Search Results (38)

During manual operations, the human brain relies on mediated visual stimuli such as color to estimate an object’s weight and adjust muscle force through the central nervous system (CNS). This study examines the neuromuscular “reality gap” induced by the color–weight illusion (CWI) during repetitive lifting tasks in an augmented reality (AR) interface. We analyzed the median frequency (MDF) and Co-Contraction Index (CCI) of the biceps and triceps muscles to quantify physiological strain under varying luminance conditions in both AR and physical environments. The results reveal that AR significantly amplifies the CWI, with black stimuli triggering an aggressive joint-stiffening strategy in the AR group (APG). Compared with the physical reality group, the AR group showed lower overall endurance (91.4 ± 22.8 vs. 100.1 ± 12.5 repetitions) and a stronger physiological response to the black stimulus. In the AR group, the black condition was associated with a terminal CCI of 84.7 ± 25.4% and an MDF decline of approximately 21.7 Hz, whereas the corresponding contrast was attenuated in the physical reality group. These findings demonstrate a critical decoupling between behavioral output and internal physiological strain, indicating that the CNS treats virtual visual cues as high-reliability signals that increase metabolic “bio-cost” despite task completion parity. This research identifies a “masking effect” where behavioral metrics hide severe ergonomic risks, providing novel approaches for managing musculoskeletal health in industrial settings and personalizing coordination training in clinical rehabilitation. Full article

Background: Age-related differences in neuromuscular coordination during multi-joint tasks are reported, but phase-specific evidence during maximal sprinting is limited. Aim: The aim of this study was to investigate phase-specific age differences in agonist–antagonist coordination of the biarticular thigh muscles during 50 m sprinting. Methods: Thirty-eight healthy trained track athletes (Adults: n = 21, age = 23.32 ± 2.98 years; Adolescents: n = 17, age = 13.65 ± 0.76 years) performed maximal 50 m sprints over force plates. Bilateral rectus femoris (RF) and biceps femoris (BF) sEMG and ground reaction forces were recorded; each stride was segmented into seven phases, and an RF–BF co-contraction index (CCI) was calculated per phase. Between-group differences in phase mean CCI were tested (α = 0.05) and quantified with Hedges’ g. Speed- and frequency-dependent modulation of CCI was evaluated using linear mixed-effects models (LME; random intercepts for participant) with Frequency × Group and Speed × Group interaction terms; ordinary least squares (OLS) fits on stride cycle-level group means were descriptive. Linear and single-breakpoint segmented models were compared using the corrected Akaike information criterion (AICc) and Akaike weights. Results: Adolescents showed higher CCI in contact (right: Adults 0.09 ± 0.05 vs. Adolescents 0.13 ± 0.07, g = 0.68; left: Adults 0.08 ± 0.04 vs. Adolescents 0.12 ± 0.06, g = 0.84) and propulsive phases (right: Adults 0.08 ± 0.05 vs. Adolescents 0.13 ± 0.08, g = 0.68; left: Adults 0.07 ± 0.04 vs. Adolescents 0.12 ± 0.07, g = 0.84; p < 0.05 for both legs in both phases). LME identified Frequency × Group interactions in the stride cycle (ΔSlope = 0.10, p < 0.001) and late swing (ΔSlope = 0.12, p < 0.05) and a Speed × Group interaction in mid swing (ΔSlope = 0.01, p < 0.05). Mid swing showed a positive CCI–speed/frequency relationship in both groups, whereas across most other phases Adults downregulated CCI as speed/frequency increased while Adolescents tended to increase CCI. Model selection supported phase-dependent single-breakpoint patterns, with breakpoints around 2.19–2.21 Hz and 6.11–9.51 m·s⁻¹ in Adults and around 2.11 Hz and 7.13–7.59 m·s⁻¹ in Adolescents. Conclusions: Maximal sprinting revealed phase-specific age differences in BF–RF co-contraction and its scaling with speed/frequency, which may help guide age-informed monitoring and training considerations in developing athletes. Full article

