. Among the Single Stage Detectors (SSD), YOLO series performed relatively well. Two-Stage Detectors like R-CNN, FPN, and Mask R-CNN generally tended to outperform SSDs. Vision Trans-formers (ViT) showed promising results amongst transformer-based models which did not generally perform better than CNNs. Finally, Generative Adversarial Networks (GANs) have been used to address the problem of smaller datasets and unbalanced data

Biological polymers such as tissue-engineered scaffolds and skin grafts with improved properties are very required in the pharmaceutical field. The wound-dressing gelatin/chitosan/moringa leaf extract (G-CH-M biopolymer) was synthesised and characterised. The blood compatibility, anti-inflammatory, antioxidant, and antibacterial activities of the biopolymer were investigated. Our results showed that S. aureus swarming motility was drastically affected. However, the biopolymer had no significant effect on the swarming motility of E. coli compared to the control. In addition, biopolymers showed high antibacterial capacities, especially against S. aureus. The biopolymer was also found to be effective in protecting plasmid DNA against oxidative stress. Furthermore, the biopolymer exhibits greatly suppressed hemolysis (lower than 2%), even at a high concentration of 50 mg/mL. These results indicated that this novel biopolymer formulation could be further developed for wound care and contamination prevention.

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine learning based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions namely; Iran, Mozambique, Sri-Lanka, and Sudan where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction , and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 to 0.98 across the four test regions at hand.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

Currently, software development is more associated with families of configurable software rather than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking (SPL). Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment's time and financial constraints, these features are rendered unfeasible. Testing subsets of configured products is thus one approach to solving this issue. In order to reduce testing effort and get better results, alternative methods of testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately CIT method produces unscalable solutions for large size SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, have also been considered. In this article, we have proposed a search-based software engineering solution using multi-objective optimization algorithms (MOEAs). In particular, the research is applied to different types of MOEA methods; Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-Dominated Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms are examined in the context of distinct objectives and two quality indicators. The results revealed how feature model attributes, implementation context and the number of objectives affect the performance of the algorithms.

Pakistan relies mostly on fossil fuel for fulfilling its energy demand which is expensive as 11 well as not friendly for the environment. The need of the hour is to shift from fossil fuels to renew-12 able energy resources like geothermal, wind, solar etc. to cater for global warming issues. Pakistan 13 has a lot of potential geothermal sites because of several fault lines and hot springs. A sound 14 knowledge of ground temperature is essential to use geothermal energy, which is obtained by drill-15 ing boreholes and putting sensors making it an expensive procedure. Thus, to avoid huge cost for 16 drilling bore holes, particularly for ground temperature analysis, a numerical approach has been 17 considered for determining ground temperature. Furthermore, correlation charts between air and 18 ground temperatures have been developed as there were no proper studies on the ground temper-19 ature of Pakistan. Then, with the help of a boreholes drilled in National University of Sciences and 20 Technology Islamabad Pakistan, the actual ground and numerically calculated temperatures have 21 been compared which shows an error margin in the range between 0.27% for higher depths to 7.3% 22 near surface. Thus, it is shown that the proposed method is easy to implement and better than large 23 scale testing methods for the depths at which geothermal energy is extracted.

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

This paper focuses on studying the mapping properties of singular integral operators on product spaces. The boundedness of such operators is established on Triebel-Lizorkin spaces whenever their kernel functions belong to Grafakos and Stefanov class. Our results generalize and improve some previously known results.

Lung cancer (LC) is one of the leading causes of cancer occurrence and mortality worldwide. Treatment of patients with advanced and metastatic LC presents a significant challenge as malignant cells use different mechanisms to resist chemotherapy. Drug resistance (DR) is a complex process that occurs due to a variety of genetic and acquired factors. Identifying the mechanisms underlying DR in LC patients and possible therapeutic alternatives for more efficient therapy is a central goal of LC research. Advances in nanotechnology resulted in the development of targeted and multifunctional nanoscale drug constructs. The possible modulation of the components of nanomedicine, their surface functionalization, and encapsulation of various active therapeutics provide promising tools to bypass crucial biological barriers. These attributes enhance the delivery of multiple therapeutic agents directly to the tumor microenvironment (TME), resulting in reversal of LC resistance to anticancer treatment. This review provides a broad framework for understanding the different molecular mechanisms of DR in lung cancer; presents novel nanomedicine therapeutics aimed to improve the efficacy of treatment of various forms of resistant LC; outlines current challenges in using nanotechnology for reversing DR; and discusses the future directions for clinical application of nanomedicine in management of LC resistance.

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: Digital health significantly affects healthcare delivery. Moreover, empirical studies on the utilization of telehealth in Dubai are limited. Accordingly, this study examines the utilization of telehealth services in Dubai Health Authority (DHA) facilities and the factors associated with telehealth appointment completion and turnaround time. Methods: This cross-sectional study examines patients who used telehealth services in DHA from 2020 through 2021 using 241,822 records. A binary logistic regression model was constructed to investigate the association between appointment turnaround time as a dependent variable and patient and visit characteristics as independent variables. Results: Of the total scheduled telehealth visits, more than three-quarter (78.55%) were completed. Older patients, non-Emiratis, patients who had their visits in 2020, patients who had video visits, and those who sought family medicine as a specialty had a shorter turn-around time to receive their appointment. Conclusions: This study identifies several characteristics associated with the turn-around time. Moreover, technological improvements focusing on specialties that can readily be addressed through telehealth and further research in this domain will improve service provision and support building an evidence base.

Interest in developing novel crop protectants has gained attention in the recent decade due to the hazardous effects of synthetic pesticides on humans and the environment. The citrus industry worldwide is threatened by the D. citri, the primary vector of phlo-em-limited bacterium (HLB) or citrus greening. However, there is no cure for citrus greening available. Diaphorina citri management largely depends on synthetic insecticides, but their massive application leads to resistance in pest populations. Therefore, alternative pest management strategies are needed. Our results indicated fewer D. citri adults settled on plants treated with ABEO than on control 48 h after re-lease. The psyllid fed on citrus leaves treated with ABEO significantly secreted less honeydew than the control. The essential oil showed potent ovicidal activity against the D. citri eggs with LC50 5.88 mg/mL. Furthermore, we also explore the fitness of D. citri on ABEO-treated and untreated Citrus × sinensis by using two-sex life table tools. The results indicated that the intrinsic rate of increase (r) was higher on untreated seedlings (0.10 d−1) than those treated with a sub-lethal dose of ABEO (0.07 d−1). Similarly, the net re-productive rate (R0) was higher for untreated seedlings (14.21 offspring) than those treated (6.405 offspring). Notably, the ABEO were safer against Aphis melifera, with LC50 ranging from 31.05 to 55.86 mg/mL, which is relatively higher than the LC50 24.40 mg/mL values against D. citri. Our results indicate that ABEO exhibits toxic and behavioral effects on D. citri that could be useful for managing this pest.

In this paper, several classes of rough Marcinkiewicz integral operators along surfaces of revolution on product spaces are investigated. We prove the Lp boundedness of these operators when their kernels functions belong to a class of functions related to a class of functions introduced by Grafakos-Stefanov. The results in this paper extend and improve several known results on Marcinkiewicz integrals.

The experience of ego dissolution and self-transcendence induced by psychedelic substances offers a profound challenge to our conventional understanding of the self and our relationship with the natural world. This paper delves into the philosophical underpinnings and implications of this phenomenon, exploring how the temporary breakdown of ego boundaries during a psychedelic experience may foster a sense of profound interconnectedness with nature. Drawing from Western and Eastern philosophical traditions, as well as recent insights from neuroscience and consciousness studies, we examine the potential of psychedelic-induced self-transcendence to catalyze a shift in our perspectives on the human-nature relationship. The paper investigates the philosophical implications of this experience, including its resonance with deep ecological philosophies and its potential to inspire more ecocentric ethical frameworks. Furthermore, we explore the possible practical implications of fostering a sense of ecological connectedness through psychedelics, such as promoting environmentally sustainable behaviors and addressing the pressing challenges of climate change and ecological degradation. Ultimately, this exploration highlights the value of engaging with altered states of consciousness as a means to reconsider our place within the intricate web of life on Earth.

While microcredits are, by the development practitioners, repeatedly hailed as a useful measure of development policy, this empirical research in Bangladesh arrives at different results: the governing and disciplinary measures are necessary to trigger the best possible repayment success. Based on interviews with the microfinance beneficiaries, loan officers, and microfinance experts in Bangladesh, I chart some of the most frequently used governing and disciplinary techniques including fictitious proximity of loan officers, their fields of visibility and surveillance processes. The “success” of credit delivery is a neoliberal stance that ensures the ceaseless debt repayment process using the intentional and planned conduct, capitalizing social capital and credit honour reputation, surveillance over business enterprises, use of equivocal language and different forms of deception. The development strategy through microfinance in Bangladesh is a development “dispositif” with a changing shift in its declared social missions.

