Search Results (800)

Background: Spondyloarthritis (SpA) typically develops before 40 years of age, but increasing life expectancy has led to a growing number of cases in older adults. It is well known that age at onset may influence disease presentation, comorbidities, and patient outcomes. Objectives: To assess whether age at onset influences SpA clinical presentation. Methods: We analyzed clinical, demographic, clinimetric, and imaging data in 272 SpA patients, grouped by onset age: early (≤40, n = 119), intermediate (41–59, n = 127), and late (≥60, n = 26). All patients had a minimum follow-up duration of 12 months. Their epidemiologic, clinic, and clinimetric data were collected, as well as patient-reported outcome measures (PROs) [Patient Global Assessment (PGA), Health Assessment Questionnaire (HAQ), FACIT-Fatigue (FACIT-F), SHORT-FORM 36 (SF-36), Hospital Anxiety and Depression Scale (HADS), Work Productivity and Activity Impairment Questionnaire (WPAI), CSI (Central Sensitization Inventory), and Psoriatic Arthritis Impact of Disease (PsAID) questionnaire]. In univariate analyses, differences in categorical variables across onset groups were assessed using Fisher’s exact test; for continuous variables, between-group comparisons were performed using the Mann–Whitney U test (two-tailed) or the Kruskal–Wallis test, as appropriate, with Bonferroni correction for post hoc analyses. Multivariable regression models were subsequently fitted, adjusting for sex, diagnosis, and disease duration. For binary outcomes, multivariable logistic regression models were used, while multivariable linear regression models (ANCOVA) were applied for continuous outcomes. The overall association between onset group and each outcome was formally tested using likelihood ratio tests, comparing models including the onset variable with nested models excluding it. A p-value < 0.05 was considered statistically significant. Results: Patients’ mean age was 60.0 ± 13.7 years; 55.9% of them were males; and there were 188 cases (69.1%) of psoriatic arthritis (PsA) and 84 cases (30.9%) of ankylosing spondylitis (AS). In early-onset patients, inflammatory back pain (IBP) was more frequent, whereas late-onset patients more often presented with joint swelling. A family history of SpA and psoriasis was less common in late-onset forms. Comorbidities, including osteoporosis, osteoarthritis, hypertension, hyperuricemia, and diabetes, were more prevalent in older-onset patients, resulting in a higher overall comorbidity burden in Groups 2 and 3. Patient-reported outcomes were largely similar across age groups, although work activity limitation was more pronounced in younger patients. Conclusions: Age at onset seems to influence SpA phenotypes: early-onset could favor axial involvement, while late-onset may associate with peripheral arthritis. Late-onset forms are associated with a more severe comorbidity burden, in particular for cardiovascular risk factors. Lung involvement proved to be more prevalent with respect to the general population, so it should be checked in the routinary assessment of SpA patients. These findings suggest that rheumatologists could tailor their routine assessments based on patients’ age at disease onset. Interestingly, work productivity seems more impacted in early-onset patients. All these points highlight the importance of age at disease onset in SpA, guiding toward personalized medicine in terms of follow-up, therapy, and more holistic patient management. Full article

The growing demand for sustainable energy alternatives highlights the need for decision support tools in biodiesel supply chains. This study proposes a mixed-integer programming (MIP) model for tactical planning in the palm oil biodiesel supply chain, focusing on refining, blending, and distribution. The model incorporates economies of scale, inventory, and transport constraints and is enhanced with valid inequalities (VI) and a warm-start heuristic procedure (WS) to improve computational efficiency. Computational experiments on simulated instances with up to 6273 variables and 47 million iterations demonstrated robust performance, achieving solutions within 15 min. The model also reduced time-to-first-feasible (TTFF) solutions by 60–75% and CPU times by 17–21% compared to the baseline, confirming its applicability in realistic contexts. The proposed model provides actionable insights for managers by supporting decisions on facility scaling, product allocation, and profitability under supply–demand constraints. Beyond palm oil biodiesel, the formulation and its VI + WS enhancement provide a transferable blueprint for tactical planning in other process industry and renewable energy supply chains, where (i) multi-echelon flow conservation holds and (ii) discrete operating scales couple throughput with fixed/variable cost structures, enabling fast scenario analyses under changing prices, demand, and capacities. Full article

