Search Results (84)

This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article

Designing energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)—trained on a synthetic dataset of 2000 EnergyPlus-simulated building design scenarios to predict both energy use intensity (EUI) and cost estimates for midrise apartment buildings in the Toronto area. All three models exhibit strong predictive performance, with R² values exceeding 0.9 for both EUI and cost. XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m², while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m². In terms of computational efficiency, the surrogate models significantly outperform a physics-based simulation model, with MLP running approximately 340 times faster and XGBoost and RF achieving over 200 times speedup. This study also examines the effect of training dataset size on model performance, identifying a point of diminishing returns where further increases in data size yield minimal accuracy gains but substantially higher training times. To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. A parametric design configuration analysis further evaluates the models’ sensitivity to changes in building envelope features. Overall, the findings demonstrate that machine learning-based surrogate models can serve as fast, accurate, and interpretable alternatives to traditional simulation methods, supporting efficient decision-making during early-stage building design. Full article

The sustainable management of organic waste is a global priority due to its environmental impact and the increasing pressure on urban and rural systems, particularly in regions with limited technological infrastructure. This study introduces and validates a comprehensive methodology based on Digital Twins (DTs) to optimize production and service systems in organic waste management. The methodology includes contextual and propositional phases and is built on a modular three-layer architecture (physical, cloud, and virtual) that enables real-time monitoring, simulation, and feedback. It was validated through a field implementation in a composting facility in Cajamarca, Colombia. The results showed a 10% increase in composting efficiency and a monthly gain of 1200 kg of compost. A statistical analysis confirmed a significant increase in process efficiency (p < 0.001) and a reduction in performance variability (p < 0.01). The return on investment reached 18,957.6% using low-cost technology. These findings demonstrate the viability and adaptability of the proposed methodology for low-tech environments and support its potential for scaling in circular economy applications across waste management and agriculture. Full article

Fiber–cement composites have been increasingly studied for sustainable construction applications, but durability issues—particularly fiber degradation in alkaline environments—remain a challenge. This study aimed to evaluate the individual and combined effects of furfurylated sisal fibers and nanoclay additions on the physical and mechanical performance of autoclaved fiber–cement composites, seeking to enhance fiber durability and matrix compatibility. All the composites were formulated with CPV-ARI cement and partially replaced with agricultural limestone to reduce the environmental impact and production costs. Sisal fibers (2 wt.%) were chemically modified using furfuryl alcohol, and nanoclays—both hydrophilic and surface-functionalized—were incorporated at 1% and 5% of cement weight. The composites were characterized for physical properties (density, water absorption, and apparent porosity) and mechanical performance (flexural and compressive strength, toughness, and modulus). Furfurylation significantly improved fiber–matrix interaction, leading to higher flexural strength and up to 100% gain in toughness. Nanoclay additions reduced porosity and increased stiffness, particularly at 5%, though excessive content showed diminishing returns. The combination of furfurylated fibers and functionalized nanoclay provided the best results in maintaining a compact microstructure, reducing water absorption, and improving mechanical resilience. Optical microscopy confirmed improved fiber dispersion and interfacial bonding in composites containing furfurylated fibers and functionalized nanoclay. These findings highlight the effectiveness of integrating surface-treated natural fibers with pozzolanic additives to enhance the performance and longevity of fiber–cement composites. Full article

The adoption of green building materials (GBMs) has become increasingly important in reducing carbon emissions and realizing zero-carbon cities. Although some scholars have investigated the decision-making of GBMs adoption in markets, they mainly focused on the impact factors of GBMs adoption without considering consumers’ multi-channel purchasing behavior. Thus motivated, this paper aims to develop a theoretical game model incorporating consumers’ multi-channel purchasing behavior and study the optimal sales strategies of GBMs manufacturers and retailers in markets for promoting GBMs adoption. To do this, not only the equilibrium outcome on sales strategy is examined, but also the effects of different GBMs sales strategies on urban environments and social welfare are theoretically verified. It is found that (1) the equilibrium sales strategy relies on two core parameters, namely matching rate and online return cost. Only when the matching rate is low and the online return cost is at a medium level can the GBMs manufacturer and retailer achieve a strategic consensus, and the equilibrium sales strategy is S (i.e., selling GBMs through the online channel, offline channel, and store-to-online channel). (2) When pursuing total profits of manufacturers and retailers in GBMs markets, the S sales strategy is 100% superior to the D sales strategy (i.e., selling GBMs only through online and offline channels). This is because the introduction of a store-to-online channel can reduce online return losses by providing consumers with physical experiences. (3) When pursuing social welfare (refers to public benefits including consumer surplus, urban environmental impacts, and others), the D sales strategy is optimal if the matching rate is relatively large and the return cost is low. (4) Under certain conditions, governments should incentivize GBMs manufacturers and retailers to adopt the D sales strategy through regulatory instruments, so as to achieve a balance between economic benefits and social benefits. Full article

