Search Results (58,771)

Peanut, as an important economic crop, is widely cultivated and rich in nutrients. Classifying peanuts based on the number of seeds helps assess yield and economic value, providing a basis for selection and breeding. However, traditional peanut grading relies on manual labor, which is inefficient and time-consuming. To improve detection efficiency and accuracy, this study proposes an improved BTM-YOLOv8 model and tests it on an independently designed pod detection device. In the backbone network, the BiFormer module is introduced, employing a dual-route attention mechanism with dynamic, content-aware, and query-adaptive sparse attention to extract features from densely packed peanuts. In addition, the Triple Attention mechanism is incorporated to strengthen the model’s multidimensional interaction and feature responsiveness. Finally, the original CIoU loss function is replaced with MPDIoU loss, simplifying distance metric computation and enabling more scale-focused optimization in bounding box regression. The results show that BTM-YOLOv8 has stronger detection performance for ‘Quan Hua 557’ peanut pods, with precision, recall, mAP50, and F1 score reaching 98.40%, 96.20%, 99.00%, and 97.29%, respectively. Compared to the original YOLOv8, these values improved by 3.9%, 2.4%, 1.2%, and 3.14%, respectively. Ablation experiments further validate the effectiveness of the introduced modules, showing reduced attention to irrelevant information, enhanced target feature capture, and lower false detection rates. Through comparisons with various mainstream deep learning models, it was further demonstrated that BTM-YOLOv8 performs well in detecting ‘Quan Hua 557’ peanut pods. When comparing the device’s detection results with manual counts, the R2 value was 0.999, and the RMSE value was 12.69, indicating high accuracy. This study improves the efficiency of ‘Quan Hua 557’ peanut pod detection, reduces labor costs, and provides quantifiable data support for breeding, offering a new technical reference for the detection of other crops. Full article

Under long-duration and high-speed flight conditions, the combined effects of external aeroheating and internal heat dissipation pose complex and challenging thermal design issues for the electronic equipment cabin of flight vehicles. This study employs a partitioned modeling strategy. By comparing the complexity of heat transfer pathways, the contact surface between the thermal protection structure (TPS) and the skin is selected as the interface. A two-way thermal coupling analysis model is established to investigate heat flux transport characteristics and coupling mechanisms between internal and external thermal environments of the electronic equipment cabin. The results indicate that the external thermal environment affects the internal environment primarily through the consumption of heat sink capacity by aeroheating penetrating the TPS, and the coupling effect intensifies with flight speed and duration. The internal thermal environment influences the external thermal environment by suppressing the penetration of aeroheating, and the coupling strength shows high sensitivity to the total internal heat dissipation. Heat conduction accounts for over 70% of the total heat transfer within the electronic equipment cabin, underscoring the importance of optimizing conductive heat transfer in thermal design. Compared to the conventional serial design approach based on the one-way coupling model, the collaborative thermal design derived from the two-way coupling model can achieve lower redundancy, lighter weight, and higher reliability. This paper is expected to provide support for the accurate thermal response prediction and collaborative thermal design of high-speed flight vehicles. Full article

This paper proposes a hybrid collaborative framework to optimize technology selection in Small and Medium-sized Enterprises (SMEs) by integrating machine learning (ML) predictions with Planning Poker, consensus-based estimation technique used in agile software development. Addressing known challenges such as cognitive bias, resource constraints, and the need for inclusive decision-making, the proposed model combines data-driven suitability analysis with stakeholder-driven consensus. ML generates quantitative, criterion-wise suitability scores based on historical SME data, providing transparent baselines for evaluation. Stakeholders independently assess candidate technologies using Planning Poker, and their consensus is blended with ML predictions through a flexible weighting mechanism. An illustrative case study on CRM tool selection illustrates the framework’s practical advantages: improved decision accuracy, transparency, and greater stakeholder engagement. The methodology is iterative, allowing for continuous learning and adaptation as new data emerges. This dual approach ensures that technology adoption decisions in SMEs are both empirically validated and contextually robust, offering a significant improvement over traditional, siloed methods. Full article

