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In this paper, a lightweight additively manufactured (AM) fixed geometry hydrodynamic thrust bearing fabricated via laser powder bed fusion (LPBF) is experimentally compared to a traditionally manufactured cast aluminum alloy thrust bearing of similar design. The purpose of this study is to evaluate how weight-saving design features in the AM bearing affect active critical hydrodynamic performance parameters to better understand in-service viability. Under various static operating conditions, performance parameters such as hydrodynamic pressure distribution, minimum oil film thickness (MOFT), bearing temperature and increase in oil temperature are measured. Compared to the traditionally manufactured bearing, the AM bearing showed an average increase in minimum oil film thickness of 53 %, an average increase in trailing edge hydrodynamic pressure of 116 %, while exhibiting an average decrease in bearing temperature of 1 %. Experimental results are compared to numerical simulation showing reasonably good agreement.

Gossypiboma is a unique postoperative complication characterized by the presence of a surgical sponge after surgery. This condition is seldom encountered. Owing to its serious medicolegal implications it remains underreported leading to a blurred clinical spectrum. This fact is also depicted in our case which presents an array of nonspecific symptoms painting a picture of possible malignancy. The case highlights the need for strict adherence to surgical protocols to avoid such instances of clinical negligence. Furthermore, our report provides the medical community with apt data to promptly diagnose such cases. A 40-year-old Asian hypertensive and diabetic female with a history of total hysterectomy presented with a 7-month history of progressive abdominal swelling, vomiting, and weight loss. The initial clinical presentation pointed to possible malignancies but subsequently revealed a 16 × 4 × 6 cm gossypiboma near the ileocecal junction. Following the removal and resection of 2.5 feet of the ileum, the patient underwent a right hemicolectomy, ileostomy, and colostomy. The postoperative recovery was uneventful, and the patient was discharged in stable condition. This case reiterates the importance of adhering to established surgical protocols and how minor negligence can have devastating and horrid effects. This case highlights the need for the fusion of medicine with technology< which prompts the use of innovations such as RFID-tagged sponges and electronic counting to minimize human error. Increased awareness and reporting of gossypibomas will aid in their timely diagnosis and management, ultimately reducing patient morbidity and associated legal challenges.

Abstract Background Trigeminal neuropathic pain (TNP) is a chronic condition characterized by heightened nociceptive responses and neuroinflammatory changes. While astrocytes are recognized as critical players in pain modulation, their specific role in influencing descending trigeminal pain pathways via ventrolateral orbitofrontal cortex (vlOFC) activity modulation remains underexplored. Therefore, we investigated the impact of optogenetic modulation of astrocytes in the vlOFC on pain hypersensitivity in a rat model of chronic constriction injury of the infraorbital nerve (CCI-ION). Method Adult female Sprague Dawley rats underwent ION constriction to mimic TNP symptoms, with naive and sham animals serving as controls. AAV8-GFAP-hChR2-mCherry, AAV8-GFAP-eNpHR3.0-mCherry, or AAV8-GFAP-mCherry were delivered to the vlOFC for in vivo optogenetic manipulation. Pain behaviors were assessed using acetone, von Frey, and elevated plus maze tests, while electrophysiological recordings from the ventrolateral periaqueductal gray (vlPAG) and ventral posteromedial (VPM) thalamus were obtained. Results Orofacial hyperalgesia, reduced vlPAG activity, and thalamic hyperexcitability were associated with vlOFC astrocytic hyperactivity in the TNP animals. In contrast, optogenetic inhibition of vlOFC astrocytes restored vlOFC glutamatergic signaling, increased vlPAG glutamatergic neuronal activity, and reduced hyperactivity in the VPM thalamus. Behavioral assessments also revealed alleviation of hyperalgesia, allodynia, and anxiety-like behaviors during the stimulation-ON phase, alongside reduced neuroinflammatory markers, including P2 × 3 and Iba-1. However, astrocytic excitation and null virus controls did not alter TNP responses, underscoring the specificity of astrocytic inhibition. Conclusion These findings suggest that the astrocytic subpopulation in the vlOFC and its robust influence on vlPAG glutamatergic neurons play a crucial role in restoring descending pain processing pathways, potentially contributing to the development of novel therapeutic approaches for TNP management.

In the digital era, video editing skills have become an essential competency for teachers to enhance the learning process. These skills enable teachers to create more interactive and engaging educational videos. However, not all teachers possess adequate video editing abilities. Therefore, this training activity aimed to improve the video editing skills of teachers at SMP Citra Harapan using the CapCut application. The training was conducted through a combination of theoretical and practical sessions, encompassing three main stages: preparation, implementation, and evaluation. The results indicated that all participants enthusiastically participated in the training, effectively understood the materials, and successfully applied video editing skills in practice. This training is expected to support teachers in producing more effective and creative educational videos.

This community service program aims to improve the understanding and skills of Posyandu cadres in optimizing the use of catfish as a source of nutrition through the innovation of the Pentole Kres product to overcome stunting in Tobimeita Village, Kendari City. The method of implementing this service uses community empowerment through education and training activities. The program evaluation uses pre-test and post-test instruments which are analyzed using a paired t-test. The results of the service showed an increase in participant understanding of 85%, while 100% of participants experienced an increase in practical skills, and the average skill score increased from 2.4 before training to 4.5 after training. Based on the results of this community service program, it can be concluded that the community empowerment approach utilizing local potential, such as catfish, is effective in improving the understanding and skills of Posyandu cadres in managing nutritional sources to combat stunting.

Abstract This work proposes a novel multi-band reflective metasurface, that is capable of linear polarization (LP), and circular polarization (CP) conversion in Ku, K, Ka, and U Bands. The metasurface design involves a combination of ring and square elements strategically arranged, and printed on a 0.76 mm thin-grounded Rogers RO3003 substrate. The metasurface achieves LP for y-polarized incident electromagnetic (EM) wave in 16.2–17.2 GHz, 23.0–25.4 GHz, 40.3–54.35 GHz frequency bands. The polarization conversion ratio (PCR) for LP frequency ranges is minimum 90% with an fractional bandwidth (FB) of 2.94%, 9.91%, and 26.9%, respectively. Moreover, metasurface achieves CP for y-polarized incident EM wave in 16.1–16.55 GHz, 17.5–22.15 GHz, 26.65–37.75 GHz, and 55.6–59.8 GHz frequency bands. In addition, the axial ratio (AR) for CP frequency ranges is less than 3 dB with a FB of 2.75%, 23.45%, 34.47%, and 7.27%. The device performance is considerably stable under oblique incidences up to 45 degrees. The metasurface unitcell is compact with a structural size of $$\text {length}=0.23\lambda _\circ$$ , $$\text {width}=0.23\lambda _\circ$$ and $$\text {thickness}=0.08\lambda _\circ$$ . The proposed prototype is fabricated, and the measured results are in good agreement with the simulated one. Overall, the proposed metasurface exhibits promising performance characteristics and holds potential for multiple applications in satellite based networks.

