Search Results (283)

This study introduces an integrated Life Cycle Assessment–Multi-Criteria Decision Analysis–Nash Equilibrium (LCA–MCDA–NE) framework to assess the feasibility of hydrogen energy storage (HES) in Philippine island grids. It starts with a cradle-to-gate LCA of hydrogen production across various electricity mix scenarios, from diesel-dominated Small Power Utilities Group (SPUG) systems to high-renewable configurations, quantifying greenhouse gas emissions. These impacts are normalized and integrated into an MCDA framework that considers four stakeholder perspectives: Regulatory (PRF), Developer (DF), Scientific (SF), and Local Social (LSF). Attribute utilities for Maintainability, Energy Efficiency, Geographic–Climatic Suitability, and Regulatory Compliance inform a 2 × 2 strategic game where net utility gain (Δ) and switching costs (C₁, C₂) influence adoption behavior. The findings indicate that the baseline Nash Equilibrium favors non-adoption due to limited utility gains and high switching barriers. However, enhancements in Maintainability and reduced costs can shift this equilibrium toward adoption. The LCA results show that meaningful decarbonization occurs only when low-carbon generation exceeds 60% of the electricity mix. This integrated framework highlights that successful HES deployment in remote grids relies on stakeholder coordination, reduced risks, and access to low-carbon electricity, offering a replicable model for emerging economies. Full article

Background/Objectives: This study explored how attribute framing (lifestyle-focused vs. technology-focused product descriptions) and popularity cueing (presence or absence of a “best-seller” label) influenced purchase likelihood for a fictitious selection of hearing aids (HAs) among Canadian adults aged 40 years and above. The study further aimed to investigate whether the effects observed were unique to HAs or applicable to less-specialized consumer technology contexts. Method: A 2 × 2 × 2 mixed experimental design compared attribute framing and popularity cueing effects across HAs and notebook computers at three technology levels (entry-level, midrange, and premium). Participants (n = 122) provided ratings indicating their purchase likelihood for each product. Results: Attribute framing showed no significant influence on purchase decisions across technology levels. The presence of a popularity cue that the midrange HA was the best-seller negatively affected purchase likelihood for the entry-level HA, with higher purchase likelihood ratings observed when this cue was absent. Participants expressed stronger purchase likelihood for premium HAs compared to premium notebook computers. Notably, these two effects were not statistically significant following correction for multiple comparisons. Conclusions: Popularity cues for HAs may have inadvertent consequences for consumer perceptions of models with differing technology levels. Findings also suggest potentially greater willingness to invest in premium health-related technologies versus familiar consumer technology. Further research involving current HA users or candidates is needed to better understand these findings. Full article

Accurate modeling of stem taper is essential for forest management decisions, including the definition of cutting cycles, the feasibility of annual harvesting, assortment classification, size and volume estimation, and ensuring sustainable production continuity. This study modeled the stem taper of Araucaria angustifolia (Bertol.) Kuntze stands in southern Brazil using Kozak’s variable-exponent model fitted with nonlinear mixed-effects techniques. Both fixed- and mixed-effects models showed high predictive performance, regardless of calibration. An unstructured (UN) covariance structure was required to reduce autocorrelation. The mixed-effects model improved predictive accuracy by up to 22%, achieved R² values above 0.99 with RMSE < 0.74 cm, and significantly reduced residual autocorrelation in diameter estimates. The most effective calibration of random effects was achieved using diameter measurements taken at heights between 0.3 and 6.3 m above ground (approximately between 1.3% and 28.3% of the total height, considering the tallest tree as a reference). This research improves the accuracy of volume estimation and the definition of timber assortments for A. angustifolia, thereby supporting forest management decision-making in southern Brazil. Full article

