Search Results (2,073)

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods suffer from suboptimal segmentation quality. To improve mural image segmentation, this study proposes an efficient mural image segmentation method termed Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating an adaptive learning strategy, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO). In ANBPO, First, to address PO’s limited global exploration capability, the adaptive learning strategy is introduced. By considering individual information disparities and learning behaviors, this strategy effectively enhances the algorithm’s global exploration, enabling a thorough search of the solution space. Second, to mitigate the imbalance between PO’s global exploration and local exploitation phases, the nonlinear factor is proposed. Leveraging its adaptability and nonlinear curve characteristics, this factor improves the algorithm’s ability to escape local optimal segmentation thresholds. Finally, to overcome PO’s inadequate local exploitation capability, the third-order Bernstein-guided strategy is introduced. By incorporating the weighted properties of third-order Bernstein polynomials, this strategy comprehensively evaluates individuals with diverse characteristics, thereby enhancing the precision of mural image segmentation. ANBPO was applied to segment twelve mural images. The results demonstrate that, compared to competing algorithms, ANBPO achieves a 91.6% win rate in fitness function values while outperforming them by 67.6%, 69.4%, and 69.7% in PSNR, SSIM, and FSIM metrics, respectively. These results confirm that the ANBPO algorithm can effectively segment mural images while preserving the original feature information. Thus, it can be regarded as an efficient mural image segmentation algorithm. Full article

Background: It is estimated that in Italy, there were 364,000 new diagnoses of neoplasms each year and that the overall incidence of blood cancers was 10% of these. Leukemia and lymphomas represented the ninth and eighth places, respectively, among the causes of death from neoplasia. Hematopoietic stem cell transplantation represented an effective treatment option for many of these malignancies, and not only that: benign and congenital diseases could also be treated. Objective: To assess knowledge among the Apulian population regarding stem cell donation and factors that could influence this choice, focusing especially on the knowledge of the residents of Puglia, Italy on how stem cells were harvested and their functions, their reasons for joining the National Registry, and the reasons that hold them back from making such a choice. Study Design: An observational and cross-sectional study was conducted, through snowball sampling methodology, until data saturation. An online survey was conducted, which included several Italian associations. The questionnaire administered contained five main sections, such as sociodemographic data, knowledge of the existence of National Registries and their adherence, the nationwide presence of various associations that promote donation, knowledge with respect to the structure, use and functions of stem cells, sources of procurement, such as bone marrow, peripheral blood and umbilical cord, and related procedures, beliefs, attitudes, values, and opinions of the Italian population regarding the topic, and degree of information and education regarding bone marrow donation. Results: A total of 567 Apulian citizens were enrolled. Of these, 75.3% were female and 96.8% were aged between 18 and 65 years. Most of participants were single (46.9%) and married (47.3%) and had a diploma (44.4%), and less had a degree (35.8%). Significant differences were recorded between gender, singles, and married participants, and participants with a diploma or a degree and the items proposed. Conclusions: A true culture of donation in our region was not clearly spread. Although something has been accomplished in recent years in terms of deceased donor donation, still a great deal needs to be achieved for living donation, which encountered a great deal of resistance. It has been deemed necessary to seek winning solutions to this issue in terms of communication and information campaigns, raising awareness and empowering citizens to express consciously their concerns about organs and tissues and to stand in solidarity with those who suffered. Full article

