Search Results (109)

Background/Objectives: Aging is closely associated with sarcopenia, which has a significant impact on muscle mass and its function. Protein supplementation (PS) brings benefits such as lean mass and strength gains during exercise training. This paper determined the optimal regimen among the composites of variate protein sources and training modalities for older individuals. Methods: We comprehensively searched the electronic databases, namely MEDLINE Complete, PEDro, the Cochrane Library, Google Scholar, EMBASE, and the China National Knowledge Infrastructure, from its inception until December 2025. We included randomized controlled trials (RCTs) that examined the effectiveness of any type of PS combined with one of three exercise types—resistance, aerobic, or multicomponent training—in untrained older adults. The main outcomes used to identify sarcopenia were assessed, including lean mass, handgrip and leg strength, and physical mobility measures. Network meta-analysis (NMA) was performed by a frequentist method using random-effects models. The estimated treatment effect was expressed as the standard mean difference (SMD) with a 95% confidence interval (CI). Any potential factor moderating the treatment effect was determined by the meta-regression analyses, including participant characteristics and methodological factors. Certainty of evidence (CoE) was assessed by the GRADE framework. Results: In total, we included 235 RCTs (20,980 participants) for analyses. A total of 10 protein sources (whey, soy, casein, milk, and the others) were identified, corresponding to 24 monotherapy and combined regimens of PS and exercise. Among the treatment arms, whey plus resistance training was ranked as the most effective treatment for muscle mass (large SMD, 1.29; CoE, moderate) and leg strength (large SMD, 1.16; CoE, moderate); additionally, whey plus multicomponent exercise training achieved the most promising effects on such sarcopenia-related physical indicators such as chair rise (large effect, SMD = 1.09; CoE: high), timed up and go (medium SMD, 0.70; CoE, high), and global mobility score (large SMD, 1.02; CoE, high). Conclusions: The treatment efficacy appears to be moderated by the participant’s conditions, PS resource, and PS dose, particularly the outcome of muscle mass and strength. The present NMA results indicate that whey protein incorporated with resistance training is the optimal program to help combat sarcopenia in older adults. Full article

Never has the analysis of bus travel times been so essential to transit planning: travelers complain about a decline in service quality, urban congestion is on the rise, and public transport companies struggle with a structural driver shortage. This research paper aims to address the urgent need to explore new tools to increase commercial speed on transit lines while avoiding costly, potentially inefficient technological investments. A data-driven, cost-neutral, and replicable methodological framework is proposed to provide a first-order estimation of the potential benefits of Transit Signal Priority (TSP) at signalized intersections. The approach is based on Automatic Vehicle Monitoring (AVM) data analysis, which is underpinned by a lean network representation logic built from origin/destination pairs of stops located upstream and downstream of signalized intersections. Bus travel inter-times across network arcs are compared between peak and off-peak periods through a two-level analytical process that progressively refines the estimation of recoverable delay. The methodology is applied to the urban bus network of Pavia (Italy), operated by Autoguidovie S.p.A. (one of the most important Local Public Transport companies in Italy), with a specific focus on the high-frequency PV3 line. Results indicate a potential reduction of up to approximately 6 h and 45 min of operating time per day at the line level (−13.5% of total driving time), and up to 2 min per trip along a 2 km corridor (−6% along the single corridor selected). The procedure integrates both infrastructural and operational perspectives, supporting preliminary decision-making on TSP implementation using only data already collected by transit agencies. Full article

This study examines the influences of various organizational performance factors on the application of Lean tools and the effects of Lean methodology implementation. Although Lean management has been widely studied, empirical evidence on the combined influence of internal organizational capabilities and external environmental pressures on Lean adoption and outcomes in transition economies remains limited. In particular, the relative importance of internal resources and competitive pressures in shaping Lean implementation results has not been sufficiently explored. Therefore, this study aims to analyze how different organizational and environmental factors influence both the application of Lean tools and the effects of Lean methodology implementation. The independent variables considered include: business performance, organizational culture, company size, technical infrastructure and resources, education and competence of employees, training for Lean methodology, management support, competitive pressure and motivation to reduce costs, degree of innovation in the company, the role of the Lean concept in strategic planning, years of company existence, and years of Lean tool implementation. The research was conducted among food industry companies in Serbia, and a total of 183 valid questionnaires were collected. The results indicate that the application of Lean tools is most strongly influenced by training for Lean methodology, followed by business performance and company size. In contrast, the effects of Lean methodology implementation are primarily affected by competitive pressure and motivation to reduce costs, as well as management support. Furthermore, the analysis shows that Lean application and Lean outcomes function as two distinct dimensions: companies may apply Lean tools without achieving significant effects if managerial support or competitive pressure is insufficient. Conversely, firms with strong competitive drivers and committed management achieve noticeably higher performance improvements even with moderate levels of Lean tool adoption. Overall, the findings suggest that the application of Lean tools largely depends on the company’s internal resources, such as employee knowledge and training, business strength, and scale of operations, while the success and outcomes of Lean implementation are more strongly driven by external competitive pressures and the degree of managerial understanding and support. By distinguishing between the determinants of Lean tool adoption and the determinants of Lean implementation outcomes, this study contributes to a clearer understanding of Lean effectiveness in the context of transition economies. Full article

