Search Results (86)

This study introduces a nonlinear fractional bi-susceptible model for COVID-19 using the Atangana–Baleanu derivative in Caputo sense (ABC). The fractional framework captures nonlocal effects and temporal decay, offering a realistic presentation of persistent infection cycles and delayed recovery. Within this setting, we investigate multiple transmission modes, determine the major risk factors, and analyze the long-term dynamics of the disease. Analytical results are obtained at equilibrium states, and fundamental properties of the model are validated. Numerical simulations based on the Toufik–Atangana method further endorse the theoretical results and emphasize the effectiveness of the ABC derivative. Bifurcation analysis illustrates that adjusting time-invariant treatment and awareness efforts can accelerate pandemic control. Sensitivity analysis identifies the most significant parameters, which are used to construct an optimal control problem to determine effective disease control strategies. The numerical results reveal that the proposed control interventions minimize both infection levels and associated costs. Overall, this research work demonstrates the modeling strength of the ABC derivative by integrating fractional calculus, bifurcation theory, and optimal control for efficient epidemic management. Full article

Gamma-aminobutyric acid (GABA) is a bioactive, non-protein amino acid recognized for its role as an inhibitory neurotransmitter in the human central nervous system. Increasing interest in functional foods has increased attention on GABA due to its potential health benefits, including antihypertensive, anxiolytic, antidepressant, and neuroprotective effects. This review summarizes the natural dietary sources of GABA and explores advanced strategies for enriching dairy products, particularly yogurt and frozen yogurt (froyo), with GABA. Key microbial species capable of GABA biosynthesis via the glutamate decarboxylase (GAD) pathway are discussed, alongside enzymatic production techniques that support controlled GABA synthesis. A major focus of this review is the evaluation of various methods for incorporating GABA into dairy matrices, including direct GABA fortification and in situ fermentation using GABA-producing strains, with comparisons of yield, sensory attributes, and product stability. Physicochemical analyses and sensory evaluations are presented as essential tools for assessing product performance. Furthermore, the review outlines the therapeutic effects of GABA-fortified foods and their potential roles in managing hypertension, stress, and neurodegenerative disorders. Key challenges, including strain-dependent variability in GABA-production, storage stability, and regulatory compliance are addressed, along with market and legislative considerations for GABA-fortified foods. Future perspectives include the development of novel high GABA-producing strains, process optimization to improve product stability and sensory acceptance, and expanded applications within the functional food sector. Overall, this review provides an integrated, up-to-date overview of technological, functional and regulatory aspects, offering a clear scientific foundation for the development and commercialization of GABA-fortified dairy products. Full article

Soil aggregate stability is a key indicator of soil health and is fundamental to soil processes such as water infiltration, nutrient cycling, carbon sequestration, erosion control, and ecosystem functionality. However, research concerning the impact of natural and anthropogenic factors on SAS across different climates, soil types, and management practices is lacking. This review synthesizes current understanding of physical, chemical, and biological mechanisms that govern the aggregate formation and stability and brings to light how the natural and anthropogenic drivers influence these processes. It highlights how clay mineralogy, root systems, microbial diversity, soil organic matter, and management practices shape the structure and turnover of aggregates essential for agricultural productivity. Key drivers of aggregate formation, categorized into natural (such as texture, clay mineral interaction, biota, and climate) and anthropogenic (such as tillage, land use changes, organic amendments) factors, have been critically evaluated. This review provides an insightful framework for soil management that may help enhance soil aggregation and promote sustainable agriculture and food security, especially under climate change. Full article

Cadmium (Cd) contamination in agricultural soils poses a significant risk to crop production and food safety. This study explored the role and mechanisms of manganese (Mn) in mitigating Cd toxicity using two rice genotypes: ZS97B (Cd-tolerant) and MY46 (Cd-sensitive). A hydroponic experiment was conducted under two Mn levels (0 and 100 µM) and three Cd levels (0, 5, 10 µM). Exposure to 10 µM Cd significantly inhibited plant growth and induced physiological disorders, with more severe effects observed in MY46 than in ZS97B. The addition of Mn markedly alleviated Cd toxicity, as reflected by increased antioxidant enzyme activities and reduced malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) contents in both roots and shoots. Gene expression analysis showed that Mn addition up-regulated genes related to antioxidant enzymes and down-regulated key Cd uptake and transport genes, including OsNramp1, OsYSL2, OsMTP9, and OsHMA3. These changes contributed to enhanced antioxidant capacity and reduced Cd accumulation in rice plants under Cd stress. Our findings demonstrate that appropriate Mn application can effectively reduce Cd accumulation and alleviate toxicity in rice grown in Cd-contaminated environments. Full article

