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Te-free thermoelectrics have garnered significant interest due to their immense thermoelectric potential and low cost. However, most Te-free thermoelectrics have relatively low performance because of the strong electrical and thermal transport conflicts and unsatisfactory compatibility of interfaces between device materials. Here, we develop lattice defect engineering through Cu doping to realize a record-high figure of merit of ~1.9 in n-type polycrystalline PbSe. Detailed micro/nanostructural characterizations and first-principles calculations demonstrate that Cu-induced interstitial defects and nanoprecipitates simultaneously optimize electron and phonon transport properties. Moreover, a robust Co/PbSe interface is designed to effectively prevent chemical reactions/diffusion; this interface exhibited a low electrical contact resistivity of ~10.9 μΩ cm2, excellent durability, and good stability in the thermoelectric module, which achieves a record-high conversion efficiency of 13.1% at a temperature difference of 460 K in segmented thermoelectric modules. This study lays the groundwork for advancing the development of Te-free selenide-based thermoelectric materials. The authors develop lattice defect engineering through Cu doping to realize a high figure of merit in n-type polycrystalline PbSe, finding that Cu induced interstitial defects and nanoprecipitates simultaneously optimize electron and phonon transport properties. [ABSTRACT FROM AUTHOR]

This study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. Through comparative experiments with various advanced models, we comprehensively evaluate the performance of our model. In the disease detection task, our model performs excellently, achieving a precision of 0.93, a recall of 0.89, an accuracy of 0.91, and an mAP of 0.90. By introducing the density loss function, we are able to effectively improve the detection accuracy when dealing with high-density regions. In the wheat spike counting task, the model similarly demonstrates a strong performance, with a precision of 0.91, a recall of 0.88, an accuracy of 0.90, and an mAP of 0.90, further validating its effectiveness. Furthermore, this paper also conducts ablation experiments on different loss functions. The results of this research provide a new method for wheat spike counting and disease detection, fully reflecting the application value of deep learning in precision agriculture. By combining the probability density attention mechanism and the density loss function, the proposed model significantly improves the detection accuracy and efficiency, offering important references for future related research. [ABSTRACT FROM AUTHOR]

Self-admitted technical debts (SATDs) refer to a solution in software development that selects suboptimal solutions to meet the current requirements and are intentionally introduced and documented by developers. SATDs in issue-tracking systems are a complement to those within source code comments. The effective identification of SATDs is crucial for software quality assurance and maintenance. Current studies focus on whether issue sections contain debt, but overlook specific SATD types. Meanwhile, they lack solutions for the challenge that SATD features are hard to learn due to the scarcity of instances containing SATDs. To address these problems, we propose a novel method, which is a weighted prompt tuning to identify SATDs, called WPTD. Specifically, WPTD employs a weighted prompt tuning to adapt the model with few-shot samples for insufficient training data. Moreover, to improve the performance of the model, WPTD constructs an SATD verbalizer by extracting keywords through mutual information and refining it with prior contextual information. Furthermore, it also improves SATD representation by extracting weights using the chi-square method and integrating them into the text. Finally, to reduce bias, WPTD computes the average score of results as final predicted distributions. We conduct comprehensive experiments on seven projects and the results show that our method significantly outperforms baseline approaches. In addition, we summarize the project-specific keywords, which can help developers better understand SATDs. [ABSTRACT FROM AUTHOR]

The strong coupling between carrier and phonon transport in thermoelectric (TE) materials severely limits improvements to the figure of merit (ZT). In this work, an approach is proposed to weaken electron‐phonon coupling, effectively enhancing the TE performance of GeTe. Alloying with Zn4Sb3 reduces excessive carrier concentration (nH), widens the bandgap, and promotes band convergence, synergistically improving charge carrier transport and ensuring a high power factor. Additional Pb doping further optimizes nH, boosting the power factor even more. Moreover, phonon transport is suppressed through Pb doping, as reflected in enhanced acoustic‐optical interactions due to the crossing of optical and acoustic modes, and a reduction in sound group velocity. This reinforced strong phonon scattering, combined with multi‐scale hierarchical nano/microstructures in the matrix, significantly reduces lattice thermal conductivity. As a result, a high ZT of 2.1 is achieved at 753 K in Ge0.9Pb0.1Te+0.9%Zn4Sb3. Alongside optimized mechanical performance, a high power density of 1.46 W cm−2 is realized at a temperature difference of 350 K in the fabricated 7‐pair TE module. The findings demonstrate the effectiveness of a stepwise optimization strategy for developing high‐performance TE materials. [ABSTRACT FROM AUTHOR]

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model's computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection. [ABSTRACT FROM AUTHOR]