Agri-food supply chain management (ASCM) involves hierarchical structures in which actors make autonomous decisions and pursue objectives that may conflict with one another, thereby hindering coordination and limiting the understanding of how these decisions affect overall chain performance. This study proposes a decentralized bilevel mixed-integer linear programming model (BLDPP) for ASCM, solved using an interactive fuzzy decision-making approach that integrates membership functions with multi-objective programming. The model was validated through a case study conducted on an agri-food supply chain in Colombia. The results show that the interactive fuzzy approach enabled the development of a planning scheme that achieved a 94% satisfaction level among all decision-makers, demonstrating its effectiveness in harmonizing potentially conflicting interests. Additionally, the resulting planning incorporated up to 99% of the total productive capacity of small producers into the purchasing plan, supporting their inclusion in the chain. These findings indicate that both the proposed management model and its solution approach offer a robust alternative for advancing toward socially sustainable management of agri-food supply chains. Full article

The worldwide transition to renewable energy systems is motivated by diminishing fossil fuel availability and the intensifying consequences of climate change. This study presents a Mixed-Integer Linear Programming (MILP) model for designing and optimising the bio-fuel and electricity supply chain in Colombia, using sugarcane as the main feedstock and integrating microalgae cultivation in vinasse. Six alternative biorefinery configurations and four microalgae conversion pathways were evaluated to inform strategic planning. The optimisation results indicate that microalgae achieve higher energy yields per unit of land than sugarcane. Ethanol production from sugarcane could meet all of Colombia’s gasoline demand, while diesel and sustainable aviation fuel derived from microalgae could supply around 9% and 16%, respectively, of the country’s consumption. Further-more, pelletised bagasse emerges as a viable alternative to replace part of the coal used in thermoelectric plants. From an economic perspective, all scenarios achieve a positive net present value, confirming their profitability. Sensitivity analysis highlights the critical factors influencing the deployment of distilleries as ethanol price, algae productivity, and sugarcane cost. Furthermore, transportation costs play a decisive role in the geographic location of microalgae-based facilities and the distribution of their products. Full article

Effective implementation of silvopastoralism, a key Nature-Based Solution for Europe’s climate goals, is hindered by a lack of decision-support tools clarifying trade-offs between efficiency and extent of carbon sequestration. To address this, we developed a multi-objective scenario analysis (4064 scenarios) to identify optimal strategies for silvopastoral expansion across the EU27 Mediterranean bioregion. We found an inverse relationship defining a clear trade-off: scenarios achieving the highest mean sequestration (up to 2.5 Mg CO₂ ha⁻¹ year⁻¹) are spatially limited, whereas those maximising total gains (approaching 10⁷ Mg CO₂ year⁻¹ in total) do so by incorporating vast areas, lowering mean rates. This trade-off is formalised by a Pareto front, from which we defined a best-balanced optimal scenario and three policy regimes (conservative, balanced, expansive). Progressing across the front involved shifting from converting primarily shrubby and sparsely vegetated lands to incorporating grasslands and mixed agro-systems. At the NUTS2 level, Spain and Greece emerged as hotspots. Notably, converting arable land was not a primary contributor to carbon gains, as the marginal carbon benefit on these productive soils is lower than on marginal lands due to their higher baseline soil carbon levels, indicating that large-scale implementation can focus on marginal lands to avoid conflicts with food security. While subject to uncertainties of the underlying land-use and carbon models, this analysis demonstrates that our framework enables policymakers to select spatially explicit strategies aligned with specific budget or sequestration goals. These insights can inform CAP eco-schemes and national LULUCF strategies. The resulting maps and code are freely available. Full article

The growing emphasis on sustainable manufacturing has motivated the integration of environmental and social factors into traditional assembly line balancing problems (ALBPs). This study introduces a Sustainable Mixed-Model Assembly Line Balancing Problem (S-MMALBP) that jointly considers task precedence, machine selection, worker allocation, carbon-emission control, and green-rating incentives. An exact optimization model is formulated to minimize total operating cost while satisfying sustainability and capacity constraints. To address the problem’s combinatorial complexity, an Adaptive Large Neighborhood Search (ALNS) metaheuristic is developed, incorporating customized destroy and repair operators, adaptive penalty updating, and a simulated-annealing-based acceptance criterion. An analytical lower bound is derived to evaluate the algorithm’s performance, and an enhanced constructive method, Precedence-Driven Task Grouping (PDTG), is proposed to generate high-quality initial solutions. Computational experiments on benchmark instances confirm that the ALNS achieves near-optimal solutions with deviations below 5% from the lower bound, while solving large instances within seconds. A real-world case study on aircraft assembly involving 166 tasks further validates the model’s applicability, achieving a cost deviation below 4% from the theoretical bound under realistic sustainability constraints. The results demonstrate that the proposed model provides an effective and scalable decision-support tool for designing environmentally and socially responsible production systems. The study is the first to incorporate sustainability and worker–machine decisions into a mixed-model ALB framework solved by a tailored ALNS and lower bound. Full article

