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Developing learning tools based on local potential is necessary because it directly relates to students' daily lives. One of them is a Popular Science Book (PSB), which highlights the potential of gastropods in coastal areas as a subject for observing biodiversity. The implementation of assessments and the value of students' critical thinking abilities still need to be improved. This research aimed to implement the PSB based on its practicality and effectiveness in improving students' critical thinking abilities. The research method used formative evaluation, focused on testing the practicality and efficacy of valid development products. Practicality was analysed based on student response tests through the percentage of responses and the implementation of the learning plan (LP) through categorization. Furthermore, effectiveness was analysed based on student learning outcomes through the percentage of completeness and achievement of critical thinking ability indicators through the N-gain test. The research results prove that the PSB developed was practical and effective. Apperception and interpretation were two of the three stages of implementation of the LP that received the highest average, where students could provide theoretical ideas based on appreciation of environmental issues. This was also supported by the student's critical thinking ability tests, where the interpretation aspect obtained the highest N-gain. The development of local potential-based PSB shows positive impacts, especially ease of use, interest in learning, motivation, apperception, and interpretation. However, it is still necessary to study in more depth the causal factors and solutions for the differences in results between the implementation of the LP at the conclusion stage and the indicators of critical thinking in the inference aspect.

Abstract The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.

Abstract Background Anaplastic large-cell lymphoma primarily involving the omentum is an extremely rare entity with variable clinical presentation. Owing to its rarity and nonspecific clinical manifestation, omental T-cell lymphoma is often diagnosed at a later stage, riddled with complications. While imaging modalities such as computed tomography scan can help a physician reach a diagnosis, cases that present with complications may require a multidisciplinary approach that combines surgical exploration along with consultation from Oncology. Case presentation We hereby report a rare case of a 66-year-old African American male patient who presented to the emergency department with complaints of acute gastrointestinal obstruction. A computed tomography scan of the abdomen and pelvis revealed evidence of an internal hernia and surgical exploration revealed a hemorrhagic and infarcted omentum. Biopsies along with immunophenotypic studies confirmed the diagnosis of anaplastic T-cell lymphoma of the omentum complicated by Massilia timonae infection. Conclusion The case highlights the significance of considering lymphoma, although rare, as a differential in a patient who presents with small bowel obstruction and the importance of investigating for malignancy for early diagnosis and treatment of primary omental lymphomas, before complications develop.

Abstract DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

Abstract As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Key Clinical Message The occurrence of terlipressin‐induced skin necrosis in cirrhotic patients is a rare but serious adverse event that warrants further investigation. Clinicians should be aware of this potential complication in cirrhotic patients receiving terlipressin therapy and closely monitor for any signs of skin necrosis. Early recognition and prompt intervention are crucial in preventing further complications and improving patient outcomes. Further research is needed to better understand the risk factors associated with terlipressin‐induced skin necrosis and to develop effective preventive strategies. Overall, healthcare providers should exercise caution when prescribing terlipressin to cirrhotic patients, weighing the potential benefits against the risks of this rare but significant adverse event. Abstract Terlipressin is commonly used to manage conditions related to portal hypertension, such as hepatorenal syndrome and esophageal variceal bleeding. Despite its therapeutic benefits, terlipressin can rarely lead to severe ischemic complications involving the skin vasculature, known as terlipressin‐induced skin necrosis. We present a 50‐year‐old male with cirrhosis and acute variceal bleeding who developed skin necrosis following terlipressin administration. We performed a comprehensive review of the literature by analyzing 18 case reports/case series comprising 22 cirrhotic patients with terlipressin‐induced skin necrosis. Among these individuals, we found a mean age of 51 years with a male predominance (78%). Further analysis showed that the onset of skin necrosis ranged from 2 to 5 days post‐terlipressin initiation, with bolus administration being predominant (85.7%). The underlying pathophysiological mechanisms of terlipressin‐induced skin ischemia are still elusive but primarily attributed to the vasoconstrictive and thrombogenic effects. Management involves terlipressin discontinuation and supportive care. Physicians should be aware of this potential complication in patients receiving terlipressin and closely observe for any signs of skin rash.