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model's ability to recognize complex agricultural disease features and to address the issue of sample imbalance efficiently. Experimental results demonstrate that the proposed method outperforms existing deep learning models in cucumber disease detection tasks. Specifically, the method achieved a precision of 93%, a recall of 89%, an accuracy of 92%, and a mean average precision (mAP) of 91%, with a frame rate of 57 frames per second (FPS). Additionally, the study successfully implemented model lightweighting, enabling effective operation on mobile devices, which supports rapid on-site diagnosis of cucumber diseases. The research not only optimizes the performance of cucumber disease detection, but also opens new possibilities for the application of deep learning in the field of agricultural disease detection. [ABSTRACT FROM AUTHOR]

This paper proposes an advanced deep learning model that integrates the Diffusion-Transformer structure and parallel attention mechanism for the tasks of growth estimation and disease detection in jujube forests. Existing methods in forestry monitoring often fall short in meeting the practical needs of large-scale and highly complex forest areas due to limitations in data processing capabilities and feature extraction precision. In response to this challenge, this paper designs and conducts a series of benchmark tests and ablation experiments to systematically evaluate and verify the performance of the proposed model across key performance metrics such as precision, recall, accuracy, and F1-score. Experimental results demonstrate that compared to traditional machine learning models like Support Vector Machines and Random Forests, as well as common deep learning models such as AlexNet and ResNet, the model proposed in this paper achieves a precision of 95%, a recall of 92%, an accuracy of 93%, and an F1-score of 94% in the task of disease detection in jujube forests, showing similarly superior performance in growth estimation tasks as well. Furthermore, ablation experiments with different attention mechanisms and loss functions further validate the effectiveness of parallel attention and parallel loss function in enhancing the overall performance of the model. These research findings not only provide a new technical path for forestry disease monitoring and health assessment but also contribute rich theoretical and experimental foundations for related fields. [ABSTRACT FROM AUTHOR]

Large-tow carbon fiber-reinforced polymer composites (CFRP) have great application potential in civil engineering due to their low price, but their basic mechanical properties are still unclear. The tensile properties of large-tow CFRP rods and plates were investigated in this study. First, the tensile properties of unidirectional CFRP rods and plates were studied, and the test results of the relevant mechanical properties were statistically analyzed. The tensile strength of the CFRP rod and plate are 2005.97 MPa and 2069.48 MPa. Second, the surface of the test specimens after failure was observed using a scanning electron microscope to analyze the type of failure and damage evolution process. Finally, the probabilistic characteristics of the mechanical properties were analyzed using normal, lognormal, and Weibull distributions for parameter fitting. Quasi-optimality tests were performed, and a probability distribution model was proposed for the mechanical properties of large-tow CFRP rods and plates. [ABSTRACT FROM AUTHOR]

While the high-voltage direct current (HVDC) transmission system is in monopolar operation, it produces thousands of amperes of ground-return currents (GRCs). Accurate computation of the GRCs is essential for assessing safety implications for nearby industrial infrastructure. Current three-dimensional forward models of GRCs are typically constructed based on discrete differential equations, and their solving efficiency is constrained by the increased degrees of freedom resulting from the fine discretization grids in high-conductivity conductors and ground points. To address this issue, we present a new resistor network (RN) forward solver based on a multi-resolution grid approach. This solver utilizes an RN to avoid the massive degrees of freedom resulting from fine discretization of high-voltage conductors and enhances grid discretization efficiency near the surface grounding system through multi-resolution grids. We demonstrate, through multiple three-dimensional geoelectrical model cases, that the proposed method reduces the forward modeling misfit to 1% and possesses only 3‰ of the required discrete elements compared to traditional approaches. Furthermore, practical HVDC grid model analyses indicate the successful application of the proposed method for GRC analysis in complex geoelectric conditions. [ABSTRACT FROM AUTHOR]

Complex optimization problems hold broad significance across numerous fields and applications. However, as the dimensionality of such problems increases, issues like the curse of dimensionality and local optima trapping also arise. To address these challenges, this paper proposes a novel Wild Gibbon Optimization Algorithm (WGOA) based on an analysis of wild gibbon population behavior. WGOA comprises two strategies: community search and community competition. The community search strategy facilitates information exchange between two gibbon families, generating multiple candidate solutions to enhance algorithm diversity. Meanwhile, the community competition strategy reselects leaders for the population after each iteration, thus enhancing algorithm precision. To assess the algorithm's performance, CEC2017 and CEC2022 are chosen as test functions. In the CEC2017 test suite, WGOA secures first place in 10 functions. In the CEC2022 benchmark functions, WGOA obtained the first rank in 5 functions. The ultimate experimental findings demonstrate that the Wild Gibbon Optimization Algorithm outperforms others in tested functions. This underscores the strong robustness and stability of the gibbon algorithm in tackling complex single-objective optimization problems. [ABSTRACT FROM AUTHOR]