Search Results (342)

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

As the environmental sustainability of blockchain technology becomes a focal point of public and academic debate, understanding how technically engaged communities frame this issue is increasingly important. This study examines 3000 long-form comments from a highly active sustainability-focused Bitcointalk thread to analyze sentiment patterns, recurring arguments, and the linguistic cues associated with community responses to environmental criticism. Using Natural Language Processing (NLP) methods, we apply Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis to classify the discourse, n-gram extraction to identify dominant thematic expressions, and a Random Forest model combined with SHapley Additive exPlanations (SHAP) to interpret the lexical features most strongly associated with sentiment polarity. The results show a strongly positive and internally consistent discourse structure: 87.63% of comments are classified as positive, while negative and neutral comments are comparatively rare. The dominant themes emphasize energy consumption as a necessary trade-off for network security, while external criticism is frequently reframed or rejected. Explanatory modeling further indicates that negative sentiment is primarily driven by terms associated with climate risk, damage, and reputational concerns when users respond to criticism. Rather than claiming to capture the cryptocurrency ecosystem as a whole, this study presents a localized case study of one Bitcointalk mega-thread and describes it as a highly homogeneous narrative space shaped by recurrent rebuttal and rhetorical reinforcement. The findings offer a focused contribution to understanding how insider communities construct sustainability narratives around blockchain energy use, while also highlighting the need for broader comparative and network-structural research in future work. Full article

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

This study provides a comprehensive examination of the inflation-hedging potential of commodity indices and futures for U.S. investors using monthly data spanning July 1959 to December 2025 for 27 individual commodities, and January 1947 to November 2025 for 13 commodity indices. We employ multiple complementary methodologies, including optimal hedge ratios with Newey–West standard errors, asymmetric hedging analysis, long-horizon regressions, rolling window stability tests, Granger causality analysis, out-of-sample validation, and Markov-switching vector error correction models (MS-VECM). Our results reveal substantial heterogeneity in hedging effectiveness across commodity sectors. Energy commodities, particularly gasoline and crude oil, demonstrate the strongest inflation-hedging properties with higher hedge ratios and hedging effectiveness. Industrial metals, represented by copper, also provide reliable hedging with stable performance across market conditions. In contrast, precious metals, including gold and silver, show weak contemporaneous hedging ability despite their traditional safe-haven reputation, though they may offer protection during specific market regimes. Agricultural commodities and livestock exhibit minimal or negative hedging effectiveness. The MS-VECM analysis confirms that hedging relationships are time-varying, with effectiveness differing significantly between stable and turbulent market regimes. These findings have important implications for portfolio construction and risk management strategies. Full article

The biofuel supply chain is a complex value chain spanning from production to consumption. Manipulating information such as geographical origin, raw material type, and quantity at the production stage can disrupt refinery production plans and cause supply–demand imbalances. Therefore, a transparent traceability system is essential. The existing centralized database architecture poses a high risk of supply chain service suspension due to even a temporary fault in the central server, and it lacks resilience. Furthermore, it is vulnerable to data forgery, making it urgent to secure information integrity. To resolve these issues, this study proposes a blockchain-based biofuel supply chain information protection system. This system utilizes Shamir’s Secret Sharing algorithm to distribute data location information across all nodes and introduces the R-snowball consensus algorithm, which combines the reputation score of nodes with the random sampling of Snowball. The system aims to secure resilience in the event of a failure, achieve reputation-based security, and provide preliminary evidence of robustness against internal and external threats under the tested conditions. Experimental results demonstrated that the proposed system achieved an average recovery time of within 0.03 s, regardless of the load volume. Furthermore, preliminary evidence under the tested conditions suggests that the security and robustness of the system were supported through the exclusion of internal malicious nodes via a reputation-based penalty logic, the defense against main chain takeover attempts in external attack scenarios involving multiple fake nodes (Sybil nodes), and the maintenance of consistent consensus times. Full article