To validate microgrid systems, precise simulations are necessary beforehand. Traditional Hardware-in-the-Loop Simulation (HILS) is used to validate systems by creating a digital twin environment that integrates software and hardware to mimic reality. However, HILS requires high investment costs for hardware, posing a significant hurdle for companies. To address this issue, this study proposes a Software-in-the-Loop Simulation (SILS) framework using SCADA/EMS and MATLAB/Simulink(R2024a). The proposed SILS framework is highly compatible with Energy Management Systems (EMSs) and Supervisory Control and Data Acquisition (SCADA), allowing near real-time data exchange and scenario-based analysis without relying on physical hardware. According to the simulation results, SILS effectively replicates the dynamic behavior of microgrid components such as solar power generation systems, energy storage systems (ESSs), and diesel generators. Solution providers can quickly conduct feasibility tests through systems that simulate actual power systems. They can test the operation of SCADA/EMS at a lower cost and reduce on-site time, thereby reducing business costs and preemptively addressing potential issues in the field. This paper demonstrates how SILS can contribute to establishing optimal operation strategies and power supply stability through case studies, including daily operation optimization and autonomous operation scenarios for microgrids. This research provides a foundation for the feasibility of microgrid solution construction by enabling software performance evaluations and the verification of economic expected returns in the early stages of a project. Full article

Regulating the process parameters in converter steelmaking is crucial for reducing the phosphorus content in molten steel and enhancing its quality. However, immoderate alteration may result in raised production costs and the occurrence of phosphorus return. This study addresses process parameter optimization challenges in converter steelmaking by proposing an improved multi-objective whale optimization algorithm (IMOWOA) that synergistically integrates metallurgical thermodynamics with data-driven modeling. The methodology constructs a physics-informed objective function linking process parameters to optimization targets, thereby resolving the disconnect between mechanistic and data-driven modeling approaches. The algorithm innovatively combines Sobol quasi-random sequences with grey wolf social hierarchy strategies to prevent premature convergence in high-dimensional search spaces while maintaining Pareto front diversity, supplemented by a reward mechanism to ensure strict adherence to multi-objective constraints. Experimental validation using steel plant production data demonstrates IMOWOA’s efficacy, achieving a 10.8% reduction in endpoint phosphorus content and a 5.79% decrease in production costs per ton of steel. Comparative analyses further confirm its superior feasibility and stability in quality-cost co-optimization, evidenced by a 12.6% improvement in hypervolume (HV) over conventional swarm intelligence benchmarks, establishing a robust framework for industrial metallurgical process optimization. Full article

Long-term intensive agricultural management practices have led to a continuous decline in farmland soil quality, posing a serious threat to food security and agricultural sustainability. Green manure, as a natural, cost-effective, and environmentally friendly cover crop, plays a significant role in enhancing soil quality, ensuring food security, and promoting sustainable agricultural development. The improvement of soil quality by green manure is primarily manifested in the enhancement of soil physical, chemical, and biological properties. Specifically, it increases soil organic matter content, optimizes soil structure, enhances nutrient cycling, and improves microbial community composition and metabolic activity. The integration of green manure with agronomic practices such as intercropping, crop rotation, conservation tillage, reduced fertilizer application, and organic material incorporation demonstrates its potential in addressing agricultural development challenges, particularly through its contributions to soil quality improvement, crop yield stabilization, water and nutrient use efficiency enhancement, fertilizer input reduction, and agricultural greenhouse gas emission mitigation. However, despite substantial evidence from both research and practical applications confirming the benefits of green manure, its large-scale adoption faces numerous challenges, including regional variability in application effectiveness, low farmer acceptance, and insufficient extension technologies. Future research should further clarify the synergistic mechanism between green manure and agronomic measures such as intercropping, crop rotation, conservation tillage, reduced fertilization and organic material return to field. This will help explore the role of green manure in addressing the challenges of soil degradation, climate change and food security, develop green manure varieties adapted to different ecological conditions, and optimize green manure planting and management technologies. Governments should comprehensively promote the implementation of green manure technologies through economic incentives, technology extension, and educational training programs. The integration of scientific research, policy support, and technological innovation is expected to establish green manure as a crucial driving force for facilitating the global transition towards sustainable agriculture. Full article

