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One of the most important aspects in determining the quality of a software product before placing it on the market is its reliability. The main problem in creating effective software that satisfies the user preferences is that it must be highly reliable. One important factor that has a remarkable influence on the overall reliability of a system is its software. Reliability is a critical aspect of software quality, and the software industry faces many challenges in its quest to produce reliable software at scale. Reliability models are a basic method for quantitatively calculating software reliability. Thus, this paper inspects the reliability of software applications as a substantial feature of this application and helps determine the extent of software reliability in performing specialized functions. This goal is accomplished by calculating the parameters of software reliability growth models (SRGMs). The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). Results show that the SVM model achieves the best mean square error.

Dyslipidemia is a major risk factor for coronary heart disease and is responsible for an estimated 2.6 million deaths annually. Hypertensive patients often suffer from dyslipidemia, which can lead to complications such as ischemic heart disease and stroke. This study examined the prevalence and factors associated with dyslipidemia among adult hypertensive patients admitted to Lira Regional Referral Hospital (LRRH) in Uganda. This cross-sectional study, conducted over three months at Lira Regional Referral Hospital (LRRH), involved 431 patients. Medical examinations and histories were recorded, and blood samples were analyzed to measure high-density lipoprotein (HDL), low-density lipoprotein (LDL-c), total cholesterol (TC), and triglycerides (TG). Dyslipidemia was defined by any of the following: TC ≥200 mg/dL, TG ≥150 mg/dL, HDL-C < 40 mg/dL, or LDL-C ≥100 mg/dL. The data were analyzed using binary logistic regression in SPSS version 26 to identify associated factors. A total of 431 patients were included in the study, with the majority being females over 65 years of age (mean age = 64.7, SD = 8.8 years). The prevalence of dyslipidemia was 48.3% (208) (95% CI = 43.2-52.9%). High LDL levels were observed in 24.4% (105), high TC in 22.5% (97), high TG in 20.0% (86), and low HDL in 20.0% (86 patients). Multivariate analysis revealed that patients with elevated blood pressure, stage 1 hypertension, and stage 2 hypertension had 1.350 (aOR = 1.350, CI = 1.194-1.525, P < 0.001), 1.290 (aOR = 1.290, CI = 1.123-1.482, P < 0.001), and 1.302 (aOR = 1.302, CI = 1.077-1.576, P = 0.007) times higher odds of having dyslipidemia, respectively. Additionally, dyslipidemia was 1.414 times more common (aOR = 1.414, CI = 1.280-1.561, P < 0.001) among current or former smokers and 1.493 times more common (aOR = 1.493, CI = 1.372-1.625, P < 0.001) in patients with diabetes mellitus. The findings highlight a high prevalence of dyslipidemia, emphasizing the need for targeted interventions. Enhancing patient education on antihypertensive therapy adherence and increasing efforts to discourage smoking are crucial steps to reduce the burden of dyslipidemia in hypertensive populations.

Sickle haemoglobin (HbS) is the most common abnormal haemoglobin mutation worldwide. In Uganda, according to the Uganda Sickle Surveillance Study (US3), the overall prevalence of sickle cell trait and disease is 13.3% and 0.7%, respectively. SCD presents with numerous complications. Acute Soft Head Syndrome (ASHS) or sickle cell cephalohematoma is among the rare complications of SCD. We report a 14-year-old adolescent male, a resident of rural Western Uganda, who presented with a history of non-traumatic painless scalp swelling for a week and multiple joints pain for 5 days. He had a similar swelling in the previous six months, which subsided after blood transfusion given at a nearby health facility. There was a history of on-and-off fever, easy fatiguability, and generalized body weakness. Complete blood counts revealed anemia, Haemoglobin Electrophoresis confirmed Sickle Cell Anemia. Head Computed Tomography concluded a subgaleal fluid collection. The diagnosis of acute soft head syndrome was established in a newly diagnosed SCD patient. This case highlights a rare diagnosis of acute soft head syndrome as well as a late diagnosis of SCD in adolescent in rural settings. In such settings, the pre-existing poor socio-economic background coupled with atypical presentation constitute a major impediment for timely diagnosis.

Abstract Background Radiologic evaluation is crucial in minimizing transplant complications and guaranteeing the donor’s safety, as a variety of anatomical variations characterizes hepatic vascularity. Computed tomographic angiography (CTA) is the gold standard in hepatic vascularity evaluation in donors before liver transplantation. Still, the procedure necessitates intravenous contrast medium injection, which entails nephrotoxicity risk and hypersensitivity reactions. These concerns about contrast medium safety encouraged new developments in non-contrast-enhanced magnetic resonance angiography (NC-MRA) techniques for imaging the vascular anatomy of the liver. Results This study was conducted on 40 patients of potential liver donors. Thirty-six cases (90%) showed excellent image quality by CTA versus 14 cases (35%) by NC-MRA; on the other hand, 21 cases (52.5%) showed good image quality by NC-MRA while versus 2 cases (5%) by CTA and NC-MRA documented 5 cases (12.5%) with fair image quality versus two cases documented by CTA. Both modalities provided diagnostic image quality in all cases. Michel’s classification was employed to identify four distinct kinds of hepatic artery anatomy using CTA. Thirty-nine cases out of 40 were successfully identified using NC-MRA. Segment IV hepatic artery origin could not be identified in 6 cases using NC-MRA versus one case using CTA. Artifacts were noted in 20 cases (50%) using NC-MRA versus 2 cases (5%) using CTA. Conclusion CTA is the gold standard for pre-liver transplant donor evaluation. The emerging technology of non-contrast MRA has an added value with fewer patient complications. It may function as an additional donor examination when CTA is unable to provide adequate diagnostic information. Non-contrast MRA yields reliable results and eliminates the necessity for contrast medium and additional radiation exposure.

Abstract Background Electrolyte abnormalities (EAs) worsen the clinical course of patients with heart failure (HF). The patterns of EAs vary among patients with HF. This study investigated patterns and factors associated with EAs among patients with HF admitted to Hoima Regional Referral Hospital (HRRH) in western Uganda. Methods This hospital-based cross-sectional study used quantitative data of 384 HF patients admitted at HRRH between 21st February and 15th May 2023. Data on sociodemographic, lifestyle, and medical characteristics were collected and presented as descriptive statistics. EAs were considered electrolyte values below or above the reference normal ranges. Bivariate and multiple logistic regression analyses were conducted to establish associations. An association with a p

Background: The world over, women are victims of violence in close and intimate relationships where they are expected to be safe and protected. Intimate partner violence (IPV) significantly impacts the physical and mental well-being of those affected. This study assessed the prevalence, pattern and effects of IPV amongst women in Abeokuta South Local Government Area (LGA) of Ogun State, South West Nigeria. Materials and Methods: This community-based cross-sectional study was carried out in Abeokuta – South LGA in Ogun State. Data were collected from 339 women in intimate relationships using a structured interviewer-administered questionnaire. Ethical approval (HPRS/381/471) was obtained from the Ogun State Ministry of Health Ethical Review Committee. Analysis was done using IBM® SPSS version 23, and results were presented in frequencies and proportions using tables and charts. Results: About three-quarters (73.2%) of the 339 respondents had experienced one form of IPV. Psychological/emotional violence, 224 (66.1%), and physical violence, 161 (47.5%), were the study’s most commonly reported forms of violence. Amongst those affected, 186 (54.9%) were ‘insulted’, 87 (25.7%) were ‘embarrassed in public’ and another 124 (36.6%) were slapped. The effects of IPV reported were bruises/lacerations, unwanted pregnancies, sexually transmitted infections and attempted suicide. Conclusion: The prevalence of IPV of all forms were high in the studied population. Therefore, the government needs to create more awareness of the problem and promote investments in women’s empowerment to reverse this trend.

