Search Results (534)

Poultry price instability remains a critical challenge for food security in Nigeria. This study examines the relationship between poultry price volatility (PPV), exchange rate (LEXR), and inflation (LCPI) from 1991 to 2024 using the Autoregressive Distributed Lag (ARDL) model. Descriptive results show that PPV had the highest variability (mean 0.65; standard deviation 1.07), while LEXR and LCPI were relatively more stable. Trend analysis indicates that poultry price volatility was high in the early 1990s but declined steadily after 2005, coinciding with persistent inflation and cycles of exchange rate depreciation and appreciation.Unit root and bounds tests confirm that the variables werecointegrated, with an F-statistic of 4.50 exceeding the upper bound at 5 percent significance. The long-run estimates reveal that inflation hada negative effect on poultry price volatility (−0.109), while the exchange rate exerteda positive effect (0.2702). The errorcorrection term (−0.336) indicates a 33.6 percent adjustment to equilibrium each period. In the short run, changes in inflation (0.942) and lagged exchange rate variations significantly influenced poultry price volatility. These findings underscore the importance of stabilizing exchange rates and controlling inflation to reduce price volatility in Nigeria’s poultry sector. Full article

Pasture-based ruminant systems link nitrogen (N) nutrition with ecosystem N cycling. Grazing ruminants convert fibrous forages into milk and meat but excrete 65 to 80% of ingested N, creating excreta hotspots that drive ammonia volatilization, nitrate leaching, and nitrous oxide (N₂O) emissions. This review synthesizes ecological and ruminant nutrition evidence on N flows, emphasizing microbial processes, biological N₂ fixation, plant diversity, and urine patch biogeochemistry, and evaluates strategies to improve N use efficiency (NUE). We examine rumen N metabolism in relation to microbial protein synthesis, urea recycling, and dietary factors including crude protein concentration, energy supply, forage composition, and plant secondary compounds that modulate protein degradability and microbial N capture, thereby influencing N partitioning among animal products, urine, and feces, as reflected in milk and blood urea N. We also examine how grazing patterns and excreta distribution, assessed with sensor technologies, modify N flows. Evidence indicates that integrated management combining dietary manipulation, forage diversity, targeted grazing, and decision tools can increase farm-gate NUE from 20–25% to over 30% while sustaining performance. Framing these processes within the global N cycle positions pasture-based ruminant systems as critical leverage points for aligning ruminant production with environmental and climate sustainability goals. Full article

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze and calibrate the key policy levers influencing owner participation timing (T*). The model, resolved using finite difference methods, captures the system’s non-linear threshold effects by simulating critical system parameters, including system instability (price volatility, ${\mathsf{\sigma }}_p$), internal friction (management fee, $w_g{g}_t$), and demand signals (transaction ratio, Q). Simulations reveal extreme non-linear system responses: a 100% increase in system instability (${\mathsf{\sigma }}_p$) delays participation by 325.5%. More critically, a 100% surge in internal friction (management fees) delays T* by 492% and triggers a 95% revenue collapse—demonstrating the risk of systemic collapse. Conversely, a 20% rise in the demand signal (Q) advances T* by 100% (immediate participation), indicating the system can be rapidly shifted to a new equilibrium by activating positive feedback loops. These findings support a sequenced calibration strategy: regulators must first manage instability via price stabilization, then counteract high friction with subsidies (e.g., 60%), and amplify demand loops. The LCC framework provides a novel dynamic decision support system for calibrating complex urban transportation systems, offering policymakers a tool for scenario testing to accelerate policy adoption and alleviate urban congestion. Full article

