Search Results (236)

This literature review provides an overview of the food safety and toxicological characteristics of various heavy metals and metalloids and the public health significance of their occurrence in food. Metals also occur as natural components of the environment, but they can enter food of animal origin and the human body primarily due to anthropogenic (industrial, agricultural, transport-related) activities. The persistent heavy metals (e.g., Hg, Pb, Cd) found in the environment are not biodegradable, can accumulate, and can enter the bodies of higher animals and subsequently, humans, where they are metabolized into various compounds with differing toxicity. Thus, due to their environmental contamination, they can accumulate in living organisms and their presence in the food chain is of great concern for human health. Regulations of the European Community in force lay down maximum levels for a limited number of metals, and the types of regulated foodstuffs of animal origin are also narrower than in the past, e.g., wild game animals and eggs are not included. The regulation of game meat (including offal) deserves consideration, given that it is in close interaction with the environmental condition of a given area and serves as indicator of it. Full article

The increasing number of natural and man-made disasters registered at the global level is causing a significant amount of damage. This represents one of the main sustainability challenges at the global level. The collapse of the Twin Towers, Hurricane Katrina, and the nuclear accident at the Fukushima power plant are some of the most representative disaster events that occurred at the beginning of the third millennium. These relevant disasters need an enhanced level of preparedness to reduce the gaps between the plan and its implementation. Among these actions, training and exercises play a relevant role because they increase the capability of planners, managers, and the people involved. By focusing on the exposure risk component, the general objective of the research is to obtain quantitative evaluations of the exercise’s contribution to risk reduction through evacuation. The paper aims to analyze serious games using a set of methods and models that simulate an urban risk reduction plan. In particular, the paper proposes a transparent framework that merges transport risk analysis (TRA) and transport system models (TSMs), developing serious game activities with the support of emerging information and communication technologies (e-ICT). Transparency is possible through the explicitation of reproducible analytical formulations and linked parameters. The core framework of serious games is constituted by a set of models that reproduce the effects of players’ choices, including planned actions of decisionmakers and travel users’ choices. The framework constitutes the prototype of a digital platform in a “non-stressful” context aimed at providing more insights about the effects of planned actions. The proposed framework is characterized by transparency, a feature that allows other analysts and planners to reproduce each risk scenario, by applying TRA and relative effects simulations in territorial contexts by means of TSMs and parameters updated by e-ICT. A basic experimentation is performed by using a game, presenting the main results of a prototype test based on a reproducible exercise. The prototype experiment demonstrates the efficacy of increasing preparedness levels and reducing exposure by designing and implementing a serious game. The paper’s methodology and results are useful for policymakers, emergency managers, and the community for increasing the preparedness level. Full article

This study takes the dual-channel fresh product supply chain involving the participation of third-party logistics (3PL) as the background to explore how 3PL makes choices between homogeneous and differentiated logistics service strategies and how the supply chain formulates optimal decisions under different logistics service strategies to achieve maximum benefits. This paper constructs a sequential game model of the three-tier supply chain composed of 3PL, a supplier, and a retailer; uses the consumer utility function to describe market demand; and considers different logistics service strategies adopted by 3PL. It compares and analyzes the equilibrium strategies under the traditional retail channel (O Model), the homogeneous cold-chain service dual-channel model (D1 Model), and the differentiated cold-chain service dual-channel model (D2 Model). The results show the following: (1) The D1 Model reduces the transportation cost of the supply chain through economies of scale. Under the D2 Model, the transportation and sales prices of the offline channels are higher than those of the online channels, while the online marketing effort is higher than that of the offline channels. (2) The profits generated by the dual-channel models (D1 Model and D2 Model) are both higher than those of O Model. In most cases, the D1 Model generates the highest system profit. However, in scenarios where consumers are highly sensitive to freshness and marketing efforts, the system profit of the D2 Model is higher than that of the D1 Model. (3) The supply chain has lower pricing and effort input when consumers are more sensitive to prices and higher pricing and effort input when consumers are more sensitive to freshness. These findings contribute valuable insights to the field of supply chain management, particularly in the context of fresh product supply chains involving 3PL. They underscore the importance of considering consumer behavior and logistics service strategies in optimizing supply chain performance and highlight the potential trade-offs between standardization and differentiation in logistics services. Full article