In coal chemical industries, the optimal allocation of gas and steam is crucial for enhancing production efficiency and maximizing economic returns. This paper proposes an optimal scheduling method using operating zone models and entropy weights for an energy system in a gas-to-methanol process. The first step is to develop mechanistic models for the main facilities in methanol production, namely desulfurization, air separation, syngas compressors, and steam boilers. A genetic algorithm is employed to estimate the unknown parameters of the models. These models are grounded in physical mechanisms such as energy conservation, mass conservation, and thermodynamic laws. A multi-objective optimization problem is formulated, with the objectives of minimizing gas loss, steam loss, and operating costs. The required operating constraints include equipment capacities, energy balance, and energy coupling relationships. The entropy weights are then employed to convert this problem into a single-objective optimization problem. The second step is to solve the optimization problem based on an operating zone model, which describes a high-dimensional geometric space consisting of all steady-state data points that satisfy the operation constraints. By projecting the operating zone model on the decision variable plane, an optimal scheduling solution is obtained in a visual manner with contour lines and auxiliary lines. Case studies based on Aspen Hysys are used to support and validate the effectiveness of the proposed method. Full article

In the context of new retail, the development of omni-channels is flourishing. The entry threshold for the clothing industry is low, and the popularity of online shopping has, to some extent, reduced consumers’ perception of the authenticity of clothing. As a result, returns are a serious issue in the clothing industry. This article focuses on a clothing retailer while addressing retail and return issues in the clothing industry. It develops and analyzes models for an online single-channel strategy and two omni-channel showroom strategies: “Experience in Store and Buy Online (ESBO)” with an experience store and “Buy Online and Return in Store (BORS)” with a physical store. These models are used to examine the pricing and return decisions of the retailer in the three strategic scenarios. Additionally, this study considers the impact of fashion trends on demand. It explores pricing and return strategies in two showroom models under the influence of the fashion trend decay factor. Moreover, sensitivity analyses and numerical analyses of the important parameters are performed. This research demonstrates the following: (1) In the case of high return transportation costs and online return hassle costs, clothing retailers can attract consumers to increase profits through establishing offline channels; (2) extending the sales time of fashionable clothing has a positive effect on profits, but blindly prolonging the continuation of the sales time will lead to a decrease in profits; (3) the larger the initial fashion level or the smaller the fashion level decay factor, the greater the optimal retailer profits. The impacts of the initial fashion level and fashion level decay factor on profits are more significant in omni-channel operations. This article aims to identify optimal strategies for retailers utilizing omni-channel operations and offer managerial insights for the sale of fashionable apparel. Full article

Introduction: The rise in youth sports participation has led to an increase in pediatric sports-related injuries in the United States, contributing to growing healthcare costs and exacerbating socioeconomic disparities. Insurance payor status is a critical factor influencing access to care, treatment delays, and health outcomes. This study examines the association between insurance payor status and outcomes in pediatric sports-related injuries. Methods: A systematic review of the Medline database was conducted. Included studies reported insurance payor status and pediatric sports orthopedic patient outcomes following surgery. Outcomes included time to be seen by a provider, treatment access, complication and revision rates, postoperative Emergency Department (ED)/Urgent Care utilization, readmission rates, hospital length of stay, pain, functional scores, discharge destinations, return to activity, and follow-up. Results: A total of 35 studies comprising 535,891 pediatric patients were included. Publicly insured or uninsured patients consistently experienced significant delays in accessing care, with average wait times for clinic visits, imaging, and surgery up to six times longer compared to privately insured patients. These delays were associated with worsened injury severity, higher rates of postoperative complications, and poorer functional outcomes. Publicly insured patients were less likely to receive advanced treatments such as bracing or physical therapy, further compounding disparities. Minority groups faced delays even when controlling for insurance status. Conclusions: Public and uninsured pediatric patients face systemic barriers to timely and equitable care, resulting in worse outcomes following sports-related injuries. Future research should explore targeted solutions to ensure equitable care for this vulnerable population. Full article