To mitigate the impact of the conductor’s inherent tension–torsion coupling effect on conductor quality during tension stringing, a method for tension–torsion analysis and structural parameter optimization of conductors is proposed based on the radial basis function neural network (RBFNN) surrogate model. The layer-wise lay ratios of conductors are selected as the structural parameters. Using the tension–torsion coupling computational method for conductors, the layer-wise lay ratios are sampled by Latin hypercube sampling (LHS) to construct the sample data by computing conductor torque under different combinations. The RBFNN surrogate model is trained with the data, and its shape parameter is optimized through Leave-One-Out Cross-Validation (LOOCV), achieving a coefficient of determination R² close to 1 with minimal errors. Targeting torque minimization, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify the optimal combination of conductor lay ratio parameters, reducing conductor torque by approximately 18% under the same axial tension. For practical applications, prioritize the optimal combination for JL/G1A-630/45-45/7 and analogous conductors, and adopt the RBFNN model for rapid torque prediction. The proposed method also serves as a reference for design optimization of conductor structural parameters. Full article

Background: The retinoid X receptor (RXR) is a ligand-activated nuclear receptor that heterodimerizes with numerous partners to regulate diverse transcriptional programs. RXR agonists, including the FDA-approved drug bexarotene, show anti-tumor activity but are limited by adverse side effects. V-125 is a next-generation RXR agonist engineered for improved selectivity, pharmacokinetics, and reduced lipogenic effects. This study compares the molecular and functional effects of V-125 and bexarotene in HER2⁺ breast cancer models. Methods: Female MMTV-Neu mice bearing mammary tumors were treated with control, V-125 (100 mg/kg diet), or bexarotene (100 mg/kg diet) for 10 days. RNA sequencing was used to identify differentially expressed genes and pathways. Candidate targets were validated by qPCR and immunohistochemistry (IHC). Immune modulation was evaluated by IHC staining for CD8 cells and CD206⁺ macrophages in tumors to capture the tumor microenvironment. Functional assays in JIMT-1 human HER2⁺ cells assessed RXR target activation and clonogenic potential in tumor cells. Results: V-125 induced broader transcriptional changes than bexarotene, including selective upregulation of Nrg1, Nfasc, Lrrc26, and Chi3l1 genes associated with improved patient survival. Pathway analysis revealed regulation of immune activation, cancer signaling, and lipid metabolism. Both V-125 and bexarotene suppressed colony formation in JIMT-1 cells, confirming previous observations about RXR-dependent inhibition of tumor cell growth. Moreover, V-125 in vivo had distinct capabilities to increase CD8 cell infiltration and reduced CD206⁺ macrophages, whereas bexarotene did not. Conclusions: V-125 but not bexarotene reprograms tumor transcriptional programs and the immune landscape in an anti-tumor manner in the MMTV-neu mouse model and in in vitro models of HER2⁺ breast cancer. This highlights its promise as a selective RXR agonist with anti-tumor and immunomodulatory activity in HER2⁺ breast cancer. Full article

This article presents a narrative, traditional literature review summarizing current research on the integration of digital technologies in waste management. The study examines how intelligent technologies, including Geographic Information Systems, Big Data analytics, and artificial intelligence, can improve energy efficiency, support sustainable resource use, and enhance the development of low emission and circular waste management systems. The reviewed research shows that the combination of spatial analysis, large-scale data processing, and predictive computational methods enables advanced modeling of waste distribution, the optimization of collection routes, intelligent sorting, and the forecasting of waste generation. Geographic Information Systems support spatial planning, site selection for waste facilities, and environmental assessment. Big Data analytics allows the integration of information from Internet of Things sensors, global positioning systems, municipal databases, and environmental registries, which strengthens evidence-based decision making. Artificial intelligence contributes to automatic classification, predictive scheduling, robotic sorting, and the optimization of recycling and energy recovery processes. The study emphasizes that the integration of these technologies forms a foundation for intelligent waste management systems that reduce emissions, improve operational efficiency, and support sustainable urban development. Full article