Abstract Thiram is a widely used fungicide in agricultural practices as spraying on crops and grains storage but its potential adverse effects on non-target aquatic organisms have raised serious concerns. In this study, firstly LC50 value of thiram has been determined as 0.744 mg/L for the first time in Labeo rohita and then, the fish were exposed to different sub-lethal concentrations of thiram (40 μg/L, 80 μg/L and 120 μg/L) to evaluate the effects on tissue growth, oxidative stress, anti-oxidant enzymes and histo-pathological parameters at days-20, 40 and 60 of the experiment. There observed a significant (p

Abstract Sentiment analysis plays an important role in understanding employee feedback and improving workplace culture. By leveraging NLP techniques to analyze this feedback accurately, organizations can pinpoint specific areas that need improvement, address employee concerns, and foster a positive work environment. These NLP-driven deep learning models offer valuable tools for E-Commerce HR and sales departments, enabling monitoring employee and users’ sentiment trends over time and assisting in implementing targeted interventions. Focusing on the e-commerce industry, this work utilizes NLP-driven deep learning methodologies to analyze employee and user feedback, aiming to identify sentiments. The proposed NLP-driven, deep learning-based framework is designed to classify user feedback into positive, negative, or neutral sentiments. The key steps in this framework include data collection, NLP-enhanced feature extraction using BERT-BiGRU, and final classification using a Graph Neural Network-based finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback from the e-commerce industry. The results demonstrate the framework’s efficacy, achieving an impressive 93.35% accuracy rate, surpassing existing benchmark methods. The research significantly benefits e-commerce by refining product portfolios and enhancing workplace culture.

The main objective of this paper is to establish some new inequalities related to the open Newton-Cotes formulas in the setting of q-calculus. We establish a quantum integral identity first and then prove the desired inequalities for $q$-differentiable convex functions. These inequalities are useful for determining error bounds for the open Newton-Cotes formulas in both classical and $q$-calculus. This work distinguishes itself from existing studies by employing quantum operators, leading to sharper and more precise error estimates. These results extend the applicability of Newton-Cotes methods to quantum calculus, offering a novel contribution to the numerical analysis of convex functions. Finally, we provide mathematical examples and computational analysis to validate the newly established inequalities.

Background and Aim: Extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli in dairy farm wastewater represents a significant threat to environmental and public health due to the dissemination of antibiotic-resistance genes. This study investigates the molecular profiles of ESBL-producing E. coli isolates harboring blaCTX-M, blaTEM, and blaSHV genes from dairy farm wastewater in East Java, Indonesia. Materials and Methods: Wastewater samples (n = 342) were collected from six major dairy regions in East Java: Pasuruan, Malang, Tulungagung, Blitar, Batu, and Kediri. The samples underwent bacterial isolation, identification, and ESBL phenotypic screening using the double-disk synergy test (DDST). Molecular genotyping of blaCTX-M, blaTEM, and blaSHV was conducted using a polymerase chain reaction. Results: Of the samples, 69.30% (237/342) tested positive for E. coli, with 32.91% (78/237) identified as ESBL producers through DDST. Molecular genotyping confirmed ESBL genes in 28.20% (22/78) of the isolates. The genotypic distribution was 77.2% (17/22) blaTEM, 4.5% (1/22) blaCTX-M, 9.0% (2/22) blaTEM + blaSHV, and 9.0% (2/22) blaTEM + blaCTX-M. These findings highlight the dominance of blaTEM and the presence of multi-gene combinations in East Java’s dairy farm wastewater. Conclusion: The identified molecular profiles underscore dairy farm wastewater as a critical reservoir for antibiotic resistance genes, particularly blaTEM. Addressing this issue necessitates stringent antimicrobial use policies, improved wastewater management, and enhanced biosecurity measures. These findings support a One Health approach to mitigate environmental and public health risks associated with AMR.

This study introduces a reflective polarization converter based on metasurface, designed to offer both Linear-to-Linear polarization (LLP) and Linear-to-Circular polarization (LCP). The design comprises two periodically arranged split ring resonators with a slotted stripe that operate in the X, Ku, and K bands. The three discrete frequency bands demonstrate over 90% Polarization Conversion Rate for LLP for oblique incidence waves up to 45°. Additionally, the proposed converter achieves Left-Hand Circular Polarization (LHCP) in two sub-bands and Right-Hand Circular Polarization (RHCP) in two sub-bands for oblique incidence waves up to 45°. The metasurface design introduced in this study exhibits significant potential for satellite applications in 6G networks, owing to its versatile LLP and LCP conversion properties in the X, Ku, and K bands.

Overall equipment effectiveness (OEE) is a lean manufacturing tool that has been reported to improve the performance of a company significantly. The adoption of OEE has been mattering in the garment manufacturing sector in Pakistan. Numerous companies have implemented OEE and improved their production in the textile sector. The present research was conducted at ABC Textile Company in Karachi. Targets at the various departments of the company were not being achieved. In this regard, the production data for the previous 90 days was collected initially. Since there were the issues with availability of materials and accessories; this was the basic and main reason for delays and unachievable targets. After certain improvements, the overall equipment effectiveness (OEE) of each department was calculated. Data analysis was conducted in Microsoft Excel. Results indicated a significant increase in the value of OEE across all the departments after the process improvement. Since there is a separate tool to increase the availability (total productive maintenance), and quality (Six Sigma); they are implemented one by one. After their integrated implementation, the boost in various parameters of productivity will be noteworthy.

The modern digital world is experiencing growing security risks, with cyber threats happening more often and becoming more complicated. These threats affect both businesses and individuals. Machine learning (ML) and deep learning (DL) have emerged as vital tools in cybersecurity, enabling the analysis of extensive datasets to identify potential cyber threats. This study proposes a novel technique utilizing ML and DL algorithms for threat detection. We began with data preprocessing, which included cleansing the data, addressing missing values through Multiple Imputation by Chained Equations (MICE), and applying transformations such as encoding and standard scaling. To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). For feature selection, we used Forward Feature Elimination (FFE), Backward Feature Elimination (BFE), and Recursive Feature Elimination (RFE) to identify the most relevant features. We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Model performance was evaluated using metrics such as accuracy, precision, recall, and F1 score, alongside loss graphs and confusion matrices. The highest accuracy at 99% was attained by the LSTM model, while the CNN demonstrated superior precision (98%), recall (97%), and F1 score (97%). Additionally, the CNN, RNN, and SVM models achieved an accuracy of 98%. These results illustrate the effectiveness of ML and DL in identifying cybersecurity threats, highlighting their potential to enhance defenses against emerging cyber risks.

