Search Results (636)

Research on commuting patterns has mainly focused on urban and metropolitan areas, and such studies are not typically applied to rural and small-town regions, where workers often face longer commutes due to limited job opportunities and inadequate public transportation. By using the Census Transportation Planning Package (CTPP) data, this research fills that gap by analyzing commuting behavior by ethnic group and industry in south-central Minnesota, which is a predominantly rural area of 13 counties in the United States. The results show that both white and minority groups in District 7 experienced an increase in excess commuting from 2006 to 2016, with the minority group in Nobles County showing a significantly higher rise. Analysis by industry reveals that excess commuting in the leisure and hospitality sector (including arts, entertainment, and food services) in Nobles County increased five-fold during this time, indicating a severe spatial mismatch between jobs and affordable housing. In contrast, manufacturing experienced a decline of 50%, possibly indicating better commuting efficiency or a loss of manufacturing jobs. These findings can help city and transportation planners conduct an in-depth analysis of rural-to-urban commuting patterns and develop potential solutions to improve rural transportation infrastructure and accessibility, such as promoting telecommuting and hybrid work options, expanding shuttle routes, and adding more on-demand transit services in rural areas. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Lactic acid bacteria (LAB) are central to food, feed, and health biotechnology, yet their genomes have long resisted rapid, precise manipulation. This review charts the evolution of LAB genome-editing strategies from labor-intensive RecA-dependent double-crossovers to state-of-the-art CRISPR and CRISPR-associated transposase systems. Native homologous recombination, transposon mutagenesis, and phage-derived recombineering opened the door to targeted gene disruption, but low efficiencies and marker footprints limited throughput. Recent phage RecT/RecE-mediated recombineering and CRISPR/Cas counter-selection now enable scar-less point edits, seamless deletions, and multi-kilobase insertions at efficiencies approaching model organisms. Endogenous Cas9 systems, dCas-based CRISPR interference, and CRISPR-guided transposases further extend the toolbox, allowing multiplex knockouts, precise single-base mutations, conditional knockdowns, and payloads up to 10 kb. The remaining hurdles include strain-specific barriers, reliance on selection markers for large edits, and the limited host-range of recombinases. Nevertheless, convergence of phage enzymes, CRISPR counter-selection and high-throughput oligo recombineering is rapidly transforming LAB into versatile chassis for cell-factory and therapeutic applications. Full article

Plant-sourced Dietary Fibers (PDFs) have garnered significant attention due to their multifaceted health benefits, particularly in glycemic control, lipid metabolism regulation, and gut microbiota modulation. This review systematically investigates advanced modification strategies, including physical, chemical, bioengineering, and hybrid approaches, to improve the physicochemical properties and bioactivity of PDFs from legumes, cereals, and other sources. Key modifications such as steam explosion, enzymatic hydrolysis, and carboxymethylation significantly improve solubility, porosity, and functional group exposure, thereby optimizing the health-promoting effects of legume-sourced dietary fiber. The review further elucidates critical structure–function relationships, highlighting PDF’s prebiotic potential, synergistic interactions with polyphenols and proteins, and responsive designs for targeted nutrient delivery. In functional food applications, cereal-sourced dietary fibers serve as a versatile functional ingredient in engineered foods including 3D-printed gels and low-glycemic energy bars, addressing specific metabolic disorders and personalized dietary requirements. By integrating state-of-the-art modification techniques with innovative applications, this review provides comprehensive insights into PDF’s transformative role in advancing functional foods and personalized nutrition solutions. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

