Advancing Open Science

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization.

This paper is dedicated to the analytical investigation of the global dynamics of an SEIR epidemiological model that incorporates latency age (the time spent by an individual in the exposed class before becoming infectious) and a general nonlinear incidence rate. In this model, to reflect the dependence of disease progress on the latency age, the exposed class is structured by the latency age, and the rate at which the latent individual becomes infected, and the removal rate are assumed to depend on the latency age. By analyzing the characteristic equations associated with each equilibrium, we study the local stability of both the disease-free and endemic steady states of the model. Moreover, it is proven that the semiflow generated by this system is asymptotically smooth, and if the basic reproduction number is greater than unity, the system is uniformly persistent. Furthermore, based on Lyapunov functional and LaSalle’s invariance principle, the global dynamics of the model are established. It is obtained that if the basic reproduction number is less than unity, the disease-free steady state is globally asymptotically stable and hence the disease dies out; however, if the basic reproduction number is greater than unity, the endemic steady state is globally asymptotically stable, and the disease persists. Numerical simulations are carried out to illustrate the main analytic results.

Rising residential land prices push up housing prices and worsen credit misallocation. These patterns emerge amid cyclical real estate fluctuations and heavy land-based public finance. Such pressures undermine macroeconomic stability and sustainable land-use. The land price circuit breaker is widely applied with a price cap and state dependence, yet its trigger mechanism and interaction with inflation targeting remain underexplored. This study addresses three core questions. First, how does the circuit breaker’s discrete trigger and rule-switching logic differ from traditional static price ceilings? Second, can the mechanism, via the collateral channel, restrain excessive land price hikes, improve credit allocation, and, thereby, stabilize land price dynamics and long-run macroeconomic performance? Third, how does the circuit breaker interact with inflation targeting, and through which endogenous channels does a strict target dampen housing prices and raise activation probability? This study develops a multi-sector DSGE model with an embedded land price circuit breaker. The price cap is modeled as an occasionally binding constraint. A dynamic price band and trigger indicator capture the policy’s switch between slack and binding states. The framework incorporates interactions among local governments, the central bank, developers, and households. It also links firms and the secondary housing market. Under different inflation-targeting rules, this study uses impulse responses, an event study, and welfare analysis to assess trigger conditions and macroeconomic effects. The findings are threefold. First, a strict inflation target increases the probability of a circuit breaker being triggered. It channels housing-demand shocks toward land prices and creates a “nominal anchor–relative price constraint” linkage. Second, once activated, the circuit breaker narrows the gap between land price and house-price growth. It weakens the procyclicality of collateral values. It also restrains credit expansion by impatient households. These effects redirect credit toward firms, improve corporate financing, reduce the decline in investment, and accelerate output recovery. Third, the circuit breaker limits new land supply and shifts demand toward the secondary housing market. This generates a supply-side effect that releases existing stock and stabilizes prices, thereby weakening the amplification mechanism of housing cycles. This study identifies the endogenous trigger logic and cross-market transmission of the land price circuit breaker under a strict inflation target. It shows that the mechanism is not merely a price-management tool in the land market but a systemic policy variable that links the real estate, finance, and fiscal sectors. By dampening real estate procyclicality, improving credit allocation, and stabilizing macroeconomic fluctuations, the mechanism offers new insights for sustainable land-use policy and macroeconomic stabilization.

Point-of-Interest (POI) recommendation predicts users’ future check-ins based on their historical trajectories and plays a key role in location-based services (LBS). Traditional approaches such as collaborative filtering and matrix factorization model user–POI interaction matrices fail to fully leverage spatio-temporal information and semantic attributes, leading to weak performance on sparse and long-tail POIs. Recently, Graph Neural Networks (GNNs) have been applied by constructing heterogeneous user–POI graphs to capture high-order relations. However, they still struggle to effectively integrate spatio-temporal and semantic information and enhance the discriminative power of learned representations. To overcome these issues, we propose Spatio-Temporal and Semantic Dual-Channel Contrastive Alignment for POI Recommendation (S${}^2$DCRec), a novel framework integrating spatio-temporal and semantic information. It employs hierarchical relational encoding to capture fine-grained behavioral patterns and high-level semantic dependencies. The model jointly captures user–POI interactions, temporal dynamics, and semantic correlations in a unified framework. Furthermore, our alignment strategy ensures micro-level collaborative and spatio-temporal consistency and macro-level semantic coherence, enabling fine-grained embedding fusion and interpretable contrastive learning. Experiments on real-world datasets, Foursquare NYC, and Yelp, show that S${}^2$DCRec outperforms all baselines, improving F1 scores by 4.04% and 3.01%, respectively. These results demonstrate the effectiveness of the dual-channel design in capturing both sequential and semantic dependencies for accurate POI recommendation.

Plants have been used in medicine for centuries to treat various diseases, with alcohol and ethanol being known as universal solvents for the extraction of medicinal plant substances. This article sheds light on Artemisia absinthium (wormwood) and absinthe usage in the history of medicine. The invention of absinthe in Switzerland in 1797 made it an integral part of everyday life and the harmful effects of the massive consumption of this product were labelled absinthism. The medicinal properties of wormwood and absinthe are explored from the earliest records of the use of wormwood from the Ebers Papyrus (copies of which date back to 1550 BC) to the military consumption of absinthe during the French invasion of Algiers in 1830. As widely accepted, A. absinthium has both anthelmintic and antiprotozoal properties. In addition, modern medicine has demonstrated antibacterial, antifungal and antibiofilm properties of the plant extracts. In order to fully utilise the therapeutic potential of A. absinthium, advances in pharmaceutical technology are essential. One promising solution could lie in nanotechnological delivery systems. In our opinion absinthe is another impressive example of how tonics containing various herbal substances were used in the history of medicine to manage infections before their efficacy was later proven in vitro and in vivo.

Rapid urbanization has exacerbated traffic congestion and associated vehicle emissions, making real-time air quality index (AQI) prediction crucial for urban environmental management. Transportation emissions, including exhaust gases and particulate matter, are the main factors causing urban environmental pollution. Vehicle emission-induced air pollution related to transportation affects public health, quality of life, and well-being on a global scale and impacts socioeconomic development and people’s livelihoods. The air quality index (AQI) is a comprehensive indicator reflecting the degree of air pollution. Understanding the pollution level in a specific area can help decision-makers manage traffic flow, reduce congestion and emissions, and improve traffic efficiency and environmental sustainability. Traditional prediction methods often have problems such as low accuracy and an inability to effectively handle complex data. Therefore, this paper explores a traffic air quality index prediction model based on the sparrow search algorithm (SSA)–variational mode decomposition (VMD)–gated recurrent unit algorithm (GRU) model, based in deep learning. Experimental results on real-world datasets demonstrate that the SSA-VMD-GRU model reduces the mean absolute percentage error (MAPE) by approximately 8% compared to the standalone GRU model, offering a robust solution for real-time AQI forecasting and practical insights for current urban traffic air quality index monitoring methods.

In recent years, due to increasing concerns about the environment and sustainable production, the effective utilization of wastes has become important. In this regard, it has become essential to obtain products with distinct properties by utilizing different types of biomass together. The world’s annual apricot production is approximately 4 million tonnes. The apricot seeds can be used to produce seed oils; however, remaining residue (apricot seed pulp) from this process is considered as waste. In this study, the co-pyrolysis of waste from the essential oil industry (apricot seed pulp, ASP, and by-products of the paper pulp industry (calcium lignosulfonate, CLS, and sodium lignosulfonate, SLS)) was carried out using mixtures at different temperatures (400–700 °C) and different ratios (4:1, 1:1, 1:4). The effects of temperature and ratio on the yields, composition, and HHV of biochars were investigated. Among biochars obtained from co-pyrolysis, the maximum HHVs of 26.91 MJ·kg⁻¹ and 25.64 MJ·kg⁻¹ were obtained for 5ASP-5CLS-400 biochar and 8ASP-2SLS-400 biochar, respectively. The results showed that the addition of ASP to CLS led to an increase in the HHVs of biochars when compared to the HHVs of biochars obtained from the pyrolysis of CLS. The use of two different types of waste has led to the production of biochars with different properties due to the synergistic effect in the pyrolysis. As a result, biochars with diverse properties, which have the potential to be utilized in various applications, were produced from different types of wastes.

Achieving sustainable agricultural development necessitates a careful balance between the competing demands of environmental sustainability and food security. While extensive research has examined the economic impacts of agricultural parks, studies focusing on their environmental effects—particularly fertilizer usage—remain limited. Addressing this gap, this study investigates the impact of China’s National Modern Agricultural Industrial Parks (NMAIP) construction on fertilizer application using county-level panel data from 2014 to 2022. By leveraging the staggered establishment of these parks as a quasi-natural experiment, we apply a multi-period Difference-in-Differences (DID) approach. The results indicate that NMAIP construction led to a significant reduction in fertilizer use, a finding robust to a series of tests including parallel trends and placebo analyses. Mechanism analysis reveals that the reduction was primarily driven by enhanced agricultural labor and technology productivity. Heterogeneity analysis further shows that the effects were more pronounced in regions with high-level fertilizer consumption, in non-major grain-producing or wheat-producing areas, and in specialized or single-function parks. Importantly, this reduction in fertilizer use did not compromise grain output; instead, it was accompanied by stable or even increased production, thereby supporting food security. Our findings demonstrate that the NMAIP policy can achieve a “win-win” outcome by simultaneously promoting fertilizer reduction and safeguarding food security, offering a viable pathway toward sustainable agricultural development and food system resilience.