Search Results (4,201)

In this paper, we integrate liquidity risk into the generalized exchange option pricing model. Based on Esscher’s transformation theory, we calculate the expectation and variance of random variables to derive an explicit expression of the generalized exchange option pricing formula considering the liquidity risk of underlying assets, which avoids the complicated calculation of the optimal parameter vector ${\mathbf{h}}^{*}$. In addition, we plot time-series liquidity curves to screen underlying stocks that fit the model framework, and employ the simulated annealing algorithm to estimate model parameters based on historical data. Finally, numerical simulations are conducted with the estimated parameters and actual market data as inputs, confirming the internal consistency of the model. Relevant parameters are then adjusted to intuitively illustrate how liquidity risk affects option pricing and the associated price movements. Full article

Precise estimation of aboveground biomass in urban forests is crucial for quantifying urban carbon stocks and supporting climate change mitigation efforts. However, the availability of allometric equations tailored for urban trees is limited. Existing equations often rely on data from harvested trees with restricted sample sizes and small diameters, thereby introducing substantial uncertainty into biomass assessments. This study utilized terrestrial laser scanning (TLS) in conjunction with a leaf-wood separation algorithm and a tree quantitative structure model (TreeQSM) as a non-destructive approach to develop new species-specific allometric models for four predominant evergreen broadleaved tree species in the urban forests of Shanghai, based on 10 sample plots and 303 trees. The results showed that TLS-derived multivariate models, which incorporated diameter at breast height (DBH), tree height, and crown diameter, consistently outperformed models that only included DBH. Compared with the TLS-derived biomass, the previously published models showed varying degrees of deviation. Notably, there was a substantial overestimation for Camphora officinarum, with a bias of +31.5%. In contrast, Elaeocarpus decipiens, Ligustrum lucidum, and Magnolia grandiflora demonstrated smaller underestimations, with biases of −9.4%, −0.9%, and −4.5%, respectively. These discrepancies were primarily attributed to the extrapolation beyond the calibration diameter at DBH ranges of the published equations. These findings highlight the critical need for urban-specific models. Because destructive harvesting was not feasible in the urban environment, the TLS-derived biomass estimates were not validated against destructively measured biomass. The equations developed in this study provide improved tools for estimating urban forest biomass and carbon accounting for the four studied species under the sampled conditions in Shanghai. Full article

Drawing on Agency Theory, Institutional Theory, and the Diffusion of Innovation (DOI) framework, this study examines the relationship between mandatory adoption of the eXtensible Business Reporting Language (XBRL) and earnings management in an emerging market. Jordan introduced compulsory XBRL reporting for listed firms in 2020, providing a natural setting to evaluate its governance implications. The analysis is based on firm-level data for 40 industrial companies listed on the Amman Stock Exchange over 2016–2023 (320 firm-year observations). Accrual-based earnings management is measured by absolute discretionary accruals from the cross-sectional Modified Jones Model,. Firm fixed-effects regressions with firm-clustered standard errors, an event-study specification with year fixed effects, and an extensive robustness battery (performance-adjusted accruals, pooled estimation, balance-sheet accruals, exclusion of the pandemic years, and a placebo adoption date) consistently show no statistically detectable change in accrual-based earnings management after adoption. By contrast, absolute abnormal production costs increase significantly after the mandate, an effect that strengthens when the COVID-19 years are excluded and disappears under a placebo date, a pattern consistent with partial substitution from accrual-based towards real-activities manipulation. The findings suggest that digital reporting mandates alone do not discipline reporting behavior in environments with limited institutional enforcement and may redirect rather than reduce managerial opportunism. Implications for regulators, auditors, and standard setters are discussed. Full article

