Scientific Reports最新文献

Limited studies have highlighted the importance of incorporating behavioural assessments into insecticide efficacy evaluations for wireworm pest control. "For this study, video tracking technology combined with a soil bioassay arena was employed to analyse the behaviour of Agriotes obscurus wireworms before, during, and after exposure to wheat seeds treated with the neonicotinoid insecticides thiamethoxam and imidacloprid at field-relevant concentrations. The analysis identified a set of behavioural key metrics for assessing the effects of these insecticides on wireworms. The results showed that these insecticides exhibited neutral attractancy towards wireworms. A brief period of feeding followed by rapid intoxication minimised damage to seeds. Furthermore, the wireworms demonstrated a specific form of behavioural resistance to neonicotinoids that did not rely on sensory input. In these insects, the rapid speed of intoxication, accompanied by drastic changes in behaviour, ensured that they received a sublethal rather than lethal dose of the insecticide. The wireworms fully recovered from all behavioural abnormalities within a week, and none died within 20 days following the exposure. In conclusion, this video tracking method provides a rapid and efficient means of assessing insecticides intended for wireworm management, offering valuable insights prior to more resource-intensive and costly field trials.

Bike Sharing Systems (BSSs) play a crucial role in promoting sustainable urban mobility by facilitating short-range trips and connecting with other transport modes. Traditionally, most BSS fleets have consisted of mechanical bikes (m-bikes), but electric bikes (e-bikes) are being progressively introduced due to their ability to cover longer distances and appeal to a wider range of users. However, the charging requirements of e-bikes often hinder their deployment and optimal functioning. This study examines the spatiotemporal variations in battery levels of Barcelona's BSS, revealing that bikes stationed in the city's expansion area (Eixample) tend to have shorter rest periods and lower average battery levels. Additionally, to improve the management of e-bike fleets, a Markov-chain approach is developed to predict both bike availability and battery levels. This research offers a unique perspective on the dynamics of e-bike battery levels and provides a practical tool to overcome the main operational challenges in their implementation.

Using the undirected network from our previous paper, we constructed 5 directed networks based on 5 electronegativity scales. The direction of the edges is determined by the differences in electronegativity. We found that indegree and outdegree can be viewed as inherent chemical properties of elements. Thus, we define a new index-comparative attractiveness (CA)-to study the relationship between this index and electronegativity. The results indicate that CA and electronegativityis are correlated and highly consistent, and can be regarded as an internal property of elements. This index can be used to predict potential binary compounds and assess the rationality of electronegativity, providing valuable insights into the study of electronegativity and a new perspective on chemical properties.

ChatGPT is a highly sophisticated AI language model that can revolutionize students' learning experiences by providing much-needed assistance and information. Given the growing trend of educational institutions integrating AI technologies into their teaching and learning processes, it is crucial to understand the factors that would lead to the acceptance and use of these technologies by students. Using a technology acceptance model, this study investigated the mediating role of satisfaction in the relationship between PU, PEU, and behavioral intention to use ChatGPT for student learning. This study used a quantitative research approach, and data were gathered from 297 students using a structured questionnaire. The analysis was conducted using structural equation modelling (SEM) in AMOS Version 26. The results indicated that PU and ease PEU significantly influenced student satisfaction. PU significantly influenced behavioral intentions to use ChatGPT. However, PEU had no direct impact on students' behavioral intention to use ChatGPT. Satisfaction had a significant influence on students' behavioral intention to use ChatGPT. Moreover, satisfaction was confirmed as a significant partial mediator between PU and behavioral intention, as well as a full mediator of the relationship between PEU and behavioral intention. These findings underscore the need to make ChatGPT more useful in academic environments to facilitate increased engagement among students and achieve better learning outcomes. This study enhances the literature on technology acceptance in the context of education, particularly regarding the application of AI tools in learning spaces.

