Search Results (228)

Accurate body-weight measurement is important for precision pig farming, but conventional weighing methods are labor-intensive and may disturb normal animal activity. Although three-dimensional sensing systems can provide reliable geometric information, their deployment cost limits large-scale application in commercial farms. This study proposes a non-contact pig weight estimation framework based on standard RGB images. The framework combines SAM 2 foreground extraction with a transformer-based dorsal segmentation network to obtain stable body contours under complex farm conditions. Cross-covariance attention and local patch interaction modules are introduced to preserve both global body structure and local boundary details during segmentation. A hybrid loss function combining focal loss and label-distribution-aware margin loss is further adopted to address foreground-background imbalance. After segmentation, 17 morphometric features are extracted from the dorsal region and used for weight prediction with XGBoost regression. Experiments were conducted on the public PIGRGB-Weight dataset containing 12,476 RGB images from 124 pigs. The proposed method achieved a mean absolute error of 2.983 kg and an $R^2$ value of 0.9891. Compared with a DeepLabV3+-based baseline under the same regression protocol, the proposed framework reduced the prediction error by 24.1%. The results indicate that improving dorsal segmentation quality can substantially enhance the stability of morphometric feature extraction from low-cost RGB images. Full article

Palmer amaranth is one of the most problematic weeds in soybean production in the United States and can cause major yield loss if not managed early. This study benchmarked eight object detection models for site-specific Palmer amaranth detection in soybean using high-resolution uncrewed aerial system (UAS) imagery, with the goal of supporting targeted herbicide application and reducing herbicide usage. The models YOLOv8m, YOLOv9m, YOLOv10m, YOLOv11m, Faster R-CNN, RetinaNet, RT-DETR, and a self-supervised Faster R-CNN variant were evaluated using five-fold cross-validation on 2064 annotated aerial RGB image tiles containing 5990 bounding-box instances across multiple growth stages and field conditions, with an additional 7615 unlabeled tiles used for self-supervised pretraining. All detectors followed an identical 150-epoch schedule with early stopping and were compared using Friedman with Iman–Davenport correction and post hoc Nemenyi tests. Detectors were assessed on three axes: detection accuracy (mAP and class-wise AP), operational spraying efficacy summarized by a threshold-independent weed coverage rate area under the curve (WCR-AUC), and computational deployment cost across batch sizes from 1 to 32. The YOLO models achieved the highest detection accuracy along with the lowest inference latency and memory use but showed weaker threshold-independent weed coverage; the two-stage Faster R-CNN models showed the opposite pattern. Weighing all three axes, YOLOv8m provided the most practical balance for real-time deployment. The study also introduced GeoCLR, a self-supervised pretraining framework that constructs positive pairs from UAS flight overlap rather than synthetic augmentation. GeoCLR produced more structured and class-discriminative features than ImageNet pretraining, and a detector fine-tuned on only half of the annotations recovered approximately 95% of full-data accuracy. Together, these results highlight the importance of operational metrics for practical model selection and show that self-supervised pretraining can reduce annotation effort for scalable precision agriculture. Full article

Underwater communication devices typically suffer from large size and high power consumption, which pose significant challenges for real-time monitoring of swimmers’ heart rate and distance. To tackle these challenges, this study successfully developed a wearable acoustic-Bluetooth dual model communication-based real-time heart rate monitoring and ranging system (WARM) for swimmers by implementing an integrated miniaturized acoustic transducer design, narrow-pulse OOK modulation, and acoustic multipath interference suppression techniques. The final self-developed system measures 47 mm × 36 mm × 18 mm and weighs 54 g. Six swimming volunteers were recruited to conduct underwater real-time heart rate monitoring and distance measurement experiments for performance evaluation of this self-developed system. Experimental results demonstrate that within an effective communication range of 2500 cm, the system achieved an average transmission power consumption of 52–58 mW, a frame loss rate of only 1.1%, and a mode-switching time of 1–2 s between the underwater acoustic and Bluetooth transmissions. In addition, the system enabled real-time heart rate monitoring and underwater ranging, with an average ranging error below 50 cm. These results verify the reliability and stability of the proposed system and provide a useful reference for the design and application of wearable underwater communication systems. Full article

Risk and loss aversion are key forms of behavioral decision-making describing how people weigh potential gains and losses. Although most of the literature on risk and loss aversion comes from the field of behavioral economics, these concepts are applicable to complex medical decision making, especially when those decisions are shaped by sociopolitical factors as in gender transitions. For clinicians providing gender-affirming care, discussions of risk and loss aversion can support the informed consent process by reducing “noise” that may obscure gender identity and embodiment goals and delay critical decisions. Using this framework and understanding the impact of oppositional sexism and the gender binary can help clinicians understand why their clients might be hesitant to transition and how they can help affirm their client’s gender identity while supporting their transition goals. This approach is especially helpful when working with individuals who undergo transition in later life who may be struggling to overcome tacit assumptions about sex and gender identity that stand in the way of pursuing gender-affirming care. Full article

Background/Objectives: Cancer cachexia (CC) is a metabolic syndrome characterized by the progressive loss of skeletal muscle and adipose tissue during tumor progression. Despite its clinical prevalence, effective therapeutic options are currently lacking. Phloretin, a natural flavonoid with potent anti-inflammatory and antioxidant properties, has unclear efficacy against CC. This study investigates the therapeutic potential of phloretin in ameliorating cancer cachexia. Methods: Mouse models of CC were established using BALB/c mice implanted with C26 colon carcinoma cells and C57BL/6 mice implanted with Lewis lung carcinoma (LLC) cells. Upon the detection of palpable tumors, phloretin (10 mg/kg) was administered daily via intraperitoneal injection. At the endpoint, hind limb skeletal muscle, inguinal white adipose tissue (iWAT), and hearts were harvested and weighed. Lean body mass was assessed by analyzing the weight of the carcass following the excision of skin, subcutaneous fat, and visceral organs. Gene expression and protein levels in muscle tissues were subsequently quantified. Results: Phloretin administration significantly alleviated tumor-induced loss of tumor-free body weight. It effectively preserved skeletal muscle mass in both C26 and LLC cachexia models, while significantly attenuating adipose tissue depletion in the C26 model. In vitro, phloretin treatment mitigated myotube atrophy induced by C26 conditioned medium. Mechanistically, phloretin inhibited STAT3 activation in skeletal muscle. This inhibition suppressed the expression of the E3 ubiquitin ligases MuRF-1 and Atrogin-1. Furthermore, phloretin concurrently modulated the autophagy pathway. Conclusions: Phloretin effectively ameliorates cancer cachexia-induced muscle wasting by targeting STAT3-mediated protein degradation and autophagy pathways. These findings suggest that phloretin represents a promising therapeutic agent for the clinical management of cancer-associated cachexia. Full article

Background: Accurate estimation of water and energy requirements is fundamental for establishing dietary reference values in young adults. However, evidence integrating objectively measured energy expenditure with detailed water turnover components remains limited in Chinese populations. Objectives: To quantify water intake, water loss, and energy expenditure in healthy young college students, and to examine how energy metabolism is associated with specific components of water turnover under free-living conditions. Methods: Twenty-one healthy adults aged 18–25 years participated in a 14-day observational study conducted in Beijing, China. Total energy expenditure (TEE) was measured over 14 days using the doubly labeled water (DLW) method. Physical activity was monitored over 7 consecutive days using a triaxial accelerometer. Water intake was assessed using multiple methods: water from beverages (including plain drinking water and other beverages) was recorded over 7 days using 24 h fluid intake records, while water from food was measured during days 5–7 using weighed food records combined with duplicate portion and direct drying methods. Urinary and fecal water loss were quantified using 24 h collections conducted during days 5–7. Metabolic water production and insensible water losses were estimated using established physiological equations. Multivariable linear regression analyses were conducted to examine associations between energy-related variables and components of water turnover. Results: Mean total daily water intake was 3023 mL, with water from beverages accounting for 54.1%, water from food for 36.7%, and metabolic water for 9.1%. Mean total daily water loss was 1931 mL, predominantly from urinary excretion (81.0%). DLW-measured TEE averaged 2018.6 kcal/day and was higher in males than in females. Most regression models examining total water intake and beverage-derived water were not statistically significant, and no consistent associations were observed between these variables and total energy intake, TEE, or PAEE. In contrast, TEE was positively associated with metabolic water production and respiratory water loss (both p < 0.001). Significant associations with total energy intake were observed for water from food and fecal water loss (both p < 0.01), whereas other water intake components showed no significant associations. Conclusions: In young adults, energy metabolism appears to be more closely associated with physiologically regulated components of water turnover than with voluntary water intake. These findings suggest a divergence between endogenous and behaviorally regulated pathways of water turnover and highlight the importance of considering component-specific water dynamics when examining hydration and energy balance, although confirmation in larger studies is warranted. Full article

Background: Early pregnancy loss, defined as the spontaneous loss of a pregnancy before 20 weeks of gestation or when the fetus weighs less than 500 g, remains a common obstetric complication, affecting up to 15% of clinically recognized pregnancies. Chromosomal abnormalities, particularly aneuploidies such as trisomies and monosomy X, account for 50–60% of first-trimester losses, with incidence increasing alongside maternal age. Additional risk factors include maternal medical conditions, uterine anomalies, infections, and modifiable lifestyle factors. Pregnancies conceived through assisted reproductive technologies also carry a slightly higher risk of miscarriage, often influenced by maternal age and embryo quality. Methods: Two pathologists, blinded to each other’s assessments, analyzed abortive material from patients who experienced spontaneous first-trimester abortion between January 2012 and January 2025 at Agostino Gemelli Hospital, Rome, Italy. Inclusion criteria were defined independently of patient demographics. No restrictions were applied regarding maternal age. With respect to gestational age, only first-trimester miscarriages (≤12 weeks of gestation) were considered. In cases of discordance, the case was reviewed and re-evaluated to reach a final diagnosis. Results: The findings of this study are presented as a proposed histopathological classification and diagnostic framework for first-trimester miscarriages. Specifically, a total of 1168 cases were categorized into eight distinct groups of miscarriage etiology based exclusively on the histomorphological features of chorionic villi and maternal decidua. Conclusions: Histopathological examination of products of conception is essential for confirming intrauterine pregnancy, identifying underlying maternal or fetal causes, and guiding future reproductive management, particularly in recurrent pregnancy loss. This study evaluates histopathological features of first-trimester losses, classifies findings by etiology, and proposes a practical diagnostic guide to support clinical decision-making and improve outcomes in subsequent pregnancies. Full article

Heavy and overloaded freight traffic strongly affects pavement performance, yet short-term weigh-in-motion (WIM) measurements are not easily converted into design-oriented traffic inputs. Using the Nairobi Southern Bypass in Kenya as a case study, this study develops axle load spectrum (ALS) and equivalent single axle load (ESAL) indicators from more than 1.5 million axle group records collected between June and December 2025 and proposes an XGBoost-based conditional axle load spectrum (CA-ALS) framework. The data revealed strongly right-skewed load distributions, with a limited number of heavily loaded axle groups dominating pavement damage. Compared with the static ALS by axle group type baseline, the CA-ALS reduced log loss from 2.7563 to 2.6709 in conditional spectrum prediction. In the December 2025 tandem axle benchmark, the CA-ALS increased the ESAL-based verification input by 6.0% at b = 4 and 11.1% at b = 5 relative to the stronger static reference. A legal-load-capped counterfactual analysis further showed that, for all heavy vehicles, observed overloading increased ESAL by 161.0% at b = 4 and 239.4% at b = 5. These results indicate that the CA-ALS provides condition-sensitive traffic inputs for design traffic verification, scenario-based pavement checks, and overload-sensitive evaluation based on short-term WIM observations. Full article

Background/Objectives: Hand loss or severe impairment significantly reduces quality of life by restricting essential daily activities and professional tasks. Despite advances in prosthetics, challenges remain in affordability, accessibility, and usability. This study aimed to design and develop a low-cost, ergonomic bionic hand prototype that integrates sustainable fabrication, intuitive control, and modular electronics. Methods: A user-centred design process guided by iterative prototyping, anatomical modelling, and functional validation. The prototype was manufactured using 3D printing techniques and assembled with modular electronic components. The design included segmented fingers, independent thumb articulation, and a tendon-like actuation system driven by micro-motors. Control was implemented through an ESP32-based board and a Bluetooth-enabled mobile application. Durability was preliminarily assessed through 500 grasp–release cycles. Results: Experimental validation confirmed the feasibility of both precision and power grips. The pinch grip successfully lifted objects to 120 g, and the power grip up to 85 g, corresponding to effective output forces of approximately 1.2 N and 0.83 N, respectively. The final prototype weighed ~350 g and maintained reliable performance during 500 grasp–release cycles. Conclusions: The developed bionic hand demonstrates that an affordable, ergonomic, and functional prosthetic can be achieved through sustainable 3D printing and accessible electronics. Future work will focus on enhancing actuation strength, long-term durability, and integration of sensory feedback, with the long-term objective of clinical testing and scalable production. Full article

Based on monolithic microwave integrated circuit (MMIC) technology, this paper presents the design and implementation of a low-cost, highly integrated Ka-band sixteen-channel transmit/receive (T/R) module, specifically tailored to meet the application requirements of phased array antennas in airborne and spaceborne radar systems, satellite communications, and 5G/6G millimeter-wave networks. The proposed module employs an MMIC-based single-channel dual-chip discrete architecture, optimally integrating amplitude-phase multifunction chips and transmit-receive multifunction chips in terms of both fabrication process and performance characteristics, achieving a favorable balance between high performance and high-integration density. Using low-cost, low-temperature co-fired ceramic (LTCC) substrates, full-silver conductive paste, and a nickel–palladium–gold plating process, a novel “back-to-back” thin-slice packaging technique is presented to improve integration, lower manufacturing costs, and boost long-term reliability. Furthermore, the design incorporates glass insulators and a direct array interconnection scheme, which significantly minimizes transmission losses and reduces interface dimensions. The final module measures 70.3 mm × 26.2 mm × 10.9 mm and weighs only 34 g. Experimental results demonstrate a transmit output power of at least 23 dBm, a receive gain exceeding 26 dB, and a noise figure below 3.5 dB, achieving a 22.5–58% reduction in volume per channel while maintaining competitive RF performance. To improve testing effectiveness and guarantee data consistency, an automated radio frequency (RF) test system based on Python 3.11.5 was also developed. This work provides a practical technical approach for the engineering realization of Ka-band phased array systems. Full article

Non-invasive monitoring technologies are increasingly being explored to support cancer care, yet most existing approaches focus on isolated parameters and fail to provide a comprehensive view of patients’ health. This study presents a feasibility evaluation of an IoT-based system designed to detect treatment-related problems in oncology patients through the integration of wearable sensors, physiological measurements, and patient-reported outcomes. A monitoring kit, including a smartwatch, tensiometer, weighing scale, and mobile device, was deployed in a cohort of 26 patients undergoing oncological treatment. Data acquisition followed a structured schedule: continuous physiological recording via the smartwatch, daily blood pressure measurements, weekly weight monitoring, and structured surveys capturing treatment-related side effects. These heterogeneous data were transformed into binary clinical metrics using rule-based feature extraction algorithms, covering conditions such as insomnia, nausea, diarrhea, abdominal pain, headache, weight loss, hypertension, and fever. Clinical specialists labeled the dataset to ensure medical validity. Machine Learning models were then trained to analyze the features and generate alerts for potential treatment complications. The results demonstrate the feasibility of integrating IoT and Artificial Intelligence techniques for continuous, patient-centered monitoring in oncology, paving the way for intelligent decision-support systems that enhance early detection and clinical management. Full article

Introduction: Minimally invasive pancreatoduodenectomy (MIPD), including laparoscopic (LPD) and robotic approaches (RPD), has gained increasing attention as an alternative to open pancreatoduodenectomy (OPD). Despite rapid technological progress, concerns persist regarding safety, reproducibility, and oncological adequacy. The publication of randomized controlled trials (RCTs) provides essential high-level evidence to reassess the true benefits and limitations of MIPD. Methods: This narrative review synthesizes all available RCTs comparing LPD and RPD with OPD. Major domains evaluated include mortality, major morbidity, intraoperative parameters, postoperative recovery, oncological outcomes, conversion, costs, and the influence of surgeon experience and institutional volume. The objective is to contextualize RCT findings rather than perform a quantitative meta-analysis. Discussion: Across studies, LPD demonstrates comparable mortality and complication rates to OPD in high-volume centers, with consistent reductions intraoperative blood loss (IBL) and shorter recovery or length of stay (LOS). RPD shows more heterogeneous results: one large trial reported improved postoperative recovery, whereas the EUROPA trial identified higher rates of pancreatic fistula (POPF) and delayed gastric emptying (DGE) alongside significantly increased costs. Both LPD and RPD achieve oncological outcomes equivalent to OPD, and 3-year survival data confirm the long-term non-inferiority of LPD. However, operative time remains longer for all minimally invasive approaches, and conversion persists as a marker of technical difficulty and incomplete learning curve. Conclusions: Current RCT evidence indicates that MIPD is safe, feasible, and oncologically sound only when performed by surgeons who have surpassed the demanding learning curve within specialized, high-volume centers. The benefits, mainly reduced IBL and faster recovery, must be weighed against longer operative times, conversion risks, and substantially higher costs for RPD. MIPD should therefore be considered an advanced option rather than a universal standard, and its broader implementation requires structured training pathways, appropriate patient selection, and institutional readiness. Full article

The V/G criterion is a critical indicator for monitoring dynamic changes during Czochralski silicon single crystal (Cz-SSC) growth. However, the inability to measure it in real time forces reliance on offline feedback for process regulation, leading to imprecise control and compromised crystal quality. To overcome this limitation, this paper proposes a novel soft sensor modeling framework that integrates both mechanism-based knowledge and data-driven learning for the real-time prediction of the crystal quality parameter, specifically the V/G value (the ratio of growth rate to axial temperature gradient). The proposed approach constructs a hybrid prediction model by combining a data-driven sub-model with a physics-informed mechanism sub-model. The data-driven component is developed using an attention-based dynamic stacked enhanced autoencoder (AD-SEAE) network, where the SEAE structure introduces layer-wise reconstruction operations to mitigate information loss during hierarchical feature extraction. Furthermore, an attention mechanism is incorporated to dynamically weigh historical and current samples, thereby enhancing the temporal representation of process dynamics. In addition, a robust ensemble approach is achieved by fusing the outputs of two subsidiary models using an adaptive weighting strategy based on prediction accuracy, thereby enabling more reliable V/G predictions under varying operational conditions. Experimental validation using actual industrial Cz-SSC production data demonstrates that the proposed method achieves high-prediction accuracy and effectively supports real-time process optimization and quality monitoring. Full article

Drone-view mixed reality (MR) in the Architecture, Engineering, and Construction (AEC) sector faces significant self-localization challenges in low-texture environments, such as bare concrete sites. This study proposes an adaptive sensor fusion framework integrating thermal and visible light (RGB) imagery to enhance tracking robustness for diverse site applications. We introduce the Effective Inlier Count ($N_{eff}$) as a lightweight gating mechanism to evaluate the spatial quality of feature points and dynamically weigh sensor modalities in real-time. By employing a $20\times 16$ grid-based spatial filtering algorithm, the system effectively suppresses the influence of geometric burstiness without significant computational overhead on server-side processing. Validation experiments across various real-world scenarios demonstrate that the proposed method maintains high geometric registration accuracy where traditional RGB-only methods fail. In texture-less and specular conditions, the system consistently maintained an average Intersection over Union (IoU) above 0.72, while the baseline suffered from complete tracking loss or significant drift. These results confirm that thermal-RGB integration ensures operational availability and improves long-term stability by mitigating modality-specific noise. This approach offers a reliable solution for various drone-based AEC tasks, particularly in GPS-denied or adverse environments. Full article

Acoustic-based anomaly detection in elevators is crucial for predictive maintenance and operational safety, yet it faces significant challenges in real-world settings, including pervasive multi-source acoustic interference within confined spaces and severe class imbalance in collected data, which critically degrades the detection performance for minority yet critical acoustic events. To address these issues, this study proposes a novel hierarchical self-distillation framework. The method embeds auxiliary classifiers into the intermediate layers of a backbone network, creating a deep teacher–shallow student knowledge transfer paradigm optimized jointly via Kullback–Leibler divergence and feature alignment losses. A self-attentive temporal pooling layer is introduced to adaptively weigh discriminative time-frequency features, thereby mitigating temporal overlap interference, while a focal loss function is employed specifically in the teacher model to recalibrate the learning focus towards hard-to-classify minority samples. Extensive evaluations on the public UrbanSound8K dataset and a proprietary industrial elevator audio dataset demonstrate that the proposed model achieves superior performance, exceeding 90% in both accuracy and F1-score. Notably, it yields substantial improvements in recognizing rare events, validating its robustness for elevator acoustic monitoring. Full article