Bloodstream infections (BSIs) are a critical concern in pediatric onco-hematological patients undergoing chemotherapy or hematopoietic stem cell transplantation (HSCT), leading to a major impact on morbidity, long-term hospitalization, and mortality. We retrospectively analyzed 202 BSIs in 145 patients, consisting of 128 with hematological malignancies, one with a solid tumor, and 16 with non-malignant hematological diseases. We collected data on patient demographics, clinical characteristics, pathogen distribution, and antimicrobial pathogen susceptibility. Gram-positive infections were the most frequent at (58.4%), followed by gram-negative (41%), and fungal infections (0.5%). Particularly, the majority of these infections occurred during the induction phase of chemotherapy, where 94 (46.5%) BSI episodes were recorded, predominantly in neutropenic patients (88.3%). The consolidation phase experienced lower BSIs (11.8%); among these patients (54.1%) were non-neutropenic. BSIs observed in (23.7%) of patients in the maintenance phase, with a higher proportion (66.6%) being neutropenic. Among the 7 patients who underwent HSCT, BSIs occurred in (4.9%) cases, mainly (70%) due to neutropenia. The most prevalent pathogens were Staphylococcus epidermidis (19.8%), Staphylococcus hominis (16.3%), and Escherichia coli (8.4%). The study highlights the critical need for vigilant monitoring and customized infection management strategies to enhance patient outcomes across chemotherapy phases and HSCT.

This study introduces a sustainable method for producing silver nanoparticles (AgNPs), focusing on sustainability and environmental protection. The technique includes the use of Salvia elegans aqueous leaf extract as a reducing agent. In addition, the research investigates the dose-dependent degradation of pUC19 DNA by silver nanoparticles, which is facilitated by the generation of singlet oxygen. The presence of S. elegans extract results in significant color changes, going from a colorless state to a dark brown hue, serving as an indication of the synthesis of AgNPs. A range of experimental methods were employed to analyze the biogenic AgNPs, such as UV–visible absorption spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The meticulously produced AgNPs demonstrate a high level of uniformity, featuring a spherical shape and a particle size of 60 nm. DNA photo-cleavage studies show that singlet oxygen plays a crucial role in triggering DNA damage caused by AgNPs, indicating their potential as powerful cytotoxic agents specifically aimed at cancer cells. Additional studies are required to clarify the effectiveness and specificity of AgNPs against various cancer cell types to assess their therapeutic potential. The cytotoxic effects of AgNPs, particularly in relation to DNA photocleavage, are of considerable interest in cancer research. This research holds promise for developing novel and sustainable cancer therapies based on the unique properties of biogenic silver nanoparticles.

IntroductionArtificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial.MethodologyThe search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews.ResultsIn this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette’s neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value

Heat stress, low mango yields and inconsistent fruit quality are main challenges for growers. Recently, licorice-root extract (LRE) has been utilized to enhance vegetative growth, yield, and tolerance to abiotic stresses in fruit trees. Potassium sorbate (PS) also plays a significant role in various physiological and biochemical processes that are essential for mango growth, quality and abiotic stress tolerance. This work aimed to elucidate the effects of foliar sprays containing LRE and PS on the growth, yield, fruit quality, total chlorophyll content, and antioxidant enzymes of ‘Osteen’ mango trees. The mango trees were sprayed with LRE at 0, 2, 4 and 6 g/L and PS 0, 1, 2, and 3 mM. In mid-May, the mango trees were sprayed with a foliar solution, followed by monthly applications until 1 month before harvest. The results showed that trees with the highest concentration (6 g/L) of LRE exhibited the maximum leaf area, followed by those treated with the highest concentration (3 mM) of PS. Application of LRE and PS to Osteen mango trees significantly enhanced fruit weight, number of fruits per tree, yield (kg/tree), yield increasing%, and reduced number of sun-burned fruits compared to the control. LRE and PS foliar sprays to Osteen mango trees significantly enhanced fruit total soluble solids ˚Brix, TSS/acid ratio, and vitamin C content compared to the control. Meanwhile, total acidity percentage in ‘Osteen’ mango fruits significantly decreased after both LRE and PS foliar sprays. ‘Osteen’ mango trees showed a significant increase in leaf area, total chlorophyll content, total pigments, and leaf carotenoids. Our results suggest that foliar sprays containing LRE and PS significantly improved growth parameters, yield, fruit quality, antioxidant content, and total pigment concentration in ‘Osteen’ mango trees. Moreover, the most effective treatments were 3 mM PS and 6 g/L LRE. LRE and PS foliar spray caused a significant increase in yield percentage by 305.77%, and 232.44%, in the first season, and 242.55%, 232.44% in the second season, respectively.

This study focuses on gene cloning, expression, biochemical and analytical characterization along with structural and functional characterization of collagenase followed with molecular docking, dynamics study and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA). The collagenase gene identified from the genome of the novel collagenase Bacillus siamensis strain Z1 is introduced into E.coli DH5α, subsequently expressed in E.coli BL21 (DE3) withisopropyl β −d – 1 − thiogalactopyranoside(IPTG) induction and further affinity purified yielding in ∼ 89.4 kDa recombinant collagenase which demonstrated alkali characteristics and thermostability determined by thermodynamic parameters. Recombinant collagenase revealed good stability when exposed to diverse biochemical components. The recombinant collagenase identity was confirmed through matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) showing specific mass peaks and via N-terminal sequence analysis as MTAVNQTISK. Moreover, the concluded N-terminal amino acid sequence from Edman degradation displayed significant resemblance. The structural and functional analysisof recombinant collagenase was analysed by Circular dichroism (CD), Proton nuclear magnetic resonance (1H NMR) spectrometry and Thermogravimetric analysis (TGA). The recombinant collagenase also showed gelatin liquefaction ability. The collagenase gene sequence is also assessed for structural and functional characterizations by using various computational tools and revealed its classification in U32 family peptidase. A Grand average of hydropathicity index (GRAVY) score of −0.295 and instability index of 37.22 was obtained. Homology model of collagenase gene was generated by employing SWISS-MODEL and structure analysis by Ramachandran plot. Molecular docking of modelled collagenase with four different substrates was carried out by PyRx and Autodocking. Highest docking score of −12.7 kcal/mol was obtained for Alaska pollock hydroxyproline containing marine collagen peptide (APHCP). Subsequently, Molecular dynamics and simulations for highest score docked complex was assessed using GROningenMAchine for Chemical Simulations (GROMACS).

Relevance. Electric power steering of vehicles successfully competes with hydraulic boosters of similar purposes, showing high efficiency, energy saving and environmental friendliness. Aim. To study and research electromechanical processes of electric power steering system based on permanent magnet synchronous motor. Methods. Mathematical and numerical modeling using the Matlab/Simulink software package. Results. The article discusses the operating modes of an electric power steering based on an electric motor with permanent magnets. The authors analyzed two classic schemes for constructing steering systems with electric power steering – with the electric motor placed on the steering column and on the steering rack. Block diagrams of the mechanical and electrical parts of the steering system have been developed. Based on mathematical dependencies characterizing the operating modes of the steering system, a complete dynamic simulation model of the electric power amplifier was obtained on the MATLAB/Simulink platform. Based on a preliminary analysis of control methods and algorithms in the system to solve the problem of control stability, PI controllers with low-pass filters are implemented to regulate the current and voltage of the electric motor depending on the speed of the vehicle and the angle of rotation of the steering wheels. The simulation results were used to design a real electric power steering for a specific vehicle. Conclusions. The complete simulation model of an electric booster based on a synchronous motor with permanent magnets in a car steering system obtained by the authors adequately reflects the dynamic control processes and can be used in the design of automotive vehicles.

