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Vision-based unmanned aerial vehicles (UAVs) have been widely studied and applied in aerial monitoring tasks; however, detecting small objects in UAV imagery remains challenging due to limited visual features, significant scale variations, dense distributions, and complex background interference. In real-world UAV scenarios, small objects often occupy only a few pixels and are easily obscured by cluttered backgrounds, which complicates stable and accurate detection. To address these issues, this study proposes MSCM-YOLO, a UAV-oriented lightweight detection framework based on YOLOv11. The framework integrates four key innovations: (1) a dedicated P2 detection head to preserve high-resolution features for extremely small and dense targets; (2) a lightweight backbone enhanced with Mobile Bottleneck Convolution (MBConv) to improve feature extraction for visually weak objects; (3) a Scale-Adaptive Attention Fusion (SAF) mechanism with a Channel-Adaptive Projection (CAP) module to effectively integrate multi-scale spatial and semantic features under large object-size variations; and (4) a Multi-Domain Feature Attention Fusion (MDFAF) module to enhance target–background discrimination in complex UAV scenes. Experiments on the VisDrone2019 dataset show that MSCM-YOLO achieves mAP50 and mAP50:95 scores of 44.41% and 27.13%, respectively, outperforming the YOLOv11 baseline by 10.77 and 7.22 percentage points. Notably, the proposed framework achieves this significant performance improvement while maintaining a balanced computational profile suitable for UAV deployment. Additional validation on the UAVDT, DIOR, and AI-TOD datasets confirms consistent improvements in mAP50, demonstrating the robustness and generalization ability of the proposed method. Overall, MSCM-YOLO provides an effective and practical solution for accurate small object detection in aerial monitoring applications.

Background Artificial intelligence (AI) has been regarded as a major success in healthcare services. At present, few studies have successfully empirically analyzed this view through large-scale multi-center trails, especially in China as well as other less-developed regions. The research aim of this work is to empirically reveal how artificial intelligence benefits in less-developed regions and reallocates medical resources. Methods This work takes the \"non-perception-perception\" public service performance evaluation model as the theoretical framework for evaluation, and the \"task-periphery\" performance structure model as the basis for forming performance indicators. This work has also adopted literature research, expert consultation, questionnaire measurement, and statistical inference as methodology, as well as a representative, advanced large-scale multi-center medical policy pilot case for performance evaluation. The case is conducted in the entire region of Puyang Prefecture, Henan Province, China. As of June 2024, 108 public healthcare institutions have been equipped with 291 modules and screened 281,663 people. 88.34 million RMB has been invested. A total of 493 questionnaires were collected (429 valid questionnaires). Results Based on the non-perceptual mode, the AI system has technical advantages, including more accurate diagnostic results (20.72% higher than the conventional rate), more detailed diagnostic data (precise to two decimal places), faster reporting (down to 0.2 seconds), standardized data collection procedures, unified healthcare collaboration platforms and lower healthcare insurance (reduced 85.7%-92.9%). Based on the perceptual mode, the overall performance value is relatively high (5.19/7 on average). The public value created by the system application is more distinct than the direct economic value. Conclusion This advanced, representative, large-scale multi-center pilot case reveals that AI has effectively promoted data standardization, regional medical cooperation, and reduced medical insurance expenditures mainly by improving the accuracy, precision, and speed of diagnosis, enabling less-developed regions to access more efficient and fair medical resources. The application of the AI system not only creates very significant economic value for primary-level medical and health institutions, but also generates huge public value (sustainable development, social satisfaction, etc.). This work points out a referential path for the healthcare development in the Global South from the perspective of AI.

The gas sensor market is growing fast, driven by many socioeconomic and industrial factors. Mid-infrared (MIR) gas sensors offer excellent performance for an increasing number of sensing applications in healthcare, smart homes, and the automotive sector. Having access to low-cost, miniaturized, energy efficient light sources is of critical importance for the monolithic integration of MIR sensors. Here, we present an on-chip broadband thermal MIR source fabricated by combining a complementary metal oxide semiconductor (CMOS) micro-hotplate with a dielectric-encapsulated carbon nanotube (CNT) blackbody layer. The micro-hotplate was used during fabrication as a micro-reactor to facilitate high temperature (>700 ∘ C) growth of the CNT layer and also for post-growth thermal annealing. We demonstrate, for the first time, stable extended operation in air of devices with a dielectric-encapsulated CNT layer at heater temperatures above 600 ∘ C. The demonstrated devices exhibit almost unitary emissivity across the entire MIR spectrum, offering an ideal solution for low-cost, highly-integrated MIR spectroscopy for the Internet of Things.

