Search Results (19,419)

Fentanyl is a potent analgesic widely used in clinical practice. Fentanyl and its analogues are seriously abused and are emerging in the illegal drug market, leading to numerous intoxication cases. However, assessment of the potency of the pharmacological effect of these novel fentanyl analogues remains limited and inconsistent across studies. The development of novel analgesics has largely relied on the assessment of mu opioid receptor (MOR) binding affinity, with insufficient verification through the assessment of antinociceptive effects. This study evaluated the antinociceptive effects of 25 fentanyl analogues to investigate the relationship between chemical structure and antinociceptive effect. In this study, hot plate tests were conducted in mice to generate time–effect and dose–effect curves for the evaluation of the antinociceptive effect of fentanyl and its analogues. The results demonstrated that the antinociceptive effects of fentanyl analogues were dose- and time-dependent. The potency of the antinociceptive effect observed in this study generally aligned with the corresponding MOR binding affinities reported in the literature, although several analogues exhibited discrepancies. Structural modifications in different regions of the fentanyl scaffold affect the antinociceptive potency to different degrees, and the duration of action also varied across fentanyl analogues. Furthermore, opioid-induced hyperalgesia (OIH) was observed following administration of several fentanyl analogues, raising potential concerns regarding their abuse liability and development for analgesic purposes. Taken together, this study systematically evaluated and compared the antinociceptive effects of fentanyl analogues. The findings clarify the relationship between chemical structure and the antinociceptive effect, providing valuable insights for drug regulation and the development of novel analgesics. Full article

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches. Full article

Phytoecdysteroids have garnered increasing interest due to their broad biological and pharmacological properties. The present study reports on the development and validation of a reliable liquid chromatography–mass spectrometry method for the detection and quantification of 20-hydroxyecdysone, turkesterone, and ponasterone. The optimized procedure improved ionization efficiency and chromatographic resolution through gradient elution using 0.1% formic acid in water and acetonitrile. Data acquisition in selective ion monitoring modes ensured high analytical precision, reproducibility, and sensitivity. The method demonstrated excellent linearity, accuracy, repeatability, and low detection limits, making it suitable for routine phytochemical and quality control applications. Application of the method to extracts from nutrient-rich superfoods, including kaniwa, spinach, quinoa, and asparagus, confirmed these plants as natural sources of phytoecdysteroids. Additionally, thirteen commercially available dietary supplements labeled as containing extracts of Rhaponticum carthamoides, Cyanotis arachnoidea, Ajuga turkestanica, or ecdysteroids were analyzed. Several products standardized to 80–95% ecdysterone contained substantially lower amounts than declared, with measured 20-hydroxyecdysone levels ranging from below the limit of detection to approximately 50 mg per capsule, whereas some non-standardized products exhibited moderate to high levels, reaching up to approximately 105 mg per capsule. Variability in turkesterone content was also observed among products marketed as standardized extracts. The method provides a simple, reliable, and accessible approach for the quantitative analysis of major phytoecdysteroids in complex plant matrices and dietary supplements. Its implementation may support phytochemical research, routine quality control, and anti-doping monitoring of ecdysteroid-containing products. Full article

We present two models for incorporating the total effect of market friction noise into the dynamic pricing of assets and European options. The first model is developed under a continuous-time Black–Scholes–Merton framework. The second model is a discrete, binomial tree model developed as an extension of the static Grossman–Stiglitz model. Both models are market-complete and provide a unique equivalent martingale measure that establishes a unique map between parameters governing the risk-neutral and real-world price dynamics. We provide empirical examples to extract the coefficients of the model, in particular those coefficients characterizing the influence of the frictions on prices. In addition to isolating the impact of noise on the volatility, the discrete model enables us to extract the noise impact on the drift coefficient. We provide evidence for the primary market friction that we believe our empirical examples capture. Full article

How to effectively leverage the agency of smallholder farmers to boost rural incomes and achieve common prosperity is a core issue in agricultural development and transformation. Taking the flower industry in four regions of Yunnan Province as a case, this paper adopts an analytical framework of government-market-society synergy to examine industrial organizational models under different synergistic approaches and their impacts on industrial prosperity, smallholder agency, and income disparities. The comparative analysis reveals that combining appropriately open market mechanisms with moderate government intervention, while coordinating household-based operations through industry associations, currently represents an effective pathway to translate industrial prosperity into rural common prosperity. Full article

Background: Environmental, social, and governance (ESG) practices have evolved from regulatory requirements to strategic drivers of competitiveness and long-term value creation, particularly in road freight logistics, where environmental impacts, greenhouse gas emissions, labor relations, and stakeholder transparency are critical. Methods: This study identifies and systematizes ESG-related critical performance factors in road logistics by combining a systematic literature review with an analysis of sustainability reports from Brazilian road freight logistics companies. Academic findings and market practices were compared to support the development of an integrated ESG monitoring and assessment dashboard. Results: The findings reveal limited standardization in sustainability monitoring and control practices, with convergence observed around a restricted set of critical performance factors across companies. Conclusions: Based on these results, a unified theoretical dashboard integrating the three ESG dimensions into structured criteria and performance indicators is proposed. The model contributes to a more systematic assessment of ESG maturity and offers a theoretically grounded framework to support sustainability monitoring and managerial decision-making in road freight logistics. Full article