The increasing emphasis on sustainability in construction materials has led to a surge of research focused on recycled aggregate self-compacting concrete (RA-SCC). However, the critical gap in predicting the compressive strength of concrete remains challenging because of the nonlinear interactions among the mix’s constituents. The distinct contribution of this study is to develop an interpretable machine learning (ML) framework to accurately forecast the compressive strength of RA-SCC and identify the most influential mix parameters. A dataset comprising 400 experimental samples was compiled, incorporating eight input variables: age, cement strength, cement, fly ash, blast furnace slag, water, recycled aggregate, and superplasticizer, with compressive strength as the output variable. Four ML algorithms such as support vector regression (SVR), random forest (RF), Multilayer Perceptron (MLP), and extreme gradient boosting (XGBoost) were trained and optimized using Bayesian-based hyperparameter tuning combined with 10-fold cross-validation. Among the evaluated models, XGBoost demonstrated superior accuracy, with R² = 0.98 and RMSE = 2.95 MPa during training, and R² = 0.96 with RMSE = 3.25 MPa during testing, confirming its robustness and minimal overfitting. SHAP (SHapley Additive exPlanations) evaluation indicates that superplasticizer, cement, and cement strength were the most dominant factors influencing compressive strength, whereas higher water content showed a negative impact. The developed framework demonstrates that explainable ML can effectively capture the complex nonlinear behavior of RA-SCC, offering a reliable tool for mix design optimization and sustainable concrete production. These findings contribute to advancing data-driven decision making in eco-efficient materials engineering. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few studies benchmark model performance across the diverse operational demands of global pharmaceutical supply chains. Predictive setbacks contribute to financial losses, reduced supply chain efficacy, and potential adverse health consequences, yet understanding these failures offers firms opportunities to refine strategy and strengthen resilience. Drawing on 1.2 million shipments spanning 39 countries, we compare traditional statistical models (ARIMA), ensemble methods (random forests, gradient boosting), and deep neural networks (LSTM, GRU, CNN, ANN) across pricing, demand forecasting, vendor management, and shipment planning. Gradient boosting produced the strongest pricing performance, while ARIMA delivered the lowest demand-forecasting errors but with limited explanatory power; neural networks captured nonlinear demand shocks and achieved superior maintenance-risk classification. We also identified three vendor performance clusters—high-performing, cost-efficient, and mixed-reliability vendors—enabling firms to better align shipment criticality with vendor capabilities by prioritizing high performers for urgent deliveries, leveraging cost-efficient vendors for non-urgent volumes, and managing mixed performers through targeted oversight. These insights highlight the value of our evidence-based roadmap for selecting algorithms in high-stakes healthcare logistics, in rapidly evolving, technologically complex global contexts where increasing algorithmic sophistication elevates the standards for safer, smarter pharmaceutical supply chains. Full article

In the context of intelligent manufacturing, the integrated scheduling problem of dual rail-guided vehicles (RGVs) and multiple parallel processing equipment in flexible manufacturing systems has gained increasing importance. This problem exhibits spatiotemporal coupling and dynamic constraint characteristics, making traditional optimization methods ineffective at finding optimal solutions. At the problem formulation level, the dual resource scheduling task is modeled as a mixed-integer optimization problem. An intelligent scheduling framework based on action mask-constrained Proximal Policy Optimization (PPO) deep reinforcement learning is proposed to achieve integrated decision-making for production equipment allocation and RGV path planning. The approach models the scheduling problem as a Markov Decision Process, designing a high-dimensional state space, along with a multi-discrete action space that integrates machine selection and RGV motion control. The framework employs a shared feature extraction layer and dual-head Actor-Critic network architecture, combined with parallel experience collection and synchronous parameter update mechanisms. In computational experiments across different scales, the proposed method achieves an average makespan reduction of 15–20% compared with numerical methods, while exhibiting excellent robustness under uncertain conditions including processing time fluctuations. Full article