Interactive Digital Media have become the most important pillars on which contemporary 21st century scenography is based, allowing for the use of many modern digital media as scenographic elements. With the steady development of digital arts, particularly digital animation, it has become a key role as a 21st century in presenting their theatrical visions. My own issue is the use of digital Media as an Interactive scenographic element, as evidenced by the design of several spatial environments within the framework of an idea and a dynamic scenario that interacts with the Several Scenes of the theatrical Performances and is displayed on screen walls or dropped on thematic elements, resulting in a type of interaction between them and the Scenic Design. My project includes instances of models exhibiting the link of designs to theme composition on stage, such as works utilized in plays, musical/motion drama, and event performances . The design used music synced with digital animated media designs as a scenographic element to reinforce the overall visual picture. The study paper examines a theory of my own project, in which I offer a dynamic exhibition of full-back digital settings in rhythm with the music, suited to its movement and the character of the performance, to be employed as a Scenographic element at a Proscenium Theater (12W x 10D x 7H).

The coexistence of SAH with T2DM is a common comorbidity. In this present study, we investigated the link between altered plasma antioxidant trace elements (ATE: Manganese, Selenium, Zinc and Copper) and fatty acids ratio (FAR: polyunsaturated/saturated) imbalance as transition biomarkers between vascular pathology (SAH) to metabolic pathology (T2DM). Our data revealed strong correlation between plasma ATE and FAR profile which is modified during SAH-T2DM association compared to healthy group. This relationship is mediated by lipotoxicity (simultaneously prominent visceral adipose tissue lipolysis, significant flow of non-esterified free fatty acids release, TG-Chol-dyslipidemia, high association of Total SFA, Palmitic acid, Arachidonic acid and PUFA 6/ PUFA 3; drop in tandem of PUFA / SFA and EPA+DHA); oxidative stress (lipid peroxidation confirmed by TAS depletion and MDA raise, concurrently drop of Zn/Cu-SOD, GPx, GSH, Se, Zn, Se/Mn, Zn/Cu; concomitantly enhance Cu, Mn and Fe); endothelial dysfunction (endotheline-1 increase); athero-thrombogenesis risk (concomitantly raise of ApoB100/ApoA1, Ox-LDL, tHcy and Lp(a)) and inflammation (higher of Hs-CRP, Fibrinogen and Ferritin). Our study open to new therapeutic targets and to better dietary management, such as to establish dietary ATE and PUFA 6 / PUFA 3 or PUFA / SFA reference values for atherosclerotic risk prevention in hypertensive-diabetics patients.

The implementation of systems condition monitoring is of utmost importance in ensuring effective maintenance, optimal efficiency, and adherence to established design criteria. This research presents a novel and economically efficient Digital Twin Model (DTM) designed for the purpose of Live Condition Monitoring (LCM) and Predictive Lifecycles. This study discusses the enhancement of DTs modelling by decreasing the number of dimensions from five to three: Physical, Digital, and Connection entities. The resulting model exhibits a notable improvement in accuracy and efficiency. The suggested Dynamic Time Model (DTM) enhances the empirical pre-set average load for both simulation and experimentation by 35.7%, which is equivalent to a 1.6-fold increase. The DTM demonstrates a significant enhancement in the empirical predefined average lifecycles of the system, as shown in the real-life case study. Specifically, the DTM achieves a 12-fold improvement compared to the simulated results and a nine-fold improvement compared to the experimental findings. The suggested Dynamic Time Management (DTM) demonstrates a significant enhancement in the average lifecycles of the system, with a 19.7% improvement (equivalent to 1.2 times greater) compared to the wireless DTM results obtained through the application of the real system load. The suggested Dynamic Time Warping (DTW) method's accuracy and efficiency are shown via a practical case study using the suspension system of a Peugeot 3008 vehicle.

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.

Objectives: One of the home-made herbal remedy being traditionally used against gout is evaluated to identify any new and characteristic marker compound which may be used as characteristic analytical marker to standardize this polyherbal remedy. Materials and method: A distinct and remarkable peak in the HPLC chromatogram of remedy was identified which was further isolated by fraction collector. This fraction was then evaporated and characterized for structure elucidation using set of various techniques such as RP-HPLC,UV, FTIR, TLC, EIMS and NMR. Results: The compound characterized was non polar compound, ester of betulin and palmitoleic acid. Although the remedy has been standardized using set of four markers as discussed in literature but this newly developed marker compound can be used to further standardize this remedy and other herbal preparations comprising of this marker compound.Conclusion: Hence the isolated compound can be used as novel analytical marker in the standardization of this herbal remedy and the other herbal preparations containing such marker compound

Soft tactile sensors based on piezoresistive materials have various large-area sensing applications. However, their accuracy is often affected by hysteresis, a phenomenon that poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fibre-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units with different layer configurations (1, 3, 6, and 12) were designed. These sensor units underwent force sequences to collect corresponding output resistances. A backpropagation network was trained using these force sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement establishes the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for force measurement and control, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Notably, the complete elimination of hysteresis in tactile sensors may not be feasible. Nevertheless, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

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.

The paper's main goal is to introduce a novel type of interpolative convex contraction and build up some fresh findings for it using the interpolative convex contractions' progressive approach. We have established certain conclusions using orbitally S-complete and Suzuki type contractions in F-metric spaces. My research aims to examine the fixed point method's efficacy in solving fractional differential equation with boundary conditions.

Findings of new targeted treatments with adequate safety evaluations is essential for better cancer cures and mortality rates. Immunotherapy holds promise for patients with relapsed disease, with the ability to elicit long-term remissions. Emerging promising clinical results in B-cell malignancy using gene-altered T-lymphocytes uttering chimeric antigen receptors have sparked a lot of interest. This treatment could open the path for a major difference in the way we treat tumors that are resistant or recurring. Genetically altered T cells used to produce tumor-specific chimeric antigen receptors are resurrected field of adoptive cell therapy by demonstrating remarkable success in the treatment of malignant tumors. Because of the molecular complexity of chimeric antigen receptors -T cells, a variety of engineering approaches to improve safety and effectiveness are necessary to realize larger therapeutic uses. In this study, we investigate at new strategies for enhancing chimeric antigen receptors-T cell therapy by altering chimeric antigen receptors proteins, T lymphocytes, and their relations with other solid tumor microenvironment (TME) aspects.

Khabib Nurmagomedov versus Conor McGregor Ultimate Fighting Championship (UFC) 229 battle was among the most controversial mixed martial art fights of the past decade. In this study, the author examined how the various popular media outlets from the Eastern and Western world portrayed Khabib Nurmagomedov after UFC 229. The author used Huntington’s (1993) clash of civilization thesis by grounding it in the Orientalism and Occidentalism paradigms to examine the phenomenon. Fairclough’s (1989, 1995) model for critical discourse analysis was employed to investigate the various Western and Eastern popular press and digital media platforms (i.e., newspapers, blogs, and sporting news websites). The author analyzed 57 (Western n = 38, Eastern n = 19) media reports per the inclusion criteria. The study results unveiled conflicting predispositions present in the Western and Eastern media for Khabib Nurmagomedov. This study contributes to the limited knowledge about how a Muslim man athlete with diverse racial and ethnic backgrounds is seen contrarily in the Western and Eastern media. The author discusses the theoretical implications of the study.

Identifying ambient noise-based (ANb) signatures of streams can help in the estimation of their erosive potential (EP) that promotes reverie landslides and soil losses in the fluvial valleys. This is particularly imperative on flooding or rainy days, leading to stronger erosion-prone conditions (colluvium and boulders) of the valley beds inferred from georadar attribute analysis. Developing such research direction can benefit the local communities, as is the case with the Cerrado region of Brazil, where these phenomena have high destructive potential with social, economic, and climatic implications. For the present study, a seasonal stream in the Federal District of Brazil was investigated by ANb monitoring supported by Ground Penetration Radar (GPR) for site characterization. The ANb monitoring was conducted (at a safe distance) with a seismometer over several durations of dry and rainy conditions. The power spectral density (PSDs) was computed as a function of several variables, including weather conditions (rainfall, wind speed, and pressure), time-frequency spectrograms, and ambient noise displacement root mean square (RMS). This analysis also considered the single station horizontal-to-vertical spectral ratio (HVSR), where rain, wind, pressure, river flow and anthropogenic signatures were evident (at selective frequency ranges). Multi-peaks that emerged on the HVSR curve were further analyzed to identify amplitude and frequency changes, and the three peaks shift on average to a lower position during the rainy period. The GPR amplitude and waveform variation features were attributed to the stratigraphy (i.e., the boundary between valid and invalid regions and coherence value) of the floodplain and regions susceptible to erosion (erosion-prone lithological spots). This approach provides the basis for non-destructive monitoring tools enabling the detection of 'seismic signatures' and weak spots of the fluvial channels for improving environmental management.

