Search Results (2,131)

Parallel Delta robots are widely used in high-speed industrial automation, particularly in food-processing operations that require short cycle times and precise manipulation. Their performance under complex loading conditions, however, remains critical for reliable integration into mechatronic production systems. In many technological processes, the end effector experiences not only gravitational forces but also additional loads caused by product contact, lateral disturbances, and dynamic gripping actions. These conditions influence actuator torque demand, structural behaviour, and workspace feasibility. To address this, the present study develops a complete kinematic formulation and a force analysis model for the ABB IRB 360 3/1130 FlexPicker^® Delta robot sourced from ABB Robotics, Västerås, Sweden. The models enable computer-based simulation of the robot’s behaviour under both vertical and horizontally oriented external forces. The results show how combined loading affects torque distribution and may restrict feasible workspace regions. These findings support motion planning, task feasibility assessment, and the reliable integration of high-speed parallel manipulators in industrial mechatronic environments. Full article

Background: Medication non-adherence is a major driver of poor therapeutic outcomes, and computer vision methods that observe facial movements offer a non-contact route to verifying oral medication intake. Objective: To map and synthesize the existing literature on computer vision techniques applicable to the monitoring of medication intake, focusing on face tracking methods, oral movement detection and deglutition recognition, and to assess their potential in supporting automatic medication adherence verification systems. Eligibility criteria: Peer-reviewed articles, conference papers, patents, theses and preprints published from 2016 onward, written in English or Portuguese, applying facial landmark tracking or face analysis to ingestion-related movements (mouth opening, hand-to-mouth motion, pill placement, mastication or deglutition); studies confined to object/pill detection without facial analysis, or to general food intake without transferability to medication, were excluded. Sources of evidence: A systematic screening of 362 initial records was conducted across six main electronic databases and repositories: Google Scholar, PubMed, ScienceDirect, arXiv, IEEE Xplore, and Espacenet. Charting methods: Data were charted with a standardized, pilot-tested extraction form capturing bibliographic attributes, dataset type, experimental environment, face tracking approach, tools/models, and target movements; extraction was performed by a single reviewer. Following the screening process, a final selection of 34 relevant studies was included for detailed analysis and mapping. Results: Among the 34 included studies, 14 employ facial landmarks, 11 utilize temporal deep learning models, 6 apply facial action models and 3 rely on hybrid multimodal approaches that combine video analysis, object detection and temporal modeling. Tasks such as detecting mouth opening or tracking pill-to-mouth movement show promising results, while accurately detecting deglutition remains a technical challenge due to high sensitivity and individual variability. Limitations: The majority of the literature relies on private or institutional datasets (31 studies) and operates in controlled laboratory environments (22 studies); only 2 studies evaluated their methods via independent external datasets, which limits the generalization of current solutions to real-world telemonitoring scenarios. Conclusions: The literature indicates the existence of solid technical foundations for developing automated medication intake verification systems. To advance the field toward practical deployment, future research must address the need for more diverse datasets, real-world validation and more robust, adaptable modeling frameworks. Full article

Volatile release immediately after opening canned foods is highly transient and may not be adequately represented by equilibrium or quasi-equilibrium headspace measurements. In this study, time-resolved proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) was used to characterize opening-burst volatile features in canned Antarctic krill processed to an equivalent sterilization intensity (F₀ ≈ 9 min). Raw krill, pretreated but non-retorted samples, and samples retorted using different temperature–time combinations were monitored online for 180 s after opening, with particular attention to the 0–60 s burst phase. Twenty-three of the twenty-five retained features reached their maximum responses within 10 s, including two within 5 s, showing that the dominant volatile changes occurred immediately after opening. The maximum background-corrected response during 0–60 s (C_max,0–60) and the corresponding area under the response curve (AUC_0–60) were used to describe transient release. The nine highest-ranked features accounted for 76.78% of the cumulative AUC_0–60 response within the retained candidate feature set. Although the retort treatments had similar F₀ values, their burst-phase fingerprints remained distinguishable. Sulfur-associated features varied markedly among treatments, whereas several low-molecular oxygenated features showed strong responses after the 121 °C high-temperature/short-time treatment. These findings show that the first seconds after opening contain substantial volatile-release information and that equivalent sterilization lethality does not necessarily produce the same opening-burst fingerprint. Time-resolved PTR-ToF-MS therefore enables direct examination of the initial post-opening volatile environment and comparison of different thermal processing routes. Chemical names are reported as putative annotations rather than definitive identifications. Full article

Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system. Full article

Risk assessment is critical for managing health threats at the human–animal–environment interface, yet sector-specific approaches can result in fragmented actions. To address this gap, the Joint Risk Assessment Operational Tool (JRA OT), an operational tool of the Tripartite Zoonoses Guide, was developed by the Food and Agriculture Organization of the United Nations, the World Health Organization, and the World Organization for Animal Health. The JRA OT provides a structured framework for joint qualitative risk assessments that integrate multisectoral expertise to identify risk pathways, assess likelihood and impact, and develop consensus-based risk management and communication options. Surveillance systems play a critical role in this process by providing the multisectoral data needed to inform risk assessments, while the JRA process helps identify information gaps and guide the strengthening of integrated One Health surveillance. Implemented in at least 52 countries, the JRA OT has informed national mandates in Indonesia, Viet Nam, and Tanzania, regional strategies in West Africa, and adaptations in Canada. Cascade training has been used to build subnational capacity to facilitate roll out. Sustained leadership commitment, multisectoral coordination, routine applications, local adaptation, and capacity building remain essential for global implementation. Full article

Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production. Full article

Selecting suitable agricultural land is critical for food security and sustainable development, particularly in arid regions facing resource scarcity and environmental degradation. This study assesses agricultural land suitability in Sohag governorate, Egypt, using a hybrid Geographic Information System (GIS), Fuzzy Analytical Hierarchy Process (FAHP) and geostatistical approach. Thirty-four representative soil profiles were morphologically described and analyzed for ten physical (slope, depth, erosion, texture, stoniness, drainage) and chemical (EC, pH, CaCO₃, organic matter) criteria, weighted using both the Analytical Hierarchy Process (AHP) and FAHP. Geostatistical analysis characterized the spatial variability in soil properties across the study area. The two suitability maps were validated using Receiver Operating Characteristic (ROC) curves and Kappa statistics: the FAHP achieved higher prediction accuracy (AUC = 0.83, Kappa = 0.91) than the AHP (AUC = 0.81, Kappa = 0.88). The AHP-based map classified 40% (1154.92 km²) as moderately suitable (S2), 33% (952.81 km²) as marginally suitable (S3), and 27% (779.57 km²) as unsuitable (N); the FAHP-based map classified 42% (1212.67 km²) as S2, 30% (866.19 km²) as S3, and 28% (808.44 km²) as N. The proposed framework may serve as a reference for similar arid environments and support progress toward Sustainable Development Goals 2, 6, 8, 9, 11, 13 and 15, conditional on local soil, water, climate and management conditions. Full article