This study examines whether daily machine learning stock rankings based on technical information contain out-of-sample ordering information and whether that information can be converted into economically implementable returns. Using a dynamically screened Nasdaq source universe from 2021 to 2026, four XGBoost objectives are evaluated in a chronological walk-forward design. Test NDCG converges to 0.495–0.504, but permutation analysis places the corresponding random-ranking mean near 0.45, indicating statistically detectable but modest cross-sectional ordering information. Economic performance is substantially weaker. Under the execution convention implied by the next-day open-to-close target, every invested portfolio is bought at the open and liquidated at the close, so round-trip turnover equals two. Pseudo-Huber Top-1, treated as an ex-post concentration diagnostic, produces a 34.9% gross annual geometric return with 89.5% volatility and an 86.5% maximum drawdown; the Newey–West mean-return test is not significant (p = 0.106). At five basis points per trading leg, its zero-cash net CAGR falls to 4.8%; crediting idle capital with the daily risk-free rate raises total-return CAGR to 9.3%, but the excess-return inference is unchanged (p = 0.297). A matched-horizon regression on SPY open-to-close returns yields a statistically insignificant net annualized alpha (p = 0.436). Hansen’s SPA test across the synchronized 12-strategy family gives p = 0.207, and the Deflated Sharpe Ratio probability for Pseudo-Huber Top-1 is 0.462. The result is also highly time- and tail-dependent. The evidence therefore supports a distinction between statistically detectable ranking information and robust implementable abnormal performance rather than a persistent trading anomaly. Full article

Predicting the Remaining Useful Life (RUL) of railway wheels is challenging because wheel–rail degradation is cumulative, stochastic, and influenced by operating conditions. This study evaluates a multi-indicator prognostic framework using real in-service measurements acquired with a CALIPRI C42 optical profilometer (NextSense GmbH, Graz, Austria). The database comprises 80 wheels from ten vehicles of the same rolling-stock type, monitored during five monthly measurement campaigns, and includes flange width (Fw), flange height (Fh), and the flange-gradient dimension (qR). The Gamma process and first-passage formulation are established tools; the contribution of this work is their common application to all three indicators on the same in-service fleet and the benchmarking of long-horizon probabilistic results against an AR(1) short-term predictor embedded in the First-Passage Auto-Regressive (FP-AR) framework using the same dataset. Median Gamma-based RUL values were 21.2–22.0 months for Fw, 13.7–17.6 months for Fh, and 5.7–9.5 months for qR, with qR showing the largest relative percentile dispersion. For one-step prediction, the FP-AR benchmark achieved global MAE/RMSE values of approximately 0.368/0.502 mm for Fw and 0.0187/0.0216 mm for Fh; qR was more difficult to predict, with global MAE/RMSE values of approximately 0.575/0.991 mm. Under the adopted intervention thresholds, these results identify qR as the most variable and operationally constraining indicator under the studied Fès–Marrakech service conditions. The proposed dual-model analysis therefore provides a position-specific, uncertainty-aware basis for comparing wheel-profile degradation indicators, while its maintenance implications remain fleet- and route-specific pending validation in additional operating contexts. Full article

This study compared conventional aquaponics (AP), biofloc-based aquaponics (FLOCponics, FP), and hydroponics (HP) in terms of juvenile eel (Anguilla japonica) growth, ‘Caipira’ lettuce (Lactuca sativa) productivity, and water-quality characteristics. The AP and FP treatments each comprised three replicate fish tank–plant cultivation systems stocked with 119 eels per tank, whereas HP was included as a control for plant production. Eels reared in FP exhibited a higher estimated final biomass and specific growth rate and a lower feed conversion ratio than those reared in AP. The total and shoot biomass of lettuce were higher in FP than in AP and were comparable to those obtained in HP. Full article

A miniature label-free biosensor based on a chemically etched single-mode optical fiber (SMF) is reported for the detection of matrix metalloproteinase-8 (MMP-8), a salivary biomarker of active periodontitis with a clinical decision threshold of 20 ng/mL. Fibers etched in 48% hydrofluoric acid to a waist diameter of 16.0 ± 1.4 µm gave a mean refractive index (RI) sensitivity of 376.8%/RIU and an RI limit of detection (LOD) of 5.9 × 10⁻⁴ RIU. Fibers were tested with MMP-8 spiked into phosphate-buffered saline (PBS) and into saliva over 0–200 ng/mL using a post-rinse protocol with per-fiber matrix subtraction. Dose–responses followed a Langmuir isotherm (Kd = 7.1 ng/mL in PBS, 12.7 ng/mL in saliva), with cohort LODs of 0.043 and 0.52 ng/mL, both well below the threshold. MMP-9 (100 ng/mL) and human serum albumin (1 mg/mL) gave negligible responses (≤2.7%, versus 68.2% for MMP-8 at 100 ng/mL); antibody immobilization was confirmed by confocal immunofluorescence. A commercial sandwich ELISA on the same spike series gave a matched-matrix LOD of 41.1 ng/mL, nearly two orders of magnitude higher. This performance requires no metal coating, nanostructuring, label, or signal amplification, only a single wet-etching step on stock telecommunications fiber. Full article

Background: The oral application of adenovirus is hindered by its poor in vitro storage stability and rapid degradation by gastric acid. To address this, a biomimetic oral adenovirus delivery system (CSP-AdV@LYO) was constructed based on three key properties of natural chrysanthemum sporopollenin (CSP): chemical inertness, intelligent “acid-shrinking/alkali-swelling” responsiveness, and mucosal adhesion via its spike structures, aiming to enhance oral stability and delivery efficiency. Methods: First, low-allergenic chrysanthemum pollen was screened using proteomics and a zebrafish allergy model. High-purity sporopollenin (SPO) was then extracted via an acidolysis method, followed by systematic characterization of its morphology, particle size, zeta potential, contact angle, and reversible acid-shrinking/alkali-swelling behavior. Subsequently, a CSP-AdV@LYO formulation was prepared by optimizing a cryoprotectant formulation (sucrose:gelatin = 1:1) and a vacuum loading process. Its protective and release properties were evaluated in vitro using simulated gastric and intestinal fluids, and its long-term stability was assessed. Further in vivo studies in mice assessed its intestinal colonization efficiency. The adhesion mechanism of the sporopollenin spike structures was investigated through mucosal retention experiments. Results: Mucosal retention experiments confirmed that the spike structures on the sporopollenin surface enhanced retention by approximately 3-fold through mechanical interlocking compared to smooth particles. In long-term stability tests, the viral genome copy number retention rate was improved more than 10-fold compared to the virus stock solution. The system enabled a steady and controlled release of the virus in simulated intestinal fluid, with the released virus maintaining its infectivity. In vivo studies demonstrated that CSP-AdV@LYO promoted efficient intestinal colonization and reduced acute mortality from 75% (AdV@LYO group) to 25%. Conclusion: By leveraging the unique physicochemical properties of chrysanthemum sporopollenin, this study successfully developed a biomimetic oral delivery system for live adenovirus that provides gastric acid protection, intelligent pH-responsive release, and mucosal adhesion. This system significantly enhances the oral stability and intestinal delivery efficiency of adenovirus while reducing systemic exposure risks. It offers a novel biomimetic strategy for the oral delivery of adenovirus and other biological macromolecules. Full article

Return predictability on the Saudi Arabian Stock Exchange (Tadawul), the largest equity market in the Middle East, remains underexplored relative to its structural distinctiveness as an oil-linked, retail-dominated emerging market. We compare 11 predictive models spanning five linear (regularised) regressors, three tree-based ensembles, and three stacked hybrid architectures. This comparison quantifies the improvement that nonlinear and ensemble methods offer over linear benchmarks for daily return prediction in this setting and identifies which method delivers the best accuracy-versus-cost trade-off for practical deployment. Using 28,750 daily observations (January 2015–December 2025), we constructed a 40-feature technical signal space spanning six families and evaluated all 11 models under a strict chronological train–validate–test protocol with an 18-month sealed holdout. A Lasso–XGBoost stacked ensemble achieves the lowest test RMSE of 0.906 and an out-of-sample Information Coefficient of 0.133, outperforming linear benchmarks by 15–27% in forecast error. Translated into a long-short strategy subject to 0.6% round-trip transaction costs, the optimal model delivers a Sharpe ratio of 0.587, an 81.1% win rate and a maximum drawdown of $-5.53\%$ across 758 trades. Sensitivity analysis confirms robustness across hyperparameter grids and rolling estimation windows. Bollinger Band Width, cross-sectional stock identity and lagged MACD signals collectively dominate feature importance rankings. Full article

Rapid urbanization, increasing demand variability, and digitalisation of urban infrastructure require intelligent decision-support systems that can improve sustainability, resilience, and operational efficiency in smart city operations. Artificial intelligence (AI) and data-driven optimization offer strong potential for adaptive urban services, including dynamic pricing, demand response, resource allocation, and energy-aware management. However, many reinforcement learning applications still focus mainly on short-term performance while giving limited attention to transparency, fairness, stability, and accountability. This study proposes a trustworthy reinforcement learning framework for AI-driven urban decision-making, using sustainable dynamic pricing and resource optimization as mechanisms for adaptive and responsible decision-making. A custom reinforcement learning environment was developed using historical e-commerce transactional data as a methodological proxy to simulate interactions among demand, resource or inventory availability, service categories, price elasticity, and changing market conditions. Three reinforcement learning algorithms, namely Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic, were evaluated under comparable experimental conditions. Performance was assessed using profitability, decision stability, fairness-oriented pricing behavior, decision consistency, and interpretability. To improve transparency, trajectory-based policy audits and SHapley Additive exPlanations were applied to identify the main factors influencing pricing decisions. The results show that the Deep Q-Network agent achieved the most balanced performance, increasing total profit by 12.58% while recording no unethical price increases under low-demand conditions. Explainability analysis showed that stock or resource levels, demand shifts, and price elasticity were the strongest positive drivers of pricing actions, whereas inventory hoarding and unfavorable price increases reduced decision quality. The findings indicate that reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles. The proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations, including demand-responsive services, resource optimization, sustainable dynamic pricing, and energy-aware management. Full article

The population of Tenualosa toli is primarily distributed in the coastal and estuarine waters of Sarawak, Malaysia, where landings have declined steadily from 1031 tons in 2003 to 586 tons in 2013. To assess stock status and support sustainable management, samples were collected between May 2016 and October 2017. Length-based stock assessment methods, including the TropFishR package and the Length-Based Bayesian Biomass (LBB) model, were used to estimate growth, mortality, exploitation, and biomass indicators. Growth analysis yielded an asymptotic length (L_∞) of 57.81 cm, a growth coefficient (K) of 0.34 yr⁻¹, a growth performance index (φ′) of 3.09, and a theoretical age at zero length (t₀) of −0.40. Mortality estimates indicated total mortality (Z) of 1.82 yr⁻¹, with natural mortality (M) of 0.49 yr⁻¹ and fishing mortality (F) of 1.33 yr⁻¹. The exploitation rate (E) was estimated at 0.73, substantially higher than the optimal level (E = 0.50), indicating excessive fishing pressure. The length at first capture (Lc₅₀ = 19.16 cm) was considerably lower than the length at first maturity (Lm₅₀ = 31.91 cm), suggesting that most individuals are harvested before reaching maturity. The optimal length (Lopt) was estimated at 35.0 cm (L_opt/L_∞ = 0.67), while the observed mean length was much smaller (L_mean/L_opt = 0.73), indicating growth overfishing. Biomass indicators were critically low, with B/B₀ = 0.017 and B/BMSY = 0.046, confirming that the stock is severely depleted and far below sustainable levels, suggesting recruitment overfishing. Overall, the results indicate that the fishery is experiencing both growth and recruitment overfishing due to intense harvesting of immature individuals and high fishing pressure. Therefore, management measures such as increasing mesh size, reducing fishing mortality to sustainable levels (F = M), and protecting spawning grounds and peak spawning seasons are urgently required. If these measures are effectively implemented, the T. toli stock could recover and reach sustainable production levels in the waters of Sarawak, Malaysia. Full article

Stock movement prediction remains challenging because financial data are non-stationary and noisy. While attention mechanisms are widely used to enhance neural networks, how different attention integration strategies affect performance and training stability has not been systematically examined. We present a multi-seed empirical analysis of MLP-based models under three integration strategies—plain MLP, naively inserted self-attention, and residual attention—together with LSTM and Transformer baselines, evaluating 16 configurations on 10 CSI 300 and 10 S&P 500 stocks under a strictly forward-looking label, a purged chronological split, and early stopping: 1600 runs in total. Interleaved naive attention, in which self-attention output replaces the forward-path representation, collapses to degenerate single-class predictions in every run on both markets, whereas strategies that preserve the original representation collapse no more often than attention-free models; this difference is decisive after correction for multiple comparisons (Holm-adjusted p < 10⁻³). In contrast, no significant accuracy differences are detected among families, including the baselines, once collapsed runs are excluded, and unconditional balanced accuracy is within 0.01 across all non-degenerate families—consistent with the limited short-horizon predictability implied by market efficiency theory. A Jacobian analysis of the attention block, verified by training diagnostics, attributes the collapse to a rank-one attention matrix that compresses the forward representation to a single scalar unless the original representation is retained. A multi-seed analysis further shows that attention weight interpretations are not reproducible across random seeds and should be validated across repeated runs. These findings show that integration strategy and representation preservation—not the mere inclusion of attention—determine whether attention-augmented lightweight models train reliably. Full article

Municipal waste management is conceptualized as a circular energy supply chain in which waste flows through parallel recovery pathways. This study examines Istanbul’s waste-to-energy transition between 2020 and 2024 using portfolio decomposition, concentration indices (Herfindahl–Hirschman index, effective source count, Theil index), and a qualitative stock–flow interpretation of İSTAÇ facility-level data. Electricity generation rose from 457 GWh in 2020 to 1332 GWh in 2024; the Herfindahl–Hirschman index fell from 1.00 to 0.47, and the effective source count rose from 1.0 to 2.1. Energy-from-waste and biomethanization accounted for 73% of the increase, while landfill-gas recovery plateaued at 690–698 GWh, contributing the remaining 27%. The study introduces the concept of a self-limiting recovery path: a technology whose resource base is depleted jointly by physical decay and a policy-mediated reduction in its replenishment. Plant-level evidence at the largely closed Odayeri site is consistent with this mechanism, though it cannot rule out ordinary gas decay kinetics or a capacity ceiling as alternatives; the reading is offered as plausible rather than demonstrated. Unlike conventional lock-in pathways, a self-limiting pathway generates self-eroding feedback, suggesting landfill gas is best treated as a transitional bridge technology. The study contributes to path-dependency theory and circular energy supply-chain research. Full article

As environmental pressures intensify, sustaining ecologically and economically important marine species such as the American lobster has become an increasing challenge. Climate-driven stressors are expected to disproportionately affect early life stages of the American lobster (Homarus americanus), as sea surface temperatures are warming more rapidly than benthic habitats. In addition, environmental pollutants, including micro- and nanoplastics, trace metals, persistent organic pollutants, and per- and polyfluoroalkyl substances, can interact with climate change to exacerbate physiological stress. Previous studies have demonstrated that changes in temperature, salinity, and pH significantly influence development, moulting, cardiac function, survival, and disease susceptibility in H. americanus. In this context, aquaculture-based approaches are being explored as complementary tools to support stock resilience and reduce reliance on wild populations. Recirculating aquaculture systems (RAS) offer a controlled framework to investigate the combined effects of environmental stressors on H. americanus physiology while assessing the feasibility of closed-system rearing. Overall, this review synthesizes current knowledge on the physiological responses of H. americanus to multiple environmental stressors, with particular emphasis on identifying optimal rearing conditions in RAS. By integrating these findings, the review aims to inform strategies for improving the sustainability and long-term viability of American lobster production under changing environmental conditions. Full article

The seismic rehabilitation of existing buildings has become a major challenge in structural engineering due to the ageing of the global building stock, increasing seismic risk, and the growing demand for sustainable and resilient infrastructure. Although numerous retrofit techniques have been developed, most existing reviews classify solutions according to construction materials or individual technologies, providing limited guidance for selecting appropriate rehabilitation strategies in engineering practice. This review proposes a strategy-oriented framework that classifies seismic rehabilitation according to primary structural objectives rather than constituent materials. Five principal strategies are critically assessed: capacity enhancement, stiffness enhancement, ductility enhancement, seismic demand reduction, and hybrid rehabilitation. Their governing structural mechanisms, representative retrofit techniques, advantages, limitations, applicability, and sustainability implications are comparatively evaluated. Building on this classification, a decision-oriented framework is introduced that links structural typology, governing deficiencies, target performance objectives, rehabilitation strategies, and representative retrofit techniques, providing a structured basis for preliminary engineering decision-making before detailed structural design and verification. The review demonstrates that effective seismic rehabilitation depends primarily on the compatibility between the selected strategy and the characteristics and deficiencies of the existing structure rather than on any individual retrofit technique. It also highlights the growing importance of hybrid interventions, performance-based design, sustainability, functional recovery, and long-term resilience. By combining a comprehensive synthesis of current knowledge with a strategy-oriented classification and practical decision-support framework, this review provides a structured methodology for comparing and selecting rehabilitation strategies across diverse building typologies and project-specific constraints. Full article