Background: Parkinson's disease (PD) is a global disease. However, there is a lack of research specifically focused on the AfrAbia population. Climate change, driven mainly by global warming, is linked to detrimental health impacts, including an increased frequency and intensity of heat waves. Neurological diseases, including neurodegenerative syndromes like Alzheimer's disease, Parkinson's disease, and motor neuron illnesses, have been increasingly seen and recorded in scientific literature. Although there is much literature demonstrating the growth in neurodegenerative disorders, the relationship between environmental variables such as global warming, air pollution, heavy metals, and pesticides and the higher occurrence of these illnesses is still unknown. Objective: The current article primarily aims to examine the relationship between climatic changes, air pollution, pesticides, heavy metals, and the occurrence of PD. Initially, we evaluate the influence of exposure to these variables on the cause or origin of Parkinson's disease. In addition, we will investigate how these variables disrupt processes such as excitotoxicity, oxidative stress, and neuroinflammation, all of which are strongly linked to Parkinson's disease (PD). Conclusions: This research presents a plausible rationale for the adverse effects of environmental influences on the brain, which are pivotal in the progression or onset of Parkinson's disease from an AfrAbian standpoint.

Various heterogeneous devices or objects shall be integrated for transplant and seamless communication under the umbrella of internet of things (IoT). It would facilitate the open accession of data for the growth of a glut of digital services. To build a general framework of IoT is very complex task because of heterogeneity in devices, technologies, platforms and services, operating in the same system. In this paper, we mainly focus on the framework for big data analytics in smart city applications , which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication for the quality life of citizens. A novel approach used in this paper, is for enhancing the energy conservation and to reduce the delay in big data gathering at tiny sensor nodes used in IoT framework. To implement the smart city scenario in terms of big data in IoT, an efficient (optimized in quality of service) WSN is required where communication of nodes is energy effcient. That is why, a new protocol QoS-IoT is proposed on the top layer of the architecture which is validated over the traditional protocols.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

Early-life adversity can profoundly impact individual development, yet some demonstrate resilience. Longitudinal studies tracking individuals over extended periods have aided our understanding of resilience and growth following adverse childhood experiences. This paper reviews insights from influential studies like the Kauai Longitudinal Study, the Adverse Childhood Experiences (ACE) Study, and the Dunedin Study. It explores key findings on risk and protective factors, adaptation trajectories, and long-term outcomes associated with resilience. Theoretical frameworks and methodological considerations are discussed. The paper examines how this research informs interventions, policies, and support systems for fostering resilience. It highlights the need for diverse samples, integrating biological and environmental factors, and investigating intergenerational impacts. By synthesizing longitudinal research, this paper elucidates complex pathways to resilience and positive development despite early adversity, offering insights for research and supporting vulnerable populations.

Longitudinal studies tracing high-risk samples over decades have illuminated powerful pathways of resilient adaptation among individuals who overcome adversities faced early in life. This research demonstrates resilience is a common phenomenon driven by dynamic interactions between individual traits and environmental resources that enable positive adjustment despite adversity exposure. For clinical social workers supporting clients impacted by childhood trauma, abuse, violence and other adversities, understanding these resilience processes is vital for developing strengths-based, trauma -informed interventions that enhance positive adaptation. This paper synthesizes key findings from landmark longitudinal resilience studies and explores specific practice implications for resilience-oriented clinical assessment, intervention planning, prevention efforts, and social worker training. By integrating resilience research insights, social workers can more effectively identify and cultivate clients' innate capacities for resilience, facilitating a journey towards posttraumatic growth.

As an on-going pandemic caused by the out-break of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or simply COVID-19 sweeps through the globe at an unprecedented rate leaving behind trails of high infection and mortality, it is crucial to understand the propagation dynamics of the virus in a host population in order to take urgent and effective remedial and mitigating steps to save life. It is already observed in many countries and communities that accurate and timely testing, tracing, and tracking of the infection lead to better containment and slowing down of the spread. In this exploratory research, the early growth dynamics of infection within a population is pursued based on real data. The study posits that the early growth in a homogenous population follows an exponential pattern motivating further rigorous quantitative treatment based on a number of analytical models such as logistic model, Richard’s model, and Gompertz model– the acceleration pattern of the outbreak is ascertained from the daily inflection data, and regression analysis against population models yields dynamic growth indices which allow very accurate prediction of the successive outbreak size when calibrated continually with updated data. The performance of the various models is evaluated with the real dataset. More, the basic reproduction number of the COVID-19 virus propagation in the community is estimated based on the on-set phase dataset using multi-compartmental epidemiological model. Also, the maximum infection size, infection doubling time and the scope of the herd immunity are also inferred for COVID-19 in a population.

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement

Impulsive buying is a growing problem of modern society with significant financial and psychological consequences, and it is necessary to examine the mechanisms underlying it. Understanding the mechanisms driving this behaviour is crucial. Impulsive buying consists of two key aspects: a cognitive aspect, characterised by a lack of planning and reflection, and an affective aspect, involving emotional responses that can occur before, during, or after a purchase. This study aimed to explore the relationship between personality traits, as measured by the HEXACO model, and tendencies toward impulsive buying. Specifically, it examined how the HEXACO traits differentially predict the cognitive and affective components of impulsive buying tendencies.

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.

Heavy rains result in significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended periods of intense precipitation. Accurate rainfall prediction is crucial in nations such as Bangladesh, where agriculture is the predominant field of occupation. The efficiency of machine learning methods is enhanced by the nonlinearity of rainfall, surpassing the effectiveness of other approaches. This study proposes the novel combination of rainfall occurrence prediction, rainfall amount prediction, and daily average temperature prediction. This research implements machine learning techniques and an ensemble-based classifier to predict rainfall occurrence, as well as machine learning regressor models and an ensemble-based regressor to predict the rainfall amount and daily average temperature, using the Bangladesh Weather Dataset. The ensemble classifier demonstrated an accuracy of 83.41% and a recall of 78.17%, exhibiting the best performance in predicting when it will rain, but its precision was the lowest, at 51.16%. The ensemble regression model outperformed the base models, including linear regression, random forest, and support vector regression in rainfall amount prediction, with the lowest mean absolute error of 0.36 and root mean squared error of 0.90. Additionally, this model provided the most precise daily average temperature prediction results with the lowest mean absolute error of 0.42 and root mean squared error of 0.54, highlighting its superiority over the other regression models in forecasting temperature. Ensemble approaches consistently exhibit superior task performance metrics.

The objective of this research is to analyze the effect of determinants of Business ethics (HR management ethics, ethics in corporate governance, and ethics in sales & marketing) on the organizational performance of the high-tech industry. The study further examines the influence of multiple serial mediations of human, structural, structural, and relational capital on the connection between Business ethics and organizational performance. Additionally, this research examines how technological change moderates the relationship between Business ethics and organizational performance. Data was collected from the information technology sector in various cities of South Asian countries through structured questionnaires. PLS-SEM modeling was used via Smart-PLS 4.0 software for analysis. The hypothesized & modified measurement and structural model were validated using the HTMT matrix, Fornell-Larcker criterion, R-square values, f-square values, outer loading, construct reliability and validity, path coefficient analysis, Blindfolding, and predictive relevance. The research results suggest that ethics in corporate governance, HR management ethics, and ethics in sales & marketing have a notable positive effect on business ethics, leading to a substantial and affirmative impact on the organizational performance of the high-tech industry. The findings further suggested that dimensions of intellectual capital (human capital, structural capital & relational capital) significantly moderate between exogenous and endogenous constructs. Moreover, technological change significantly & positively moderates the relationship between Business ethics and organizational performance of the high-tech industry. In conclusion, this research provides insights into how these variables interact and what potential mechanisms act upon them. Organizations can make informed decisions about improving their performance and ethical standards by understanding these relationships. This research offers substantial theoretical and managerial implications to research scholars and industry practitioners.