The socks, hosiery, bodywear (SHB) industry is a critical segment of the textile sector, characterized by high-volume production and rapid delivery requirements, making efficiency and resource optimization essential. A corporate sustainability system is needed to minimize environmental impact, ensure long-term competitiveness, and align operations with global sustainability standards. Thus, this research aims to propose an integrated Corporate Sustainability System (CSS) framework that synergizes Lean Manufacturing (LM), Digital Transformation (DT), and sustainability transition through a methodological triangulation of (1) a narrative review, (2) in-depth expert interviews, and (3) a comprehensive Turkish case study. The proposed framework integrates foundational lean principles such as 5S, TPM, and Value Stream Mapping with Industry 4.0 technologies, including RFID traceability, real-time ERP integration and machine vision systems. Empirical demonstration through the case study reveals that establishing foundational lean maturity is a critical foundation for successful digital adoption. Furthermore, the study demonstrates that transitioning from manual tracking to integrated digital platforms resolves data silos and enhances the transparency of customer revisions and warehouse accuracy. The framework also incorporates human-centric Lean 5.0 improvements, proving that ergonomic interventions such as rail-mounted cable systems are vital for operational sustainability. Ultimately, the CSS provides a scalable model that aligns SHB production with global mandates like the EU Green Deal and CBAM, positioning the sector for long-term competitive advantage in an increasingly eco-conscious global market. Full article

This article presents the simulation methodology and results of dual-fuel combustion for internal combustion engines (ICE). Simulations were performed in ANSYS Forte^®, which modeled flame propagation using the G-equation model, and results were validated against experimental data. The article also presents results from simulations performed in Converge CFD^®, which used the SAGE combustion model, presented in previous work. Typical combustion modelling challenges in such ICE simulations are discussed, and the applied methodology is described. The range of methane-air equivalence ratio was 0.47 ≤ ϕ ≤ 0.57 across four load conditions with a rotational velocity range of 1228 ≤ RPM ≤ 1800. The methane-air combustion at these low equivalence ratios led to the required tuning of the stretch factor coefficient used in the flame speed model in ANSYS Forte^® due to methane’s thermo-diffusive effects at lean equivalence ratios. As a result, the flame stretch factor coefficient was found to increase with decreasing equivalence ratio. The study thus demonstrates the importance of flame stretch sensitivity and thermo-diffusive instabilities in ICE combustion through CFD combustion simulations. Full article

Disruptive global events such as the COVID-19 pandemic have exposed critical vulnerabilities in the construction industry’s reliance on lean principles and Just-In-Time (JIT) methodologies. These disruptions, categorized as Black Swan Events (BSEs), challenged conventional supply chain management (SCM) and risk management (RM) strategies, resulting in delayed projects and increased costs. This paper explores how BSEs affect construction supply chains and evaluates the industry’s evolving response through RM and resilience-building strategies. A Joanna Briggs Institute (JBI) scoping review of the literature (2000–2024) synthesized evidence across SCM, RM, Lean Construction, JIT, and BSEs, triangulating 86 peer-reviewed studies with authoritative industry reports. The review reveals a lack of integrated research addressing these themes holistically for the construction sector. Key findings show that while JIT and lean approaches optimize efficiency, they fall short during high-impact, low-probability disruptions. Evidence indicates a selective shift toward Just-In-Case (JIC) practices; however, the extent and persistence of this transition vary by project context and merit further study. The study proposes a future research agenda emphasizing interdisciplinary models that integrate lean methods with resilience and anticipatory strategies. These insights aim to support construction firms in developing supply chains that are not only efficient but also adaptable and better prepared for future BSEs. Full article

Although technologies used in non-fossil methane and fossil resources to produce blue hydrogen are relatively mature, a system-integrated approach to reference system (RS)-based purification of H₂, CO₂ capture and storage, and UHS is relatively unexplored and requires research to fill gaps in the literature regarding balanced permutations and geological viability for net-zero requirements. This research proposes a system-integrated process for H₂ production through a PSA-based purification technique coupled with amine-based CO₂ capture and underground hydrogen storage (UHS). The intellectual novelty of the research is its first quantitative treatment of synergistic effects such as heat recovery and pressure-matching across units. Additionally, a site separation technique is applied, where H₂ and CO₂ reservoirs are selected based on the permeability of rock formations and fluids. On a research methodology front, a base case of a steam methane reforming process with the production of 99.99% pure H₂ at a production rate of 5932 kg/h is modeled and simulated using Aspen Plus™ to create a balanced permutation of mass and energy across units. As per the CO₂ capture requirements of this research, a capture of 90% of CO₂ is accomplished from the production of 755 t/d CO₂ within the model. The compressed CO₂ is permanently stored at specifically identified rock strata separated from storage reservoirs of H₂ to avoid empirically identified hazards of rock–fluid interaction at high temperatures and pressures. The lean amine cooling of CO₂ to 60 °C and elimination of tail-gas recompression simultaneously provides 5.4 MW_th of recovered heat. The integrated design achieves a net primary energy penalty of 18% of hydrogen’s LHV, down from ~25% in a standalone configuration. This corresponds to an energy saving of 8–12 MW, or approximately 15–18% of the primary energy demand. The research computes a production cost of H₂ of 0.98 USD per kg of H₂ within a production atmosphere of a commercialized WGS and non-fossil methane-based production of H₂. Additionally, a sensitivity analysis of ±23% of the energy requirements of the reference system shows no marked sensitivity within a production atmosphere of a commercially available WGS process. Full article

This study establishes a comprehensive structural isomorphism between Conway’s Game of Life and the entrepreneurial process, analysing the latter as a complex adaptive system governed by non-linear dynamics rather than linear predictability. Through a rigorous qualitative approach based on a systematic literature review and abductive inference, the research identifies and correlates four fundamental dimensions: uncertainty, adaptability, growth, and sustainability. Transcending traditional metaphorical comparisons, this paper introduces a novel mathematical model that modifies Conway’s deterministic logic by incorporating an «Agency» variable (A). This critical addition quantifies how an entrepreneur’s internal capabilities can counterbalance environmental pressures (neighbourhood density) to determine survival thresholds, effectively transforming the simulation into a «Game of Life with Agency» where participants actively influence their viability potential (Ψ). The analysis explicitly correlates specific algorithmic configurations with real-world business phenomena: high-entropy initial states («The Soup») mirror early-stage market uncertainty where outcomes are probabilistic; «gliders» represent the necessity of strategic pivoting and continuous displacement for survival; and «oscillators» symbolise dynamic sustainability through rhythmic equilibrium rather than static permanence. Furthermore, the study validates the «Gosper Glider Gun» pattern as a model for scalable, generative growth. By bridging abstract systems theory with managerial practice, the research positions these simulations as «mental laboratories» for decision-making. The findings theoretically validate iterative methodologies like the Lean Startup and conclude that successful entrepreneurship operates on the «Edge of Chaos», providing a rigorous framework for navigating high stochastic uncertainty. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

Accurate land cover mapping is essential for environmental monitoring and agricultural management. Sentinel-2 imagery, with high spatial resolution and open access, provides valuable opportunities for operational classification. Convolutional neural networks (CNNs) have demonstrated state-of-the-art results, yet their adoption is limited by high computational demands and limited methodological transparency. This study proposes a lightweight CNN for soil–vegetation classification, in Dolj County, Romania. The architecture integrates three convolutional blocks, global average pooling, and dropout, with fewer than 150,000 trainable parameters. A fully documented workflow was implemented, covering preprocessing, patch extraction, training, and evaluation, addressing reproducibility challenges common in deep leaning studies. Experiments on Sentinel-2 imagery achieved 91.2% overall accuracy and a Cohen’s kappa of 0.82. These results are competitive with larger CNNs while reducing computational requirements by over 90%. Comparative analyses showed improvements over an NDVI baseline and a favorable efficiency–accuracy balance relative to heavier CNNs reported in the literature. A complementary ablation analysis confirmed that the adopted three-block architecture provides the optimal trade-off between accuracy and efficiency, empirically validating the robustness of the proposed design. These findings highlight the potential of lightweight, transparent deep learning for scalable and reproducible land cover monitoring, with prospects for extension to multi-class mapping, multi-temporal analysis, and fusion with complementary data such as SAR. This work provides a methodological basis for operational applications in resource-constrained environments. Full article

Lean manufacturing emphasized reducing waste and improving efficiency, with the 5S methodology, Seiri (Sort), Seiton (Set in Order), Seiso (Shine), Seiketsu (Standardize), and Shitsuke (Sustain) as key tools. This study explored 5S implementation in a laboratory of a university, which initially suffered from disorganization, inefficiencies, and wasted resources. The intervention involved data collection, discussions with lab technicians and students, and layout mapping. After applying the first 4S steps, the lab realized marked improvements in organization, cleanliness, and workflow. Designated storage improved space use, while time-motion studies showed an average 78.6 s reduction in activity times, saving 632 s weekly. A 54% efficiency enhancement has also been achieved. The successful implementation created a safer and more efficient lab environment. The final step, Shitsuke, ensured sustained improvements through training, cleaning schedules, and time management tools. This paved the way towards a green university. Full article