This study focuses on the synthesis, characterization, and use of zinc oxide nanoparticles (ZnONPs) derived from W. bifurcata fruit peel extract. ZnONPs are frequently synthesized utilizing a green technique that is both cost-effective and ecologically friendly. ZnONPs were characterized utilizing analytical techniques. Ultra Violet visible (UV-Vis) spectra showed peaks at 364 nm, confirming the production of ZnONPs. Scanning Electron Microscope analysis indicated that the nanoparticles generated were spherical/agglomerated, with diameters ranging from 11 to 25 nm. FTIR spectroscopy was used to identify the particular functional groups responsible for the nanoparticles’ reduction, stabilization, and capping. Phytochemical analysis of the extract revealed that flavonoids, saponins, steroids, triterpenoids, and resins were present. The antibacterial activity of W. bifurcata synthesised nanoparticles was evaluated against pathogenic bacteria. The ZnONPs antioxidant activity was assessed using DPPH assay. The in vitro cytotoxicity was assessed against prostate cancer PC3 cells. The wound healing potential was assessed by employing in vitro scratch assay and in vivo excision model in Wistar rats. Because of its environmentally benign production, low toxicity, and biocompatibility, ZnONPs exhibited potential antibacterial, antioxidant, anticancer, and wound healing activities, indicating that they could be used in cancer treatment and wound management. Further study is required to examine the fundamental mechanisms and evaluate the safety and effectiveness of the test sample in clinical situations.

L-glutaminase is a unique enzyme with catalytic activity and the ability to modulate glutamine levels, making it a valuable enzyme with numerous potential applications. L-glutaminase triggers a distinctive reaction by converting L-glutamine into glutamic acid while releasing ammonia concurrently. This enzymatic process holds potential applications across diverse industries, notably in food and pharmaceuticals. The primary objective of the present research was to identify and isolate a fungal strain proficient in L-glutaminase production from soil found in maritime environments. The physical and nutritional conditions were optimized for maximum synthesis of L-glutaminase under solid state fermentation (SSF) and submerged fermentation conditions (SmF). The isolated organism was identified as Fusarium solani-melongenae strain CRI 24 by morphological and 18S rRNA analysis. The optimum carbon source under SmF and SSF was found to be starch (0.2% w/v). Wheat bran as solid substrate among others showed optimum enzyme activity. On the seventh day of incubation, at pH 8.0 and 0.7% L-glutamine concentration under SSF and SmF, the highest enzyme activity was detected. The greatest enzyme activity in SSF was seen at a moisture content of 10%. Fusarium solani-melongenae species produced the enzyme under optimal conditions and 4.20 and 4.73-fold increase (from 0.8 U/mL to 3.61 U/mL and from 0.781 U/mL to 3.69 U/mL) was achieved after optimization in submerged and in solid state fermentation, respectively. The selective isolation and optimization processes described in this work are a promising technique for the industrial production of L-glutaminase and can be applied in the pharmaceutical and food industries.

The systematic identification of appropriate sites for different rainwater harvesting (RWH) structures may contribute to better success of crop production in such areas. One approach to improving crop yields in North Kordofan, Sudan, that is mostly adaptable to the changing climate is in-field water harvesting. The main objective of this study is to employ a geographical information system (GIS) in order to identify the most suitable sites for setting in situ water harvesting structures, aiming to address climate change in this area. A GIS-based model was developed to generate suitability maps for in situ RWH using multi-criteria evaluation. Five suitability criteria (soil texture, runoff depth, rainfall surplus, land cover, and slope) were identified; then, five suitability levels were set for each criterion (excellent, good, moderate, poor, and unsuitable). Weights were assigned to the criteria based on their relative importance for RWH using the analytical hierarchy process (AHP). Using QGIS 2.6.1 and ArcGIS 10.2.2 software, all criterion maps and suitability maps were prepared. The obtained suitability map for the entire region showed that 40% of the region area fell within the “good” class, representing 7419.18 km2, whereas 26% of the area was “excellent”, occupying 4863.75 km2. However, only 8.9% and 15.6% of the entire region’s area were “poor” and “unsuitable” for RWH, respectively. The suitability map of the delineated pilot areas selected according to the attained FAO data revealed that one location, Wad_Albaga, was found to be in an excellent position, covering an area of 787.811 km2, which represents 42.94% of the total area. In contrast, the Algabal location had 6.4% of its area classified as poor and the remaining portion classified as excellent. According to the findings from the validated trial, Wad_Albaga is located in a good site covering 844 km2, representing 46.04%, while Algabal is classified as a moderate site, covering 341 km2 or 18.6% of the area. This study concluded that the validation of the existing trial closely matched the suitability map derived using FAO data. However, ground data from field experiments provided more accurate results compared to the FAO suitability map. This study also concluded that using GIS is a time-saving and effective tool for identifying suitable sites and discovering the most appropriate locations for rainwater harvesting (RWH).

Introduction The Middle East and North African (MENA) region is characterised by high and complex migration flows, yet little is known about the health of migrant populations, their levels of underimmunisation and access to healthcare provision. Data are needed to support regional elimination and control targets for key diseases and the design and delivery of programmes to improve health outcomes in these groups. This protocol describes a suite of seven systematic reviews that aim to identify, appraise and synthesise the available evidence on the burden and health outcomes, policies and access (barriers and facilitators) related to these mobile populations in the region.Methods Seven systematic reviews will cover three questions to explore the: (1) burden and health outcomes, (2) policies and (3) healthcare barriers and facilitators for the following seven disease areas in migrants in the MENA region: tuberculosis, HIV and hepatitis B and C, malaria and neglected tropical diseases, diabetes, mental health, maternal and neonatal health, and vaccine-preventable diseases. We will search electronic databases for studies in any language (year 2000–2023), reference-check relevant publications and cross-check included studies with experts. We will search for grey literature by hand searching key databases and websites (including regional organisations and MoH websites) for country-specific guidelines and talking to our network of experts for local and regional reports and key datasets. We will assess the studies and policies for their quality using appropriate tools. We will meta-analyse the data by disease outcome if they are of sufficient volume and similarity. Where meta-analysis is not possible and where data are on policy or access, we will narratively synthesise the evidence using summary tables, figures and text.Dissemination We anticipate disseminating the findings through peer-reviewed publications, conferences and other formats relevant to all stakeholders. We are following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and protocols will be registered on International Prospective Register of Systematic Reviews.

Since ancient times, bioactive phytocompounds from different parts of medicinal plants have been used to heal various disease ailments and they are now regarded as a valuable source of disease prevention globally. Kalanchoe pinnata is a member of the Crassulaceae family; it has a long history of usage in traditional ayurvedic treatment. Analysis of bioactive compounds for their potential anti-type-2 diabetes mellitus (T2DM) mechanism along with in-vitro and in-silico approaches was studied in the present research. The alpha-amylase and alpha-glucosidase inhibitory activity of methanolic extract of Kalanchoe pinnata (α-amylase: IC50 29.50 ± 0.04 μg/ml; α-glucosidase IC50 32.04 ± 0.35 μg/ml) exhibit a high degree of similarity to the standard drug acarbose (IC50 35.82 ± 0.14 μg/ml). Different biological databases were used to list phytocompounds from the plant, and ADME analysis using swissADME was carried out to screen compounds that obeyed the Lipinski rule of 5 and were employed further. STRING and KEGG pathway analysis was performed for gene enrichment analysis followed by network pharmacology to identify key target proteins involved in DM. AMY2A, NOX4, RPS6KA3, ADRA2A, CHRM5, and IL2 were identified as core targets for luteolin, kaempferol, alpha amyrin, stigmasterol compounds by modulating neuroactive ligand interaction, P13-AKT, MAPK, and PPAR signaling pathways. Molecular docking was performed to study the binding affinity among bioactive compounds of K. pinnata against aldose reductase, alpha-amylase, alpha-glucosidase, and dipeptidyl peptidase IV. Alpha-amylase-friedelin [FRI] and alpha-amylase-acarbose [STD] complexes were subjected to molecular simulation for a 200 ns duration that depicted the stability of the compounds and proteins. In the current study, employing dual approach in-silico and in-vitro enzyme assays has yielded a comprehensive and strong understanding of its potential therapeutic properties, making a significant step towards the development of novel anti-diabetic treatment.