In order to optimize the pressure relief gas extraction process for the 1504 working face in East 2 of Dalong Coal Mine based on its mining and gas conditions, a physical model of pressure relief gas extraction in the airspace using two preliminary extraction processes—a high-level oblique borehole and a directional long borehole—was established using COMSOL 6.2 software. The changes in the gas extraction effect of high-level oblique boreholes were analyzed through a simulation of the advancement of the working face, and the reasons for the low utilization rate of the high-level oblique boreholes were outlined. The effects of the horizontal distance of the directional long boreholes from the side of the air return lane, the borehole spacing, and the negative pressure of the boreholes on the gas extraction effect were analyzed, and the gas extraction process of the directional long boreholes was optimized and applied in the field. The results showed that the directional long borehole gas extraction process had a better extraction effect, a higher borehole utilization rate, and superior cost savings, and was thus was the preferred process. Additionally, the optimal parameters were a 30 m horizontal distance of the boreholes from the side of the air return lane, a 30 m spacing between the boreholes, and a 20 kPa negative extraction pressure. Full article

Dispatching goods is becoming more difficult to manage in the field of logistics due to the high demand for order shipments. This is related to the increasing popularity of the use of e-commerce platforms by consumers, where products are required to be delivered rather than being bought in physical stores. Dispatch management is one of the critical components in a supply chain since it covers the coordination of tasks among stakeholders from the warehouse to the consumer’s doorstep. In this study, the authors propose a framework leveraging geographic information to sustain logistics operations, specifically in terms of managing last-mile delivery and return trip orders. This includes scheduling, communications, and the inventorying of the shipment status of goods. A mobile application built on this framework was integrated with a waypoint order optimization algorithm considering an entire route that traverses all the required pick-up and delivery points. It was pilot tested with an actual dispatch operation of a logistics company, yielding decreases of 92% and 43% in the average turnaround time and carbon footprint per completed service request, respectively, a decrease of 57% in operations cost, and an increase of 72% in profit. With the adoption of this framework, this study aims to contribute to the overall efficiency and sustainability of logistics operations in a wider geographic range. Full article

Background: Traumatic brain injury (TBI) is referred to as a “silent epidemic” due to its limited awareness in the general public. Nevertheless, it can cause chronic, lifelong physical and cognitive impairments with severe impact on quality of life, resulting in high healthcare costs and loss of employment. To evaluate the outcome after mild and moderate TBI, “return to work (RTW)” is a relevant parameter, reflecting the socio-economic consequences of TBI. Our study aims to summarize RTW-rates to raise awareness on the impact of non-severe TBI. Methods: We performed a systematic literature review screening the databases Medline, Embase and Web of Science for studies reporting RTW in mild to moderate TBI. Studies that reported on RTW after mild or moderate TBI (defined by GCS > 9) in adults, with a minimum follow-up of six months were included. Risk of bias was assessed using the QUIPS tool. Results: We included 13 studies with a total 22,550 patients. The overall RTW rate after at least six months, varies between 37% and 98%. Full RTW is reported in six of the included 13 studies and varies between 12% and 67%. In six studies (46%) the RTW-rate by the end of follow-up was ≤60%, with four studies being from high-income countries. Conclusion: Mild and moderate TBI have a high impact on employment rates with diverging rates for RTW even between high-income countries. Increasing the societal awareness of this silent epidemic is of utmost importance and is one of the missions of the Swiss Brain Health Plan. Full article

In athletic training and research, the evaluation of sprint speed is widely used, and its accurate measurement is especially demanding. High-cost photocells are the gold-standard system for sprint time assessment, although low-cost smartphone applications can be a suitable option. This study assesses the validity and reliability of an application to measure sprint time compared to photocells. Five physically active subjects completed six sprints of 10 m and 20 m at maximal speed and a 5 m go and return sprint to evaluate the validity of the Photo Finish^® app (Version 2.30). To assess reliability, six trials of 5 m go and return sprints were measured by two smartphones. The validity results showed a mean bias of 0.012 s (95% CL: 0.000, 0.024) between the application and the photocells for the 10 m sprint, 0.007 s (95% CL: −0.007, 0.022) for the 20 m sprint and 0.005 s (95% CL: −0.005, 0.017) for the 5 m go and return test. The results also found R² between both systems (R²= 0.9863, 0.990 and 0.958) for each distance (10 m, 20 m and 5 m go and return, respectively). As for reliability, the application showed outstanding consistency between two smartphones operating simultaneously (ICC 0.999; R²: 0.999). This study shows that the Photo Finish^® app is an accurate and reliable tool to measure sprint time with an error of 0.09 s. Full article