The purpose of this paper is to investigate the firm-level determinants of the cost of debt in a bank-based emerging economy, where debt serves as the primary external financing mechanism, enabling firms to maintain operations, pursue growth opportunities, and ensure long-term financial sustainability. Using panel data from non-financial firms listed on the Casablanca Stock Exchange over the period 2018–2024, we document a robust nonlinear relationship between financial leverage and the cost of debt, whereby low and moderate debt levels reduce borrowing costs by signaling creditworthiness and financing capacity, while excessive indebtedness reverses this effect, with an optimal threshold estimated at approximately 34.8% of total assets. Firms with stronger growth prospects further benefit from more favorable financing conditions, as creditors interpret sustained asset expansion as a signal of financial strength and long-term viability. Financial performance is also found to reduce the cost of debt, although this effect is not fully robust to endogeneity controls. In contrast, asset tangibility, firm size, firm age, and liquidity do not emerge as significant determinants, suggesting that creditors in the Moroccan market adopt a financial health-oriented approach when assessing credit risk, placing greater emphasis on leverage and growth prospects than on collateral-based or reputational signals. Overall, the study highlights the coexistence of linear and nonlinear dynamics in debt pricing, thereby enriching the corporate finance literature and providing insights for managers and policymakers seeking to reduce borrowing costs, enhance access to debt financing, and support sustainable value creation. Full article

The rapid proliferation of Narrowband Internet of Things (NB-IoT) devices necessitates robust, privacy-preserving intrusion detection systems. While Federated Learning (FL) mitigates data privacy risks through localized training, it introduces vulnerabilities to model poisoning and computational bottlenecks on edge devices. To address these challenges, we propose a secure, hardware-optimized Blockchain-Federated Learning (BC-FL) framework. Deploying a lightweight Hybrid CNN-RNN model on Edge Gateways, we relieve end-sensors of heavy computational tasks. To overcome the ‘cold-start’ problem, we introduce a Domain-Adaptive Transfer Learning strategy, dynamically adapting a pre-trained binary classifier to a multi-class task (Normal, Mirai, Bashlite). Furthermore, a lightweight blockchain ledger provides an immutable audit trail and a reputation-based isolation mechanism to penalize malicious nodes. Evaluated on the N-BaIoT dataset, the proposed 3-class CNN-RNN model achieves 95.62% overall accuracy, with precision/recall/F1-scores of 0.99/0.91/0.95 for Mirai and 0.93/0.99/0.96 for Bashlite attacks. The framework reduces communication bandwidth by 96% compared to centralized learning. During simulated Byzantine attacks, the reputation mechanism successfully banned malicious nodes, maintaining a robust 95.62% global accuracy. This framework offers a highly scalable, secure, and computationally feasible solution for real-time anomaly detection in resource-constrained IoT edge environments. Full article

Cryptocurrencies have become increasingly in demand in Ukraine’s wartime economy, yet little is known about how entrepreneurs perceive them in terms of trust, business use, and reputation. This study examines trust dynamics in cryptocurrencies among Ukrainian small-to-medium enterprise (SME) entrepreneurs under wartime conditions, exploring their association with business behavior, investment decisions, and reputational perceptions. The analysis is based on a survey of 561 Ukrainian entrepreneurs. The results show a statistically significant increase in trust in cryptocurrencies during the war. Higher trust is associated with more intensive operational use of cryptocurrencies and greater importance in investment portfolios. Entrepreneurs who associate cryptocurrencies with traditional liquid assets are more likely to assign them a stronger investment role. The use of cryptocurrencies affects both cryptoassets’ reputations and entrepreneurs’ business reputations. Greater engagement with cryptocurrencies is associated with a higher likelihood of viewing their use as a reputational advantage. However, overall assessments remain cautious due to regulatory uncertainty, financial risks, and potential involvement in tax evasion or speculative activities. Different perceived value propositions of cryptocurrencies are also linked to distinct behavioral strategies. Overall, the findings suggest that, in wartime Ukraine, trust in cryptocurrencies is shaped by their practical usefulness during periods of financial disruption and by their implications for entrepreneurs’ reputations. Full article