This paper proposes a novel Convolutional Kolmogorov-Arnold Network (CKAN) model for Intrusion Detection Systems (IDS) in an IoT environment. The CKAN model is developed by replacing the Multi-Layer Perceptrons (MLPs) layers with Kolmogorov-Arnold Networks (KANs) layers inside the Convolutional Neural Networks (CNN) architecture. The KANs give high performance compared to the MLPs layers with fewer parameters. The performance of the proposed CKAN model has been evaluated against other well-known Deep Learning (DL) models like CNN, recurrent neural networks (RNN), and Autoencoder. The evaluation process has been carried out with three benchmark datasets: NSL_KDD, which is treated as a standard IDS dataset; CICIoT2023; TONIoT, which are IoT IDS datasets. The results point out the superiority of the CKAN model over other DL models for both binary and multi-classification tasks as per the accuracy, precision, recall, and F1 score. The proposed CKAN model achieved accuracies of 98.71%, 99.22%, and 99.93% for binary classification, and 99.2%, 98.84%, and 93.3% for multi-classification on the NSL_KDD, CICIoT2023, and TONIoT datasets, respectively. The CKAN model gives better performance metrics with a smaller number of parameters compared to other DL models. In this way, our findings point out that KANs are promising for being a substitute for MLPs.

Utilizing Unmanned Aerial Vehicles (UAVs) as flying edge nodes to support task offloading from terminal devices has recently attracted significant research attention. However, the literature lacks a systematic perspective on this emerging topic. The goals are to understand the volume and trends of research, identify use case scenarios and proposed architectures, classify the core topics addressed, explore group techniques explored, recognize task types considered, and summarize open issues needing further work. Publications are mapped by type and source from 2019 to 2023 to assess the maturity and activity level in this field over time. Various use case scenarios for UAV-enabled Mobile Edge Computing (MEC) task offloading are identified and categorized, and different proposed architectures for offloading between UAV-MEC platforms are summarized. Techniques for offloading decision-making and performance enhancement are grouped to identify popular and less explored methods. The literature is also mapped based on the types of tasks considered for offloading to UAV-enabled MEC platforms to recognize the focus areas. Open issues that are briefly discussed across papers but require additional research are summarized on basis of the gaps identified. This systematic perspective consolidates existing research in an organized manner to guide future works and establish a coherent taxonomy to organize future studies and reviews. Overall, mapping trends helps characterize research maturity, guiding its continued development.

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal.

The Indian Clove (Syzygium aromaticum) is a naturally occurring spice with significant biological properties. It is primarily found in India and Indonesia, but can also be cultivated in other coastal areas. Clove is commonly used as a natural preservative and in the production of natural medicines. One of the notable benefits of clove is its ability to help treat diseases caused by various bacteria and fungi, including E. coli, P. aeruginosa, S. aureus, Z. mobilis, and C. albicans. Additionally, clove possesses antioxidant properties that can help reduce cell damage caused by free radicals. Clove has also shown potential in inhibiting the growth of cancer cells and reducing the activity of enzymes such as amylase, glucosidase, and lipase, which can be beneficial for managing diabetes. The study demonstrated that Silver nanoparticles (AgNPs) synthesized from Indian clove leaf extract exhibited promising antimicrobial, anticancer, antidiabetic, and antioxidant activities. This suggests that these nanoparticles could have versatile applications in antimicrobial therapy, cancer treatment, diabetes management, and antioxidant supplementation. However, further research is needed to understand the underlying mechanisms of these activities and to evaluate the safety and efficacy of these nanoparticles in more complex biological systems. In summary, clove possesses diverse biological properties and has potential applications in various therapeutic areas. The synthesis of AgNPs from clove leaf extract shows promise in multiple fields, but more research is required for a deeper understanding of its mechanisms and broader applications.

Polytropic equations (Lane–Emden [LE] equations) are valuable because they offer a simple explanation for a star’s interior structure, interstellar matter, molecular clouds, and even spiral arms that can be calculated and used to estimate various physical parameters. Many analytical and numerical methods are used to solve the polytropic LE equation. The series expansion method played an essential role in many areas of science and has found application in many branches of physical science. This work uses the series expansion method to examine N-dimensional polytropes (i.e., slab, cylinder, and sphere). To solve LE-type equations, a computational method based on accelerated series expansion (ASE) is applied. We calculate several models for the N-dimensional polytropes. The numerical results show good agreement between the ASE and numerical and analytical models of the N-dimensional polytropes.

The limitations of the current vaccination strategy for the Kyasanur Forest Disease virus (KFDV) underscore the critical need for effective antiviral treatments, highlighting the crucial importance of exploring novel therapeutic approaches through in silico drug design. Kyasanur Forest Disease, caused by KFDV, is a tick-borne disease with a mortality of 3–5% and an annual incidence of 400 to 500 cases. In the early stage of infection, the envelope protein plays a crucial role by facilitating host–virus interactions. The objective of this research is to develop effective antivirals targeting the envelope protein to disrupt the virus–host interaction. In line with this, the 3D structure of the envelope protein was modeled and refined through molecular modeling techniques, and subsequently, ligands were designed via de novo design and pharmacophore screening, yielding 12 potential hits followed by ADMET analysis. The top five candidates underwent geometry optimization and molecular docking. Notably, compounds L4 (SA28) and L3 (CNP0247967) are predicted to have significant binding affinities of −8.91 and −7.58 kcal/mol, respectively, toward the envelope protein, based on computational models. Both compounds demonstrated stability during 200 ns molecular dynamics simulations, and the MM-GBSA binding free-energy values were −85.26 ± 4.63 kcal/mol and −66.60 ± 2.92 kcal/mol for the envelope protein L3 and L4 complexes, respectively. Based on the computational prediction, it is suggested that both compounds have potential as drug candidates for controlling host–virus interactions by targeting the envelope protein. Further validation through in-vitro assays would complement the findings of the present in silico investigations.

In Nuclear Power Plants (NPPs), online condition monitoring and the fault detection of structures, systems and components (SSCs) can aid in guaranteeing safe operation. The use of data-driven methods for these tasks is limited by the requirement of physically consistent outcomes, particularly in safety-critical systems. Considering the importance of regulating valves (e.g., safety relief valves and main steam isolation valves), this work proposes a multistage Physics-Informed Neural Network (PINN) for fault detection in such components. Two stages of the PINN are built by developing the process model of the regulating valve, which integrates the basic valve sizing equation into the loss function to jointly train the two stages of the PINN. In the 1st stage, a shallow Neural Network (NN) with only one hidden layer is developed to estimate the equivalent flow coefficient (a key performance indicator of regulating valves) using the displacement of the valve as input. In the 2nd stage, a Deep Neural Network (DNN) is developed to estimate the flow rate expected in normal conditions using inputs such as the estimated flow coefficient from the 1st stage, the differential pressure, and the fluid temperature. Then, the residual, i.e., the difference between the estimated and measured flow rates, is fed into a Deep Support Vector Data Description (DeepSVDD) to detect the occurrence of faults. Moreover, the deviation between the estimated flow coefficients of normal and faulty conditions is used to interpret the consistency of the detection result with physics. The proposed method is, first, applied to a simulation case implemented to emulate the operating characteristics of regulating the valves of NPPs and then validated on a real-world case study based on the DAMADICS benchmark. Compared to state-of-the-art fault detection methods, the obtained results from the proposed method show effective fault detection performance and reasonable flow coefficient estimation, thus guaranteeing the physical consistency of the detection results.

