Search Results (6,605)

Salmonellosis remains a persistent threat to global food safety and poultry productivity, compounded by rising antimicrobial resistance. Here, we report the in silico design and immunoinformatic validation of a broad-spectrum, edible multi-epitope vaccine targeting conserved adhesion and biofilm-associated proteins (FimH, AgfA, SefA, SefD, and MrkD) of Salmonella spp. Two constructs were engineered by integrating cytotoxic (CTL) and helper (HTL) epitopes with β-defensin-3 (HBD-3) or lipopolysaccharide (LPS) adjuvants, optimized for expression in Chlorella vulgaris. Structural modeling confirmed native-like folding (z-scores −2.58 and −5.22) and high stability indices. Molecular docking and dynamics revealed that the LPS-adjuvanted construct (Construct 2) forms a highly stable complex with Toll-like receptor 3 (HADDOCK score −63.4; desolvation energy −50.2 kcal/mol), indicating potent innate immune activation. Immune simulations predicted strong IgM-to-IgG class switching and durable humoral responses, consistent with effective antigen clearance. Codon optimization achieved high adaptability for algal expression (CAI = 0.93; GC ≈ 65%), supporting scalable microalgae-based production. Compared with current parenteral vaccines, offering a low-cost, non-invasive way to curb Salmonella in poultry, this edible vaccine platform reduces dependence on antibiotics. Our approach, which combines computational vaccinology with a safe-by-design sustainable biomanufacturing perspective, outlines a One Health framework for advancing antimicrobial stewardship and food safety. Full article

Rural communities in developing countries face rising livelihood vulnerability due to climate change, agricultural price volatility, and dependence on linear production systems. This study examines whether circular utilization of cashew by-products can strengthen rural economies through a field-based case study in rural Cambodia. Primary data were collected through on-site observations, semi-structured interviews with farm owners and rural workers, and farm-level economic assessments. The results indicate that cashew apple juice processing is not financially viable as a standalone activity under prevailing wage and market conditions, producing negative net profits across all examined processing volumes. By contrast, integrating cashew apple utilization with other by-products shows more favorable outcomes. Cashew nut shells and pruning residues generate relatively stable supplementary income for farm operators, while cashew apple collection creates additional employment opportunities, particularly during off-harvest periods and low-yield years, helping to stabilize household labor income. Rather than relying on capital-intensive technologies, the observed practices represent low-cost and locally feasible circular economy approaches suitable for medium-sized commercial farm-based systems, with potential analytical transferability to smallholder contexts. Overall, these findings suggest that integrated by-product utilization may reduce income volatility and support sustainable rural community development in similar cashew-producing contexts. Full article

Efficient waste management is a critical challenge in urban areas. This paper explores the optimization of power consumption in a smart bin management system using LoRa (long-range) communication technology. LoRa’s low-power, wide-area capabilities make it an ideal choice for IoT-based waste management systems. However, energy efficiency remains a crucial factor for ensuring the long-term sustainability of such systems, to avoid frequent intervention and reduce operating costs. This study employs advanced optimization techniques to minimize the energy usage of LoRa nodes while maintaining a reliable data transmission and system performance. By integrating a dynamic scheduling algorithm based on the usage of bins, and a custom adaptive data rate and power algorithm, the proposed solution significantly reduces the system’s energy impact. The performance of the system is evaluated through simulations and real-world deployment, where the results demonstrate a significant reduction in energy usage, over 84%, a longer battery life, and fewer maintenance interventions. The findings provide a scalable and energy-efficient framework for deploying smart waste management systems in resource-constrained environments. Full article

Biomolecules play pivotal roles in cellular signaling, metabolic regulation and the maintenance of physiological homeostasis in the human body, and their dysregulation is closely associated with the onset and progression of various human diseases. Consequently, the development of highly sensitive, selective, and stable detection platforms for these molecules is of significant value for drug discovery, pharmaceutical quality control, pharmacodynamic studies, and personalized medicine. In recent years, electrochemical biosensors, particularly those integrated with functional nanomaterials and biorecognition elements, have emerged as powerful analytical platforms in pharmaceutics and biomedical analysis, owing to their high sensitivity, exquisite selectivity, rapid response, simple operation, low cost and suitability for real-time or in situ monitoring in complex biological systems. This review summarizes recent progress in the electrochemical detection of representative biomolecules, including dopamine, glucose, uric acid, hydrogen peroxide, lactate, glutathione and cholesterol. By systematically summarizing and analyzing existing sensing strategies and nanomaterial-based sensor designs, this review aims to provide new insights for the interdisciplinary integration of pharmaceutics, nanomedicine, and electrochemical biosensing, and to promote the translational application of these sensing technologies in drug analysis, quality assessment, and clinical diagnostics. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

