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Plasma is considered as the fourth state of matter, and atmospheric cold plasma (cold plasma) is a type of plasma consisting of ionized gases containing excited species of atoms, molecules, ions, and free radicals at near room temperature. Cold plasma is generated by applying high voltage to gases, causing it to ionize thus forming plasma. Although cold plasma has been found to break seed dormancy and improve germination rate, only a few studies have explored the potential of cold plasma against insect herbivory. Given that cold plasma produces reactive oxygen and nitrogen species that can activate plant signalling molecules, it is plausible that cold plasma can have differential effects against insect herbivores. To test this, we evaluated the effectiveness of cold plasma on a polyphagous lepidopteran pest, Fall armyworm (FAW) [Spodoptera frugiperda (Lepidoptera: Noctuidae)] on rice (Oryza sativa L.) using an atmospheric plasma jet reactor that generated cold plasma using ambient air as the source gas. We treated rice seeds from two commonly grown Arkansas cultivars (Jewel and Diamond) with cold plasma, followed by irrigation with Cold Plasma-Activated Water (PAW). We then independently tested FAW growth on an artificial diet partially made with PAW. Our results show that cold plasma significantly affected the feeding, growth, and development of FAW, irrespective of the rice varieties. The effects of cold plasma treatment resulted in reduced damage by FAW, lower mass gain and longer pupation period on FAW compared to the untreated control. However, the effects of cold plasma on rice growth and development were dependent on the rice varieties. Cold plasma treatment also induced detrimental effects on FAW leading to ~ 25% mortality on cold plasma-treated plants when compared to untreated controls. Collectively, these findings offer significant evidence of the potential of cold plasma as a novel component for sustainable pest management. [ABSTRACT FROM AUTHOR]

Objective: Functional magnetic resonance imaging data pose significant challenges due to their inherently noisy and complex nature, making traditional statistical models less effective in capturing predictive features. While deep learning models offer superior performance through their non-linear capabilities, they often lack transparency, reducing trust in their predictions. This study introduces the Time Reversal (TR) pretraining method to address these challenges. TR aims to learn temporal dependencies in data, leveraging large datasets for pretraining and applying this knowledge to improve schizophrenia classification on smaller datasets. Methods: We pretrained an LSTM-based model with attention using the TR approach, focusing on learning the direction of time in fMRI data, achieving over 98 % accuracy on HCP and UK Biobank datasets. For downstream schizophrenia classification, TR-pretrained weights were transferred to models evaluated on FBIRN, COBRE, and B-SNIP datasets. Saliency maps were generated using Integrated Gradients (IG) to provide post hoc explanations for pretraining, while Earth Mover's Distance (EMD) quantified the temporal dynamics of salient features in the downstream tasks. Results: TR pretraining significantly improved schizophrenia classification performance across all datasets: median AUC scores increased from 0.7958 to 0.8359 (FBIRN), 0.6825 to 0.7778 (COBRE), and 0.6341 to 0.7224 (B-SNIP). The saliency maps revealed more concentrated and biologically meaningful salient features along the time axis, aligning with the episodic nature of schizophrenia. TR consistently outperformed baseline pretraining methods, including OCP and PCL, in terms of AUC, balanced accuracy, and robustness. Conclusions: This study demonstrates the dual benefits of the TR method: enhanced predictive performance and improved interpretability. By aligning model predictions with meaningful temporal patterns in brain activity, TR bridges the gap between deep learning and clinical relevance. These findings emphasize the potential of explainable AI tools for aiding clinicians in diagnostics and treatment planning, especially in conditions characterized by disrupted temporal dynamics. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic affected a broad spectrum of people's lives very quickly. Although the pandemic could influence people's fertility behaviours in several ways, there is little knowledge about such influence in diverse socioeconomic and cultural settings. This study investigated the impact of the COVID-19 pandemic on fertility behaviours and desires among women in a lower-middle-income country, India. Data and methods: Our study analyzed cross-sectional data from 13 states and union territories (UTs) in India that were surveyed in pre- and post-lockdown periods by the 2019/21 National Family Health Survey (NFHS). The data were analysed using the descriptive analysis technique and the multilevel logit model. All these analyses were performed using the technique developed for complex sample design. Results: The poverty-stricken states of Uttar Pradesh and Jharkhand and the Odisha state with moderate socioeconomic status experienced a significant decrease in contraceptive use and non-significant changes in the desire for birth and sexual activities after the lockdown. Contraceptive use significantly increased after the lockdown in the rich states of Punjab, Delhi, and Tamil Nadu, as well as in the Arunachal Pradesh state with moderate socioeconomic status. The changes in fertility behaviours in Uttar Pradesh, Jharkhand, and Odisha may influence fertility positively, while those changes in Punjab, Delhi, Tamil Nadu, and Arunachal Pradesh may influence fertility negatively. Conclusion: At the aggregate level, there was a significant increase in desire for another child and a decrease in contraceptive use after the lockdown, which may influence fertility positively but can be compensated by reduced sexual activities. The influence of the fertility trends in the states with pro-natalist changes on India's recent fertility trend could be greater than those with anti-natalist changes, which can be better understood by analyzing reliable data from a couple of years following the 2019/21 NFHS. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains. [ABSTRACT FROM AUTHOR]