Zinc oxide nanoparticles (ZnO NPs) are establishing themselves as an important class of nanomaterials due to their exceptional physicochemical properties and wide range of applications. Due to their affordability, lack of toxicity, and strong biocompatibility, ZnO NPs find extensive use in the field of biomedicine. ZnO NPs are promising in biomedicine, especially for their ability as anticancer and antimicrobial agents. The ecologically sustainable preparation of metallic NPs using different plant extracts is a viable alternative to more conventional synthesis methods. The present study investigates the effects of changing the physical conditions on ZnO NPs synthesis from Bixa orellana (B. orellana) extract using the precipitation method. Confirmation and characterization of the ZnO NPs were achieved by analytical techniques. EDS results verified that highly pure ZnO NPs were synthesized. X-ray diffraction analysis verified the crystal nature of the synthesized NPs and their crystalline particle size of 82.66 nm. The XRD graphs strongly indicate the formation of wurtzite ZnO due to the presence of the (100), (002), and (101) planes. The antibacterial activity was assessed through the utilization of agar disc diffusion. The findings revealed that ZnO NPs exhibited significant efficacy in inhibiting the growth of both Gram-positive and Gram-negative bacteria. The zone of inhibition with the greatest diameter (22 mm) was reported for the bacterial strain B. cereus. The present investigation provides evidence that B. orellana leaves extract is capable of producing ZnO NPs, which play a crucial role in its antibacterial action. Additional investigation is necessary to validate the role of diverse phytochemicals in the synthesis of ZnO NPs and their applications in diverse fields such as agriculture, cosmetics, food, and healthcare.

Hepatocytes play a crucial role in host response to infection. Ehrlichia is an obligate intracellular bacterium that causes potentially life-threatening human monocytic ehrlichiosis (HME) characterized by an initial liver injury followed by sepsis and multi-organ failure. We previously showed that infection with highly virulent Ehrlichia japonica (E. japonica) induces liver damage and fatal ehrlichiosis in mice via deleterious MyD88-dependent activation of CASP11 and inhibition of autophagy in macrophage. While macrophages are major target cells for Ehrlichia, the role of hepatocytes (HCs) in ehrlichiosis remains unclear. We investigated here the role of MyD88 signaling in HCs during infection with E. japonica using primary cells from wild-type (WT) and MyD88-/- mice, along with pharmacologic inhibitors of MyD88 in a murine HC cell line. Similar to macrophages, MyD88 signaling in infected HCs led to deleterious CASP11 activation, cleavage of Gasdermin D, secretion of high mobility group box 1, IL-6 production, and inflammatory cell death, while controlling bacterial replication. Unlike macrophages, MyD88 signaling in Ehrlichia-infected HCs attenuated CASP1 activation but activated CASP3. Mechanistically, active CASP1/canonical inflammasome pathway negatively regulated the activation of CASP3 in infected MyD88-/- HCs. Further, MyD88 promoted autophagy induction in HCs, which was surprisingly associated with the activation of the mammalian target of rapamycin complex 1 (mTORC1), a known negative regulator of autophagy. Pharmacologic blocking mTORC1 activation in E. japonica-infected WT, but not infected MyD88-/- HCs, resulted in significant induction of autophagy, suggesting that MyD88 promotes autophagy during Ehrlichia infection not only in an mTORC1-indpenedent manner, but also abrogates mTORC1-mediated inhibition of autophagy in HCs. In conclusion, this study demonstrates that hepatocyte-specific regulation of autophagy and inflammasome pathway via MyD88 is distinct than MyD88 signaling in macrophages during fatal ehrlichiosis. Understanding hepatocyte-specific signaling is critical for the development of new therapeutics against liver-targeting pathogens such as Ehrlichia.

Objectives: The present study was carried out to investigate COVID-19 vaccination coverage among populations of internally displaced persons (IDPs), refugees, and host communities in northern Iraq and the related underlying factors.Methods: Through a cross-sectional study conducted in five governorates in April–May 2022, 4,564 individuals were surveyed. Data were collected through an adapted questionnaire designed to gather data on participants.Results: 4,564 subjects were included (59.55% were 19–45 years old; 54.51% male). 50.48% of the participants (51.49% of host communities, 48.83% of IDPs, and 45.87% of refugees) had been vaccinated with at least one dose of COVID-19 vaccine. 40.84% of participants (42.28% of host communities, 35.75% of IDPs, and 36.14% of refugees) had been vaccinated by two doses, and 1.56% (1.65% of host communities, 0.93% of IDPs, and 1.46% of refugees) were vaccinated with three doses.Conclusion: Sociodemographic factors including age, gender, education, occupation, and nationality could affect vaccination coverage. Moreover, higher acceptance rate of vaccination is associated with belief in vaccine safety and effectiveness and trust in the ability of the vaccine to prevent complications.

Underwater plasma discharge is considered a nontoxic and effectual purification approach to control waterborne bacterial pathogens. In the present study, Vibrio cholerae contaminated drinking water was sterilized by using underwater capillary discharge generation via high voltage, oxygen (O2{{\rm{O}}}_{2}) injection, and hydrogen peroxide (H2O2{{\rm{H}}}_{2}{{\rm{O}}}_{2}) addition. The effects of oxidant species generated by plasma discharge on V. cholerae disinfection have been studied and reported. The electrical and optical analysis of capillary discharge revealed the generation of reactive oxygen species (OH.{{\rm{OH}}}^{.}, H and O), which are highly useful for bacterial disinfection along with enhanced power and energy of discharge pulses. Complete elimination of V. cholerae (0 CFUs (colony forming units)) from the water after O2{{\rm{O}}}_{2} injected and H2O2{{\rm{H}}}_{2}{{\rm{O}}}_{2} added plasma discharge was achieved, and 100% inactivation of V. cholera from drinking water was proven. The retardant effect of the initial V. cholerae colonies with time variation was reported through optical density (OD600 nm) measurements. The time course study of bactericidal activity of plasma treatment on V. cholerae, observed every 12 h up to 36 h, revealed a high retardant effect on V. cholerae CFUs. Underwater capillary discharge is an efficient approach for the inactivation of V. cholerae from drinking water.

Urban expansion along the coastal sites is one of the major causes of the deterioration of mangrove habitats. This study aimed to report the current status of different mangrove environments in Saudi Arabia. Mangrove soil and sediment–water samples were collected from the coasts of the Arabian Gulf and the Red Sea for their physical, chemical, and heavy metal analysis. The mean values (n = 42) of heavy metals in the soil showed that Fe (2200 ppm) > Mn (91.9 ppm) > Zn (66.11 ppm) > Cr (62.40 ppm) > Ni (33.64 ppm) > Cu (31.06 ppm) > Pb (30.69 ppm) > Co (27.06 ppm). While the mean values (n = 15) of heavy metals in the sediment–water were as follows; Cr (0.1978 ppm) > Fe (0.1016 ppm) > Pb (0.0792 ppm) > Co (0.0706 ppm) > Mn (0.0356 ppm) > Cu (0.0317 ppm) > Ni (0.0215 ppm). The average values of Zn, Ni, Cu, Pb, and Co were greater than those reported previously in the Red Sea and Arabian Gulf coastal sediments. At the same time, Mo and Cd were not detected in any studied soil sample. The correlation analysis revealed that the EC of mangrove soil was positively correlated with SOM, Ca, Mg, Na, and K, while the EC of sediment–water was positively correlated with Na, K, and Cu. Landfilling, sewage pollution, mismanagement of solid waste, and contamination due to other anthropogenic activities may lead to heavy metal hazards and the loss of large areas of mangrove plants in this region. Therefore, the findings of this study should be considered to understand and design the management strategy of mangrove forests in the area of study.