Capital flow, integral to the global economy, is significantly influenced by business potential and institutional environments. As one of the world’s largest economies, China’s outflow plays a crucial role in the rapid development of its economy. This study examines domestic investment into public and private components to avoid aggregation bias, whether China’s outward foreign direct investment (OFDI) serves as a substitute or complement to local investments, and how local institutional quality mediates this relationship. We employed Dynamic Autoregressive Distributed Lag model ARDL simulation methods for the period of 1996–2021 in order to control endogeneity, auto-correlation, cross-sectional bias, as well as heteroscedasticity issues, which normally arise in time-series datasets. Our findings reveal that OFDI has a dual impact on local economies. Firstly, OFDI has a generally positive effect on private and public investment, but this relationship is nonlinear. Furthermore, institutional quality significantly influences private investment more than public investment. Additionally, higher interest rates are shown to adversely affect both private and public investments by increasing borrowing costs. These results offer valuable insights for policymakers aiming to optimize investment flows and economic stability. Specifically, fostering institutional quality can amplify the positive spillovers of OFDI on private investment, while mitigating its crowding-out effects on public investment. Full article

Climate change and injudicious nitrogen addition alter the soil physico-chemical properties and microbial activity in oligotrophic forest soil, which disrupts the nitrogen cycle balance. Nevertheless, recommended fertilizer forms and levels are considered to be crucial for stable nitrogen application. We established a short-term field trial for the first time using a randomized complete block design under the yellow larch forest, with six treatments applied, including urea CO(NH₂)₂, ammonium chloride NH₄Cl, and sodium nitrate NaNO₃ at concentrations of 10 and 20 kg N hm⁻² yr⁻¹, each extended by three replicates. The gene abundances were measured using quantitative PCR (qPCR), in which the abundance levels of AOA (amoA) and nirS were higher under high CO(NH₂)₂ 2.87 × 10¹⁰ copies g⁻¹ dry soil and low NO₃⁻ 8.82 × 10⁹ copies g⁻¹ dry soil, compared to CK, representing 2.8-fold and 1.5-fold increases, respectively. We found niche partitioning as revealed despite AOA (amoA) increasing in number, AOB (amoA) contributing more to ammonia oxidation while nirS proved opportunistic under stress conditions. This was supported by distinct significant correlations among factors, in which soil urease enzymatic activity (S-UE) was associated with AOA (amoA) and nirK, while AOB (amoA) and nirS positively correlated with NH₄⁺ content and soil potential of hydrogen (pH), respectively. Among the applied treatments, high-level NO₃⁻ increased total nitrogen content and had a significant effect on soil N-acetyl-β-d-glucosaminidase (S-NAG) and soil acid protease (S-ACPT) activity. In summary, we observed an increase in Larix olgensis growth with high nitrogen retention. Full article

This study developed and evaluated plant-based mayonnaise formulations in which egg yolk was replaced with aquafaba (15–25%) and stabilized with gum tragacanth (0.3–1.0%). Formulations were prepared using canola oil and stored at 4 °C for 28 days. Aquafaba extract was characterized for total phenolic content (TPC) and total flavonoid content (TFC), while mayonnaise samples were assessed for physicochemical composition, creaming index, antioxidant activity, viscosity, texture, sensory properties, and microbiological stability. Total phenolic content (TPC) rose from 17.52 mg GAE/g at 10 µg to 135.34 mg GAE/g at 100 µg (p < 0.05), while total flavonoid content (TFC) increased from 76.95 to 192.42 mg TE/g over the same concentration range. These increases demonstrate the high antioxidant potential of aquafaba extract. The 25% aquafaba + 1% gum tragacanth formulation (T₃) showed the highest protein content, viscosity, firmness, and antioxidant capacity, with improved storage stability compared to the control. FTIR analysis identified functional groups such as phenols, esters, and carboxylic acids, suggesting contributions to antioxidant activity and emulsion stability. Sensory evaluation indicated strong acceptance for T₃. These results demonstrate that aquafaba combined with gum tragacanth can effectively replace egg yolk while maintaining desirable quality attributes. Full article