Abstract The present research investigates the double-chain deoxyribonucleic acid model, which is important for the transfer and retention of genetic material in biological domains. This model is composed of two lengthy uniformly elastic filaments, that stand in for a pair of polynucleotide chains of the deoxyribonucleic acid molecule joined by hydrogen bonds among the bottom combination, demonstrating the hydrogen bonds formed within the chain’s base pairs. The modified extended Fan sub equation method effectively used to explain the exact travelling wave solutions for the double-chain deoxyribonucleic acid model. Compared to the earlier, now in use methods, the previously described modified extended Fan sub equation method provide more innovative, comprehensive solutions and are relatively straightforward to implement. This method transforms a non-linear partial differential equation into an ODE by using a travelling wave transformation. Additionally, the study yields both single and mixed non-degenerate Jacobi elliptic function type solutions. The complexiton, kink wave, dark or anti-bell, V, anti-Z and singular wave shapes soliton solutions are a few of the creative solutions that have been constructed utilizing modified extended Fan sub equation method that can offer details on the transversal and longitudinal moves inside the DNA helix by freely chosen parameters. Solitons propagate at a consistent rate and retain their original shape. They are widely used in nonlinear models and can be found everywhere in nature. To help in understanding the physical significance of the double-chain deoxyribonucleic acid model, several solutions are shown with graphics in the form of contour, 2D and 3D graphs using computer software Mathematica 13.2. All of the requisite constraint factors that are required for the completed solutions to exist appear to be met. Therefore, our method of strengthening symbolic computations offers a powerful and effective mathematical tool for resolving various moderate nonlinear wave problems. The findings demonstrate the system’s potentially very rich precise wave forms with biological significance. The fundamentals of double-chain deoxyribonucleic acid model diffusion and processing are demonstrated by this work, which marks a substantial development in our knowledge of double-chain deoxyribonucleic acid model movements.

ABSTRACTMoney laundering (ML) is a critical source of extracting the money illegally from the financial system. It is linked to various types of crimes, including corruption, exploitation of a specific community, drug use, and many others. Detection of ML operations is a difficult task on a global scale due to the large volume of financial transactions. However, it also allows criminals to use financial systems to carry out fraudulent transactions. It mainly concern minimizing the potentially risks associated with money laundering. Anti-money laundering-(AML) tools based on AI-driven applications are now tracking transactions to overcome this challenge. A total of 112 research papers are assessed to identify the literature’s gaps and suggest new directions for the research area accordingly. The findings of this systemic literature review work will not only open new paths for the research community, but will also assist the state agencies in developing an optimal AML system to counter these major issues and provide a healthy environment for their residents. This article seeks to assess the existing situation from various angles and open up new pathways for future research directions to investigate and build high levels of authenticity and security in the financial industry using artificial intelligence (AI).

Objective: To compare the efficacy of intravenous Paracetamol versus intravenous Tramadol in terms of post-operative analgesia in patients undergoing laparoscopic cholecystectomy. Study Design: Quasi-experimental study. Place and Duration of Study: Department of General Surgery, Pak-Emirates Military Hospital (PEMH), Rawalpindi Pakistan, Jan 2020 to Feb 2021. Methodology: A total of 88 patients admitted for planned laparoscopic cholecystectomy were screened as per the selection criteria and were included in the study. They were further divided into two Groups. Each Group received 44 patients. Group-A received intravenous Paracetamol 1 g, while Group-B received intravenous Tramadol 100 mg, both given 6 hourly over 24 hours. Both Groups were evaluated for pain at regular interval via a Visual Analog Scale for pain, for the first 24 hours post-operatively. Results: Mean Visual Analog Scale score post-recovery at 0 minutes was 6.57±1.47 with Paracetamol and 6.84±1.39 with Tramadol, (p=0.38). The difference in both Groups remained non-significant when checked at regular intervals till 18 hours post-recovery where intravenous Paracetamol showed a clearly superior Visual Analog Scale score of 1.00±0.94 versus 1.66±1.06 with Tramadol, (p= 0.007). Thereafter, the difference disappeared again at 24 hours post-recovery. Conclusion: Paracetamol provides analgesia effectively when compared to Tramadol in the first 24-hour post-operative period. Moreover, it seems to be devoid of the adverse effect profile seen with opioid analgesics. Considering the results obtained, the routine use of Paracetamol as first-line analgesic post-operatively is highly recommended.

