Search Results (1,029)

Trade-finance instruments may support export performance by alleviating financing constraints and reducing international payment risks. This study examines the short- and long-run relationships between Turkish Eximbank instruments and Turkey’s aggregate exports using annual data for 2001–2024. Three nested log-linear Autoregressive Distributed Lag (ARDL) models jointly incorporate short-term credits, medium- and long-term credits, insured shipment values and the real effective exchange rate. Intermediate-goods imports and external demand are added in the second model, while tariff and Russia–Ukraine war-period controls are introduced in the third. ADF, PP and KPSS tests examine the integration properties of the continuous variables and ARDL bounds tests evaluate the existence of long-run relationships. The bounds-test results support a relationship with levels in all three specifications, while the negative and significant error-correction coefficients indicate convergence toward equilibrium. Insured shipments have a positive and statistically significant short-run coefficient in every model and a positive long-run coefficient in the benchmark model. However, their long-run significance disappears after imported-input conditions and external demand are controlled for. Neither credit category exhibits a statistically significant relationship with exports. Intermediate-goods imports are positively associated with exports in both horizons, while external demand has a positive long-run relationship. The REER generally has the expected negative sign, but its significance is specification dependent. The findings identify insurance as the strongest short-run correlate of exports while showing that its long-run aggregate relationship is sensitive to macroeconomic conditions. Recipient-level data are required to establish causal program effects. Full article

Marine cross-medium acoustic–radio collaborative networks must route traffic across heterogeneous underwater acoustic and radio links while coping with energy imbalance, congestion, and mobility-induced link instability. This paper proposes Q-Learning AODV, an AODV extension that integrates distributed Q-value updating and multipath route maintenance into existing RREQ, RREP, and HELLO procedures. The routing reward combines normalized residual energy, queue availability, inter-node distance/link stability, relative velocity, and, for air–sea links, elevation-angle information. The protocol maintains multiple node-disjoint candidate paths and forwards data through the currently highest-valued path. NS-3 simulations are reported for underwater-to-underwater, underwater-to-air, and air-to-underwater communication scenarios. Relative to conventional AODV, Q-Learning AODV increases packet delivery ratio from 55.8% to 88.3%, from 68.3% to 76.1%, and from 86.9% to 91.9%, corresponding to relative improvements of 58.2%, 11.4%, and 5.8%, respectively. The results indicate improved delivery reliability and communication-subsystem energy balancing at the cost of additional state exchange, Q-table storage, and route-selection computation. Full article

Proton exchange membrane fuel cell (PEMFC) durability under dynamic operation remains a major challenge because the observed voltage decay may combine persistent and transient performance changes. This study presents a condition-aware and data-driven analysis of PEMFC degradation under a dynamic fuel cell load cycle (FC-DLC). A public single-cell PEMFC dataset was reconstructed into 3076 dynamic cycles over 1008.24 h of operation and complemented with polarization curves measured directly after dynamic operation and after 12 h of shutdown rest. Load-resolved voltage indicators, polarization descriptors, direct-to-after-rest difference metrics, hysteresis indices, and uncertainty-evaluated short-horizon forecasting models were developed. The dynamic analysis showed that voltage degradation was strongly current-dependent, with the early-to-late voltage drop increasing from 23.09 mV at 0 A to more than 76 mV at the highest current levels. Over the common 100–1000 h comparison window, maximum power decreased by 9.31% in the direct condition and by 12.00% in the after-rest condition, whereas the voltage–current area decreased by 11.79% and 10.40%, respectively. Therefore, the after-rest temporal losses were not uniformly smaller and depended on the selected indicator and current region. The comparison between direct and after-rest curves revealed persistent after-rest minus direct voltage differences of 30–45 mV in medium- and high-current regions even after 1000 h. A sensitivity analysis showed that the voltage-cleaning threshold had no measurable effect on the reported dynamic indicators. For high-load voltage forecasting, Ridge regression achieved RMSE values of 0.00936 V and 0.01134 V at 50- and 100-cycle horizons, improving upon the persistence baseline by 29.7% and 23.5%, respectively. These error reductions were statistically significant, although the corresponding $R^2$ values remained negative on the late-life temporal holdout. The ablation analysis further showed that the complete feature set was not systematically optimal, and the best-performing feature group depended on the target and forecasting horizon. Nominal 90% conformal coverage was adequate at 50 cycles (91.25%) but decreased to 55.03% at 100 cycles, indicating loss of calibration under the longer temporal horizon. Overall, the proposed framework integrates load-dependent voltage-loss characterization, direct and after-rest measurement conditions, feature-group ablation, persistence benchmarking, and uncertainty evaluation without assigning the observed measurement-condition difference to a unique reversible or irreversible mechanism or claiming a validated remaining-useful-life or maintenance-decision system. Full article