Metal oxide nanoparticles (NPs), particularly ZnO NPs, have garnered significant attention in addressing global water-related challenges. This study introduces NPs agents for acidic water treatment by synthesizing ZnO nanostructures via ultrasound-assisted green synthesis utilizing Ginger extract. The research investigates the influence of solution pH on the physical properties of NPs and their photocatalytic efficiencies in treating acidic (pH = 5) and neutral (pH = 7) water through the photodegradation of methylene blue (MB) under ultraviolet (UV) illumination. Results indicate that the solution pH, varying between 7 and 13, significantly controls the morphologies of ZnO NPs, yielding hexagonal plates, barley-like structures, and nanoflakes. The band gap energies of the synthesized NPs are quite independent of the solution pH, but their crystallite sizes decrease with increasing pH values. Notably, ZnO NPs synthesized at pH = 11 exhibit the highest BET-specific surface area of 26.74 m²/g, correlating with their superior photocatalytic activity. The optimal degradation efficiencies of MB in acidic conditions (pH = 5) reach 93.54% and 86.04% when utilizing 10 and 5 mg of the irradiated photocatalyst, respectively, after a reaction time of 160 min. These findings underscore the potential of ZnO NPs as a cost-effective and environmentally sustainable solution for efficient acidic wastewater treatment.

This study introduces a novel forecasting tool for West Nile Virus (WNV) risk at the municipal level, based on a previously developed climate-dependent epidemiological model coupled with a data-driven model and field data in an ensemble framework. The modelling system was evaluated at the municipal level in the regions of Central Macedonia (RCM) and Thessaly (RTH) in Greece for the period 2020-2024 and classifies the municipalities according to the risk level of occurrence of a WNV human case (WNVhc) within each forecast season. The modelling system produces seasonal forecasts updated monthly throughout the entire mosquito breeding season (April-September). From the first forecast of the year, WNVhc were correctly predicted at 71% of the infected municipalities in RCM and 60% of the infected municipalities in RTH, percentages that increased by at least 20% after July when surveillance data were incorporated. The observed number of infected humans was within the predicted range, highlighting the part of the region with an expected outbreak. The approach shows that the coupling of a climate-dependent epidemiological model at local level with a data-driven model, incorporating entomological data to predict the risk of WNVhc, can be a useful tool in planning control strategies.

Lung cancer is the leading cause of cancer-related deaths, of which adenocarcinoma is the most common subtype. Despite this, lung adenocarcinoma and its metastasis are poorly understood, due to difficulties in feasibly recapitulating disease progression and predicting clinical benefits of therapy. We outline a methodology to develop immunogenic orthotopic lung adenocarcinoma mouse models, by injecting cell-specific cre viruses into the lung of a genetically engineered mouse, which mirrors cancer progression defined by the International Association for the Study of Lung Cancer. Evaluation of different cre virus/concentrations models demonstrate remarkable consistency in cancer initiation and metastasis, allowing for high throughput, while showing differences in timing and severity, offering greater flexibility when selecting models. Histological and immune profiles reflect clinical observations suggesting similar mechanisms are recapitulated and preliminary data show resultant tumors to be responsive to clinical treatments. We present a clinically relevant, next-generation murine model for studying lung adenocarcinoma.

Accurate residential load forecasting is a key to achieve grid stability and efficient energy management. However, it becomes challenging due to the non-linear and seasonally fluctuating energy usage of domestic users. Existing statistical and machine learning-based forecasting models struggle to produce accurate forecasts due to dynamic and stochastic user behaviors for energy usage. On the other hand, pairwise ensemble methods can achieve higher forecasting accuracy in short-term load forecasting, but are not scalable and face generalization issues that often lead to overfitting and complexity in managing multivariate data. To address these limitations, we propose to integrate LightGBM, XGBoost and CatBoost models to systematically address the limitations of existing ensemble-based forecasting models. This integration aims to leverage the strengths of each ensemble method, where LightGBM handles generalization across multiple sites, XGBoost avoids overfitting the model, and CatBoost effectively manages categorical features. We implement our proposed model using a real-world, publicly available dataset for 13 residential locations in North America and Europe. The proposed model outperforms other state-of-the-art algorithms with the lowest root mean squared logarithmic error (RMSLE) values of 0.1898, while the coefficient of determination (R²) calculated from the data is 0.9745. Other evaluation measures such as root mean square error (RMSE), coefficient of variation of the root mean square error (CVRMSE) and mean bias error (MBE) also support the proposed approach regarding the efficiency of the model.Finally, we also perform an ablution study to show predictive efficacy of incremental model integration.