Key Clinical Message Early recognition and management of seronegative celiac disease, even in the absence of typical serological markers, can prevent complications and ensure better health outcomes in pediatric patients. Consideration of a gluten‐free diet in similar cases can lead to significant clinical improvement. Abstract Celiac disease, characterized by its diverse clinical manifestations, often necessitates adherence to a gluten‐free diet, particularly in pediatric patients for optimal growth and development. This report presents the case of an 11‐year‐old male who exhibited recurrent symptoms of fever and diarrhea progressing to edema and pallor, with a history dating back to age 3. Laboratory findings revealed pancytopenia, hypoalbuminemia, and proteinuria. Despite negative serological markers, noninvasive tests, along with clinical improvement on a gluten‐free diet and supportive measures within a month, suggested celiac disease complicated by transient protein‐losing enteropathy and vitamin B12 deficiency. It is important to note that other malabsorption disorders can also show clinical improvement following a gluten‐free diet. Additionally, the antibiotic treatment received by the patient could have addressed other possible causes of malabsorption, complicating the differential diagnosis. This case highlights the importance of early recognition and management of celiac disease, especially in pediatric patients, to prevent complications and promote optimal health outcomes.

Blockage of cross-drainage hydraulic structures is a significant concern in water resources and civil engineering projects, particularly in urban areas experiencing increased debris supply. During storms or floods, debris can accumulate and restrict the flow capacity of these structures, leading to potential failures and adverse impacts on flood levels. While some argue that blockage at culverts is a non-issue, scientific research supports its significance in specific regions. However, in context of rivers and dams, blockage by Large Wood (LW) is an established issue with plenty of research in terms of its hydraulic impacts, dynamics, modeling and scouring impacts. Specifically in Australasia the Australian Rainfall and Runoff (ARR) initiative recognized the importance of studying blockage at culverts and introduced guidelines incorporating it into design and modeling. These guidelines also included post flood visual inspections of structures to understand blockage, however, this approach has been criticized by hydraulic engineers arguing that post flood visuals can not be considered as the representation of the peak floods blockage. Recently, an approach of using visual information to interpret the blockage has been adopted as a new dimension to the problem. This paper, therefore, highlights the advances, challenges, and opportunities in studying blockage, emphasizing the need for data-driven approaches and interdisciplinary collaboration. Understanding and addressing blockage are crucial for ensuring the efficient operation and longevity of hydraulic structures and promoting the resilience of infrastructure systems in the face of evolving environmental conditions.

Water and fertilizer losses have often affected wheat yield per unit area in China and Pakistan. The 2-year field experiments were conducted at six research stations. The experimental sites in China were Tongzhou, Qiliying and Yuanyang. In Pakistan were Faialsaabad, TandoJam and Dokri. Fertilizer treatments were: N275, N207 and N135 kg ha−1 in Tongzhou and N125, N105 and N90 kg ha−1 in Faisalabad. While the irrigation treatments were: W210–230, W160–190 and W110–140 mm ha−1 in Qiyliying, W350, W305 and W250 mm ha−1 in Yuanyang, W435, W400 and W365 mm ha−1 in TandoJam and W380, W340 and W300 mm ha−1 in Dokri. The grain yields, water and N losses were simulated using the WHCNS model. The model showed a good ability to capture the effects of management practices and read the environmental diversity. The study revealed that major contributors to water and N losses were N leaching (r=0.98), evaporation and drainage (r=0.77–0.96) rates. In Tongzhou, the N207 treatment maintained yield, NUE and decreased N loss. In Faisalabad, the N125 increased yield but led to higher N loss and reduced NUE. Furthermore, the decline in surface soil water content was a common issue in Pakistani sites and wheat yields were 27.48–41.60 % lower than in China. In Qiliying, the W160–190 and W110–140 and in Yuanyang, the W300 and W250 treatments decreased the annual average water loss by 33.49–12.38 mm and 66.06–55.01 mm, respectively. In TandoJam, the W400 and W365 and in Dokri, the W380 and W340 treatments decreased water loss by 72.34–45.87 mm and 56.84–37.82 mm compared to the full irrigation amount. Overall, results showed that 17.39–23.81 % of water use could be decreased without yield penalty in Qiliying and 14.29 % in Yuanyang. In Pakistani sites of TandoJam and Dokri, a decreased irrigation amount of 10.5 % is recommended.

Background: Human face is highly vascular region and vascularity to the skin and other organs is an important indicator of health and disease. Change in blood flow is affected by aging, diabetes, high blood triglycerides, cigarette smoking etc. With so many factors that can alter blood flow in the skin, normal blood flow is important to know for comparison to diseased state. Blood flow in superficial arteries of face has not yet been described, therefore this study was designed to establish baseline blood flow values in arteries of face. Materials and Methods: Blood flow of right and left side was assessed at level of facial and infraorbital artery. Categorical variables were presented in form of frequency and percentages was done by using Mann-Whitney U. Wilcoxon Signed Ranks test was used to compare left and right facial and infra orbital arteries. Results: Peak systolic velocity of right and left facial artery had a significant difference having right side mean of 67.02±12.48 and left side mean as 72.67±11.69. Facial artery diameter of right and left side also had significant difference with mean of 0.14±0.02 and 0.15±0.02 respectively. Conclusion: No difference was found between vascularity in male & female and left or right side. The study might be useful to establish normal baseline values of various parameters on both sides of face in male and female adults. This study may become important reference for future studies measuring blood flow and even progression of vascular diseases may be assessed by indexes developed on the basis of these studies.

Purpose: This paper aims to deeply understand online trying to be entrepreneurs' intentions at starting online firms in Pakistan by investigating the influence of the internet as an evil godhead’s disposition and interactive computer communications worthiness of people’s willing to act on their own initiative toward self- employment stimulation, understood either as an informal chance or an instance of an ability to make profit using the internet called an online business. Design/Methodology/Approach: This research created a theoretical model drawing on the planned behavior theory to investigate the impact of online entrepreneurial attitude, subjective norms, perceived control behavior, and online business opportunity recognition on OEI and OEB. The methodology used in this study is PLS structural equation modeling (PLS-SEM). The researchers analyzed collected responses from 260 online questionnaires using Smart PLS 4.0.9. 8software. Findings: The results showed a significant positive influence of online entrepreneurial attitude, subjective norms, perceived behavioral control and online business opportunity recognition on OEI and OEB in the Pakistan. Interestingly, despite that many prior studies showing the significant impact of subjective norms on OEI, the analysis showed insignificant impact of online entrepreneurial subjective norms on OE. More results are demonstrated in the study. . Implications/Originality/Value: This study results contribute to an enhanced reciprocal relationship by adding depth to the digital presence and services through online business opportunities which are turned around once they occur and begin. Consequently, there is the provision of rich four insights into the aforementioned TPB constructs of entrepreneur’s starting an online e-business other than it. Furthermore, Pakistan-specific focus on said phenomenon has hitherto not been effectively articulated. Thus, this research does not merely seek to fill this gap but also deal with it uniquely by examining how individuals act when setting up their business online within Pakistan through empirical analysis.

Tuberculosis (TB) is a global health issue, and the increasing resistance to anti-tuberculosis therapy (ATT) highlights the need for innovative treatment regimens. Among the different collateral effects of resistance development, collateral sensitivity (CS) is a phenomenon in which drug-resistant mutants display increased susceptibility to other drugs. The mechanisms of CS in Mycobacterium tuberculosis (MTB) include increased effective drug concentration due to altered transport proteins (MmpL3, MchK), disruption in pathways of cell wall synthesis (inhibited GlmR) and redox homeostasis (katG and mshA mutations), enhanced target affinity (EfpA), and downregulation of drug-deactivating enzymes (BlaC). Drugs that exploit CS include oxidative stress-inducing agents (isoniazid, ethionamide, bedaquiline, clofazimine, auranofin, thioacetazone, thiocarlide, etc.), efflux pump inhibitors (BRD 8000 and BRD 9327), rifampicin, beta-lactams, aminoglycosides (streptomycin, amikacin, and kanamycin), macrolides (erythromycin), and antimicrobial peptides. CS-based treatment strategies involve creating new drug combinations and drug cycling protocols to reduce the development of resistance, choosing drugs that exploit CS when resistance is present, and artificially emulating CS mechanisms by developing drugs that target novel molecular targets. Limitations of this concept include a lack of evolutionary conservation of CS, in vivo-exclusive pathogenic mutants, only slight reduction in minimum inhibitory concentrations, emergence of escape mutants, scarce data on horizontal resistance gene transfer in MTB, and a lack of clinical relevance of some tested compounds. However, the closed pan-genome and low intrinsic mutation rate of MTB could theoretically make CS-based regimens feasible. Still, the empirical evidence for such clinical use is lacking and future research could focus on it.