Traditional fossil-based adhesives, hindered by issues such as formaldehyde emission, dependence on fossil resources, and poor biodegradability, struggle to meet the global demand for low-carbon green development. Biomass-based adhesives have thus emerged as a core alternative. Among them, chitin/chitosan derived from biomass waste such as shrimp and crab shells demonstrates significant potential in the adhesive field due to its renewability, controllable structure, biocompatibility, and inherent antibacterial properties. However, mainstream biomass adhesives like soy protein and starch adhesives suffer from poor water resistance and insufficient wet adhesion strength. Pure chitin/chitosan-based adhesive systems also exhibit low wet strength retention. Furthermore, the overall development faces challenges including high extraction costs, insufficient performance synergy, poor industrial compatibility, and a lack of standardized systems. This review follows the framework of “resource–extraction–modification–performance–application–challenges” to systematically summarize relevant research progress. It clarifies the molecular structure and intrinsic advantages of chitin/chitosan, outlines extraction processes such as acid/alkali and enzymatic methods, and characterization techniques including FT-IR and XRD. The review focuses on analyzing modification strategies such as composite modification, chemical modification, biomineralization, and biomimetic design, and verifies the application potential of these adhesives in wood processing, biomedicine, paper-based packaging, and other fields. Research indicates that chitin/chitosan-based adhesives provide an effective pathway for the green transformation of the adhesive industry. Future efforts should concentrate on developing green extraction processes, designing multifunctional integrated systems, and achieving full resource utilization of biomass. Additionally, establishing comprehensive standardized systems and promoting the translation of laboratory research into industrial applications are crucial to driving the industry’s green transition. Full article

The multi-objective optimization of multicomponent superalloys has long been impeded by not only the complex interactions among multiple elements but also the low efficiency and high cost of traditional trial-and-error methods. To address this issue, this study proposed a thermodynamic calculation data-driven optimization framework that integrates machine learning (ML) and multi-objective screening based on domain knowledge. The core of this methodology involves introducing a commercial reference alloy and rapidly generating a large-scale thermodynamic dataset through ML models. After training, the ML models were verified to be more efficient at predicting phase transition temperatures and γ′ volume fractions than the CALPHAD methods. Focusing on the mechanical properties, critical strength indices, including solid solution strengthening, precipitation strengthening, and creep resistance based on the calculated γ/γ′ two-phase compositions, were compared with the reference alloy and set as the critical screen criteria. Optimal alloys were selected from the 388,000 candidates. Compared with the reference alloy, two new alloys were experimentally verified to have superior or comparable compressive yield strength and creep resistance at 900 °C at the expense of oxidation resistance and density, while maintaining comparable cost. This work demonstrates the significant potential of combining high-throughput thermodynamic data with intelligent multi-objective optimization to accelerate the development of new alloys with tailored property profiles. Full article

Very Low Earth Orbit (VLEO) satellites, operating at altitudes below 450 km, provide tremendous potential in the domain of remote sensing. Their proximity to Earth offers high resolution, low latency, and rapid revisit rates, allowing continuous monitoring of dynamic systems and real-time delivery of vertically integrated earth observation products. Nonetheless, the application of VLEO is not yet fully realized due to numerous complexities associated with VLEO satellite development, considering atmospheric drag, short satellite lifetimes, and social, political, and legal regulatory fragmentation. This paper reviews the recent technological developments supporting sustainable VLEO operations with regards to aerodynamic satellite design, atomic oxygen barriers, and atmospheric-breathing electric propulsion (ABEP). Furthermore, the paper provides an overview of the identification of regulatory and economic barriers that extort additional costs for VLEO ranging from frequency band allocation and space traffic management to life-cycle cost and uncertain commercial demand opportunities. Nevertheless, the commercial potential of VLEO operations is widely acknowledged, and estimated to lead to an economic turnover in the order of 1.5 B USD in the next decade. Learning from the literature and prominent past experiences such as the DISCOVERER and CORONA programs, the study identifies key gaps and proposes a roadmap to sustainable VLEO development. The proposed framework emphasizes modular and serviceable satellite platforms, hybrid propulsion systems, and globally harmonized governance in space. Ultimately, public–private partnerships and synergies across sectors will determine whether VLEO systems become part of the broader space infrastructure unlocking new capabilities for near-Earth services, environmental monitoring, and commercial innovation at the edge of space. Full article