Medicinal mushroom production utilising rural cultivation (solid state fermentation) requires approximately six months compared to culinary mushroom production (7 days). Urban cultivation (submerged liquid fermentation) can be used as a sustainable method of producing medicinal mushroom biomass. In this study, chicken patties were fortified with liquid-fermented Ganoderma lucidum flour (GLF) and Pleurotus djamor mushroom biomass flour (PDF) at concentrations of 3%, 6%, and 9%. These were compared to a negative control (0% mushroom flour chicken patty) and a commercial patty. Chicken patties fortified with 3% PDF and 9% GLF recorded the lowest cooking loss, at 5.55% and 10.3%, respectively. Mushroom chicken patties exhibited lower cooking losses and significant changes in colour and texture compared to control samples. Notably, 3% GLF chicken patty achieved the highest overall acceptability score of 6.55 followed by 9% PDF chicken patty (6.08) (p < 0.05). Biomass flour of liquid-fermented Ganoderma lucidum (ENS-GL) and Pleurotus djamor (ENS-PD) were extracted for their endopolysaccharide and analysed for their functional properties. All elemental, FT-IR, and NMR spectroscopy analyses revealed the existence of a comparable beta-glucan polymer structure, linkages, and absorptions when compared to the Laminarin standard. In addition, ENS-GL also proved to possess higher antimicrobial activities and significant antioxidant levels (DPPH-scavenging activity, ferric reduction potential and total phenolic content) compared to ENS-PD. Overall, this study revealed that sustainable liquid-fermented Ganoderma lucidum, a medicinal mushroom, outperformed Pleurotus djamor, a culinary mushroom, as a potential alternative flour for combating hunger in the future. Full article

Sparse matrix–vector multiplication (SpMV) plays a significant role in the computational costs of many scientific applications such as 2D/3D robotics, power network problems, and computer vision. Numerous implementations using different sparse matrix formats have been introduced to optimize this kernel on CPUs and GPUs. However, due to the sparsity patterns of matrices and the diverse configurations of hardware, accurately modeling the performance of SpMV remains a complex challenge. SpMV computation is often a time-consuming process because of its sparse matrix structure. To address this, we propose a machine learning-based tool, namely Elegante+, that predicts optimal scheduling policies by analyzing matrix structures. This approach eliminates the need for repetitive trial and error, minimizes errors, and finds the best solution of the SpMV kernel, which enables users to make informed decisions about scheduling policies that maximize computational efficiency. For this purpose, we collected 1000+ sparse matrices from the SuiteSparse matrix market collection and converted them into the compressed sparse row (CSR) format, and SpMV computation was performed by extracting 14 key sparse matrix features. After creating a comprehensive dataset, we trained various machine learning models to predict the optimal scheduling policy, significantly enhancing the computational efficiency and reducing the overhead in high-performance computing environments. Our proposed tool, Elegante+ (XGB with all SpMV features), achieved the highest cross-validation score of 79% and performed five times faster than the default scheduling policy during SpMV in a high-performance computing (HPC) environment. Full article

The opioid drug overdose death rate remains a significant public health crisis in the U.S., where an opioid epidemic has led to a dramatic rise in overdose deaths over the past two decades. Since 1999, opioids have been implicated in approximately 75% of the nearly one million drug-related deaths. Research indicates that the epidemic is caused by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and social isolation. Impeding this research is the lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolution. To address this issue, we sourced data from Reddit, where people share self-reported experiences with opioid substances, specifically using opioid drugs through different routes of administration. To achieve this objective, an opioid overdose dataset is created and manually annotated in binary and multi-classification, along with detailed annotation guidelines. In traditional manual investigations, the route of administration is determined solely through biological laboratory testing. This study investigates the efficacy of an automated tool leveraging natural language processing and transformer model, such as RoBERTa, to analyze patterns of substance use. By systematically examining these patterns, the model contributes to public health surveillance efforts, facilitating the identification of at-risk populations and informing the development of targeted interventions. This approach ultimately aims to enhance prevention and treatment strategies for opioid misuse through data-driven insights. The findings show that our proposed methodology achieved the highest cross-validation score of 93% for binary classification and 91% for multi-class classification, demonstrating performance improvements of 9.41% and 10.98%, respectively, over the baseline model (XGB, 85% in binary class and 81% in multi-class). Full article

