Search Results (6,104)

In this study, material and energy losses were systematically assessed, together with a comprehensive economic and environmental evaluation, for an industrial expanded polystyrene (EPS) recycling process implemented under a circular economy framework at a company located in Medellín, Colombia. The system boundaries were clearly defined, and detailed mass and energy balances were performed using operational data collected over a six-month period. The process achieved a yield of 78.09 percent in the production of densified polystyrene from post-consumer EPS, with the main material losses attributed to solid residues and water losses during processing. The total energy consumption was 7350.34 kWh, of which 55.46 percent corresponded to energy losses, predominantly thermal losses associated with the EPS melting stage. Techno-economic evaluation indicated that the process is financially viable over a twelve-year operational horizon. Furthermore, the environmental assessment demonstrated a 68.44 percent reduction in carbon footprint, underscoring the strong potential of this recycling route as a sustainable and effective alternative for the management of recyclable solid waste. Full article

Background: The healthcare sector plays a significant role in environmental degradation, particularly through energy consumption, emissions, and resource use associated with hospital operations. Despite growing global awareness of the impacts, environmental sustainability remains only partially embedded with the design, planning, management, and evaluation of hospital facilities, and empirical evidence is still limited. Methods: This exploratory study employed a mixed-method, two-phase approach. First, a scoping literature review identified key environmental dimensions and approaches for environmental sustainability in hospitals infrastructures. Second, a structured survey was distributed to Italian hospitals from Lombardy Region, between May and June 2024, to assess environmental performance and environmental strategy adoption. Results: Eight (n = 8) core environmental sustainability dimensions emerged from the review: energy efficiency, resource and waste management, transportation and mobility, materials and construction, environmental compliance, emissions, site sustainability, and design strategies. The subsequent based on these dimensions, gathered responses from (n = 18) healthcare facilities from Lombardy region, Italy. Findings revealed substantial gaps, since key measures such as on-site renewable capacity, water reuse systems, environmental certification application and health-island mitigation practices appear to be adopted sporadically. In addition, many of the surveyed facilities show consumption levels that exceed the benchmarks outlined in the literature. Discussion: The findings of this study reveal a notable misalignment between the sustainability debate, maturity promoted in the academic literature and the actual practices implemented in the Italian regional context. This mismatch highlights the importance of developing more uniform evaluation tools, policy requirements, and strengthening the organizational capabilities, to improve environmental performance in Italian hospital facilities. Full article

Bio-based fertilisers (BBFs), produced from various types of biological waste using different processing methods, have demonstrated encouraging levels of agronomic efficiency and environmental benefits, consistent with the principles of sustainable development (SD). Nevertheless, bringing these newly developed products to market remains difficult due to limited farmer awareness, perceived risks, and regulatory uncertainties. In this paper, we examine the attitudes, opinions, and awareness of farmers regarding the use of various BBFs in their fertilisation practices. We applied a survey research method, using the Paper and Pen Personal Interview (PAPI), and answers were collected by agricultural advisors. A questionnaire, consisting of open, closed, and Likert scale questions, focusing on general information about farmers, current practices regarding fertiliser use, and the determinants of fertiliser choice, was used. Descriptive statistics, cross-tabulations, chi-square tests, Cramer’s V coefficients, 95% confidence intervals, and McNemar’s test were used to analyse the data. This study was conducted in all 16 voivodeships in Poland, collecting a total of 800 responses. Factors influencing the negative or positive attitude of farmers toward this practice were identified. Currently, mineral fertilisers remain the dominant choice among Polish farmers due to their accessibility, cost-effectiveness, and agronomic performance. There is observed growing, albeit cautious, interest in alternative fertilisation strategies and the correct understanding of sustainable agriculture practices. About half of farmers expressed willingness to partially replace mineral fertilisers with organic options, but only a minority showed interest in adopting BBFs. The findings indicate that concerns about contaminants, heterogeneous quality, limited availability, and regulatory uncertainty continue to constrain interest in BBFs. Although respondents recognised potential environmental benefits, economic and agronomic considerations remained the primary drivers of decision-making. As the survey was conducted in late 2021, the results reflect pre-2022 market conditions and should be interpreted as a baseline rather than a direct indication of current attitudes. Overall, this study provides insights into behavioural and structural factors influencing fertiliser use in Poland and highlights areas where further policy, advisory, and market developments may help support more sustainable nutrient management. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time (\(T_{95}\) and worst-case exposure) and decreases both event energy \(E_{\mathrm{event}}\) and CO₂-equivalent \(CO_{\mathrm{2event}}\) while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load \(U_{\mathrm{energy}}\) and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article

The increasing deployment of hybrid renewable energy systems has introduced significant challenges in optimal energy dispatch and load balancing due to the intrinsic stochasticity and temporal variability of renewable sources, along with the multi-dimensional optimization requirements of simultaneously achieving economic efficiency, grid stability, and environmental sustainability. This paper presents GreenMind, a scalable Deep Reinforcement Learning framework designed to address these challenges through a hierarchical multi-agent architecture coupled with Long Short-Term Memory (LSTM) networks for predictive energy management. The framework employs specialized agents responsible for generation dispatch, storage management, load balancing, and grid interaction, achieving an average decision accuracy of 94.7% through coordinated decision-making enabled by hierarchical communication mechanisms. The integrated LSTM-based forecasting module delivers high predictive accuracy, achieving a 2.7% Mean Absolute Percentage Error for one-hour-ahead forecasting of solar generation, wind power, and load demand, enabling proactive rather than reactive control. A multi-objective reward formulation effectively balances economic, technical, and environmental objectives, resulting in 18.3% operational cost reduction, 23.7% improvement in energy efficiency, and 31.2% enhancement in load balancing accuracy compared to state-of-the-art baseline methods. Extensive validation using synthetic datasets representing diverse hybrid renewable energy configurations over long operational horizons confirms the practical viability of the framework, with 19.6% average cost reduction, 97.7% system availability, and 28.6% carbon emission reduction. The scalability analysis demonstrates near-linear computational growth, with performance degradation remaining below 9% for systems ranging from residential microgrids to utility-scale installations with 2000 controllable units. Overall, the results demonstrate that GreenMind provides a scalable, robust, and practically deployable solution for predictive energy dispatch and load balancing in hybrid renewable energy systems. Full article

Decision-making is increasingly guided by the integration of Multi-Criteria Decision-Making (MCDM) and Machine Learning (ML) approaches. Despite their complementary strengths, the literature lacks clarity on which forms of integration exist, what contributions they offer, and how to determine the most effective form for a given decision problem. This study systematically investigates integration modes through a methodology that combines a literature review, expert judgment, and statistical analyses. It develops a novel categorization of integration modes based on methodological characteristics, resulting in five distinct modes: sequential approaches (ML → MCDM and MCDM → ML), hybrid integration (MCDM + ML), and performance comparison approaches, including ML vs. MCDM and ML vs. ML evaluated through MCDM. In addition, new evaluation criteria are introduced to ensure rigor, comparability, and reliability in assessing integration forms. By applying correspondence, cluster, and discriminant analyses, the study reveals distinctive patterns, relationships, and gaps across integration modes. The primary outcome is a novel evidence-based framework designed to guide researchers and practitioners in selecting the appropriate integration modes based on problem characteristics, methodological requirements, and application context. The findings reveal that sequential approaches (ML → MCDM and MCDM → ML) are most appropriate when efficiency, structured decision workflows, bias reduction, minimal human intervention, and the management of complex multi-variable decision problems are key objectives. Hybrid integration (MCDM + ML) is better suited to dynamic and data-rich environments that require flexibility, continuous adaptation, and a high level of automation. Performance comparison approaches are most appropriate for validation-oriented studies that evaluate outputs (MCDM[ML vs. ML]) and benchmark alternative methods (ML vs. MCDM), thereby supporting reliable method selection. Furthermore, the study underscores the predominance of integration modes that combine value-based MCDM methods with classification-based ML algorithms, particularly for enhancing interpretability. Environmental science and healthcare emerge as leading domains of adoption, primarily due to their high data complexity and the need to balance diverse, multi-criteria stakeholder requirements. Full article

Bioinsecticides offer eco-friendly alternatives to chemical insecticides and thereby meet the need for sustainable pest control. Entomopathogenic fungi (EPF) represent one of the core classes of microbial insecticides, distinguished by their advantageous contact-based mode of action. Several products have been successfully commercialized, and with continuing improvements to the technology, the market size for EPF continues to grow. The translation of EPF into reliable field performers relies upon formulation technologies that ensure product quality, stability, virulence, and cost-effectiveness. Current formulations comprise diverse solid and liquid states (e.g., wettable powders, oil dispersions) that deliver a range of propagules (conidia, blastospores, microsclerotia). While advanced approaches like nanoparticle encapsulation show promise, some limitations hinder their widespread use. Major constraints include maintaining fungal viability during storage/transport and protecting propagules from harsh environmental factors post-application. Regulatory requirements also present significant barriers to widespread uptake. Addressing these formulation challenges through continued research is essential for advancing mycoinsecticide technology and increasing their contribution to integrated pest management. This review aims to present the latest scientific advances in EPF formulation technologies and application strategies, alongside an overview of current regulatory frameworks and an up-to-date analysis of registered microbial biopesticide products in some of the world’s largest markets. Full article

Sustainable finance increasingly relies on Environmental, Social, and Governance (ESG) data, yet forecasting ESG-based stock indices remains challenging in an emerging-market context. Using Thailand as a representative case due to limited historical information, this study constructs a realistic simulated SET ESG Index using free-float-adjusted market capitalization and semiannual rebalancing rules that reflect the methodology of the Stock Exchange of Thailand. Using this index as the forecasting target, this study compares traditional statistical time series models (ARIMA, SARIMA, SARIMAX) with seven deep learning architectures (RNN, GRU, LSTM, DF-RNN, DeepAR, DSSM, Deep Renewal) to evaluate performance in multi-step (36-day) prediction. Results reveal that deep learning models significantly outperform statistical approaches, with GRU delivering the highest accuracy and the most consistent robustness across reduced-data scenarios. These findings highlight the ability of advanced AI techniques to capture nonlinear ESG market dynamics better. This study provides a replicable modeling pipeline for ESG index forecasting in data-constrained contexts, with practical implications for sustainable investment decision-making, risk management, and market resilience in emerging economies. Full article

Large quantities of wastewater (OMW) are generated by the olive oil industry, requiring sustainable management to mitigate environmental impacts. The main goal of this work is to evaluate the possibility of using the homogeneous Fenton process as a pretreatment of OMW, as well as the iron (Fe (II) and Fe (III)) addition to improve the methane production through AD. The Fenton process achieved chemical oxygen demand (COD) and total phenolic compound (TPh) removals of 17–47% and 75–94%, respectively. However, methane production did not improve compared with untreated OMW, which yielded about 82 NmL CH₄/ g COD_i. The increase in H₂S production from about 2 mL in raw OMW to more than 8 mL in treated OMW may justify the inhibition of AD. Supplementing AD with 2 mg/L of Fe (III) increased methane production by 65% and significantly reduced H₂S due to FeS precipitation. The addition of 1 and 2 mg/L of Fe (II) also increased methane production by 82 and 59%, respectively, but no reduction in H₂S was observed. Therefore, although the Fenton pretreatment effectively reduces recalcitrant organic matter, it does not necessarily enhance methane production. A balance must be achieved between improving OMW characteristics and minimizing adverse impacts on AD performance. Full article

Crop row line detection is essential for precision agriculture, supporting autonomous navigation, field management, and growth monitoring. To address the low detection accuracy of rapeseed seedling rows under complex field conditions, this study proposes a detection framework that integrates an improved BiSeNetV2 with a dynamic sliding-window fitting strategy. The improved BiSeNetV2 incorporates the Efficient Channel Attention (ECA) mechanism to strengthen crop-specific feature representation, an Atrous Spatial Pyramid Pooling (ASPP) decoder to improve multi-scale perception, and Depthwise Separable Convolutions (DS Conv) in the Detail Branch to reduce model complexity while preserving accuracy. After semantic segmentation, a Gaussian-filtered vertical projection method is applied to identify crop-row regions by locating density peaks. A dynamic sliding-window algorithm is then used to extract row trajectories, with the window size adaptively determined by the row width and the sliding process incorporating both a lateral inertial-drift strategy and a dynamically adjusted longitudinal step size. Finally, variable-order polynomial fitting is performed within each crop-row region to achieve precise extraction of the crop-row lines. Experimental results indicate that the improved BiSeNetV2 model achieved a Mean Pixel Accuracy (mPA) of 87.73% and a Mean Intersection over Union (MIoU) of 79.40% on the rapeseed seedling dataset, marking improvements of 9.98% and 8.56%, respectively, compared to the original BiSeNetV2. The crop row detection performance for rapeseed seedlings under different environmental conditions demonstrated that the Curve Fitting Coefficient (CFC), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) were 0.85, 1.57, and 1.27 pixels on sunny days; 0.86, 2.05 and 1.63 pixels on cloudy days; 0.74, 2.89, and 2.22 pixels on foggy days; and 0.76, 1.38, and 1.11 pixels during the evening, respectively. The results reveal that the improved BiSeNetV2 can effectively identify rapeseed seedlings, and the detection algorithm can identify crop row lines in various complex environments. This research provides methodological support for crop row line detection in precision agriculture. Full article

