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Bees play a crucial role in terrestrial ecosystems. However, beekeepers are unable to monitor the state of beehives (bees and environment) all the time, which often results in bees escaping or even dying. Currently, some researchers provided the scheme of intelligent beehive monitoring system equipped with the Internet of Things (IoT), There remain two challenges: accurately monitor the environmental status around the hive and accurately track and monitor bees in real time. With the development of the IoT and computer vision algorithms, we hope to provide an automated and efficient system to meet the above challenges. In this paper, we proposed a hive monitoring system, and build a visualization module in the cloud to monitor the activity of bee colonies and the environmental dynamic changes. (1) We proposed a multi-bee tracking algorithm to solve the problem of monitoring bees at the door of the hive; (2) we constructed a dataset containing various complex scenes, named BEE22, for training and testing the performance of our algorithm; (3) we designed a bee counting rule, based on results of multi-bee tracking algorithm, to reasonably count the bees entering or leaving the beehive; (4) we have deployed multiple sensors around(center, margin, door, and environment) the hive to accurately reflect the changes in the environment around the hive. Experimental results demonstrate the effectiveness and excellence of our system. In particular, the tracking performance of the multi-bee tracking algorithm reaches 83.5 % ± 0.7 % Multiple Object Tracking Accuracy (MOTA) and 77.3 %±0.2 % Multiple Object Tracking Precision (MOTP), speeds up to 16 frames per second, compared with other algorithms, MOTA and Identity F1 Score (IDF1) are improved by 5.4 % and 8.2 % respectively. Moreover, our counting algorithm also achieved excellent results, with root mean square error (RMSE) of 1.3 ± 0.1, 0.2 ± 0.0, and 1.6 ± 0.1 in counting the number of bees current, entry, and out scene in an episode, respectively. After that, the system will be deployed and monitored for a long time in the actual scenario, it was found that the activity of bees decreased significantly under heavy rainfall conditions. Additionally, the activity of the bee colony will also increase accordingly, when the amplitude is 500 dB to 2000 dB, the temperature of the center of the beehive is 25 °C to 37 °C, and the humidity is 48 % to 67 %. In summary, our system can provide valuable information for bee farmers to make control decisions on hives.

Abstract A wearable sensing system that can reconstruct dynamic 3D human body models for virtual cloth fitting is highly important in the era of information and metaverse. However, few research has been conducted regarding conformal sensors for accurately measuring the human body circumferences for dynamic 3D human body reshaping. Here, we develop a stretchable spring‐sheathed yarn sensor (SSYS) as a smart ruler, for precisely measuring the circumference of human bodies and long‐term tracking the movement for the dynamic 3D body reconstruction. The SSYS has a robust property, high resilience, high stability (>18 000), and ultrafast response (12 ms) to external deformation. It is also washable, wearable, tailorable, and durable for long‐time wearing. Moreover, geometric, and mechanical behaviors of the SSYS are systematically investigated both theoretically and experimentally. In addition, a transfer learning algorithm that bridges the discrepancy of real and virtual sensing performance is developed, enabling a small body circumference measurement error of 1.79%, noticeably lower than that of traditional learning algorithm. Furtherly, 3D human bodies that are numerically consistent with the actual bodies are reconstructed. The 3D dynamic human body reconstruction based on the wearing sensing system and transfer learning algorithm enables excellent virtual fitting and shirt customization in a smart and highly efficient manner. This wearable sensing technology shows great potential in human‐computer interaction, intelligent fitting, specialized protection, sports activities, and human physiological health tracking.

In recent decades, phytoplankton proliferation and sediment input to rivers (especially urban rivers) have become more dramatic under the compound pressure of climate change and human activities. Given the generally narrow width of rivers and current high spatial resolution satellites, which are limited by band settings, bandwidth, and the signal-to-noise ratio, UAVs with their exceptional spatiotemporal resolution can be used as a useful tool for river environmental monitoring and inversion uncertainty assessment. In this study, UAV-based hyperspectral (X20P) and multispectral (P4M) images, along with Sentinel-2 MultiSpectral Instrument (MSI), Landsat-8 Operational Land Imager (OLI) and Landsat-9 OLI2 data, were used to assess the uncertainty in retrieving chlorophyll-a (Chla) and suspended sediment (SS) concentrations in rivers. Chla and SS models based on UAV and satellite data were constructed using stepwise multiple regression and typical Chla and SS retrieval algorithms, respectively, and the performance of the models was the focus of our research. The results demonstrated that in the Chla concentration inversion, each sensor performed as follows: X20P > P4M > Landsat9 OLI2 > Sentinel-2 MSI > Landsat8 OLI, and the performance in the SS concentration inversion was as follows: X20P > Sentinel-2 MSI > P4M > Landsat9 OLI2 > Landsat8 OLI. In addition, the uncertainty of high spatial resolution satellite retrievals was analyzed with the assistance of the UAV-based model. Results showed that narrow bandwidths and finely tuned band settings are more essential for the Chla inversion. The typical Chla retrieval algorithm, NDCI, is only effective in certain bands (band 1 from 684 to 724 nm and band 2 from 660 to 680 nm). It is also noted that Landsat8 and Landsat9 lack some key band settings (e.g., the red-edge band of 700–710 nm), severely limiting practical application in relation to Chla. However, specific variances in different sensor bands have a relatively small impact on SS inversion, for example, the correlation between SS and the R/B (a typical SS retrieval algorithm) constructed by each sensor ranged from 0.68 to 0.77. Chla monitoring, on the other hand, necessitates a higher spatial resolution than SS monitoring. The accuracy decreased markedly when UAV images were resampled to 10 m and 30 m spatial resolution. However, it is not as crucial for the SS inversion, images with the original spatial resolution (RMSE