In the context of infrastructure modernization, enhancing the durability of reinforced concrete (RC) structures is crucial for achieving sustainable and resilient development. Impressed current cathodic protection (ICCP) is a popular technique to improve corrosion resistance of RC structures exposed to chloride-rich environments but may also induce localized acidification in the external anode mortar due to continuous OH− consumption and H+ generation. This phenomenon leads to the dissolution of calcium hydroxide and acidification erosion damage on the anode metal and mortar, undermining the long-term performance of the protection system. This study uses modified aggregates that are incorporated with Ca(OH)2 to improve the corrosion resistance of anode metal and mortar. Results from electrochemical measurements, pH monitoring, and XRD analysis show that the Ca(OH)2-loaded aggregates extended the stable alkaline buffer time of simulated pore solution during ICCP by 1.5 to 2 times longer and exhibited good resistance to the mortar acidification. These findings offer a promising pathway for safeguarding RC structures and advancing infrastructure modernization by integrating protective functionalities at the material level.

The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote calm and emotional relief—offer immersive environments that facilitate affective engagement and sustained user involvement. This study proposes a computational framework that integrates sentiment analysis and topic modeling to investigate the affective mechanisms and behavioral dynamics associated with relaxing gameplay. We analyzed nearly 60,000 user reviews from the Steam platform in both English and Chinese, employing a hybrid methodology that combines sentiment classification, dual-stage Latent Dirichlet Allocation (LDA), and multi-label mechanism tagging. Emotional relief emerged as the dominant sentiment (62.8%), whereas anxiety was less prevalent (10.4%). Topic modeling revealed key affective dimensions such as pastoral immersion and cozy routine. Regression analysis demonstrated that mechanisms like emotional relief (β = 0.0461, p = 0.001) and escapism (β = 0.1820, p < 0.001) were significant predictors of longer playtime, while Anxiety Expression lost statistical significance (p = 0.124) when contextual controls were added. The findings highlight the potential of relaxing video games as scalable emotional regulation tools and demonstrate how sentiment- and topic-driven modeling can support system-level understanding of affective user behavior. This research contributes to affective computing, digital mental health, and the design of emotionally aware interactive systems.

A new signal processing technique has been developed for resistive metal oxide (MOX) gas sensors to enable high-bandwidth measurements and enhanced selectivity at PPM levels (<5 PPM VOCs). An embedded micro-heater is thermally pulsed from a temperature of 225 to 350 °C, which enables the chemical reaction kinetics of the sensing film to be extracted using a fast Fourier transform. Signal processing is performed in real-time using a low-cost microcontroller integrated into a sensor module. Three sensors, coated with SnO2, WO3 and NiO respectively, were operated and processed at the same time. This approach enables the removal of long-term baseline drift and is more resilient to changes in ambient temperature. It also greatly reduced the measurement time from ~10 s to 2 s or less. Bench-top experimental results are presented for 0 to 200 ppm of acetone, and 0 ppm to 500 ppm of ethanol. Our results demonstrate our sensor system can be used on a mobile robot for real-time gas sensing.