The agglomeration of the Unmanned Aerial Vehicle (UAV) industry is a key driver of the low-altitude economy. To understand how UAV industrial agglomeration emerges across cities with different socioeconomic foundations, this study investigates its dynamic configurational pathways. It develops an analytical framework that integrates the institutional environment, market conditions, and knowledge-based capabilities. Using panel data for 280 Chinese cities from 2017 to 2023, we apply panel data qualitative comparative analysis (QCA) to identify configurational pathways toward UAV industrial agglomeration. Seven socioeconomic conditions are considered: science and technology expenditure, policy support, infrastructure, social consumption level, financial development, urban innovation capacity, and human capital. The results show that UAV industrial agglomeration arises from the joint effects of multiple conditions, not from any single factor. We identify six pathways that are grouped into three archetypes: institution–knowledge-driven, institution–market-driven, and multidimensional synergistic configurations. The dominant pathways shift over time and differ across city sizes. These findings provide macro-level evidence on the mechanisms underpinning UAV industrial agglomeration. They also offer implications for strengthening the UAV industrial ecosystem. Full article

The agriculture sector plays a pivotal role in global economies, and optimizing its perishable food supply chain (PFSC) is vital to ensuring food security and transparency. The purpose of the study is to develop a blockchain-based smart contract to secure and provide transparency about perishable goods in the PFSC while delivering the goods between the stakeholders, such as farmers, mandis, and wholesalers. The study enhances collaboration between stakeholders by implementing smart contracts. The delivery status and the transactions have been safely recorded and verified by the stakeholder in the PFSC to ensure data integrity all the way through. The blockchain application has reduced fraud and streamlined the flow of goods and information. Moreover, this study emphasizes providing farmers with a straightforward route to the market to empower them. The benefits for the stakeholders are optimizing inventory control and developing appropriate decision-making skills. A three-echelon PFSC can become more resilient and is able to meet changing market demands by implementing blockchain-based smart contracts. Finally, the study employs blockchain technology to establish a decentralized and efficient PFSC, confirming a tamper-resistant system and enhancing stakeholder trust and collaboration. Full article

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

Despite widespread corporate endorsement of the Sustainable Development Goals (SDGs), systematic evidence on how top management in emerging economies prioritizes and frames SDG-related issues over time remains limited. Existing studies are often based on manual or single-year analyses, restricting comparability, scalability, and longitudinal insight. This study examines how corporate managerial communication aligns with and emphasizes SDGs across sectors and over time in two major emerging economies, India and South Africa. Using an AI-driven natural language processing (NLP) pipeline, we analyse 2400 annual reports from 600 publicly listed companies covering the period 2020–2023. A fine-tuned SDG-BERT multi-label classification model is applied to extract and classify SDG-related content from top management communications, enabling sectoral, temporal, and cross-country comparison of SDG relevance. The results reveal a strong and persistent emphasis on SDG 12 (Responsible Consumption and Production) across both countries, alongside sector-specific variation and differing patterns of SDG diversity over time. South African firms exhibit greater variation in SDG emphasis across years, while Indian firms display more concentrated and stable SDG framing. Overall, the findings highlight systematic imbalances in SDG-related managerial communication and persistent underrepresentation of several social SDGs. The study contributes methodologically by demonstrating the value of validated AI-assisted longitudinal text analysis for large-scale SDG research and empirically by providing comparative insights into how corporate SDG narratives evolve in emerging market contexts. Full article

The global shift toward electric vehicles (EVs) has accelerated as governments pursue low-carbon transport systems and sustainable mobility transitions. In emerging economies such as Thailand, however, consumer adoption remains influenced by a complex interplay of policy incentives, perceived benefits, and charging-related uncertainties. This study investigates the determinants of EV adoption intention by integrating ecosystem and policy support with perceived value and perceived risk within a unified analytical framework. Grounded in customer perception theory and technology adoption perspectives, this research addresses the fragmented treatment of these factors in prior studies. Data were collected from 400 respondents with prior EV experience and analyzed using structural equation modeling to examine both direct and mediated relationships. The findings reveal that ecosystem and policy support significantly strengthen adoption intention, primarily by enhancing perceived value and reducing perceived risk. These results highlight the pivotal role of perception-based mechanisms in translating policy initiatives into consumer commitment. The study suggests that effective EV promotion in Thailand and similar emerging markets requires coordinated ecosystem development, clear policy communication, and reliable charging infrastructure to sustain long-term adoption momentum. Full article

Energy security represents a fundamental pillar of economic sustainability, being defined as a state’s ability to ensure continuous, reliable, and affordable access to energy resources. In the context of recent geopolitical shifts, such as worldwide military conflicts, the vulnerabilities of energy systems have become evident, highlighting the interdependence between energy security and economic stability. Analyzing energy security involves assessing the diversification of sources, supply routes, critical infrastructure, and the degree of dependence on imports. The transition to renewable sources, in line with the objectives established by the European Union, contributes to reducing the risks associated with fossil market volatility and to strengthening economic resilience. At the same time, the integration of digital technologies and the development of storage capacities increase the flexibility of energy systems. Evaluating energy security must include indicators regarding price accessibility, environmental sustainability, and institutional capacity for crisis management. By aligning energy policies with macroeconomic and climate strategies, states can prevent major energy crises, mitigate the impact of external shocks, and ensure long-term sustainable economic development. The study highlights and brings to light Romania’s energy security situation by conducting an in-depth analysis of the Romanian Power System and assessing the most severe vulnerabilities and risks that could jeopardize the proper functioning of the system and the supply to electricity consumers. Based on these findings, various strategies for the safety, security, and resilience of the Romanian Power System have been developed. Full article

Basalt fiber-reinforced concrete (BFRC) exhibits superior mechanical performance, durability, and environmental benefits, making it a promising material for promoting green and low-carbon construction. This study develops a novel multi-objective mix design optimization method for BFRC under cost and carbon emission constraints, presents a framework that considers tensile strength (f_t) as a core design objective, and establishes high-precision strength prediction models via gene expression programming (GEP). Material cost and carbon emission functions were formulated based on market data, while compressive strength (f_c) and tensile strength (f_t) prediction models were established using using GEP implemented in MATLAB 2018b with seven mix design variables, including cement dosage, aggregate parameters, and basalt fiber (BF) characteristics (diameter, length, and dosage). Multiple constraints covering material quantities, mix ratios, workability, and density were incorporated into the optimization model, which was solved via the non-dominated sorting genetic algorithm II (NSGA-II). The method identifies the optimal cement dosage, aggregate proportions, and BF dosage to maximize tensile strength (f_t) while minimizing cost and carbon emissions. Computational results suggest that within the target strength range of 30–60 MPa, the proposed design yields reductions of 10–20% in carbon emissions and 12–18% in costs compared to conventional methods, offering potential advantages for sustainable construction. Unlike existing multi-objective studies, which focus on compressive strength, this work addresses critical factors of tensile strength (f_t) and prediction inaccuracy, proposing a systematic low-carbon design framework for potential BFRC applications. Full article

The global functional food market continues to expand, and edible mushrooms are emerging as high-value ingredients due to their rich nutritional profile, particularly their high protein content, balanced amino acid composition, and dietary fiber. This growing industrial interest is reflected in the registration of more than 322 patents in the past five years according to the Derwent Innovation patent database. Recent advances include the integration of precision mycology (PM) and omics-based approaches, such as CRISPR-Cas9, into solid-state fermentation and submerged fermentation, enabling improvements in natural umami flavor and bioactive composition. Innovative products, including meat analogues with fibrous textures, functional beverages such as kombucha and juices, and fermented dairy products such as yogurts and cheeses, have been formulated to deliver prebiotic, antioxidant, and immunomodulatory properties. Future trends indicate a shift towards the production of high-value nutraceutical peptides and biomass, together with the adoption of artificial intelligence (AI) and the Internet of Things (IoT) to enhance bioreactor automation and scalability. Nevertheless, significant challenges remain, including regulatory constraints, the scarcity of clinical validation in humans, and the need for strict control over the bioaccumulation of heavy metals in mushroom-derived raw materials. Addressing these gaps will be critical for advancing regulatory frameworks, improving industrial standardization, and supporting the translational development of mushroom-based functional foods. Full article

This paper examines the impact of public debt servicing on private investment in Egypt within the debt overhang hypothesis. While existing research largely focuses on the debt–growth relationship, limited attention has been given to how debt servicing burdens affect private capital formation. Using annual data from 1990 to 2023, the study employs an Autoregressive Distributed Lag (ARDL) model to estimate short-run and long-run dynamics between private sector gross fixed capital formation and key mac-roeconomic variables. Results provide statistically significant evidence of a long-run debt overhang effect, whereby debt servicing exerts a persistent negative impact on private investment. Short-run effects appear temporarily expansionary but dissipate as servicing pressures accumulate. The analysis focuses on Egypt-where debt servicing pressures have repeatedly intensified in response to external shocks and exchange rate adjustments-but offers broader implications for emerging market and developing economies. The paper contributes to the literature by identifying repayment capacity as the key transmission channel through which public debt affects private investment. In contexts characterized by liquidity constraints, external vulnerabilities, and refinancing risks, debt servicing burdens-rather than debt levels alone-constitute the binding con-straint on private capital formation. Accordingly, the findings emphasize the im-portance of assessing debt sustainability through servicing obligations and repayment pressures. Full article