Search

The widespread habit of consumption of raw meat along with poor hygiene during handling, transportation, and processing are the major contributors to the high prevalence of food-borne diseases in Ethiopia. To assess the occurrences and antimicrobial susceptibility patterns of Listeria monocytogenes in raw meat, samples were collected from abattoir and butcher shops in Jimma town. A laboratory-based cross-sectional study design was used. One hundred meat samples were obtained from abattoir and butcher shops in Jimma Town. The Listeria species were isolated and identified using traditional culture and biolog. A test for antimicrobial susceptibility was carried out utilizing ten distinct antibiotics currently used in both veterinary and human medicine. From 100 samples collected, 29 (29 %; 95 % CI: 21.0–38.5 %) were found positive for Listeria species. Of this, 15 (30 %, 95 % CI: 34.4–68.6 %) samples were from the butcher shops, and 14 (28 %, 95 % CI: 31.4–65.6 %) were from the abattoir. L. innocua (48 %) was dominant followed by L. seeligeri (28 %). L. monocytogenes accounts for 7 % of the isolates. L. monocytogenes isolates were susceptible to gentamicin, vancomycin, sulfamethoxazole, chloramphenicol, and cloxacillin. Tetracycline, penicillin, nalidixic acid, and cloxacillin, however, are resistant to them. This finding indicated the presence of Listeria species with particular significance to L. monocytogenes in consumable bovine meat. Therefore, training is needed on hygienic meat handling practices for meat handlers to minimize the risk of foodborne diseases. Lowering the level of antibiotic resistance also requires controlling the optimal use of antibiotics in animals bred for food.

This study investigates the interactions between climate change and human health with a particular focus on Qatar, using the DPSIR (Driving Forces, Pressures, States, Impacts, Responses) framework. Key drivers, including economic development and population growth, contribute to increased greenhouse gas (GHG) emissions, exerting pressure on Qatar's climate through rising temperatures and altered precipitation patterns, as modeled by the MIT Regional Climate Model (MRCM). The findings reveal critical gaps in understanding the state of climate-health interactions, including insufficient disease data, incomplete climate-health linkages, and significant research gaps. These limitations hinder targeted responses to climate-sensitive diseases, which have shown an increase over the years. The study identifies the pathways through which climatic shifts contribute to immediate health risks, such as heat-related illnesses and respiratory conditions, as well as long-term impacts, including chronic diseases and mental health challenges. Despite Qatar's efforts through national and international strategies, the DPSIR analysis highlights the urgent need for enhanced research, improved data collection, and tailored actions to address these challenges. Strengthened adaptation, resilience-building, and emission reduction strategies remain essential for safeguarding public health in the face of accelerating climate change.

The low sugar content of tropical range grasses makes them difficult to ensiling. This study identified sugar rich (>7 % on dry matter basis) accessions of Sehima nervosum (SN), which is adequate to initiate lactic acid production during ensiling. Sehima is a prominent range grass of Sehima-Dichatnthium grasslands in India followed by Africa, Pakistan, China and Australia. In this study, 92 diverse SN germplasm accessions were evaluated for biomass yield, nutritional quality and sugar content. Green forage yield was higher in rainy season (ranged 2.2 to 58.1 t ha−1) compared to post rainy season (varied between 0.48 and 44.0 t ha−1). The approximates values expressed (%), crude protein ranged from 3.15 to 6.10, neutral detergent fiber from 67.13 to 81.95, acid detergent fiber from 42.03 to 56.05, cellulose from 24.49 to 37.98 and lignin from 3.78 to 7.97. Sugar content varied from 27.04 to 123.32 mg g−1 DM. To advance this experiment, the next phase involved selecting 19 accessions of SN with >7 % sugar content on DM and promising forage yield. These accessions again evaluated for nutritional quality, subjected to in vitro tests, and then assessed for ensiling potential. Among 19 accessions, carbohydrate bound to lignin (Cc) ranged from 15.35 % to 25.63 % total carbohydrates. The total digestible nutrients (TDN) ranged from 33.07 % to 44.32 %, digestible energy (DE) from 1.46 to 1.95 Kcal per kg of dry matter (DM), and metabolizable energy (ME) from 1.20 to 1.60 Kcal per kg of DM. The intake of dry matter, digestible dry matter, and relative feed value varied from 1.55 to 1.77, 45.88 %–51.51 %, and 57.96 %–66.94 %, respectively. Methane production, as a percentage of the total gas, was at its lowest for IG-99-195 (7.91 %) and reached its highest for IG-01-391 (14.97 %). Silage pH ranged from 4.57 (IG-02-703) to 5.61 (IG-99-181) and lactic acid from 0.094 % to 1.774 % DM. Accessions IG-02-703, IG-02-713, and IG-2045-1 had good silage quality, with suitable pH and lactic acid levels. Planting these in pastures and grazing lands could improve the availability of quality fodder during lean periods.

