Search Results (1,746)

Conventional chemical analysis relies on costly laboratory instrumentation, while current e-nose systems are expensive for widespread deployment. New opportunities for low-cost, accessible e-nose applications are emerging for diverse fields due to the rapid evolution of inexpensive sensor technologies. We developed a framework that enables rapid integration of newly available low-cost gas sensors into functional e-nose systems, continuously evaluating them as they become commercially available. By characterizing their performance in multi-sensor arrays that mimic biological olfaction, the framework demonstrates effective odor discrimination in a low-cost e-nose system through coordinated behavior of a heterogeneous sensor array. Our testing approach includes sensor sensitivity, selectivity, and stability, which are to be combined with appropriate pattern recognition and AI algorithms in the future for effective chemical discrimination. This work provides a pathway for continuously updating e-nose technology with the latest available sensors in a cost-effective manner, thereby making advanced chemical sensing accessible for resource-limited settings and enabling large-scale deployment in real-world applications with future potential applications such as food quality monitoring, environmental sensing, smart agriculture, etc. Full article

Microalgae are photosynthetic organisms that produce bioproducts of commercial interest and are efficient sequestering CO₂. The monitoring and control processes are areas for improvement to increase the efficiency of its production. There are sensor options for monitoring microalgae cultures, but the vast majority rely on microcontrollers, often lacking the robustness required for applications in more demanding conditions. Also, commercial systems with industrial capabilities can fit the above purpose, but they require licensing and are expensive. Therefore, this work presents the technical details of developing an open-source platform to monitor environmental variables using Modbus industrial sensors and Python used to control the photoperiod and for measuring pH, dissolved oxygen, electrical conductivity, water and air temperatures, photosynthetic photon flux density, irradiance, and turbidity in three photobioreactors containing the microalgae Chlorella vulgaris. The resulting time series showed that the platform preserved data and had a low outlier rate. pH measurements showed that during photosynthesis, the microalgae used CO₂ as their carbon source. Dissolved oxygen and culture medium temperature had an almost perfect Pearson’s anticorrelation with air-sparging. However, with aeration interruption, the correlation was $-0.804$, because dissolved oxygen depends on illumination, aeration, temperature, and biomass quantity, as shown in the time series. Full article

Fireworks have been used in China for more than a millennium, though they are an increasing part of celebration globally. Consumption of fireworks is on the rise despite increased regulation of their use. This review examines the key themes that are apparent in contemporary research: contamination of air, water and soil, in addition to waste debris, noise and light pollution, along with contemporary approaches to mitigate environmental impact. Research is, as expected, more frequent from countries with high fireworks use, so some rather small countries such as the Netherlands, Malta and Iceland are notably active. Concentrations of emitted gases (especially SO₂) and fine particles are frequently studied, along with associated toxic metals and semimetals (especially Cu, Zn, Cd, As, Ba and Sr). There are many projections of effects of fireworks, but relatively few epidemiological studies of health outcomes or the impact of contamination on local ecosystems. Fireworks waste and debris is an environmental problem; it is expensive to clear and aesthetically unpleasing. Excessive noise (up to 137 dB) created by fireworks affects pets and wildlife, as well as posing a risk to pyrotechnicians. Fireworks produce bursts of light that can be distracting to motorists and disturb wildlife, while smoke particles cause lowered visibility. Green fireworks and festivals of light with lasers or drone technology present routes to lower impact. Contemporary society is sympathetic towards restricting fireworks, but recognition of their cultural importance remains. Full article

Integration of LiDAR and thermal sensing has become increasingly important in robotics, infrastructure diagnostics, environmental monitoring, and autonomous perception systems. LiDAR sensors provide accurate three-dimensional geometric information but do not directly capture thermal properties of observed objects, whereas thermal cameras provide temperature distributions without explicit spatial structure. Fusion of both sensing modalities enables thermally augmented 3D scene reconstruction and spatial localization of temperature anomalies. This paper presents a practical LiDAR–thermal fusion framework for three-dimensional localization of heat sources using an Ouster OS1 LiDAR sensor and a FLIR A70 thermal camera. The proposed framework includes intrinsic thermal-camera calibration, extrinsic LiDAR–thermal calibration, multimodal data synchronization, projection of LiDAR points onto the thermal image plane, and assignment of temperature values to spatial points. Additionally, a dedicated thermally distinguishable calibration target is proposed to enable reliable multimodal feature extraction under low-contrast LWIR imaging conditions. The developed framework was experimentally validated using real radiometric thermal data and LiDAR point clouds acquired under laboratory conditions. Quantitative evaluation demonstrated reprojection errors below 1 pixel and a mean hottest-point localisation error of approximately 4.1 cm at a distance of 12.3 m. The results confirm that accurate spatial localisation of thermal anomalies can be achieved using a geometry-based multimodal fusion approach without relying on computationally expensive learning-based methods. The proposed framework emphasises practical deployment, deterministic calibration, and applicability in scenarios where limited training data or constrained computational resources make learning-based approaches difficult to apply. The proposed system may be applied to building energy diagnostics, industrial inspection, technical infrastructure monitoring, and robotic perception systems that require reliable spatial localisation of heat sources under real measurement conditions. Full article

Polymer-based sandwich structures are widely used for their lightweight and tailorable properties, but interfacial failure phenomena often govern their performance. Among these, Mode I skin/core debonding is a critical mechanism that limits structural reliability. This review provides a unified and critical assessment of experimental methodologies for Mode I fracture characterisation, focusing on the ASTM D8637/D8637M standard and alternative setups, including Double Cantilever Beam (DCB), Single Cantilever Beam (SCB), and Climbing Drum Peel (CDP) tests. Alongside the influence of geometrical factors, processing conditions and intrinsic polymer properties on Mode I characterisation are detailed. Conventional DCB setups are shown to introduce mixed-mode effects due to asymmetric loading. In contrast, the modified DCB-UBM setup achieves near-pure Mode I conditions at the expense of increased complexity. Comparative analysis indicates that the SCB configuration with a roller base outperforms the standardised flexible-rod setup, particularly for specimens with non-linear responses. The review also indicates that Mode I debonding behaviour is strongly influenced by several factors, including interfacial adhesion quality, constituent material properties, manufacturing-induced defects, specimen configurations, and environmental factors. Therefore, the interpretation of debonding performance requires a comprehensive structure–property–processing framework. Moreover, geometric constraints imposed by ASTM D8637/D8637M are also revisited, demonstrating that reduced-dimension specimens can yield comparable fracture toughness, thereby enabling greater design flexibility. Additionally, while the standard prescribes Modified Beam Theory (MBT) and Area Method (AM) for initiation and propagation, both methods provide comparable propagation toughness under linear conditions. For non-linear systems, alternative data reductions based on CDP concepts, with the SCB–roller base setup, are effective. Based on this assessment, key challenges and potential improvements are identified, guiding the development of more accurate and reliable testing methodologies for polymer sandwich structures. Full article

The production of inexpensive, effective sorbents from natural materials for the purification of water bodies and/or soils is a pressing problem. Therefore, the purpose of this manuscript is to summarize current approaches to the use of brown coal (lignite) and its processing products (humic acids, HAs) as sorbents for the purification of aqueous and soil environments from heavy metal ions and other pollutants. Modification of lignite (chemical, biological, physicochemical) or the creation of lignite–mineral composites significantly increases its sorption capacity and stability: after modification, the sorption capacity can reach more than 85 mg of heavy metals per g of sorbent, which is only 3 times lower than that of specialized, expensive sorbents. Also, good results are achieved in the case of sorption of water-soluble organic drugs, dyes, etc. Humic acids obtained from brown coal have better selectivity and efficiency than the original lignite, and slightly worse than the modified one, in terms of removing cadmium, lead, copper, and other toxic elements; and also, can complex with organic xenobiotics. Current research trends indicate growing interest in multifunctional composite sorbents, environmentally friendly extraction technologies, and the development of materials with enhanced selectivity and regeneration ability. Future studies should focus on improving the understanding of sorption mechanisms, optimizing modification strategies, scaling up lignite-based technologies for practical environmental applications, and developing waste-free technologies to produce sorbents from lignite. Full article

A virtual training environment offers clear advantages for agricultural robotics. It provides a safe setting in which perception, navigation, and control algorithms can be evaluated without risking damage to either the robot or the crop. It also supports efficient data generation: large volumes of training data can be collected under diverse environmental conditions that would be costly, slow, and often season-dependent in real-world deployments. This broader variability improves model adaptability, reduces the risk of overfitting, and leads to more robust operation. In this paper, we argue that digital twin technology should therefore be understood not merely as a passive mirror of a physical robot, but as an active training environment in which multiple sensor-related subprocesses can be developed, tested, validated, and refined jointly. This paper is based on our experiences with digital twin technology used in the development of a vineyard robot, including a self-driving rover, sensor simulation, procedural map generation, and agriculture-specific movement models. Our contribution is threefold: we reinterpret the digital twin as a training space, propose a layered framework for training agricultural robots in virtual environments, and explain why agriculture is a particularly strong use case, given variable field conditions, expensive real-world experimentation, and persistent labor scarcity. To validate this framework, we present the simulation-based evaluation of an autonomous reinforcement learning agent. The agent has been trained entirely in this virtual environment, which successfully navigated to 155 out of 161 target points in a simulated vineyard demonstration environment. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article

This study establishes a warehouse model specific to the handling and packaging of fresh lemons in southeast Spain (Fino and Verna, both conventional and organic) into two types of packaging (mesh and corrugated box) and carries out an economic and environmental evaluation using Life Cycle Costing (LCC) and Life Cycle Assessment (LCA). The boundary of the assessment is the warehouse gate, not considering the following phases (distribution and consumption). The warehouse model is based on data from on-site surveys in four medium-sized companies representative of the studied area. The results of this production phase are analyzed, as well as the aggregates of the whole production chain, from cultivation to the dispatching of packaged products at the gate of the warehouse. Production costs between 0.450 €·kg⁻¹ and 0.545 €·kg⁻¹, with organic options being generally more expensive, although it is the packaging that accounts for the biggest differences. The cost in the aggregate production chain shows a wider range, from 0.73 €·kg⁻¹ to 0.99 €·kg⁻¹. In terms of employment, the production chain generates 0.58 agricultural work unit (AWU)·ha⁻¹. The environmental results of the production chain show that the warehouse phase (handling and packaging) has a significant environmental impact. The handling stage shows little variation between lemon varieties or types of management, in contrast to the cultivation phase, where significant differences are observed. In comparative terms, in the production chain: conventional management has a greater environmental impact than organic management in most categories. The Verna variety has a greater impact than Fino and corrugated box packaging is systematically more impactful than mesh. Fresh lemons from southeast Spain have a low global warming impact (0.096–0.152 kg CO₂ eq·kg lemon⁻¹) compared to the literature, mainly due to the low impact during the cultivation phase. Full article

To address the challenges of combinatorial explosion and expensive evaluations in truck–drone (truck–UAV) collaborative delivery under complex geographical constraints, this paper proposes a Surrogate-assisted Rezone-Enhanced Multi-objective Adaptive Evolutionary Algorithm (SRE-MAEA). As a knowledge-driven decomposition-based surrogate-assisted framework, the proposed algorithm aims to synergistically optimize a four-dimensional conflicting objective space consisting of economic cost, social satisfaction, environmental emissions, and battery resilience. To overcome the curse of dimensionality in high-dimensional and strongly constrained environments, SRE-MAEA constructs an adaptive Rezone Search architecture. By dynamically deconstructing the decision space, it transforms global search pressure into refined knowledge mining within high-potential local regions. The core mechanism incorporates an intelligent sampling strategy based on the Multi-Armed Bandit (MAB). By utilizing real-time evolutionary feedback to dynamically prioritize the Pareto contribution of each rezone, the MAB achieves pruning-level scheduling of expensive evaluation resources. Simulation results on 15 benchmark instances with clustered, random, and mixed spatial distributions demonstrate that SRE-MAEA exhibits superior convergence boundaries and distribution uniformity in terms of IGD and HV metrics, significantly outperforming state-of-the-art regression-based strategies. Furthermore, computational efficiency analysis confirms that by precisely identifying invalid search paths via the MAB mechanism, SRE-MAEA maintains a high-precision Pareto front while reducing the average CPU time by approximately 35.2–48.5%. This effectively resolves the computational bottleneck caused by complex battery resilience integral models. This research provides an efficient algorithmic paradigm for resilient logistics scheduling in extreme environments and holds significant academic value and engineering application prospects. Full article

Background: This study aims to evaluate the impact of energy and logistics factors on the milk producer price index to support evidence-based policies that maintain price stability at an optimal level. Methods: Annual data for 2000–2023 are used, including the milk producer price index, milk production volume, transport CPI, diesel price, CO₂ emissions from agriculture, and renewable energy consumption (percentage of total energy consumption). A log-linear ARDL model is applied to examine both short- and long-run asymmetric effects of diesel prices, transport costs, and agricultural CO₂ emissions on milk production dynamics. Results: The research results indicate that energy expenses, logistics considerations, and environmental metrics have statistically significant asymmetric influences on milk production. This underscores the varying short-term adjustments and enduring long-term economic effects throughout the supply chain. Conclusions: Energy and cost factors on the supply side significantly influence the stability of milk markets. Therefore, improving transportation efficiency, encouraging the use of renewable energy sources, and addressing environmental impacts can contribute to consistent and sustainable pricing. Specific policies—including investments in transport infrastructure, subsidies for green energy targeting dairy producers, carbon pricing with support tailored to the sector, and digitalization of supply chains—can enhance resilience and ensure price stability. Full article

