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Abstract Photodetection has attracted significant attention for information transmission. While the implementation relies primarily on the photonic detectors, they are predominantly constrained by the intrinsic bandgap of active materials. On the other hand, photothermoelectric (PTE) detectors have garnered substantial research interest for their promising capabilities in broadband detection, owing to the self-driven photovoltages induced by the temperature differences. To get higher performances, it is crucial to localize light and heat energies for efficient conversion. However, there is limited research on the energy conversion in PTE detectors at micro/nano scale. In this study, we have achieved a two-order-of-magnitude enhancement in photovoltage responsivity in the self-rolled tubular tellurium (Te) photodetector with PTE effect. Under illumination, the tubular device demonstrates a maximum photovoltage responsivity of 252.13 V W−1 and a large detectivity of 1.48 × 1011 Jones. We disclose the mechanism of the PTE conversion in the tubular structure with the assistance of theoretical simulation. In addition, the device exhibits excellent performances in wide-angle and polarization-dependent detection. This work presents an approach to remarkably improve the performance of photodetector by concentrating light and corresponding heat generated, and the proposed self-rolled devices thus hold remarkable promises for next-generation on-chip photodetection.

The excessive phosphorus input generated by human activities has broken the nutritional balance of ecosystem. This study focused on the Three Gorges Reservoir Region, where human activities are concentrated. It involved the innovative selection of county subunits to provide a clearer analysis of different components contributing to net anthropogenic phosphorus input (NAPI) and factors impacting it on a smaller scale, opening up a new horizon for more accurately formulating phosphorus management policies. The results showed that NAPI of the reservoir region maintained a high level (2641.84 kg·km−2·a−1), while showing significant spatial variation due to high input (>4200 kg·km−2·a−1) at an urban agglomeration in the reservoir region. Phosphorus fertiliser made the largest (63 %–64 %) contribution to NAPI in the reservoir region, but different compositions of the component determining NAPI were shown among the counties. The sensitive coefficient (2.36) of phosphorus fertiliser in the reservoir region was the largest, the sensitive coefficients and proportional contributions in counties also varied among the counties. The findings of this study showed that reducing the input of phosphorus fertiliser and improving the utilization rate of phosphorus are direct methods for reducing NAPI in the Three Gorges Reservoir Region. Furthermore, improving the methods of treating human domestic waste and sewage can alleviate the high input pressure of phosphorus. The spatial differentiation of NAPI in the analyzed counties was mainly affected by the increase in phosphorus fertiliser and gross output value of agriculture, along with increasing urbanization, population densification, and greater economic activity in Chongqing compared with the other counties. Moreover, the components, sensitivities, contributions and impact factors in the counties were heterogeneous due to socio-economic, agricultural, and geographical characteristics, so it might be more appropriate to formulate phosphorus management policies on a smaller county-based scale.

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Acquiring a comprehensive understanding of cropping patterns and their spatiotemporal distribution is crucial for sustainable agricultural development and ecological environment protection. However, the similarity of crop spectra and the diversity of ecosystem types hinder the accurate mapping of cropping patterns, especially in agricultural landscape regions. Hence, taking Xinghua County as study area, this article proposed a novel method for integrating multidimensional feature indices and phenological windows, named phenological window feature (PWF), to achieve efficient and accurate mapping of cropping patterns. In this study, we adopt a two-step approach. First, time-series curves of feature indices were constructed using Sentinel-1/2 satellite data to determine the phenological windows of different cropping patterns and construct PWF sets. Then, the ruleset threshold method (RTM) and random forest (RF) algorithms were used to map cropping patterns including wheat-rice, crayfish-rice, vegetable-rice, rice-rapeseed, rapeseed-vegetable, and year-round vegetables. The results indicate that the phenological windows extracted from the cropping patterns in the study area were 30–120, 90–135, and 200–270 days, respectively. The overall accuracies of RTM and RF, based on PWF, were 85.91% and 89.50%, respectively, and the kappa coefficients for RTM and RF were 0.831 and 0.872, respectively. In terms of classification performance, RF slightly outperformed RTM. The study demonstrates that PWF proposed in this article can be effectively utilized for mapping cropping patterns in complex agricultural landscape regions.