Thiamine (vitamin B1) and riboflavin (vitamin B2) are essential vitamins because of their important health impact. Twelve different green leafy vegetable-samples including Solanum nigrum, Alternanthera sessilis, Amaranthus spinosus, Cucurbita moschata, Pisum sativum, Chenopodium album, Alternanthera philoxeroides, Amaranthus blitoides, Glinus oppositifolius, Amaranthus viridis, Coccinia grandis, and Corchorus capsularis indigenous to Shariatpur district in Bangladesh were collected, dried, stored and analyzed. The extraction procedures involved acid hydrolysis followed by enzymatic hydrolysis of the dried vegetable samples. A Shimadzu Prominence HPLC system operating in isocratic elution mode on a C18 column (4.6 mm × 250 mm, 5 μm) was utilized to achieve chromatographic separation of the vitamins while a fluorescence detector recorded the signals. The outcome showed that the HPLC method offered excellent sensitivity and selectivity for thiamine and riboflavin determination in leafy vegetables. The calibration curves exhibited good linearity (R2 > 0.99), and the recovery percentages were found to be excellent also, at 108.22 % and 100.22 % for thiamine and riboflavin, respectively. Comparative analyses revealed variations in thiamine and riboflavin contents in different leafy vegetables, highlighting the importance of including a diverse range of leafy vegetables in the diet to meet recommended daily intake levels of these vitamins.

Epilepsy is one of the most common neurological disorders affecting approximately 50 million people worldwide. It impacts people of all genders and ages, but evidence suggests a higher incidence rate in children and the elderly.Given that childhood epilepsy has the risk of causing developmental epileptic encephalopathy, which is associated with intellectual, behavioral, and/or motor disabilities, proper assessment of children with new-onset epilepsy at an early stage is essential to prevent threats affecting neurodevelopmental processes. The aim of this study was to investigate whole genome sequencing data of children diagnosed with epilepsy. Our results revealed an identification of a novel mutation in a 2-year-old male patient who suffered from recurrent epileptic seizures of unknown etiology. The detected variant is heterozygous and located in gene CHRNA2 (chr8:27321348, NM_000742, c.612G > A, p.Trp204∗) in exon 6. The databases such as Varsome, GeneCards, and NCBI did not reveal any matches with previously identified variants, implying the novelty of the finding. Moreover, according to various prediction tools (MutationTaster, SIFT, CADD, FATHMM-MKL, LRT, DANN, Eigen, and BayesDel), the mutation is characterized as pathogenic, which corresponds to the American College of Medical Genetics and Genomics (ACMG) classification. According to the findings, mutation of the CHRNA2 gene is closely associated with two disorders known as autosomal dominant nocturnal frontal epilepsy (ADNFLE), and benign familial infantile epilepsy (BFIS). Comparison of proband's clinical manifestations showed that it is difficult to attribute precisely the patient's symptoms to either of the conditions, however the evidence suggests that the patient's symptoms are more consistent with those of ADNFLE. In this report, we expanded the spectrum of existing variations in the CHRNA2 gene contributing and associated with the development of epilepsy with the important and novel causative genetic variant.

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques.

