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The global surge in the electric vehicle market shows a growing commitment to sustainable transportation. However, Malaysia’s progress in EV development has been slow. This article presents the challenges to EV adoption in Malaysia. The study identifies critical issues such as inadequate charging infrastructure, uneven distribution of charging stations, high costs of ownership, and low consumer awareness, acceptance, and safety. These challenges collectively impact the adoption rate of EVs in the Malaysian market. The study also suggests potential solutions, such as expanding the charging infrastructure, developing cost-effective strategies, implementing fast charging with renewable integration, enforcing safety protocols and standards, and establishing a battery recycling industry. By addressing these key areas, the research highlights a feasible path to boost EV adoption in Malaysia that aligns with international trends and promotes a greener future.

Abstract The study of genotype × environment interaction is crucial for plant breeders to introduce new cultivar(s) with improved and stable yield performance. The productivity of chickpea crops is very low in Pakistan. It requires the selection of genotypes with optimal productivity for diverse environmental conditions. Fourteen different chickpea genotypes were assessed using the linear mixed model to evaluate genotypes across four diverse chickpea growing regions, including Attock (Punjab), Bhakkar (Punjab), Karak (Khyber Pakhtunkhwa), and Larkana during the year 2017–19. The environmental effect was pronounced and contributed significantly to variation (25.8%) in grain yield. Analysing genotype × environmental interactions at multiple locations facilitates ranking superior genotypes that excel in specific or diverse environments. Notably, the genotypes, viz., Fakhr-e-Thal and Bhakkar-2011, demonstrated superior performance in terms of overall grain yield. Utilising a multitrait stability index, Bittal-2016 and Thal-2006 exhibited the most stable genotypes across various environments and were observed suitable for diverse growing regions. While, for specific environmental conditions, genotypes, i.e., KK-1, Noor-2013 and Fakhr-e-thal, exhibited higher yields and stability. This showed their adaptability to the particular environment. The present study revealed that Larkana as the mega environment was conducive to higher yield, while Attock, Bhakkar, and Karak sites were less favorable for KK-3, DG-89, and Dasht. The BLUP outperformed the AMMI in our analysis of evaluating multi-location trials. The integration of WAASBY and MTSI indices proved useful in differentiating between high yielding genotypes and those with stable performance. These tools are essential for breeders aiming to select genotypes that will perform consistently across diverse environmental conditions.

Abstract In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

One of the interesting energy technologies is a hybrid (asymmetric) device, has an exceptional power density, a quick charging/discharging process, and outstanding cycle stability. Herein, tungsten oxide (W18O49) and its nanocomposites with reduced graphene oxide (rGO) and cerium oxide (CeO2) were hydrothermally synthesized. The phase stability and crystalline nature were confirmed by X-ray diffraction (XRD). Scanning electron microscopy (SEM) was employed to analyze the morphological features of the synthesized materials, while UV–visible and photoluminescence techniques were used to evaluate their optical characteristics. The rGO/W18O49 nanocomposites exhibit an improved specific capacity of 723 C/g at 1 A/g which is better than W18O49 (519 C/g at 1 A/g) and CeO2/W18O49 (385 C/g at 1 A/g). XPS assessment confirms that the rGO increases conductivity by lowering binding energies in W and O bondings. Due to the high specific capacity of rGO/W18O49, the material is employed as an anode material for hybrid supercapacitors. The fabricated device depicts an excellent energy density of 11.25 Wh/kg with 2250 W/kg power density at 3 mA/g current density. Moreover, the fabricated device showed a remarkable rate capability of ∼ 93.26 % with 88.29 % columbic efficiency.

This study focuses on revealing closed-form solutions for traveling waves in the nonlinear dynamical model of dust-acoustic waves in multispecies of dusty plasma. Plasma consists of cold dust grains, along with isothermal electrons and positive ions for balance. Fluid equations simplify through reductive perturbation, yielding the modified Korteweg–de Vries equation for analyzing weak dust-acoustic solitons. Applying this method to a specific scenario involving negatively charged dust grains and protons with all electrons bound to the dust grains shows support for rarefactive supersonic solitons, contrasting with typical compressive ion-acoustic solitons. Introducing streaming reveals a subtle soliton amplitude decrease. Using the Generalized exp(-S(φ)) expansion method effectively derives various soliton solutions, including kink, anti-kink, singular, hyperbolic functions, and diverse solitary waves. Furthermore, the equation’s dynamic behaviors are explored through equilibrium point bifurcations. Employing a Galilean transformation, the model is transformed into a planar dynamical system, and qualitative inspection is performed. Phase portraits depict bifurcation outcomes. Our methodology is significant for tackling the novel problem and applying previously untested approaches in this context, resulting in the development of multiple exact and novel optical soliton solutions. This highlights the efficiency and broad application of our new strategy to addressing nonlinear challenges in astrophysics, laboratories, space, and technology.

Coccidiosis is a significant disease of poultry and is usually treated using various synthetic anticoccidial drugs. However, the effectiveness of this approach has been compromised due to drug resistance. Medicinal plants are being considered as potential alternatives to these drugs. As part of ongoing research, an investigation was conducted to assess the anticoccidial potential of Linum usitatissimum seed extract (LUE) and its impact on hematological parameters in broiler chickens with experimental coccidiosis. A total of 108 broiler chicks were divided into six groups. The chicks in Groups I, II, and III were given plant extract at three different concentrations: 4%, 5%, and 6%, respectively, at one week of age. Group IV was the positive control - these chickens were treated with toltrazuril. Group V served as the negative control, meaning they were infected but not given any medication. Group VI was the normal control group. All groups, except Group VI, were orally infected with 60,000 sporulated oocysts when they were 18 days old. After 7 days of giving orally infection, six birds from each group were euthanized so that we could examine their feed conversion ratio (FCR), oocyst score, lesion score, fecal score, serum chemistry and hematology. The results showed that the L. usitatissimum extract exhibited anticoccidial activity. It improved the FCR and reduced lesion, oocyst, fecal scores and blood and serum chemistry.

Intelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially when dealing with complex or lengthy texts. In contrast, abstractive summarization represents a more sophisticated approach. Rather than extracting content from the source, abstractive models generate summaries using new language, often incorporating words and phrases not found in the original text. This allows for more natural, human-like summaries that better capture the key ideas in a fluid and cohesive manner. This study introduces two advanced models for generating titles from the abstracts of scientific articles. The first model employs a Gated Recurrent Unit (GRU) encoder coupled with a greedy-search decoder, while the second utilizes a Transformer model, known for its capacity to handle long-range dependencies in text. The findings demonstrate that both models outperform the baseline Long Short-Term Memory (LSTM) model in terms of efficiency and fluency. Specifically, the GRU model achieved a ROUGE-1 score of 0.2336, and the Transformer model scored 0.2881, significantly higher than the baseline LSTM model, which reported a ROUGE-1 score of 0.1033. These results underscore the potential of abstractive models to enhance the quality and accuracy of summarization in academic and scholarly contexts, offering more intuitive and meaningful summaries.

Abstract This paper presents a rule‐based energy management system (EMS) designed for a standalone DC microgrid incorporating solar photovoltaic (PV), fuel cell, battery energy storage system (BESS), and electric vehicle. The unpredictable nature of renewable energy sources and the instability of loads pose challenges for maintaining DC bus voltages and power‐sharing arrangements, impacting the microgrid's smooth operation. The proposed EMS aims to ensure power balance between generation and demand, mitigating vulnerabilities of the DC bus to voltage instability caused by fluctuations from both the load and source sides. This is achieved through an autonomous DC bus voltage stabilization strategy, involving the maintenance of a nominal state of energy (SoE) for the BESS and hydrogen fuel consumption for the fuel cell within predefined lower and upper limits. By regulating these two factors, the EMS algorithm facilitates optimal performance of the PV, battery, and fuel cell components. Consequently, the EMS provides decision‐making instructions to each individual energy source, ensuring efficient operation under various conditions. The effectiveness of the proposed EMS is evaluated through hardware‐based testing on a DC microgrid and simulations in the MATLAB Simulink environment across multiple operating scenarios.

Abstract Energy harvesting from ambient sources present in the environment is essential to replace traditional energy sources. These strategies can diversify the energy sources, reduce maintenance, lower costs, and provide near-perpetual operation of the devices. In this work, a triboelectric nanogenerator (TENG) based on silane-coupled Linde type A/polydimethylsiloxane (LTA/PDMS) is developed for harsh environmental conditions. The silane-coupled LTA/PDMS-based TENG can produce a high output power density of 42.6 µW/cm2 at a load resistance of 10 MΩ and operates at an open-circuit voltage of 120 V and a short-circuit current of 15 µA under a damping frequency of 14 Hz. Furthermore, the device shows ultra-robust and stable cyclic repeatability for more than 30 k cycles. The fabricated TENG is used for the physiological monitoring and charging of commercial capacitors to drive low-power electronic devices. Hence, these results suggest that the silane-coupled LTA/PDMS approach can be used to fabricate ultra-robust TENGs for harsh environmental conditions and also provides an effective path toward wearable self-powered microelectronic devices.

Abstract The adoption of transformer networks has experienced a notable surge in various AI applications. However, the increased computational complexity, stemming primarily from the self-attention mechanism, parallels the manner in which convolution operations constrain the capabilities and speed of convolutional neural networks (CNNs). The self-attention algorithm, specifically the matrix-matrix multiplication (MatMul) operations, demands a substantial amount of memory and computational complexity, thereby restricting the overall performance of the transformer. This paper introduces an efficient hardware accelerator for the transformer network, leveraging memristor-based in-memory computing. The design targets the memory bottleneck associated with MatMul operations in the self-attention process, utilizing approximate analog computation and the highly parallel computations facilitated by the memristor crossbar architecture. Remarkably, this approach resulted in a reduction of approximately 10 times in the number of multiply-accumulate (MAC) operations in transformer networks, while maintaining 95.47% accuracy for the MNIST dataset, as validated by a comprehensive circuit simulator employing NeuroSim 3.0. Simulation outcomes indicate an area utilization of 6895.7 $$\mu m^2$$ μ m 2 , a latency of 15.52 seconds, an energy consumption of 3 mJ, and a leakage power of 59.55 $$\mu W$$ μ W . The methodology outlined in this paper represents a substantial stride towards a hardware-friendly transformer architecture for edge devices, poised to achieve real-time performance.

Abstract The integration of artificial intelligence (AI) in cardiovascular imaging has revolutionized the field, offering significant advancements in diagnostic accuracy and clinical efficiency. However, the complexity and opacity of AI models, particularly those involving machine learning (ML) and deep learning (DL), raise critical legal and ethical concerns due to their "black box" nature. This manuscript addresses these concerns by providing a comprehensive review of AI technologies in cardiovascular imaging, focusing on the challenges and implications of the black box phenomenon. We begin by outlining the foundational concepts of AI, including ML and DL, and their applications in cardiovascular imaging. The manuscript delves into the "black box" issue, highlighting the difficulty in understanding and explaining AI decision-making processes. This lack of transparency poses significant challenges for clinical acceptance and ethical deployment. The discussion then extends to the legal and ethical implications of AI's opacity. The need for explicable AI systems is underscored, with an emphasis on the ethical principles of beneficence and non-maleficence. The manuscript explores potential solutions such as explainable AI (XAI) techniques, which aim to provide insights into AI decision-making without sacrificing performance. Moreover, the impact of AI explainability on clinical decision-making and patient outcomes is examined. The manuscript argues for the development of hybrid models that combine interpretability with the advanced capabilities of black box systems. It also advocates for enhanced education and training programs for healthcare professionals to equip them with the necessary skills to utilize AI effectively. Patient involvement and informed consent are identified as critical components for the ethical deployment of AI in healthcare. Strategies for improving patient understanding and engagement with AI technologies are discussed, emphasizing the importance of transparent communication and education. Finally, the manuscript calls for the establishment of standardized regulatory frameworks and policies to address the unique challenges posed by AI in healthcare. By fostering interdisciplinary collaboration and continuous monitoring, the medical community can ensure the responsible integration of AI into cardiovascular imaging, ultimately enhancing patient care and clinical outcomes.

Abstract This paper proposes measurement and reduction of eye dose in real time for the physician and the assistant performing fluoroscopy guided arterial puncture. Eye dose rates were measured for 30 fluoroscopy-guided punctures of bilateral femoral arteries in pigs. Fifteen fluoroscopy-guided punctures were performed using real time radiation dosimeter without auditory and visual feedback and other fifteen punctures were done using real time radiation dosimeter with visual and auditory feedback worn on forehead by an interventional cardiologist having experience of more than 10 years. The mean radiation dose rate for eyes of physician during arterial puncturing with real time radiation dosimeter with auditory feedback was 0.07 mSv/h (n = 15) whereas it was 0.18 mSv/h (n = 15) without visual and auditory feedback. The percentage of reduction with the device was 61% for eyes. In case of assistant the reduction was 33% for eyes (n = 15). The real time visual and auditory feedback dosimeter has reduced the eye dose rate of the physician and assistant and also helped him staying away from the X-ray source. Real time radiation dosimeters can be an effective tool to measure and reduce the dose to the eyes. The radiation eye dose rate for physician and assistant was significantly reduced by using real time radiation dosimeter with visual and auditory feedback. The real time radiation dosimeter not only helps in measuring but also help in minimizing the radiation dose rate for the physician and assistant in real time.