The Chongqing section of the Three Gorges Reservoir area (TGRA) is a high-risk geological catastrophe warning zone in the Yangtze River Basin. This study aimed to categorize landslides and improve the accuracy of landslide susceptibility evaluations by combining the artificial neural networks (ANN) and the normalized frequency ratio (NFR) models. Considering that the indicator factors have different effects on various landslides, the landslides were separated into four types, that is, giant, large, medium, and small. Thirteen indicator factors were selected through correlation analysis, including elevation, lithology, precipitation, land use, population density, and so on. Combined with 7777 historical landslide events, an ANN-NFR coupling model was proposed. The susceptibility grade prediction of landslides for the entire region was conducted using a GIS platform. Compared to the NFR model, the ANN-NFR model could improve the accuracy of landslide susceptibility evaluation by approximately 4%. This indicates that the ANN-NFR model is an effective method to calculate the weights of indicator factors. The FR value of large landslides is 9.201 in the very susceptibility region, and the model evaluation effect is superior to that of the other three types of landslides. The success rate of the ANN-NFR model after landslide classification was 78.9%, and the prediction rate was 78.6%, both of which were greater than the unclassified ANN-NFR model. Therefore, the ANN-NFR (landslide classified) model could improve the prediction accuracy and can provide a scientific basis for disaster prevention, mitigation, and management in the TGRA.

Non-point source (NPS) pollution poses a significant threat to the water environment, yet controlling it at the watershed scale remains a formidable challenge. Understanding the characteristics and drivers of nitrogen (N) and phosphorus (P) outputs at the watershed scale, along with identifying cost-effective best management practices (BMPs), is crucial for effective pollution control. In this study, we utilized the Wangjiaqiao watershed within the Three Gorges Reservoir Area (TGRA) as a case study to explore the characteristics of N and P load outputs and their dominant drivers by combining the SWAT model and a geographic detector. Based on our analysis of N and P loads within the watershed, we employed the entropy weight method to evaluate the reduction efficiency and cost-effectiveness of 64 BMP scenarios, encompassing seven measures (vegetative filter strips, parallel terraces, 10% fertilizer reduction, 30% fertilizer reduction, residue cover tillage, grass mulching, and returning farmland to forest) and their combinations. Our findings revealed the following: (1) spatial heterogeneity in NPS loads within the watershed, primarily influenced by land use, fertilizer application, and surface runoff, with interactive enhancement effects among driving factors; (2) the differential effectiveness of BMPs at the watershed level, with structural measures, particularly terracing, exhibiting higher efficacy and achieving reduction rates of 28.12% for total nitrogen (TN) and 37.69% for total phosphorus (TP); the combined BMPs showed improved reduction efficiency, but not merely additive; and (3) in terms of cost-effectiveness, 30% fertilizer reduction emerged as the most beneficial among the individual measures. Moreover, a combination of vegetative filter strips, parallel terraces, and 30% fertilizer reduction demonstrated significant improvements in TN and TP reductions (48.05% and 61.95%, respectively), suggesting their widespread applicability. Overall, our study provides insights into developing a cost-effective BMP strategy for the Wangjiaqiao watershed and offers valuable guidance for NPS pollution management in similar small watersheds within the TGRA.

Weather conditions play a pivotal role in embryo development and parental incubation costs, potentially impacting the clutch size and incubation behavior of birds. Understanding these effects is crucial for bird conservation. Reeves's Pheasant (Syrmaticus reevesii) is a threatened species endemic to China, which is characterized by female-only incubation. However, there is a lack of information regarding the impact of weather conditions on clutch size and incubation behavior in this species. Using satellite tracking, we tracked 27 wild female Reeves's Pheasants from 2020 to 2023 in Hubei Province, China. We explored their clutch size and incubation behavior, as well as their responses to ambient temperature and precipitation. Clutch size averaged 7.75 ± 1.36, had an association with average ambient temperature and average daily precipitation during the egg-laying period, and was potentially linked to female breeding attempts. Throughout the incubation period, females took an average of 0.73 ± 0.46 recesses every 24 h, with an average recess duration of 100.80 ± 73.37 min and an average nest attendance of 92.98 ± 5.27%. They showed a unimodal recess pattern in which nest departures peaked primarily between 13:00 and 16:00. Furthermore, females rarely left nests when daily precipitation was high. Recess duration and nest attendance were influenced by the interaction between daily mean ambient temperature and daily precipitation, as well as day of incubation. Additionally, there was a positive correlation between clutch size and recess duration. These results contribute valuable insights into the life-history features of this endangered species.

In this work, we propose a SiC-NSFET structure that uses a PTS scheme only under the gate, with SiC layers under the source and drain, to improve the leakage current and thermal reliability. Punch-through stopper (PTS) doping is widely used to suppress the leakage current, but aggressively high PTS doping will cause additional band-to-band (BTBT) current. Therefore, the bottom oxide isolation nanosheet field-effect transistor (BOX-NSFET) can further reduce the leakage current and become an alternative to conventional structures with PTS. However, thermal reliability issues, like bias temperature instability (BTI), hot carrier injection (HCI), and time-dependent dielectric breakdown (TDDB), induced by the self-heating effect (SHE) of BOX-NSFET, become more profound due to the lower thermal conductivity of SiO2 than silicon. Moreover, the bottom oxide will reduce the stress along the channel due to the challenges associated with growing high-quality SiGe material on SiO2. Therefore, this method faces difficulties in enhancing the mobility of p-type devices. The comprehensive TCAD simulation results show that SiC-NSFET significantly suppresses the substrate leakage current compared to the conventional structure with PTS. In addition, compared to the BOX-NSFET, the stress reduction caused by the bottom oxide is avoided, and the SHE is mitigated. This work provides significant design guidelines for leakage and thermal reliability optimization of next-generation advanced nodes.

In this paper, we investigate the effects of negative bias instability (NBTI) and self-heating effect (SHE) on threshold voltage in NSFETs. To explore accurately the interaction between SHE and NBTI, we established an NBTI simulation framework based on trap microdynamics and considered the influence of the self-heating effect. The results show that NBTI weakens the SHE effect, while SHE exacerbates the NBTI effect. Since the width of the nanosheet in NSFET has a significant control effect on the electric field distribution, we also studied the effect of the width of the nanosheet on the NBTI and self-heating effect. The results show that increasing the width of the nanosheet will reduce the NBTI effect but will enhance the SHE effect. In addition, we extended our research to the SRAM cell circuit, and the results show that the NBTI effect will reduce the static noise margin (SNM) of the SRAM cell, and the NBTI effect affected by self-heating will make the SNM decrease more significantly. In addition, our research results also indicate that increasing the nanosheet width can help slow down the NBTI effect and the negative impact of NBTI on SRAM performance affected by the self-heating effect.

This study examined the program effectiveness of Health Rocks!, a youth substance use prevention program. Merging data from 128,544 youth participants in 21 states between 2009 and 2021, this study compared self-reported levels of knowledge, skills, and internal and external assets before and after the program. Youth reported significant increases in substance use knowledge, stress-coping skills, and assets to make healthy life decisions across eleven years. The magnitude of effects varied by individual characteristics, including gender, school grade level, race, ethnicity, and residence. We provide recommendations for the evaluation of substance use prevention program effectiveness. [ABSTRACT FROM AUTHOR]

Ensuring the optimal operating temperature is imperative for achieving efficient performance in proton exchange membrane fuel cells. Consequently, this study introduces a dual-model predictive control strategy to regulate the water pump and cooling fan in a cooling system. Initially, we establish an electrochemical and thermal model for fuel cell stacks and validate the model's accuracy through experimental data. The system model is linearized, and the model predictive control (MPC) controller is formulated using the MATLAB/Simulink toolbox. Subsequently, it is collaboratively simulated with the electrochemical model of the fuel cell stack and the temperature model. To evaluate the effectiveness of the MPC controller, we conducted a comparative analysis with the traditional proportional-integral-derivative (PID) control and water pump MPC under step load, uniform load increase, and variable target scenarios. The findings indicate that in contrast to the PID control, the MPC controller significantly decreases the stack temperature difference fluctuation by more than 50%, maintaining the stack temperature within ±0.6 K of the set value. Furthermore, we independently assessed the performance of the MPC controller under varying ambient temperatures. The findings illustrate that the dual MPC method proficiently adapts cooling parameters across different ambient temperature ranges (288.15 K-308.15 K), ensuring the stable performance of the fuel cell. The model is linearized, and the simulation work is explained mainly on the MATLAB/Simulink platform. In order to compare the effectiveness of the MPC controller, the comparison with the MPC controller strategy of the water pump is added, which can better reflect the effectiveness of the proposed collaborative MPC controller strategy. [ABSTRACT FROM AUTHOR]