This study addressed the core challenge of intelligent pest and disease monitoring and early warning in smart horticultural production by proposing a multimodal deep learning framework based on multi-parameter environmental sensor arrays. The framework integrates visual information with electrical signals to overcome the inherent limitations of conventional single-modality approaches in terms of real-time capability, stability, and early detection performance. A long-term field experiment was conducted over 18 months in the Hetao Irrigation District of Bayannur, Inner Mongolia, using three representative horticultural crops—grape (Vitis vinifera), tomato (Solanum lycopersicum), and sweet pepper (Capsicum annuum)—to construct a multimodal dataset comprising illumination intensity, temperature, humidity, gas concentration, and high-resolution imagery, with a total of more than $2.6\times {10}^6$ recorded samples. The proposed framework consists of a lightweight convolution–Transformer hybrid encoder for electrical signal representation, a cross-modal feature alignment module, and an early-warning decision module, enabling dynamic spatiotemporal modeling and complementary feature fusion under complex field conditions. Experimental results demonstrated that the proposed model significantly outperformed both unimodal and traditional fusion methods, achieving an accuracy of $0.921$, a precision of $0.935$, a recall of $0.912$, an F1-score of $0.923$, and an area under curve (AUC) of $0.957$, confirming its superior recognition stability and early-warning capability. Ablation experiments further revealed that the electrical feature encoder, cross-modal alignment module, and early-warning module each played a critical role in enhancing performance. This research provides a low-cost, scalable, and energy-efficient solution for precise pest and disease management in intelligent horticulture, supporting efficient monitoring and predictive decision-making in greenhouses, orchards, and facility-based production systems. It offers a novel technological pathway and theoretical foundation for artificial-intelligence-driven sustainable horticultural production. Full article

Water management in Arid and Semi-Arid Regions (ASAR) relied on large-scale, centralized systems that expanded potable water access. However, high energy requirements, rising operational costs, and limited adaptability to climate variability now put their sustainability under question. According to this study, hybrid rainwater–graywater systems (HRGSs) are emerging as decentralized approaches that can reduce the stress on centralized water systems, increase water supply during dry season, and lower the risk of flooding during rainy seasons. Identifying and evaluating a comprehensive sustainability framework of HRGSs for ASARs remains underexplored. To address this gap, a systematic review of literature indexed in two databases, Scopus and Engineering Village, was performed. Forty studies met the inclusion criteria and were critically appraised to delineate their scope, recurring patterns, and frameworks. Moreover, this study developed a comprehensive sustainability framework specific to the ASAR context, proposing key indicators for HRGS evaluation across environmental, economic, and social aspects with their indicators. Proposing a new sustainability framework provides a basis for guiding future research, technology design, and policy development aimed at implementing HRGS in ASAR contexts. Full article

Sequential twin-tunnel excavation has become increasingly common as urban rail networks expand, making both deformation control and construction-phase carbon management essential for sustainable underground development. This study investigates the spatiotemporal development of ground settlement induced by parallel Earth Pressure Balance shield tunnelling in a twin-tunnel section of the Hangzhou Metro, based on long-term field monitoring. The settlement process is divided into three stages—immediate construction settlement, time-dependent additional settlement, and long-term consolidation—each associated with distinct levels of energy input, grouting demand, and embodied-carbon release. Peck’s Gaussian function is used to model transverse settlement troughs, and Gaussian superposition is applied to separate the contributions of the leading and trailing tunnels. The results indicate that the trailing shield induces ahead-of-face settlement at approximately two excavation diameters and produces a deeper–narrower settlement trough due to cumulative disturbance within the overlapping interaction zone. A ratio-type indicator, the Twin-Tunnel Interaction Ratio (TIR), is proposed to quantify disturbance intensity and reveal its environmental implications. High TIR values correspond to amplified ground response, prolonged stabilization, repeated compensation grouting, and increased embodied carbon during construction. Reducing effective TIR through coordinated optimization of shield attitude, face pressure, and grouting parameters can improve both deformation control and carbon efficiency. The proposed framework links geotechnical behaviour with environmental performance and provides a practical basis for risk-controlled, energy-efficient, and low-carbon management of sequential shield tunnelling. Full article

Colorectal cancer (CRC) remains a major cause of morbidity and mortality worldwide, with its incidence and biological behavior influenced by both genetic and environmental factors. Emerging evidence highlights notable sex differences in CRC, with men generally exhibiting higher incidence rates and poorer prognoses, while women often display stronger immune responses and distinct molecular profiles. Traditional screening tools, such as colonoscopy and fecal-based tests, have improved survival through early detection but are limited by invasiveness, cost, and adherence issues. In this context, biosensors have emerged as innovative diagnostic platforms capable of rapid, sensitive, and non-invasive detection of CRC-associated biomarkers, including genetic, epigenetic, and metabolic alterations. These technologies integrate biological recognition elements with nanomaterials, microfluidics, and digital systems, enabling the analysis of biomarkers such as proteins, nucleic acids, autoantibodies, epigenetic marks, and metabolic or VOC signatures from blood, stool, or breath and supporting point-of-care applications. Electrochemical, optical, piezoelectric, and FET platforms enable label-free or ultrasensitive multiplexed readouts and align with liquid biopsy workflows. Despite challenges related to standardization, robustness in complex matrices, and clinical validation, advances in nanotechnology, multi-analyte biosensing with artificial intelligence are enhancing biosensor performance. Integrating biosensor-based diagnostics with knowledge of sex-specific molecular and hormonal pathways may lead to more precise and equitable approaches in CRC detection, selection of therapeutic regimes and management. Full article

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning. Full article