For mine ventilation-on-demand (VOD) scenarios, conventional joint optimisation of airflow augmentation and energy saving in mine ventilation systems is often constrained in practical engineering applications by shrinkage of the feasible region, limited adjustable resistance margins, and strongly multi-modal objective functions. These factors tend to result in low solution efficiency, pronounced sensitivity to initial values and insufficient solution robustness. In response to these challenges, a two-layer intelligent evolutionary optimisation framework, termed ES–Hybrid JADE with Competitive Niching, is developed in this study. In the outer layer, four classes of evolutionary algorithms—CMAES, DE, ES, and GA—are comparatively assessed over 50 repeated test runs, with a combined ranking based on convergence speed and solution quality adopted as the evaluation metric. ES, with a rank_mean of 2.0, is ultimately selected as the global hyper-parameter self-adaptive regulator. In the inner layer, four algorithms—COBYLA, JADE, PSO and TPE—are compared. The results indicate that JADE achieves the best overall performance in terms of terminal objective value, multi-dimensional performance trade-offs and robustness across random seeds. Furthermore, all four inner-layer algorithms attain feasible solutions with a success rate of 1.0 under the prescribed constraints, thereby ensuring that the entire optimisation process remains within the feasible domain. The proposed framework is applied to an exhaust-type dual-fan ventilation system in a coal mine in Shaanxi Province as an engineering case study. By integrating GA-based automatic ventilation network drawing (longest-path/connected-path) with roadway sensitivity analysis and maximum resistance increment assessment, two solution schemes—direct optimisation and composite optimisation—are constructed and compared. The results show that, within the airflow augmentation interval [0.40, 0.55], the two schemes are essentially equivalent in terms of the optimal augmentation effect, whereas the computation time of the composite optimisation scheme is reduced significantly from approximately 29 min to about 13 s, and a set of multi-modal elite solutions can be provided to support dispatch and decision-making. Under global constraints, a maximum achievable airflow increment of approximately 0.66 m³·s⁻¹ is obtained for branch 10, and optimal dual-branch and triple-branch cooperative augmentation combinations, together with the corresponding power projections, are further derived. To the best of our knowledge, prior VOD airflow-augmentation studies have not combined feasibility-region contraction (via sensitivity- and resistance-margin gating) with a two-layer ES-tuned JADE optimiser equipped with Competitive Niching to output multiple feasible optima. This work provides new insight that the constrained airflow-augmentation problem is intrinsically multimodal, and that retaining multiple basins of attraction yields dispatch-ready elite solutions while achieving orders-of-magnitude runtime reduction through prediction-based constraints. The study demonstrates that the proposed two-layer intelligent evolutionary framework combines fast convergence with high solution stability under strict feasibility constraints, and can be employed as an engineering algorithmic core for energy-efficiency co-ordination in mine VOD control. Full article

Background: The anterior cruciate ligament (ACL) and medial collateral ligament (MCL) display strikingly different healing behaviors, despite their similar structural roles within the knee. The epiligament (EL)—a vascular and cellular envelope surrounding each ligament—has emerged as a critical determinant of repair capacity. The aim of this study was to perform a region-specific, comparative analysis of EL molecular profiles in the ACL and MCL to elucidate the mechanisms underlying their contrasting reparative outcomes. Methods: Human ACL and MCL specimens were obtained from 12 fresh knee joints. Immunohistochemical labeling for CD34, α-smooth muscle actin (α-SMA), and vascular endothelial growth factor (VEGF) was performed across proximal, mid-substance, and distal EL regions. Quantitative image analysis using IHC Profiler for ImageJ generated semiquantitative (negative, low-positive, positive) distributions, and inter-ligament comparisons were quantified using t-tests (p  <  0.05). Results: Distinct, region-specific EL signatures were identified. The ACL EL exhibited strong proximal α-SMA expression (0% neg/66.8% low+/33.2%+) and notable distal CD34 positivity (0% neg/83.3% low+/16.7%+), while VEGF expression was confined to the mid-substance (≈55% low+/26%+). In contrast, the MCL EL was largely negative for CD34 and VEGF across all regions, showing a homogeneous but functionally oriented α-SMA profile: proximally negative, sparse mid positivity, and high distal low-positive staining (93.4% low+). Differences in proximal and distal CD34 and α-SMA expression between the ACL and MCL were highly significant (p  <  0.0001–0.001), confirming a mechanistic divergence in EL organization. Conclusions: The ACL EL is regionally heterogeneous, vascularly biased, and enriched in contractile α-SMA+ cells, suggesting localized but poorly coordinated reparative potential. In contrast, the MCL EL is structurally uniform, with distributed α-SMA activity supporting stable wound contraction and tissue continuity, despite limited angiogenic signaling. These findings indicate that the ACL’s failure to heal is not attributable to the absence of progenitor or angiogenic factors, but rather to its fragmented spatial organization and dominant contractile phenotype. Therapeutically, preserving and modulating the EL, particularly its CD34+ and α-SMA+ compartments, could be key to enhancing intrinsic ACL repair and improving outcomes in ligament reconstruction and regeneration. Full article