This study explores the potential for afforestation in Portugal that could balance wood and non-wood forest production under future climate change scenarios. The Climate Envelope Models (CEM) approach was employed with three main objectives: (1) to model the current distribution of key Portuguese forest species—eucalypts, maritime pine, umbrella pine, chestnut, and cork oak—based on their suitability for wood and non-wood production; (2) to project their potential distribution for the years 2070 and 2090 under two Shared Socioeconomic Pathway (SSP) scenarios: SSP2–4.5 (moderate) and SSP5–8.5 (high emissions); and (3) to generate integrated species distribution maps identifying both current and future high-suitability zones to support afforestation planning, reflecting climatic compatibility under fixed thresholds. Species’ current CMEs were produced using an additive Boolean model with a set of environmental variables (e.g., temperature-related and precipitation-related, elevation, and soil) specific to each species. Species’ current CEMs were validated using forest inventory data and the official Land Use and Land Cover (LULC) map of Portugal, and a good agreement was obtained (>99%). By the end of the 21st century, marked reductions in species suitability are projected, especially for chestnut (36%–44%) and maritime pine (25%–35%). Incorporating future suitability projections and preventive silvicultural practices into afforestation planning is therefore essential to ensure climate-resilient and ecologically friendly forest management. Full article

In the modern era, demand forecasting enhances the decision-making tasks of industries for controlling production planning and reducing inventory costs. However, the dynamic nature of the fashion and apparel retail industry necessitates precise demand forecasting to optimize supply chain operations and meet customer expectations. Consequently, this research proposes the Formicary Zebra Optimization-Based Distributed Attention-Guided Convolutional Recurrent Neural Network (FZ-DACR) model for improving the demand forecasting. In the proposed approach, the combination of the Formicary Zebra Optimization and Distributed Attention mechanism enabled deep learning architectures to assist in capturing the complex patterns of the retail sales data. Specifically, the neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate extracting the local features and temporal dependencies to analyze the volatile demand patterns. Furthermore, the proposed model integrates visual and textual data to enhance forecasting accuracy. By leveraging the adaptive optimization capabilities of the Formicary Zebra Algorithm, the proposed model effectively extracts features from product images and historical sales data while addressing the complexities of volatile demand patterns. Based on extensive experimental analysis of the proposed model using diverse datasets, the FZ-DACR model achieves superior performance, with minimum error values including MAE of 1.34, MSE of 4.7, RMS of 2.17, and R² of 93.3% using the DRESS dataset. Moreover, the findings highlight the ability of the proposed model in managing the fluctuating trends and supporting inventory and pricing strategies effectively. This innovative approach has significant implications for retailers, enabling more agile supply chains and improved decision making in a highly competitive market. Full article

Against the backdrop of global climate change and China’s “dual-carbon” goals, carbon emissions from the construction phase of transportation infrastructure, particularly the rapidly expanding railway network, have garnered significant attention. However, systematic research and general evaluation models targeting the factors influencing carbon emissions during the railway bridge construction phase remain insufficient. To address this gap, this study presents a novel low-carbon evaluation model that integrates the analytic network process (ANP) and the fuzzy comprehensive evaluation (FCE) method. First, a carbon accounting model covering four stages—material production, transportation, construction, and maintenance—is established based on life cycle assessment (LCA) theory, providing a data foundation. Second, an innovative low-carbon evaluation index system for railway bridges, comprising 5 criterion layers and 23 indicator layers, is constructed. The ANP method is employed to calculate weights, effectively capturing the interdependencies among indicators, while the FCE method handles assessment ambiguities, forming a comprehensive evaluation framework. A case study of the bridge demonstrates the model’s effectiveness, yielding an evaluation score of 82.38 (“excellent” grade), which is consistent with expert judgement. The ranking of indicator weights from the model is highly consistent with the actual carbon emission inventory ranking (Spearman coefficient of 0.714). Key indicators—C21 (use of high-performance materials), C22 (concrete consumption), and C25 (transportation energy consumption)—collectively account for approximately 60% of the total impact, accurately identifying the major emission sources. This research not only verifies the model’s efficacy in pinpointing critical carbon sources but also provides a scientific theoretical basis and practical tool for low-carbon decision-making and optimization in the planning and design stages of railway bridge projects. Full article

Temperature Swing Absorption (TSA) is the primary candidate for the Isotope Rebalancing and Protium Removal (IRPR) system within the envisioned EU-DEMO fusion reactor fuel cycle. TSA separates a mixed hydrogen isotope stream into two product streams using a semi-continuous process. One stream, enriched in heavy isotopes, is used to re-establish the required deuterium-to-tritium fuel ratio. The second, enriched in protium, is stripped off from the fuel cycle to counteract the protium build-up. Separation is achieved by cycling an isotope mixture between two columns filled with metallic absorption materials that have opposite isotope effects of metal hydride formation. The selection of these materials, the operation parameters and the column geometry allow for adjusting the resulting enrichments. To identify suitable operation parameters, a TSA process model is developed which depicts the process dynamics and interactions between the columns. A modified process operation mode is introduced, which enables higher system throughputs and non-cryogenic operation, i.e., operational temperatures between 0 to 130 °C, while reducing the tritium inventory due to shorter cycling times by reduced amplitudes of the temperature swings. Finally, simulations of a TSA system at relevant scale confirm the suitability of TSA technology for the separation task of the EU-DEMO IRPR system. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

As the global climate discourse shifts from mitigation to achieving net-negative emissions, there is a critical need for replicable, real-world models of climate-positive development at a regional scale, particularly in the Global South. This study addresses this gap by conducting a detailed greenhouse gas (GHG) inventory of four diverse provinces in Thailand and analyzing the results through the newly proposed Climate-Positive Pathways Framework (CPPF). Our findings reveal that all four provinces function as significant net-negative GHG sinks. They achieve this status through three distinct archetypes: a Conservation-Dependent pathway, an Agricultural Frontier pathway, and a novel Agro-Sink pathway. Most significantly, in the Agro-Sink model, we find that in specific economic contexts, managed agricultural landscapes can surpass natural forests as the primary driver of regional carbon removal. This typology provides a new, landscape-scale paradigm for cleaner production, proposing these three archetypes as transferable, evidence-based models for regional policymakers. This underscores that effective climate action requires context-specific regional planning that strategically leverages both natural and agricultural capital. Full article

Pharmaceutical supply chains face high inventory and stockout risks because of short product shelf lives and volatile demand. To enhance coordination efficiency and reduce drug waste, this study examines a two-echelon supply chain comprising a manufacturer and multiple medical institutions. We built a joint ordering and transshipment optimization model that simultaneously incorporates shelf-life constraints, the first-in–first-out (FIFO) policy, inventory capacity limits, and peer-level transshipment. Under deterministic and stochastic demand, we solved the model using Bayesian optimization and Monte Carlo simulation. The results show that moderate inventory transshipment effectively mitigates risk from demand uncertainty and increases total supply-chain profit; under stochastic demand, the optimal strategy relies more heavily on coordinated transshipment to reduce excess inventory and near-expiry waste. Full article

Small and medium-sized enterprises (SMEs) play a central role in employment and regional economic development, yet they are highly vulnerable to shocks such as pandemics, energy price spikes, and supply chain disruptions. Scenario modelling, stress testing, and digital twins are used to assess resilience, yet most applications focus on large firms in single-country settings. This article develops a model to stress test the resilience of Slovenian SMEs, calibrated with parameters and mechanisms derived from South African SME resilience studies. A system dynamics model with stocks for cash, inventory, and productive capacity is specified and subjected to demand, supply, financial, and compound shock scenarios, with and without resilience measures such as liquidity buffers, customer and supplier diversification, and basic digital planning capabilities. Results indicate non-linear tipping points where small reductions in liquidity sharply increase the likelihood of distress, and show that combinations of liquidity, diversification, and collaborative supply chain practices reduce the depth and duration of output losses. The study demonstrates how evidence from an African context can inform resilience strategies in a small European economy and provides a transparent, portable modelling architecture that can be adapted to other settings. Implications are discussed for SME managers and for policies supporting sustainable, resilient enterprise ecosystems. Full article

This paper presents an energy-focused analysis of structural materials used in passenger cars, with a particular emphasis on the impact of construction materials on total energy consumption throughout the vehicle’s life cycle. Three production periods (2000, 2010, and 2020) were analysed for B- and C-segment vehicles using inventory data from Life Cycle Assessment databases, the scientific literature, and certified dismantling stations. The embodied energy of key material groups—steel, aluminium, plastics, and other materials—was calculated based on representative mass shares and material-specific energy intensity indicators. The computational model was supplemented with statistical analyses (Kolmogorov–Smirnov test, Levene’s test, ANOVA, and Tukey’s post hoc tests) to verify whether observed temporal trends were statistically significant. The results indicate that total material-related energy inputs increased from approximately 57 GJ to 64 GJ per vehicle, while the specific energy intensity per kilogram decreased from 47.6 MJ/kg to 42.6 MJ/kg. Aluminium exhibited a pronounced reduction in unit energy intensity due to the rising share of secondary materials, whereas plastics and other materials showed substantial increases. Steel remained the largest contributor in absolute terms because of its dominant mass share. This study highlights the growing importance of the production phase in the environmental balance of modern vehicles, particularly in the context of the rising share of lightweight materials and recycling-based components. The results emphasise the importance of energy-efficient material use and underscore the significance of material selection and recycling strategies in reducing energy demand within the automotive sector. Full article