There is growing interest in the discovery of novel xanthine oxidase inhibitors for gout prevention and treatment with fewer side effects. This study aimed to identify the xanthine oxidase (XO) inhibitory potential and drug-likeness of the metabolites present in the methanolic leaves extract of Anastatica (A.) hierochuntica L. using in vitro and in silico models. The extract-derived metabolites were identified by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Molecular docking predicted the XO inhibitory activity of the identified metabolites and validated the best scored in vitro XO inhibitory activities for experimental verification as well as predictions of their anticancer, pharmacokinetic and toxic properties, oral bioavailability, and endocrine disruption using SwissADMET, PASS, ProTox-II and Endocrine Disruptome web servers. A total of 12 metabolites with a majority of flavonoids were identified. Rutin, quercetin and luteolin flavonoids demonstrated the highest ranked docking score of -12.39, -11.15 and -10.43, respectively, while the half-maximal inhibitory concentration (IC50) values of these metabolites against XO activity were 11.35 µM, 11.1 µM and 21.58 µM, respectively. In addition, SwissADMET generated data related to physicochemical properties and drug-likeness of the metabolites. Similarly, PASS, ProTox-II and Endocrine Disruptome prediction models stated the safe and potential use of these natural compounds. However, in vivo studies are necessary to support the development of prominent and promising therapeutic use of A. hierochuntica methanolic leaves extract-derived metabolites as XO inhibitors for the prevention and treatment of hyperuricemic and gout patients. Furthermore, the predicted findings of the present study open a new paradigm for these extract-derived metabolites by revealing novel oncogenic targets for the potential treatment of human malignancies.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

Water pollution with dyes is a critical environmental issue because off the huge amount of dyes disbarred annually which cause severe damage for the ecosystem and human life. On the other hand, the core-shell based magnetic materials have showed amazing character for controlling the material synthesis with targeted structure to enhance the adsorptive-removal of pollutants. Herein, Fe3O4 core-TiO2/mesoSiO2 and Fe3O4 core-mesoSiO2/TiO2 double shell nanoparticles were prepared by first (R1) and second (R2) routes. The preparation procedure is controlled to prepare the magnetic core with further coating layers from silica and titania for the uptake of methylene blue from aqueous solutions. The reported adsorption capacities for R1-0.2, R1-0.4 and R2 samples were 46, 38 and 50 mg/g respectively at pH 6 after 80 min contact time form 50 ppm methylene blue solution. The adsorption process of methylene blue onto Fe3O4core-meso SiO2/TiO2 double shell was well fitted with the pseudo-second-order kinetic model and Freundlish isotherm which suggested the quick and multilayer adsorption mechanism. In addition, results of thermodynamic investigation indicated that the surfaces of Fe3O4 core-mesoSiO2/TiO2 and Fe3O4 core-TiO2/mesoSiO2 double shell exhibit spontaneous tendency to adsorb methylene blue from the aqueous solutions. These results will encourage the further application of Fe3O4 core-meso SiO2/TiO2 and Fe3O4 core-TiO2/meso SiO2 double shell for removal of other dyes and pollutants from wastewater.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

Developments of new probability models for data analysis are keen interest of importance for all fields. The log-Dagum distribution has a prominent role in the theory and practice of statistics. In this article, a new family of continuous distributions generated from a log Dagum random variable called the log-Dagum Weibull distribution is proposed. The key properties of the proposed distribution are derived. Its density function can be symmetrical, left-skewed, right-skewed and reversed-J shaped and can have increasing, decreasing, bathtub hazard rates shaped. The model parameters are estimated by the method of maximum likelihood and illustrate its importance by means of applications to real data sets.

This paper presents how social media platforms and their components could be integrated into the KMS for the purpose of managing and sustaining Somali Indigenous knowledge (IK). The discovery of knowledge and its utilization is now fuelled by the advancement of technology, which connects users to external sources through using different platforms. The rapidly increasing use of SM and mobile technologies create opportunities to form knowledge networks that can facilitate the process of creating, preserving, and sharing knowledge and skills that are unique to communities in the Somali context. Somali society being an oral society with rich IK and other cultural practices lacked a framework in place to organize the existing knowledge in various methods. The paper refers to existing methodology E-learning and KM functions. This method first identifies functional similarities between SM, E-Learning, and KM systems and their interactions if they are properly integrated. The proposed framework consists of two functional parts; KM and SM. The KM part involves knowledge capturing, validation, formatting, storing, and distribution. SM part of the framework involves knowledge presentation, sharing, and application. Since the Somalis are said to be an oral society, this framework helps to capture and disseminate IK residing in knowledge holders.

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.

While the beneficial effects of using magnetic and electromagnetic treatment (ET) of brackish and saline waters on soil salinity reduction in the root zone were widely reported more studies are needed to answer questions about the soil salt leaching efficiency and the effect of the duration of the exposure to ET. For this aim, pot experiments were conducted using an Aqua-4DR physical water treatment device. The first experiment included two trials. The first considered 5 concentrations: C0:1.0; C1:4.5; C2:9; C3:13.5; and C4:18 dSm-1. The results revealed that the volume and the salt concentrations of the drained waters were significantly higher under irrigation with ET saline waters than those provided by untreated waters. The drained fraction of water varied from 20 to 26% under irrigation with untreated water and increased from 33 to 56% under irrigation with electromagnetized water indicating an improvement of salt leaching. The second trial was carried out with different irrigation doses. The result showed that the higher the dose, the more obvious and significant ET effect. The different treatment durations of water exposure revealed that the volume and salinity of drained water significantly increase as the ET duration increases. An increase in ET duration induces also an increase in soil water content of around 2.5%. Based on the experimental findings, we may conclude that the ET of saline water can reduce the adverse effect of salinity on the top soil, but these leached salts are carried away in depth and there is no concentration limit of water to the effect of the ET.

Background and Objective: Segmentation of the femur in Dual Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models. Methods: Convolutional neural network (CNN) based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and its effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods. Results: Our study demonstrated that FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis. Conclusions: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.

This paper investigates the improvement in solar energy collection and conversion using Al₂O₃-Water nanofluids in a solar flat plate collector (SFPC). The efficiency of a solar flat plate collector using water as the fluid medium is analyzed experimentally and theoretically. For theoretical analysis, a mathematical model in MATLAB is used to simulate and is validated by the experimental results. To enhance the solar energy collection and conversion efficiency of the SFPC, Al₂O₃-Water nanofluid was selected as the fluid medium. The nanofluid properties like density, specific heat capacity, thermal conductivity and viscosity are analyzed and compared for several models of Al₂O₃-Water nanofluids and the best model was selected to modify the simulation. Effect of particle diameter in the nanofluid was found to be marginal on the nanofluid properties. The optimum volume concentration of the nanofluid was found to be 4% giving an efficiency increase of 7.78% in the SFPC over the use of water. This reduces the area of the SFPC by 10.5%.

This study investigated the relationship between environmental, social, and governance (ESG) disclosure and the performance of Saudi Arabian companies. We analysed panel data from the 100 non-financial companies listed on the Saudi stock exchange (Tadawul) from 2017 to 2022. Using fixed effects, random effects, and generalised method of moments (GMM) models to account for endogeneity concerns, we examined the impact of ESG disclosure on the return on assets (ROA), return on equity (ROE), and Tobin’s Q. An ESG index was constructed through principal component analysis of individual environmental, social, and governance scores. Our results indicate a significant positive relationship between ESG disclosure and companies’ key performance variables across all models. These findings are consistent with stakeholder theory and signalling theory, suggesting that comprehensive ESG practices can lead to better financial performance and serve as a positive signal to stakeholders. The study also reveals sector-specific differences, with non-manufacturing firms showing stronger positive relationships between ESG disclosure and performance measures compared to manufacturing firms. Additionally, we find that firm size, age, and liquidity are important factors influencing the ESG–performance relationship. This research contributes to the growing literature on ESG and corporate performance in emerging markets, offering valuable insights for policymakers, investors, and corporate practitioners in Saudi Arabia’s evolving sustainable business landscape. Our findings underscore the importance of ESG disclosure in driving sustainable and responsible business practices in the region.

In this project, our foremost intention is to analyze the angle of deflection of photon by NED black holes with many horizons in the context of non-linear electrodynamics. For the accomplishment of our intention at first we manipulated the optical metric of the desired black holes for the calculation of Gaussian curvature. After that, the angle of deflection in leading orders is attained from the desired black holes in weak field limit by using the famous Gauss-Bonnet theorem. Moreover, we evince the effect of plasma medium on the angle of deflection. At last, the graphical impact of the angle of deflection for NED black holes with many horizons is examined with respect to black hole charge and impact parameter in both the plasma and non plasma medium. In addition, we discuss the rigorous bound for grey body factor of the desired black hole and also observer its graphical behavior.