Aimed at the problems of traditional wood species identification relying on manual experience, slow identification speed, and insufficient robustness, this study takes hyperspectral images of cross-sections of 10 typical wood species commonly found in Puer, Yunnan, China, as the research object. It comprehensively applies various spectral and texture feature extraction technologies and proposes an intelligent wood species identification method based on the fusion of multimodal texture-dominated features and deep learning. Firstly, an SOC710-VP hyperspectral imager is used to collect hyperspectral data under standard laboratory lighting conditions, and a hyperspectral database of wood cross-sections is constructed through reflectance calibration. Secondly, in the spectral space construction stage, a comprehensive similarity matrix is built based on four types of spectral similarity indicators. Representative bands are selected using two Max–Min strategies: partitioned quota and coverage awareness. Multi-scale wavelet fusion is performed to generate high-resolution fused images and extract interest point features. Thirdly, in the texture space construction stage, three types of texture feature matrices are generated based on the PCA first principal component map, and interest point features are extracted. Fourthly, in the complementary collaborative learning stage, the ST-former model is constructed. The weights of the trained SpectralFormer++ and TextureFormer are imported, and only the fusion weights are optimized and learned to realize category-adaptive spectral–texture feature fusion. Experimental results show that the overall classification accuracy of the proposed joint model reaches 90.27%, which is about 8% higher than that of single-modal models on average. Full article

Consumer food choices play a significant role in supporting sustainable, resilient, and equitable food systems by shaping the environmental, economic, and social impact of diets. To determine whether environmental concerns and innovativeness drive Europeans to buy more sustainable foods, quantitative data were collected from 3131 adults in three countries. A Logistic Regression Model was developed to assess the quantitative impact of variables on consumers’ likelihood to choose sustainably produced foods. Respondents who paid attention to whether food items are produced and/or packaged in an environmentally friendly way were 94% and 48% more likely to purchase sustainably produced products, respectively. Readiness to purchase a dairy product that the buyer had never heard of resulted in a 15% increase in the likelihood of selecting sustainably produced foods. Additionally, respondents living in Germany were 30% more likely to choose sustainable products compared to Polish consumers, while Czech consumers were 10% less likely to do so. Implementing campaigns focusing on promoting sustainable diets could consequently determine and accelerate the adoption of environmentally friendly production practices in the food system. Our findings provide evidence for policymakers, the business community, and educators who aspire to improve the health of people and the planet as a whole. Full article

Underwater images often suffer from severe color distortion, low contrast, and reduced visibility, motivating the widespread use of image enhancement as a preprocessing step for downstream computer vision tasks. However, recent studies have questioned whether enhancement actually improves object detection performance. In this work, we conduct a comprehensive and rigorous evaluation of nine state-of-the-art enhancement methods and their interactions with modern object detectors. We propose a unified evaluation framework that integrates (1) a distribution-level quality assessment using a composite quality index (Q-index), (2) a fine-grained per-image detection protocol based on COCO-style mAP, and (3) a mixed-set upper-bound analysis that quantifies the theoretical performance achievable through ideal selective enhancement. Our findings reveal that traditional image quality metrics do not reliably predict detection performance, and that dataset-level conclusions often overlook substantial image-level variability. Through per-image evaluation, we identify numerous cases in which enhancement significantly improves detection accuracy—primarily for low-quality inputs—while also demonstrating conditions under which enhancement degrades performance. The mixed-set analysis shows that selective enhancement can yield substantial gains over both original and fully enhanced datasets, establishing a new direction for designing enhancement models optimized for downstream vision tasks. This study provides the most comprehensive evidence to date that underwater image enhancement can be beneficial for object detection when evaluated at the appropriate granularity and guided by informed selection strategies. The data generated and code developed are publicly available. Full article

In aerospace manufacturing, laser welding of TC4/TA18 dissimilar titanium alloys in bottom-locking configurations is essential for lightweight design, yet the residual stress behavior of such joints remains insufficiently understood. This study systematically examines the influence of laser power on residual stress distribution in laser-welded TC4/TA18 bottom-locking tubular joints. Welded specimens were fabricated at three distinct laser power levels (600 W, 800 W, and 1000 W). Experimental characterization included macroscopic morphology analysis and residual stress measurement using the blind-hole drilling method, among other techniques. Concurrently, a three-dimensional thermo-elastic-plastic finite element model was established based on ABAQUS 2022 to simulate the transient temperature field and stress–strain field during the welding process. The results indicate that due to the differences in thermophysical properties between the two titanium alloys and the wall thickness effect, both the temperature field and residual stress distribution of the TC4/TA18 dissimilar titanium alloy bottom-locking joints exhibit significant asymmetry. Laser power exerts a selective influence on the residual stress field: within the parameter range of this study, increasing laser power can significantly reduce the peak hoop stress of TA18 thin-walled tubes and TC4 thick-walled tubes, as well as the peak axial stress of TC4 thick-walled tubes, while remarkably increasing the peak axial stress of TA18 thin-walled tubes. The numerical simulation results are in good agreement with the experimental data, verifying that the established finite element model is an effective tool for predicting welding outcomes. Full article