The rapid integration of renewable energy sources into power grids has created an urgent need for accurate energy demand and supply forecasting models capable of managing the inherent variability of renewable energy generation. The combination of fluctuating consumer demand patterns and high variability across different energy sources presents significant challenges in maintaining a reliable balance between supply and demand. To address these challenges, we propose a Attention-driven Bayesian-Optimized Hybrid Ensemble Framework (ABHEF), evaluated on Jeju Island’s energy mix data. ABHEF integrates state-of-the-art models—ConvBiLSTM (Convolutional Bidirectional Long Short-Term Memory), ETCN (Enhanced Temporal Convolutional Network), TFT (Temporal Fusion Transformer), and DAT (Dual Attention Transformer)—to capture both short-term fluctuations and long-term trends in energy data. The proposed framework is evaluated on actual energy demand and supply data from Jeju Island, along with key weather attributes, thereby enhancing the model’s real-world applicability and accuracy. Bayesian optimization was applied to each model to determine optimal hyperparameters, to ensure the peak predictive performance. The output of the base models was stacked, and four meta-models (Gradient Boosting, LGBM, Ridge, and CatBoost) were applied. Among these, CatBoost demonstrated the best performance and was selected as the final meta-model. For hourly supply prediction, our selected meta-model achieved a 52% reduction in MAE and a 50% reduction in RMSE compared to BiLSTM, the best-performing standalone time-series model, validated through a consistent evaluation of accuracy metrics across all models on the same dataset. For hourly demand predictions, it achieved a 43% reduction in MAE and a 34% reduction in RMSE. For daily supply predictions, it demonstrated a 76% reduction in MAE and a 77% reduction in RMSE, while for daily demand predictions, the reductions were 70% in MAE and 69% in RMSE. These results highlight the superior accuracy of the proposed framework, offering significant benefits for energy management and resource planning in renewable energy systems.

The Extensible Markup Language (XML) files are extensively used for representing structured data on the web for file configuration, exchanging data between distinct applications, web development, and many other applications. Consequently, effective parsing techniques are necessary for XML files to enhance the performance of applications. The existing parsing techniques have their strengths and weaknesses affecting the performance of applications. Researchers point out that the selection of an efficient and appropriate parser is the most challenging issue regarding a particular condition. This paper proposes a framework XML Parsing Optimization using Hybrid Machine Learning (XPOHML) that makes use of Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques for efficient XML parsing. The newly developed framework performs analysis and prediction of different XML parsers using profiling, classification, performance evaluation, and finally generates code for efficient parsing. The XML profiling phase of the XPOHML framework generates a dataset by evaluating the performance of PXTG, SAX, StAX, DOM, and JDOM parsing models on separate cores by applying numerous file sizes. The Classification phase produces the classification model by applying ANN and SVM techniques to identify the appropriate parsing model. The performance evaluation phase of XPOHML assesses the performance of both parsing models through classification metrics (accuracy). Additionally, based on evaluation outcomes, the code generation phase produces an efficient parsing model of XML files. The newly designed and developed XPOHML framework has shown a meaningful improvement in the performance of parsing XML files.

Feed ingredients, particle size, and texture are well-acknowledged factors affecting nutrient digestibility, feed utilization, gut health development, and overall production performance of poultry birds. Therefore, it has been a topic of interest for nutritionists and feed producers to optimize poultry production efficiency. Grinding, the second most expensive process during feed manufacturing, has been proposed to modify ingredient particle size. It is investigated that changes in the feed ingredients' particle size can significantly affect poultry birds' production, thus increasing particle size to the recommended level for improving poultry performance and saving costs from over-grinding. Providing feed in pelleted form is an advanced form of feeding with many benefits compared to mash, such as improved nutrient digestibility, feed palatability, and reduced ingredient segregation. The modern poultry industry has traditionally fed birds with pelleted diets.Moreover, pellet quality and conditioning temperature significantly impact bird production performance. The feed form and feed ingredient particle size also significantly influence digestive system development and intestinal microflora, affecting birds' overall performance. This review paper will discuss the published literature on feed form and particle size on bird performance, passage rate, gut development, feed intake, and nutrient digestibility.

Current data on cybercrime shows a rising influence of online threats, leading to significant financial impacts across vital industries like finance, healthcare, and energy. These impacts encompass the theft of confidential information, service interruptions, and expenses tied to breach remediation, underscoring the urgent necessity for strengthened cybersecurity strategies. Machine learning (ML) is highly effective in signifying cybersecurity standards, leveraging large-scale data analysis, pattern recognition, and adaptability to emerging threats. Unlike other traditional methods such as rule-based that relay on predefined criteria. This study presents an automated ML framework to recommend cybersecurity standards, audits, and compliance measures, enhancing and monitoring cybersecurity infrastructure systematically. Nine cybersecurity standards, including seven international (e.g., ISO/IEC 27001:2022, NIST, CIS) and two national standards (UAE, KSA), were analyzed using data from official sources. Data preprocessing addressed duplicates and missing values, with validation via Pearson Correlation and Chi-square tests (Chi-square = 55.79, p = 0.0017). Attributes were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) technique and refined through Recursive Feature Elimination (RFE). A content-based filtering (CBF) recommender system, aligned with organizational maturity levels, was developed and enhanced with a feedback loop for user insights. The recommendation model was validated across three organizational growth scenarios: ad-hoc, managed, and adaptive. The UAE standard recommended 158 controls for Scenario-1, while KSA led in Scenario-2. We evaluate the performance of the model using 3-ML classifiers including Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Model performance was assessed using the F1 score and ROC AUC score. Testing in ad-hoc, managed, and adaptive scenarios showed the UAE standard recommended 158 controls for Scenario-1, while KSA led Scenario-2. The RF classifier achieved 81% accuracy and an ROC AUC score of 0.98. This framework supports alignment with global standards, enhances cybersecurity governance, and enables ongoing cybersecurity maturity tracking, aiding resilience and improving GCI rankings.

The integration of Information Centric Networking (ICN) with the Internet of Things (IoT) will meet the expectations of end users by providing an admirable network system. ICN enhances the IoT by utilizing in-network caching regarding data dissemination with the help of various paths labeled by the name of the path by which the data is returned. Caching is a significant technique for improving content accessibility, reducing hops while transferring data, and finally shortening data access time, which in turn improves IoT networks. This study develops a novel caching strategy that caches content in suitable locations at highly requested nodes, thereby improving the information caching efficiency of ICN-based IoT systems. When comparing the proposed caching scheme with other caching methods, it pay attention to the data retrieval latency, cache hit ratio, and the average number of hops. These results consistently show that the proposed strategy enhances cache performance by a high margin. The future context of the utilization of the specified caching strategy will lie in the advancements of fog, edge, and ad hoc networks concerning the concept of IoT and new trends like 5 G and 6 G.

In the ever-expanding digital landscape, the abundance of user-generated content on consumer platforms such as Booking and TripAdvisor offers a rich source of information for both travellers and hoteliers. Sentiment analysis, a fundamental research task of natural language processing (NLP) is used for mining sentiments and opinions within this vast reservoir of text reviews. A more specific type of sentiment analysis, i.e., aspect-based sentiment analysis (ABSA), is used when processing customer reviews is required. In ABSA, we aim to capture aspect-level sentiments and intricate relationships between various aspects within reviews. This article proposes a novel approach to ABSA by introducing a novel technique of word sense disambiguation (WSD) and integrating it with the Transformer architecture bidirectional encoder representations from Transformers (BERT) and graph convolutional networks (GCNs). The proposed approach resolves the intriguing ambiguities of the words and represents the review data as a complex graph structure, facilitating the modeling of intricate relationships between different aspects. The combination of bidirectional long short-term memory (BiLSTM) and GCN proves effective in capturing inter-dependencies among various aspects, providing a nuanced understanding of customer sentiments. The experiments are conducted on the RABSA dataset (an enhanced and richer hotel review data collection), and results demonstrate that our approach outperforms previous baselines, showcasing the effectiveness of integrating WSD in ABSA. Furthermore, an ablation study confirms the significant contribution of the WSD module to the overall performance. Moreover, we explore different similarity measures and find that cosine similarity yields the best results when identifying the real sense of a word in a given sentence using WordNet. The findings of our work and future work related to our work create lots of interest for people in the tourism and hospitality industry. This research gives another boost to the concept of the potential of NLP techniques in sentiment analysis. It emphasizes that if we combine the potential of NLP techniques along with state-of-the-art machine learning frameworks, we can shape the future of this field.