The quality of vegetables and fruits are judged by their visual features. Misclassification of fruits and vegetables lead to a financial loss. To prevent the loss, superstores need to classify fruits and vegetables in terms of size, color and shape. To improve the accuracy of models for classifying fruits and vegetables, researchers have introduced various CNN architectures like VGG16, AlexNet, DenseNet-121 and different attention-based variants by compromising models' complexity leading to high computational cost and overlooking feature's contribution for interpretable predictions. Additionally, the major drawback in most of the existing works is the utilization of the limited dataset and number of classes for fruit and vegetable classification task instead of the benchmark dataset like Fruit-360 which comprises of 141 distinct fruit and vegetable classes. In this study, an explainable artificial intelligence (XAI) driven enhanced attention-CNN (EA-CNN) is proposed for accomplishing the fruit and vegetable classification task accurately and efficiently. The proposed EA-CNN model (a) detects the class of fruits and vegetable accurately through visual features by manipulating undiscover customized pooling technique and enhanced attention feature extraction mechanism, (b) classifies the fruits and vegetables efficiently through a simplified architecture, (c) provides interpretable predictions through XAI approach. By utilizing entire fruit-360 database, this research noteworthily enhances the model's capability to classify wide range of fruits and vegetables, thus providing an effective and reliable solution for practical applications. The proposed study outclasses baseline models with regard to accuracy and computational cost on fruit-360 benchmark dataset. EA-CNN achieves better accuracy of 98.1 % in smaller number of iterations as compared to benchmark models. Moreover, to further prove the efficiency, generalization ability, robustness, scalability and adaptability of this research, the architecture of proposed EA-CNN model is validated on another real-world dataset named as ‘Fruit Recognition’. The experimental outcomes display that the proposed EA-CNN model produces substantial performance on Fruit-Recognition dataset as well by attaining a generalized accuracy of 96 %.

Security in data transmission is becoming problematic due to the internet's explosive growth as well as the spread of social media and digital advertising platforms, which have produced enormous volumes of data every day. In order to safeguard sensitive data, network security is becoming more and more important as technology is adopted. Effective information security measures are needed to protect against a variety of dangers, given the expanding number of users. A key factor in improving security is network security attributes, which include message encryption, password breaking, safeguarding wireless networks, and finding security holes. This paper adopts the Multi-Criteria Decision Making (MCDM) approach, utilising a fuzzy TOPSIS-based method, with the objective of systematically evaluating resilience in network security, especially against DDoS attacks. The research assesses different security attributes and vulnerabilities to offer actionable insights on how best to strengthen organizational cybersecurity frameworks. The methodology encompasses conducting network security audits to identify vulnerabilities that could compromise commercial operations or expose sensitive data. The findings provide critical insights that can inform targeted actions to address these vulnerabilities and enhance resource protection. The results indicate that network N6 is the most secure under DDoS attacks, followed by networks N1, N5, N3, N4, and N2. This research is significant as it aids strategic decision-making, strengthens network defenses, and enhances overall cybersecurity resilience in an era of evolving cyber threats. By addressing the complexities of network security assessments, this study makes a crucial contribution to the ongoing efforts of organisations to safeguard their data, employees, and customer information from sophisticated cyber threats.

Context: Rice is indeed a staple food in many parts of the world, including Bangladesh. This research aimed to evaluate the two essential vitamin levels in different processed rice sunned, parboiled, cooked sunned, cooked parboiled, and washed cooked rice. Objective: The study focused on analyzing the thiamine (vitamin B1) and riboflavin (vitamin B2) contents in 10 different varieties of paddy samples grown in the Kurigram district of Bangladesh. Methods: A high performance liquid chromatographic (HPLC) technique assembled with fluorescence detector was employed for these analyses. Two calibration curves were generated to analyze vitamin B1 and B2 (for thiamine the regression coefficient, r2 = 0.9992 and riboflavin r2 = 0.9989). The study demonstrated a recovery rate of 96.88 % for vitamin B1 and 104.24 % for vitamin B2. Overall, these findings provide valuable insights into the vitamin B1 and B2 contents across different rice samples. Results and conclusion: The results showed that the parboiled samples had the highest thiamine content, ranging from 342.50 ± 0.3 to 673.0 ± 7.5 μg/100 g, compared to the sunned samples, which had a range of 209.5 ± 0.3 to 337.4 ± 4.3 μg/100 g. Conversely, the sunned samples had the highest riboflavin content, ranging from 26.4 ± 0.2 to 59.6 ± 0.3 μg/100 g, while the parboiled samples had a range of 3.0 ± 0.1 to 26.6 ± 0.01 μg/100 g. Additionally, it was observed that the steaming process increased the thiamine content and reduced the riboflavin content in parboiled rice samples but decreased both the thiamine and riboflavin in sunned rice samples. Another study showed a gradual decrease in the levels of both vitamins B1 and B2 in sunned, cooked sunned and washed cooked sunned rice samples. Significance: These findings provide valuable insights into the impact of processing methods, such as parboiling and steaming, on the thiamine and riboflavin contents in rice. The results suggest that parboiled rice may be a preferable choice for individuals seeking higher thiamine content in their diet, while sunned rice may contain higher levels of riboflavin. The results shed light on the nutritional profiles of different rice varieties and highlight the potential impact of processing techniques on the vitamin content of rice.