Maple syrup production has the potential to promote sustainable rural economic development in regions with suitable forest and climate conditions. Kentucky emerges as a promising candidate due to its extensive maple tree inventory and favorable seasonal patterns. However, the broader economy-wide implications of developing a maple syrup industry in the state remain underexplored. To fill this knowledge gap, this study employs a customized static single-region computable general equilibrium (CGE) modeling approach for Kentucky under nine scenarios based on production capacities and potential levels. The results consistently show positive impacts on net household income, social welfare (measured by equivalent variation), government revenues, and state GDP across all scenarios. Medium production capacities generate the most balanced and efficient outcomes, while high-potential scenarios, especially under small and large scales produce the largest absolute gains. These results underscore the viability of maple syrup production as an economic development strategy and highlight the role of production scale in maximizing benefits. Furthermore, expanding maple syrup production can enhance rural livelihoods by diversifying forest-based income and promoting long-term stewardship. As a non-timber forest product, maple syrup tapping provides economic incentives to maintain healthy forests, strengthening rural sustainability and resilience. Our findings indicate that developing this industry beyond traditional regions can generate meaningful economic benefits while encouraging sustainable resource use when appropriately scaled and managed. Full article

This paper introduces a novel, integrated optimization framework for sustainable multi-site logistics planning, which simultaneously addresses production, inventory, and distribution decisions. The proposed hybrid methodology combines a Genetic Algorithm (GA) with Linear Programming (LP) to minimize total logistics costs while proactively integrating environmental impact assessment. The model determines optimal production schedules across multiple facilities, manages inventory levels, and solves the Vehicle Routing Problem (VRP) for distribution. A key innovation is the incorporation of a CO₂ emission forecasting module directly into the optimization loop, allowing the algorithm to anticipate and mitigate the environmental consequences of logistics decisions during the planning phase, rather than performing a post-hoc evaluation. The framework was implemented in Python 3.13.4, utilizing the PuLP library for LP components and custom-developed GA routines. Its performance was validated through a numerical case study and a series of sensitivity analyses, which investigated the effects of fluctuating demand and key cost parameters. The results demonstrate that the inclusion of emission forecasting enables the identification of solutions that achieve a superior balance between economic and environmental objectives, leading to significant reductions in both total costs and predicted CO₂ emissions. This work provides practitioners with a scalable and practical decision-support tool for designing more sustainable and resilient multi-echelon supply chains. Full article

Improving responsiveness and efficiency in production systems requires an understanding of how manufacturing flexibility and inventory management interact under conditions of uncertainty. This study examines the combined effect of four types of flexibility, machine, labor, routing, and volume, together with the use of buffers, on the total flow time of production batches. A total of 84 experimental configurations were simulated, of which 35 were feasible and statistically valid, using a discrete-event simulation model developed in Arena and validated with industrial data. The results show that combining high machine and labor flexibility reduces total flow time from 5450 to 3050 min (a 44% decrease), whereas routing and volume flexibility exhibit minor effects. Moreover, the inclusion of buffers further improves performance, reducing times by approximately 1000 min in low-flexibility configurations. These findings provide robust quantitative evidence to guide the design of adaptive production systems by jointly evaluating the flexibility and inventory management dimensions that are typically studied in isolation. Full article

Groundwater resources are scarce in the cold and arid regions of north China. Moreover, regional water resource replenishment without external sources remains difficult. This water deficit has become a major factor restricting the sustainable development of regional vegetable production. The effective utilization of rainwater harvesting for irrigated agricultural production is necessary to suppress droughts and floods in farming under the semi-arid climate of this area in order to both guarantee a stable supply of vegetables to the market in south and north China and promote the balanced development of regional agriculture–resource–environment integration. In this study, based on continuous simulation and Python modeling, we simulated and analyzed the water supply and production effects of irrigation with harvests and stored rainwater on tomatoes under different water supply scenarios from 1992 to 2023. We then designed and tested a water-saving and high-yield project for rainwater-irrigated greenhouses in 2024 and 2025 under natural rainfall conditions in northwestern Hebei Province based on the reference irrigation scheme. The water supply satisfaction rate, water demand satisfaction rate, and volume of water inventory of tomato fields under different water supply scenarios increased with the rainwater tank size, and the corresponding drought yield reduction rate of tomato decreased. Under the actual rainfall scenarios in 2024 and 2025, a 480 m² greenhouse with a 14.4 m³ rainwater tank for producing tomatoes irrigated with rainwater drip from the greenhouse film collected 127.7 and 120.5 m³ of rainwater, respectively. The volume of the rainwater tank was exceeded 8.3 and 8.0 times, and up to 93.8% and 95.0% of the irrigated groundwater was replaced; additionally, the average yield of the small-fruited tomato ‘Beisi’ was 50,076.6 kg·hm⁻² and 48,110.2 kg·hm⁻², reaching 96.1% and 92.3% of the expected yield. Conclusion: The irrigation strategy based on the innovative “greenhouse film–rainwater harvesting–groundwater replenishment” model developed in this study has successfully achieved a high substitution rate of groundwater for greenhouse tomato production in the cold and arid regions of north China while ensuring stable yields by mitigating drought and waterlogging risks. This model not only provides a replicable technical framework for sustainable agricultural water resource management in semi-arid areas but also offers critical theoretical and practical support for addressing water scarcity and ensuring food security under global climate change. Full article