Abstract Anorexia nervosa (AN) and depression are complex conditions with high comorbidity, impacting mental and physical health. Recent research suggests gut microbiota plays a significant role in their development and progression. This correspondence highlights the critical gaps in understanding the gut–brain axis in AN and depression, emphasizing the need for comprehensive research. We discuss recent findings, including altered gut microbiota profiles and inflammatory factors, and identify key areas for future investigation. These include large-scale, multi-center studies to quantify microbiota profiles, mechanistic understanding of gut microbiota alterations, and diagnostic and therapeutic advances using microbiota as biomarkers. Addressing these gaps is crucial for developing effective treatment strategies and improving patient outcomes. By exploring the underlying mechanisms, validating biomarkers, and developing targeted therapies, we can enhance diagnostic accuracy, improve treatment outcomes, and better support patients with AN and depression. This research has the potential to transform the management of these conditions and improve quality of life for those affected. Level of evidence Low level of evidence as it is a personal opinion.

This study aims to analyze the governance of higher education institutions facing specific challenges, using a dynamic system model to gain a deeper understanding compared to traditional governance approaches. The approach used in this study is qualitative, with a case study design and purposive sampling technique to select participants. Data were collected through interviews, observations, and document analysis, which were then analyzed using thematic analysis. The study results indicate that effective governance relies on adaptive leadership, transparency in decision-making, and active stakeholder participation. This study contributes by developing a measurable framework to assess these governance elements and demonstrating how a dynamic system model can improve institutional performance. Practical implications of this study include the importance of aligning leadership practices with the specific needs of the institution, as well as the need to increase stakeholder engagement. Recommendations generated from this study are expected to be applied to other higher education institutions facing similar challenges to improve governance effectiveness and institutional performance.

The phenomenon of stress in prisoners is currently an urgency for various researchers. This stress occurs because it is experienced due to changes in the atmosphere of the general public, but entering a criminal facility makes them feel uncomfortable, starting from mental pressure, anxiety, anti-social behavior, and even suicide due to stress. Therefore, there is a need for case management so that inmates can face situations of stress and fear when undergoing initial formation in state detention centers. This study used a qualitative research method with a case study research design, employing observation, study documentation, and interviews for data collection. There were 6 informants, 1 of whom was a prisoner, along with 1 guard officer, 1 registration officer, and 3 inmates. This research emphasizes interactive data analysis and qualitative confirmation of source triangulation results. The object of research lies in how prisoners can undergo an excellent initial coaching period to manage stressful situations and fears. The results of this research indicate that correctional guardians have a role in providing counseling guidance on the stress of inmates during initial training at the Painan Detention Center, where the stress levels of inmates are very high. The effective counseling guidance methods or techniques used include a religious approach to reduce stress on inmates during the initial training period. This action seeks to reduce stress levels and provide assistance to prisoners. This research presents a novelty that lies in the experience of stress management during the initial guidance of inmates.

The prioritization of bug reports based on severity is a crucial aspect of bug triaging, enabling a focus on more critical issues. Traditional methods for assessing bug severity range from manual inspection to the application of machine and deep learning techniques. However, manual evaluation tends to be resource-intensive and inefficient, while conventional learning models often lack contextual understanding. This study explores the effectiveness of large language models (LLMs) in predicting bug report severity. We propose a novel approach called SevPredict using GPT-2, an advanced LLM, and compare it against state-of-the-art models. The comparative analysis between the proposed approach and state-of-the-art approaches suggests that the proposed approach outperforms the state-of-the-art approaches in terms of performance evaluation metrics. SevPredict shows improvements over the best-performing state-of-the-art approach (BERT-SBR) with 1.72% higher accuracy, 2.18% higher precision, and 4.94% higher MCC. The improvements are even more substantial when compared to the approach by Ramay et al., with SevPredict demonstrating 10.66% higher accuracy, 10.39% higher precision, 3.29% higher recall, 7.19% higher F1-score, and a remarkable 41.27% higher MCC. These findings not only demonstrate the superiority of our GPT-2-based approach in predicting the severity of bug reports but also highlight its potential to significantly advance automated bug triaging and software maintenance. This research introduces a severity prediction tool named SevPredict.

This article discusses the Lontaraq Meongpalo Karellae manuscript from an anthropological perspective. The manuscript narrates the story of a cat subjected to violence by humans, leading to the descent of celestial beings known as Dewata Sangiangseri to administer 'retribution' against those who mistreat animals. The manuscript also explores the relationships between humans, nature, and other living beings to foster harmonious coexistence for environmental preservation. This narrative imparts ethical lessons from an anthropological and cultural standpoint.The study employs an anthropological approach to analyze cultural practices among the Bugis community, particularly in maintaining harmony among humans, nature, and the surrounding environment. Through the Maddoja Bine ritual, these practices substantiate how the Lontaraq Meongpalo Karellae manuscript provides concrete implications for Bugis ethnic entities to nurture and care for the environment. Furthermore, this research enables a deeper understanding of the dynamics of traditional Bugis society, especially in the contexts of social interaction, family structure, and inherited cultural values. Oral traditions, myths, and rituals constitute integral aspects of the cultural identity of the community, effectively manifesting as relational entities within historical and environmental contexts, thereby underscoring their relevance in contemporary anthropological studies.

Abstract Islanding detection is a critical issue in grid‐connected distributed microgrid systems. Distributed generation in the current power system has caused many challenges. Consequently, detecting quick and effective islanding is the most critical issue to minimise equipment failure, avoid danger, and maintain grid safety. There are various techniques for islanding identification in microgrids. Three classifications have been applied to categorise these strategies, which are: active, passive, and hybrid. This paper proposes and demonstrates an efficient and accurate approach to islanding detection based on the Variance Autocorrelation Function of a Modal Current Envelope (VAMCE) technique. Demodulation techniques including synchronous real demodulation, square law demodulation, asynchronous complex square law demodulation, and the quadrature demodulation technique are employed to detect the envelope of the 3‐phase current signal. The VAMCE methodology is better suited for islanding detection because of its response to current sensitivity under islanding scenarios but not under normal conditions. Several simulations under various settings, including normal and islanded scenarios are used to analyse this method. These simulations have demonstrated different situations, such as when the system works normally and when it does not. The VAMCE along with the quadrature demodulation technique outperforms the others. The proposed solution is not only more accurate but also much faster compared to other methods. The proposed approach can identify normal and islanded situations in just 0.4 s.

