Search Results (2,629)

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

Accurate day-ahead wind speed forecasting is essential for wind power forecasting and electricity market participation under increasing renewable energy penetration. This study proposes a hybrid forecasting framework that combines raw global forecasts from GFS and IFS, the KMA KIM-RDAPS regional forecast, and dynamically downscaled GFS/IFS forecasts generated with alternative boundary-layer physics. Seven forecast members were synthesized using arithmetic averaging, performance-weighted averaging, and LightGBM-based machine learning (ML) regression. The framework was evaluated over Jeju Island, Republic of Korea, using 10 m Automatic Weather Station observations from 2023 to 2024 and 80 m meteorological mast observations from 2023. For the AWS evaluation, 2023 was used for training and validation, and 2024 was reserved for independent testing. The site-specific LightGBM synthesis achieved the most consistent improvement, reducing the median site-wise MAE across 31 AWS sites to 0.90 m s⁻¹, corresponding to a 39.2% improvement relative to the best non-downscaled member and 47.2% relative to the unweighted multi-model mean. In the 80 m mast-based diagnostic assessment, the same approach reduced derived normalized power MAE to 11.4%. These results indicate that ML synthesis of multi-source NWP forecasts can improve day-ahead wind speed and power-oriented forecast information over complex island terrain. Full article

Frankincense is a natural oleo-gum resin obtained from several Boswellia tree species, playing important roles in supporting the spiritual, cultural, and socioeconomic livelihoods of communities across East Africa. Despite their cultural and economic value, the Ethiopian market still lacks scientifically based criteria to evaluate and properly classify this raw material, with traditional grading relying on gum size, color, collection area, and impurity content. Frankincense-derived essential oil value is much higher than that of gum, making this valorization route very enticing. This work compares the extraction potential and chemical profiles of hydrodistilled essential oils from various commercial grades and also different Ethiopian harvest locations (Afar, Humera, Assosa, Shire, Metema, South Omo, Borena and Jigjiga). The essential oils were extracted using hydrodistillation with a Clevenger-type apparatus, and their chemical composition was identified with GC-MS. The results revealed no substantial quantitative and qualitative differences among commercial grades, showing that essential oils can be obtained indiscriminately from classification. As for harvesting locations, both the extraction yield and essential oil compositions varied substantially. With the economic value of frankincense essential oil around six times that of the raw resin required to obtain it, these results show the importance of revising the commercial grading system to reflect chemical composition and promote the value-added processing of both black and white frankincense, rather than relying mainly on raw resin exports. Full article

Corporate sustainability and the reliability of ESG reporting have gained relevance in the evaluation of listed companies, particularly in emerging capital markets, where reporting practices are still in their early stages of development. The purpose of this study is to analyze the relationship between the quality of ESG reporting, the risk of greenwashing estimated using a proxy derived from reported information, and the market value of companies listed on the Bucharest Stock Exchange. The research employs a mixed-methods design, involving content analysis of annual reports, sustainability reports, and sustainability statements for 25 companies over the 2020–2024 period. The scores corresponding to the Environmental, Social, and Governance dimensions, as well as the proxy for greenwashing risk, were developed using an ordinal scoring grid, which was validated through inter-rater assessment. During the course of the study, the empirical relationships were tested using pooled OLS specifications on short panel data, incorporating the natural logarithm of market capitalization, financial controls, year effects, and sector dummy variables. The results highlight the presence of an association between the quality of ESG reporting and market value, particularly for environmental and social dimensions, while the greenwashing risk proxy exhibits a limited statistical influence. The study contributes to the literature on ESG reporting in emerging markets and highlights the need for a cautious interpretation of indicators constructed based on corporate disclosures. Full article