Synthetic azo dyes are the largest class of industrial colorants having widespread application in textile, food, cosmetic, and pharmaceutical industries. Moreover, they are persistent and toxic, threatening aquatic environments as well as human health. Disperse red 1 (DR1), a mono-azo dye belonging to the disperse dye group and widely used in polyester dyeing, cosmetics, and other applications, is of particular concern due to its mutagenic potential and resistance to conventional treatment processes. The present study investigated an integrated DR1 removal strategy using thermotolerant Bacillus licheniformis Tol1 as well as its EPS-stabilized biogenic silver nanoparticles (AgNPs). With a maximum tolerable concentration of 0.5 g L⁻¹, B. licheniformis Tol1 showed a maximum decolorization of 70.86% (0.2 g L⁻¹, 55 °C). However, response surface methodology (RSM) based on the Box–Behnken design showed an actual decolorization efficiency of 73.13%. The artificial neural network (ANN) model predicted an accuracy of R² = 0.9933, confirming the robustness and reliability of the experimental findings. To enhance dye removal efficiency, Tol1 EPS-stabilized AgNPs were synthesized via a green method and characterized using UV-Vis, SEM-EDAX, TEM, AFM, FTIR, DLS and zeta potential. Characterization of AgNP confirmed the formation of spherical stable AgNPs with an average size of 19.99 ± 0.38 nm, indicating polydisperse colloids nature with moderate electrostatic stability. A sunlight/H₂O₂-assisted process (photocatalytic experiments) demonstrated DR1 decolorization (80.72 ± 1.72% within 5 h under sunlight) following pseudo-first-order kinetics (k = 0.271 h⁻¹). Furthermore, FTIR analysis confirmed the degradation of the chemical structure of DR1 through the disappearance of the characteristic azo (–N=N–) bond, indicating cleavage of the dye molecule. Overall, the present study provides a dual biological–nanotechnological approach for DR1 decolorization using single bacteria as well as its polysaccharide-stabilized AgNP, a sustainable eco-friendly future approach. However, further studies on complete mineralization, transformation products, toxicity evaluation, detailed catalyst reusability, and silver (Ag) leaching are needed to facilitate the practical implementation for wastewater treatment. Full article

Modern smart kitchen automation requires reliable vision-based tools to provide user-advisory decision support during domestic culinary processes. However, standard deep learning models utilizing closed-set Softmax classifiers typically misclassify unknown or Out-of-Distribution (OOD) kitchen objects with high confidence, posing safety and reliability risks. To address this problem without clous dependency, this study introduces a localized open-set metric learning framework based on a modified MobileNetV2 architecture. The conventional Softmax classification layer is replaced with a feature embedding layer evaluated via Cosine Similarity and a calibrated decision threshold. This architecture tracks targeted food items across four operational stages—counter-raw, in-oven-raw, in-oven-cooked, and counter-cooked—while identifying and rejecting OOD objects. To ensure reproducibility, comprehensive experimental validations were conducted on a dedicated internal dataset, providing direct baseline comparisons against mainstream backbones (ResNet50, EfficientNet-B0, and Vision Transformers) stripped of their Softmax layers and evaluated under identical metric constraints. The results demonstrate that the proposed framework achieves a Macro F1-score 92.6% and ultra-low inference latency of 11.5 ms, ensuring an optimized trade-off between Macro $F_1$-score and inference speed on edge computing environments. This framework establishes a robust, self-contained solution for open-set object recognition in localized smart home appliances. Full article

Lactic Acid Bacteria (LAB) from traditional fermented foods serve as a promising source of novel exopolysaccharides (EPSs) with desirable techno-functional properties. In this study, the EPS production ability of LAB strains from traditional pickles was tested and a strong slimy isolate grown in a sucrose-rich environment was further selected. The isolate was identified as Leuconostoc mesenteroides VB113 and its EPS production characteristics were determined. HPLC analysis revealed that glucose was the only monomer in the EPS repeating structure, whereas ¹H and ¹³C NMR spectroscopy analysis demonstrated a branched dextran structure with 20% (1 → 3)-linked α-D-glucose units. Dextran VB113 showed a molecular weight of 2.4 × 10⁶ Da tested by GPC analysis. The functional groups within dextran VB113 were determined by FTIR analysis, and XRD analysis demonstrated its amorphous physical status. TGA and DSC analysis were applied for thermal characterization and a degradation temperature of 272.25 °C was observed for dextran VB113, suggesting its high thermal resistance. The micro-structural features of dextran VB113 were tested by SEM and AFM analysis that revealed spherical and fibrillary properties, respectively. These findings revealed the importance of testing different niches to obtain novel EPS-producing LAB strains that can be further evaluated to enhance the techno-functional properties of food systems. The branched nature of dextran VB113, together with its molecular weight and thermal characteristics, offers practical advantages for its potential usage as a thermal-stable thickening agent, emulsion stabilizer, and fat replacer in high-temperature food processing applications, such as bakery products, processed dairy, and plant-based meat analogs. Full article