Ionizing radiation from natural and many synthetic sources is a remarkable tool in many scientific, production, quality control, food preservation, medical, security, and other technological processes. The need to protect humans (public and personnel), gadgets, the environment, and animals from the harmful effects of radiation, while maintaining and expanding the scope of application has made radiation protection an important topic to discuss. Among the methods and materials available for radiation control, shielding and the use of glass shields are the most effective and attractive methods and materials, respectively. In this report, the basic parameters for measuring shielding competences, basic shielding materials and their shortcomings, and glass shields are discussed. Five categories of glasses, namely, borate, germanate, silicate, phosphate, and tellurites, with important shielding attributes, are reviewed. The role of chemical composition, density, and mean atomic number as gamma shielding delineating factors was emphasized. The weaknesses and comparable advantages of each glass system were presented as well. The review concludes by looking at the trend and future of glass shields and research in radiation technology. The data and analysis presented in this review provides scientists and radiation protection technologist on the impact of certain chemical oxides on shielding efficacies of different glass systems.

Lately, the herb's essential oils have received the intense attention of researchers and pharmaceutical agencies. The chemical composition, antioxidant, analgesic activity, anti-hyperpyrexia, and hepatorenal protective effects of the Kleinia anteuphorbium (L.) DC. Essential oil (KAEO) were evaluated. The KAEO was yanked by steam distillation and subjected to a GC/MS system. The antioxidant powers of KAEO were evaluated by DPPH, NO, and FRAP tests. Besides the anti-hyperpyrexia, the analgesic effects were assessed. Moreover, the hepatorenal curative effects were evaluated in a carbon tetrachloride-rat model. Sixty-eight active components were identified by GC/MS. α-pinene was the most dominant monoterpene in KAEO. KAEO showed anti-hyperpyrexia, anti-captive, and great antioxidant powers through DPPH, NO, and FRAP. The oral LD50 of KAEO was 187.5 mg/kg in rats. Furthermore, KAEO treatment succeeded in reliving the adverse effects of CCL4 in rats as a standard toxic model through reliving the hepatic pathological and oxidative stress and restoring the hepatic and renal functions. Overall, the obtained results demonstrated that KAEO might be used as a potential natural medicine to relieve a variety of modern symptoms and hepatorenal disorders associated with oxidative stress.

Yogurt, a popular dairy product renowned for its nutritional benefits and probiotic content, serves as a functional food with potential health-promoting properties. The objective of this study was to investigate whether incorporating green banana peel polyphenol extract (GBPPE) into yogurt formulations enhances the viability and functionality of probiotics while also potentially improving the overall quality and health-promoting properties of the yogurts. GBPPE was extracted and added to the yogurt formulation at 0.0 %, 0.5 %, 1 %, and 2 %. Various physico-chemical properties of GBPPE as well as a range of physical, biochemical, sensory, and microbial assessments of formulated yogurts were carried out. Compared to the control, yogurt containing GBPPE improves functional characteristics by increasing antioxidant activity while having no detrimental impact on physicochemical and organoleptic properties. In terms of antioxidant capabilities, all fortified yogurts showed significantly (p

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption.