Sensors are now used by farmers and agronomists to help them improve their operations. They use sensor data transmitted via IoT to remotely monitor their crops. Farmers today manage crops in a controlled environment to increase yields in the name of modern farming. Crop productivity, on the other hand, is influenced by the severity of the weather and disease variations. The primary objective of this paper is to present a novel Multisensor Machine-Learning Approach (MMLA) for classifying multisensor data. The fusion strategy supports high-quality data analysis in agricultural contexts for cultivation recommendations. Based on the proposed recommendation system, eight crops were classified: cotton, gram, groundnut, maize, moong, paddy, sugarcane, and wheat. Crop species were classified using three machine learning algorithms: J48 Decision Tree, Hoeffding Tree, and Random Forest. To evaluate the performance of the proposed multi-text classifier, only the top eight classes were investigated. The classifier’s performance is measured in terms of precision, recall, F-measure, MCC, ROC Area, and PRC Area class, and the results are compared with the state-of-the-art classifiers. The Random forest algorithm has the lowest error measure of RMSE at 13%, RAE at 38.67%, and RRSE at 44.21%, demonstrating effectiveness in classifying the agriculture text. Thus, the use of a multisensor data fusion approach based on crop recommendation provides greater precision in prediction, resulting in a significant increase in crop yield while also creating awareness in the condition-based environmental monitoring system.

Background: Hernias are repeatedly encountered in surgical clinics owing to risks such as constipation, weightlifting, gestation, weight gain, and ongoing ailments similar to bronchial asthma and prostatic enlargement. The study intended to examine various viewpoints of the population's knowledge and beliefs concerning hernia and its management. Methods: Individuals in this cross-sectional study were randomly picked from those at greater risk of acquiring abdominal wall hernias (AWHs). To report their awareness levels toward AWH, the data were calculated as frequency, expressed as a percentage, and displayed as tables and figures. Results: Eighty-six percent of the patients did not have a hernia during the study, but all had experienced hernias before the study date. About 36.9% of participants showed a poor level of knowledge regarding AWHs. Participants' knowledge was notably correlated with gender, age, marital status, previous abdominal surgery, presence of interfamilial hernias, and having asthma or chronic cough (P < 0.05). According to 55% of respondents, pain and discomfort after an operation are the most prevalent complications, while 15.1% reported no postoperative complications. According to 41.4% of respondents, laparoscopic surgery was the best treatment option, followed by open surgery and lifestyle modifications in 28% and 24.8%, respectively. Conclusion: Compared to females, males were more likely to suffer from AWHs. Hernia's risk factors, management methods, and surgical complications were not well understood by participants. This may entail holding seminars to raise awareness of AWHs among the community members.

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called “SETL_BMRI”, which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model’s effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy.

The main objective of this research was to perform Gymnema sylvestre (Asclepiadaceae) extract’s phytochemical screening and identify its therapeutic potential. Using a Soxhlet apparatus, the powdered plant material was extracted using ethyl acetate. The preliminary phytochemical analysis confirmed the presence of alkaloids, flavonoids, phenols, glycosides, and steroids. Gas chromatography–mass spectroscopy analysis of the extract was performed and confirmed the presence of 11 compounds. As per the quantitative analysis, the extract exhibited a phenolic content of 948 µg gallic acid equivalent/g dry weight, a total flavonoid content of 398 µg quercetin equivalent/g dry weight, and an alkaloid content of 487 µg atropine equivalent/g dry weight. As per the in vitro cytotoxicity test using A549 cells, the IC50 (half-maximal inhibitory concentration) value for the extract was found to be 76.06 ± 1.26 µg/mL, indicating its cytotoxic effect on the cells. The ethyl acetate extract showed significant antibacterial efficacy, as evidenced by a zone of clearance measuring 3 mm against Escherichia coli and 6 mm against Bacillus subtilis. For anthelmintic activity, the earthworm paralysis time induced by G. sylvestre extract (10 mg/mL) was 28.13 ± 0.8 min, and the time of death was 68.21 ± 1.72 min. In comparison, the reference drug, piperazine citrate (10 mg/mL), caused paralysis in 22.18 ± 1.02 min and resulted in death at 66.22 ± 2.35 min. Similarly, the coagulation time was notably prolonged, with blood clot formation observed at 1 min and 40 s, at a concentration of 1 mg/mL, which underscores the potential anticoagulant or hemostatic modulation properties of G. sylvestre extract. The test extract showed good inhibition of alpha-amylase activity and exhibited an IC50 value of 15.59 µg/mL. The IC50 value for DPPH (2,2-diphenyl-1-picrylhydrazyl)-scavenging activity for the extract was 19.19 µg/mL. Based on the GCMS results, the compound 2,7-dimethyl-undecane was selected for its anticancer potential. Docking studies were conducted with the epidermal growth factor receptor (EGFR) protein, specifically the 5WB7 variant associated with lung cancer. The docking score was −4.5, indicating a potential interaction. Key interaction residues such as ASN328, VAL350, and THR358 were identified. Overall, this research provides valuable insights into the phytochemical composition and diverse biological activities of G. sylvestre extract, offering a foundation for further exploration of its medicinal and pharmacological potential.

Aim: This study aimed to evaluate the correlation between vertical dimension of occlusion (VDO) and various facial measurements in a sample of Sudanese adults. Material and Methods: A total of 113 dental students (33 males and 80 females) with a mean age of 21.7±1.26 years were enrolled in this study. Different facial measurements including (Eye-Mouth, Eye-Eye, Eye-Ear, and Ear Height) were compared with two different measurements of VDO: N-Gn (from the tip of the nose to the tip of the chin), and Sn-Me (from the base of the nose to the bottom of the chin). Pearson’s correlation coefficient test was utilized for the correlation between the measured parameters. A p-value of less than 0.05 was considered significant for all analyses. Results: A significant positive correlation was shown between all measured facial distances and both measured VDO distances. Though, the strongest correlation was seen for the eye-mouth distance (r= 0.725, p

Bixa orellana (B. Orellana) is a frequently utilized plant that has grown in significance in pharmaceutical applications. The leaves extract of B. orellana was used in the current study for preliminary phytochemical analysis in terms of both quantitative and qualitative, which indicates the presence of phenols, alkaloids, and flavonoids. Furthermore, the extract demonstrated antifungal activity at 30 mm zone of clearance against Candida albicans and antibacterial activity at 16 mm zone of clearance against Bacillus nakamuria by well diffusion method against different strains. The plant extract showed a MIC of 20 µg/mL and MBC of 157.11 µg/mL against E. coli by ELISA and broth dilution method, respectively. To evaluate the phytochemicals in the extract, further purification of the extract by TLC, column chromatography, and component analysis by GC–MS, which reported 18 components, and UV spectrum were performed. A number of therapeutic applications for the extract were observed, including antidiabetic activity (anti-glucosidase) of 98.34% inhibition at 100 µg/mL, anti-lipase of 100% inhibition at 100 µg/mL, and anti-amylase activity of 100% inhibition at 100 µg/mL, antioxidant activity (anti-DPPH activity) of 59.74% inhibition at 100 µg/mL, anthelmintic at 1 min by 1000 µg/mL, antifibrinolytic at 20 secs by 1000 µg/mL and cytotoxic activity against A549 lung cell lines, wherein, the cell viability was below 40 % at the highest dose (100 µg/mL), with an IC50 value of 39.9 µg/mL.