Higher-education institutions increasingly use AI-enabled chatbots, personalised communication, recommendation systems, and predictive information services in academic marketing. Adoption of these systems depends not only on technical availability, but also on institutional trust, emotional engagement, and skepticism regarding the reliability, transparency, and autonomy implications of AI. This study examines the Trust-Tech Nexus framework using stakeholder survey data collected at MIT Art, Design and Technology University, Pune, India (N = 300). The analysis combines psychometric validation, WLSMV confirmatory factor analysis for ordered indicators, and cross-validated predictive modelling. Four three-item constructs were measured with five-point Likert indicators, as follows: AI Adoption, Institutional Trust, Emotional Engagement, and AI Skepticism. Reliability and convergent validity were acceptable, and the WLSMV CFA showed strong practical fit (CFI = 0.991, TLI = 0.988, RMSEA = 0.040, SRMR = 0.039). Discriminant validity was supported by HTMT and Fornell–Larcker evidence, while Harman’s single-factor result was treated only as an initial diagnostic. Construct-only ridge regression produced positive out-of-sample predictive evidence (CV R-squared = 0.352; RMSE = 0.642; MAE = 0.501). Exploratory classification results were moderate and are interpreted only as supplementary segmentation evidence because the binary targets were derived from the AI Adoption composite. The study supports a validated four-construct measurement structure and moderate predictive association in one institutional context, while avoiding causal claims. Full article

Although it is generally accepted that material characteristics influence the sensing and locomotion of autonomous mobile robots (AMRs), this knowledge is mostly anecdotal and remains fragmented. This study aims to shed light on the relationship between building finishing materials and AMR performance. To address the lack of literature on the subject, this exploratory mixed-methods review combines an AMR market survey, collection of failure cases, and review of robot navigation mechanisms. As a result, with additional expert assessment, this study derived a relational diagram containing five primary relationships for sensing (i.e., color on obstacle detection, texture and transparency on obstacle detection and mapping accuracy) and five for locomotion (i.e., slipperiness and unevenness on speed and path consistency, wheel-mark resistance on surface preservation). Potential research themes (e.g., sensitivity by robot specifications, BIM-based information utilization, and robot-specific signage systems) were also derived by thematic analysis. By establishing a foundational research framework that clarifies how architectural material choices dictate robotic reliability, this review contributes to designing experimental scenarios for future empirical validations in robot-inclusive spaces. Full article

Standard cryptocurrency transaction cost models assume flat geometry and assign execution cost as a proportional fee. This paper proposes GEODEX, a framework that models execution slippage as the geodesic arc length on the Fisher information manifold of a Markov-switching GARCH maximum-entropy model, augmented by a joint curvature–topological fragmentation alarm. The Curvature-Fragmentation Law (Proposition 2) is an analytically derived heuristic. Its empirical validity is confirmed across four crisis episodes. Ablation confirms that each geometric component contributes uniquely: removing the geodesic increases mean squared prediction error by 2.9%, removing topological data analysis by 2.1%, and removing curvature by 1.5%. On five cryptocurrency markets (BTC, ETH, XRP, LTC, and BCH), over 2253 daily observations, the framework achieves competitive prediction error and is the only single-signal model retained in the Model Confidence Set at $\alpha =0.10$ against eight benchmarks. A joint curvature–topological alarm fires a median of two days before price-based circuit breaker thresholds across four crisis episodes, including the Terra collapse (May 2022) and FTX bankruptcy (November 2022). Online inference requires under one second; full offline calibration requires approximately 28 h. The framework requires no additional data beyond the upstream estimation pipeline and supports SDG 10 (Reduced Inequalities) and SDG 16 (Strong Institutions) by enabling accessible geometric liquidity intelligence for regulators and smaller market participants. Full article

Even though the functional food market has rapidly increased in recent years, the links between bioactive-rich formulations and consumers’ health benefit are not fully established, mainly because of insufficient consideration of food matrix effects. This review provides a comprehensive and integrated evaluation of how food matrix properties (structural and physicochemical) affect the bioaccessibility of plant bioactive compounds. Unlike many reviews that focus on a single nutrient approach, we highlight quantitative evidence of how bioaccessibility can be affected by matrix properties, illustrating the interactions between main food components (lipids, proteins, dietary fiber and minerals). This review integrates fragmented information among different areas of food and nutrition sciences, i.e., food structure, gastrointestinal science, mineral chemistry, protein chemistry, providing a holistic framework for Quality by Design (QbD) functional food development. Synergisms and antagonistic behaviors, threshold effects, and concentration-dependent behaviors are analyzed comparatively for the most common plant-derived bioactives, such as polyphenols, carotenoids, curcuminoids and minerals (iron, zinc and calcium). We propose a matrix-informed optimization as a prerequisite for credible health claims and sustainable plant-based nutrition strategies. This can ultimately serve as a foundation for next-generation functional food development based on bioaccessibility, supporting the central argument that functional food development should move from composition-based fortification to bioaccessibility-based matrix engineering. Full article

To make electric mobility possible and acceptable on a large scale, it is necessary to integrate electric vehicle (EV) charging infrastructure in residential energy systems. Solar surplus charging, a special case of controlled charging, is a popular and promising operating mode of installed systems. Comparison of different home energy management systems (HEMSs) in combination with a dedicated EV charging station reveals differences in control quality. Within the research project Wallbox-Inspektion, a test setup has been developed. The derived procedures determine the main criteria for evaluating the quality of solar surplus charging. The core question is: “How well does the EV charging power follow the reference?”. This contribution explains the tests for standby consumption and control quality of control steps and presents an approach to determine the impact on use case scenarios. Further, different solar charging systems (i.e., charging station, HEMS, energy meter) available on the market are compared and discussed regarding the quality of implemented solar charging strategies. Full article

Marine biological invasions are rapidly reshaping Mediterranean ecosystems, with growing consequences for biodiversity and fisheries. This study investigates recent changes in the composition of commercially important crustacean assemblages along the south-eastern coast of Sicily (central Mediterranean), focusing on penaeid shrimps (Penaeus aztecus and Penaeus kerathurus) and stomatopods (Erugosquilla massavensis and Squilla mantis). Field surveys were conducted during the fishing seasons of 2021 and 2025 at major landing sites and markets (Portopalo di Capo Passero, Syracuse and Catania), using standardized subsampling protocols applied to catches obtained by trammel nets and bottom trawls. Species composition was quantified through repeated sampling events, and temporal differences were analyzed using non-parametric tests and binomial generalized linear models, incorporating year and fishing gear as explanatory variables. Quantitative data were complemented by local ecological knowledge derived from structured interviews with professional fishers. Across the four-year interval, both taxonomic groups exhibited a pronounced shift in species dominance. The proportion of the invasive shrimp P. aztecus increased from approximately 20% in 2021 to over 80% in 2025, while the invasive stomatopod E. massavensis rose from about 2% to nearly 90% of total landings. These changes were statistically significant and independent of fishing gear. Fishers’ perceptions closely mirrored the quantitative trends, confirming the rapid replacement of native species by non-indigenous taxa and highlighting emerging socio-economic implications for local fisheries. Our findings document a rapid shift in the composition of commercial crustacean landings in Sicilian coastal waters, with invasive species becoming the dominant component of catches within a few years. This study underscores the need for adaptive fisheries management and integrated monitoring frameworks capable of responding to accelerating biological invasions in Mediterranean marine ecosystems. Full article

Sustainable fresh food supply chains depend heavily on effective coordination under uncertain market conditions. Citrus export systems are particularly sensitive to perishability, seasonality, and cold-chain constraints. Under these conditions, reliable price forecasts are important for export planning, logistics efficiency, and sustainable supply-chain management. This study examines the relationship between orange and mandarin export prices in Türkiye using monthly data from 2016 to 2025. Export prices are proxied by real unit values derived from official trade statistics. The analysis applies Augmented Dickey–Fuller tests, Johansen cointegration analysis, and Vector Error Correction Models (VECMs). Forecast performance is evaluated using a rolling-origin framework and compared with ETS, SARIMA, XGBoost, and a seasonal naïve benchmark. The results identify one cointegrating relationship between the two export markets. The estimated long-run coefficient is 0.92, indicating near one-to-one price co-movement. Adjustment toward equilibrium is asymmetric, with orange prices responding faster (ECT = −0.44) than mandarin prices (ECT = −0.21). Forecasting results show that VECM-based models outperform all alternative specifications. The robust VECM achieves the lowest forecast errors (MAPE = 8.3%), compared with 9.8% for XGBoost, 10.6% for SARIMA, 11.5% for ETS, and 14.1% for the seasonal naïve benchmark. Diebold–Mariano tests confirm that these improvements are statistically significant. The findings indicate that orange and mandarin export prices should be analyzed jointly rather than separately. In closely connected citrus supply chains, cointegration-based forecasting models provide more reliable forecasts and a stronger analytical basis for sustainable market coordination. Full article

Regulatory risk measurement under Basel III’s Fundamental Review of the Trading Book places Expected Shortfall (ES) at the center of market risk capital, yet the fourth-order Edgeworth expansion, still widely used for Value-at-Risk (VaR) and ES calculations, can produce negative densities in the tail regions where these measures concentrate, while saddlepoint approximations preserve positivity but face their own limits in heavy-tailed and sub-Gaussian settings. Whether either method delivers reliable tail estimates in the rare-disaster regimes documented in the empirical consumption-disaster literature therefore remains an open question. We address it by comparing the two approximations across 648 rare-disaster parameter combinations and five additional distributional families (Student-t, Hansen skewed-t, generalised error distribution (GED), two-sided jump mixture, and generalised hyperbolic), and by deriving a closed-form characterisation of the Edgeworth validity envelope. We establish three core findings. First, the validity envelope is bounded above by a sharp kurtosis ceiling at ${\gamma }_2=4$ and laterally by a non-monotone skewness boundary peaking at $|{\gamma }_1^{\ast ,\max }| \approx 0.685$ at ${\gamma }_2\approx 2.533$; $87.5\%$ of the rare-disaster grid falls outside it. Second, accuracy is threshold-dependent: Edgeworth dominates at moderate quantiles, saddlepoint at extreme quantiles, with negative-density regions inflating Edgeworth ES error from $6.20\%$ inside the envelope to $47.04\%$ outside it. Third, these results reconcile only when point probability, density validity, and integrated-tail accuracy are treated as distinct accuracy criteria. The findings have direct implications for ES-based regulatory capital in heavy-tailed regimes and motivate a regime-conditional rather than universal approximation choice. Full article

We compare classical and modern regression models for next-day cryptocurrency forecasting on 14 USD-denominated coins across three liquidity tiers from 2018 through 2025, and we use the resulting panel to formally test three pre-specified hypotheses. The features are a strictly past-only 28-element set; the evaluation uses expanding-window walk-forward cross-validation with nested hyperparameter tuning, stationary block-bootstrap 95% confidence intervals, and pairwise Diebold–Mariano tests. Methodologically, we derive a bias-variance bound that turns the ‘no model beats the mean’ observation from a null finding into a predicted outcome under weak-form market efficiency. Empirically, (H1) the threshold–effect interaction is not supported (slope −1.7 × 10⁻⁴, 95% CI [−4.8 × 10⁻⁴, +1.4 × 10⁻⁴], p = 0.25). (H2) Statistical loss minimisation is decoupled from risk-adjusted economic outcome: the cluster-bootstrapped 95% CI for the Spearman rank correlation between the within-ticker MAE rank and within-ticker post-cost Sharpe rank is [−0.39, +0.10] overall, lies *strictly below zero* on the mid-cap (CI [−0.71, −0.04]) and long-tail (CI [−0.26, −0.09]) tiers, and decisively rejects perfect alignment (ρ = +1) on every tier. None of the seven (ticker, model) pairs with annualised Sharpe ≥ 0.5 has a hit rate significantly different from 0.5; high-Sharpe outcomes reflect return skew, not directional skill—formally predicted by a closed-form Sharpe–MSE decoupling proposition we derive in Section 3.6 under non-zero return skewness. (H3) Lo–MacKinlay variance ratio tests show top-tier coins are indistinguishable from a random walk (|z| ≤ 1.5 at q ∈ {2, 5, 10}), while mid- and long-tail tiers reject the random-walk null at q = 2 (z = −2.36, z = −2.60). The findings extend across two robustness layers. An AR(1)-GARCH(1,1) baseline produces R² ≈ −0.005 on every tier and is indistinguishable from Lasso, supporting the bias-variance bound; Giacomini–White conditional predictive ability tests reject equal predictive ability between Lasso and tree-based models on every coin in every tier, complicating naive DM interpretations; and a forward-walking 2026-Q1 holdout—83 daily observations per coin entirely outside the training window—confirms that H1 is even more decisively null on unseen data and that the H3 efficiency conclusion holds. Together, these results give a formally tested EMH-style picture for daily crypto: no model meaningfully forecasts log-returns; statistical accuracy and trading P&L are decoupled by an analytically derived mechanism; and weak-form efficiency is approximately satisfied in most liquid coins and in the convergence across the cross-section. Full article

Item-to-group recommendation identifies the most compatible user groups for a specific item provider to enable precision marketing, such as recommending fruit products to the most receptive consumer communities. Existing graph-based recommendations typically treat social relationships as static binary links, failing to capture variations in interaction intensity driven by user preferences. Moreover, these models largely overlook the structural relevance of intra-group connections, leading to unreliable group representations. To address these challenges, we propose the Preference-Weighted Neighbor-Aware Group Recommendation Network (PNGRN). Specifically, social edges are first reweighted using preference signals derived from historical user–item rating interactions, thereby suppressing socially connected but preference-inconsistent neighbors during aggregation. Structurally cohesive candidate groups are then identified via k-core decomposition, retaining only subgraphs where every member has at least k internal connections. A neighbor-aware graph convolutional network (GCN) module is further introduced to incorporate external social neighborhood features into group representations. This ensures that the learned group profiles reflect both internal structural stability and the external social context. Experiments on three real-world datasets demonstrate that PNGRN consistently outperforms competitive baselines across all evaluation metrics. Notably, on the MovieLens-1M dataset, PNGRN achieves up to a 9.85% improvement in Precision@20 and a 8.98% gain in NDCG@20. These results validate the necessity of coupling topological density with external social influence, and this work offer a scalable framework for precision group-targeted marketing. Full article

Secure data collaboration among mutually distrustful organizations requires more than encrypted storage: it also needs accountable ownership control, auditable access governance, privacy-preserving transaction execution, and reliable settlement when data are exchanged as digital assets. This paper proposes TrustTrade, a unified multi-party secure data management and transaction framework designed for cross-organization data sharing, trading, and compliance-sensitive analytics. TrustTrade integrates policy-bound data capsules, a tamper-evident provenance ledger, adaptive threshold escrow, verifiable data-payment settlement, and selective audit with revocation rebinding. On four real-dataset-derived workloads, TrustTrade reaches a $90.4$–$94.8\%$ settlement rate, with a $92.5\%$ average that is $6.4$ percentage points higher than the strongest baseline average. Under adversarial request injection, TrustTrade reduces unauthorized release to $0.31\%$ and atomicity violation to $0.38\%$, corresponding to $93.6\%$ and $93.0\%$ reductions compared with Plain-Market, respectively; compared with Fixed-Escrow, unauthorized release is reduced by $77.4\%$. TrustTrade also achieves $96.7\%$ dispute-resolution accuracy while maintaining practical settlement latency. These results indicate that jointly designing secure data management and secure data transaction protocols offers a practical path toward trustworthy multi-party data ecosystems. Full article