Purpose: This study investigated how variations in approach speed and distance influence lower-limb muscle activation, joint co-contraction ratios (CCRs), and mechanical joint stiffness during single-leg approach run jump landings (ARJSL), to clarify adaptive neuromuscular strategies for joint stiffness regulation. Methods: Twenty-five physically active male university students performed ARJSLs under six randomized conditions combining two approach speeds (fast > 4.0 m/s; slow < 4.0 m/s) and three approach distances (3, 6, and 9 m). Surface electromyography (sEMG) from five dominant-limb muscles—rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and soleus—was analyzed across three movement phases: pre-activation, downward (braking), and push-off. Knee and ankle CCRs were computed, while kinematic and kinetic data were used to calculate mechanical joint stiffness via inverse dynamics. A two-way repeated-measures ANOVA evaluated the main and interaction effects of approach speed and distance. Results: Significant speed × distance interactions were observed for tibialis anterior activation, several CCRs, and eccentric ankle stiffness (p < 0.05). Pre-activation knee CCR increased with longer, faster approaches, indicating anticipatory joint pre-stiffening. During braking, greater ankle co-contraction under fast–9 m conditions coincided with reduced mechanical ankle stiffness, suggesting a compensatory yielding strategy under high kinetic loads. In the push-off phase, faster approaches elicited higher concentric stiffness at the hip and ankle, supporting efficient energy transfer. Rectus femoris and gastrocnemius activation scaled with both approach speed and distance. Conclusions: Athletes adapt neuromuscular co-contraction and mechanical stiffness in a coordinated, phase-dependent manner to balance protection and performance. These insights may inform targeted training strategies for enhancing jump efficiency and mitigating ACL injury risk. Full article

Stroke often leads to persistent upper limb dysfunction that impairs activities of daily living, yet objective biomechanical indicators for precise assessment remain limited. This study aimed to characterize phase-specific impairments in energy output, torque stability, and muscle coordination during the hand-to-mouth (HTM) task and to explore their potential for improving rehabilitation evaluation. Motion data from 20 stroke patients and 20 healthy controls were recorded using wearable surface electromyography and inertial measurement unit systems. A musculoskeletal model was applied to calculate joint torque, mechanical work, torque smoothness, and a novel torque-based co-contraction index across four movement subphases. These phase-specific metrics demonstrated significant correlations with clinical motor impairment scores, confirming their clinical validity. Significant dynamic features were then selected to construct machine learning models for group classification. Stroke patients showed reduced output capacity, increased torque fluctuations, and abnormal co-contraction patterns that varied across subphases. Among the classifiers, the quadratic support vector machine achieved the best performance, with an accuracy of 84.6% and an AUC of 0.853, surpassing models based on whole-task features. These findings demonstrate that phase-specific biomechanical features sensitively capture neuromuscular deficits in stroke survivors and highlight the potential of phase-specific biomechanics to inform future individualized rehabilitation assessment and treatment planning. Full article

Objective: Head-to-head evidence comparing closed-kinetic-chain (CKC) and open-kinetic-chain (OKC) training on balance in middle-aged women with knee osteoarthritis (KOA) is limited. Purpose: To compare 10-week hip abduction/external rotation-focused CKC versus OKC on static and dynamic balance. Methods: Twenty-two women with KOA were randomized to CKC (n = 11) or OKC (n = 11) and trained twice weekly for 10 weeks. The primary outcome was the center of pressure (COP) during single-leg stance (AP/ML range, excursion, velocity, and RMS); the secondary outcome was the Y-Balance Test (YBT) composite score. Results: CKC produced significant within-group reductions across all COP variables and significant YBT increases for both affected and unaffected limbs (p < 0.05). OKC showed only small changes in select COP indices and no meaningful change in YBT. Post-intervention between-group comparisons consistently favored CKC for AP/ML and total COP excursion/velocity and for the YBT composite score (p < 0.05). Conclusions: Under weight-bearing conditions, a hip-focused CKC program that promotes multi-joint coordination and co-contraction yields broader and more consistent improvements in postural stability and dynamic balance than OKC in middle-aged women with KOA. These findings support prioritizing CKC when rehabilitation aims include gait and functional stability. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