Alternative proteins denote non-traditional, high-protein foods. These innovative sources aim to compete with conventional animal products by providing protein-rich, sustainable, nutritious, and flavorful options. Currently, five main categories of alternative proteins are being developed: plant-based proteins, cultured meat, single-cell proteins, edible insects, and seaweed. Nonetheless, several chemical and microbiological food safety hazards are associated with these alternatives Incorporating novel protein sources into food products may heighten the prevalence of existing food allergies. This could arise from extracting proteins from their natural matrices and utilizing them at significantly higher concentrations. Additionally, the introduction of new proteins may lead to the development of novel food allergies. Proteins that are currently seldom or never consumed may cause primary sensitisation or trigger cross-reactivity with known allergens. To date, alternative proteins have not been thoroughly studied for their allergenic potential, and there is no standardised method for assessing this risk. This review aims to explore non-traditional protein sources, discussing their nutritional and functional properties, as well as their potential allergenicity based on available research. We conducted a literature search in PubMed and Embase databases. We used specific keywords and MESH terms. A total of 157 studies were included in the review. The studies reviewed in our analysis reveal significant limitations, such as inconsistent methodologies, limited participant numbers, and a lack of long-term data, which hinder the ability to make clear conclusions regarding the safety of these new proteins for individuals with allergies. To address current challenge, future research should integrate food science, regulatory perspectives and advanced technologies. Full article

Industrial energy systems are increasingly required to reduce operating costs and CO₂ emissions while integrating variable renewable energy sources. Managing these objectives under uncertainty requires advanced optimization strategies capable of delivering reliable and real-time decisions. To address these challenges, this study focuses on the short-term operational planning of an industrial energy supply system using the rolling horizon approach (RHA). The RHA offers an effective framework to handle uncertainties by repeatedly updating forecasts and re-optimizing over a moving time window, thereby enabling adaptive and responsive energy management. To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. To overcome these limitations, we employ the BO–IPOPT, integrating the global search capabilities of BO with the efficient local convergence and constraint fulfillment of the IPOPT. Applied to a large-scale real-world case study of a food and cosmetic industry in Germany, the proposed BO–IPOPT method outperformed state-of-the-art solvers in both solution quality and robustness, achieving up to 97.25%-better objective function values at the same CPU time. Additionally, the influence of key parameters, such as forecast uncertainty, optimization horizon length, and computational effort per RHA iteration, was analyzed to assess their impact on system performance and decision quality. Full article

The art of food–drink pairing has always fascinated gourmets and cooking enthusiasts. While wine has long held pride of place on the table, natural mineral water plays a central role in this new concept. Through the Pyramid of Natural Mineral Waters, we aim to explore the relationships between the structure of water and food, flavors and aromas, revealing a world of previously unexplored nuances and tastes. This new approach is based on the analysis of the fixed residue of water, i.e., the amount of mineral salts dissolved in it. The fixed residue gives the water unique organoleptic characteristics, influencing the perception of flavors and sensations in the mouth. By analyzing the technical data sheet of mineral waters designed by us, it is possible to identify their main characteristics and combine them in a consistent way with various dishes, as proposed in the pyramid scheme. There are many possible combinations between natural mineral waters and foods, depending on numerous factors, including the type of water and the salts dissolved in it, the type of food, the cooking method, and the types of sauces and condiments present in the dish. To guide consumers in this fascinating universe, the figure of the water sommelier, or so-called hydro-sommelier, was born. As expert connoisseurs of natural mineral waters, they are able to recommend the ideal water for every occasion, maximizing the taste characteristics of the food served at the table. This study is completed with the construction of the Pyramid of Natural Mineral Waters, which relates the composition of water, specifically the salient characteristics related to dissolved minerals, with the respective food combinations recommended by us, in relation to the structure of both water and food. Full article

Oleogels are a subclass of organogels that present a healthier alternative to traditional saturated and trans solid fats in food products. The unique structure and composition that oleogels possess make them able to provide desirable sensory and textural features to a range of food products, such as baked goods, processed meats, dairy products, and confectionery, while also improving the nutritional profiles of these food products. The fact that oleogels have the potential to bring about healthier food products, thereby contributing to a better diet, makes interest in the subject ever-increasing, especially due to the global issue of obesity and related health issues. Research studies have demonstrated that oleogels can effectively replace conventional fats without compromising flavor or texture. The use of plant-based gelators brings about a reduction in saturated fat content, as well as aligns with consumer demands for clean-label and sustainable food options. Oleogels minimize oil migration in foods due to their high oil-binding capacity, which in turn enhances food product shelf life and stability. Although oleogels are highly advantageous, their adoption in the food industry presents challenges, such as oil stability, sensory acceptance, and the scalability of production processes. Concerns such as mixed consumer perceptions of taste and mouthfeel and oxidative stability during processing and storage evidence the need for further research to optimize oleogel formulations. Addressing these limitations is fundamental for amplifying the use of oleogels and fulfilling their promise as a sustainable and healthier fat alternative in food products. As the oleogel industry continues to evolve, future research directions will focus on enhancing understanding of their properties, improving sensory evaluations, addressing regulatory challenges, and promoting sustainable production practices. The present report summarizes and updates the state-of-the-art about the structure, the properties, and the applications of oleogels in the food industry to highlight their full potential. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which integrates essential components including a knowledge base, a mathematical-model-based expert system, an enhanced optimization framework, and a real-time feedback mechanism. Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). This innovation achieves an efficient balance between exploration and exploitation in model solving and improves system adaptability. Numerical experiments demonstrate RLEHBA’s superior performance over State-of-the-Art algorithms on the CEC 2017 benchmark. A case study of China’s 2026 pork regulation confirms the system’s practical value in stabilizing the supply-demand balance and optimizing resource allocation. Finally, some targeted managerial insights are proposed. This study constructs a replicable framework for intelligent livestock regulation, and it also holds transformative significance for sustainable and adaptive supply chain management in global agri-food systems. Full article

Per- and polyfluoroalkyl substances (PFASs) comprise a diverse array of synthetic chemicals that resist environmental degradation. They are increasingly recognised as endocrine-disrupting compounds (EDCs). These chemicals, found in non-stick cookware, food packaging, and industrial waste, accumulate in human tissues and fluids, raising substantial concerns regarding their impact on female reproductive health. Epidemiological studies have demonstrated associations between PFAS exposure and reduced fertility; nevertheless, the underlying molecular pathways remain inadequately understood. This narrative review investigates the multifaceted effects of PFASs on ovarian physiology, including its disruption of the hypothalamic–pituitary–ovarian (HPO) axis, alteration of anti-Müllerian hormone (AMH) levels, folliculogenesis, and gonadotropin receptor signalling. Significant attention is directed towards the emerging association between PFASs and polycystic ovarian syndrome (PCOS), wherein PFAS-induced hormonal disruption may exacerbate metabolic issues and elevated androgen levels. Furthermore, we analyse the current data regarding PFAS exposure in women undergoing treatment based on assisted reproductive technologies (ARTs), specifically in vitro fertilisation (IVF), highlighting possible associations with diminished oocyte quality, suboptimal embryo development, and implantation failure. We examine potential epigenetic and transgenerational alterations that may influence women’s reproductive capabilities over time. This study underscores the urgent need for further research and regulatory actions to tackle PFAS-related reproductive toxicity, particularly in vulnerable populations, such as women of reproductive age and those receiving fertility treatments. Full article

For the first time, the numerous scattered data on birds (wild and domestic) have been collected based on their medieval bone remains discovered on the modern territory of the Republic of Bulgaria. The collected information is about a total of 37 medieval settlements from the time of the First and Second Bulgarian Empires. Among the settlements studied are both the two medieval Bulgarian capitals (Pliska and Veliki Preslav), as well as other cities, smaller settlements, military fortresses, monasteries, and inhabited caves. The data refer to a total of 48 species of wild birds and 6 forms of domestic birds of 11 avian orders: Accipitriformes, Anseriformes, Ciconiiformes, Columbiformes, Falconiformes, Galliformes, Gruiformes, Otidiformes, Passeriformes, Pelecaniformes, and Strigiformes. The established composition of wild birds amounts to over one tenth (to 11.5%) of the modern avifauna in the country. Five of the established species (10.4%) have disappeared from the modern nesting avifauna of the country—the bearded vulture, the great bustard, the little bustard, the gray crane, and the saker falcon (the latter two species have reappeared as nesters in the past few years). First Bulgarian Empire (681–1018): Investigated settlements—22. Period covered—five centuries (7th to 11th c.). Found in total: at least 44 species/forms of birds, of which 39 species of wild birds and 5 forms of poultry. Second Bulgarian Empire (1185–1396): Investigated settlements—15. Period covered—3 centuries (12th to 14th c.). Found in total: at least 39 species/forms of birds, of which 33 species of wild birds and 6 forms of poultry. The groups of raptors, water, woodland, openland, synanthropic and domestic birds were analyzed separately. The conclusion was made that during the two periods of the Middle Ages, birds had an important role in the material and spiritual life of the population of the Bulgarian lands. Birds were mainly used for food (domestic birds), although some were objects of hunting. No traces of processing were found on the bones. Birds were subjects of works of applied and monumental art. Their images decorated jewelry, tableware, walls of buildings and other structures. Full article

Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily on image datasets for disease recognition, they often overlook the complementary role of textual features in enhancing visual understanding. To address this problem, we proposed a cross-modal data fusion via a vision-language model for crop disease recognition. Our approach leverages the Zhipu.ai multi-model to generate comprehensive textual descriptions of crop leaf diseases, including global description, local lesion description, and color-texture description. These descriptions are encoded into feature vectors, while an image encoder extracts image features. A cross-attention mechanism then iteratively fuses multimodal features across multiple layers, and a classification prediction module generates classification probabilities. Extensive experiments on the Soybean Disease, AI Challenge 2018, and PlantVillage datasets demonstrate that our method outperforms state-of-the-art image-only approaches with higher accuracy and fewer parameters. Specifically, with only 1.14M model parameters, our model achieves a 98.74%, 87.64% and 99.08% recognition accuracy on the three datasets, respectively. The results highlight the effectiveness of cross-modal learning in leveraging both visual and textual cues for precise and efficient disease recognition, offering a scalable solution for crop disease recognition. Full article

Wheat diseases threaten yield and food security, highlighting the need for rapid, accurate diagnosis in precision agriculture. However, current remote sensing methods often lack hierarchical spectral semantic perception or rely on single-source data and simple fusion, limiting diagnostic performance. To address these challenges, this study proposed MSFNet, a novel multi-source fusion network with enhanced hierarchical spectral semantic perception, to achieve the precise regional classification of wheat diseases. Specifically, a multi-source fusion module (MSFM) was developed, employing a dual-branch architecture to simultaneously enhance spatial–spectral semantics and comprehensively explore complementary cross-modal features, thereby enabling the effective integration of critical information from both modalities. Furthermore, a hierarchical spectral semantic fusion module (HSSFM) was developed, which employs a pyramid architecture integrated with attention mechanisms to fuse hierarchical spectral semantics, thereby significantly enhancing the model’s hierarchical feature representation capacity. To support this research, we constructed a new multispectral remote sensing dataset, MSWDD2024, tailored for wheat disease region diagnosis. Experimental evaluations on MSWDD2024 demonstrated that MSFNet achieved 95.4% accuracy, 95.6% precision, and 95.6% recall, surpassing ResNet18 by 6.0%, 6.0%, and 5.8%, respectively, and outperforming RGB-only models by over 12% across all metrics. Moreover, MSFNet consistently exceeded the performance of existing state-of-the-art methods. These results confirm the superior effectiveness of MSFNet in remote sensing-based wheat disease diagnosis, offering a promising solution for robust and accurate monitoring in precision agriculture. Full article