Health systems face pressure to strengthen resilience against supply chain disruptions while maintaining cost-effective service delivery. This mixed-methods study describes a pilot project that integrated 3D printing services into a Canadian provincial health authority. Quantitative data were derived from internal clinical engineering work orders, where a scenario-based economic analysis compared original equipment manufacturer (OEM) procurement with modelled 3D-printed parts. Using conservative assumptions, selected non-electronic structural parts were assigned a fixed unit cost. Qualitative data were collected from two focus groups with clinical engineers and other end-users. Results from an exploratory scenario-based economic analysis suggest that substituting selected structurally simple clinical engineering parts with 3D-printed alternatives would be associated with modelled cost impacts ranging from a 67.4% net increase (OEM prices halved and 3D-printing costs doubled) to a 69.6% cost reduction (OEM prices increased by 10% and 3D-printing costs decreased by 20%). Demand changes affected absolute savings but not the percent difference (58.1% under ±50% quantity changes), and a pessimistic procurement scenario (OEM prices decreased by 30% and 3D-printing costs increased by 50%) reduced savings to 10.3%. Focus groups highlighted perceived benefits and implementation challenges associated with integrating additive manufacturing. Implementation was facilitated through an outsourcing model, which was perceived to shift certain responsibilities and risk-management functions to the vendor. Long-term adoption will require clearer communication and targeted education. This pilot study suggests that, under constrained regulatory scope and scenario-based assumptions, additive manufacturing may contribute to supply chain resilience and may be associated with modelled cost advantages for selected low-risk components. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article

Background/Objectives: Obesity is a multifactorial condition and a major public health challenge. Conventional treatment centers on caloric restriction, which is often unsustainable and may cause stigma and psychoemotional harm. This study aimed to describe the methodology and assess the effects of a multicomponent nutritional intervention not focused on caloric restriction on psychoemotional outcomes. Women were selected as the target population because of the higher prevalence of obesity-related psychoemotional distress, body dissatisfaction, and weight-related stigma in this group, as well as their greater vulnerability to the psychosocial impacts of weight-focused interventions. Methods: This randomised, parallel, open-label trial included 89 obese women from primary care in Viçosa, Brazil. The participants were allocated into three groups: Group 1 (Control), which received a personalised hypocaloric diet (from 500 to 1000 kcal/day); Group 2 (NutrirCom (NutrirCom is a multicomponent, person-centred nutritional intervention protocol that is not focused on caloric restriction, conceived by a group of researchers at the Federal University of Viçosa for the care of women with obesity in Primary Health Care. It integrates nutritional, psychoemotional, behavioural, and social strategies, with an emphasis on promoting eating autonomy, mental health, and quality of life through a humanised, integrated, and sustainable approach, aiming to enhance the effectiveness of health care delivery and clinical practice)), which received 10 individual NutrirCom-based sessions; and Group 3 (NutrirCom + Social Support), which combined individual NutrirCom sessions with monthly group meetings for social support. Randomisation was stratified by body mass index via Excel^® with concealed allocation. The six-month intervention assessed changes in stress, anxiety, depression, and self-compassion, along with anthropometric and metabolic markers. Results: All groups presented reductions in waist circumference, fasting glucose, and total body fat, with increased lean mass. Anxiety remained unchanged in Group 1 but decreased significantly in Groups 2 (p = 0.002) and 3 (p = 0.005). Only Group 2 showed a significant reduction in depression symptoms (p = 0.023). Self-compassion improved significantly in groups 2 and 3. Conclusions: NutrirCom is a low-cost, scalable, and human-centered intervention that integrates emotional, social, and nutritional aspects of care. This approach shows promise as a sustainable strategy for obesity treatment in primary health care. Registration: Brazilian Registry of Clinical Trials (ReBEC) (no. RBR-87wb8x5). Full article

This study presents a sustainable valorization strategy for wastewater treatment plant (WWTP) residual sands through their hydrothermal conversion into a dual-phase FAU/LTA zeolite and evaluates its adsorption performance toward methylene blue (MB) as a model cationic contaminant. The synthesized material (ZEO-RS) exhibited a low Si/Al ratio (~1.7), well-developed FAU supercages with minor LTA domains, and high structural integrity, as confirmed by XRD, FTIR, XRF, SEM and PZC analyses. ZEO-RS demonstrated rapid adsorption kinetics, reaching approximately 92% of equilibrium uptake within 30 min and following a pseudo-second-order kinetic model (k₂= 2.73 g·mg⁻¹·h⁻¹). Equilibrium data were best described by the Langmuir isotherm, yielding a maximum adsorption capacity of 34.2 mg·g⁻¹ at 20 °C, with favorable separation factors (0 < r_L < 1), while Freundlich fitting indicated moderate surface heterogeneity. Thermodynamic analysis revealed that MB adsorption is spontaneous (ΔG° = −11.98 to −12.56 kJ·mol⁻¹), mildly endothermic (ΔH° = +5.26 kJ·mol⁻¹), and entropy-driven (ΔS° = +0.059 kJ·mol⁻¹·K⁻¹). FTIR evidence, combined with pH-dependent behavior, indicates that adsorption proceeds via synergistic electrostatic attraction, pore confinement within FAU domains, and partial ion-exchange interactions. Desorption efficiencies conducted under mild acidic, neutral, and alkaline conditions resulted in low MB release (1–8%), indicating strong dye retention and high framework stability. Overall, the results demonstrate that WWTP residual sands are an effective and scalable low-cost precursor for producing zeolitic adsorbents, supporting their potential application in sustainable water purification and circular-economy-based wastewater treatment strategies. Full article