Two-dimensional Ti4C3 MXene has recently emerged as a promising electrode for Lithium-ion batteries (LIBs) because of its outstanding ion-transport abilities and high Li-absorbability. This study employed molecular dynamics simulation to explore the mechanical stability of Ti4C3 MXene subjected to various temperatures, strain rates, and vacancy concentrations. A slightly superior tensile strength and elasticity modulus have been observed along zigzag directions, measuring 148.14 GPa and 29.17 GPa, respectively. On the other hand, armchair-oriented Ti4C3 MXene shows a considerably greater fracture strain of 0.259 due to its strain-hardening tendency at lower temperatures. Elevated temperature decreases both fracture strength and fracture strain, which is opposite to the effect of strain rate. Armchair loading has been revealed to be more sensitive to strain rate than its counter direction. Unlike temperature and strain rate, point vacancy significantly deteriorates the elastic modulus of Ti4C3 MXene. Carbon vacancies are more probable than titanium vacancies, which have less formation energy. The atomistic deformation profile supports the predicted values of fracture strain from stress-strain behavior. This in-depth study offers a detailed understanding of the mechanical behavior of Ti4C3 MXene under diverse circumstances, which will aid further experimental study and be beneficial for adopting Ti4C3 as anode materials in LIBs.

As the demand for energy in world is increasing rapidly and there is pursuit for renewable energy sources which is cheap, easy to generate and requires low maintenance, solar energy is a top contender. The world is in dire need of photovoltaic solar cells that can aid in keeping up with the hiking energy demands. However, thin film solar cells (TFSCs) which are developed for cost effectivity, suffer in performance due to reduced thickness. Therefore, this computational study uses Finite-Difference Time-Domain (FDTD) and delves into the scope of using a 1-D nano-grating structure embedded into absorbing layer of cadmium telluride TFSC to enhance the optoelectronic performance. A unique nano-grating structure with SiO2 at its core and aluminium coating aims to enhance the performance of CdTe TFSC with cost effectivity and ease of fabricability as the main motifs. Additionally, an evolutionary computational technique, Particle Swarm Optimization (PSO), was used to determine the optimal parameters of nano-gratings on CdTe TFSCs, i.e., nano-grating height, angle, duty cycle, aluminium coating thickness and grating substrate thickness. The PSO algorithm resulted in a nano-grating structure that yielded an efficiency increase of 52.86% and increase in short-circuit current density of 25.45% with respect to bare CdTe TFSCs. Subsequently, the performance of the optimal nano-grating structure was also observed to be insensitive to variation of wide range of incidence angle of light, a key feature preferred for versatile application of TFSCs.

The widespread use of imidacloprid (IMI) in pest control presents significant environmental challenges due to its persistence and low removal efficiency. This study introduces magnetic Covalent Organic Frameworks (COFs) functionalized with Fe₃O₄ nanoparticles (Fe₃O₄@HMN-COF, Fe₃O₄@MAN-COF, and Fe₃O₄@SIN-COF) as efficient adsorbents for IMI removal from water. These COFs, engineered with nitrogen-rich structures and extensive π-electron systems, achieve superior adsorption through π-π interactions, hydrophobic interactions, and hydrogen bonding. Characterization via FT-IR, XRD, and nitrogen sorption isotherms confirmed their high hydrophilicity, stability, and large surface areas. The magnetic properties of the COFs facilitated easy separation from water, enhancing practicality. Kinetic studies for all COFs indicated a pseudo-second-order model, suggesting chemisorption, with adsorption capacities of 600 mg/g for Fe₃O₄@HMN-COF, 480 mg/g for Fe₃O₄@MAN-COF, and 375 mg/g for Fe₃O₄@SIN-COF. Thermodynamic analyses revealed spontaneous and endothermic adsorption processes. Reusability tests showed minimal capacity loss over multiple cycles, underscoring their practical applicability. Practical tests in honey and fruit samples confirmed high efficacy, demonstrating the COFs' versatility. The study also optimized the photocatalytic degradation of imidacloprid using these COFs, with Fe₃O₄@HMN-COF achieving 98.5 % efficiency under optimal conditions (10 mg L−1 IMI, 0.01 g catalyst dose, pH 11, 30 °C, UV light). These findings highlight the potential of magnetic COFs for sustainable environmental remediation of pesticide-contaminated water.

