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Background Pump thrombosis is a serious complication of continuous-flow left ventricular assist device (CF-LVAD) therapy. In this study, we aim to report a novel protocol of an intermittent, low-dose, and slow infusion of tissue plasminogen activator (alteplase). Case summary We treated seven LVAD pump thrombosis events (HeartMate® II and HeartWare) in four patients with a median age of 52 years (31–63), and all were female. The protocol was applied from January 2015 to December 2018, and it consisted of an intermittent, low-dose, and slow infusion of systemic thrombolytic therapy in the intensive care unit. This therapy resulted in successful resolution of pump thrombosis in six out of seven events. Bleeding complication occurred in one patient, which included a ruptured haemorrhagic ovarian cyst and a small cerebellar intra-parenchymal haemorrhage. All patients were discharged home in a stable condition, except one patient who died during hospitalization because of severe sepsis, pump thrombosis with subsequent pump exchange, and multi-organ failure. Discussion A low-dose, prolonged, and systemic thrombolytic infusion protocol is an effective and relatively safe treatment that can lead to a sustained resolution of pump thrombosis with low bleeding complications and failure rates. [ABSTRACT FROM AUTHOR]

In this article, the ratio-dependent prey-predator system perturbed with time noise is numerically investigated. It relates to the population densities of the prey and predator in an ecological system. The initial prey-predator models only depend on the time and a couple of the differential equations. We are considering a model where the prey-predator interaction is influenced by both space and time and the need for a coupled nonlinear partial differential equation with the effect of the random behavior of the environment. The existence of the solutions is guaranteed by using Schauder's fixed point theorem. The computation of the underlying model is carried out by two schemes. The proposed stochastic forward Euler scheme is conditionally stable and consistent with the system of the equations. The proposed stochastic non-standard finite difference scheme is unconditionally stable and consistent with the system of the equations. The graphical behavior of a test problem for different values of the parameters is shown which depicts the efficacy of the schemes. Our numerical results will help the researchers to consider the effect of the noise on the prey-predator model. [ABSTRACT FROM AUTHOR]

This paper deals with the study of the reaction–diffusion epidemic model perturbed with time noise. It has various applications such as disease in population models of humans, wildlife, and many others. The stochastic SIR model is numerically investigated with the proposed stochastic backward Euler scheme and proposed stochastic implicit finite difference (IFD) scheme. The stability of the proposed methods is shown with Von Neumann criteria and both schemes are unconditionally stable. Both schemes are consistent with systems of the equations in the mean square sense. The numerical solution obtained by the proposed stochastic backward Euler scheme and solutions converges towards an equilibrium but it has negative and divergent behavior for some values. The numerical solution gained by the proposed IFD scheme preserves the positivity and also solutions converge towards endemic and disease-free equilibrium. We have used two problems to check our findings. The graphical behavior of the stochastic SIR model is much adjacent to the classical SIR epidemic model when noise strength approaches zero. The three-dimensional plots of the susceptible and infected individuals are drawn for two cases of endemic equilibrium and disease-free equilibriums. The results show the efficacy of the proposed stochastic IFD scheme. [ABSTRACT FROM AUTHOR]

Abstract This study projects future water demand scenarios in the Upper Indus Basin, focusing on reference, high population growth, increased irrigation, and lower population growth scenarios. The baseline scenario indicates a significant rise in water demand from 35.74 billion cubic meters (BCMs) in 2020 to 60.28 BCM by 2035, driven by population growth and increased domestic water consumption. High population growth exacerbates this demand, reaching 62.96 BCM by 2035. This research aims to address domestic water needs under various growth scenarios, considering factors such as population growth rate and per capita consumption. The study employs integrated hydrological modeling to simulate water demand under different socioeconomic conditions. Key methods include analyzing baseline water demand, projecting future scenarios, and evaluating the impact of increased irrigation and population growth on water resources. Results reveal that without intervention, stagnant water supply management will lead to severe water shortages. Increased irrigation, influenced by a 3% growth in irrigated land, pushes agricultural water demand to 56.37 BCM by 2035. Mitigation efforts, such as a 15% reduction in domestic water consumption, could decrease overall demand to 51.23 BCM by 2035. Further reductions are explored through a 50% cut in agricultural water consumption, involving efficient irrigation techniques. The study highlights the critical role of technology and farmer awareness in achieving these reductions, despite current irrigation scheme losses of 20%. A lower population growth scenario shows a contrasting trend, with water demand decreasing to 49.11 BCM by 2035, attributed to a 1.8% population growth rate and decreased per capita consumption to 82 m3 per day. These findings underscore the importance of proactive water management strategies, technological advancements, and demographic considerations in addressing future water demand challenges in the Upper Indus Basin. This research provides proper insight into the impact of varied socioeconomic scenarios on water resources and the necessity for strategic interventions.