With the acceleration of the oilfield development process during the high water content period, the contradiction between the increase in energy consumption and the decrease in the energy efficiency of the gathering and transportation system has become increasingly obvious. This paper develops a grey relational analysis model using a combination of AHP and EWM. Based on the characteristics of light oil production, a four-level evaluation indicator system is developed. Based on game theory, AHP can provide subjective weights, the EWM can provide objective weights, and subjective and objective combinations are used for a more reasonable assignment. Concurrently, the 0.05 distinguishing coefficient and the ideal reference values are selected as the GRA reference sequence to evaluate the energy consumption of the gathering and transportation system as a whole and each subsystem. The analysis of a light oil block indicates significant room for improvement in the energy efficiency correlation across the system. Taking the central processing station as an example, the grey relational degree of electricity consumption per unit of injected water is measured at 0.12, marking it as the weakest link in the system. This study supports efficiency enhancement by identifying energy consumption bottlenecks within the system. Full article

With the increase in urban–rural disparities in China, rural public transportation systems in low-density areas face unique challenges, especially in the contexts of sparse population, complex topography, and uneven resource allocation; research on public transportation in low-density areas has had less attention compared to high-density urban areas. Therefore, how to solve the dilemma of public transportation service provision in low-density rural areas due to sparse population and long travel distances has become an urgent problem. In this paper, a dynamic optimization model based on a two-layer planning framework was constructed. The upper layer optimized the topology of multimodal transportation nodes through the Floyd shortest path algorithm to generate a composite network of trunk roads and feeder routes; the lower layer adopted an improved Logit discrete choice model, integrating the heterogeneous utility parameters, such as time cost, economic cost, and comfort, to simulate and realize the equilibrium allocation of stochastic users. It was found that the dynamic game mechanism based on the “path optimization–fairness measurement” can optimize the travel time, mode, route, and bus stop selection of rural residents. At the same time, the mechanism can realize the fair distribution of rural transportation network subjects (people–vehicles–roads). This provides a dynamic, multi-scenario macro policy reference basis for the optimization of a rural transportation network layout. Full article

Promoting carbon emission reduction in road freight transportation is important to achieve low-carbon development. The carbon trading mechanism is an effective market mechanism to promote carbon emission reduction. The digital and networked features of the online freight platform (OFP) service supply chain (SSC) not only help the platform reduce carbon emissions but also facilitate the government’s achievement of efficient and economic supervision of carbon emissions. Therefore, this paper proposes two types of carbon trading mechanism based on the OFP SSC to investigate the carbon emission reduction decision of the OFP, namely an absolute emission cap-based allocation (AC) model and an intensity-based allocation (IC) model. By using game theory, we then analyze the optimal solutions of the OFP SSC under the non-participation in carbon trading market (NC model), the AC model, and the IC model. By comparing these decisions, we explore the impact of the carbon trading mechanism on the OFP SSC. Results show the following: (1) Carbon trading mechanisms reduce OFP emissions, particularly under IC models with high free allowances. (2) High initial allowances and low service costs under the carbon trading mechanism enhance the OFP’s profit. (3) The carbon trading mechanism can reduce the carbon emissions of the road freight sector when initial allowances are sufficient or the off-platform trucker’s carbon emission coefficient is low. The study concludes that the IC model optimizes emission cuts while maintaining platform profitability. From a managerial perspective, the government should adopt dynamic allowance policies and incentivize the OFP’s participation through data integration. OFPs must balance network growth with low-carbon technology adoption to align commercial and environmental objectives. Full article

This paper proposes a two-stage stochastic non-cooperative game model to solve relief supplies procurement and distribution optimization of multiple rescue organizations in crisis rescue. Rescue organizations with limited budgets minimize rescue costs through relief supply procurement, storage, and transportation in an uncertain environment. Under a mild assumption, we establish the existence and uniqueness of the equilibrium point and derive the optimality conditions by using the duality theory, characterizing the saddle point in the Lagrange framework. The problem is further reformulated as a constraint system governed by Lagrange multipliers, and its optimality is characterized by the Karush–Kuhn–Tucker condition. The economic interpretation of the multipliers as shadow prices is elucidated. Numerical experiments verify the effectiveness of the model in cost optimization in crisis rescue scenarios. Full article

Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. Focusing on complex smart city scenarios—including intelligent transportation coordination and UAV swarm scheduling—we identify technical breakthroughs in MARL applications for policy space modeling and distributed decision optimization. By incorporating bio-inspired optimization approaches, the investigation particularly highlights evolutionary computation mechanisms for dynamic strategy generation in search planning, alongside population-based learning paradigms for enhancing exploration efficiency in policy refinement. The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems. Full article