Poor circulation, unresolved inflammation, neuropathy, and infection make wound care difficult. Manilkara zapota (M. zapota) antibacterial and antioxidant properties may help speed up the healing process. The present investigation aimed to evaluate the wound healing activity of M. zapota bark ethanolic extract (MZE) by employing in-vitro migration scratch assay and in-vivo animal models. Wistar albino rats were used for the in-vivo wound healing models. No treatment was given to Group I; Group II received povidone-iodine (5% W/W); Group III received MZE (5% W/W); and Group IV received MZE (10% W/W). Linear incision models and excision wound models were used to induce injury. The ointments were applied immediately to the wounds after causing the injury. The percentage of wound contraction, the length of the epithelization period, and the wound’s tensile strength were all calculated. The scratch assay assessed the test drug’s potential for wound healing in-vitro. H2O2 and DPPH scavenging assays were used to measure antioxidant activity. A p < 0.05 was used to define statistical significance. On days 4, 8, 12, 16, and 20, the wound contraction potential of animals treated with MZE ointment was significantly higher (p < 0.001) than that of the control group. On day 20, the proportion of wound contraction in MZE-treated animals was 99.88%, compared to 83.86% in untreated animals. The test group had a significantly (p < 0.01) faster time to full epithelization than the control group. In the incision model, the control group had considerably lower mechanical strength (p < 0.001) than animals treated with MZE. In addition, MZE caused a significant increase (p < 0.001) in total protein and hydroxyproline levels. In the scratch experiment, test drug-treated cells showed a higher rate of cell migration than untreated cells. Furthermore, animals treated with MZE showed increased levels of epithelial tissue, collagen proliferation, and keratinization. To summarize, the current study found that M. zapota improved wound healing activity both in vitro and in vivo, as evidenced by the study results. M. zapota extract has significant wound-healing potential and could be a viable source of wound-healing nutraceuticals.

Introduction: Pharmacovigilance (PV) is critical in determining the risk–benefit ratio of medicines and encouraging their safe, rational, and effective use, hence enhancing patient safety and care. Pharmacists, as drug experts, share responsibility for ensuring that medicines remain safe. Objective: The study aimed to assess the knowledge, attitude, and practice of hospital pharmacists towards PV and adverse drug reaction (ADR) reporting and to know factors that discourage them from reporting ADRs in Najran, Saudi Arabia. Methods: A pre-tested self-administered questionnaire was distributed to all the pharmacists working in government hospitals who consented to participate in the study. Data was collected over three months, from 01 June 2018 to 31 Aug 2018. Data were analyzed using Statistical Package for Social Science (SPSS) software for Windows, version 23. Descriptive statistics such as frequency and percentages, mean ± standard deviation (SD) were calculated, and the Pearson's Chi-square test was used to examine the relationship between different variables. Results: A total of 145 questionnaires were distributed, and the response rate obtained was 70.3%. The definition of PV and ADR were correctly identified by 42% and 68.3% of participants, respectively. A noteworthy finding is that 95% of participants were aware of the existence of the ADR reporting system, and 88.9% knew the responsible regulatory agency. Participants showed a positive attitude towards PV and ADR reporting; 90.1% considered ADR reporting a part of professional obligation, and 94.1% believed that there should be collaboration between pharmacists and other healthcare professionals. A majority of participants (86.1%) had identified an ADR during their practice, and 71.3% had reported an ADR. The unavailability of a professional environment to discuss ADR and insufficient pharmacotherapy/clinical knowledge was cited as the main factors discouraging from reporting ADRs. Conclusions: Overall, the pharmacists had an average to good knowledge of and positive attitude towards PV and ADR reporting and a good ADR reporting practice. The concept of PV and ADR reporting should be further strengthened, and there is a vast potential for the same.

Looking at the evolution of mobile phones, communications technology, and the Internet, one can see a clear shift in their usage in the past decade as mobile payment has become an important research area in the field of information technology. However, many financial institutions have adopted mobile payments. Except that only a limited number of clients are used. Several information systems theories/models have been proposed to examine the factors that could influence user adoption. However, the literature on the field is still in its infancy. This paper, reviews and systematically analyzes the existing mobile payment acceptance and adoption literature that include UTAUT/TAM as a theoretical model to reveal mobile payment adoption research's current situation. The current study also provides a basis for future researchers in the mobile payment adoption study, as it provides a summary of related literature in the field, the models used, and the factors that have an impact on customer intent. Accordingly, the UTAUT, TAM models, with their extensions, are one of the models most used in examining and understanding the necessary factors that could influence mobile payment applications' adoption. The research revealed that 37 factors most commonly than a literature review on factors of adoption mobile payment applications since 2015. It was found that the factors of perceived trust and perceived risks are among the most critical factors in which the models are expanded, as they have an impact on the customer's acceptance of any new technology innovation. Therefore, emphasis must be placed on the factors of perceived trust and perceived risks to increase the applicability of UTAUT, TAM models to the mobile payment context.

Abstract This study aimed to investigate the synergistic effect of the cold atmospheric plasma (CAP) and heterogeneous photocatalytic processes in an aqueous solution to enhance water purification efficacy and reduce the energy cost required by CAP. 0.1% Ag/TiO2-reduced graphene oxide (rGO) nanoparticles (NPs) photo-composite were prepared and fully characterized. Data showed that Ag nanoparticles and the rGO play an important role in increasing the efficiency of the whole treatment process and the photo-composite (0.1% Ag/TiO2-1% rGO at 400 °C) revealed the highest phenol removal rate with excellent reusability. Also, complete inactivation (~ 5log10 reduction) of both E. coli and S. aureus by NPs was observed without CAP exposure, whereas a minimal effect (0.1–0.5 log10) on viruses (Adenovirus (AdV), rotavirus, and ɸX174) was observed after 10 min incubation. Interestingly, the photocatalytic virus inactivation test was promising, as it resulted in > 4.7log10 reduction of AdV at 2 min treatment, whereas

In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delay-sensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26%and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

The mechanical components are produced by various fabrication methods, although forged products have excellent mechanical characteristics at a minimal cost. The stress and temperature analysis process in the closed die hot forging contributed to finding failure regions in these dies through simulations in the FE program. This enables the process to be improved and reduced time and mineral losses. A simplified model was used to represent the forming process, with a temperature of (1150-950 °C) was simulated using MSC Simufact software. The forge fastener head product is formed with a horizontal mechanical press of 800 tones. In this research, the workpiece material used Ck45 alloy steel, 56NiCrMoV 7 tool material. The results illustrate the maximum equivalent stresses values, and the maximum value was 739.70 MPa / 240.64 on lower die and product at a heating temperature of 950 °C, respectively. The local plastic deformation would be expected at places where the maximum stress is generated and exceeds the yield strength of the die material.

The mobility nature of the wireless networks and the time-sensitive tasks make it necessary for the system to transfer the messages with a minimum delay. Cloud Radio Access Network (C-RAN) reduces the latency problem. However, due to the trustlessness of 5G networks resulting from the heterogeneity nature of devices. In this article, for the edge devices, there is a need to maintain a trust level in the C-RAN node by checking the rates of devices that are allowed to share data among other devices. The SDN controller is built into a macro-cell that plays the role of a cluster head. The blockchain-based automatically authenticates the edge devices by assigning a unique identification that is shared by the cluster head with all C-RAN nodes connected to it. Simulation results demonstrate that, compared with the benchmark, the proposed approach significantly improves the processing time of blocks, the detection accuracy of malicious nodes, and transaction transmission delay.

الأهداف: تهدف الدراسة إلى بيان مفهوم المشهور المبني على قول ضعيف عند المالكية، وبيان الأصول التي استند عليها المالكية في تشهير الأقوال من خلال آراء ضعيفة، كالاستحسان ومراعاة الخلاف، وبيان دور تلك الصنعة الأصولية، في تقليل الخلاف الفقهي الفروعي، بين المذاهب عمومًا، وبين فقهاء المذهب المالكي خصوصًا. المنهجية: استخدمت الدراسة المنهج النقلي في إيراد النصوص، والمنهج الاستقرائي في جمع المادة العلمية من مصادر السادة المالكية -الفقهية منها والأصولية- بالإضافة لغيرها من الكتب الحديثة، التي أرّخت لتطور المذهب، واستخدمت الدراسة –كذلك- المنهج التحليلي، بتحليل آراء الفقهاء، ومناقشتها. النتائج: توصلت الدراسة إلى أن المالكية بنوا "القول المشهور على قول ضعيف" من خلال أصول فقهية متعددة، كان من أهمها الاستحسان ومراعاة الخلاف، وتوصلت الدراسة إلى بيان دور هذين الأصلين في إيجاد نقاط التقاء بين فقهاء المذهب المالكي خصوصًا، وبقية المذاهب عمومًا، كما أشارت الدراسة إلى أصل النظر في المآل وأثره في بناء المشهور على أقوال ضعيفة. الخلاصة: دراسة التطبيقات الفقهية التي بنى فيها المالكية قولًا مشهورًا على أقوال ضعيفة، مع التركيز على أصول أخرى غير الاستحسان ومراعاة الخلاف، كسد الذرائع والنظر في المآلات، ومحاولة ربطها بمقاصد الشريعة، والتركيز على دورها الكبير في تقليل الخلاف، وإيجاد مساحات التقاء مشتركة مع المذاهب الفقهية المختلفة.

Cancer is driven by both genetic and epigenetic changes that impact on gene expression profiles and the resulting tumourigenic phenotype. Enhancers are transcriptional regulatory elements that are key to our understanding of how this rewiring of gene expression is achieved in cancer cells. Here, we have harnessed the power of RNA-seq data from hundreds of patients with oesophageal adenocarcinoma (OAC) or its precursor state Barrett’s oesophagus coupled with open chromatin maps to identify potential enhancer RNAs and their associated enhancer regions in this cancer. We identify ~1000 OAC-specific enhancers and use these data to uncover new cellular pathways that are operational in OAC. Among these are enhancers for JUP, MYBL2, and CCNE1, and we show that their activity is required for cancer cell viability. We also demonstrate the clinical utility of our dataset for identifying disease stage and patient prognosis. Our data therefore identify an important set of regulatory elements that enhance our molecular understanding of OAC and point to potential new therapeutic directions.

This paper aims to develop an evolutionary deep learning based hybrid data driven approach for short term load forecasting (STLF) in the context of Bangladesh. With the lapse of time, the power system is getting complex. Penetration of intermittent renewable energy (RE) into the grid, changing prosumer load pattern with the need of demand side management (DSM) has thrown a challenge for dynamic power system operation and control. Load forecasting plays a significant role in this dynamic operation and control. In addition, it directly affects the future planning of network expansion, unit commitment and economic energy mix for power market. Day ahead short-term forecasting is very crucial for day to day operation. As such, various conventional and modified methods have been used over time for short-term prediction. Nevertheless, the existing approaches like age old statistical methods, artificial intelligence (AI), machine learning (ML), deep learning (DL) techniques alone cannot provide effective accuracy all the time. Hence, an integrated genetic algorithm (GA)-bidirectional gated recurrent unit (Bi-GRU) hybrid data driven technique (GA-BiGRU) is proposed in this work. The developed method is validated in Bangladesh power system (BPS) network by providing day ahead forecasting of electrical load of the whole country. Besides, the performance of the prediction model is compared with some existing approaches such as long short-term memory network (LSTM), gated recurrent unit (GRU) and integrated genetic algorithm-gated recurrent unit (GA-GRU) in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The outcome gives an indication of better forecasting accuracy of proposed GA-BiGRU evolutionary DL technique compared to others.

The major goal of the study was to isolate bacteria synthesizing protease enzyme from a soil sample taken in Dandeli, Karnataka, India. Furthermore, screening, production, and optimization of medium components for maximum protease activity, partial purification of crude enzymes, and application of protease produced by Bacillus nakamurai were carried out. At 72 hrs and pH 6, the optimum incubation time, pH, and temperature were evaluated (acidic and thermophilic). As a substrate, casein was employed. Plackett-Burman screening was performed, and KH2PO4, xylose, MnCl2, and peptone were discovered to be essential components in protease production media. Following the production and optimization processes, partial purification was performed using ammonium sulfate precipitation, with the maximum protease activity at 60% ammonium sulfate, and further dialysis was performed using precipitated enzyme, yielding enzyme activity of 0.747 U/mL. The protease enzyme proved effective at removing egg yolk stains as well as degrading (dehairing) chicken feathers and hairs from goat skin. Bacillus nakamurai PL4 can be utilized for the industrial-scale production of proteases to fulfill current demands. Thus, such optimized parameters can optimize protease production and their application across various industries.

BackgroundIn Nigeria, results from the pilot of the Test and Treat strategy showed higher loss to follow up (LTFU) among people living with HIV compared to before its implementation. The aim of this evaluation was to assess the effects of antiretroviral therapy (ART) initiation within 14 days on LTFU at 12 months and viral suppression.MethodsWe conducted a retrospective cohort study using routinely collected de-identified patient-level data hosted on the Nigeria National Data Repository from 1,007 facilities. The study population included people living with HIV age ≥15. We used multivariable Cox proportional frailty hazard models to assess time to LTFU comparing ART initiation strategy and multivariable log-binomial regression for viral suppression.ResultsOverall, 26,937 (38.13%) were LTFU at 12 months. Among individuals initiated within 14 days, 38.4% were LTFU by 12 months compared to 35.4% for individuals initiated >14 days (p14 days was not statistically significant.ConclusionLTFU was higher among individuals who were initiated within 14 days compared to greater than 14 days after HIV diagnosis. There was no difference for viral suppression. The provision of early tailored interventions to support newly diagnosed people living may contribute to reducing LTFU.

Introduction: The objective of a complete denture prosthesis is restoring aesthetics, comfort, and function by the replacement of missing dental and alveolar structures employing a stable prosthesis. Case Report: Many conditions can complicate the treatment plan and fabrication of a complete denture prosthesis. Complete denture fabrication in clinically compromised conditions is a challenging task for the dentist. In this clinical report, we present comprehensive management of a patient with denture-induced hyperplasia, flabby ridge, and severely resorbed edentulous ridge. The three-part strategy for management of the above-mentioned challenges can provide high-quality complete dentures, based on recognized prosthodontic principles. This first part will discuss the management of denture induced hyperplasia by elimination of the inflammation and excision of the lesion. Part two will cover management of the flabby ridge using a modified window technique for the impression of maxillary flabby tissues for an improved and controlled application of the impression material that is usually obtainable in dental practice. Part three highlights the rehabilitation procedure of the resorbed mandibular ridge using a functional impression technique with minimum soft tissue displacement and neutral zone arrangement of teeth to improve stability of the denture. Conclusion: rehabilitation of a patient with denture induced hyperplasia, flabby ridge, and severely resorbed edentulous ridges was successful.