Aquaculture is central to global food security, but intensification of production has increased disease risk and environmental pressure. The gut microbiome of aquatic organisms is now recognized as a key mediator of host physiology, nutrition, immunity, and pathogen resistance, making it a promising alternative to antimicrobial-based disease control. This review summarizes current knowledge on the composition, assembly, and functional roles of the gut microbiome in fish and crustaceans of aquacultural importance. Major bacterial phyla include Proteobacteria, Firmicutes, Bacteroidetes, Fusobacteria, Actinobacteria, and Verrucomicrobia. Community structure is shaped by environment, diet, host age, genetics, stress, and stochastic processes, with differences between marine and freshwater systems. The microbiome contributes to immune defense through short-chain fatty acid production, Toll-like receptor signaling, cytokine regulation, mucosal immunoglobulin responses, antimicrobial peptide and bacteriocin production, and competitive exclusion of pathogens. It also supports productivity by improving nutrient assimilation, vitamin and enzyme synthesis, and feed conversion. Probiotics, prebiotics, and synbiotics are discussed as strategies for targeted microbiome modulation, although unstable colonization and the lack of standardized protocols remain major challenges. Overall, targeted microbiome manipulation offers a promising route toward sustainable, antimicrobial-reduced aquaculture. Full article

The Internet of Plants (IoP) represents the convergence of Artificial Intelligence (AI), Big Data analytics, and the Internet of Things (IoT) within protected agricultural systems. This study presents an IoP platform designed to collect, process, and analyze real-time environmental data using specialized IoT sensors capable of monitoring critical variables, including carbon dioxide concentration (CO₂), pH, air temperature, and relative humidity in hydroponic production systems. The proposed framework integrates advanced machine-learning algorithms, including Random Forest Regressor and Long Short-Term Memory (LSTM) neural networks, to process large volumes of environmental data and support crop management. In addition, the platform incorporates Vapor Pressure Deficit (VPD) and Growing Degree Days (GDD) analyses to provide crop-specific recommendations and support informed decision-making. This platform establishes a benchmark for smart agriculture in Mexico’s Laja–Bajío region, facilitating informed decision-making and maximizing the sustainability of food systems. Experimental validation was conducted under both controlled and semi-controlled environments using Swiss chard (Beta vulgaris subsp. cicla L.) and lettuce (Lactuca sativa L.) cultivated in hydroponic systems. These environments represented contrasting climatic conditions, allowing evaluation of platform stability and forecasting performance under varying thermal regimes. The Random Forest Regressor model, trained using growth chamber data consisting of 19,836 valid observations, reproduced the deterministic VPD relationship with a coefficient of determination (R²) of 0.90 and a root mean square error (RMSE) of 0.08 kPa, confirming internal consistency and identifying temperature as the dominant contributing variable rather than predicting an independent outcome. The dynamic alarm system, integrated with crop phenological stages, demonstrated greater effectiveness than conventional static-threshold approaches by generating alerts according to crop developmental requirements. Furthermore, the web-based visualization platform enabled users to interpret environmental conditions through intuitive graphical representations, facilitating decision-making without requiring specialized technical expertise. The results demonstrate the feasibility of the IoP platform as a comprehensive environmental management tool for protected agricultural systems. The proposed framework provides a scalable solution for precision agriculture applications in the Laja–Bajío region of Mexico and in other regions with similar production systems. Full article