Reward processing is a vital function for health and survival and is impaired in various psychiatric and neurological disorders. Using a monetary gambling task, the current study aims to elucidate neural substrates in the reward network underlying the evaluation of win versus loss outcomes and their association with behavioral characteristics, such as impulsivity and task performance, and neuropsychological functioning. Functional MRI was recorded in thirty healthy, male community volunteers (mean age = 27.4 years) while they performed a monetary gambling task in which they bet with either 10 or 50 tokens and received feedback on whether they won or lost the bet amount. Results showed that a set of key brain structures in the reward network, including the putamen, caudate nucleus, superior and inferior parietal lobule, angular gyrus, and Rolandic operculum, had greater blood oxygenation level-dependent (BOLD) signals during win relative to loss trials, and the BOLD signals in most of these regions were highly correlated with one another. Furthermore, exploratory bivariate analyses between these reward-related regions and behavioral and neuropsychological domains showed significant correlations with moderate effect sizes, including (i) negative correlations between non-planning impulsivity and activations in the putamen and caudate regions, (ii) positive correlations between risky bets and right putamen activation, (iii) negative correlations between safer bets and right putamen activation, (iv) a negative correlation between short-term memory capacity and right putamen activity, and (v) a negative correlation between poor planning skills and left inferior occipital cortex activation. These findings contribute to our understanding of the neural underpinnings of monetary reward processing and their relationships to aspects of behavior and cognitive function. Future studies may confirm these findings with larger samples of healthy controls and extend these findings by investigating various clinical groups with impaired reward processing. Full article

The mosaic tesserae that are the topic of this study were found during an archeological excavation in a Roman villa at Aiano, in the municipality of San Gimignano, Tuscany (Italy). Many thousands of tesserae were found in the site in many different stratigraphic units (US). For this study, 392 tesserae mainly from three US (US 1095, US 5010 and US 5015 being the most consistent ones) were selected for non-invasive analyses. They might be tesserae coming from different places, collected to be reused or melted down to create new glass objects. The characterization of the tesserae, divided in various groups depending on their color, is an important tool in evaluating their compositional homogeneity/inhomogeneity. The presence of certain markers, such as the opacifiers based on Sb or Sn, could be helpful also in approximately dating the tesserae, since, as reported in the literature, various opacifiers were used in different periods. A well-established diagnostic protocol, based only on non-invasive techniques, allowed us to study a large number of tesserae, which certainly did not derive from a single mosaic and could have been of very different ages, compositions and origins. This procedure has proven to be a winning tool for this aim. Full article

Experimental research in the field of science and technology of polymeric materials and their hybrid organic-inorganic systems has been and will continue to be based on the execution of tests to establish robust structure-morphology-property-processing correlations. Although absolutely necessary, these tests are often time-consuming and require specific efforts; sometimes, they must be repeated to achieve a certain reproducibility and reliability. In this context, the introduction of methods like the Design of Experiments (DoEs) has made it possible to drastically reduce the number of experimental tests required for a complete characterization of a material system. However, this does not seem enough. Indeed, further improvements are being observed thanks to the introduction of a very recent approach based on the use of artificial intelligence (AI) through the exploitation of a “machine learning (ML)” strategy: this way, it is possible to “teach” AI how to use literature data already available (and even incomplete) for material systems similar to the one being explored to predict key parameters of this latter, minimizing the error while maximizing the reliability. This work aims to provide an overview of the current, new (and up-to-date) use of AI/ML strategies in the field of sol-gel-derived hybrid materials. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

Against the backdrop of the accelerated transition to sustainable development in global emerging markets, the synergistic mechanism between environmental governance innovation and corporate green transformation has become a key issue in realizing high-quality development. As the world’s largest emerging economy, China’s new Environmental Protection Law (EPL), implemented in 2015, has promoted green technology innovation and performance improvement of heavily polluting enterprises by strengthening environmental regulation. This paper takes Chinese A-share listed companies as samples from 2012–2023, treats the EPL as a quasi-natural experiment, and applies the DID method to explore the path of its impact on the performance of heavily polluting firms, with a focus on analyzing the mediating effect of green technological innovation and the moderating role of firm size and regional differences. The study revealed the following findings: the implementation of the EPL significantly improves the performance of heavily polluting enterprises, which verifies the applicability of “Porter’s hypothesis” in emerging markets; green technological innovation plays a partly intermediary role in the process of policy affecting enterprise performance, indicating that environmental regulation achieves win–win economic and environmental benefits by driving the innovation compensation mechanism; and there is significant heterogeneity in policy effects, with large-scale firms and firms in the eastern region experiencing more pronounced performance improvements, reflecting differences in resource endowments and institutional implementation strength within emerging markets. This study provides empirical evidence for emerging market countries to optimize their environmental governance policies and construct a “regulation–innovation–performance” synergistic mechanism, which will help green economic transformation and ecological civilization construction. Full article