Vehicle emissions are key precursors to near-ground ozone and secondary aerosol formation. While China’s clean air actions have significantly reduced particulate pollution, ozone levels continue to rise in some city clusters, calling for a deeper understanding of volatile organic compound (VOC) emissions from gasoline vehicles. This study systematically evaluated the impacts of fuel composition (China 6b vs. Methyl tert-butyl ether -free (MTBE-free) gasoline), engine type (Port fuel injection (PFI) vs. Gasoline direct injection (GDI)), and ambient temperature (25 °C vs. −7 °C) on VOC emissions and ozone formation potential (OFP) under the World Harmonized Light-Duty Test Cycle (WLTC). Results of dynamometer experiments showed that MTBE-free gasoline reduced total VOC emissions by 47% compared to China 6b fuel, with aromatics accounting for 69% of this reduction. PFI vehicles exhibited higher VOC emissions than GDI vehicles at 25 °C, though this difference diminished at −7 °C. Low temperatures significantly increased VOC emissions and OFP, increasing by a factor of 10–13 compared to 25 °C. Aromatics were the dominant OFP contributors under all conditions. Our findings highlight the importance of fuel reformulation and temperature-specific emission controls in mitigating ozone pollution, particularly under cold-start conditions. Full article

The deterioration of concrete hydraulic structures caused by chemical factors, seepage, and environmental stress necessitates advanced protective coatings that enhance durability, flexibility, and environmental sustainability. Conventional protective systems often exhibit limited durability under combined hydraulic, thermal, and chemical stress. In this study, a novel polyfluorosilicone-based coating system is presented, which integrates a deep-penetrating nano-primer for substrate reinforcement, a crack-bridging polymer intermediate layer for impermeability, and a polyfluorosilicone topcoat providing UV and weather resistance. The multilayer architecture addresses the inherent trade-offs between adhesion, flexibility, and durability observed in conventional waterproofing systems. Informed by a mechanistic study of interfacial adhesion and failure modes, the coating exhibits outstanding high mechanical and performance characteristics, including a mean pull-off bond strength of 4.56 ± 0.14 MPa for the fully cured triple-layer coating system, with cohesive failure occurring within the concrete substrate, signifying a bond stronger than the material it protects. The system withstood 2.2 MPa water pressure and 200 freeze–thaw cycles with 87.2% modulus retention, demonstrating stable mechanical and environmental durability. The coating demonstrated excellent resilience, showing no evidence of degradation after 1000 h of UV aging, 200 freeze–thaw cycles, and exposure to alkaline solutions. This water-based formulation meets green-material standards, with low volatile organic compound (VOC) levels and minimal harmful chemicals. The results validate that a multi-scale, layered design strategy effectively decouples and addresses the distinct failure mechanisms in hydraulic environments, providing a robust and sustainable solution. Full article

The fiberboard industry remains heavily reliant on synthetic, formaldehyde-based adhesives, which, despite their cost-effectiveness and strong bonding performance, present significant environmental and human health concerns due to volatile organic compound (VOC) emissions. In response to growing sustainability imperatives and regulatory pressures, the development of non-toxic, renewable, and high-performance bio-based adhesives has emerged as a critical research frontier. This review, conducted through both narrative and systematic approaches, synthesizes current advances in green adhesive technologies with emphasis on lignin, tannin, starch, protein, and hybrid formulations, alongside innovative synthetic alternatives designed to eliminate formaldehyde. The Evidence for Policy and Practice Information and Coordinating Centre (EPPI) framework was applied to ensure a rigorous, transparent, and reproducible methodology, encompassing the identification of research questions, systematic searching, keywording, mapping, data extraction, and in-depth analysis. Results reveal that while bio-based adhesives are increasingly capable of approaching or matching the mechanical strength and durability of urea–formaldehyde adhesives, challenges persist in terms of water resistance, scalability, cost, and process compatibility. Hybrid systems and novel crosslinking strategies demonstrate particular promise in overcoming these limitations, paving the way toward industrial viability. The review also identifies critical research gaps, including the need for standardized testing protocols, techno-economic analysis, and life cycle assessment to ensure the sustainable implementation of these solutions. By integrating environmental, economic, and technological perspectives, this work highlights the transformative potential of green adhesives in transitioning the fiberboard sector toward a low-toxicity, carbon-conscious future. It provides a roadmap for research, policy, and industrial innovation. Full article

Driven by carbon neutrality policies, the cumulative issuance volume of the global green bond market has surpassed $2.5 trillion over the past five years, with China, as the second largest issuer, accounting for 15%. However, there exists a yield difference of up to 0.8% for bonds with the same credit rating across different policy regions, and the premium level fluctuates dramatically with market cycles, severely restricting the efficiency of green resource allocation. This study innovatively constructs a Bayesian hierarchical spatiotemporal model framework to systematically analyze pricing deviations through a three-level data structure: the base level quantifies the impact of bond micro-characteristics (third-party certification reduces financing costs by 0.15%), the temporal level captures market dynamics using autoregressive processes (premium volatility increases by 50% during economic recessions), and the spatial level reveals policy regional dependencies using conditional autoregressive models (carbon trading pilot provinces and cities form premium sinkholes). The core breakthroughs are: 1. Designing spatiotemporal interaction terms to explicitly model the policy diffusion process, with empirical evidence showing that the green finance reform pilot zone policy has a radiation radius of 200 km within three years, leading to a 0.10% increase in premiums in neighboring provinces; 2. Quantifying the posterior distribution of parameters using the Markov Chain Monte Carlo algorithm, demonstrating that the posterior mean of the policy effect in pilot provinces is −0.211%, with a half-life of 0.75 years, and the residual effect in non-pilot provinces is only −0.042%; 3. Establishing a hierarchical shrinkage prior mechanism, which reduces prediction error by 41% compared to traditional models in out-of-sample testing. Key findings include: the contribution of policy pilots is −0.192%, surpassing the effect of issuer credit ratings, and a 10 yuan/ton increase in carbon price can sustainably reduce premiums by 0.117%. In 2021, the “dual carbon” policy contributed 32% to premium changes through spatiotemporal interaction channels. The research results provide quantitative tools for issuers to optimize financing timing, investors to identify cross-regional arbitrage, and regulators to assess policy coordination, promoting the transformation of the green bond market from an efficiency priority to equitable allocation paradigm. Full article

In order to solve the problems of high volatility and insufficient absorption effect when using chemical by-product washing oil to treat benzene-containing waste gas, this study innovatively proposed a composite solvent screening method based on the solvation free energy (ΔGsol), and reasonably predicted the absorption performance of 26 solvents for benzene. Through theoretical calculation and experimental verification, tetraethylene glycol dimethyl ether (TGDE) was finally determined to be the optimal composite component of washing oil. The absorption efficiency of the composite solvent reached 96.2%, and the regeneration efficiency was stable after 12 cycles with a mass loss of only 2.4%. Quantum computing simulation revealed that the dispersion force is dominant between benzene and the solvent, and TGDE enhances the electrostatic interaction through weak hydrogen bonds. The synergistic effect of the two improves the absorption performance. This study provides theoretical and technical support for the development of efficient and renewable benzene waste gas recovery solvent systems. Full article