Abstract In the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods. The proposed framework not only secure medical data against cyber threats but also protects the secret security keys. Initially, a Quantum Key Distribution (QKD) is employed to generate a shared key, which is then used to secure the symmetric keys via One-Time Pad (OTP) encryption. Next, bit-planes are extracted from each color component. The rows and columns of the extracted bit-planes are scrambled using random sequences which are generated by a 6D hyperchaotic Chen system and the Ikeda map. To further increase confusion in the original data, multiple-step pixel scrambling operations such as pixel shuffling, pixel value shuffling, and rotational and flipping operations are implemented. After the confusion phase, a combination of affine transformations with non-linear functions, Discrete Cosine Transform (DCT) with complex modulation, Discrete Wavelet Transform (DWT) with random phase modulation, bilinear transformation, and nonlinear polynomial mapping are employed to create diffusion in the scrambled components. These multiple encryption operations aim to maximize randomness in the final ciphertext image. Additionally, to reduce computational complexity, only the Most Significant Bit-Planes (MSBs) are encrypted, as they contain more than 94% of the plaintext information. Several experimental results and analyses are conducted to assess the proposed encryption framework, including entropy analysis, key sensitivity analysis, correlation analysis lossless analysis, and histogram analysis. Furthermore, the framework is tested against various cyberattacks such as brute-force attacks, clipping attacks, and noise attacks on the ciphertext images, to demonstrate its resilience against such threats.

Background: Coronary artery bypass graft, a surgical way to bypass blocked portions of coronary arteries, comes with many postoperative complications including a decline in pulmonary function. Some preoperative exercises help reduce such pulmonary complications and improve postoperative pulmonary function. Objectives: to determine the effects of preoperative respiratory rehabilitation on post-operative pulmonary functioning in CABG patients. Methods: a randomized clinical trial with two groups was conducted in the Armed Forces Institute of Cardiology & NIHD Rawalpindi from June 2022- July 2023. The n=86 participants included ages between 40-65 years, those undergoing elective CABG surgery for any cardiac disease, patients who covered at least 400 steps on a 6-minute walk test, patients with ejection fraction 45% or more, and those who reserved at 4th postoperative day of CABG. Group A received Pre and Postoperative Respiratory Rehabilitation. Both groups received postoperative care for 4 days. All the patients were assessed for FEV1, FVC, and their ratio (FEV1/FVC) at baseline, on the 7th day, and on the 4th postoperative day of CABG surgery Results: the experimental group significantly improved FVC compared to the control group, with moderate improvements observed (p=0.03*) by Day 7 and substantial improvements (p

Background: Apolipoprotein Ɛ4 has been established as a risk factor for cognitive impairment and dementia among Caucasians; however, the relationship among Black Africans has not been consistent. Aims: This study aimed to determine the sex-related differences in mild cognitive impairment (MCI) and its association with apolipoprotein E (APOE) polymorphism and sociodemographic factors amongst community-dwelling older persons in Kano, Nigeria. Materials and Methods: One hundred and forty-three participants (males = 65 and females = 78) were randomly recruited. APOE was genotyped from peripherally extracted genomic DNA using the sequence-specific primer polymerase chain reaction with Hhal digestion. Cognition was assessed using the Montreal Cognitive Assessment Basic (MoCA-B). Data were analysed using the SPSS software. Results: The mean age of the participants was 65.69 years. The prevalence of MCI was 63.64%, and females were more affected (73.08% vs. 52.31%, χ2 = 6.609; P = 0.010). The frequency of APOE genotypes was: E3/3-48%, E3/4-25.9%, E2/3-15.4%, E2/4 and E4/4-4.2%, and E2/2-2.8%, while its allelic distribution was: Ɛ3-68.2%, Ɛ4-19.2%, and Ɛ2-12.6%. Age and years of formal education were the independent predictors of the MoCA-B score. They explained 68% of the variance in MoCA-B score, while possession of at least one Ɛ4 allele (χ2 = 37.949, P < 0.001), low level of formal education (χ2 = 20.035, P = 0.001), being female (χ2 = 6.609, P = 0.010), being single (χ2 = 6.303, P = 0.012), and having a lower monthly income (χ2 = 16.884, P = 0.001) were associated with MCI. Conclusions: The prevalence of MCI among older persons in Kano, Nigeria, is high and is associated with low levels of education, female sex, advanced age, low income, and APOE Ɛ4.

Abstract Aims The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease. Methods In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three‐step approach includes pre‐processing of the dataset, applying feature selection method on pre‐processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10‐fold cross‐validation provided the high accuracy. Results The proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively. Conclusions In conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages.

The Ewing sarcoma family of tumors (ESFT) stands for a group of rare and aggressive malignancies consisting of high grade poorly differentiated small round blue cells. In our case report, we aim to describe the clinical presentation, diagnosis and the treatment plan for our patient. A 16-year-old girl presented to the ENT outpatient department of the Mayo Hospital Lahore with the complaints of right nasal obstruction, epistaxis from right nose, swelling on the right cheek and headaches. Examination revealed a soft, tender swelling on her right cheek below the right eye. Imaging revealed a heterogeneous opacity in the right nose and right maxillary sinus with erosion of the sinus walls.Very few such cases have been reported in international literature. Being a rare case, our article will add insights to the literature regarding complex clinical presentation, diagnostic evaluation and treatment of the sinonasal Ewing sarcoma.

This research project examines the deployment of Business Intelligence (BI) utilising Tableau to enhance data visualisation at PT PP London Sumatra Indonesia Tbk, with a particular focus on the palm oil processing and sales sector. Palm oil is a significant commodity in the Indonesian economy. This study highlights the significance of accessing accurate data in order to inform business decisions. The implementation of Business Intelligence (BI) facilitates the processing of data from disparate sources into informative visualisations, thereby enabling the identification of operational trends and patterns. The methodology comprised the collection of data on a daily basis, the cleansing of said data using ETL processes, and the presentation of the data in a visual format through Tableau. The results demonstrated an improvement in operational efficiency and a reduction in the time taken to make decisions, while also providing insights that could inform strategic decisions made by management. It is anticipated that this implementation will assist the company in meeting market challenges and improving product quality and business processes.

This paper presents a comprehensive study of the material selection, manufacturing, and Electro-Mechanical testing of radomes made from Polymer Matrix Sandwich Composite materials. The objective is to achieve optimal electromagnetic (EM) and mechanical performance using a combination of PMI foam core and hybrid face sheets (Kevlar & S Glass) sandwich composite structures. The selection of the material configuration was based on a Multi Criteria Decision Matrix (MCDM) method to meet the requirements of Electromagnetic Interference (EMI) transparency and aerodynamic/inertial loads during flights. A full-scale nose radome was manufactured using a hand-layup technique assisted by vacuum bagging, specifically designed for flight vehicles with speeds up to

Antibiotics have been used as growth promotors to enhance poultry"s growth performance and feed efficiency. However, their use leads to various side effects like antimicrobial resistance (AMR), destruction of beneficial bacteria, and microbes in the gut, and dysbiosis with time. Various alternatives have been used over the years to overcome the poultry problems and these are necessarily required to replace antibiotics to prevent these side effects. Among alternatives, probiotics are the best option because of their unique pharmacological and therapeutic properties. They are living and nonpathogenic microbes that have health benefits for the host. In poultry diets, they are used as feed additives. They help in proper feed digestion to make beneficial nutrients available for more feed intake and faster growth. They play a significant role in the host by modulation of the host"s immune response thus protecting the host from many infections. They contribute to health by offering anti-mutagenic, anti-carcinogenic, anti-hypertensive, hypocholesterolemic, and immune-modulating effects. They help to enhance the production and quality of poultry meat and eggs. The selection of suitable probiotic strains is of great concern to achieve all optimal effects of probiotics. Therefore, this review explores probiotics, their properties, mode of action, and effects on commercial egg and meat production.