It is well known that there is a significant conflict of interest between high-speed rail (HSR) operators and logistics companies. This study proposes an HSR freight pricing strategy based on a multi-objective optimization framework and a freight mode splitting model based on the Logit model. A utility function was constructed to quantify the comprehensive utility of different modes of transportation by integrating five key influencing factors: economy, speed, convenience, stability, and environmental sustainability. A bi-objective optimization model was developed to balance the cost of the logistics with the benefits of high-speed rail operators to achieve a win–win situation. The model is solved by the TOPSIS method, and its effectiveness is verified by the freight case of the Zhengzhou–Chongqing high-speed railway in China. The results of this study showed that (1) HSR has advantages in medium-distance freight transportation; (2) increasing government subsidies can help improve the market competitiveness of high-speed rail in freight transportation. This research provides theoretical foundations and methodological support for optimizing HSR freight pricing mechanisms and improving multimodal transport efficiency. Full article

The Relative Age Effect (RAE) refers to the advantage that relatively older athletes within the same age group may have in sports. While this phenomenon has been widely documented in numerous disciplines, its presence in orienteering remains largely unexplored. This study aimed to analyse the existence of RAE among Spanish orienteers selected for international competitions organised by the International Orienteering Federation (IOF) between 1987 and 2023. A total of 384 participations (225 male, 159 female) were examined across the European Youth Orienteering Championships (EYOC), Junior World Orienteering Championships (JWOC), and the European and World Orienteering Championships (EOC + WOC). The distribution of birth dates by quartiles and semesters was compared using chi-square tests, Cramér’s V, Z-tests, and odds ratios with 95% confidence intervals. The results revealed a significant RAE in male athletes, particularly in JWOC, where those born in the first quartile were up to 3.77 times more likely to be selected than those in the third quartile. In contrast, no significant associations were found in female athletes, which may reflect structural or developmental differences related to sex. These gender-based disparities highlight the importance of integrating sex-specific considerations into selection policies. Overall, the findings suggest a selection bias favouring relatively older males, which may hinder the development of late-born talent. Therefore, it is recommended that selection criteria be reassessed to ensure fairer and more inclusive talent identification and development in youth and elite orienteering. Full article

This study aimed to describe the training leading to the Zwift Academy (ZA) Finals of a world-class road cyclist who earned a professional contract after winning the contest. Four years of daily power meter data were analyzed (male, 25 years old, 68 kg, VO₂max: 85 mL·min⁻¹·kg⁻¹, and 20-min power: 6.37 W·kg⁻¹), focusing on load, volume, intensity, and strategies. Early training alternated between long, moderate-intensity sessions and shorter high-intensity sessions, with easy days in between. Gradually, the structure was progressively modified by increasing the duration of moderate-intensity (MIT) and high-intensity (HIT) and, subsequently, moving them to “high-volume days”, creating a sort of “all-in days” with low-intensity (LIT), MIT, and HIT. Moderate use of indoor training and a few double low-volume, low-intensity sessions were noted. These data provide a deep view of a 4-year preparation period of ZA, providing suggestions for talent identification and training, thereby highlighting the importance of gradual progression in MIT and HIT. Full article

This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. The dataset encompasses records of total medals by country, event categories, and athletes’ participation from the Olympic Games held between 1896 and 2024. We use K-means clustering to analyze medal trends, categorizing 234 nations into four groups (${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$, ${\alpha }_4$). Among these, ${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$ represent medal-winning countries, while ${\alpha }_4$ consists of non-medal-winning nations. For the $\alpha$1, $\alpha$2, and $\alpha$3 groups, 2–3 representative countries from each are selected for trend analysis, with the United States serving as a case study. This study extracts ten factors that may influence medal wins from the dataset, including participant data, the number of events, and medal growth rates. Factor analysis is used to reduce them into three principal components: Factor analysis condenses ten influencing factors into three principal components: the event scale factor (F1), the medal trend factor (F2), and the gender and athletic ability factor (F3). An ARIMA model predicts the factor coefficients for 2028 as 0.9539, 0.7999, and 0.2937, respectively. Four models (random forest, BP Neural Network, XGBoost, and SVM) are employed to predict medal outcomes, using historical data split into training and testing sets to compare their predictive performance. The research results show that XGBoost is the optimal medal predicted model, with the United States projected to win 57 gold medals and a total of 135 medals in 2028. For non-medal-winning countries (${\alpha }_4$), a three-layer fully connected neural network (FCNN) is constructed, achieving an accuracy of 85.5% during testing. Additionally, a formula to calculate the host effect and a Bayesian linear regression model to assess the impact of exceptional coaching on athletes’ medal performance are proposed. The overall trend of countries in the $\alpha$1 group is stable, but they are significantly affected by the host effect; the trend in the $\alpha$2 group shows an upward trend; the trend in the $\alpha$3 group depend on the athletes’ conditions and whether the events they excel in are included in that year’s Olympics. In the $\alpha$4 group, the probabilities of the United Arab Republic (UAR) and Mali (MLI) winning medals in the 2028 Olympic Games are 77.47% and 58.47%, respectively, and there are another four countries with probabilities exceeding 30%. For the eight most recent Olympic Games, the gain rate of the host effect is 74%. Great coaches can bring an average increase of 0.2 to 0.5 medals for each athlete. The proposed models, through an innovative integration of clustering, dimensionality reduction, and predictive algorithms, provide reliable forecasts and data-driven insights for optimizing national sports strategies. These contributions not only address the gap in predicting first-time medal wins for non-medal-winning nations but also offer guidance for policymakers and sports organizations, though they are constrained by assumptions of stable historical trends, minimal external disruptions, and the exclusion of unknown athletes. Full article

In this study, curdlan was investigated as a substitute for egg-white protein, and the effects of different concentrations (0.2%, 0.4%, 0.6%, 0.8%, and 1.0%) on lipid oxidation and the physicochemical properties of a novel low-salt surimi gel containing transglutaminase (TGase) and lysine were evaluated. The results indicated that adding appropriate curdlan concentrations (0.2%–0.4%, especially 0.4%) significantly inhibited lipid oxidation in the surimi gel, achieving the highest L* and whiteness values. The fracture strength, WHC, hardness, and chewiness of the gel increased by 23.87%, 6.70%, 32.80%, and 13.49%, respectively, compared to the control gel containing egg-white protein (p < 0.05). At 0.4% curdlan, the gel also enhanced the crosslinking within the surimi, improved its resistance to shear stress, significantly increased the G’ value, and shortened the T₂₁, T₂₂, and T₂₃ relaxation times, inhibiting the conversion of immobilized to free water in the gel and promoting a denser three-dimensional network structure. However, excessive curdlan amounts (0.6%–1.0%) led to a notable deterioration in the gel performance, causing a more irregular microstructure, the formation of larger cluster-like aggregates, and a negative effect on color. In conclusion, the combination of 0.4% curdlan with TGase and Lys is effective for preparing low-salt surimi products. Full article

Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article

With a low surface recombination velocity, it is possible to increase the efficiency of solar cells as the thickness is decreased. A maximum appearing in the efficiency versus thickness curve is mostly due to the same trend in the short-circuit current versus thickness curve. The trend of the short-circuit current versus thickness curve will be clearly discussed based on the view of competition between generation and recombination rates near the rear surface. If surface passivation can be well introduced, the win-win situation for the material cost and efficiency can be achieved based on our results. Full article