Penelitian ini bertujuan untuk mengungkap bagaimana pemilik usaha warung kopi dapat mempertahankan keberlanjutan usahanya dari perspektif akuntansi dan nilai-nilai Pancasila. Penelitian ini menggunakan metode kualitatif, dengan pendekatan fenomenologi. Informan yang dipilih untuk penelitian ini adalah tiga orang pemilik warung kopi di kota Surabaya. Hasil penelitian menunjukkan bahwa pemilik usaha warung kopi jalanan memanfaatkan berbagai strategi untuk menjaga keberlangsungan bisnisnya. Strategi tersebut meliputi pengelolaan keuangan yang sederhana, juga membangun hubungan baik dengan pelanggan dan komunitas lokal. Selain itu, penelitian ini menemukan bahwa nilai-nilai Pancasila, seperti ketuhanan, kemanusiaan, persatuan, gotong royong serta keadilan sosial memainkan peran penting dalam membentuk budaya bisnis yang inklusif dan berkelanjutan. Para pemilik usaha warung kopi menanamkan nilai-nilai ini dalam praktik bisnis sehari-hari, yang tidak hanya memperkuat hubungan dengan pelanggan tetapi juga dengan masyarakat sekitar. Penelitian ini berkontribusi untuk memperkaya literatur tentang akuntansi dan bisnis, sekaligus menyoroti pentingnya nilai-nilai Pancasila dalam mempertahankan usaha mikro di Indonesia. Temuan ini memberikan wawasan baru tentang bagaimana nilai-nilai lokal dapat diintegrasikan dalam praktik bisnis untuk mendukung keberlanjutan ekonomi di tingkat mikro.

Abstract The authors explore the various obstacles and possible approaches that the UK may take to fulfil its goal of having net‐zero greenhouse gas emissions by 2050. The paper thoroughly examines several aspects of this project, such as the modernisation of infrastructure, the energy transition, the economic effects, the obstacles to research and development, changes in behaviour, and the frameworks for policy and regulation. With a 44% decrease from 1990 levels by 2021, it showcases the UK's noteworthy achievement in lowering emissions and its ambitious initiatives, such as the £12 billion Ten Point Plan, to accelerate this development. The difficulties of switching from reliance on fossil fuels to renewable energy sources, their implications for the economy, and the necessity of green technology innovation are all covered in the article. It also discusses the behavioural sides of this shift, highlighting the need to change one's lifestyle and engage the public. To address these issues, the importance of international cooperation and policymaking is emphasised. Insights into potential remedies are provided by the article, which includes energy efficiency initiatives, investments in renewable energy, assistance for clean technology R&D, green funding options, public awareness campaigns, international cooperation, and regulatory frameworks. Every one of these alternatives is examined for possible effects and obstacles. The article concludes that reaching net zero in the UK is a complex but necessary objective that calls for a concerted strategy that strikes a balance between social and economic concerns and environmental sustainability.

Abstract Conventional control charts track changes in the process by using predefined process parameters. Conversely, during online monitoring, adaptive control charts modify the process parameters. To improve the process dispersion monitoring in various operational environments, this study presents an adaptive exponentially weighted moving average (AEWMA) control chart based on support vector regression (SVR). This study investigates the efficacy of different kernels such as linear, polynomial, and radial basis functions (RBF) within the SVR framework. By adapting the smoothing constant to the shift’s size in process dispersion, the suggested SVR-based AEWMA control chart makes better use of the strengths of the RBF kernel to identify shifts in the process dispersion. To demonstrate the method’s effectiveness, real-life data is used in a practical application, highlighting the adaptability and reliability of the SVR-based AEWMA control chart for monitoring process dispersion. The code and supplementary data set file may be found at ( https://github.com/muhammadwaqaskazmi/ARL-SDRL-Codes ).

Abstract Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm.