Technological advancements have significantly progressed and innovated across various industries. However, these advancements have also led to an increase in cyberattacks using malware. Researchers have developed diverse techniques to detect and classify malware, including a visualization-based approach that converts suspicious files into color or grayscale images, eliminating the need for domain-specific expertise. Nonetheless, converting files to color and grayscale images may result in the loss of texture details due to noise, adversely affecting the performance of machine learning models. The aim of this study is to present to assess the texture features and noise contributions of the red, green, and blue channels in color images. We propose a novel method to enhance model performance in terms of accuracy, precision, recall, f1-score, memory utilization, and computing cost during testing and training. This study introduces an approach involves separating color channels into individual red, green, and blue datasets and using various Discrete Wavelet Transform levels to reduce dimensions and remove noise. The extracted texture characteristics are then used as input for machine learning classifiers for training and prediction. Through comprehensive evaluation, we compare the performance of grayscale images with that of the red, green, and blue datasets. The results show that grayscale images significantly lose critical textural artifacts and perform worse than the color channels. Notably, employing extra tree classifiers yielded the best results, achieving an accuracy of 98.37%, precision of 98.64%, recall of 97.60%, and an f1-score of 98.04% with the red channel of color dataset.

Due to complex nature of metabolic pathways, E. coli metabolic model kinetic parameters are difficult to detect experimentally. Thus, obtaining accurate kinetic data for all reactions in an E. coli metabolic model is a technically-challenging process. So, Garra Rufa-inspired Optimization (GRO) Algorithm is applied to the primary metabolic network of E. coli as a model to estimate small-scale kinetic parameters and increase the kinetic accuracy. Also, the Differential Algebraic Equations (DAE) is used to represent the glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate, and acetate production pathways of Escherichia coli in the metabolic network. Based on the behavior of the Garra Rufa fish, a route is modelled in which particles are sorted into groups and each group is guided by the best value. In addition, the fitness of the group leaders determines whether or not these particles are able to switch groups. In this study, experimental data was used to estimate seven kinetic parameters. However, the numerical results of The Relative Error (RE) and the Mean Error (ME) reveal that the observed and anticipated data are in line with the results. As a result of this new method, it was discovered that small-scale and even whole-cell dynamic models can be estimated accurately.

Abstract Real-world decision-making problems often include multi-polar uncertainties dependent on multi-dimensional attributes. The m-polar fuzzy (mF) sets can efficiently handle such multi-faceted complications with T-norm based weighted aggregation techniques. The Aczel–Alsina T-norms offer comparatively flexible and accurate aggregation than the other well-known T-norm families. Consequently, this work introduced novel mF Aczel–Alsina aggregation operators (AOs), including weighted averaging (mFAAWA, mFAAOWA, mFAAHWA) and weighted geometric (mFAAWG, mFAAOWG, mFAAHWG) AOs. The fundamental properties, including boundedness, idempotency, monotonicity, and commutativity are investigated. Based on the proposed AOs, a decision-making algorithm is developed and implemented to solve two detailed multi-polar site selection problems (for desalination plant and for wind-power plant). Finally, a comparison with mF Dombi and mF Yager AOs reveals that different T-norm based AOs may yeild different solutions for the same problem.