Athletes engaged in dynamic sports experience a shortened red blood cell (RBC) lifespan and accelerated turnover due to RBC destruction. This accelerated RBC turnover might have a positive impact on exercise performance by increasing the number of young red blood cells with a high oxygen-carrying capacity. However, accelerated turnover might also be a result of intravascular haemolysis caused by RBC destruction during exercise, impairing RBC function and oxygen transport. Therefore, we aimed to evaluate the relationship between reticulocyte count as an indicator of short-term RBC profile changes and exercise capacity. We retrospectively evaluated elite Italian athletes engaged in endurance or mixed sports disciplines selected for the 2023 European Games or 2024 Olympic Games. Athletes underwent blood tests, echocardiography, and cardiopulmonary exercise tests. We assessed the relationship between reticulocytes and the peak value of VO₂ (peak VO₂) and anaerobic threshold (AT). In addition, the effects of age, sex, haemoglobin concentration, stroke volume, peak heart rate, and reticulocytes on peak VO₂ and AT were assessed using multiple linear regression. Of the 105 athletes, reticulocyte count (0.059 ± 0.024 × 10¹²/L) negatively correlated with peak VO₂ (45.5 ± 9.1 mL/min/kg) (p = 0.022) and AT (27.6 ± 7.9 mL/min/kg) (p = 0.040). Using multivariate linear regression analysis, reticulocytes were independent predictors of peak VO₂ and AT (95% confidence interval: −192.3 to −45.9; p = 0.001; 95% confidence interval: −143.4 to −13.8: p = 0.018, respectively). Our findings indicated a negative relationship between reticulocyte count and peak VO₂ or AT. The life span of reticulocytes was close to the period of transient decline in RBC function that occurred after high-intensity exercise; therefore, the changes in reticulocytes might be related to the decline in exercise performance owing to this decline in RBC function. Full article

In a multi-beam communication scenario where Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communications coexist, the limited spectrum of resources force V2V users to reuse the orthogonal frequency bands allocated to I2V, inevitably introducing cross-layer interference between I2V and V2V. Furthermore, the adoption of a multi-beam communication architecture exacerbates beam interference, significantly degrading the overall network’s communication and sensing performance. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) beam allocation algorithm, termed IMSBA, which jointly optimizes beam direction, transmission power, and spectrum resource allocation to effectively mitigate the interference between I2V and V2V while maximizing the overall network performance. Specifically, IMSBA employs a joint optimization framework combining Multi-Agent Proximal Policy Optimization (MAPPO) with a Stackelberg game. Within this framework, MAPPO leverages vehicle perception data to dynamically optimize V2V beam steering and frequency selection, while the Stackelberg game reduces computational complexity through hierarchical decision-making and optimizes the joint power allocation among V2V users. Additionally, the proposed scheme incorporates a V2V cooperative sensing domain-sharing mechanism to enhance system robustness under adverse conditions. The experimental results demonstrated that, compared with existing baseline schemes, IMSBA achieved a 92.5% improvement in V2V energy efficiency while significantly enhancing both communication and sensing performance. This study provides an efficient and practical solution for spectrum-constrained scenarios in millimeter-wave Internet-of-Things (IoT), offering substantial theoretical insights and practical value for the efficient operation of intelligent transportation system (ITSs). Full article

Ovotransferrin (OVT) is one of the major proteins of egg white and is known to bind and transport irons in animals. OVT exerts bacteriostatic and bactericidal activities due to its iron-binding capacity. OVT effectively controls the growth of various pathogenic microorganisms, including Pseudomonas, E. coli, Staphylococcus, Proteus, and Klebsiella species, as well as inhibiting the replication of viruses. OVT also has antioxidant, anti-inflammatory, anti-hypertensive, anticancer, and immuno-stimulating properties. For instances, OVT quenches free radicals, induces antioxidant enzymes, suppresses pro-inflammatory cytokines, increases immune cells, reduces angiotensin-converting enzymes, and inhibits the proliferation of cancer cells. In this review, the beneficial effects of OVT in both in vitro and in vivo, particularly livestock, are described. Because of its antimicrobial properties, OVT supplementation in livestock feed would be an excellent alternative to antibiotics, which reducing the development of antibiotic-resistant pathogenic microorganisms. Thus, OVT could be a game-changing protein for the growth, performance, and healthy life of animals. Full article

We investigate the application of the Shapley value in addressing risk-related challenges, focusing on two primary areas. The first area explores the role of the Shapley value in the financial sector, specifically in managing portfolio risk. By conceptualizing a portfolio of assets as a cooperative game, we analyze the contribution of individual securities to the reduction in overall portfolio risk. The second area addresses emergency facility logistics, where the Shapley value is utilized to optimize the selection of potential facility locations and mitigate the risks associated with the storage and transportation of hazardous materials. Using Markowitz’s mean-variance framework, the Shapley value facilitates a fair and efficient allocation of risk across portfolio assets, identifying both risk-increasing and risk-reducing assets. Through numerical experiments, we demonstrate that the Shapley value offers valuable insights into the equitable distribution of financial resources and the strategic placement of facilities to manage systemic risks. These findings highlight the practical advantages of integrating game-theoretic approaches into risk management strategies to enhance fairness, efficiency, and the robustness of decision-making processes. Full article