Classical swine fever (CSF) is a highly contagious transboundary animal disease that causes major economic losses to the global swine industry and remains notifiable to the World Organisation for Animal Health (WOAH). Handling and transport of infectious classical swine fever virus (CSFV)-positive samples require biosafety level 3 (BSL-3) containment, limiting diagnostic capacity and interlaboratory exchange in many regions. In this study, we evaluated the efficacy of PrimeStore^® Molecular Transport Medium (PS-MTM) for eliminating detectable CSFV infectivity while preserving viral ribonucleic acid (RNA) detectability for downstream molecular detection. Validation assays were performed using the CSFV Alfort/187 reference strain and clinical samples collected from pigs experimentally infected with the Catalonia01 and Margarita CSFV strains. Residual infectivity was assessed by virus isolation, whereas viral RNA detection and stability were evaluated using the WOAH-recommended real-time reverse transcription quantitative polymerase chain reaction(RT-qPCR) assay. Following PS-MTM treatment, no residual infectivity was detected in either cell culture-derived virus stocks or clinical samples, including extensive replicate testing and three subsequent blind passages. RT-qPCR analyses confirmed preservation and stability of detectable CSFV RNA for at least 60 days under the storage conditions evaluated, including refrigerated storage and room temperature storage for Catalonia01-derived samples. Overall, these findings demonstrate that PS-MTM eliminates detectable CSFV infectivity while preserving viral RNA for molecular diagnosis. The implementation of PS-MTM may facilitate safer handling, transport, and interlaboratory exchange of CSFV-positive samples, contributing to improved biosafety, diagnostic harmonization, and global surveillance capacity for this transboundary animal disease. Full article

The objectives of this study are to evaluate the level of awareness and readiness for adoption of practices related to ESG criteria and sustainability in the construction industry. Following a systematic literature review it is revealed that by 2023, only 19 scientific publications had addressed ESG issues specifically within the construction sector while in the past two years, nearly three times as many were published, a fact that demonstrates the growing research interest in ESG practices in the construction sector. It provides new empirical insights by focusing on small to medium enterprises in the Greek construction industry rather than large construction companies with established ESG reporting strategies. Interviews using open-ended questionnaires constructed based on the core ESG indicators defined in the 2022 ESG Disclosure Guidelines published by the Athens Stock Exchange Data collection, were carried out to collect data on the subject. It is shown that most SMEs demonstrate low awareness of formal ESG frameworks and insufficient readiness to adopt ESG practices. In some cases, it was found that sustainability actions exist for limited numbers of ESG indicators, but they are not integrated into a structured ESG company strategy. Overall, the results emphasize the emerging importance of ESG criteria for the construction sector and highlight the need for clearer guidelines, stronger support mechanisms, and standardized assessment tools to advance ESG adoption and support the industry’s improvement in its sustainability performance. To support industry transition, the study concludes with a practical ESG implementation roadmap designed to help Greek construction SMEs progress toward structured, measurable, and scalable sustainability practices. Full article

This paper develops a hybrid fractional-order framework for medium-horizon forecasting of wheat, corn, and soybean prices, combining a Caputo fractional differential equation with exogenous macro-climatic drivers (weather, crude oil, exchange rate, inflation) and a machine learning residual-correction layer. Existence, uniqueness, and Ulam–Hyers stability are established for both Caputo and Atangana–Baleanu formulations via Banach fixed-point theory, with numerical illustration through a fractional Adams–Bashforth–Moulton predictor–corrector scheme. Formal unit-root tests (ADF, KPSS, Phillips–Perron) confirm $I\left(1\right)$ behaviour in log-price levels and stationarity in first differences. Residuals are corrected using XGBoost and two feedforward neural networks (MLP-A, MLP-B), producing a family of hybrid forecasting models. Using 25 years of monthly FRED data (January 2000–December 2024), multi-method long-memory diagnostics (Hurst exponent, Lo’s modified $R/S$ test, DFA, local Whittle estimation) confirm near unit-root fractional integration in log-price levels ($H\in [0.985,1.044]$), with estimated fractional orders $\widehat{\alpha }\in \{0.737,0.884,0.451\}$ for wheat, corn, and soybean. Under a strict rolling-origin protocol (17 origins, horizons $h\in \{1,5,6,12\}$ months), conventional benchmarks remain competitive at $h=1$, but their MAPE degrades to 16.6–31.9% at $h=12$, while Frac+XGBoost error stays flat at 3.9–14.5%. This horizon-robust advantage holds across three market regimes (COVID-19 pandemic shock, 2021–2022 super-cycle, 2023–2024 normalisation) and is confirmed by Holm–Bonferroni-corrected Diebold–Mariano tests ($p<0.001$ at $h=12$). The model’s structural advantage emerges at $h\ge 5$ months, supporting procurement planning, food-security buffer stocks, and import budgeting. Full article