To identify hypertension, Ballistocardiograph (BCG) signals can be primarily utilized. The BCG signal must be thoroughly understood and interpreted so that its application in the classification process could become clearer and more distinct. Various unhealthy habits such as excess consumption of alcohol and tobacco, accompanied by a lack of good diet and a sedentary lifestyle, lead to hypertension. Common symptoms of hypertension include chest pain, shortness of breath, blurred vision, mood swings, frequent urination, etc. In this work, two pragmatic models are proposed for the detection of hypertension using BCG signals and machine learning models. The first model uses K-means clustering, the maximum overlap discrete wavelet transform (MODWT) and the Empirical Wavelet Transform (EWT) techniques for feature extraction, followed by the Binary Tunicate Swarm Algorithm (BTSA) and Information Gain (IG) for feature selection, as well as two efficient hybrid classifiers such as the Hybrid AdaBoost–-Maximum Uncertainty Linear Discriminant Analysis (MULDA) classifier and the Hybrid AdaBoost–Random Forest (RF) classifier for the classification of BCG signals. The second model uses Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and the Random Feature Mapping (RFM) technique for feature extraction, followed by IG and the Aquila Optimization Algorithm (AOA) for feature selection, as well as two versatile hybrid classifiers such as the Hybrid AutoRegressive Integrated Moving Average (ARIMA)–AdaBoost classifier and the Time-weighted Hybrid AdaBoost–Support Vector Machine (TW-HASVM) classifier for the classification of BCG signals. The proposed methodology was tested on a publicly available BCG dataset, and the best results were obtained when the KPCA feature extraction technique was used with the AOA feature selection technique and classified using the Hybrid ARIMA–AdaBoost classifier, reporting a good classification accuracy of 96.89%. Full article

Background: Dyspepsia is a common indication for gastroscopy, yet its diagnostic yield for malignancy and other clinically significant findings (CSF) remains low. Improved risk stratification is therefore essential to guide endoscopic referral. This study evaluates the diagnostic yield of gastroscopy in dyspepsia and investigates the predictive roles of age, ethnicity, and alarm symptoms. Methods: This retrospective single-center study was conducted at a university-affiliated hospital in Israel and included 3022 patients who underwent gastroscopy for dyspepsia over a five-year period. Multivariate logistic regression identified independent predictors of CSF, and receiver operating characteristic (ROC) analysis determined optimal age thresholds for malignancy and CSF. Results: Functional dyspepsia accounted for 55.9% of cases, while precancerous gastric lesions and upper gastrointestinal malignancies were identified in 12.8% and 0.79%, respectively. In multivariable models, age ≥ 50 years (OR = 2.59; CI: 2.02–3.32) and alarm symptoms (OR = 1.79; 95% CI: 1.33–2.41) independently predicted CSFs. Malignancy was similarly associated with age ≥ 50 years (OR = 4.89; CI: 1.11–21.60) and alarm symptoms (OR = 31.42; CI: 10.26–96.19). ROC analysis identified optimal age thresholds of 50 years for CSF (AUC = 0.65) and 54 years for malignancy (AUC = 0.72). Ethnicity did not independently predict malignancy, though minority patients showed differing precancerous lesion patterns. Conclusions: Age ≥ 50 years and alarm symptoms significantly increased the likelihood of CSFs and malignancy, supporting a selective approach to gastroscopy. ROC-derived thresholds may support reconsideration of age criteria in settings with similar epidemiologic patterns, highlighting the need for region-specific risk stratification. Full article

Early fault diagnosis in induction motors is important to maintain correct operation in terms of energy and efficiency, as well as to achieve a reduction in costs associated with maintenance or unexpected stoppages in production processes. These motors are widely used in industry due to their reliability, low cost, and great robustness; however, over time, they may be exposed to wear that can affect their performance, endanger the integrity of operators, or cause unexpected shutdowns that generate economic losses. Corrosion in the bearings is one of the most common failures, which is mainly triggered by high humidity in combination with high temperatures. However, despite its relevance, it has not been widely explored as a cause of failure in induction motors. Unlike failures that occur in specific or localized areas, corrosion in bearings does not manifest through specific frequencies associated with the phenomenon, since the corrosion occurs extensively on the surface of the raceway, making early diagnosis difficult with conventional techniques based on spectral analysis. Therefore, this work proposes an approach for the analysis of magnetic stray flux and vibration signals under different levels of corrosion using statistical and non-statistical parameters to capture variations in the dynamic behavior of the motors while employing genetic algorithms to select the most relevant parameters for each signal and optimize the configuration of an ensemble learning algorithm. The classification of the bearing condition is achieved using support vector machines in combination with the bagging method, which increases the robustness and accuracy of the model in the presence of signal variability. A classification accuracy between the healthy state and two gradualities greater than 99% was obtained, indicating that the proposed approach is reliable and efficient for corrosion diagnosis. Full article

This study focuses on the operational energy consumption of existing thermal power plant buildings in China’s hot-summer, cold-winter regions. Unlike conventional civil buildings, thermal power plant structures feature intense internal heat sources, large spatial dimensions, specialized ventilation requirements, and year-round industrial waste heat. Consequently, the energy consumption characteristics and energy-saving logic of their building envelopes remain understudied. This paper innovatively employs a combined experimental approach of field monitoring and energy consumption simulation to quantify the actual thermal performance of building envelopes (particularly exterior walls, doors, and windows) under current operating conditions, identifying key components for energy-saving retrofits of the main plant building envelope. Due to the fact that most thermal power plants were designed relatively early, their envelope structures generally have problems such as poor insulation performance and insufficient air tightness, resulting in severe energy loss under extreme weather conditions. An energy consumption simulation model was established using GBSEARE software. By focusing on heat transfer coefficients of exterior walls and windows as key parameters, a design scheme for energy-saving retrofits of building envelopes in thermal power plants located in hot-summer, cold-winter regions was proposed. The results show that there is a temperature gradient along the height direction inside the main plant, and the personnel activity area in the middle activity level of the steam engine room is the most unfavorable area of the thermal environment of the steam engine room. The heat transfer coefficient of the envelope structure does not meet the current code requirements. The over-standard rate of the exterior walls is 414.55%, and that of the exterior windows is 177.06%. An energy-saving renovation plan is proposed by adopting a composite color compression panel for the external wall, selecting 50 mm flame-retardant polystyrene EPS foam board for the heat preservation layer, adopting 6 high-transmittance Low-E + 12 air + 6 plastic double-cavity for the external windows, and adding movable shutter sunshade. The energy-saving rate of the building reached 55.32% after the renovation. This study provides guidance for energy-efficient retrofitting of existing thermal power plants and for establishing energy-efficient design standards and specifications for future new power plant construction. Full article

A cobalt-containing zeolitic imidazolate framework (ZIF-67) was carbonized by different routes to composite materials (cZIFs) composed of metallic Co, Co₃O₄, and N-doped carbonaceous phase. The effect of the carbonization procedure on the water pollutant removal properties of cZIFs was studied. Higher temperature and prolonged thermal treatment resulted in more uniform particle size distribution (as determined by nanoparticle tracking analysis, NTA) and surface charge lowering (as determined by zeta potential measurements). Surface-governed environmental applications of prepared cZIFs were tested using physical (adsorption) and electrochemical methods for dye degradation. Targeted dyes were methylene blue (MB) and methyl orange (MO), chosen as model compounds to establish the specificity of selected remediation procedures. Electrodegradation was initiated via an intermediate reactive oxygen species formed during oxygen reduction reaction (ORR) on cZIFs serving as electrocatalysts. The adsorption test showed relatively uniform adsorption sites at the surface of cZIFs, reaching a removal of over 70 mg/g for both dyes while governed by pseudo-first-order kinetics favored by higher mesoporosity. In the electro-assisted degradation process, cZIF samples demonstrated impressive efficiency, achieving almost complete degradation of MB and MO within 4.5 h. Detailed analysis of energy consumption in the degradation process enabled the calculation of the current conversion efficiency index and the amount of charge associated with O₂^•−/^•OH generation, normalized by the quantity of removed dye, for tested materials. Here, the proposed method will assist similar research studies on the removal of organic water pollutants to discriminate among electrode materials and procedures based on energy efficiency. Full article