This study investigated the effects of dietary carbohydrate levels on growth performance, body composition, and hepatic expression of metabolic genes in Chinese hook snout carp (Opsariichthys bidens). Fish were fed five isonitrogenous diets with graded α-starch levels (8%, 14%, 20%, 26%, and 32%) for 56 days. The diet containing 14% α-starch significantly increased the weight gain rate (WGR) and specific growth rate (SGR) of O. bidens (p < 0.05). Both broken-line and polynomial regression analyses on WGR and SGR consistently indicated an optimal dietary α-starch level of approximately 14–17%. High carbohydrate diets significantly elevated plasma glucose, triglyceride, and cholesterol levels, as well as hepatosomatic and intraperitoneal fat indices. Gene expression analysis revealed that moderate carbohydrate intake upregulated lipoprotein lipase (lpl), hormone-sensitive lipase (hsl), and carnitine palmitoyltransferase 1 (cpt1) gene expressions, enhancing lipolysis and β-oxidation, whereas excessive carbohydrate intake (>26% α-starch) suppressed these pathways but strongly induced acc1 gene expressions, promoting lipogenesis. Additionally, glycogen metabolism genes (glycogen synthase (gys) and glycogen phosphorylase (pyg) and glycolysis-related phosphofructokinase (pfk) were responsive to carbohydrate supply, while oxidative metabolism gene cs was downregulated under excessive carbohydrate, implying reduced mitochondrial oxidative metabolism. Overall, O. bidens exhibited limited carbohydrate utilization, with optimal intake supporting growth and metabolic balance, whereas excessive intake redirected glucose toward glycogen and lipid accumulation, leading to metabolic imbalance.

An optical system for gaseous chloroform (CHCl3) detection based on wavelength modulation photoacoustic spectroscopy (WMPAS) is proposed for the first time by using a distributed feedback (DFB) laser with a center wavelength of 1683 nm where chloroform has strong and complex absorption peaks. The WMPAS sensor developed possesses the advantages of having a simple structure, high-sensitivity, and direct measurement. A resonant cavity made of stainless steel with a resonant frequency of 6390 Hz was utilized, and eight microphones were located at the middle of the resonator at uniform intervals to collect the sound signal. All of the devices were integrated into an instrument box for practical applications. The performance of the WMPAS sensor was experimentally demonstrated with the measurement of different concentrations of chloroform from 63 to 625 ppm. A linear coefficient R2 of 0.999 and a detection sensitivity of 0.28 ppm with a time period of 20 s were achieved at room temperature (around 20 °C) and atmosphere pressure. Long-time continuous monitoring for a fixed concentration of chloroform gas was carried out to demonstrate the excellent stability of the system. The performance of the system shows great practical value for the detection of chloroform gas in industrial applications.

Stanniocalcin 2 (STC2) is a secreted glycoprotein involved in multiple biological processes. To systemically study the biological role of STC2 in chickens, phylogenetic tree analysis and conservation analysis were conducted. Association analysis between variations in the STC2 gene and the economic traits of Gushi-Anka F2 was conducted. The tissue expression patterns of STC2 expression in different chicken tissues and liver at different stages were detected. The biological role of STC2 in chicken liver was investigated through overexpression and interfering methods in the LMH cell line. Correlation analyses between STC2 expression and lipid components were conducted. (1) The phylogenetic tree displayed that chicken STC2 is most closely related with Japanese quail and most distantly related with Xenopus tropicalis. STC2 has the same identical conserved motifs as other species. (2) rs9949205 (T > C) found in STC2 intron was highly significantly correlated with chicken body weight at 0, 2, 4, 6, 8, 10 and 12 weeks (p < 0.01). Extremely significant correlations of rs9949205 with semi-evisceration weight (SEW), evisceration weight (EW), breast muscle weight (BMW), leg muscle weight (LMW), liver weight and abdominal fat weight (AFW) were revealed (p < 0.01). Significant associations between rs9949205 and abdominal fat percentage, liver weight rate, breast muscle weight rate and leg muscle weight rate were also found (p < 0.05). Individuals with TT or TC genotypes had significantly lower abdominal fat percentage and liver weight rate compared to those with the CC genotype, while their body weight and other carcass traits were higher. (3) STC2 showed a high expression level in chicken liver tissue, which significantly increased with the progression of age (p < 0.05). STC2 was observed to inhibit the content of lipid droplets, triglycerides (TG) and cholesterol (TC), as well the expression level of genes related to lipid metabolism in LMH cells. (4) Correlation analysis showed that the STC2 gene was significantly correlated with 176 lipids in the breast muscle (p < 0.05) and mainly enriched in omega-3 and omega-6 unsaturated fatty acids. In conclusion, the STC2 gene in chicken might potentially play a crucial role in chicken growth and development, as well as liver lipid metabolism and muscle lipid deposition. This study provides a scientific foundation for further investigation into the regulatory mechanism of the STC2 gene on lipid metabolism and deposition in chicken liver.