Colistin [polymyxin E] is a veterinary exclusive antimicrobial and one of the oldest antibiotics that is currently recognised as one of the highest priority critically important antimicrobials for humans. It is extensively used for multiple medical and non-medical purposes particularly for growth promotion, prophylaxis/metaphylaxis and therapeutic in food animals. As a result, colistin resistance was widespread along the food-chain linking to multidrug resistant bacterial infections in humans reflected by greater use of colistin in healthcare settings resulted in different types of bans/restrictions in food-producing animals, around the world. This report debates the testimonies of 23 countries representing the bulk of global food producing animal production, providing robust evidence on the essential need for colistin in animal production.

Multi-walled carbon nanotubes (MWCNTs) and graphene oxide (GO) reinforced carbon foam (CF) composite were prepared by direct pyrolysis of MWCNTs, GO and mesophase coal tar pitch. The effect of additive amount of the mixture of MWCNTs and GO on the microstruture and properties of carbon foam was analzyed by transmission electron miscroscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Four-probe resistance meter, universal testing machine, and laser thermal conductivity tester respectively. The result shows that MWCNTs and GO had significant impact on the microstructure of carbon foam. Futhermore, the electrical, mechanical and thermal properties of carbon foam composites were significantly enhanced by increasing the additive amount. Maximum compressive strenght of 19.2 MPa and Young’s modulus of 56.8 MPa of CF composite were observed. Similarly, Highest thermal conductivity of 30.91 W/m.K and electrical conductivity of 27.2 ×103 S/m were observed at 2 wt. % of MWCNTs-GO additive loading.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

Brain disorders pose a significant global healthcare challenge due to their complexity and profound impact on individuals and society. This review explores recent advancements in brain disease research, deepening our understanding and progress towards treatments. Technological innovations, like high-resolution imaging and optogenetics, play a pivotal role in illuminating cellular structures and dissecting neural circuits. Genetic analysis identifies dis-ease-associated genes and mutations, yet translating findings to clinical applications remains challenging. Epigenomics, transcriptomics, and proteomics provide molecular insights, un-covering disrupted pathways in conditions like Alzheimer's disease. Stem cell models elucidate developmental mechanisms, and immunotherapies, including CAR T cells, emerge as prom-ising strategies for brain cancers like glioblastoma. Genome editing tools, exemplified by CRISPR, enable precise modification of disease genes in models, deepening our understanding of pathogenesis. Multifaceted approaches integrating human studies, animal models, and bi-oinformatics propel the field forward. However, complexities persist, as disease specificity for cell types or brain regions remains an enigma, and genetic factors interact with environmental influences, highlighting disease intricacies. Ultimately, interdisciplinary efforts combining bi-ophysical and psychospiritual perspectives are essential to fully understand brain health and unlock transformative treatments for these debilitating disorders. A comprehensive under-standing of brain disorders is vital to improve the quality of life for millions and alleviate the global burden of neurological afflictions. Continued collaborative research will lead to an era of improved brain health worldwide.

Management of chronic inflammation and wounds has always been a key issue in the pharmaceutical and healthcare sector. Curcumin (CCM) is an active ingredient extracted from turmeric rhizomes that has antioxidant, anti-inflammatory, and antibacterial activities, thus showing significant effectiveness toward wound healing. However, its shortcomings such as poor water solubility, poor chemical stability and fast metabolic rate limit its bioavailability and long-term use. In this context, hydrogels appear to be a versatile matrix for carrying and stabilizing drugs due to the biomimetic structure, soft porous microarchitecture, and pleasant biomechanical properties. The drug loading/releasing efficiencies can also be controlled by use of highly crystalline and porous metal organic frameworks (MOFs). Here, a flexible hydrogel composed of sodium alginate (SA) matrix and CCM-loaded MOFs was constructed for long-term drug release and antibacterial activity. The morphology and physicochemical properties of composite hydrogels were analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), ultraviolet visible spectroscopy (UV-Vis), Raman spectroscopy and mechanical property tests. The results showed that the composite hydrogel was highly twistable and bendable to mechanically comply with human skin. The as-prepared hydrogel could capture efficient CCM for slow drug release as well as effective killing of bacteria. Therefore, such composite hydrogel is expected to provide a new management system for chronic wound dressings.

Glass samples (59.5 – x) B2O3 – 20Na2O – 20CaO – xV2O5 – Er2O3 – 0.5AgCl (x = 0 – 2.5 mol%) were prepared using melt – quenching method to investigate the structural and optical properties of the glass. The structural of the glass were characterized by using XRD, TEM and FTIR, meanwhile the optical properties were characterized by UV – VIS absorption. The XRD patterns confirmed the amorphous nature of the prepared glass samples. FTIR confirmed the presence of VO4, VO5, BO3, and BO4 vibration, and this result showed NBO increased with increasing vanadium concentration. The UV – Vis – NIR spectra exhibits six absorption band centered at 490, 520, 540, 660, 800, and 980 nm. The optical band gap (Eopt), Urbach energy and refractive index shown decrease, increase and increase, respectively. The Judd – Ofelt intensity parameter reveal the trends was Ω2 > Ω4 > Ω6. There are three emission bands at 516 nm, 580 nm, and 673 nm which are represented by 2H11/2 – 4I15/2, 4S3/2 – 4I15/2, and 4F15/2 – 4I15/2, respectively under 800 nm excitation was obtained.

Soil erosion is one of the most important environmental problems which can have various negative consequences, such as land degradation affecting the sustainable development and the agricultural production, especially for developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. RUSLE model take into account all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity and anti-erosion farming practices. The equation is thus implemented under Geographic Information System (GIS) “Arc GIS Desktop”. The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate estimated at more than 30 t/ha/year and 4.20% are affected by high mean annual soil loss ranging from 20 to 30 t/ha/year. The most eroded areas were identified in west southern, central and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control in the global scale of Tunisia.

It is a theoretical exportation for mass transpiration and thermal transpiration of Casson nanofluid over an extending cylindrical surface. The Stagnation point flow through porous matrix is influenced by magnetic field of form strength. Appropriate similarity functions are availed to yield the transmuted system of leading differential equations. Existence for the solution of momentum equation is proved for various values of Casson parameter β, magnetic parameter M, porosity parameter Kp and Raynolds number Re in two situations of mass transpiration (suction/injuction). Moreover, uniqueness results are discussed and for skin friction factor are established to attain accuracy for large injection values. Thermal and concentration profiles are delineated numerically by applying Runge-Kutta method and shooting technique.

The aim of this paper is to develop a composite coating on St 304 steel employing TIG process. Ti wire cored with graphite powder is used as the means of coating material. The process parameters are varied in order to develop coating with optimum characteristics (i.e., hardness and wear resistance). The microstructure of the coating is analyzed with SEM and XRD. It is found that both of the hardness and wear resistance increase as the current increases while both of these properties decrease as the travelling speed increases. It is found that the coated samples with composite layers have more hardness than the substrate and it could range up to 1100 HV being almost 4.5 times higher than the hardness of St 304. Likewise, the wear resistance of coating is observed to be 4.5 times higher than that of substrate. The high performance of coating, as revealed by microstructural analysis, is due to formation of TiC and Cr₂₃C₆ .The optimum conditions to produce the coating are proposed to be 120 A current and 3.17 mm/s travel speed.

Our study essentially concerns the dynamic behavior of an SIRS epidemic model in discrete time. Two equilibrium points are obtained; one is disease-free while the other is endemic. We are interested in the endemic fixed point as well as its asymptotic stability. Depending on the parameters which are estimated using the data from US Department of health and SIRS modelling with optimization, two Flip and Transcritical bifurcations appear. We illustrate their diagrams, as well as their bifurcation curves using the method of Carcasses \cite{carcasses1993determination,carcasses1995singularities} for the Flip bifurcation and by an implicit function deduced from such an equation for the Transcritical bifurcation. We use the scanning of the parametric plane to have a global view of the behavior of the model and to highlight the zones of stability of the existing singularities. A superposition of the bifurcation curves with the parametric plane can show the overlap of the curves with the boundaries of the stability domains, which confirms the smooth running of the simulation and its correspondence with the theory, we finish this article with constrained optimal control applied to infection rate and recruitment rate for an SIRS discrete epidemic model. Pontryagin's maximum principle is used to determine these optimal controls. Finally using COVID-19 data in the USA, we obtain results that demonstrate the effectiveness of the proposed control strategy to mitigate the spread of the pandemic.

In this paper a new monolithic Eulerian formulation in the framework of non-classical continuum is presented for the analysis of fluid-strucutre interaction problems. In this respect, Cosserat continuum theory taking into account the micro-rotational degrees of freedom of the particles is considered. Continuum description of the model and variational formulation of the governing flow dynamics for non-classical -fluid-structure interaction nCFSI is presented. The model is analyzed by computing a well known benchmark problem by Hecht and Pironneau [16]. The algorithmic description is presented and implemented using FreeFEM++. Code is validated with the benchmark solution of Turek and Hron [38] in case of flow around a flag attached with cylinder. New microstructral behavior of the solution is studied and numerical simulations and results are shown in the form of figures. Some interesting feature of the flow is observed and microstructural characteristics are discussed.