Conventional urea nitrogen (N) fertilizers are characterized by low use efficiency, resulting in substantial economic losses and environmental degradation. To address this issue, we developed a novel carbon-based stabilized coated urea by incorporating biochar, the urease inhibitor NBPT, and the nitrification inhibitor DCD through a low-energy in situ coating process. This study evaluated the effects of this fertilizer on N transformation and loss via soil column leaching and ammonia volatilization experiments, as well as its impact on choy sum (Brassica chinensis L.) yield, N use efficiency (NUE), and product quality under field conditions. Results indicated that coatings containing both NBPT and DCD (specifically, formulations with 0.5%NBPT + 1.0%DCD, and 1.0%NBPT + 1.5%DCD) significantly reduced cumulative ammonium-N leaching by 41.5–53.8% and nitrate-N leaching by 45.3–59.4% compared to conventional urea. All coated treatments suppressed ammonia volatilization by over 10%, with the highest inhibition (26.92%) observed in the treatment with 1.0%NBPT + 1.5%DCD. The synergistic coating also modulated key soil enzyme activities involved in N cycling. Field trials demonstrated that the formulations with 0.5%NBPT + 1.0%DCD and 0.5%NBPT + 1.5%DCD increased choy sum yield by 56.1% and 58.1%, respectively, while significantly improving NUE and agronomic efficiency. Moreover, these treatments enhanced vegetable quality by reducing nitrate content and increasing vitamin C and soluble sugar concentrations. In conclusion, this carbon-based stabilized coated urea, which integrates biochar with NBPT and DCD, represents a promising strategy for minimizing N losses, improving NUE, and advancing sustainable vegetable production. Full article

Given the deteriorating situation of ambient ozone (O₃) pollution in some areas of China, understanding the mechanisms driving O₃ formation is essential for formulating effective control measures. This study examines O₃ formation mechanisms and RO_x (OH, HO_2, and RO₂) radical cycling driven by photochemical processes in Bozhou, located at the junction of Jiangsu–Anhui–Shandong–Henan (JASH), a region heavily affected by O₃ pollution, by applying a zero-dimensional box model (Framework for 0-Dimensional Atmospheric Modeling, F0AM) coupled with the Master Chemical Mechanism (MCM v3.3.1) and Positive Matrix Factorization (PMF 5.0) to characterize O₃ pollution, identify volatile organic compound (VOC) sources, and quantify radical budgets during pollution episodes. The results show that O₃ episodes in Bozhou mainly occurred in June under conditions of high temperature and low wind speed. Oxygenated volatile organic compounds (OVOCs), alkanes, and halocarbons were the dominant VOCs groups. The CH₃O₂ + NO reaction accounted for 24.3% of O₃ production, while photolysis contributed 68.7% of its removal. Elevated VOCs concentrations in Bozhou were largely maintained by anthropogenic sources such as vehicle exhaust, solvent utilization, and gasoline evaporation, which collectively enhanced O₃ production. The findings indicate that O₃ formation in the region is primarily regulated by NO_x availability. Therefore, emission reductions targeting NO_x, along with selective control of OVOCs and alkenes, would be the most effective strategies for lowering O₃ levels. Model simulations further highlight Bozhou’s strong atmospheric oxidation capacity, with OVOC photolysis identified as the dominant contributor to RO_x generation, accounting for 33% of the total. Diurnal patterns were evident: NO_x-related reactions dominated radical sinks in the morning, while HO₂ + RO₂ reactions accounted for 28.5% in the afternoon. By clarifying the mechanisms of O₃ formation in Bozhou, this study provides a scientific basis for designing ozone control strategies across the JASH junction region. In addition, ethanol was not directly measured in this study; given its potential to generate acetaldehyde and affect local O₃ formation, its possible contribution introduces additional uncertainty that warrants further investigation. Full article

This work presents a data-driven framework for early detection of polymer melt instability in industrial injection moulding using Long Short-Term Memory (LSTM) time-series models. The study uses six months of continuous production data comprising approximately 280,000 injection cycles collected from a fully operational thermoplastic injection line. Because melt behaviour evolves gradually and conventional threshold-based monitoring often fails to capture these transitions, the proposed approach models temporal patterns in torque, pressure, temperature, and rheology to identify drift conditions that precede quality degradation. A physically informed labelling strategy enables supervised learning even with sparse defect annotations by defining volatile zones as short time windows preceding operator-identified non-conforming parts, allowing the model to recognise instability windows minutes before defects emerge. The framework is designed for deployment on standard machine signals without requiring additional sensors, supporting proactive process adjustments, improved stability, and reduced scrap in injection moulding environments. These findings demonstrate the potential of temporal deep-learning models to enhance real-time monitoring and contribute to more robust and adaptive manufacturing operations. Full article

This research tackles the challenge of forecasting nonlinear time series data with stochastic structural variations by proposing the Markov switching autoregressive model with time-varying parameters (MSAR-TVP). Although effective in modeling dynamic regime transitions, the Classical MSAR-TVP faces challenges with complex datasets. To address these issues, a Bayesian MSAR-TVP framework was developed, incorporating flexible parameters that adapt dynamically across regimes. The model was tested on two periods of U.S. real GNP data: a historically stable segment (1952–1986) and a more complex, modern segment that includes more economic volatility (1947–2024). The Bayesian MSAR-TVP demonstrated superior performance in handling complex datasets, particularly in out-of-sample forecasting, outperforming the Bayesian AR-TVP, Classical MSAR-TVP, and Classical MSAR models, as evaluated by mean absolute percentage error (MAPE) and mean absolute error (MAE). For in-sample data, the Classical MSAR-TVP retained its stability advantage. These findings highlight the Bayesian MSAR-TVP’s ability to address parameter uncertainty and adapt to data fluctuations, making it highly effective for forecasting dynamic economic cycles. Additionally, the two-year forecast underscores its practical utility in predicting economic cycles, suggesting continued expansion. This reinforces the model’s significance for economic forecasting and strategic policy formulation. Full article

This review critically examines photoresponsive supercapacitors based on TiO₂/graphene hybrids, with a particular focus on their emerging dual role as energy-storage devices and environmental sensors. We first provide a concise overview of the electronic structure of TiO₂ and the key attributes of graphene and related nanocarbons that enable efficient charge separation, transport, and interfacial engineering. We then summarize and compare reported device architectures and electrode designs, highlighting how morphology, graphene integration strategies, and illumination conditions govern specific capacitance, cycling stability, rate capability, and light-induced enhancement in performance. Particular attention is given to the underlying mechanisms of photo-induced capacitance enhancement—including photocarrier generation, interfacial polarization, and photodoping—and to how these processes can be exploited to embed sensing functionality in working supercapacitors. We review representative studies in which TiO₂/graphene systems operate as capacitive sensors for humidity, gases, and volatile organic compounds, emphasizing quantitative figures of merit such as sensitivity, response/recovery times, and stability under repeated cycling. Finally, we outline current challenges in materials integration, device reliability, and benchmarking, and propose future research directions toward scalable, multifunctional TiO₂/graphene platforms for self-powered and environmentally aware electronics. This work is intended as a state-of-the-art summary and critical guide for researchers developing next-generation photoresponsive supercapacitors with integrated sensing capability. Full article

This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability. Full article

Globally, the transition of energy structure towards clean and low-carbon is accelerating, with the increasing grid integration ratio of renewable energy. However, the inherent intermittency, volatility and randomness of such energy sources are in fundamental conflict with the traditional operation mode of existing power systems, which not only restricts the absorption capacity of renewable energy, but also poses severe challenges to the safe and stable operation of power systems. The integration of renewable energy sources into existing power systems poses numerous challenges that can be mitigated through the utilization of demand-side flexible resources. Among these, air-conditioning (AC) loads, as a prominent example, offer significant potential for enhancing flexibility in power systems. Nonetheless, determining an optimal AC control strategy to achieve the desired power response remains challenging, particularly in practical control settings where reliance on a single timescale control strategy may prove inadequate to address fluctuations in power system flexibility requirements. This paper investigates the characteristics of direct start-stop control and duty cycling control within a multi-timescale, source-load coordinated scheduling framework. Furthermore, a hybrid control strategy that combines these two methods is proposed, accompanied by the formulation of a power curtailment model tailored to the hybrid control strategy. Case study results demonstrate that the hybrid control strategy effectively augments AC load flexibility and enhances scheduling feasibility, thereby supporting the stable operation of the power system. Full article