Search Results (7,936)

In recent years, real-time monitoring of broiler chicken weight has become crucial for assessing growth and health status. Currently, obtaining weight data often relies on manual collection. However, this process is cumbersome, labor-intensive, and inefficient. This paper proposes a broiler carcass weight detection model based on deep learning image segmentation and regression to address these issues. The model first segments broiler carcasses and then uses the pixel area of the segmented region as a key feature for a regression model to predict weight. A custom dataset comprising 2709 images from 301 Taihu yellow chickens was established for this study. A novel segmentation network, AR2U-AtNet, derived from R2U-Net, is proposed. To mitigate the interference of background color and texture on target carcasses in slaughterhouse production lines, the Convolutional Block Attention Module (CBAM) is introduced to enable the network to focus on areas containing carcasses. Furthermore, broilers exhibit significant variations in size, morphology, and posture, which impose high demands on the model’s scale adaptability. Selective Kernel Attention (SKAttention) is therefore integrated to flexibly handle broiler images with diverse body conditions. The model achieved an average Intersection over Union (mIoU) score of 90.45%, and Dice and F1 scores of 95.18%. The regression-based weight prediction achieved an $R^2$ value of 0.9324. The results demonstrate that the proposed method can quickly and accurately determine individual broiler carcass weights, thereby alleviating the burden of traditional weighing methods and ultimately improving the production efficiency of yellow-feather broilers. Full article

Intense thermo-mechanical coupling effects during cutting generate residual stress within the surface layer of a workpiece. This residual stress is a critical factor influencing the fatigue life, corrosion resistance, and dimensional stability of mechanical components, making its accurate prediction and control essential for improving product performance. To address the often generalized treatment of residual stress prediction modeling in existing literature, this paper presents a systematic review of recent advances in surface residual stress prediction for cutting operations. It details the formation mechanisms and significance of residual stress, focusing on four primary modeling approaches: empirical models based on experimental data, analytical models founded on metal cutting and elastoplastic theory, finite element models that simulate actual machining conditions, and hybrid models. A comprehensive analysis and comparison of these four model types is provided, summarizing their respective advantages and limitations. Furthermore, this paper identifies potential future research directions and development trends in residual stress prediction modeling, serving as a valuable reference for work in this field. Full article

The extraction of land gas resources requires efficient methods to address the issue of pipeline obstruction due to the accumulation of natural gas hydrates. The existing ground heating, downhole throttling, and decompression measures are energy-intensive. The metal-reinforced composite heat-insulation pipe serves as the production string for terrestrial natural gas wells, effectively minimizing temperature loss of natural gas within the wellbore. This innovation eliminates the need for ground heating equipment and downhole throttling devices in large-scale gas well production, thereby fundamentally achieving environmentally sustainable natural gas extraction, energy conservation, and cost reduction. This research simulates the operational circumstances and environmental characteristics of the Sulige gas field. Utilizing predictions and analyses of the formation characteristics of natural gas hydrate, the gas–solid two-phase flow DPM model, RNG k-ε turbulence model, heat transfer characteristics, and population balance model are employed to examine the concentration distribution, pressure distribution, velocity distribution, and heat transfer characteristics of natural gas hydrate within the vertical tube of the structure. The findings indicate that a reduction in natural gas production or an increase in hydrate volume fraction leads to significant accumulation of hydrate adjacent to the tube wall, while the concentration distribution of hydrate is more uniform at elevated production conditions. The pressure distribution of hydrate under each operational state exhibits a pattern characterized by a high central concentration that progressively diminishes towards the periphery. The unit pressure drop of hydrate markedly escalates with an increase in flow rate. As the ambient temperature of the formation rises or the flow rate escalates, the thermal loss of the hydrate along the pipeline diminishes, resulting in an elevated exit temperature. Minimizing the thermal conductivity of the composite pipe can significantly decrease the temperature loss of the hydrate along the pipeline, greatly aiding in hydrate inhibition during the extraction of natural gas from terrestrial wells. This paper’s research offers theoretical backing for the enduring technical application of metal-reinforced composite insulating pipes in terrestrial gas fields, including the Sulige gas field. Full article