This study investigates the use of various industrial waste materials—silica fume (SF), cement kiln dust (CKD), calcium carbide residue (CCR), rice husk ash (RHA), and ground granulated blast furnace slag (GGBS)—as eco-friendly stabilizers for expansive clay soil (ECS). Laboratory tests were conducted to assess the impact of different proportions (3 %, 6 %, and 9 %) of these additives on the soil's physical, mechanical, and microstructural properties. Results indicated that the inclusion of industrial waste significantly improved the soil's behavior, with notable reductions in liquid limit (up to 37.66 %), plasticity index (up to 74.76 %), and swell potential. Additionally, unconfined compressive strength (UCS) and shear strength increased substantially, with UCS values rising from 114.64 kPa to 1582.91 kPa at 30 days of curing for 9 % GGBS. Microstructural analyses confirmed the formation of cementitious compounds, which enhanced soil particle bonding and durability. These findings suggest that industrial waste materials can serve as effective and sustainable alternatives to traditional soil stabilizers, offering both performance improvements and environmental benefits.

Metal oxide semiconductors are highly promising due to their excellent photocatalytic performance in the photodegradation of industrial waste containing refractory chemical compounds. A hybrid structure with other semiconductors provides improved photocatalytic performance. In this work, porous and two-dimensional (2D) hexaniobate-bismuth vanadate (Nb6-BiVO4) Z-scheme hybrid photocatalysts are synthesized by chemical solution growth (CSG) of BiVO4 over electrophoretically deposited Nb6 thin films. The structural and morphological analysis of Nb6-BiVO4 hybrid thin films evidenced the well-crystalline uniform growth of monoclinic scheelite BiVO4 over lamellar Nb6 nanosheets. The Nb6-BiVO4 hybrid thin films exhibit a highly porous randomly aggregated nanosheet network, creating the house-of-cards type morphology. The Nb6-BiVO4 hybrid thin films display a strong visible light absorption with band gap energy of 2.29 eV and highly quenched photoluminescence signal, indicating their visible light harvesting nature and intimate electronic coupling between hybridized species beneficial for photocatalytic applications. The visible-light-driven photodegradation performance of methylene blue (MB), rhodamine-B (Rh-B) dyes, and tetracycline hydrochloride (TC) antibiotic over Nb6-BiVO4 hybrid are studied. The best optimized Nb6-BiVO4 thin film shows superior photocatalytic activity for photodegradation of MB, Rh-B dyes, and TC antibiotic with photodegradation rates of 87.3, 92.8, and 64.7 %, respectively, exceptionally higher than that of pristine BiVO4. Furthermore, the mineralization study of Nb6-BiVO4 thin film is conducted using chemical oxygen demand (COD) analysis. The optimized Nb6-BiVO4 thin film shows superior percentage COD removal of 83.33, 85.42, and 61.36 % for MB, Rh-B dyes and TC antibiotic, respectively. The present results highlight the expediency of hybridization in enhancing the photocatalytic activity of pristine BiVO4 by minimizing its charge recombination rate and improving chemical stability.

The rise of antibiotic resistance underscores the urgent need for novel antibacterial agents. DNA gyrase, an essential enzyme involved in bacterial DNA replication, is a promising target for antibacterial therapy. Computational approaches offer a cost-effective means to design and screen potential inhibitors, such as eugenol derivatives. This study aims to computationally design eugenol derivatives as potential antibacterial agents targeting DNA gyrase, assess their binding affinities, evaluate physicochemical properties, and toxicity, and select lead compounds for further investigation. Molecular docking simulations were conducted to investigate the binding affinities of eugenol derivatives and controls to DNA gyrase. Physicochemical properties and toxicity assessments of eugenol were evaluated. Lead compounds were selected based on drug likeness, toxicity, and binding affinity. Molecular docking studies revealed varying binding affinities of eugenol derivatives to DNA gyrase, with lead compounds exhibiting superior affinity compared to eugenol. Physicochemical properties indicated moderate lipophilicity and low aqueous solubility for eugenol. Toxicity assessment revealed mutagenicity and tumorigenicity. De novo compound synthesis generated 244 novel compounds, with 44 selected based on drug-likeness, toxicity, and binding affinity as lead candidates. These findings provide valuable insights for the development of novel antibacterial agents targeting DNA gyrase, with implications for combating antibiotic resistance.