Abstract Background Subtropical forest plant diversity, characterized by a wide range of species adapted to seasonal variations, is vital for sustaining ecological balance, supporting diverse wildlife, and providing critical ecosystem services such as carbon sequestration and soil stabilization. The Changa Manga Forest, an ecologically rich area with varied vegetation, was analyzed to understand the intricate relationship between plant diversity and environmental factors. This study investigates the diversity patterns, vegetation structure, and environmental influences on forest biodiversity. Methods A comprehensive survey was conducted across 127 stands within the Changa Manga Forest to document plant species and classify vegetation communities. Soil samples were collected and analyzed for key physicochemical parameters, while multivariate statistical methods, including hierarchical clustering and ordination, were applied to examine the relationships between vegetation structure and environmental factors. Diversity indices and beta diversity components were calculated to assess variations across plant communities. Results The species were classified into six distinct vegetation communities: Neltuma-Ziziphus-Malvestrum (NZM), Broussonetia-Lantana-Morus (BLM), Dalbergia-Lantana-Solanum (DLS), Morus-Abutilon-Ricinus (MAR), Eucalyptus-Vachellia-Sorghum (EVS), and Bombax-Leucaena-Croton (BLC). Analyses using hierarchical clustering and ordination methods revealed significant differences in species composition among these communities, with NZM and DLS exhibiting the highest dissimilarity. Canonical Correspondence Analysis (CCA) indicated that environmental factors such as soil pH, available phosphorus (AP), and organic matter percentage (OM%) are crucial in shaping plant distribution, though the total explained variation remained relatively low. Diversity indices varied significantly among communities, with the NZM community showing the highest Shannon and Simpson diversity, while EVS exhibited the lowest. The beta diversity analysis revealed a high species turnover between certain communities, indicating complex ecological interactions. Our results indicate significant variability in plant community composition and diversity patterns, influenced by edaphic factors and environmental gradients. We anticipate that future environmental changes, such as shifts in soil properties, precipitation patterns, and increased human activity, may exacerbate declines in local plant species richness and disrupt community structures. To preserve the invaluable biodiversity of the study area for future generations, it is essential to implement timely and effective conservation and management strategies.

This research proposes a text data security method using a combination of Linear Congruential Generator (LCG), Advanced Encryption Standard (AES) Cipher Block Chaining (CBC) mode, and Least Significant Bit (LSB) steganography technique on digital images. The message scrambling process using LCG produces ASCII characters as noise that is inserted in the original message. After that, the message is encrypted using AES-256 CBC to provide additional security. The encryption result is then hidden in the digital image through LSB steganography technique. Tests were conducted on images with JPEG and BMP formats to measure the visual quality after the data insertion process, as measured by PSNR (Peak Signal-to-Noise Ratio). The test results show a PSNR value of 56.60 dB for JPEG images and 70.84 dB for BMP images. In addition, the insertion process in JPEG images degrades the image quality, mainly due to lossy compression, compared to the lossless BMP format. This study concludes that the proposed combination of methods is effective in hiding messages in images, but is susceptible to compression on lossy formats such as JPEG. The use of lossless image formats such as BMP or PNG is recommended to maintain data integrity.

Abstract Based on sociocultural theory and pragmatism philosophical underpinnings, this study explores the classroom assessment practices of tertiary EFL practitioners, focusing on their alignment with contemporary constructivist assessment trends, methods, and approaches, rather than psychometrical assessments. The study extends knowledge on language teachers’ preparedness for implementing classroom assessment literacy. Participants were tertiary EFL instructors from four higher educational institutions in Saudi Arabia. Adopting an explanatory sequential mixed-methods research design, data were collected through self-reported questionnaires, classroom observations, and a review of assessment documents and artifacts. Descriptive statistics were used to analyze quantitative data and thematic analysis qualitative data. The dominance of traditional over alternative assessment methods was demonstrated, indicating gaps in teachers’ classroom assessment literacy. The findings highlight the contradiction, discrepancy, and complexity of the relationship between teachers’ articulated and exercised assessment practices. They provide baseline data for L2 classroom assessment policy, a classroom-based assessment framework, and a guide for teachers’ professional development in language assessment literacy.