The rise in social media has improved communication but also amplified the spread of hate speech, creating serious societal risks. Automated detection remains difficult due to subjectivity, linguistic diversity, and implicit language. While prior research focuses on high-resource languages, this study addresses the underexplored multilingual challenges of Arabic and Urdu hate speech through a comprehensive approach. To achieve this objective, this study makes four different key contributions. First, we have created a unique multi-lingual, manually annotated binary and multi-class dataset (UA-HSD-2025) sourced from X, which contains the five most important multi-class categories of hate speech. Secondly, we created detailed annotation guidelines to make a robust and perfect hate speech dataset. Third, we explore two strategies to address the challenges of multilingual data: a joint multilingual and translation-based approach. The translation-based approach involves converting all input text into a single target language before applying a classifier. In contrast, the joint multilingual approach employs a unified model trained to handle multiple languages simultaneously, enabling it to classify text across different languages without translation. Finally, we have employed state-of-the-art 54 different experiments using different machine learning using TF-IDF, deep learning using advanced pre-trained word embeddings such as FastText and Glove, and pre-trained language-based models using advanced contextual embeddings. Based on the analysis of the results, our language-based model (XLM-R) outperformed traditional supervised learning approaches, achieving 0.99 accuracy in binary classification for Arabic, Urdu, and joint-multilingual datasets, and 0.95, 0.94, and 0.94 accuracy in multi-class classification for joint-multilingual, Arabic, and Urdu datasets, respectively. Full article

Traditional fermented foods are appreciated worldwide for their cultural significance and health-promoting properties. However, traditional fermentation production suffers from many obstacles such as microbial variability, varying quality, and lack of scalability. The implementation of smart fermentation technologies, including biosensors, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), hold the key to the optimization of microbial process control, enhance product consistency, and improve production efficiency. This review summarizes modern developments in real-time microbial monitoring, IoT, AI, and ML tailored to traditional fermented foods. Despite significant technical advancements, challenges related to high costs, the absence of standardized frameworks, and access restrictions for small producers remain substantial limitations. This review proposed a future direction prioritizing modular, scalable solutions, open-source innovation, and environmental sustainability. In alignment with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure), smart fermentation technologies advance sustainable industry through innovation and serve as a critical bridge between traditional craftsmanship and Industry 4.0, fostering inclusive development while preserving microbial biodiversity and cultural heritage. Full article

As global climates shift, plants are increasingly exposed to biotic and abiotic stresses that adversely affect their growth and development, ultimately reducing agricultural productivity. To counter these stresses, plants produce secondary metabolites (SMs), which are critical biochemical and essential compounds that serve as primary defense mechanisms. These diverse compounds, such as alkaloids, flavonoids, phenolic compounds, and nitrogen/sulfur-containing compounds, act as natural protectants against herbivores, pathogens, and oxidative stress. Despite the well-documented protective roles of SMs, the precise mechanisms by which environmental factors modulate their accumulation under different stress conditions are not fully understood. This review provides comprehensive insights into the recent advances in understanding the functions of SMs in plant defense against abiotic and biotic stresses, emphasizing their regulatory networks and biosynthetic pathways. Furthermore, we explored the unique contributions of individual SM classes to stress responses while integrating the findings across the entire spectrum of SM diversity, providing a comprehensive understanding of their roles in plant resilience under multiple stress conditions. Finally, we highlight the emerging strategies for harnessing SMs to improve crop resilience through genetic engineering and present novel solutions to enhance agricultural sustainability in a changing climate. Full article

In the current digital era, the Google Play Store and the App Store are major platforms for the distribution of mobile applications and games. Billions of users regularly download mobile games and provide reviews, which serve as a valuable resource for game vendors and developers, offering insights into bug reports, feature suggestions, and documentation of existing functionalities. This study showcases an innovative application of fine-tuned RoBERTa for detecting bugs in mobile phone games, highlighting advanced classification capabilities. This approach will increase player satisfaction, lead to higher ratings, and improve brand reputation for game developers, while also reducing development costs and saving time in creating high-quality games. To achieve this goal, a new bug detection dataset was created. Initially, data were sourced from four top-rated mobile games from multiple domains on the Google Play Store and the App Store, focusing on bugs, using the Google Play API and App Store API. Subsequently, the data were categorized into two classes: binary and multi-class. The Logistic Regression, Convolutional Neural Network (CNN), and pre-trained Robustly Optimized BERT Approach (RoBERTa) algorithms were used to compare the results. We explored the strength of pre-trained RoBERTa, which demonstrated its ability to capture both semantic nuances and contextual information within textual content. The results showed that pre-trained RoBERTa significantly outperformed the baseline models (Logistic Regression), achieving superior performance with a 5.49% improvement in binary classification and an 8.24% improvement in multi-class classification, resulting in cross-validation scores of 96% and 92%, respectively. Full article