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data. Unlike traditional methods, which often lack transparency in decision-making, our approach focuses on early detection, offering a proactive strategy to mitigate the risks of sepsis. By integrating advanced machine learning algorithms with interpretability techniques, our method not only provides accurate predictions but also offers clear insights into the factors influencing the model's decisions. Moreover, we introduce a preference-based matching algorithm to evaluate disease severity, enabling timely interventions guided by the analysis outcomes. This innovative integration significantly enhances the effectiveness of our approach. We leverage a clinical health dataset comprising 1,552,210 Electronic Health Records (EHR) to train our interpretable machine learning models within a cloud computing framework. Through techniques like feature importance analysis and model-agnostic interpretability tools, we aim to clarify the crucial indicators contributing to septic shock prediction. This transparency not only assists healthcare professionals in comprehending the model's predictions but also facilitates the integration of our system into existing clinical workflows. We validate the effectiveness of our interpretable models using the same dataset, achieving an impressive accuracy rate exceeding 98% through the application of oversampling techniques. The findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management. [ABSTRACT FROM AUTHOR]

Reasons for the emerging mismatch between education and the labor market, its impact and the resolving mechanisms have been well studied. However, mismatch continues to be very problematic. Moreover, the new era of digitalization and artificial intelligence is accelerating this crisis. Acknowledging this tenet, we investigate further—whether a mismatch finds a match in the digitalized era. An individualist approach to answer each research question was considered so descriptive statistics, multinomial logistic regression and interviews are adopted under were implemented using the framework of both quantitative and qualitative methods. Findings suggest that the overall performance of the fully mismatched group (Science graduates) is comprehensively better compared to their counterpart (Business graduates). Digitalization has further generated a mismatch since business graduates are now increasingly irrelevant. This paper supplements the discourse on education and job market mismatch by using a distinct measurement strategy. We argue that digitalized skills are now the required competence to do jobs in any sector. Hence, all types of schools (business, science, social sciences) should equip their graduates with digitized skills in order to prevent them being replaced by science graduates. [ABSTRACT FROM AUTHOR]

Recent advances in wearable electronics have been propelled by the rapid growth of microelectronics and Internet of Things. The proliferation of electronic devices and sensors relies heavily on power sources, predominantly batteries, with significant implications for the environment. To address this concern and to reduce carbon emissions, there is a growing emphasis on renewable energy harvesting technologies, among which textile‐based triboelectric nanogenerators (T‐TENGs) stand out as an innovative and sustainable solution due to having the interesting characteristics like large contact area, lightweight design, flexibility, comfort, scalability, and breathability. T‐TENGs can harness mechanical energy from human movement and convert it into electric energy. However, one of the challenges is low electric power output, which can be addressed by meticulous selection of material pairs with significant differences in work function and optimizing contact areas. The incorporation of carbon‐based nanomaterials, such as carbon nanotubes and graphene, emerges as a key strategy to enhance output. This review delineates recent progress in T‐TENGs incorporating carbonaceous nanofillers, comprehensively addressing fundamental classification, operational mode, structural design, working performance, and potential challenges that are hindering commercialization. By doing this, this review aims to stimulate future investigations into sustainable, high‐performance smart wearables integrated with T‐TENGs. [ABSTRACT FROM AUTHOR]

The advent of blockchain technology has transformed traditional methods of information exchange, shifting reliance from centralized data centers to decentralized frameworks. While blockchain’s decentralization and security are strengths, traditional consensus mechanisms like Proof of Work (PoW) and Proof of Stake (PoS) face limitations in scalability. PoW achieves decentralization and security but struggles with scalability as transaction volumes grow, while PoS enhances scalability, but risks centralization due to monopolization by high-stake participants. Sharding, a recent advancement in blockchain technology, addresses scalability by partitioning the network into shards that process transactions independently, thereby improving throughput and reducing latency. However, cross-shard communication, essential for transactions involving multiple shards, introduces challenges in coordination and fault tolerance. This research introduces a shard-based hybrid consensus model, PoSW, which combines PoW and PoS to mitigate the limitations of both mechanisms. By integrating PoW’s fairness with PoS’s scalability in a shard-based blockchain, the proposed model addresses key issues of scalability and monopolization. We evaluate the model against state-of-the-art consensus algorithms, including Monoxide and Practical Byzantine Fault Tolerance (PBFT). The results show that the proposed PoSW model reduces communication overhead compared to PBFT and improves resource utilization over Monoxide. In addition to performance gains, the security analysis demonstrates that the PoSW model provides robust defense against common blockchain attacks such as the 51% and Sybil attacks, etc. The proposed approach is particularly suited for applications like decentralized finance (DeFi) and supply chain management, which require both high scalability and robust security. The contributions of this research include the development of the PoSW hybrid consensus mechanism, its comparative evaluation with leading algorithms, and a thorough security analysis. These contributions represent a significant step forward in addressing blockchain’s scalability, fairness, and security challenges. [ABSTRACT FROM AUTHOR]

Cellulose, a naturally abundant biopolymer, holds great potential as a sustainable alternative to synthetic fibers. However, the limited understanding and awareness surrounding cellulose utilization, particularly from agricultural origins, have impeded the complete harnessing of this highly biodegradable resource. This study aimed to extract and characterize cellulose from jute and banana fibers. The extracted cellulose exhibits a light yellow to white color, and microscopic analysis of the fibers showed micro‐fibrils. X‐ray diffraction (XRD) characterization indicated that the extracted cellulose from biomass primarily consists of cellulose II structures, except for the treated banana fiber (M:L = 1:8), which contains both cellulose I and II. Moreover, increasing the M:L ratio of alkali treatment enhanced the percentage of cellulose‐II, as observed from the XRD data. The findings of this study carry significant implications for the efficient production of cellulose fibers, with diverse applications spanning from high‐volume products like regenerated fibers, automotive parts, packaging, absorbent products (diapers), textiles, and precast concrete, drug delivery mediums, electronics, additive manufacturing, bone and tissue scaffolding, and so on. This research opens the door to harnessing the potential of cellulose derived from jute and banana fibers in various industries. Highlights: Extraction cellulose using the kraft process.Isolated cellulose shows a micron‐sized structure.Optimal extraction achieved with M:L ratio of 1:4.Applications of isolated cellulose: regenerated fibers, packaging, absorbent products (diapers), textiles, and so on. [ABSTRACT FROM AUTHOR]