Abstract The mung bean crop (Vigna radiata (L.) R. Wilczek) is widely recognized as a key source of pulse food worldwide. However, this crop suffers substantial yield losses due to humid environments, particularly from infestations by the fungal pathogen Macrophomina phaseolina, which causes charcoal rot disease. This infestation results in significant agronomic losses, affecting both the crop’s growth characteristics and overall yield. Previous research suggests that these losses can be mitigated through environmentally friendly soil amendments, such as biochar, as well as by applying various nanofungicides. This study aims to explore the potential of biochar and zinc oxide nanoparticles (ZnONPs) to reduce the severity of charcoal rot disease and enhance the agronomic traits and yield of mung bean plants affected by this disease. The experiment was conducted in triplicate, applying ZnONPs at three concentrations (5, 10, and 20 mg. L− 1) via foliar spraying, combined with two levels of biochar (20 g and 40 g per pot). Positive and negative control treatments were also included for comparison. The results demonstrated that applying 40 g of biochar per pot and 20 mg. L− 1 of foliar-applied ZnONPs increased the activities of the anti-oxidative defence enzymes. Additionally, this treatment strategy boosted the plants’ disease resistance mechanisms, leading to lower mortality rates and reduced levels of malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) by 61.7% and 49.23%. Moreover, the treatment positively impacted key growth parameters, increasing total chlorophyll content by 43%, plant height by 47%, and legume count per plant by 80.4%. The application of biochar and ZnONPs also improved seed protein content, reflecting an enhancement in nutritional quality. This study supports the use of biochar and ZnONPs as biostimulants to manage yield losses in mung bean crops affected by charcoal rot disease. The future prospects of using ZnONPs and biochar as treatments in agriculture are promising, as they offer innovative, eco-friendly solutions to enhance crop productivity, improve soil health, and reduce reliance on synthetic chemicals, paving the way for more sustainable and resilient agricultural systems.

Abstract Glutathione Peroxidase 1 (GPx1) gene has been reported for its role in cellular redox homeostasis, and the dysregulation of its expression is linked with the progression of diverse cancers. Non-synonymous single nucleotide polymorphism (nsSNPs) have been emerged as the crucial factors, playing their role in GPx1 overexpression. To understand the deleterious mutational effects on the structure and function of GPx1 enzyme, we delved deeper into the exploration of possibly damaging nsSNPs using in-silico based approaches. Eight widely utilized computational tools were employed to roughly shortlist the deleterious nsSNPs. Their damaging effects on structure and function of the genes were evaluated by using different bioinformatics tools. Subsequently, the three final proposed deleterious mutants including mutations rs373838463, rs2107818892, and rs763687242, were docked with their reported binder, TNF receptor-associated factor 2 (TRAF2). The lowest binding affinity and stability of the docked mutant complexes as compared to the wild type GPx1 were validated by molecular dynamic simulation. Finally, the comparison of RMSD, RMSF, RoG and hydrogen bond analyses between wild-type and mutant’s complexes validated the deleterious effects of proposed nsSNPs. This study successfully identified and verified the possibly damaging nsSNPs in GPx1 enzyme, which may be linked the progression of various types of cancer. Our findings underscore the value of in-silico approaches in mutational analysis and encourage further preclinical and clinical trials.

Abstract With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection.

Abstract The transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

Abstract Significant attempts have been made to make the electrical grid more intelligent and responsive to better meet customers' requirements while boosting the stability and efficiency of current power systems. Smart grid technologies, which have just recently emerged, facilitated the incorporation of demand response (DR) by introducing an information and communication backbone to the current system. The Internet of Things (IoT) has emerged as a key technology for smart energy grids. Security concerns have emerged as a major obstacle to the widespread adoption of IoT‐enabled devices because of the inherent Internet connectivity of these smart gadgets. Therefore, security is a crucial factor to address before the widespread implementation of IoT‐based devices in power grids. In this study, the framework and architecture of smart grids that are enabled by the IoT are first examined. Then, the role of IoT for DR in smart grids and different approaches adopted worldwide to make DR schemes more effective, have been discussed in detail. Finally, the authors discuss how IoT‐enabled smart grids can benefit from cutting‐edge solutions and technologies that make them more secure and resistant to cyber and physical attacks.

Abstract Background 17.9 million deaths worldwide were attributable to cardiovascular diseases. Basic life support is one of the crucial strategies that could increase chances of cardiac arrest victims’ survival rate by nurses and other healthcare providers. Aim The aims of this study was to examine the retention of the BLS knowledge among nurses in Palestine. Methods A descriptive cross-sectional design was used to collect data from 108 nurses between February 2022 and April 2022 from two AHA-ITCs in Palestine. The instrument consisted of two sections; demographics and knowledge test which was developed by the researcher and contain a written examination containing 25 multiple-choice questions. Results Out of 160 distributed questionnaires, 108 were completed by nurses as a convenience sampling technique. Over half of the participants were male (54.6%), and the majority had a bachelor’s degree in nursing (75%). Analysis identified that there was a significant difference (t (107) = 18.02, p

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values.

Abstract Coal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations.