Search Results (666)

Smallholder farming remains a critical livelihood source for rural communities in South Africa, particularly in the Eastern Cape Province. However, cropland abandonment has become an escalating concern, undermining food security, household incomes, and the long-term sustainability of agricultural systems. This study assessed the socio-economic and climate-related factors influencing cropland abandonment in the former homelands of Transkei. A mixed-methods approach was used, combining a quantitative survey, a qualitative focus group discussion, and a key informant interview. Data were analysed using descriptive statistics, a double-hurdle model, and thematic analysis. The descriptive results revealed that the average respondent was 57 years, with a predominantly male majority (57.47%), a primary education (40.27%), and a mean average household size of 5.4. About 51.58% of household heads were married and 48.42% were single, with a mean household income of R63 155 (3680.26 USD). The econometric results from the first hurdle model indicated that education level, farming experience, rainfall variability, access to irrigation, and off-farm income significantly influenced the decision to abandon cropland. The second hurdle model demonstrated that the extent of cropland abandonment was shaped by labour availability, access to credit, rainfall patterns, cooperative membership, and farming experience. The study concluded that cropland abandonment in the former Transkei was influenced by different factors. Therefore, the study would recommend targeted policy interventions that strengthen human capital, improve access to agricultural support services, and promote youth participation and collective farming structures to revitalise smallholder agriculture and enhance rural food security. Full article

Background: The global number of individuals living with dementia is projected to rise from 57.4 million in 2019 to 152.8 million by 2050. Alongside this increase, the worldwide economic burden of dementia continues to grow, with the overall societal cost estimated at US$1313 billion in 2019—substantially higher than earlier projections. Objectives: To analyze the impact of dementia on long-term-care costs, we conducted a multivariate analysis to take into account overlaps with various other diseases. Methods: In this study, we conducted a multivariate analysis to assess the effect of major diseases on annual expenditure on long-term care by linking Japanese National Health Insurance and long-term-care insurance claims. Results: In a two-part analysis using a hurdle model, the first stage of multivariate logistic regression analysis of the presence or absence of disease showed that Parkinson’s disease had the highest multivariate-adjusted odds ratio, followed by Alzheimer’s disease and schizophrenia. In the second stage of the generalized linear model with log link–Gamma analysis of residents with positive costs, the disease with the highest exponential function (exp(b)) was Alzheimer’s disease, followed by stroke sequelae. Conclusions: To examine the impact of dementia on long-term-care costs, it is necessary to use multivariate analysis to avoid overlap with other diseases. Full article

Employees were required to adopt new working methods within a very short time frame during the COVID-19 period through digitalization. While digitalization has been largely perceived as an enabler during the pandemic, its impact on employee health and well-being remains complex and underexplored, particularly in the public sector, where employees have less discretion to adapt digital tools. This study examines how rapid workplace digitalization during COVID-19 affected employee health and well-being in the public sector. Drawing on the job demands–resources (JD-R) framework, we focus on three specific forms of digital work—digital meetings, digital clearance, and digital training—selected because they represent distinct theoretical pathways through which digitalization affects well-being, such as digital meetings and digital training can increase job demands that can deplete employee energy and increase stress, whereas digital clearance operates as a job resource that reduces bureaucratic hurdles and enhances autonomy. To test these ideas, this study uses data from the 2020 Australian Public Service Commission Census (n = 108,085), and applies ordinal and multinomial generalized structural equation modeling (GSEM) to assess the effects of three new ways of working—digital meetings, digital clearance, and digital training—on employees’ health and well-being, as well as the mediating roles of organizational support. The results demonstrate that while digital clearance is positively associated with employee health and well-being, digital meetings and digital training are negatively associated. Organizational support mediates these relationships, underscoring its importance in mitigating adverse effects. These findings highlight the mixed consequences of digitalization for public employees’ health and well-being and point to the need for supportive organizational strategies in times of crisis. As a practical implication, this study suggests that public sector organizations should prioritize employee mental health in teleworking policies, adopt employee-centered digital transformation strategies that provide adequate resources and training support, and implement digital clearance processes that enhance employee well-being, particularly during a crisis. Full article

This review examines the implementation dimensions of integrated lemon biorefinery systems, including cascade valorisation design, circular-economy integration, life-cycle assessment, techno-economic feasibility, and regulatory frameworks. Bibliometric analysis of Web of Science data (2015–2025) reveals exponential growth in citrus-biorefinery research, with lemon representing a burgeoning subset. Techno-economic assessments indicate that cascade biorefineries recovering essential oils, pectin, polyphenols, nanocellulose, and bioenergy can achieve cumulative revenues of USD 400–650 per tonne of dry peel. Whilst small-scale units (<500 tonnes per year) struggle to achieve viability, industrial simulations demonstrate Internal Rates of Return exceeding 18% at processing scales above 100,000 tonnes annually (2025 basis). Life-cycle assessments confirm environmental benefits, with greenhouse gas reductions of 60–85% relative to conventional disposal. Critical success factors include adopting green extraction technologies to preserve bioactive integrity and mitigating D-limonene inhibition in downstream anaerobic digestion. These findings establish essential oil extraction and pectin recovery as commercially mature technologies, whilst integrated multi-product lemon biorefineries remain economically promising based on techno-economic modelling and pilot-scale demonstrations, provided regulatory hurdles are effectively navigated. Full article

Objectives: Rhizoma Coptidis alkaloids (RCAs) have been proven highly promising in diabetes therapy. However, poor solubility, low bioavailability, and a lack of an effective delivery strategy are major hurdles to improving clinical outcomes. Herein, mPEG-PLGA nanoparticles were employed to deliver RCA orally to enhance anti-diabetic effects. Methods: The RCA-loaded nanoparticles (RCA NPs) were prepared using the emulsion solvent diffusion method. The physicochemical properties of RCA NPs were characterized by morphology, particle size, zeta potential, polydispersity index, drug loading, and drug release. Pharmacokinetic and tissue distribution were determined by UPLC-MS/MS. The hypoglycemic effect was evaluated in a type 2 diabetes mouse model. To illustrate potential mechanisms of action, the expression of PI3K/Akt signaling pathway-related genes and their proteins was detected by RT-PCR and Western blot, respectively. Results: The prepared RCA NPs were spherical in structure, with a particle size of approximately 145 nm and a sustained drug release profile (approximately 50% within 24 h). Compared with RCAs, RCA NP bioavailability increased approximately 2.2-fold, and the hypoglycemic, hypolipidemic, hepatoprotective, anti-inflammatory effects were significantly improved. The better outcome might be due to upregulation of expression and phosphorylation levels within the IRS1/PI3K/AKT/GLUT4 signal pathway in liver tissues. Conclusions: RCA NPs hold great potential for further clinical translation. Full article

This review systematically evaluates the interdisciplinary convergence of artificial intelligence (AI) and polymer science in cancer therapy. Beyond mere description, we provide an integrated framework spanning synthetic optimization, biocompatibility prediction, and the design of tumor microenvironment (TME)-responsive carriers. We highlight how AI algorithms (ML, DL, and RNNs) transform traditional trial-and-error methods into a data-driven paradigm, enabling precise spatiotemporal drug release and individualized pharmacokinetic modeling. Crucially, this work addresses the critical gap between computational modeling and clinical realization by providing a balanced critical analysis of current bottlenecks, including the “small data” challenge, publication bias, and regulatory hurdles. We conclude with a roadmap for AI-guided precision oncology, shifting the focus from predictive accuracy to mechanistic interpretability and prospective in vivo validation. Full article

The fish community structure of the Zhoushan Fishing Ground is undergoing change due to overfishing, climate variability, and other anthropogenic stressors. To investigate community-level environmental responses and interspecific associations in this region, we used 11 consecutive years (2014–2024) of spring bottom trawl survey data from the Zhoushan Fishing Ground and integrated environmental covariates to build a two-part hurdle model within the Hierarchical Modelling of Species Communities (HMSC) framework. The results showed that the spatial random effect had the highest contribution (41%), followed by the interannual trend (18%), indicating that community occurrence patterns are primarily shaped by the superposition of stable spatial structuring and long-term change. Depth was significant for more species, whereas salinity was significant for the fewest. Residual correlations further revealed that the focal fish species could be partitioned into two assemblages with one linking species. Meanwhile, within the two-part hurdle model, the direction and significance of responses to the same covariate were not always consistent, supporting that species occurrence probability and positive biomass are governed by different ecological processes. Overall, this study provides a transferable quantitative framework for community assessment in coastal fishing grounds and offers a more operational chain of evidence for ecosystem-based fisheries management. Full article

Drones have long been explored by commercial and military users for supply. While several systems offering small payloads in drone delivery have seen operational use, large-scale supply drones have yet to be adopted. A range of setbacks cause this, including technological and operational challenges that hinder their adoption. Here, we evaluate these challenges from a conceptual modelling perspective and forecast their applicability once these barriers are overcome. This study uses technology trend modelling and bibliometric activity mapping methodologies to predict the applicability of specific technologies that are currently identified as operational challenges. Specifically for supply drones, we model trends in technological improvements of battery technology and aircraft control, and project its focus on landing zone autonomy and powertrain. The prediction also focuses on the current state of hybrid power and higher levels of automation required for landing zone operations. These models are validated through several published case studies of small delivery drones and then applied to assess the feasibility and constraints of larger supply drones. A case study involving the conceptual design of a supply drone large enough to move a shipping container is presented to illustrate the critical technologies required to transition large supply drones from concept to operational reality. Key technologies required for large-scale supply drones have yet to build up a critical mass of research activity, particularly on landing zone autonomy and powertrain. Moreover, additional constraints beyond technological and operational challenges could include limitations in autonomy, certification hurdles, regulatory complexity, and the need for greater social trust and acceptance. Full article

Advancements in biomaterials have transformed the field of endodontics, shifting treatment approaches from mechanical interventions to biologically driven regenerative therapies. This narrative review explores the evolving landscape of endodontic biomaterials, emphasizing their roles in disinfection, obturation, root repair, surgical procedures, and regenerative endodontics. Key materials such as mineral trioxide aggregate (MTA), Biodentine, and calcium-enriched mixture (CEM) cement demonstrate superior sealing, biocompatibility, and osteogenic potential compared to traditional materials. The integration of nanotechnology, bioactive components, and smart drug delivery systems has further enhanced antimicrobial properties and tissue interaction. Clinical applications, including regenerative procedures using platelet-rich fibrin and case-based biomaterial usage, are discussed to illustrate their relevance and effectiveness in real-world practice. Despite significant progress, challenges such as regulatory hurdles, economic limitations, and translational gaps persist. Emerging trends such as 3D printing, personalized medicine, and multifunctional scaffolds offer promising directions for future endodontic care. Continued interdisciplinary collaboration is essential to overcome current barriers and facilitate widespread adoption of next-generation biomaterials. Unlike prior reviews that categorize endodontic biomaterials descriptively by material class or technological advancement, this review introduces an indication-based comparative framework aligning biomaterial properties with specific clinical decision points and corresponding levels of evidence. By integrating biological mechanisms, translational considerations, and clinical application within a structured decision-oriented model, the manuscript offers analytical synthesis rather than a purely descriptive overview. Full article

Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is examined regarding the identification of novel disease drivers, prediction of synthetic lethality interactions, and prioritization of therapeutic targets. Finally, addressing persistent challenges related to data sparsity, model interpretability, and target druggability and validation hurdles, emerging opportunities driven by Generative AI, Large Multimodal Models (LMMs), Explainable AI (XAI), and multidimensional feasibility assessment frameworks are discussed in the context of advancing precision medicine. Full article

Mycelium-based composites (MBCs) have emerged as promising biofabricated materials that align with circular economy and clean technology goals by utilizing fungal networks to transform lignocellulosic residues into functional, biodegradable composites. Despite the MBC’s potentials, the intrinsic nature of the fungal strain, substrate physico-chemical composition and engineering property variability remain significant hurdles that should be critically surmounted. Substrate treatment is central to determining growth kinetics, microstructural uniformity, and mechanical performance in MBC production. This review highlights recent advancements in physical, chemical, biological, and hybrid pretreatment methods, including comminution, pasteurization, alkali hydrolysis, enzymatic conditioning, microwave-assisted hydrolysis, ultrasound pretreatment, steam explosion, plasma activation, and irradiation. These technologies collectively enhance substrate digestibility, aeration, and permeability while reducing contamination. Optimization parameters—temperature, pH, C:N ratio, moisture content, particle size, porosity, and aeration—are examined as critical process levers influencing hyphal density, bonding efficiency, and composite uniformity. Evidence suggests that properly engineered substrate treatments accelerate colonization, strengthen hyphal networks, and significantly improve compressive, tensile, and flexural material properties. The review discusses emerging process control tools such as AI-assisted modeling, micro-CT porosity analysis, and sensor-integrated bioreactors that enable reproducible and energy-efficient fabrication. Collectively, the findings position substrate engineering as a foundational technology for scaling high-performance mycelium composites and advancing sustainable material innovation. Full article

The growing consumer demand for minimally processed, “clean-label” foods is increasing interest in innovative technologies that maintain quality while ensuring microbial safety. This study sheds light on how the protein:lipid ratio in meat-like model matrices modulates the effectiveness of combined high-intensity ultrasound (20 kHz) and carvacrol treatments applied against Escherichia coli ATCC 25922. Three emulsified systems with geometrically spaced protein:lipid ratios (0.33, 1.0, 3.0) were subjected to combinations of ultrasound and carvacrol (0–1200 ppm) at $30\pm 2$ °C. To address the rheological non-linearity, the matrix index was log-transformed, and the process was modeled using both Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). While both models achieved high predictive accuracy ($R^2>0.96$), lack-of-fit analysis revealed that the reduced polynomial RSM model provided a more robust and statistically valid representation of the process compared to the ANN, which exhibited significant overfitting to experimental noise ($p<{10}^{-9}$). The results highlighted a distinct matrix dependency: ultrasound alone provided the fastest inactivation in the high-lipid matrix, while the high-protein matrix exhibited much slower kinetics due to viscous damping. Consequently, the explicit mathematical relationships derived from the RSM model are proposed as the preferred, transparent kernel for future digital twins and autonomous process-control systems in smart food-processing lines. Full article