Beef production and consumption generate significant environmental and health costs for society, negative externalities that are not generally reflected in retail prices. Plant-based meat alternatives (PBMAs) are products designed to emulate conventional meat by using plant-based ingredients that are purported to produce significantly fewer negative externalities than beef but are often substantially more expensive. Are the premiums paid by consumers for PBMAs worth the potential environmental and nutritional benefits received by society from choosing them instead of beef? To address this question, we analyze the impacts of two well-known PBMAs in the USA, Beyond Burger^® and Impossible Burger^®, compared to those of beef on global warming potential, water consumption, dietary risks, and the market retail prices of each product. Results show that the public benefits of Beyond Burger^® and Impossible Burger^® are equivalent to USD 2.39 and 2.31, while additional private costs are USD 0.81 and 1.08 per patty, indicating ratios of 2.96 and 2.13, respectively, between the public benefits and the premiums paid by consumers. These results, while conditional on these specific products, the datasets available, and assumptions of the methods used, suggest that some level of public support for PBMAs may be justified. Full article

The growing demand for concrete poses a significant environmental challenge, but alkali-activated high-performance concrete (AA-HPC) offers a more sustainable alternative by potentially reducing carbon emissions and ecological harm. This study explores the latest developments in machine learning (ML) applications aimed at predicting the compressive strength of AA-HPC, with a focus on minimizing experimental expenses, construction duration, and environmental impact. Among nine evaluated ML models, the combination of extreme gradient boosting (XGBoost) with the African vultures optimization algorithm (AVOA) emerged as the most effective. AVOA proved highly efficient in optimizing model parameters, achieving the lowest root mean square error (RMSE) during hyperparameter tuning. On the training dataset, XGB-AVOA reached an R² of 0.994 and an RMSE of 2.368, while on the testing dataset, it maintained superior performance with an R² of 0.975 and an RMSE of 5.664. These findings highlight AVOA’s strength in fine-tuning XGBoost compared to alternative optimizers such as grey wolf optimizer (GWO), whale optimization algorithm (WOA), social spider optimization (SSO), and gorilla troops optimizer (GTO). To support practical implementation, a graphical user interface (GUI) has also been developed, allowing researchers to efficiently utilize the XGB-AVOA model for accurate, cost-effective, and time-saving predictions in laboratory environments. Full article

Environmental changes driven by land use and climate variability profoundly affect basin water balance, yet their separate and combined effects remain poorly understood in data-scarce regions. This study investigates the individual and combined impacts of land use/land cover (LULC) and climate change on seasonal runoff in the Rokel-Seli River Basin (RSRB), Sierra Leone, over two periods (1965–1990 and 1991–2016). Using LULC maps derived from 1988 and 2013 Landsat imagery and the Soil and Water Assessment Tool (SWAT), we simulated hydrological responses under four scenario frameworks. The results reveal a marked expansion of urban, bare, and agricultural land at the expense of forest cover. The SWAT model satisfactorily captured streamflow dynamics during calibration and validation. Land use change alone increased wet-season runoff by 6.55% and decreased dry-season runoff by −13.15%, whereas climate change contributed changes of +24.87% and −31.43%, respectively. A double mass curve analysis and Budyko framework further revealed a regime shift toward higher runoff efficiency (runoff coefficient increased from 0.67 to 0.69), indicating a loss of basin retention capacity. Notably, land use change partially masked the full hydrological deficit induced by climate change, acting as a counter-buffering mechanism. This study provides critical evidence for water resource authorities and local stakeholders to develop adaptive land use and water conservation strategies in data-scarce tropical basins, emphasizing the need to consider both climatic and anthropogenic drivers in seasonal water availability assessments. Full article

This review analyzes the economic, social, and environmental dimensions of water buffalo (Bubalus bubalis) production and its contribution to the Sustainable Development Goals (SDGs). A scoping review following PRISMA-ScR guidelines was conducted using the Web of Science (2020–2026), resulting in 225 included studies. Buffalo production is a multipurpose system that generates value through milk, meat, hides, manure, draft power, and animal-assisted services, with greater longevity than most livestock species. Economically, it supports income diversification, resource efficiency, and functions as a financial asset that can be sold to cover unexpected expenses. Socially, it enhances food security by providing nutrient-dense products, particularly milk with bioactive compounds associated with potential health benefits, and promotes women’s participation in livestock management and household economies. Environmentally, buffalo systems efficiently utilize low-quality forages, are adapted to marginal conditions, contribute to wetland conservation, and provide ecosystem services. These contributions align with several SDGs (1, 2, 5, 8, 12, 13, and 15). However, sector expansion is constrained by limitations in nutrition, management, veterinary services, and reproductive efficiency, as well as environmental challenges related to methane emissions and life cycle impacts. While global methane emissions from buffalo are lower due to their smaller population, emission intensity remains system-dependent and represents a critical challenge. In conclusion, water buffalo production represents a multifunctional and context-dependent system with significant potential to support sustainable development, although targeted innovations are required to improve productivity and address environmental challenges. Future research should integrate One Health and One Welfare approaches, develop long-term studies, and expand research under diverse experimental and field conditions to better characterize the potential health implications of buffalo-derived products. In addition, strengthening circular economy strategies, including region-specific diets to reduce emissions, remains a priority. Full article