In the face of economic, social, and environmental pressures, the issue of sustainable development has garnered widespread attention in the Logistics Service Supply Chain (LSSC) with risk attitudes under Technical Output Uncertainty. In this regard, this paper first constructs an optimal emission reduction investment game model for an LSSC composed of Logistics Service Integrators (LSIs) and Logistics Service Providers (LSPs) against the backdrop of Technical Output Uncertainty. To this end, it quantifies the participants’ risk attitudes using a mean-variance model to analyze optimal emission reduction investment decisions for centralized and decentralized LSSC under different levels of risk tolerance. Subsequently, it designs a joint contract with altruistic preferences for sharing emission reduction costs in the LSSC. This contract analyzes the parameter constraints for achieving Pareto optimization within the supply chain. Finally, the study employs a case simulation to analyze the changes in expected revenues for centralized LSSC and joint contracts under different risk tolerance levels. The study reveals that (1) in a centralized LSSC, under risk-neutral attitudes, there exists a unique optimal emission reduction investment, which yields the highest expected return from emission reduction. However, under risk-averse attitudes, the expected return is always lower than the optimal expected return under risk neutrality. (2) In a decentralized LSSC, the emission reduction investment decisions of the Logistics Service Providers are similar to those in a centralized LSSC. (3) Under risk-neutral attitudes, the cost-sharing and altruistic preference-based joint contract can also coordinate the risk-averse LSSC under certain constraints, and by adjusting the cost-sharing and altruistic preference parameters, the expected returns can be reasonably allocated. Full article

The convergence of blockchain and metaverse technologies is poised to redefine how Global Value Chains (GVCs) create, capture, and distribute value, yet scholarly insight into their joint impact remains scattered. Addressing this gap, the present study aims to clarify where, how, and under what conditions blockchain-enabled transparency and metaverse-enabled immersion enhance GVC performance. A systematic literature review (SLR), conducted according to PRISMA 2020 guidelines, screened 300 articles from ABI Global, Business Source Premier, and Web of Science records, yielding 65 peer-reviewed articles for in-depth analysis. The corpus was coded thematically and mapped against three theoretical lenses: transaction cost theory, resource-based view, and network/ecosystem perspectives. Key findings reveal the following: 1. digital twins anchored in immersive platforms reduce planning cycles by up to 30% and enable real-time, cross-border supply chain reconfiguration; 2. tokenized assets, micro-transactions, and decentralized finance (DeFi) are spawning new revenue models but simultaneously shift tax triggers and compliance burdens; 3. cross-chain protocols are critical for scalable trust, yet regulatory fragmentation—exemplified by divergent EU, U.S., and APAC rules—creates non-trivial coordination costs; and 4. traditional IB theories require extension to account for digital-capability orchestration, emerging cost centers (licensing, reserve backing, data audits), and metaverse-driven network effects. Based on these insights, this study recommends that managers adopt phased licensing and geo-aware tax engines, embed region-specific compliance flags in smart-contract metadata, and pilot digital-twin initiatives in sandbox-friendly jurisdictions. Policymakers are urged to accelerate work on interoperability and reporting standards to prevent systemic bottlenecks. Finally, researchers should pursue multi-case and longitudinal studies measuring the financial and ESG outcomes of integrated blockchain–metaverse deployments. By synthesizing disparate streams and articulating a forward agenda, this review provides a conceptual bridge for international business scholarship and a practical roadmap for firms navigating the next wave of digital GVC transformation. Full article

Construction machinery operations are intrinsically linked to critical societal challenges, including safety risks and carbon emissions. In response to the high incidence of fatal accidents during installation and dismantling phases, the Chinese government has officially promoted integrated installation–dismantling services to enhance construction safety since 2023. However, the economic viability of this policy for leasing companies remains largely underexplored. To address this gap, this paper develops a leasing-oriented closed-loop construction machinery supply chain model that incorporates integrated installation–dismantling services under an industrial internet platform. The study first compares and analyzes the product leasing demand, installation and dismantling demand, and supply chain profits under both centralized and decentralized decision-making scenarios. Based on these analyses, a profit–cost sharing joint contract is designed to coordinate the supply chain. Furthermore, the interrelationships among key parameters are examined through a sensitivity analysis and numerical simulation. The results reveal that enhancing leasing information services increases both the demand for construction machinery and the platform’s operating costs. These costs are positively correlated with the product’s selling price, leading to higher purchasing costs for lessees. Similarly, improving information services for installation and dismantling raises the platform’s operating costs and enhances service levels, which in turn increases installation and dismantling costs for lessees. The findings demonstrate that within a certain range of cost-sharing and leasing-sharing proportional coefficients, the joint contract enables the supply chain to achieve Pareto optimization. This approach simultaneously alleviates economic pressure on lessees, improves construction safety, and promotes the integration of installation and dismantling services. Full article

Within the logistics service supply chain, where logistics service integrators and providers collaborate, the role of integrators in orchestrating resources and enhancing supply chain efficiency is pivotal. To motivate logistics service providers to invest in logistics capacity, thereby maintaining supply chain resilience and achieving coordination, a joint contract incorporating cost and benefit sharing is proposed in a novel Stackelberg game model. By analyzing the behavior of various decision-making models—centralized, coordinated, and decentralized—in supply chains, we find that the proposed Stackelberg game model effectively correlates higher investment levels with increased supply chain resilience. The coordination mechanism significantly improves both the level of resilience and the investment amount, leading to enhanced overall profitability. Sensitivity analysis reveals the impact of market pricing, demand stockout penalty costs, and substitute demand prices on these outcomes. This research contributes a new theoretical framework and practical insights into supply chain coordination and investment strategies. Full article

In recent years, the escalation in emergency occurrences has underscored the pressing need for expedient responses in delivering essential supplies. Efficient integration and precise allocation of emergency resources under joint government–enterprise stockpiling models are pivotal for enhancing emergency response effectiveness and minimizing economic repercussions. However, current research predominantly focuses on contract coordination and cost-sharing within these joint reserve modes, overlooking significant discrepancies in emergency supply classification standards between government and enterprise sectors, as well as the asymmetry in cross-sectoral and cross-regional supply information. This oversight critically impedes the timeliness and accuracy of emergency supply responses. In practice, manual judgment has been used to match the same materials under differing classification standards between government and enterprise reserves. Still, this approach is inefficient and prone to high error rates. To mitigate these challenges, this study proposes a methodology leveraging the BERT pre-trained language model and TextCNN neural network to establish a robust mapping relationship between these classification criteria. The approach involves abstracting textual representations of both taxonomical classes, generating comparable sentence vectors via average pooling, and calculating cosine similarity scores to facilitate precise classification mapping. Illustrated with China’s Classification and Coding of Emergency Supplies standards and Global Product Classification standards, empirical validation on annotated data demonstrates the BERT-TextCNN model’s exceptional accuracy of 98.22%, surpassing other neural network methodologies such as BERT-CNN, BERT-RNN, BERT-BiLSTM, etc. This underscores the potential of advanced neural network techniques in enhancing emergency supply management across diverse sectors and regions. Full article

Misreporting is prevalent in supply chain characterized by asymmetric information, and its impact on the supply chain is substantial and cannot be overlooked. In order to explore the impact of fresh supplier’s misreporting decisions on fresh supply chain, this paper takes the fresh supply chain with a single fresh supplier and a single e-commerce enterprise as the research object, and constructs five Stackelberg game models based on the differences of supply chain information transparency and power structure. Particularly, the effect of fresh-keeping level on the after-sales rate and market demand of the product is incorporated into the model, and the following conclusions are drawn by solving and analyzing the decision results of the different models: (1) When the supplier has the decision advantage, it will not choose to misreport. But when it loses the decision advantage, it will produce the misreporting behavior. Supplier misreporting is detrimental to the retailer and the supply chain; specifically, it can lead to lower fresh-keeping level and higher after-sales rates. (2) In the decentralized decision-making model, it is more beneficial for the supply chain that the supplier has the leading right of decision-making. In the absence of misreporting, the leader’s profit is always higher than that of the follower. When there is misreporting, even if the retailer is the dominant player, its profit is still lower than the manufacturer’s. (3) Both supply chain profit and fresh-keeping level are positively correlated with the coefficient of consumer perception of freshness and the coefficient of sensitivity to fresh-keeping technology, and are more significant under the centralized decision-making model. Furthermore, in response to supplier misreporting behavior, this paper achieves coordination in the fresh supply chain by designing a joint contract and confirms the effectiveness of this contract through an arithmetic analysis. Full article

To reduce the losses caused by insufficient preservation efforts during transportation, the preservation effort level has been the focus of research. In the fierce competition of online sales, it is particularly important to reduce the cost of damaged goods by improving the level of preservation efforts. Therefore, according to Stackelberg game theory, this article establishes five decision-making models and incorporates the damage rate and preservation effort level into the research. Finally, this article coordinates the online shipping supply chain (SC) through a joint contract. After comparing and analyzing the model results, research has found that: (1) in centralized model, the level of preservation effort reaches its optimal level and the system benefit is maximized; (2) under third-party logistics (TPL) leading decision-making, the different bearers of cargo damage costs will not affect the profits of both parties and the system; (3) among the four decentralized models, the level of preservation efforts and system profit are highest when the decision is led by online store and TPL bears the cost of damaged goods; and (4) under a given sharing ratio, when the logistics service quotation satisfies a certain range of condition, the online shopping SC can achieve Pareto improvement. This paper studies the differences and reasons for decision models in the supply and demand relationship between online stores and TPL, which provides fresh product e-commerce decision-makers with a theoretical basis. Full article