Keyphrase Prediction (KP) is essential for identifying keyphrases in a document that can summarize its content. However, recent Natural Language Processing (NLP) advances have developed more efficient KP models using deep learning techniques. The limitation of a comprehensive exploration jointly both keyphrase extraction and generation using pre-trained language models spotlights a critical gap in the literature, compelling our survey paper to bridge this deficiency and offer a unified and in-depth analysis to address limitations in previous surveys. This paper extensively examines the topic of pre-trained language models for keyphrase prediction (PLM-KP), which are trained on large text corpora via different learning (supervisor, unsupervised, semi-supervised, and self-supervised) techniques, to provide respective insights into these two types of tasks in NLP, precisely, Keyphrase Extraction (KPE) and Keyphrase Generation (KPG). We introduce appropriate taxonomies for PLM-KPE and KPG to highlight these two main tasks of NLP. Moreover, we point out some promising future directions for predicting keyphrases.

Abstract Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Many patients with a hematologic malignancy have other pre-existing conditions at the time of consideration of an allogeneic stem cell transplant (allo-HSCT). Among these, mild-to-moderate chronic kidney disease (CKD) is a common comorbid condition that can potentially impact the rates of non-relapse mortality among transplant patients. While the risk of severe CKD on allo-HSCT is well recognized, there remains a paucity of data in terms of the impact of mild-to-moderate CKD on patient outcomes in this setting. Using data from the National Inpatient Sample database, we aimed to investigate the impact of mild-to-moderate CKD on hospitalization outcomes for patients undergoing an allo-HSCT. Multivariate analysis revealed that CKD patients had a 31% higher risk of all-cause mortality (OR = 1.31, 95% CI: 1.01–1.70; p = 0.04) and a higher risk of other common hospitalization complications, including acute kidney injury, acute pulmonary edema, cardiac arrhythmias, and septic shock. While this study has limitations, including its retrospective nature and lack of specific medication data, it underscores the importance of considering CKD as a significant risk factor in allo-HSCT outcomes.

Abstract Background Crop diversification is considered as an imperative approach for synchronizing the plant nutrient demands and soil nutrient availability. Taking two or more crops from the same field in one year is considered as multiple cropping. It improves the diversity and abundance of soil microbes, thereby improving the growth and yield of crops. Therefore, the present study was conducted to explore the effects of different multiple winter cropping on soil microbial communities in paddy fields. In this study, eight rice cropping patterns from two multiple cropping systems with three different winter crops, including Chinese milk vetch (CMV), rape, and wheat were selected. The effects of different multiple winter cropping on soil microbial abundance, community structure, and diversity in paddy fields were studied by 16 S rRNA high-throughput sequencing and real-time fluorescence quantitative polymerase chain reaction (PCR). Results The results showed that different multiple winter cropping increased the operational taxonomic units (OTUs), species richness, and community richness index of the bacterial community in 0 ~ 20 cm soil layer. Moreover, soil physical and chemical properties of different multiple cropping patterns also affected the diversity and abundance of microbial bacterial communities. The multiple cropping increased soil potassium and nitrogen content, which significantly affected the diversity and abundance of bacterial communities, and it also increased the overall paddy yield. Moreover, different winter cropping changed the population distribution of microorganisms, and Proteobacteria, Acidobacteria, Nitrospira, and Chloroflexi were identified as the most dominant groups. Multiple winter cropping, especially rape-early rice-late rice (TR) andChinese milk vetch- early rice-late rice (TC) enhanced the abundance of Proteobacteria, Acidobacteria, and Actinobacteria and decreased the relative abundance of Verrucomicrobia and Euryarchaeota. Conclusion In conclusion, winter cropping of Chinese milk vetch and rape were beneficial to improve the soil fertility, bacteria diversity, abundance and rice yield.

Abstract Microtubule associated proteins (MAPs) are widely expressed in the central nervous system, and have established roles in cell proliferation, myelination, neurite formation, axon specification, outgrowth, dendrite, and synapse formation. We report eleven individuals from seven families harboring predicted pathogenic biallelic, de novo, and heterozygous variants in the NAV3 gene, which encodes the microtubule positive tip protein neuron navigator 3 (NAV3). All affected individuals have intellectual disability (ID), microcephaly, skeletal deformities, ocular anomalies, and behavioral issues. In mouse brain, Nav3 is expressed throughout the nervous system, with more prominent signatures in postmitotic, excitatory, inhibiting, and sensory neurons. When overexpressed in HEK293T and COS7 cells, pathogenic variants impaired NAV3 ability to stabilize microtubules. Further, knocking-down nav3 in zebrafish led to severe morphological defects, microcephaly, impaired neuronal growth, and behavioral impairment, which were rescued with co-injection of WT NAV3 mRNA and not by transcripts encoding the pathogenic variants. Our findings establish the role of NAV3 in neurodevelopmental disorders, and reveal its involvement in neuronal morphogenesis, and neuromuscular responses.

China’s rapid urbanization has significantly impacted the environment, escalating resource, energy, and material consumption. Sustainable urban development has become a critical issue, with eco-efficiency emerging as a key metric for its assessment. This study employs eco-efficiency analysis, incorporating environmental contamination as an undesirable output, using data envelopment analysis (DEA) and a modified super-efficiency model for ranking cities. Empirical research was conducted on 30 Chinese provincial capital cities using real-world data. Results reveal that while many cities are eco-efficient, inefficiency is concentrated in underdeveloped regions of the southwest and northwest. Conversely, some eco-efficient cities exhibit high levels of pollution and intensive resource use, including land, energy, and water. The modified ranking methodology identified Yinchuan, Lanzhou, and Guiyang as the least eco-efficient cities, while Haikou, Fuzhou, and Beijing ranked as the top performers. The study highlights the need to reform the GDP-oriented development model and evaluation systems, continually upgrade industrial structures, and prevent the migration of heavy industries from more developed to less developed regions. These findings provide actionable insights for policymakers to balance urban growth with environmental sustainability.

As a sustainable energy source, sound is abundant in the environment, but its effective conversion into electrical energy remains a technical challenge. Triboelectric nanogenerators (TENGs) have made significant progress in addressing this issue, enabling the efficient capture of low-frequency sound waves and their transformation into usable power. This review explores the core principles behind acoustic energy harvesting using TENGs and examines key advancements in materials and resonator designs, including Helmholtz and quarter-wavelength configurations, which enhance energy conversion. Recent breakthroughs in TENG technologies are also highlighted, focusing on specialized mechanisms and innovative structural designs that improve sound wave collection and energy transfer. The review also looks into the various applications of TENGs, including harnessing urban noise, enhancing voice recognition systems, and powering wearable devices. Finally, challenges and future directions for expanding the use of acoustic energy harvesting technologies are discussed,focusing on their potential to provide practical and scalable energy solutions for various applications.

Nanotechnology is a critical emerging technology that enables contributions, innovation and long-term impact in food, medicine and agriculture. Nanomaterials can improve the quantitative and qualitative development of safer, healthier and higher-quality semi-perishable or perishable functional foods. Nanotechnologies outperform traditional food manufacturing techniques in extending the shelf life of the food product, reducing contamination and producing higher-quality food. Nanotechnology applications improve the bioavailability, flavor, texture and stability of food by altering the particle diameter, potential clustering and charge density of food nanoparticles. The present review article highlights the role of nanotechnology in product development and quality maintenance as well as its potential role in agriculture.

The research aimed to enhance the shelf life of sugarcane juice through carbonation, employing different concentrations of juice mixed with syrup. Treatments included T0 (100% juice), T1 (80% juice), T2 (60% juice), T3 (40% juice), and T4 (20% juice). Physicochemical analyses encompassed color, pH, total soluble solids, and titratable acidity, with T0 (pH: 5.2, total soluble solids: 18.5°Brix) showing significant values. However, T4 exhibited the most significant (p ≤ .05) results, suggesting its potential for shelf-life extension. Additionally, bioactive compounds such as ascorbic acid, total phenolic, and flavonoid contents were measured, with T4 demonstrating noteworthy levels. Antioxidant assays (DPPH: 65% inhibition, TFC: 45 mg QE/100 ml, TPC: 130 mg GAE/100 ml, and FRAP: 320 µmol Fe2+/L) highlighted T4 the most effective, showcasing robust antioxidant activity. Sensory evaluation, conducted at refrigeration temperatures of 4–10°C, favored T4, indicating its superior acceptability. In conclusion, Carbonated sugarcane juice (20% T4) extends shelf life, preserves quality, and boosts antioxidants.