Abstract Background Neurodegenerative disorders, including Alzheimer’s disease (AD), have been linked to alterations in tryptophan (TRP) metabolism. However, no studies to date have systematically explored changes in the TRP pathway at both transcriptional and epigenetic levels. This study aimed to investigate transcriptomic, DNA methylomic (5mC) and hydroxymethylomic (5hmC) changes within genes involved in the TRP and nicotinamide adenine dinucleotide (NAD) pathways in AD, using three independent cohorts. Methods DNA derived from post-mortem middle temporal gyrus (MTG) tissue from AD patients (n = 45) and age-matched controls (n = 35) was analyzed, along with DNA derived from blood samples from two independent cohorts: the German Study on Ageing, Cognition, and Dementia in Primary Care Patients (AgeCoDe) cohort (n = 96) and the Dutch BioBank Alzheimer Center Limburg (BBACL) cohort (n = 262). Molecular profiling, including assessing mRNA expression and DNA (hydroxy)methylation levels, was conducted using HumanHT-12 v4 Expression BeadChip and HM 450 K BeadChip arrays, respectively. Functional interactions between genes and identification of common phenotype-specific positive and negative elementary circuits were conducted using computational modeling, i.e. gene regulatory network (GRN) and network perturbational analysis. DNA methylation of IDO2 (cg11251498) was analyzed using pyrosequencing. Results Twelve TRP- and twenty NAD-associated genes were found to be differentially expressed in the MTG of AD patients. Gene sets associated in the kynurenine pathway, the most common TRP pathway, and NAD pathway, showed enrichment at the mRNA expression level. Downstream analyses integrating data on gene expression, DNA (hydroxy)methylation, and AD pathology, as well as GRN and network perturbation analyses, identified IDO2, an immune regulatory gene, as a key candidate in AD. Notably, one CpG site in IDO2 (cg11251498) exhibited significant methylation differences between AD converters and non-converters in the AgeCoDe cohort. Conclusion These findings reveal substantial transcriptional and epigenetic alterations in TRP- and NAD-pathway-associated genes in AD, highlighting IDO2 as a key candidate gene for further investigation. These genes and their encoded proteins hold potential as novel biomarkers and therapeutic targets for AD.

Abstract Trigeminal neuropathic pain (TNP), migraine, and cluster headache (CH) profoundly impact the quality of life and present significant clinical challenges due to their complex neurobiological underpinnings. This review delves into the pivotal role of the hypothalamus in the pathophysiology of these facial pain syndromes, highlighting its distinctive functions and potential as a primary target for research, diagnosis, and therapy. While the involvement of the hypothalamus in migraine and CH has been increasingly supported by imaging and clinical studies, the precise mechanisms of its role remain under active investigation. The role of the hypothalamus in TNP, in contrast, is less explored and represents a critical gap in our understanding. The hypothalamus’s involvement varies significantly across these conditions, orchestrating a unique interplay of neural circuits and neurotransmitter systems that underlie the distinct characteristics of each pain type. We have explored advanced neuromodulation techniques, such as deep brain stimulation (DBS) and optogenetics, which show promise in targeting hypothalamic dysfunction to alleviate pain symptoms. Furthermore, we discuss the neuroplastic changes within the hypothalamus that contribute to the chronicity of these pains and the implications of these findings for developing targeted therapies. By offering a comprehensive examination of the hypothalamus’s roles, this paper aims to bridge existing knowledge gaps and propel forward the understanding and management of facial neuralgias, underscoring the hypothalamus’s critical position in future neurological research.

Abstract This study objective to assess the effects of Waste Engine Oil (WEO) and Sasobit on improving the rheological and physicochemical properties of Reclaimed Asphalt Pavement (RAP), both of which are considered waste materials. Seven asphalt mixes were prepared with varying percentages of these materials. The study included rheological testing, Fourier Transform Infrared Spectroscopy (FTIR) analysis, cost–benefit analysis, and final stage to select blend selection. Highway agencies typically limit RAP usage to 20% due to concerns such as fatigue and low-temperature cracking. To address these issues and increase the RAP content, rejuvenators are often required to soften the aged binder. This study investigated the use of WEO and Sasobit as rejuvenators, with WEO added at dosages of 4% and 8%, and Sasobit at 1%, 2%, and 3%. The penetration and viscosity methods were employed to determine the optimal amounts of WEO and Sasobit. The selected mixtures were tested for rutting resistance, fatigue performance, and low-temperature cracking susceptibility. FTIR was used to further analyze the blends, along with a cost analysis. The results indicated that 4% WEO was effective in rejuvenating 25% of the reclaimed binder, offering a cost-effective solution. While Sasobit did not function as a rejuvenator, it proved beneficial as a viscosity reducer.

The genus Coccinella is reviewed with seven species found in Pakistan: C. luteopicta (Mulsant, 1866), C. marussii Kapur, 1973, C. iranica Dobzhansky, 1926, C. transversalis Fabricius, 1781, C. septempunctata Linnaeus, 1758, C. transversoguttata transversoguttata Faldermann, 1835 and C. undecimpunctata Linnaeus, 1758. Information on prey, host plants, distribution and an identification key for Coccinella species in Pakistan is provided. Additionally, newly-sequenced partial COI (cytochrome-c-oxidase subunit I) for C. luteopicta and C. marussii were used to determine their phylogenetic positions within the genus Coccinella.This study comprehensively reviews the genus Coccinella in Pakistan and highlights Coccinella luteopicta as a new country record. Morphological features of adults, including male genital characters and an identification key to known species in Pakistan are presented. Records of prey, host plants and distributions for all identified species are included. The new data (COI-barcode) shows that C. luteopicta (Mulsant, 1866) was recorded first in Pakistan.

Abstract Reformulating the physical processes associated with the evolution of different ailments in accordance with globally shared objectives is crucial for deeper comprehension. This study aims to investigate the mechanism by which the HB virus induces harmful inflammation of the liver, with a focus on early detection and therapy using corticosteroids or chemotherapy. Based on the developed hypothesis, a new mathematical model has been created for this purpose. The recently developed system for HBV is $$SEI_{1} I_{2}R$$ S E I 1 I 2 R , which is examined both quantitatively and qualitatively to determine its actual effect on stability. Reliable conclusions are ensured by examining the system’s boundedness, positivity, existence, uniqueness, and conducting local and global stability analysis-all crucial components of epidemic models. Global stability is tested using Lyapunov first derivative functions to assess the overall impact of asymptomatic persons and chemotherapy treatment. Additionally, the Lipschitz condition is used to confirm the unique solutions for the newly built HBV model using methods from fixed point theory, thus meeting the requirements for uniqueness and existence. Since the population must maintain this property, positivity is confirmed using global derivatives and Lipschitz criteria to calculate the rate of change in each sub-compartment. Applying the Mittag-Leffler kernel with a fractal-fractional operator to continuously monitor the HBV virus for liver cirrhosis infection yields dependable results. Furthermore, the current situation regarding the HBV outbreak pertaining to liver cirrhosis infection, along with the control measures implemented following early diagnosis through asymptomatic measures and chemotherapy treatment under constant observation, are established to prevent chronic stage infections. Simulations have been used to study the true behavior and impact of HBV in asymptomatic persons receiving chemotherapy for liver cirrhosis infection in the community. This research is essential for understanding the spread of viruses and developing control strategies based on our validated findings to mitigate the risk factors associated with liver cirrhosis.

Abstract Background Oral cavity cancer (OCC) represents a significant public health burden in South Asia, ranking as the most prevalent cancer affecting males in Pakistan and contributing considerably to morbidity and mortality. The objective of this study was to evaluate the survival rates in patients diagnosed with OCC in the Pakistani population and identify the factors influencing survival. Methods This was a retrospective cohort study conducted at the Aga Khan University Hospital Karachi, Pakistan. A total of 2238 cases diagnosed with OCC from May 2011 through December 2020, who met the inclusion criteria, were included. Survival analyses at 1-, 3- and 5-year intervals were performed using the Kaplan-Meier method. Adjusted hazard ratios (HR) and their 95% confidence intervals (CIs) were estimated using a Cox regression model to identify the factors significantly influencing survival. Results Among all cases, 54.0% were diagnosed in individuals 50 years of age and below and 73.0% were tobacco users. Overall survival, 1-year, 3-year, and 5-year survival rates were 54.9%, 88.9%, 81.6%, and 75.9% respectively. After adjustment for all the significant confounding and prognostic factors, age was an important factor showing that adults > 50 years had poorer survival compared to ≤ 50 years (HR = 1.43, 95% CI = 1.11–1.83). Poorly differentiated grade of OCC compared to well-differentiated grade (HR = 2.49, 95% CI = 1.61–3.84) and stage IV compared to earlier stages (HR = 3.56, 95% CI = 2.13–5.94) were also associated with poor survival. Conclusion Notably, there was a high occurrence of late stage (III and IV) OCC in individuals aged ≤ 50 years, with the buccal mucosa being the most common site involved. Moreover, a large proportion of our patients had a history of tobacco use. Patients with advanced-stage disease and poorly differentiated grade showed poor survival. Therefore, public awareness about tobacco control for OCC prevention and earlier diagnosis of OCC is urgently needed in our Pakistani population.

Purpose – The usage of social media at the workplace has become an undeniable reality, yet the role of social media use (SMU) in job-related outcomes is still unclear. This study uncovers a chain process through which SMU may strengthen job security perception of employees through social media disorder (SMD) and networking behavior. Design/methodology/approach – This quantitative study used ratings of 197 Emirati students enrolled in a higher education institution located in United Arab Emirates (UAE). The respondents were professionals serving in different public and private organizations in UAE. Findings – The statistical results supported a significant serial mediation of SMD and networking behavior between SMU and job security perceptions of employees. Practical implications – This study offers implications for employees and their supervisors about the usage of social media for strengthening their perceptions of job security. Originality/value – This study contributed to the existing stream of research on SMU to explain a chain process through which employees may benefit from social media to strengthen their perceptions of job security.

Abstract The preservation of the left colic artery (LCA) during rectal cancer resection remains a topic of controversy, and there is a notable absence of robust evidence regarding the outcomes associated with LCA preservation. And the advantages of robotic-assisted laparoscopy (RAL) surgery in rectal resection remain uncertain. The objective of this study was to assess the influence of LCA preservation surgery and RAL surgery on intraoperative and postoperative complications of rectal cancer resection. Patients who underwent laparoscopic (LSC) or RAL with or without LCA preservation resection for rectal cancer between April 2020 and May 2023 were retrospectively assessed. The patients were categorized into two groups: low ligation (LL) which with preservation of LCA and high ligation (HL) which without preservation of LCA. A one-to-one propensity score-matched analysis was performed to decrease confounding. The primary outcome was operative findings, operative morbidity, and postoperative genitourinary function. A total of 612 patients were eligible for this study, and propensity score matching yielded 139 patients in each group. The blood loss of the LL group was significantly less than that of the HL group (54.42 ± 12.99 mL vs. 65.71 ± 7.37 mL, p

Abstract Background Digital literacy influences academic behaviors, especially in medical education, where students encounter considerable academic challenges. However, the effects of this phenomenon on academic self-efficacy and procrastination remain inadequately understood. Objectives This research explores the relationships between digital literacy and academic self-efficacy, emphasizing the mediating role of academic procrastination and its various dimensions among medical students. Methods A descriptive cross-sectional survey was conducted from seven medical colleges from October to December 2023. The research employed meticulously validated measurement tools encompassing a digital literacy scale, academic self-efficacy scale, and academic procrastination scale and collected data on 659 medical students. Descriptive statistics and inferential testing (mediation analysis) were performed to investigate the direct and indirect relationships of the study variables. Results The empirical results confirmed that digital literacy positively influenced academic self-efficacy and negatively correlated with academic procrastination. Among the five dimensions of procrastination assessed, self-efficacy emerged as the most potent mediator, significantly reducing procrastination and enhancing academic self-efficacy, followed by affective procrastination, time management, task avoidance, and behavioral procrastination. Conclusion The findings underscore the critical role of digital literacy in fostering academic self-efficacy and reducing procrastination among medical students. Educators can develop more effective interventions to support medical students in their academic journey by targeting specific dimensions of procrastination, particularly self-efficacy-related procrastination. Future research should consider longitudinal studies to confirm causality and explore these dynamics in diverse educational settings.

Abstract Arsenic (As) contamination in drinking water has been highlighted for its environmental significance and potential health implications. Iron-based filters are cost-effective and sustainable solutions for As removal from contaminated water. Applying Machine Learning (ML) models to investigate and optimize As removal using iron-based filters is limited. The present study developed Deep Learning Neural Network (DLNN) models for predicting the removal of As and other contaminants by iron-based filters from groundwater. A small Original Dataset (ODS) consisting of 20 data points and 13 groundwater parameters was obtained from the field performances of 20 individual iron-amended ceramic filters. Cubic-spline interpolation (CSI) expanded the ODS, generating 1600 interpolated data points (IDPs) without duplication. The Bayesian optimization algorithm tuned the model hyper-parameters and IDPs in a Stratified fivefold Cross-Validation (CV) setup trained all the models. The models demonstrated reliable performances with the coefficient of determination (R2) 0.990–0.999 for As, 0.774–0.976 for Iron (Fe), 0.934–0.954 for Phosphorus (P), and 0.878–0.998 for predicting manganese (Mn) in the effluent. Sobol sensitivity analysis revealed that As (total order index (ST) = 0.563), P (ST = 0.441), Eh (ST = 0.712), and Temp (ST = 0.371) are the most sensitive parameters for the removal of As, Fe, P, and Mn. The comprehensive approach, from data expansion through DLNN model development, provides a valuable tool for estimating optimal As removal conditions from groundwater.

Poropuntius tawarensis is an endemic fish of Laut Tawar that is currently classified as Endangered according to the IUCN Red List. The decline in its population is caused by habitat degradation, overfishing, and the introduction of alien species that act as predators This study aims to analyze the diversity and distribution patterns the population of kawan fish (Poropuntius tawarensis) in Lake Laut Tawar, Central Aceh Regency. Fish samples were collected from four different locations and analyzed using the Shannon-Wiener and Morisita indices. The results indicate that the diversity level of kawan fish (Poropuntius tawarensis) is classified as a "Low level of diversity," with significant differences between sampling locations. All locations (Hakim Balai Bujang: 0.796845; Toweren: 0.301815; Kelitu: 0.753952; Kala Segi: 0.467728) show a low level of diversity, which is H' 1. Analysis using the Standardized Morisita Index indicates that the distribution pattern of kawan fish (Poropuntius tawarensis) in the four study locations tends to be clumped dispersion. This is evidenced by Morisita index values greater than 1 at all sites (Hakim Balai Bujang: 1.2834; Toweren: 1.0212; Kelitu: 1.0053; Kala Segi: 1.0134). These values suggest that the fish tend to concentrate in specific areas within each village rather than being evenly distributed. Keywords: Poropuntius tawarensis Laut Tawar Lake Diversity Distribution Patterns

Objective: To provide a model and explain the obstacles to the universalization of women's sports from the point of view of sports science professors and working women in Mashhad and to provide a model that leads to expansion and development.Method: mixed (qualitative-quantitative). Qualitative statistical population: 45 articles and 20 sports science experts who were selected by targeted sampling, quantitative statistical population: all female employees of Mashhad city, 384 people were selected by cluster sampling. In order to extract elements and group them from Thematic analysis method was used. The proposed model was tested using structural equations based on partial least squares approach. Structured and semi-structured interviews were used to collect data and seven barriers were identified.Findings: seven identified obstacles: personal and physical, family, religious and religious, cultural and social, gender perspective, lack of work of sports organizations and other obstacles: lack of a comprehensive and codified program and lack of attention to public sports and leaders of sports organizations due to Not having the necessary knowledge and non-specialist management have worsened the situation and the policies governing public sports are very unclear and designed on a daily basis. Optimal use of the country's available capital resources has not been implemented in women's sports. Innovation and strengths of the research: lack of plan for the development of women's sports and lack of decision making and planning to encourage and facilitate the presence of financial sponsors in women's public sports

Financial management is an important instrument to improve the well-being of individuals. Individuals must have an understanding of financial management to make financial decisions. This study aims to find out how financial knowledge, financial attitudes, and personality affect financial management behavior. The respondents of this study amounted to 95 from micro, small, and medium enterprises in the batik sector in Yogyakarta. SPSS software is used to process research data. The results show that personality, financial attitudes, and financial knowledge improve financial management behavior. The conclusion of this study explains how individual financial management behavior can be improved with financial knowledge, financial attitudes, and personality.

Objective: This study aimed to determine the prevalence and survival rate of trauma patients who underwent resuscitative thoracotomy (RT) in a level I trauma center in southern Iran.Methods: This cross-sectional descriptive study conducted at Rajaee Hospital (Shiraz, Iran) from March 2018 to October 2022, included trauma patients who underwent RT surgery. Demographic information, vital signs at arrival, mechanism of injury, type of trauma, admission and discharge dates, length of hospital stay, blood transfusions, associated injuries, and clinical and laboratory parameters were evaluated. Statistical analyses were conducted using SPSS software.Results: A total of 147 trauma patients underwent RT. The patients’ mean age was 39±18.49 years. The majority of participants were men (82.40%). The most prevalent type of damage was blunt trauma, with a survival rate of 3%, followed by penetrating trauma, which had a 20% survival rate. The leading cause of trauma-related fatalities was road traffic accidents (78.9%). The majority of RT procedures (75.5%) took place in cardiopulmonary resuscitation (CPR) rooms. The survival group had lower systolic and diastolic blood pressure levels than the non-survival (83.0±34.96 vs. 97.83±33.10) and (40.75±20.91 vs. 62.48±25.36), respectively. Survivors exhibited a significantly higher Glasgow Coma Scale than non-survivors (8.40±6.14 vs. 4.75±3.84).Conclusion: The study revealed a low survival rate among trauma patients undergoing RT. Blunt chest trauma emerged as an independent predictor of poor outcomes. Future studies should further explore indications and outcomes of RT to better inform clinical practice.

The hybridization of metal-organic framework (MOF) with inorganic layers would lead to the discovery of novel hybrid materials that can provide a compelling strategy for enhancing its photocatalytic and electrochemical response. In the present study, a highly efficient multifunctional hybrid material was developed by exploiting the defective layer formed on AZ31 Mg alloy through plasma electrolytic oxidation (PEO) as a nucleation and growth site for Co-MOF. The concentrations of the organic linker 2-Methylimidazole (2,MIm) and cobalt nitrate as a source of Co2+ ions were varied to control the growth of the obtained Co-MOF. Lower concentrations of the 2, MIm ligand favored the formation of leaf-like MOF structures through an anisotropic, two-dimensional growth, while higher concentrations led to rapid, isotropic nucleation and the creation of polyhedral Co-MOF structures. The sample characterized by polyhedral Co-MOF structures exhibited superior electrochemical stability, with the lowest corrosion current density (3.11 ​× ​10−9 A/cm2) and the highest top layer resistance (2.34 ​× ​106 ​Ω ​cm2), and demonstrated outstanding photocatalytic efficiency, achieving a remarkable 99.98 ​% degradation of methylene blue, an organic pollutant, in model wastewater. To assess the active adsorption sites of the Co-MOF, density functional theory (DFT) was utilized. This study explores the changes in morphologies of the coatings of Co-MOF with the change of solution concentration to form coatings with enhanced properties on the metallic substrate, which could establish the groundwork for the development of next-generation multifunctional frameworks with diverse applications.

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.

Abstract The gastrointestinal tract of felines is inhabited by an active and intricate population of microorganisms whose alteration creates disturbances in the immune response and can affect health and disease states. Studies using various analytical methods have identified peculiar trends in various illnesses, with Firmicutes being the most prevalent phylum, followed by Bacteroidetes, Proteobacteria, and Actinobacteria. However, more Firmicutes and fewer Bacteroidetes have been observed in cats infected with Feline coronavirus. Alterations in the composition of these gut microbiota can be solved by microbiota modification through dietary fiber, probiotics, and fecal microbiota transplantation. Therefore, it is critical to understand the composition of the gut microbiota, the changes in and roles of the gut environment, and the importance of these concepts for overall health while considering the exchange of microbes between humans and domestic animals. This review provides comprehensive information on feline gut microbiota composition, modulation, and analytic methods used for characterizing the gut microbiota.