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations $({\sigma }_r,{\sigma }_a,{\sigma }_c)$ are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows. Full article

Background/Objectives: The functional architecture connecting speech production and reading comprehension remains unclear in multilinguals. This study investigated the cross-modal interaction between these systems in trilinguals to resolve the debate between Age of Acquisition (AoA) and usage frequency. Methods: We recruited 144 Uyghur (L1)–Chinese (L2)–English (L3) trilinguals, a population uniquely dissociating acquisition order from social dominance. Participants completed a production-to-comprehension priming paradigm, naming pictures in one language before performing a lexical decision task on translated words. Data were analyzed using linear mixed-effects models. Results: Significant cross-language priming confirmed an integrated lexicon, yet a fundamental asymmetry emerged. The top-down influence of production was governed by AoA; earlier-acquired languages (specifically L1) generated more effective priming signals than L2. Conversely, the bottom-up efficiency of recognition was driven by social usage frequency; the socially dominant L2 was the most receptive target, surpassing the heritage L1. Conclusions: The trilingual lexicon operates via “Two Rules”: a history-driven production system (AoA) and an environment-driven recognition system (Social Usage). This asymmetrical baseline challenges simple bilingual extensions and clarifies the dynamics of multilingual language control. Full article

The results of life cycle assessments (LCAs) are used for sustainable decision-making. Parameters that impact environmental performance during a product’s lifetime, e.g., operational electricity mix, are subject to future change. Especially in energy technologies with a long lifespan, future developments need to be included early in the tender stage by utilizing prospective LCA. Existing studies of transformers show that the significant global warming potential (GWP) impact results from operational electricity losses. The influence on industrial LCA from considering future developments already in a project’s tender phase is analyzed in this paper by implementing a future German electricity mix for the operation stage of a power transformer. The results show a reduction of 85% in the transformer’s GWP by integration of prospective data from 2030 and 2050 and 90% when prospective data are integrated in 5-year steps until 2050. This reflection of further developments makes product LCA results more accurate and therefore impacts, e.g., reported scope 3 emissions. The increased resolution of a product model allows focusing on parameters that are relevant during a product’s lifetime and developing, e.g., tailor-made circularity measures to improve the product’s environmental performance, thereby supporting sustainable decision-making. Full article

Sustainable development, which integrates economic progress with environmental stewardship to serve societal needs, seeks a balanced approach to resource utilization and intergenerational equity. Implementing carbon policies to limit emissions in production is an effective measure that also puts pressure on the supply chain’s profitability. Meanwhile, the emergence of the price reference effect affects consumers’ behavior and the decisions of supply chain members. This study constructs a dual-channel supply chain model under three carbon policy scenarios within a manufacturer-led Stackelberg game framework. The model is solved analytically to examine equilibrium outcomes and investigate the influence of channel competition, the price reference effect, and carbon policies on profitability and carbon emissions across different scenarios. The results are as follows. (1) As consumers’ online channel preference increases, manufacturers’ profits turn from falling to rising, especially under a lower carbon tax (higher carbon quota), with profit growing earlier. (2) A stronger price reference effect encourages higher emission reduction efforts, selling prices, and profits in smaller markets. However, this effect can reduce prices and profits due to increased competition and pricing pressure in larger markets. (3) The influence of carbon tax and emission quota on emission reduction and price depends on the initial carbon emission of the product, and their interaction has different impacts on total profits at different initial emission levels. (4) Within the mixed policy, the supply chain can obtain better economic and environmental benefits at a specific range of basic market demand. This study provides valuable references for formulating tactics to cope with low-carbon demand and price reference effects, as well as for developing effective environmental protection policies. Full article