Efficient and clean separation of residual plastic mulch film is the primary bottleneck hindering its resource-oriented reutilization. Currently, the field faces critical technical challenges, most notably the elusive motion mechanisms of flexible materials and the inherent difficulty of film–impurity separation. To address these issues, this study investigates a drum-type mulch-film impurity-removal unit by modeling the throw-off motion mechanism of the material stream, followed by comprehensive multiphysics simulation and optimization. First, to overcome the simulation hurdles typical of flexible materials, “Meta-particles” and the “Bonding V2” contact model were implemented on the EDEM platform to establish a discrete element method (DEM) framework. The resulting analysis revealed a non-linear transport trajectory and morphological evolution within the drum flow field, characterized by a “wall-adhering–slipping–throwing” sequence. These findings were further quantified through MATLAB-based numerical calculations to determine collision frequency and axial residence behavior. Second, ANSYS modal analysis verified the dynamic stability of the frame structure, confirming that the operating frequency (2.37 Hz) remains well below the first natural frequency (6.77 Hz). Furthermore, Box–Behnken response surface methodology (RSM) was employed to elucidate the coupled effects of key process parameters. The results demonstrated that separation efficiency and impurity-removal mass are predominantly governed by the quadratic terms of the inclination angle and rotational speed, respectively. After multi-objective optimization and engineering refinement, the optimal operating parameters were established: a film length of 220 mm, an inclination angle of 3°, and a drum rotational speed of 25 r/min. Bench tests indicated that, under these optimal conditions, the impurity-removal rate stabilized between 71.5% and 72.4%, satisfying the design requirement (≥70%). By elucidating the drum’s throw-off screening mechanism, this study achieves a coordinated improvement in both impurity-removal mass and separation efficiency, resolving long-standing engineering uncertainties regarding film–impurity trajectories and providing a theoretical foundation for the clean treatment of waste mulch film. Full article

In modern communication networks, particularly satellite-based systems, data security faces significant challenges from vulnerabilities such as signal interception, jamming, and latency during long distance transmissions. Traditional cryptographic methods are increasingly vulnerable to quantum computing threats, underscoring the need for advanced solutions to protect data integrity, confidentiality, and availability. This research investigates the fusion of quantum cryptography and Machine Learning (ML) to improve security in satellite communication. The Quantum Key Distribution (QKD), which is grounded in quantum mechanics, enables unbreakable encryption by detecting eavesdropping via quantum state disturbances. The CatBoost ML algorithm is applied to a dataset of 10,000 records featuring categorical attributes for prioritizing security elements such as anomaly detection, encryption types, and access controls. The model yields an accuracy of 89.23% and Area under Curve the Receiver Operating Characteristic (AUC-ROC) score of 94.56%, effectively predicting threat levels. Feature importance reveals anomaly detection (28.5%) and quantum encryption (22.3%) as primary contributors. While hurdles such as high implementation costs and transmission range limitations persist, this quantum ML synergy provides a proactive, adaptive framework for resilient, future-ready communication networks. Full article

In the era of Industry 4.0, Zero-Defect Manufacturing is critical for injection molding but faces three major hurdles: severe class imbalance, the “black-box” nature of AI models, and the lack of scalability across machines. This study presents a comprehensive framework addressing these challenges. Using industrial datasets, we evaluated state-of-the-art supervised algorithms. Results show that CatBoost outperforms other architectures. Crucially, we demonstrate that maximizing accuracy is insufficient; instead, we introduce a cost-sensitive threshold optimization that minimizes economic risk, identifying an optimal classification threshold significantly lower than the standard. To enhance trust, SHAP analysis reveals that motor power and specific nozzle temperatures are the primary defect drivers. Finally, we validate a transfer learning approach using LightGBM, proving that models can be adapted to new datasets with minimal retraining. The implementation of cost-sensitive thresholding reduces total failure costs by over 75% compared to standard classification, while the transfer learning approach cuts the data requirements for new machine adaptation by more than half, providing a high-impact, scalable solution for sustainable smart manufacturing. Full article