Formally stipulating rental terms in farmland transfer contracts is essential to safeguarding transacting parties’ rights, anchoring market price signals, and underpinning the rule-based governance of rural land markets. Drawing on survey data from 1496 rural households across three Chinese provinces, this study empirically examines how connection strength between transacting parties shapes the decision to formally stipulate rental terms in farmland transfer contracts. Baseline estimates show that greater connection strength is significantly and negatively associated with the probability of formal rental term stipulation, a pattern robust to alternative model specifications and variable operationalizations. Mechanism analysis reveals that stronger connections inhibit formal stipulation by concurrently heightening reputational constraints among parties suppressing demand for formal enforcement mechanisms and attenuating perceived transactional risk, which erodes the perceived value of the risk-bounding function that written clauses provide. Heterogeneity analysis further shows that this inhibitory effect is concentrated among ordinary farm household transfers and disappears among new-type agricultural business entities, where institutional rationality crowds out connection-based governance logic. Beyond its direct effect on contract formalization, greater connection strength indirectly undermines the price-anchoring function of written agreements, exposing realized rents to systematic connection-based discounting. These findings carry direct implications for the demand-side redesign of contract formalization policy and the development of county-level rental price guidance systems in rural China. Full article

Radon (222Rn) is a naturally occurring radioactive gas. It is colourless, odourless, and tasteless, produced through the spontaneous decay of uranium in soil and rocks. Among school-aged children, exposure to radon is a major public health concern because, during school hours, learners spend an average of 6–8 h daily inside school buildings, often on the ground floor or in basement classrooms, where radon levels tend to be highest. This study aims to contextualize radon exposure among children in educational settings, with a focus on the associated risk factors. A systematic review of the literature on radon exposure in classrooms among schoolchildren was conducted, analysing associated risk factors and methods of radon measurement. A literature search was performed across reputable databases to ensure compliance with systematic review standards. The quality of the evidence was appraised using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool. A total of 32 studies met the inclusion criteria and were analyzed. Radon levels measured in classrooms exhibit variability based on geographic location. Certain classrooms in Continental Europe and North America exceed the WHO reference limit of 100 Bq/m³, as well as regional thresholds, including the European Union limit of 300 Bq/m³ and the United States Environmental Protection Agency (EPA) limit of 148 Bq/m³. Indoor radon exposure in classrooms is a worldwide concern because children are particularly vulnerable during their formative years. Those attending daycare centers and kindergartens are at greater risk due to their nascent respiratory systems. Full article

Environmental, social, and governance (ESG) performance has become an important component of corporate sustainability and responsible governance, yet its economic implications remain contested, especially in state-owned enterprises (SOEs) that are expected to balance commercial goals with broader social responsibilities. This study examines the relationship between ESG performance and firm performance in Chinese listed SOEs, with particular attention to the mediating role of corporate reputation. The results show that ESG performance is positively associated with firm performance. Corporate reputation, risk-taking, and financial constraints are identified as important transmission channels through which ESG performance affects firm outcomes. Further analysis reveals a threshold effect in the ESG–performance relationship: when corporate reputation is relatively low, ESG investment may weaken firm performance; however, once reputation exceeds a critical threshold, ESG performance significantly improves firm performance. These findings enrich the literature on corporate sustainability and ESG value creation by showing that the performance effect of ESG is conditional on reputational capital. The study also provides practical implications for managers and policymakers seeking to promote sustainable corporate transformation in state-owned enterprises. Full article

Current research directed toward exploring the complexities of experiences within video gaming culture often comprises male-majority yet mixed-gender samples. Although valuable, these findings do not provide a male-representative overview of male gamers and risk diluting male gamer experiences as universal to all gamers, losing valuable gendered perspectives. In our study, we aimed to bridge this research gap by addressing: “What are the experiences of male gamers in online video gaming environments?” Through a qualitative, exploratory approach, underpinned by social constructionist epistemology, we conducted semi-structured interviews with 12 Australian adult male-identifying people who self-identified as online gamers (aged 18–36 years). Interviews were analysed through Reflexive Thematic Analysis, and findings present an overview of the complex social dynamics that shape male gamer experiences. Participants discussed experiences with toxicity online and frequently attributed problematic behaviour to characteristics they described as unrepresentative of male gamers broadly. They further described the sophisticated nature of online socialisation regarding the depth of bonds formed through gaming, which, at times, constitute larger online communities. These were navigated through a multitude of social criteria, revealing the underlying sociological structures that maintain dynamics within gaming environments. As such, broader concerns for the sociocultural status of men arose, particularly the problematisation of masculinity, which participants countered through identity management strategies aimed at restoring their reputation. Our findings highlight implications surrounding the importance of accounting for gendered meaning within gaming-based academic discourse and encourage public discourse surrounding problematic behaviour online to be redirected toward systems-level approaches. Full article

The persistence of greenwashing as a strategic corporate behavior reflects a financial tradeoff between risk and return. Current literature lacks an integrative framework explaining how these risks and institutional arrangements vary across distinct contexts. This study maps the intellectual structure and contextual heterogeneity of corporate greenwashing research through a bibliometric analysis of 818 publications indexed in the Web of Science Core Collection from 2000 to 2025. The results indicate an evolutionary shift in research focus from early ethical and reputational debates toward empirical investigations of capital market consequences, ESG controversies, and the dark side of corporate sustainability. This transition is accompanied by thematic movement from voluntary disclosure and legitimacy concerns toward mandatory compliance, sustainable finance, green bond pricing, and digital detection using artificial intelligence and natural language processing. The analysis reveals substantial structural heterogeneity. Heavy-asset industries are closely associated with technological decoupling under physical and compliance constraints, whereas financial and service sectors rely heavily on information asymmetry, green label arbitrage, and greenhushing. These sectoral patterns intersect with regional governance trajectories shaped by market-driven, regulation-oriented, and state-led contexts, generating distinct incentive structures and risk conditions, while firm-level governance further moderates these behaviors. The findings position greenwashing as a context-dependent corporate strategy and provide a structured synthesis for future research and differentiated regulatory responses. Full article

Organizations are currently in a stage where the volume of financial transactions and data is constantly growing. The same goes for risks associated with the use of data for risk management and strategic decision-making. The likelihood of transactional errors generally increases with data volume and process complexity, while fraud, although less frequent, may have more severe financial, compliance, and reputational consequences for organizations. Continuous auditing practices and well-established enterprise risk management (ERM) processes, combined with AI-driven pattern recognition, trend analysis and segmentation, can enhance timely detection and proper investigation of suspicious transactions. In areas with large volumes of transactions, the audit sampling process may be a lengthy process and pose a detection risk. Using machine learning (ML) models to support critical business processes could prove effective in managing enterprise risk overall. The current study offers new perspectives on managing risk and assurance with ML model output for flagging possible risky transactions within ERP (SAP) systems data. The study population consists of 69,158 finalized billing records extracted from the SAP production environment of a private sector organization, which covers a six-month operational period. The dataset was divided into an 80/20 train–test split, yielding 55,326 training and 13,832 test instances across six classification categories. The study examines the ML methods’ outcomes from billing datasets and their applicability in enhancing audit, assurance, and ERM processes by evaluating output data results from two supervised classification algorithms—multinomial logistic regression (SoftMax regression) and XGBoost—against various criteria generally accepted as risky in audit engagements. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and average precision (AP) from precision–recall curves. The results confirm that XGBoost achieves 99% overall accuracy with a macro F1-score of 0.965, outperforming logistic regression (macro F1 = 0.863), and that ML output allows early investigation and follow-up procedures to minimize the risk of fraud and errors and optimize risk management activities, thus strengthening internal control frameworks. Full article

Supply chain risk management has become a core element of corporate strategy, yet systematic evidence on how innovation information disclosure affects supply chain risk remains scarce. We study how innovation information disclosure in firms’ MD&A sections affects supply chain risk. Using data on Chinese A-share listed firms from 2012 to 2023, we find that firms disclosing more innovation-related content face significantly a lower supply chain risk. This result remains true following instrumental variable estimation, propensity score matching, entropy balancing, and controlling for province- and industry-specific time trends. We provide supportive evidence for three circumstances: firms that disclose more have a broader and more diverse set of supply chain partners; they engage in more joint patenting with partners, consistent with higher switching costs and more stable relationships; and they exhibit stronger reputations and commercial credit capacity, consistent with partnerships reinforced through both trust and financial ties. The effect is concentrated among non-SOEs, high-tech firms, firms in competitive industries, and firms outside the digital economy, all settings in which information asymmetry is more severe and alternative channels for conveying innovation capabilities are limited. We also document asymmetric vertical spillovers: downstream customers’ innovation disclosure prompts upstream suppliers to become more transparent, but the reverse does not hold. Supply chain risk, by contrast, affects connected firms in both directions. These findings extend the literature on the economic consequences of innovation disclosure from capital markets to supply chain management. Full article