Fossil fuels have been the main source of energy for decades. However, they are non-renewable resources that take millions of years to replenish from decomposed organic matter. As they are depleting at an alarming rate, a shift towards more sustainable fuels is gaining popularity. Biodiesel is emerging as a biodegradable and renewable energy source that serves as a promising alternative to conventional fuels. It addresses the challenges of greenhouse gas emissions while ensuring energy security. Among potential feedstocks, sunflower oil demonstrates unique advantages due to its high oil yield, favorable fatty acid composition, and availability. Despite extensive research on biodiesel, no comparative study has yet synthesized the four generations of biodiesel feedstocks while integrating optimization strategies with a particular focus on sunflower oil and sustainability trade-offs. This review aims to fill that gap by providing a comprehensive analysis of biodiesel production pathways, highlighting sunflower oil within a broader sustainability framework. The four generations are assessed based on feedstock potential, efficiency, and yield, while optimization processes for sunflower oil-based biodiesel are examined in terms of economic feasibility, limitations, and environmental impacts. The principal findings highlight the low free fatty acid composition of sunflower oil compared to other feedstocks, which makes it efficient for transesterification. Challenges such as production costs, land consumption, and food chain disruption are also discussed. Finally, innovative insights are presented for improving the viability of biodiesel through advanced technologies and supportive policies. Full article

The exponential growth of multimodal orthopedic data, ranging from longitudinal Electronic Health Records to high-resolution musculoskeletal imaging, has rendered manual analysis insufficient. This has established Large Language Models (LLMs) as algorithmically necessary for managing healthcare complexity. However, their deployment in high-stakes surgical environments presents a fundamental algorithmic paradox: while generic foundation models possess vast reasoning capabilities, they often lack the precise, protocol-driven domain knowledge required for safe orthopedic decision support. This review provides a structured synthesis of the emerging algorithmic frameworks required to build modern clinical AI assistants. We deconstruct current methodologies into their core components: large-language-model adaptation, multimodal data fusion, and standardized data interoperability pipelines. Rather than proposing a single proprietary architecture, we analyze how recent literature connects specific algorithmic choices such as the trade-offs between full fine-tuning and Low-Rank Adaptation to their computational costs and factual reliability. Furthermore, we examine the theoretical architectures required for ‘agentic’ capabilities, where AI systems integrate outputs from deep convolutional neural networks and biosensors. The review concludes by outlining the unresolved challenges in algorithmic bias, security, and interoperability that must be addressed to transition these technologies from research prototypes to scalable clinical solutions. Full article

The emergence of low-cost edge devices has enabled the integration of automatic speech recognition (ASR) into IoT environments, creating new opportunities for real-time language assessment. However, achieving reliable performance on resource-constrained hardware remains a significant challenge, especially on the Artificial Internet of Things (AIoT). This study presents an AIoT-based framework for automated English-speaking assessment that integrates architecture and system design, ASR benchmarking, and reliability analysis on edge devices. The proposed AIoT-oriented architecture incorporates a lightweight scoring framework capable of analyzing pronunciation, fluency, prosody, and CEFR-aligned speaking proficiency within an automated assessment system. Seven open-source ASR models—four Whisper variants (tiny, base, small, and medium) and three Vosk models—were systematically benchmarked in terms of recognition accuracy, inference latency, and computational efficiency. Experimental results indicate that Whisper-medium deployed on the Raspberry Pi 5 achieved the strongest overall performance, reducing inference latency by 42–48% compared with the Raspberry Pi 4 and attaining the lowest Word Error Rate (WER) of 6.8%. In contrast, smaller models such as Whisper-tiny, with a WER of 26.7%, exhibited two- to threefold higher scoring variability, demonstrating how recognition errors propagate into automated assessment reliability. System-level testing revealed that the Raspberry Pi 5 can sustain near real-time processing with approximately 58% CPU utilization and around 1.2 GB of memory, whereas the Raspberry Pi 4 frequently approaches practical operational limits under comparable workloads. Validation using real learner speech data (approximately 100 sessions) confirmed that the proposed system delivers accurate, portable, and privacy-preserving speaking assessment using low-power edge hardware. Overall, this work introduces a practical AIoT-based assessment framework, provides a comprehensive benchmark of open-source ASR models on edge platforms, and offers empirical insights into the trade-offs among recognition accuracy, inference latency, and scoring stability in edge-based ASR deployments. Full article