Fermented milk product "Laban" in Libya is one of the most a traditional fermented milk product consumed a refreshing drink, particularly in the warm season The average values of the physicochemical including titratable acidity, pH, total solids, and fat were 0.73%, 4.16, 8.12%, and 1.54% respectively. Coliform, yeast and mold counts were 21×10⁴, 39×10⁴, and 41 ×10³ cfu/ ml., respectively. Most strains of coliform bacteria were Serratia odorifera, Escherichia coli 1, E. coli 2. and Klebsiella oxytoca. The average Lactococcus, Streptococcus, Mesophilic Lactobacillus / Leuconostoc and Thermophilic Lactobacillus counts were 99 ×10⁷, 96 ×10⁷, 93 ×10⁷ and 15 ×10⁷ cfu / ml. respectively. A total of 142 lactic acid bacteria (LAB) isolates were identified to the genus level as Lactobacillus (48.59%), Lactococcus (43.66%), Streptococcus (4.93%) and Leuconostoc (2.82%). Sugar fermentation tests revealed the most frequent Lactobacillus species found to be Lactobacillus delbrueckii ssp. lactis (62.32%), followed by Lactobacillus plantarum (31.88%). Furthermore, other selected LAB isolates were identified by API 50 CH test as Lactococcus lactis ssp. lactics, Lactobacillus pentosus, Lactobacillus brevis, and Leuconostoc mesenteroides ssp. cremoris. Thus, our research documented the lactic acid bacteria strains and will provides fundamental basic and useful information for further studies of strain selection starter culture, with regard to the industrial production of fermented dairy milk products.

In this paper, we establish certain Lp bounds for several classes of rough Marcinkiewicz integrals over surfaces of revolution on product spaces. By using these bounds and using an extrapolation argument, we obtain the Lp boundedness of these Marcinkiewicz integrals under very weak conditions on the kernel functions. Several previous results on Marcinkiewicz operators are essentially extended or improved. Our results represent natural extensions and improvements of several known results on Marcinkiewicz integrals over symmetric spaces.

We prove Lp estimates of a class of generalized Marcinkiewicz integral operators with mixed homogeneity on product domains. By using these estimates along with an extrapolation argument we obtain the boundedness of our operators under very weak conditions on the kernel functions. Our results in this paper improve and extend several known results on both generalized Marcinkiewicz integrals and parabolic Marcinkiewicz integrals on product domains.

Optimization using Genetic Algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the Genetic Algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case of one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and damp the undesired attitudes. The proposed crossover can be achieved in two stages: the first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system shows improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

The aim of this work is to analyze and investigate the shape preserving properties of ternary 4-point non-stationary interpolating subdivision schemes constructed by Beccari et al. [1] with a tension parameter !k+1 which can reproducing exponential. Moreover, the conditions on the initial control points are developed that allow user to generate shape preserving limit curves after a nite number of subdivision steps and generalize these results in limiting case. Signicance of derived conditions are illustrated through graphs and the whole discussion is followed by examples.

Severe malaria in adults is not well studied in Sahelian Africa. Clinical features and mortality associated with severe Plasmodium falciparum malaria in adult patients hospitalized in Kiffa, southern Mauritania, were analysed. Patients over 15 years old admitted for severe malaria between August 2016 and December 2019 were included in the present retrospective study. The World Health Organization (WHO) criteria were used to define severe malaria. The presenting clinical characteristics and outcome were compared. Of 4266 patients hospitalized during the study period, 573 (13.4%) had a positive rapid diagnostic test for malaria, and 99 (17.3%; mean age, 37.5 years; range 15–79 years; sex-ratio M/F, 2.1) satisfied the criteria for severe malaria. On admission, the following signs and symptoms were observed in more than one-fourth of the patients: fever (98%), impairment of consciousness (81.8%), multiple convulsions (70.7%), cardiovascular collapse (61.6%), respiratory distress (43.4%), severe anaemia ≤ 80 g/L (36.4%), haemoglobinuria (27.3%), and renal failure (25.3%). Patients were treated with parenteral quinine or artemether. Fourteen (14.1%) patients died. Multiple convulsions, respiratory distress, severe anaemia, haemoglobinuria, acute renal failure, jaundice, and abnormal bleeding occurred more frequently (P < 0.05) in deceased patients. Mortality due to severe falciparum malaria is high among adults in southern Mauritania. An adoption of the WHO-recommended first-line treatment for severe malaria, i.e. parenteral artesunate, is required to lower the mortality rate associated with severe malaria.

Breast and Cervical cancer are significant causes of morbidity and mortality among women worldwide. Usually, conventional therapies and early detection has shown success in metastasis and relapse rates and improved overall survival of the patients. The elimination of the efficacy of chemotherapies and target therapies because cancer stem cells create resistance against chemotherapy, metastasis, and relapse. Studies reveal that cancer stem cell markers are important for improving efficiencies of targeted therapies. Our study aims to analyze the stem cell markers and target the most significant stem cell marker by phytochemicals and their derivates. Through the analysis, it was observed that CXCR4 and SOX-2, mir-1, mir-125, and mir-429 may have a crucial role in stemness of breast and cervical cancer respectively, while BPMD & MD simulations revealed Di-valinoyl curcumin as a potential compound for CXCR4 targeting.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

In this article, the usual factorials and binomial coefficients have been generalized and extended to the negative integers. Basing on this generalization and extension, a new kind of polynomials has been proposed, which led directly to the non-classical hypergeometric orthogonal polynomials and the non-classical second-order hypergeometric linear DEs. The resulting polynomials can be used in non-relativistic and relativistic QM, particularly, in the case of the Schrödinger equation, and Dirac equations for an electron in a Coulomb potential field.

This study measures key indicators of perceived precarity and decent working conditions and their relative effects on life satisfaction of workers in small and medium-sized enterprises. Using a random sample survey, we interviewed 103 workers in four districts of Sylhet division in Bangladesh. Utilizing Smart PLS application for structural equation modeling, we iterated the samples 1000 times which resulted in a new sample size standing at 499. We used a Likert-type five-point scaling technique, with appropriate items related to the latent constructs of the model, for path analysis. We found perceived precariousness has the highest causal relations with a path coefficient of 0.706 with social dialogue (an indicator of decent working conditions) followed by 0.539 with working hour issue, 0.345 with life satisfaction and 0.301 with the unacceptable work issue. The importance-performance map analysis confirms that perceived precarity is the most critical construct for worker’s life satisfaction. Thus, in a ceteris paribus situation, an increase of one point in the performance on the perceived precarity is expected to increase in the performance on the life satisfaction by a total effect level of 0.428. Therefore, we recommend further attention should be paid by the policymakers to the issue of consciousness of the precarity at the workplace for a better life satisfaction of workers.

The continuing public health issue known as COVID-19 (the 2019 Novel Corona virus infection) has a global emphasis. Despite (or perhaps because of) the fact that there are significant gaps in our understanding of COVID-19 epidemiology, transmission dynamics, research methods, and management breakout poses a new kind of global hazard. The good news is that there is currently enough knowledge about the epidemic process to allow for the creation of mathematical forecasting models. We modify a conventional SEIR epidemic model to the unique dynamic compartments and epidemic features of COVID 19 as it spreads in a population with a diverse age structure. Although many US states and other nations around the world followed lockdown and reopening processes, we perform some analysis on using some techniques of the epidemic course. A new perspective of fractional calculus known as piecewise derivatives of fractional order is used to study the proposed model. Sufficient conditions are established to show the existence theory. In addition, a numerical scheme based on Newton’s polynomials is established to simulate the approximate solutions of the proposed model by using various fractional orders. Some real data results are also shown with comparison of the numerical results.

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.

Molecular dynamics simulations were performed to examine the impact of temperature (ranging from 250K to 350K) on the structural behavior of cellulose chains when dissolved in an aqueous cuprammonium hydroxide Cuam solvent system. By monitoring the root mean square deviation (RMSD) and radius of gyration (Rg) values at each temperature, we found that the geometric deformation of the cellulose chains changed significantly at T=300K. In contrast, Rg and RMSD values remained stable at temperatures in the range of 250K, 270K and the range of 330K, 350K. Calculating the interaction energy between the solvent and cellulose chains revealed that temperature significantly influences the cellulose dissolution process in the Cuam solvent system and that T=300K is an efficient temperature for its dissolution. The number of intra- and interchain hydrogen bonds was also calculated as a function of temperature, and the analysis confirmed that there is no breakage of these bonds at temperatures below 300 K (i,e: 250K, 270K) and above 310 K (I,e, : 330K and 350K) either between two native chains or between a native chain and another derivative.