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.

Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.

This article presents the notion about Slow Invariant Manifold (SIM) and their fundamental role in model reduction techniques (MRTs) for challenges encountered in mechanical engineering within dissipative systems of chemical kinetics. Focusing on the reaction routes of complex mechanisms, we construct and compare primary approximations of the SIM through MRTs, including the Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM). These methods effectively transform high-dimensional complex problems into lower dimensions, solving them without compromising crucial information about the complex systems modified for homogeneous reactive systems. Employing the sensitivity analysis by using the MATLAB's toolbox, we present the numerical findings in a tabular format obtained through MRTs. This study provides the understanding about the accessible exploration of numerical solutions, improving insights of the complex variation within the system.

The primary objective of this study is to investigate the effects of the Fractional Order Kepler Optimization Algorithm (FO-KOA) on photovoltaic (PV) module feature identification in solar systems. Leveraging the strengths of the original KOA, FO-KOA introduces fractional order elements and a Local Escaping Approach (LEA) to enhance search efficiency and prevent premature convergence. The FO element provides effective information and past expertise sharing amongst the participants to avoid premature converging. Additionally, LEA is incorporated to boost the search procedure by evading local optimization. The single-diode-model (SDM) and Double-diode-model (DDM) are two different equivalent circuits that are used for obtaining the unidentified parameters of the PV. Applied to KC-200, Ultra-Power-85, and SP-70 PV modules, FO-KOA is compared to the original KOA technique and contemporary algorithms. Simulation results demonstrate FO-KOA's remarkable average improvement rates, showcasing its significant advantages and robustness over earlier reported methods. The proposed FO-KOA demonstrates exceptional performance, outperforming existing algorithms by 94.42 %–99.73 % in optimizing PV cell parameter extraction, particularly for the KC200GT module, showcasing consistent superiority and robustness. Also, the proposed FO-KOA is validated of on SDM and DDM for the well-known RTC France PV cell.

In the antibiotic resistance era, utilizing understudied sources for novel antimicrobials or antivirulence agents can provide new advances against antimicrobial resistant pathogens. In this study, we aimed to investigate antibacterial and antibiofilm activities of Posidonia oceanica (L.) Delile against Enterococcus faecalis ATCC 29212, Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922 and Klebsiella pneumoniae ATCC 700603 and several S. aureus clinical isolates obtained from medical devices, including patient urinary catheters and breast implant infections, with varying antibiotic recalcitrance profiles. The ethanolic and methanolic extracts from P. oceanica rhizome exhibited significant antibacterial activity against E. faecalis and S. aureus, as well as drug resistant S. aureus clinical isolates. Furthermore, significant antibiofilm activity was observed against S. aureus and E. faecalis treated with ER, MR1, and MR2. P. oceanica extracts also exhibited synergistic antimicrobial activity with ciprofloxacin against E. faecalis, sensitizing E. faecalis to a lower ciprofloxacin concentration. Collectively, our data demonstrate the selective antibacterial and antibiofilm activity of the extracts of P. oceanica against Gram-positive bacteria and clinical isolates along with potentiation of current antibiotics, which suggests that P. oceanica can be further investigated as a potential source for novel therapeutic options in the treatment of drug resistant bacterial infections.

Debriefing is a process in physical or online learning that encourages learners to reflect on their own learning experiences. Effective questioning techniques and evoking positive experiences are methods for regulating and guiding students toward an environment that promotes mental health. The purpose of this scoping review is to identify effective debriefing questioning techniques and experiences for addressing the three presences in the Community of Inquiry framework, namely social presence, cognitive presence, and instructor presence, among learners in an educational or occupational setting. The result of the synthesis provided a comprehensive picture of which question types and experiences are present for the projection of each or a combination of the three presences. On Google Scholar, Education Resources Information Center, ResearchGate, and ScienceDirect, a search pertaining to debriefing strategy and questioning techniques was performed. From 2002 to 2020, 265 articles on debriefing strategy and questioning techniques were eliminated, leaving only 60 articles that were largely relevant. Results indicated that open-ended questions that are oriented toward higher-order thinking with the purpose of stimulating, following up, and clarifying are prevalent. Based on the Debriefing Experience Scale, the majority of the learners’ experiences involve Learning and Making Connections with Learning, followed by Appropriate Facilitator Guidance, Facilitator Skill in Conducting Debriefing, and then Analyzing Thoughts and Feelings. Questioning is a relevant aspect of facilitating experiences in different types of presence, and the types of questions used can influence the quality of those experiences.

Thevetia peruviana (T. peruviana; Family: Apocynaceae), commonly known as Lucky Nut, is a traditionally and medicinally important plant, and the barks of the plant are traditionally used as anti-inflammatory, anti-diabetic, and antibacterial remedies. Thus, this study aimed to evaluate bioactive phytochemicals and in-vitro biological activities from the bark of T. peruviana using methanolic (TPM) and dichloromethane (TPD) extracts. The GC-MS analysis showed the presence of 54 and 39 bioactive compounds in TPM and TPD, respectively. The TPM extract has a higher level of total polyphenolic contents (TPC: 70.89 ± 1.08 and 51.07 ± 0.78 mg GAE/g extracts, while TFC: 56.89 ± 1.16 and 44.12 ± 1.76 Qu.E/g extracts for TPM and TPD, respectively). Herein, the results of antioxidant activities were also found in correlation with the total polyphenolic contents i.e., depicting the higher antioxidant potential of TPM compared to TPD. The significant inhibitory activities of extracts were observed against tyrosinase (TPM; 59.43 ± 2.87 %, TPD; 53.43 ± 2.65 %), lipoxygenase (TPM; 77.1 ± 1.2 %, TPD; 59.3 ± 0.1 %), and α-glucosidase (TPM; 71.32 ± 2.44 %, TPD; 67.86 ± 3.011 %). Furthermore, in comparison to co-amoxiclave, the antibacterial property against five bacterial strains was significant assayed. The compounds obtained through GC-MS analysis were subjected to in-silico molecular docking studies, and the phyto-constituents with maximum binding scores were then subjected to ADME analysis. The results of in-silico studies revealed that the binding affinity of several phyto-constituents was even greater than that of the standard inhibitory ligands. ADME analysis showed bioavailability radars of phyto-constituents having maximum docking scores in molecular docking. The results of this study indicated that T. peruviana has bioactive phytochemicals and therapeutic potential and may provide a basis for treating metabolic disorders (inflammatory diseases like rheumatism and diabetes), bacterial infections, and skin-related problems.

In today's digital age, ensuring high-quality healthcare services is paramount for both physical and mental well-being. This is particularly challenging in developing countries like Bangladesh, where marginalized individuals, especially women, face barriers to accessing essential healthcare. Financial constraints and limited awareness often hinder their ability to avail themselves of private or public healthcare services. Besides, women have distinct healthcare difficulties compared to men, and they are also more likely to catch certain illnesses and diseases. However, there is a noticeable gap in the existing literature, with insufficient research specifically dedicated to addressing the healthcare challenges faced by this underprivileged group of women in developing countries. In this study, we delve deeper into the healthcare challenges faced by underprivileged women in Bangladesh, including issues related to menstruation and gynecological disorders through a field study in a slum area. Drawing valuable insights from this field study, we propose leveraging voice bots in the telecommunications sector as an innovative solution to provide accessible and targeted healthcare support for marginalized women. We present the prototype of such a voice bot to be available 24/7 with detailed workflow and performance evaluation. We expect that our study will contribute to the ICTD scholarship by informing future interventions for economically disadvantaged communities as well as informing policymakers grappling with the healthcare challenges faced by underprivileged women.

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992–2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km2, constituting 45.0 % of the area in 1992, to 291.5 km2 (36.0 %) in 2023, representing a total decrease of 72.4 km2. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km2 (22.0 %) in 1992 to 99.8 km2 (12.3 %) in 2023, a decrease of 78.1 km2. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.