Sustainable Development (SD) has increasingly become a core strategic direction. This study centers on the automotive industry, serving as the primary focus for both research and empirical analysis. If Taiwan’s automotive industry can successfully achieve transformation, it will generate a more advanced multiplier effect on the overall development of Taiwan’s industries. The study confirms that Lean Management (LM) and SD can effectively produce synergistic effects. Digital Transformation (DT) is increasingly recognized as a key driver of future business development. Exploring the interrelationships among SD, LM, and DT presents a strategic and practical research direction. Findings from this study suggest that integrating LM and DT can generate synergies that enhance resource efficiency, minimize waste, and improve both environmental and social performance. The primary objective of this study is to develop a structured framework connecting SD, LM, and DT by utilizing the House of Quality (HoQ) from the Quality Function Deployment methodology. The research employs multiple attribute decision-making techniques, including the Fuzzy Delphi Method, the Fuzzy Analytic Hierarchy Process, and the Compromise Ranking Method. By constructing and analyzing two HoQs, the study identifies key LM practices and DT technologies that serve as critical strategies for advancing SD performance. Finally, with regard to LM practices, no previous research has attempted to conduct a hierarchical classification. This study is the first to construct a hierarchical structure for Just-in-Time and Jidoka. Full article

This study assesses De Lone and McLean’s Information System (D&M IS) Success Model concerning DAS throughout small and medium enterprises (SMEs) in Saudi Arabia (SA). The present work mainly sought to evaluate the impact of information quality (IQ), system quality (SysQ), service quality (SrvQ) serving, system utilization, and user satisfaction (Usat) on the usage of the Digital Accounting System (DAS), which is posited to ultimately improve the quality of sustainable decision-making. The research utilized a quantitative methodology, employing a self-administered questionnaire to collect data from 328 decision-makers who are knowledgeable about actual DAS usage by SMEs in SA. Subsequent to gathering data, validation was conducted via Structural Equation Modeling (SEM) by utilizing smart-PLS software. The findings indicate that SysQ and IQ significantly influenced system utilization, although SrvQ did not. DAS was determined to significantly influence user happiness. Moreover, system utilization and user satisfaction positively influenced DAS, thereby affecting the sustainability of decision-making and reflecting the overall benefits of DAS. This work enhances the current IS literature by identifying the characteristics that affect the net advantages of DAS, with the suggested model evaluated in SMEs in SA utilizing DAS. This study serves as a reference to elucidate the significance of DAS and offers consequences, limitations, and prospects for further research. Full article

Logistics and warehouse operations experience an increasing pressure to adopt sustainable practices. The logistics industry generates substantial material waste, with cardboard being the primary packaging material. Adopting Circular Economy (CE) principles to control this waste is important for enhancing sustainability. However, there is a lack of studies on transforming warehouses into more sustainable operations. This paper studies the ability to transform the linear supply chain of a distribution warehouse into a circular supply chain by applying lean manufacturing principles to eliminate cardboard waste. A structured framework is presented to outline the project’s methodology and illustrate the steps taken to apply the concept of CE. The paper also tests the capability to simulate warehouse operations with engineering software using limited available data to generate various scenarios. This study contributes by showing how discrete-event simulation combined with VSM and 6R principles can provide operational insights under data-constrained conditions. Bridging the gap between theory and practice. Multiple operational scenarios were modelled and run, including peak and off-peak demand periods, as well as a sensitivity analysis for recycling durations. A comparative evaluation is shown to demonstrate the effectiveness of each alternative and determine the most feasible solution. Results indicate that introducing recycling activities created some bottlenecks in the system and reduced its efficiency. Furthermore, suggestions for future improvements are presented, ensuring that on-site actions are grounded in a simulation that reflects reality. Full article

This research investigates the optimization of operational efficiency and cost reduction through the enhancement of material flow management within production line stations. Departing from conventional static analyses, the study employs advanced simulation tools to pinpoint performance bottlenecks and inefficiencies via dynamic modelling techniques. The Ishikawa diagram serves as the primary tool for conducting root-cause analysis. Simultaneously, the 5S methodology is implemented to foster workplace organization, standardization, and hygiene practices. In contrast to traditional optimization frameworks, the proposed strategy integrates real-time performance tracking systems, complemented by adaptive feedback mechanisms. This integration permits ongoing assessment of the production process, facilitating iterative improvement cycles. Empirical data gathered from monitored cycle times, equipment utilization rates, and defect frequencies substantiate the validation of implemented changes. The resulting optimized system significantly minimizes downtime and waste, thereby advancing sustainable and scalable operations. Ultimately, this research demonstrates that the fusion of simulation-based insights with lean management principles leads to considerable improvements in manufacturing productivity and overall product quality. Full article