The pressure-gradient Micro-Electro-Mechanical Systems (MEMS) vector hydrophone is a novel type of sensor capable of simultaneously acquiring both scalar and vectorial information within an acoustic field. Conventional azimuth estimation methods struggle to achieve high-resolution localization using a single pressure-gradient MEMS vector hydrophone. In practical marine environments, the multiple signal classification (MUSIC) algorithm is hampered by significant resolution performance loss. Similarly, the complex acoustic intensity (CAI) method is constrained by a high-resolution threshold for multiple targets, often resulting in inaccurate azimuth estimates. Therefore, a cross-spectral model between the acoustic pressure and the particle velocity for the pressure-gradient MEMS vector hydrophone was established. Integrated with an improved particle swarm optimization (IPSO) algorithm, a high-resolution azimuth estimation method utilizing this hydrophone is proposed. Furthermore, the corresponding Cramér-Rao Bound is derived. Simulation results demonstrate that the proposed algorithm accurately resolves two targets separated by only 5° at a low signal-to-noise ratio (SNR) of 5 dB, boasting a root mean square error of approximately 0.35° and a 100% success rate. Compared with the CAI method and the MUSIC algorithm, the proposed method achieves a lower resolution threshold and higher estimation accuracy, alongside low computational complexity that enables efficient real-time processing. Field tests in an actual seawater environment validate the algorithm’s high-resolution performance as predicted by simulations, thus confirming its practical efficacy. The proposed algorithm addresses key limitations in underwater detection by enhancing system robustness and offering high-resolution azimuth estimation. This capability holds promise for extending to multi-target scenarios in complex marine settings. Full article

Background: Emotional dysregulation (ED) is a transdiagnostic construct implicated in a broad range of psychiatric conditions. However, the influence of gender on ED remains understudied, particularly among adolescents with severe mood and behavioral disorders. Furthermore, few studies have controlled for confounding effects of specific psychiatric diagnoses. Methods: We assessed 182 adolescents (80.8% female; mean age 15.7 years) referred to our clinical institution. Participants completed the Cyclothymic–Hypersensitive Temperament Questionnaire (CHTQ), the Reactivity, Intensity, Polarity, and Stability Questionnaire (RIPoSt-Y), and the K-SADS-PL interview. Results: Females reported significantly higher levels of CHTQ mood lability (7.53 vs. 5.94, p = 0.012), RIPoSt-Y affective instability (62.33 vs. 53.31, p = 0.023), and interpersonal sensitivity (30.80 vs. 24.97, p < 0.001). They also exhibited higher rates of cyclothymic–hypersensitive temperament (46.6% vs. 14.7%, p = 0.001). Regression analysis revealed that gender and specific psychiatric diagnoses exerted significant independent effects on ED dimensions. Mood lability/hypersensitivity was significantly predicted by bipolar disorder (p = 0.001), depressive disorder (p = 0.002), and female sex (p = 0.025). Affective instability was independently predicted by bulimia nervosa (p = 0.019), depressive disorder (p = 0.004), and female sex (p = 0.033). Significant predictors for interpersonal sensitivity included female sex (p = 0.002), depressive disorder (p = 0.008), bulimia nervosa (p = 0.044), and the absence of conduct disorder (p = 0.048). Conclusions: Female adolescents with severe psychiatric presentations exhibited higher levels of ED, specifically regarding mood lability, affective instability, and interpersonal sensitivity. These associations persisted independently of current mood disorder diagnoses or comorbidities. While findings from this clinical cohort may not be fully generalizable to the general population, they highlight the need for gender-informed clinical interventions for adolescents characterized by severe ED. Full article

Objectives: To explore if MRI can monitor treatment and predict outcome in patients with human epidermal growth factor 2 (HER2)-negative breast cancer receiving neoadjuvant chemotherapy (NACT) with or without bevacizumab. Methods: Multiparametric MRI was performed at baseline and after 12 and 25 weeks of NACT. MRI assessment included tumour size, apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI), and signal intensity–time curves and vascular volume transfer constant (K^TRANS) from dynamic contrast-enhanced MRI (DCE). The reference standards were pathological complete response (pCR) at the time of surgery, and 10-year recurrence-free survival. Receiver operating characteristics analyses were performed to assess the predictive value of the MRI parameters. MRI findings and outcomes were compared between the treatment groups. Results: Seventy women were included from November 2008 to July 2012, with a median age of 49.5 years and median tumour diameter of 47 mm. Fourteen patients (20.0%) achieved pCR, while eleven (15.7%) had recurrence during the 10-year follow-up. The treatment significantly reduced tumour size, increased ADC, decreased K^TRANS, and shifted the signal intensity–time curves towards more benign shapes. The DCE parameters changed significantly more in the bevacizumab group. In the bevacizumab group, baseline K^TRANS predicted pCR (Area under curve (AUC) = 0.73), but the difference in pCR-rates between the treatment groups was not significant (p = 0.07). Only tumour size and shrinkage at 12 weeks predicted pCR (AUC = 0.71–0.85) regardless of size measuring method. No MRI parameters predicted survival. Conclusions: All MRI parameters reflected treatment response, but no parameter predicted survival or benefit from adding bevacizumab to chemotherapy. Full article

With the increasing frequency extreme weather events associated with climate change, real-time monitoring of rainfall intensity is critical for water resource management, disaster warning, and other applications. Traditional methods, such as ground-based rain gauges, radar, and satellites, face challenges like high costs, low resolution, and monitoring gaps. This study proposes a novel real-time rainfall intensity monitoring method based on deep learning and audio signal processing, using acoustic features from rainfall to predict intensity. Conducted in the semi-arid region of Northwest China, the study employed a custom-designed sound collection device to capture acoustic signals from raindrop-surface interactions. The method, combining multi-feature extraction and regression modeling, accurately predicted rainfall intensity. Experimental results revealed a strong linear relationship between sound pressure and rainfall intensity (r = 0.916, R² = 0.838), with clear nonlinear enhancement of acoustic energy during heavy rainfall. Compared to traditional methods like CML and radio link techniques, the acoustic approach offers advantages in cost, high-density deployment, and adaptability to complex terrain. Despite some limitations, including regional and seasonal biases, the study lays the foundation for future improvements, such as expanding sample coverage, optimizing sensor design, and incorporating multi-source data. This method holds significant potential for applications in urban drainage, agricultural irrigation, and disaster early warning. Full article

Recent studies have reported a rapid increase in short-duration, high-intensity rainfall over the Seoul Metropolitan Area (SMA), primarily associated with mesoscale convective systems (MCSs), highlighting the need for high-resolution and multi-platform observations for accurate forecasting. To address this challenge, the Korea Meteorological Administration (KMA) established the Korean Precipitation Observation Program (KPOP), an intensive observation network integrating radar, wind lidar, wind profiler, and storm tracker measurements. This study introduces the design and implementation of the KPOP network and evaluates its observational and forecasting value through a heavy rainfall event that occurred on 17 July 2024. Wind lidar data and weather charts reveal that a strong low-level southwesterly jet and enhanced moisture transport from the Yellow Sea played a key role in sustaining a quasi-stationary, line-shaped rainband over the metropolitan region, leading to extreme short-duration rainfall exceeding 100 mm h⁻¹. To investigate the impact of KPOP observations on numerical prediction, preliminary data assimilation experiments were conducted using the Korean Integrated Model-Regional Data Assimilation and Prediction System (KIM-RDAPS) with WRF-3DVAR. The results demonstrate that assimilating wind lidar observations most effectively improved the representation of low-level moisture convergence and spatial structure of the rainband, leading to more accurate simulation of rainfall intensity and timing compared to experiments assimilating storm tracker data alone. These findings confirm that intensive, high-resolution wind observations are critical for improving initial analyses and enhancing the predictability of extreme rainfall events in densely urbanized regions such as the SMA. Full article

Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950 to 2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches were developed and tested under operationally realistic conditions—Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), XGBoost—using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional major-hurricane targets due to lower event frequencies and stronger predictability limits. Full article

(1) Background: We aimed to determine rates of bacteremia and multidrug resistance (MDR) bacteremia and associated risk factors among neonates receiving parenteral nutrition (PN). (2) Methods: This is a multicenter study conducted in three neonatal intensive care units in Saudi Arabia, including 414 neonates who received PN. Associations were assessed using Chi-square or Fisher’s Exact tests when applicable and logistic regression analyses were conducted to determine factors predicting outcomes. Odds ratios with their 95% confidence intervals were computed, and a p value < 0.05 was considered statistically significant. (3) Results: PN was started within the first 10 days of life in 74.4% of cases. Fat emulsion was administered to 38.9% of the newborns. Blood cultures were positive in 24.9% of patients. Among the positive cultures, 4.9% were confirmed to have MDR bacteria. The mortality rate following bacteremia was 7.8%. The use of fat emulsion (p = 0.003), birth weight < 700 g (p < 0.001), and a gestational age within 27 weeks (p < 0.001) predicted bacteremia. (4) Conclusions: There was an association between the PN and bacteremia. Significant predictors of bacteremia were the use of fat emulsion, birth weight < 700 g, and a gestational age within 27 weeks. Full article

The identification of non-circular critical slip surfaces in slopes using metaheuristic algorithms remains a frontier challenge in geotechnical engineering. Such approaches are particularly effective for assessing the stability of heterogeneous slopes, including earth dams. This study introduces ODACO, a comprehensive program developed to determine the optimum cross-section of earth dams with berms. The program employs the Max–Min Ant System (MMAS), one of the most robust variants of the ant colony optimization algorithm. For each candidate cross-section, the critical slip surface is first identified using MMAS. Among the stability-compliant alternatives, the configuration with the most efficient shell geometry is then selected. The optimization process is conducted automatically across all loading conditions, incorporating slope stability criteria and operational constraints. To ensure that the optimized cross-section satisfies seismic performance requirements, an artificial neural network (ANN) model is applied to rapidly and reliably predict seismic responses. These ANN-based predictions provide an efficient alternative to computationally intensive dynamic analyses. The proposed framework highlights the potential of optimization-driven approaches to replace conventional trial-and-error design methods, enabling more economical, reliable, and practical earth dam configurations. Full article

Exchanges between ground and surface water strongly influence how rivers thermally respond. Ground-to-surface water connections are particularly important during periods of intense atmospheric heat waves. In salmonid-rich rivers of Quebec, elevated summer temperatures can induce thermal stresses, threatening aquatic ecosystems. This study’s objective was to evaluate the influence of groundwater discharge on river water temperature, using a 3D coupled flow and heat transfer model calibrated with one year of field data. The results show that groundwater inflow reduced the peak river temperatures by 1.5–3.2 °C during heat waves, representing up to 40% of the river’s thermal budget under low-flow conditions. In both rivers, groundwater prevented the temperatures from exceeding the 20–22 °C threshold critical for salmonid survival. These findings underscore the importance of integrated hydrothermal modeling for predicting ecological vulnerability under climate change. Full article

Core body temperature is a critical physiological indicator for assessing and diagnosing animal health status. In bovines, continuously monitoring this metric enables accurate evaluation of their physiological condition; however, traditional rectal measurements are labor-intensive and cause stress in animals. To achieve intelligent, contactless temperature monitoring in cattle, we proposed a non-invasive method based on thermal imaging combined with environmental data fusion. First, thermal infrared images of the cows’ faces were collected, and the You Only Look Once (YOLO) object detection model was used to locate the head region. Then, the YOLO segmentation network was enhanced with the Online Convolutional Re-parameterization (OREPA) and High-level Screening-feature Fusion Pyramid Network (HS-FPN) modules to perform instance segmentation of the eye socket area. Finally, environmental variables—ambient temperature, humidity, wind speed, and light intensity—were integrated to compensate for eye socket temperature, and a random forest algorithm was used to construct a predictive model of rectal temperature. The experiments were conducted using a thermal infrared image dataset comprising 33,450 frontal-view images of dairy cows with a resolution of 384 × 288 pixels, along with 1471 paired samples combining thermal and environmental data for model development. The proposed method achieved a segmentation accuracy (mean average precision, mAP_50–95) of 86.59% for the eye socket region, ensuring reliable temperature extraction. The rectal temperature prediction model demonstrated a strong correlation with the reference rectal temperature (R² = 0.852), confirming its robustness and predictive reliability for practical applications. These results demonstrate that the proposed method is practical for non-contact temperature monitoring of cattle in large-scale farms, particularly those operating under confined or semi-confined housing conditions. Full article

Phytoremediation is an eco-friendly and in situ solution for remediating heavy metal-contaminated soils, yet practical application requires timely and accurate effectiveness evaluation. However, conventional chemical analysis of plant parts and soils is labor-intensive, time-consuming and limited for large-scale monitoring. This study proposed a rapid sensing framework integrating laser-induced breakdown spectroscopy (LIBS) with deep transfer learning and spectral indices to assess phytoremediation effectiveness of Sedum alfredii (a Cd/Zn co-hyperaccumulator). LIBS spectra were collected from plant tissues and diverse soil matrices. To overcome strong matrix effects, fine-tuned convolutional neural networks were developed for simultaneous multi-matrix quantification, achieving high-accuracy prediction for Cd and Zn (R²_test > 0.99). These predicted concentrations enabled calculating conventional phytoremediation indicators like bioconcentration factor (BCF), translocation factor (TF), plant effective number (PEN), and removal efficiency (RE), yielding recovery rates near 100% for TF and PEN. Additionally, novel spectral indices (SIs)—directly derived from characteristic wavelength combinations—were constructed to bypass intermediate quantification. SIs significantly improved the direct evaluation of Zn removal and translocation. Finally, a decision-level fusion strategy combining concentration predictions and SIs enhanced Cd removal assessment accuracy. This study validates LIBS combined with intelligent algorithms as a rapid sensor tool for monitoring phytoremediation performance, facilitating sustainable environmental management. Full article

Conventional descriptive statistical approaches in per- and polyfluoroalkyl substance (PFAS) environmental forensics often fail under small-sample, ecosystem-level complexity, challenging the optimization of sampling, monitoring, and remediation strategies. This study presents an advance from passive description to adaptive decision-support for complex PFAS contamination. By integrating Bayesian optimization (BO) via Gaussian Processes (GP) with a Generalized Linear Mixed Model (GLMM), we developed a signal-extraction framework for both understanding and action from limited data (n = 18). The BO/GP model achieved strong predictive performance (GP leave-one-out R² = 0.807), while the GLMM confirmed significant overdispersion (1.62), indicating a patchy contamination distribution. The integrated analysis suggested a dominant spatiotemporal interaction: a transient, high-intensity perfluorooctane sulfonate (PFOS) plume that peaked at a precise location during early November (the autumn recharge period). Concurrently, the GLMM identified significant intra-sample variance (p = 0.0186), suggesting likely particulate-bound (colloid/sediment) transport, and detected n-ethyl perfluorooctane sulfonamidoacetic acid (NEtFOSAA) as a critical precursor (p < 0.0001), thus providing evidence consistent with the source as historic 3M aqueous film-forming foam. This coupled approach creates a dynamic, iterative decision-support system where signal-based diagnosis informs adaptive optimization, enabling mission-specific actions from targeted remediation to monitoring design. Full article