In the context of managing museums, historical, artistic, and archaeological heritage, an advanced decision support system (DSS) can serve as the engine for a business game platform, optimizing decision paths and management strategies. In complex, multi-parameter scenarios, the final decision is often only part of the process; it is equally essential to follow the decision-making path, that is, the sequence of actions necessary to reach the objective. The DSS presented here simplifies the problem by transforming the initial n-dimensional space, defined by the critical success factors (CSFs) selected by experts, into a two-dimensional space. Indeed, thanks to this approach, the computational complexity is reduced to the point that the technological solution can be used even on standard desktop computers and not only on high-performance computing systems. Moreover, the user does not necessarily need to be an IT expert but rather a specialist in the cultural domain. Through grid-based motion algorithms and a hierarchy of CSF priorities, the system quickly identifies optimal solutions in the 2D plane and then maps them back to the n-dimensional space to maintain consistency with the original context. Since the correspondence between n-dimensional micro-states and two-dimensional macro-states is not one-to-one, the DSS returns the specific micro-state of interest from the optimal macro-state, selecting the most effective path. This research aims to develop algorithms that by minimizing entropy and optimizing the system’s dynamics, build optimal paths in the 2D plane, with algorithms capable of restoring the solution in the initial space. Several use cases in the form of business games have been conducted, demonstrating the value of the proposed solution. The result of this work is a simulation environment useful for museum experts to analyze the impact of their management strategies. Thanks to the ability to assign weights to each of the critical success factors (CSFs), the system can display both qualitative and quantitative simulations of museum dynamics as the weights associated with different CSFs vary. Given the system’s generality, it is applicable to various fields where complex business games are required, such as cultural heritage management, logistics, transportation, healthcare systems, and, more broadly, any context where strategic business analysis is needed for the economic enhancement of resources and their optimization. Full article

As the core carrier of the low-carbon transportation transformation, the sustainable optimization of the supply chain of new energy vehicles is crucial to reduce carbon emissions throughout the life cycle and improve resource utilization efficiency. However, the current problems, such as resource waste, duplicate production, and low logistics efficiency caused by insufficient supply chain information coordination, have become bottlenecks restricting the green development of the industry. A large number of studies have shown that information collaboration plays a key role in reducing risks and costs, improving quality and innovation capabilities, adaptability, performance, and supply chain competitiveness in the new energy vehicle supply chain. Although the advantages of supply chain information collaboration are widely known, supply chain information collaboration has not been widely adopted in actual operation, and there are almost no studies on the lack of adoption or the restriction of the development of supply chain information collaboration. Based on the research methods of the modified Delphi technique and analytic hierarchy process (AHP), this paper finds that the lack of information quality, information security, and information collaboration motivation are important factors restricting the collaborative development of information in the new energy vehicle supply chain. Furthermore, an optimization model of the new energy vehicle supply chain information co-evolution game combined with traceability and blockchain technology is proposed, and it is found that the evolutionary game model that solves the stability of information quality and information security has a significant effect on the information collaborative optimization of the new energy vehicle supply chain. This study proposes an information co-evolution game model combined with blockchain traceability technology, which can improve the level of information collaboration in the supply chain of new energy vehicles, significantly reduce the “bullwhip effect” and redundant inventory in the supply chain, reduce energy waste and carbon emissions caused by information asymmetry, and improve the overall energy efficiency of the supply chain, so as to provide theoretical support for the sustainable and green supply chain transformation of the new energy vehicle industry. Full article

To enhance the security and supervision of emergency materials from their source to transport, thus preventing safety incidents, a tripartite evolution model integrating the government, material production enterprises, and logistics entities is proposed. This model, situated within the framework of a government-established blockchain platform, combines evolutionary game theory and prospect theory. It examines the strategy evolution of emergency material’s security subjects amid diverse government reward punishment measures and enterprise risk perceptions. Our findings indicate that employing dynamic reward–punishment measures not only conserves government regulatory resources but also incentivizes enterprises to enhance the quality of emergency materials security. This dynamic approach effectively encourages responsible behavior, averting rebellious tendencies or speculative mindsets in enterprises with deviating risk perceptions regarding emergency materials security. Full article