The COVID-19 pandemic, declared in early 2020, is an unprecedented global health crisis, causing over 7.0 million deaths and ongoing challenges. While the pharmaceutical industry expedited vaccine development, mutant SARS-CoV-2 strains remain a major fear. Moreover, concerns regarding the long-term health repercussions of COVID-19-affected individuals persist since individuals affected by mild and moderate to severe SARS-CoV-2 infection experience long-term cardiovascular complications, liver dysfunction, pulmonary afflictions, kidney impairments, and most importantly neurocognitive deficits. In recent studies, we documented pathophysiological changes in various organs following the post-acute infection of mice with murine hepatitis virus-1 (MHV-1), a coronavirus, at both 7 days and 12 months after infection. One part of the body that can be drastically affected by SARS-CoV-2 is the skin. Studies have shown major changes in the skin post-acute SARS CoV-2 infection in humans. However, long-term dermatologic changes post-COVID have never been explored. For the first time, we show several cutaneous findings both at the acute stages and long-term post-infection of mice with MHV-1 coronavirus (a promising experimental model to study acute and long-COVID). Precisely, we found destruction of the epidermal layer, an increase in the number of hair follicles, extensive collagen deposition in the dermal layer, and hyperplasticity of the sebaceous glands at the acute stages, along with thinning of the panniculus carnosus, as well as the adventitial layer, which corresponds well with studies in humans. In contrast, the cutaneous investigation in the long-COVID phase shows the absence of hair follicles from both the epidermal and dermal layers, the destruction of adipose tissues, and the devastation of the epidermal layer. Further, treatment of these mice with a 15 amino acid synthetic peptide, SPIKENET (SPK), which was effective in preventing Spike glycoprotein-1 binding with host receptors, as well as has a potent anti-inflammatory response to severe inflammatory stimulus) restored the loss of hair follicles and re-architected the epidermal and dermal layers. Additionally, destruction in fatty tissue in the infected mice was successfully restored post-treatment with SPK. These findings suggest that SARS-CoV-2 initiates the changes early post-infection, leading to devastating skin alterations in the long term which can be prevented by our newly identified peptide drug SPK.

The Gulf of Suez is a sedimentary rift-basin with thick evaporites and complex structural settings that presents challenges to achieving quality imaging based on seismic reflection data. Such subsurface settings and the lack of 3D seismic data poses challenges in building reservoir models to guide hydrocarbon exploration and development, especially in southern parts the Gulf of Suez. This study attempts to overcome the challenges of both 2D seismic interpretation and building a static reservoir model of two Miocene reservoir formations, Ras Ghara Field, Southern Gulf of Suez. Integration of well log data analysis of nine wells and regional-tectonics cognizant 2D seismic interpretation of nineteen 2D seismic lines enabled a reliable 3D static reservoir model. The Miocene reservoir characterization reveals heterogeneity in lithofacies distribution, with thick evaporite sequences causing seismic reflection distortions. Significant hydrocarbon accumulations are associated with horst fault blocks. The Miocene reservoirs demonstrate effective porosity of 14-33%, water saturation of 16-65%, and hydrocarbon saturation of 35-84%. Two new prospects are identified based on structure, facies distribution, and petrophysical results, with estimated original oil in place (OOIP) of 2,002,404 stock tank barrels (STB) and 1,175,580 STB for the Kareem Formation, and 6,302,916 STB and 2,841,894 STB for the Rudeis Formation.

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform adaptive filtering techniques and algorithms normally used in adaptive control, especially when the plant is nonlinear. The deeper the controller the better the inverse function approximation, provided that the nonlinear plant have an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to parameter change of the nonlinear plant in that, under such change, the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. Settling times and rise times of the step response are shown to improve in the DL-based AIC system.

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.

Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average (MA) colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher-order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, like sensor networks.

The present work is focused on the numerical solution of the complete energy equation used in fluid film lubrication. The work was motivated by the fact the complete energy equation has no analytic solution that could be used for validations. Its accuracy and computation time are related to the employed numerical method and to the grid resolution. The natural discretization method (NDM) applied on different grids is systematically compared with the spectral method (the Lobatto Point Colocation Method or LPCM) with different polynomial degrees. A one dimensional inclined slider is used for the numerical tests and the energy equation is artificially decoupled from Reynolds. This approach enables to focus all the attention on the numerical solution of the energy equation. The results show that the LPCM is one or two orders of magnitudes more efficient than the NDM in terms of computation time. The energy equation is then coupled with Reynolds equation in a thermo-hydrodynamic analysis of the same 1D slider; the numerical results confirm again the efficiency of the LPCM. A thermo-hydrodynamic analysis of a two-lobe journal bearing is then presented as a practical application.

The purpose of this paper is to study the influence of diffusion and thermal loading due to pulsed laser heating on a thermoelastic solid with voids in the context of the model of three-phase-lag. The non-dimensional governing equations are formulated and then the exact expressions for the temperature field, the displacement components, the stress components, the concentration field and the change in the volume fraction field are obtained using the normal mode technique. The material is an isotropic, homogeneous elastic half-space and heated by a non-Gaussian laser beam with pulse duration of ps. The magnesium crystal element is used as an application to compare the predictions induced by diffusion and laser pulse on poro-thermoelastic medium of the model of three-phase-lag with those for the theory of Green–Naghdi of type III. The results obtained for thermal changes are verified by comparison with two theories of thermoelasticity, namely the model of three-phase lag (3PHL) and the Green and Nagdi type III theory. Plot the expressions to examine the effect of the laser pulse at two different values of time and the effect void parameter. This study also yields some interesting special cases.

Spectrometers have a wide range of applications ranging from optical to non-optical spectroscopy. The need for compact, portable, and user-friendly spectrometers has been the pivot of attention from small laboratories to the industrial scale. Here, the Czerny Turner configuration-based optical spectrometer simulation design is carried out using ZEMAX OpticStudio. A compact and low-cost optical spectrometer in the visible range has been developed by using diffraction grating as a dispersive element and USB-type WebCAM CCD (Charge-coupled device) as a detector instead of an expensive commercial diffraction grating and detector. Using National Instruments LabVIEW, data acquisition, processing, and displaying techniques are made possible. We have employed different virtual images in LabVIEW programs to collect the pixel-to-pixel information and wavelength-intensity information from the image captured using the WebCAM CCD. Finally, we have shown that the OpticStudio-based spectrometer and experimental measurements of the developed spectrometer are in good agreement.

A novel coronavirus has been reported as the causative pathogen of the Coronavirus disease 2019 (COVID-19) outbreak Wuhan city, China in December 2019. Due to the rapid spreading of COVID-19 worldwide, it has been announced as a pandemic by the World Health Organization (WHO). Hospitalized patients in Wuhan are associated with the Huanan seafood wholesale market where live animals, such as poultry, bats, snakes, frogs, rabbits, marmots, and hedgehogs are sold in that market which suggests a possible zoonotic infection. Therefore, it is essential to identify the potential animal reservoir, and the possibility of infection for other animal species. This short review aims to provide an overview on the relation between severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) infection and animals.

Since the start of the COVID-19 pandemic, the world has witnessed a digital revolution that has transformed the way information are broadcast and consumed. This rapid evolution was accompanied by an “infodemic” (Beaulieu, 2019), a transformation of workplaces (Lulli LG, 2021)and difficulties in managing information flows (Bendahan & Nader, 2020). In this scientific article, we examine the communication strategies adopted by hospital structures as they are perceived by staff, highlighting their psychological and professional experiences throughout the crisis period. The results of the study carried out among hospital staff (n=200) demonstrate that the perception of communication strategies is generally positive for 78.56% of participants (M=1.85). The assessments of the staff interviewed as for the effect of this communication on the psychological experience and on the management of professional tasks have a positive trend respectively (M=2.15) and (M=1.86)

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

A low-cost medium power carbon dioxide (CO₂) laser system is designed, constructed, and characterized to produce coherent, monochromatic laser radiation in the Infrared region. The laser cavity is simulated and designed by using ZEMAX optic studio. A switch-mode high tension pump source is designed and constructed using a flyback transformer and simulated using NI Multisim to study the voltage behavior at different node points. A prototype cooling system/chiller is designed and built using the Thermo-Electric Coolers (TEC) to remove the excess heat produced during laser action. Various parameters, like pumping mechanism, chiller stability, efficiency, output power, and current at different applied voltages, are studied. The chiller efficiency at different output powers of the laser is analyzed, which clearly shows that the chiller's cooling rate is good enough to compensate for the heat generated by the laser system. The center wavelength of the carbon dioxide laser is 10.6μm with FWHM of 1.2nm simulated in the ZEMAX optic studio. The output beam penetration through salt rock (NaCl), wood, and acrylic sheet at various output powers is analyzed to measure the penetration depth rate of the CO₂ laser.