The catalytic pyrolysis of post-consumer polystyrene (PS) offers a potential route to obtain styrene-rich liquid fractions from plastic waste. In this study, natural zeolites were modified by thermal activation (AT-ZN), acid treatment (AA-ZN), and protonic ion exchange (H-ZN), and their performance was evaluated under different pyrolysis temperatures (400–500 °C), heating rates (10–20 °C min⁻¹), and catalyst loadings (5–10 wt.%). Thermogravimetric analysis indicated that zeolite incorporation shifted the apparent PS degradation profile toward lower temperatures, suggesting that the modified solids altered the polymer’s thermal conversion behavior. Product-yield analysis showed that H-ZN provided the most favorable phase distribution, producing high liquid fractions while limiting solid-residue formation. AT-ZN exhibited an intermediate, comparatively stable response. In contrast, AA-ZN promoted greater solid formation and lower liquid recovery, suggesting that more severe catalytic conditions may favor secondary reactions and the accumulation of carbonaceous residues. Targeted GC–MS analysis revealed that styrene was the dominant aromatic compound among the quantified products, with H-ZN consistently showing the highest styrene proportion in the analyzed liquid fraction. Correlation analysis and ANOVA further indicated that the influence of temperature, catalyst loading, and their interactions depended strongly on the zeolite modification route. Overall, the results demonstrate that the route of modification of the natural zeolite strongly affected its composition, textural properties, acidity distribution, thermal behavior, and catalytic performance during PS pyrolysis. XRF, N₂ adsorption–desorption, NH₃-TPD, TGA/DTG, and FTIR characterization showed that AA-ZN exhibited the highest Si/Al ratio and BET surface area, whereas H-ZN presented the highest total acidity and the largest contribution of medium- and strong-acid sites. The combined characterization and pyrolysis results indicate that the preservation of styrene-rich liquid products was governed by the balance between acid-site distribution and pore accessibility, rather than by surface area or total acidity considered in isolation. Full article

Lys49 phospholipase A2 (Lys49-PLA₂) homologues are catalytically inactive snake venom toxins with diverse biological activities, but their functional characterization requires recombinant production strategies to overcome the limitations inherent to native venom-derived proteins. Here, we produced recombinant BaMtx (rBaMtx), a Lys49-PLA₂ homologue from Bothrops atrox, in the Pichia pastoris KM71 expression system and evaluated whether its biochemical and biological properties were preserved. rBaMtx was secreted into the culture medium, purified by cation-exchange chromatography, and characterized by SDS-PAGE, Western blotting, RP-HPLC (85.9% purity), and MALDI-TOF mass spectrometry, confirming a molecular mass of 13,821 Da. Despite lacking PLA₂ activity, rBaMtx retained pronounced myotoxicity in vivo, inducing dose-dependent plasma creatine kinase release and skeletal muscle damage comparable to or greater than native BaMtx. In vitro, rBaMtx reduced viability of murine 4T1 breast carcinoma cells (IC₅₀ = 60.94 µg/mL), altered cell morphology, modestly increased IL-1β release, and produced context-dependent effects in paired human tumor and non-tumor cell models, differing from native BaMtx and crude venom. These findings establish rBaMtx as a reproducible model for investigating the mechanisms and biomedical potential of catalytically inactive Lys49-PLA₂ homologues. Full article

Commodity prices are notoriously hard to forecast, and whether the returns of commodity exchange-traded funds (ETFs) can be predicted remains an open question. We compare three deep learning models, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, for forecasting the returns of six Deutsche Bank commodity ETFs covering agriculture (DBA), base metals (DBB), broad commodities (DBC), energy (DBE), oil (DBO), and precious metals (DBP). Using daily price data from January 2007 to December 2025, we predict daytime returns (open to close) and overnight returns (previous close to open) separately, over five horizons of 1, 5, 30, 60, and 180 trading days. Each model sees a 20-day window of price-based features, returns, rolling averages and volatilities, momentum, and recent lags, built from all six ETFs. All models are trained on a strict chronological split and judged by two simple, decision-oriented measures: how often they call the direction correctly, and the risk-adjusted return (annualized Sharpe ratio) of a stylized long–short strategy that ignores transaction costs. Formal significance tests with HAC corrections for overlapping targets, bootstrap confidence intervals, and comparisons with ARIMA, random forest, and simpler benchmarks corroborate strong predictability in overnight DBP and daytime DBB at medium horizons. Predictability turns out to be highly specific to the asset, the trading session, and the horizon. Overnight returns of the precious metals ETF (DBP) are by far the most predictable: the correct direction is called 71.6% of the time at 60 days and 76.7% at 180 days, with Sharpe ratios reaching about 15. Base metals (DBB) daytime returns are predictable at 30 days and oil (DBO) daytime returns at 180 days, whereas one-day-ahead forecasts and agricultural returns (DBA) stay essentially unpredictable. The Transformer has a slight edge at longer horizons and the GRU at shorter ones. Key directional accuracy and Sharpe ratio results are confirmed by Newey–West HAC significance tests and Diebold–Mariano forecast comparison tests with the Harvey–Leybourne–Newbold small-sample correction; HAC standard errors at the 180-day horizon exceed naïve OLS errors by a factor of approximately 7.4, and we explicitly flag results that do not survive this correction. A three-fold expanding walk-forward validation scheme corroborates the main findings, with DBP overnight and DBO daytime predictability persisting across all evaluation windows. Deep learning architectures statistically and economically outperform logistic regression, ridge regression, and momentum baselines on the most predictable configurations. An anomalous failure of all models on DBA daytime returns at the 180-day horizon is diagnosed as a regime-driven artefact associated with post-2021 commodity inflation, not a general feature of agricultural return dynamics. The broader lesson is that splitting returns into daytime and overnight components exposes predictable structure that conventional close-to-close returns hide. Full article

This study investigates the oversubscription in public issues of Small and Medium Enterprises in India, across different quantiles. This study further explores the non-linearity in factors through machine learning. Utilising data from 1014 IPOs during the period 2012 to 2024 listed on SME exchanges of NSE and BSE, this study applies OLS regression, quantile regression and a Random Forest model. SHapley Additive exPlanations values are computed to validate and extend econometric findings. Underpricing, listing delay and hot markets are the prominent factors affecting oversubscription. The Random Forest model outperforms OLS, revealing non-linear effects of predictors. This study offers crucial insights enabling policymakers and regulators to refine disclosure regulations and strengthening investor protection measures, while firms can efficiently structure their offerings and attract significant investor interest. Full article

The Makara, a potent hybrid guardian figure from Indian architectural sculpture, poses a question for the study of artistic exchange: how did this complex motif travel across Asia and transform into a simplified painted emblem on Chinese export ceramics? We address this question by analyzing a corpus of textual sources, sculptural reliefs, and ceramic assemblages, with a focused case study on ninth-century Changsha kiln production and the pivotal evidence from the Belitung shipwreck. The analysis reveals a profound morphological and functional shift. In stone, the Makara appears as a powerful, three-dimensional guardian in complex crocodilian or elephantine forms. In contrast, on mass-produced Changsha bowls, it is abbreviated to a stylized, two-dimensional head, often paired with a pearl. We argue that this transformation was not a simple degradation but a sophisticated act of modular translation. The shift in medium from stone carving to swift underglaze painting and the demands of a global market forced a functional reinvention: the static, architectural guardian became a portable, commercial emblem. Ultimately, the analysis frames the Changsha artisan as an active cultural translator and the Makara bowls as an early, successful example of customized export design for a global audience. Full article

Digitalization and sustainability are increasingly important in container terminal development, yet their operational-level integration remains insufficiently explained in medium-sized terminals. This study examines how digital systems support sustainability-related outcomes in a medium-sized container terminal in the Port of Klaipėda, Lithuania. An exploratory qualitative case study was conducted using document analysis and semi-structured interviews with three key informants representing operational, planning/administrative, and technical or sustainability-related functions. Qualitative content analysis was applied to identify digital technologies, operational mechanisms, sustainability outcomes, implementation barriers, and managerial responses. The findings show that the terminal operating system, vehicle booking system, automated gate solutions, and port community system support sustainability mainly indirectly, through improved planning, information exchange, gate coordination, reduced congestion, and more efficient resource use. However, these effects are constrained by system fragmentation, limited interoperability, manual data transfer, uneven access to real-time information, and insufficiently targeted staff training. The strongest documented sustainability effects relate to LED lighting and solar energy infrastructure, while other digital-green links remain mainly qualitative or potential. The study provides exploratory, case-based evidence on how digitalization and sustainability are operationally connected in the analyzed medium-sized container terminal. Rather than proposing a new general theory, the study offers an analytical classification structure that helps distinguish documented, indirect and potential digitalization–sustainability links. The findings provide practical recommendations for system integration, staff training and sustainability performance monitoring. Full article

Among the micropollutants of medium-depth waters in isolated inhabited areas, the inorganic ones deserve special attention: nitrate anion (NO₃⁻) and phosphate anions (HxPO₄^−(3−x)). The individual removal of these anions from water is widely studied, with different methods being found: chemical, ion exchange, or biological. This paper presents a membrane method for the simultaneous removal of nitrate anion and phosphate anions from dilute synthetic aqueous solutions. The developed method is nanofiltration using composite membranes made of cellulose acetate (CA) and silver nanoparticles (Agnp). The composite membranes were made by phase inversion of the dimethylformamide (DMF) solution containing the two components (CA–Agnp) on a polypropylene (PP) capillary fiber using deionized water as a coagulant. The DMF solution of CA containing Agnp was obtained by dissolving black-and-white cinematographic films (exposed and unexposed to light). CA–Agnp–PP composite membranes were tested for the simultaneous removal of nitrate anion and phosphate anions from aqueous solution by nanofiltration at pressures ranging from 5 to 25 bars. A removal of over 98% of phosphate anions and more than 95% of nitrate anions was achieved. Fluxes of 10 L·m⁻²·h⁻¹ were obtained for the working pressure of 15 atm, depending on the pH, flow rate, and concentration of the feed water. The variable parameters considered were also the concentrations of CA and Agnp. Full article

Integrated circuits (ICs), a major fraction of waste electrical and electronic equipment (WEEE), represent an important secondary source of gold (Au). However, recovering high-purity Au from ICs remains challenging due to the high silicon dioxide content that encapsulates Au within the IC core and the presence of complex base-metal mixtures that hinder selective purification. This study proposes a simplified end-to-end process that integrates mechanical liberation, magnetic separation, oxidative chlorination, ion-exchange purification and Au recovery from isolated ICs. Unlike conventional multi-stage comminution routes, the proposed pretreatment combines hydraulic pressing, milling/sieving and magnetic separation to maximize Au exposure while minimizing dust generation, metal losses and base-metal interference, which is subsequently subjected to oxidative leaching and purification. Optimal extraction conditions, determined through a Taguchi design (2.5 M HCl, 0.34 M NaClO, 40 °C, solid–liquid ratio 1 g/40 mL, 3 h), achieved a Au leaching efficiency of 89%. The resulting multi-metal leachate was treated with a strong anionic ion-exchange resin, increasing Au purity from 8% to 86% after thiourea elution in a sulfuric-acid medium. Final Au recovery was completed by reductive precipitation with sodium borohydride, yielding complete solidification (~100% efficiency). A comparative life-cycle assessment showed that this recycling route offers favourable environmental performance relative to primary mining. Beyond achieving efficient Au recovery, this work establishes an integrated recovery route for isolated ICs that combines process simplification with environmental positive impact, addressing an important gap in WEEE recycling. Full article

Wood-based materials are a significant source of formaldehyde (HCHO), which is a volatile organic compound (VOC) classified as a human carcinogen that has adverse effects on the indoor air quality (IAQ) and health. This study evaluates HCHO emissions from six materials commonly used indoors, including medium-density fiberboard, melamine, pine boards, birch boards, oak boards, and alder boards, using the desiccator method in accordance with ASTM D5582. The experimental results were integrated with an indoor air model to estimate exposure under representative residential conditions. The measured emissions ranged from 0.235 ± 0.01 to 1.023 ± 0.10 mg m⁻³, with composite materials exhibiting the highest values. The risk analysis showed that all samples exceeded international reference values, with the greatest concern arising in scenarios of chronic exposure. The modeling indicated that indoor air concentrations depend heavily on material load and ventilation, reaching values as high as 0.155 mg m⁻³ under conditions of low air exchange. Overall, the results show that material composition, installed quantity, and ventilation conditions are key factors influencing indoor HCHO concentrations. This study offers an integrated approach that combines experimental measurement and exposure estimation, contributing to informed material selection and the assessment of their impact on IAQ. Full article