Climate-related disturbances are increasingly identified as major determinants of food insecurity in fragile environments, yet empirical evidence remains scarce in conflict-affected areas such as the eastern Democratic Republic of Congo (DRC). Using high-frequency FAO DIEM-Monitoring surveys (2022–2026), this paper estimates the causal effect of climate shocks on household food security in the provinces of Ituri, North Kivu, South Kivu, and Tanganyika. Food security is measured with five validated indicators (FCS, HDDS, HHS, FIES, and rCSI); capturing multiple dimensions of access, consumption, and coping. To address endogeneity in self-reported shock exposure, the authors apply an instrumental-variable Conditional Mixed Process (IV-CMP) model and triangulate results with a pseudo-panel fixed-effects ordered probit model based on FIES severity. Objective climate proxies (rainfall and NDVI) are used to reinforce robustness. The results indicate that climate shocks lead to a significant deterioration in food consumption and raise the likelihood of households experiencing both moderate and severe food insecurity, thereby undermining sustainable food security. Effects are heterogeneous across provinces, reflecting the interaction between climate variability, conflict intensity, and market access constraints. Female-headed households are disproportionately affected in some areas, highlighting gendered vulnerabilities, while income, education, livestock ownership, and land access mitigate vulnerability. These findings highlight the need for integrated and sustainability-oriented policy responses combining climate-smart agriculture, adaptive social protection, early warning systems, and conflict-sensitive programming. Overall, this research advances existing knowledge by delivering rigorous causal insights based on multiple food security indicators and advanced econometric techniques in a highly fragile setting. Full article

Contemporary food systems are increasingly shaped by financialization, corporate concentration, and unequal distributions of power that influence food production, food environments, dietary exposures, and population health. However, the relationships among food-system financialization, food sovereignty, and oral health remain insufficiently integrated within food-security, sustainability, and One Health research. This structured narrative review critically synthesized interdisciplinary evidence from Scopus, Web of Science, PubMed/MEDLINE, Google Scholar, citation searching, and authoritative institutional sources to examine these relationships and develop an integrative conceptual framework. The synthesis indicates that financialization may influence health through market concentration, commodity dependence, corporate control of food environments, and the expansion of ultra-processed foods, whereas food sovereignty may modify these pathways by strengthening agency, equitable resource distribution, local governance, and ecological resilience. Dietary exposures and related biological mechanisms provide plausible pathways through which these structural processes may contribute to oral-health outcomes and inequalities. The proposed framework integrates food-system structures, governance, food environments, dietary exposures, biological pathways, oral health, and One Health implications within a common analytical model. Oral health is therefore proposed as a potential biological interface through which food-system transformations and inequalities may become measurable. Empirical research is required to test these pathways and evaluate the framework’s applicability across populations and food-system contexts. Full article

Shiga toxin-producing Escherichia coli (STEC) is a zoonotic pathogen of global relevance whose persistence in food processing environments is frequently mediated by biofilm formation. The acquisition of stx prophages and associated regulatory mutations can impose adaptive restrictions on this phenotype. This study analyzed 216 whole-genome sequences (WGS) to investigate the genomic determinants of biofilm formation in a diverse collection of E. coli isolates (STEC and non-STEC) isolated from cattle feedlots. Genomes were characterized for serogroup, MLST, stx subtyping, integrity of the mlrA and rpoS regulators, single nucleotide polymorphisms (SNPs) in the csg/bcs operons, antimicrobial resistance (AMR) genes, and plasmid replicons. The biofilm phenotype was quantitatively evaluated using a crystal violet assay at 15 °C for 96 h. Results demonstrated higher biofilm forming ability among non-STEC isolates compared to STEC strains, with strains simultaneously carrying both stx1 and stx2 exhibiting the lowest prevalence of biofilm formation (3.3%). Although the rpoS mutation did not show a significant overall association (p = 0.354) with biofilm formation, it was universally present across all O157:H7 isolates, and likely enhanced mlrA disruption toward csgD repression. In addition, stx-positive isolates accumulated significantly more SNPs in the curli (csg) and cellulose (bcs) structural genes. Exploratory gene-level association tests and multiple correspondence analysis further indicated that stx status was associated with broader differences in non-stx accessory gene composition. In addition, there was no statistical association between AMR classes and biofilm-forming capacity. Furthermore, STEC strains demonstrated a lower frequency of resistance to aminoglycosides, phenicols, and tetracyclines. Finally, the plasmid replicons IncX1 and IncFII(pSE11) were present and associated with biofilm-positive isolates. In conclusion, biofilm suppression in STEC suggests a multifactorial and evolutionary phage-induced regulatory trade-off, whereas biofilm persistence in non-STEC strains is independently driven by mobile genetic elements and potential accessory determinants. Full article