GaN high-electron-mobility transistor (HEMT) has superior features of wide band gap, high electron mobility and very high electric field strength due to its material advantages. By using the GaN HEMT, switching frequency can be enhanced up to megahertz with extremely high efficiency. Unfortunately, GaN HEMTs accomplished by undesirable switching oscillations and voltage spikes due to extremely fast switching frequencies with very high ${dv}/{dt}$ , ${di}/{dt}$ and parasitic parameters. In this paper, RLC equivalent circuit models are developed for turn on and turn off conditions, including all parasitic components. In addition, the relative effect of each parasitic parameter is analyzed and estimated. Moreover, simple mathematical model is developed for theoretical analysis of switching oscillation phenomenon and, for guidance of snubber or damping circuit design. To validate these simple equivalent circuit models, both circuit simulation and experimental measurements are employed.

Early detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural network and a long short-term memory was developed as the proposed model. Seven other classifiers were treated as the baseline models: logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, artificial neural network, long short-term memory, and convolutional neural network. All models were trained using a healthcare dataset of 5110 patients’ health profiles. A synthetic minority oversampling technique was deployed to balance the data. Metrics such as accuracy, precision, F1-score, recall, area under the curve, and confusion metrics were used to evaluate the models’ performance. With a 95.9% accuracy, the proposed model outperformed the models employed in this study and improved the accuracy of prior studies that used the same dataset. The Shapley Additive Explanations method was applied to explain the result obtained by the best model. The proposed model was created to predict ischemic stroke. It considers each patient’s profile, allowing for personalized decision-making in resource-constrained settings.

Abstract The northeastern Indian Ocean exhibits distinct hydrographic characteristics influenced by various local and remote forces. Variations in these driving factors may alter the physiochemical properties of seawater, such as dissolved oxygen levels, and affect the diversity and function of microbial communities. How the microbial communities change across water depths spanning a dissolved oxygen gradient has not been well understood. Here we employed both 16S rDNA amplicon and metagenomic sequencing approaches to study the microbial communities collected from different water depths along the E87 transect in the northeastern Indian Ocean. Samples were collected from the surface, Deep Chlorophyll Maximum (DCM), Oxygen Minimum Zone (OMZ), and bathypelagic layers. Proteobacteria were prevalent throughout the water columns, while Thermoproteota were found to be abundant in the aphotic layers. A total of 675 non-redundant metagenome-assembled genomes (MAGs) were constructed, spanning 21 bacterial and 5 archaeal phyla. The community structure and genomic information provided by this dataset offer valuable resources for the analysis of microbial biogeography and metabolism in the northeastern Indian Ocean.

Hasil survei awal yang dilakukan oleh tim pengabdian, permasalahan yang dihadapi oleh mitra peternak itik di Desa Batee Puteh, Langsa Lama, Kota Langsa, Aceh yaitu: 1) Kurangnya pemahaman mitra tentang budidaya ternak itik yang dapat meningkatkan produktivitas dan kualitas telur serta daging itik; 2) Pengeluaran untuk pakan ternak yang terbilang cukup mahal sehingga diperlukan alternatif pakan dari bahan alami. Kayambang dapat dimanfaatkan untuk bahan membuat pakan ternak yang kaya nutrisi. Solusi yang ditawarkan oleh tim pengabdian berupa: 1) Penyuluhan mengenai cara meningkatkan produktivitas dan kualitas telur serta daging itik; 2) Pelatihan dan pendampingan mitra dalam pembuatan pakan dari fermentasi kayambang. Hasil dari kegiatan yaitu mitra mengalami peningkatan pengetahuan dan keterampilan dalam membuat pakan dari kayambang sebesar 39,3 %; tingkat kepuasan peserta terhadap kegiatan PKM yaitu sebesar 47,35 % untuk kategori sangat setuju dan 52,65 % lainnya setuju dan berharap keberlanjutan program untuk kegiatan pemberdayaan lainnya.

Identification of nutritious rice varieties through non-destructive detection technology is important for high-quality seed production. With the development of technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining attention in the scientific community. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain filling stage. Different models (stepwise multiple linear regression (SMLR) and the Back Propagation Neural Network (BPNN)) for estimating rice protein content based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R2 = 0.9516 and RMSE = 0.3492, indicating high accuracy and stability in the model. Overall, hyperspectral imaging technology combined with various models could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner.

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions.