Search Results (252)

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

We present a model that integrates the mapping of the phenology and climatic suitability for the spotted lanternfly (SLF), Lycorma delicatula (White, 1845) (Hemiptera: Fulgoridae), to provide guidance on when and where to conduct surveillance and management of this highly invasive pest. The model was designed for use in the Degree-Day, Establishment Risk, and Phenological Event Maps (DDRP) platform, which is an open-source decision support tool to help to detect, monitor, and manage invasive threats. We validated the model using presence records and phenological observations derived from monitoring studies and the iNaturalist database. The model performed well, with more than >99.9% of the presence records included in the potential distribution for North America, a large proportion of the iNaturalist observations correctly predicted, and a low error rate for dates of the first appearance of adults. Cold and heat stresses were insufficient to exclude the SLF from most areas of the conterminous United States (CONUS), but an inability for the pest to complete its life cycle in cold areas may hinder establishment. The appearance of adults occurred several months earlier in warmer regions of North America and Europe, which suggests that host plants in these areas may experience stronger feeding pressure. The near-real-time forecasts produced by the model are available at USPest.org and the USA National Phenology Network to support decision making for the CONUS. Forecasts of egg hatch and the appearance of adults are particularly relevant for surveillance to prevent new establishments and for managing existing populations. Full article

Per- and polyfluoroalkyl substances (PFAS) have been found worldwide in water, soil, plants, and animals, including humans. A primary route of exposure for humans and animals to PFAS is through the diet and drinking water. Perfluorooctane sulfonate (PFOS), a long-chain PFAS with a relatively long half-life, has been associated with adverse health effects in humans and laboratory animals. There are few toxicokinetic studies on PFOS in domestic livestock raised for human food consumption, which are critical for assessing human food safety. This work aimed to develop a simple daily accumulation model (DAM) for predicting PFOS residues in edible beef cattle muscle. A one-compartment toxicokinetic model in a spreadsheet format was developed using simple calculations to account for daily PFAS into and out of the animal. The DAM was used to simulate two case studies to predict resultant PFOS residues in edible beef cattle tissues. The results demonstrated that the model can reasonably predict PFOS concentrations in beef cattle muscle in a real-world scenario. The DAM was then used to simulate dietary PFOS exposure in beef cattle throughout a typical lifespan in order to derive a generic bioaccumulation factor. The DAM is expected to work well for other PFAS in beef cattle, PFAS in other livestock species raised for meat, and other chemical contaminants with relatively long half-lives. Full article

This study investigated the integration of advanced Atmospheric Water Generators (AWGs) within the design process of building energy systems, focusing on the water–energy nexus in the context of a real-life hospital building. It is based on a simulation approach, recognised as a viable means to analyse and enhance AWG potentialities. However, the current state of research does not address the issue of AWG integration within building plant systems. This study contributes to fill such a research gap by building upon an authors’ previous work and proposing an enhanced methodology. The methodology describes how to incorporate a multipurpose AWG system into the energy simulation environment of DesignBuilder (DB), version 7.0.0116, through its coupling with AWGSim, version 1.20d, a simulation tool specifically developed for atmospheric water generators. The chosen case study is a wing of the Mondino Hospital in Pavia, Italy, selected for its complex geometry and HVAC requirements. By integrating AWG outputs—covering water production, heating, and cooling—into DB, this study compared two configurations: the existing HVAC system and an enhanced version that includes the AWG as plant support. The simulation results demonstrated a 16.3% reduction in primary energy consumption (from 231.3 MWh to 193.6 MWh), with the elimination of methane consumption and additional benefits in water production (257 m³). This water can be employed for photovoltaic panel cleaning, further reducing the primary energy consumption to 101.9 MWh (55.9% less than the existing plant), and for human consumption or other technical needs. Moreover, this study highlights the potential of using AWG technology to supply purified water, which can be a pivotal solution for hospitals located in areas affected by water crises. This research contributes to the atmospheric water field by addressing the important issue of simulating AWG systems within building energy design tools, enabling informed decisions regarding water–energy integration at the project stage and supporting a more resilient and sustainable approach to building infrastructure. Full article

Urbanization poses mental health risks for urban dwellers, whereas natural environments offer mental health benefits by providing restorative experiences through visual stimuli. While urban waterfront spaces are recognized for their mental restorative potential, the specific environmental features and individual visual behaviors that drive these benefits remain inadequately understood. Grounded in restorative environments theory, this study investigates how these factors jointly influence restoration. Employing a controlled laboratory experiment, subjects viewed real-life images of nine representative spatial locations from the waterfront space of Guangzhou Long Bund. Data collected during the multimodal experiments included subjective scales data (SRRS), physiological measurement data (SCR; LF/HF), and eye-tracking data. Key findings revealed the following: (1) The element visibility rate and visual characteristics of plant and building elements significantly influence restorative benefits. (2) Spatial configuration attributes (degree of enclosure, spatial hierarchy, and depth perception) regulate restorative benefits. (3) Visual behavior patterns (attributes of fixation points, fixation duration, and moderate dispersion of fixations) are significantly associated with restoration benefits. These findings advance the understanding of the mechanisms linking environmental stimuli, visual behavior, and psychological restorative benefits. They translate into evidence-based design principles for urban waterfront spaces. This study provides a refined perspective and empirical foundation for enhancing the restorative benefits of urban waterfront spaces through design. Full article

The cement industry is a significant contributor to global environmental impacts, and Life Cycle Assessment (LCA) has emerged as a critical tool for evaluating and managing these burdens. This review uniquely synthesizes recent advancements in the LCA methodology and provides a detailed comparison of cement production impacts across major producing regions, notably highlighting China’s role as the largest global emitter. It covers the core LCA phases, including goal and scope definition, inventory analysis, impact assessment, and interpretation, and emphasizes the role of LCA in quantifying cradle-to-gate impacts (typically around 0.9–1.0 t CO₂ per ton of cement), evaluating the emissions reductions provided by alternative cement types (such as ~30–45% lower emissions using limestone calcined clay cements), informing policy frameworks like emissions trading schemes, and guiding sustainability certifications. Strategies for environmental load reduction in cement manufacturing are quantitatively examined, including technological innovations (e.g., carbon capture technologies potentially cutting plant emissions by up to ~90%) and material substitutions. Persistent methodological challenges—such as data quality issues, scope limitations, and the limited real-world integration of LCA findings—are critically discussed. Finally, specific future research priorities are identified, including developing country-specific LCI databases, integrating techno-economic assessment into LCA frameworks, and creating user-friendly digital tools to enhance the practical implementation of LCA-driven strategies in the cement industry. Full article

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet. Full article

Safflower (Carthamus tinctorius L.) is a plant in the family of Asteraceae, and the dried tubular flowers are used as medicine, which contain active ingredients such as safflower yellow pigment and safflower glycosides. They play important roles in many fields. NF-Y, as an important transcription factor in plants, regulates a variety of plant life activities. In this study, we identified and analysed 11 CtNF-Y gene family members from safflower for the first time. Their core motifs, which are conserved structural domains, gene structures, and cis-acting elements, are described in this study. In addition, there was good collinearity between safflower CtNF-Y and other species. Protein–protein interaction network analysis showed that the CtNF-YA1 and CtNF-YB subfamilies were the core proteins of the interaction network. Real-time quantitative PCR (qRT-PCR) studies showed that the expression level of the CtNF-Y gene was regulated by safflower flower colour and safflower flowering period. Subcellular localisation results showed that three CtNF-Y proteins were located in the nucleus, the cellular regulatory centre of the plant. This study will provide valuable insights into the selection of key candidate genes in the network of regulatory mechanisms for the formation of safflower flower colour and flowering time. Full article

The integration of renewable energy with circular economy strategies offers effective pathways to reduce greenhouse gas emissions while enhancing local energy independence. This study analyses three real-world projects implemented in Spain that exemplify this synergy. LIFE Smart Agromobility converts pig manure into biomethane to power farm vehicles, using anaerobic digestion and microalgae-based upgrading systems. Smart Met Value refines biogas from a wastewater treatment plant in Guadalajara to produce high-purity biomethane for the municipal fleet, demonstrating the viability of energy recovery from sewage sludge. The UNDERGY project addresses green hydrogen storage by repurposing a depleted natural gas reservoir, showing geochemical and geomechanical feasibility for seasonal underground hydrogen storage. Each project utilises regionally available resources to produce clean fuels—biomethane or hydrogen—while mitigating methane and CO₂ emissions. Results show significant energy recovery potential: biomethane production can replace a substantial portion of fossil fuel use in rural and urban settings, while hydrogen storage provides a scalable solution for surplus renewable energy. These applied cases demonstrate not only the technical feasibility but also the socio-economic benefits of integrating waste valorisation and energy transition technologies. Together, they represent replicable models for sustainable development and energy resilience across Europe and beyond. Full article

Postharvest spoilage of horticultural produce is a significant challenge, contributing to substantial food waste and economic losses. Traditional preservation methods, such as chemical preservatives and fungicides, are increasingly being replaced by sustainable, chemical-free alternatives. Microbial interventions using beneficial bacteria, fungi, and yeasts have emerged as effective solutions to enhance the postharvest quality and extend shelf life. Advancements in omics technologies, such as metabolomics, transcriptomics, and microbiomics, have provided deeper insights into plant–microbe interactions, facilitating more targeted and effective microbial treatments. The integration of artificial intelligence (AI) and machine learning further supports the selection of optimal microbial strains tailored to specific crops and storage conditions, further enhancing the treatment efficacy. Additionally, the integration of smart cold storage systems and real-time microbial monitoring through sensor technologies offers innovative approaches to optimize microbial interventions during storage and transport. This review examines the mechanisms through which microbes enhance the postharvest quality, the role of omics technologies in improving microbial treatments, and the challenges associated with variability and regulatory approval. Amid growing consumer demand for organic and sustainable solutions, microbial-based postharvest preservation offers a promising, eco-friendly alternative to conventional chemical treatments, ensuring safer, longer-lasting produce while reducing food waste and environmental impact. Full article

The massive integration of variable renewable energy sources (RESs) poses the gradual necessity for new power system architectures with wide implementation of distributed battery energy storage systems (BESSs), which support power system stability, energy management, and control. This research presents a methodology and realization of a set of 11 BESS models based on different machine learning methods. The performance of the proposed models is tested using real-life BESS data, after which a comparative evaluation is presented. Based on the results achieved, a valuable discussion and conclusions about the models’ performance are made. This study compares the results of feedforward neural networks (FNNs), a homogeneous ensemble of FNNs, multiple linear regression, multiple linear regression with polynomial features, decision-tree-based models like XGBoost, CatBoost, and LightGBM, and heterogeneous ensembles of decision tree modes in the day-ahead forecasting of an existing real-life BESS in a PV power plant. A Bayesian hyperparameter search is proposed and implemented for all of the included models. Among the main objectives of this study is to propose hyperparameter optimization for the included models, research the optimal training period for the available data, and find the best model from the ones included in the study. Additional objectives are to compare the test results of heterogeneous and homogeneous ensembles, and grid search vs. Bayesian hyperparameter optimizations. Also, as part of the deep learning FNN analysis study, a customized early stopping function is introduced. The results show that the heterogeneous ensemble model with three decision trees and linear regression as main model achieves the highest average R² of 0.792 and the second-best nRMSE of 0.669% using a 30-day training period. CatBoost provides the best results, with an nRMSE of 0.662% for a 30-day training period, and offers competitive results for R²—0.772. This study underscores the significance of model selection and training period optimization for improving battery performance forecasting in energy management systems. The trained models or pipelines in this study could potentially serve as a foundation for transfer learning in future studies. Full article

The main objective of this study is to assess the environmental life cycle of the materials, components, and elements of a mono-Si photovoltaic power plant towards their sustainable development. Currently, photovoltaic installations are considered to be environmentally friendly systems that produce “green” energy. During their exploitation, no pollutants are emitted into the environment. However, the processes of manufacturing and post-used management of their materials, components and elements are associated with both high demand for energy and matter, as well as with emissions of harmful substances into the atmosphere, water, and soil. For this reason, from the perspective of the entire life cycle, photovoltaic power plants may contribute to the deterioration of human health, the reduction in the quality of the environment, and the depletion of non-renewable fossil resources. Due to these potential threats, it was considered appropriate to conduct a Life Cycle Assessment of a real 2 MW photovoltaic power plant located in northern Poland, in terms of compliance with the main assumptions of sustainable development. The analysis was conducted using the Life Cycle Assessment (LCA) methodology (the ReCiPe 2016 model). Impacts on the environment was assessed in three areas: human health, ecosystem quality, and material resources. Two scenarios were adopted for the post-used management of materials, components, and elements: landfill disposal and recycling. Based on the conducted research, it was found that, among the assessed groups of photovoltaic power plant components (photovoltaic modules, supporting structure, inverter station, and electrical infra-structure), photovoltaic modules have the highest level of harmful impact on the environment (especially the manufacturing stage). The use of recycling processes at the end of their use would reduce their harmful impact over the entire life cycle of a photovoltaic power plant and better fit with the main principles of sustainable development. Full article

Scar development is a notable clinical and aesthetic issue in soft tissue healing, frequently compromising functionality and quality of life. Conventional treatments demonstrate limited efficacy in avoiding fibrosis and facilitating regenerative repair. Nevertheless, natural compounds have surfaced as viable alternatives owing to their biocompatibility, multitarget bioactivity, and historical application in traditional medicine. This review examines the therapeutic potential of plant-derived substances, marine agents, and microbial metabolites in influencing critical stages of wound healing, including inflammation, oxidative stress, fibroblast activation, and extracellular matrix remodeling. While these agents have demonstrated beneficial effects in preclinical models, their direct impact on functional or aesthetic clinical outcomes remains under investigation. We propose a hierarchical framework linking molecular mechanisms to clinical endpoints, suggesting that improvements at the cellular and molecular level may eventually support better healing quality. Natural bioactives, especially when integrated into advanced delivery systems such as hydrogels and nanocarriers, show promise for enhancing the regenerative microenvironment. By contextualizing these mechanisms within real-world therapeutic goals, this review highlights both the potential and limitations of natural products in the pursuit of improved soft tissue healing. Further translational research is needed to determine how modulation of these processes may reduce scarring and approach clinically meaningful outcomes. Full article

Root-knot nematodes (RKNs), specifically Meloidogyne incognita, are notoriously difficult to eliminate as endophytic nematodes, and cause severe damage to various plants. Cucumber (Cucumis sativus), which is a cash crop widely grown across the world, is often infected by RKNs. ELONGATED HYPOCOTYL5 (HY5), a member of the bZIP transcription factor family, plays a vital role in hormone, nutrient, abiotic stress, biotic stress, and oxygen species (ROS) signaling pathways. However, the involvement of HY5 in the defense against RKNs has rarely been reported. The present study initially explored the response of CsHY5 to RKNs. The results indicated that the hy5 mutant had a higher number of nematodes and galls in the root system and exhibited a higher susceptibility to RKNs compared with the wild type (WT). Particularly, the root-knot nematodes in hy5 plants completed their life cycle more quickly and produced more eggs. The activities of defense-related hormones and antioxidant enzymes were measured, and the results indicated that JA, jasmonoyl-isoleucine (JA-Ile), abscisic acid (ABA), peroxidase (POD), and ascorbate peroxidase (APX) were significantly elevated in the wild type, but were not induced or decreased in the mutant. Through transcriptome sequencing analysis and quantitative real-time PCR (qRT-PCR), it was found that when RKNs infect plants, the key genes of jasmonic acid (JA) synthesis, CsAOC and CsAOS, as well as the key gene of the antioxidant system, CsPOD, were all significantly induced. Nevertheless, this induction effect disappeared in the hy5 mutant. Generally, CsHY5 plays a role in the response of cucumber to RKNs, and its deletion increases the sensitivity of cucumber to RKNs. These results suggest that CsHY5 may affect the resistance of cucumber to RKNs by affecting antioxidant enzyme activities and hormone content. Full article

The relationships between mycotoxins content in food commodities or feedstuffs and the foodborne diseases is well known. So far, the available data mainly include chemical methods of mycotoxins decontamination for agricultural commodities or raw materials, including mycotoxin binders. Therefore, the possible use of some natural and cost-effective supplements such as herbs, fungi, microorganisms, or plants with powerful and safe protection against mycotoxin-induced health ailments is the main subject of this review paper. Various antagonistic microorganisms or yeast with fungicidal properties, as well as some herbs or plants that suppress fungal development and the subsequent production of target mycotoxins and/or have protective effect against mycotoxins, are deeply studied in the literature, and practical suggestions are given in this regard. The protection by degradation, biotransformation, or binding of mycotoxins by using natural additives such as herbs or plants to feedstuffs or foods has also been thoroughly investigated and analyzed as a possible approach for ameliorating the target adverse effects of mycotoxins. Possible beneficial dietary changes have also been studied to potentially alleviate mycotoxin toxicity. Practical advice are provided for possible application of the same natural supplements in real-life practice for combating mycotoxin-induced health ailments. Natural feed supplements and bioactive compounds appeared to be safe emerging approaches to preventing health ailments caused by mycotoxins. However, the available data mainly address some in vitro studies, and more in vivo experiments are necessary for introducing such approaches in the real-life practice or industry. Generally, target herbal supplements, antioxidants, or polyenzyme complements could be used as powerful protectors in addition to natural mycotoxin binders. Bioactive agents and enzymatic degradation are reported to be very successful in regard to PAT and OTA, whereas antagonistic microorganisms/fungi/yeasts have a successful application against AFs and PAT-producing fungi. Full article