Background: Helicobacter pylori infection is a global health concern, contributing to gastritis, peptic ulcer disease, and gastric cancer. However, knowledge about H. pylori epidemiology in Somalia is limited. This scoping review aims to synthesize evidence on H. pylori prevalence in Somalia and associated factors. Methods: We systematically retrieved six scholarly investigations on H. pylori prevalence in Somalia published until 2023 from academic databases. We extracted data on prevalence, demographics, and covariate factors. Results: H. pylori infection varied (32.4% to 56.5%) across studies, with higher rates in adults and outpatient settings. Temporal variability was observed. Gastritis symptoms and female gender showed an association with H. pylori infection, while age, family history, diet, lifestyle, and comorbidities had inconclusive associations. Conclusions: Somalia faces a substantial H. pylori burden, reaching 56.5% in symptomatic adults seeking outpatient care. Temporal prevalence fluctuations require further investigation. These findings inform research and clinical management. Population-based studies are essential to establish a national prevalence profile. This research addresses a critical knowledge gap in Somalia's H. pylori epidemiology, guiding public health strategies. Journals in gastroenterology, infectious diseases, and public health may consider this for publication.

In a previous series of papers relating to the Combined Gravitational Action (CGA), we have exclusively studied orbital motion without spin. In the present paper, we apply CGA to any self-rotating material body, i.e., an axially spinning massive object, which itself may be locally seen as a gravito-rotational source because it is capable of generating the gravito-rotational acceleration, which seems to be unknown to previously existing theories of gravity. The consequences of such an acceleration are very interesting, particularly for Compact Stellar Objects. Independently of the equation of state, it is found that the minimum radius of a stable neutron star is three times its gravitational radius, R_min = 3GM_NS/c², and its critical and maximum internal magnetic field strength cannot exceed the value of 3×10¹⁸ G.

Global warming and climate change have been the major concern for multiple disciplines. Population growth, urbanization, and industrialization are major contributing factors to such a problem by escalating the use of fossil fuels to meet their energy demands. The building sector has the largest share in using the total globally produced energy and emitting tons of greenhouse gas emissions. Emerging eco-friendly technologies such as solar and wind energy harvesting are being extensively explored but still, they are insufficient. Nature-inspired technologies can be the solution to our problem. For instance, algae are microorganisms that use water, light, and CO2 to produce energy and sustain life. Exploiting such characteristics in the built environment is termed algae building technology, a very efficient and green application for a sustainable future. Algae-integrated façades show great versatility through biomass and energy production, wastewater treatment, shading, and thermal and acoustic insulation. In this paper, algae will be introduced as a robust tool toward a greener and more sustainable future, The algae-building technology and its implementation will be demonstrated. Steps for applying this sustainable approach in Egypt will be discussed.

The minimum requirement for uncertainty estimation is to use only intermediate precision (R_w), especially for measurands lacking a reference measurement system such as thyroid functions tests (TFT). In this study, measurement uncertainty (MU) for TSH and FT4 from long-term internal quality control (IQC) data was estimated while reference change values (RCV) were calculated from estimated MU. Furthermore, intermediate precision (R_w) was used to establish appropriate risk-based QC frequency. Twenty fore months of third party IQC data were collected retrospectively, on the Abbott ARCHITECT i1000sr analyzer from INSTITUT PASTEUR OF MSILA laboratory, ALGERIA. The MU, RCV and sigma-metric were estimated simply from the intermediate precision (R_w), while a nomogram relating sigma performance to run size was used to establish QC frequency. The MU for the TSH and FT4 was 12% and 8% respectively. The U _one-sided for the TSH and FT4 was 10%, 6.6% respectively. MU and U _one-sided of TSH and FT4 met quality requirements for permissible uncertainty (pU %) and allowable total error (ATE %). When monitoring thyroid replacement therapy, an upward minimum change (RCV) of 54% and 22% or a downward of 35% and 18% in serum TSH and FT4 respectively, would be considered significant. Optimal QC strategy for serum TSH was selected to run 4 QC materials every 190 patients sample and to use a multi-rule (1_3s/2_2s/R_4s/4_1s). Our results suggest that MU estimation from long-term IQC alone may be acceptable for TFT to assist physician in results interpretation and to establish appropriate QC frequency.

With the global increase of photovoltaic (PV) modules deployment in recent years, the need to explore and realize their reported failure mechanisms has become crucial. Despite PV modules being considered reliable devices, failures and extreme degradations often occur. Some degradations and failures can be minor and cause no critical harm if within the expected range. Others, may start mildly and then deteriorate faster to become catastrophic, especially in harsh environments. This paper conducts a state-of-the-art literature review to scan PV failures, types, and their root cause based on PV’s constructed components (from protective glass to junction-box). It outlines the hazardous consequences beyond PV module failures, describing what harm they can bring to the PV system. As we delve into the literature, it becomes clear that every component is vulnerable to certain types of failures; some can deteriorate within themselves, and others infect further PV components resulting in emerging more severe failures. In the end, the review briefly summarises PV failure detection techniques, emphasising electrical characterization techniques, and disclosing the need to engage more electrical parameters. Most importantly, this review can prepare the stage for the PV research community to identify the most prevalent degradation processes. This, in turn, encourages researchers to investigate them throughout modelling and experimental studies to forecast them at the early onset in order to protect the PV systems from hazardous malfunctions.

This study investigates resource adequacy and renewable energy integration in the United States, European Union, and Pakistan amid global energy market liberalization and greenhouse gas re-duction efforts. It explores how these regions are adapting to the surge in renewable sources like wind and solar, which, despite their financial and environmental benefits, challenge resource adequacy and the economic viability of traditional energy sources. In the US and EU, significant improvements have been introduced in wholesale electricity markets and capacity accreditation mechanisms, which enhanced the large-scale deployment of renewables. This shift has prompted a reevaluation of resource adequacy, leading to increased deployment of battery storage and demand response. Presently, gas-based generation is largely upholding resource adequacy, however, future trends indicate a move towards greater consumer participation, energy efficiency, and utility-scale storage, with a decline in fossil fuel use. Pakistan aims to adopt a liberalized market structure by balancing competitive markets with legacy contracts. Public pressure is driving a shift from costly fossil-based generation to renewables. Similarly, a trend in the rise of behind-the-meter solar generation can be witnessed. In future, Pakistan may also experience resource adequacy challenges. It will likely need to implement battery storage, demand response, and modern capacity accreditation tools, by drawing lessons from developed markets.

The coronavirus disease pandemic 2019 (COVID-19) has emerged in Wuhan province, China in December 2019 and has spread over all countries. The current study was carried out to predict active, death and cured rate of COVID 19 in Algeria for a future period of 35 days using FB prophet model. Results shoed that the active rate and the death rate decrease for the next days while the cured rate increase. The active, cured and death rates are estimated at 19.7% 78.85% and 2.55% respectively. These results highlight the importance of FB prophet model in COVID-19 prediction which could help national authorities in adopting the best preventive measures.

Flooding during the rainy season poses a significant threat to the city of Hargeisa, impacting the well-being of both its residents and the local ecosystems. This research focuses on urban flood control, with a specific emphasis on finding sustainable solutions for managing large stream in Hargeisa. The study identifies five key dimensions, including infrastructure, transportation, environmental soil, life, and property, as critical factors in addressing the issue. By employing a comparative method and collecting data through closed-ended questions, the research aims to analyze and weigh eleven potential solutions, seeking to identify the most effective approaches. The findings of the study indicate that constructing a dam emerges as a promising and sustainable solution, with a weightage of 70 percent. This highlights the potential of a dam in mitigating the impact of floods in the region. Additionally, considering the government's economic capacity, the implementation of check dams is identified as another viable option, with a weightage of 95 percent for reducing the flood power of the stream. These solutions offer practical and effective measures to address the recurring problem of flooding in Hargeisa. To support future decision-making and planning, the research underscores the need for establishing a comprehensive rainfall data database. This database would store relevant information on rainfall patterns, enabling better understanding and prediction of flood events. The study emphasizes the importance of collaboration between local government authorities, environmental organizations, and stakeholders to develop and implement effective flood control strategies. This research provides a systematic approach that can inform and benefit regional and global researchers, policymakers, and municipal governments in their efforts to address urban flood control challenges, not only in Hargeisa but also in similar contexts worldwide.