The changes in the composition of intestinal microbiota and metabolites have been linked to digestive disorders in calves, especially neonatal calf diarrhea. Bovine rotavirus (BRV) and bovine coronavirus (BCoV) are known to be the primary culprits behind neonatal calf diarrhea. In this study, we analyzed changes in the fecal microbiota and metabolites of calves with neonatal diarrhea associated with BRV and BCoV infection using high-throughput 16S rRNA sequencing and metabolomics technology. The microbial diversity in the feces of calves infected with BRV and BCoV with diarrhea decreased significantly, and the composition changed significantly. The significant increase of Fusobacterium and the reductions of some bacteria genera, including Faecalibacterium, Bifidobacterium, Ruminococcus, Subdoligranulum, Parabacteroides, Collinsella, and Olsenella, etc., were closely related to diarrhea associated with BRV and BCoV infection. Metabolites in the feces of BRV and BCoV-infected calves with diarrhea were significantly changed. Phosphatidylcholine [PC; 16:1(9 Z)/16:1(9 Z)], lysophosphatidylethanolamine (LysoPE; 0:0/22:0), lysophosphatidylcholine (LysoPC; P-16:0) and LysoPE (0:0/18:0) were significantly higher in the feces of BRV-infected calves with diarrhea. In contrast, some others, such as desthiobiotin, were significantly lower. BRV infection affects glycerophospholipid metabolism and biotin metabolism in calves. Two differential metabolites were significantly increased, and 67 differential metabolites were significantly reduced in the feces of BCoV-infected calves with diarrhea. Seven significantly reduced metabolites, including deoxythymidylic acid (DTMP), dihydrobiopterin, dihydroneopterin triphosphate, cortexolone, cortisol, pantetheine, and pregnenolone sulfate, were enriched in the folate biosynthesis, pantothenate and CoA biosynthesis, pyrimidine metabolism, and steroid hormone biosynthesis pathway. The decrease in these metabolites was closely associated with increased harmful bacteria and reduced commensal bacteria. The content of short-chain fatty acids (SCFAs) such as acetic acid and propionic acid in the feces of BRV and BCoV-infected calves with diarrhea was lower than that of healthy calves, which was associated with the depletion of SCFAs-producing bacteria such as Parabacteroides, Fournierella, and Collinsella. The present study showed that BRV and BCoV infections changed the composition of the calf fecal microbiota and were associated with changes in fecal metabolites. This study lays the foundation for further revealing the roles of intestinal microbiota in neonatal calf diarrhea associated with BRV and BCoV infection.

Curcumin, the major active compound of turmeric, has shown potential benefits for poultry health and production in various studies. However, its specific role in enhancing the egg quality and liver health of laying hens, as well as its underlying mechanisms, have yet to be determined. Here, a total of 600 Su Qin No.1 Laying hens, aged 55 weeks and with similar laying rates, were randomly placed into five groups, with 10 replicates of 12 hens each. Curcumin doses of 0, 100, 200, 400, and 800 mg/kg were added to the basal diet to form the experimental groups. After an 8-week feeding period, no significant changes were observed in the production performance of laying hens due to curcumin supplementation. However, additional tests revealed that a 200 mg/kg curcumin supplementation improved albumen height, yolk color, Haugh unit, and eggshell thickness, while reducing the thin albumen’s weight and proportion. This was accompanied by a significant down-regulation of the mRNA expression level of the Prolactin Receptor (Prlr) in the oviduct magnum. Furthermore, the number of hepatic lipid droplets and the hepatic triglyceride (TG) content, as well as malondialdehyde (MDA) levels were significantly reduced, indicating improved hepatic lipid metabolism and oxidative status. This was accompanied by a significant reduction in the expressions of sterol regulatory element binding protein-1 gene (Srebp-1), fatty acid synthase gene (Fasn), as well as fatty acid synthase (FASN), which are closely related to fatty acid synthesis in the liver. Overall, these findings suggest that curcumin supplementation at a dosage of 200 mg/kg could lead to significant improvements in egg quality and hepatic lipid metabolism.

Laser welding with high beam intensity generates a metal vapor, which partially displaces the metal melt, forming a narrow keyhole-shaped fusion zone. This study conducted theoretical analysis of the keyhole formation processes within fiber laser welding, based on three types of experimental observation. During the welding mode transition process, the keyhole formation was abrupt due to the jump-like absorptivity variation in the concave molten pool. In the primary stage of the laser keyhole welding process (when the laser was turned on), the keyhole depth grew rapidly and then slowed down before reaching a steady state. The keyhole formation time was related to the welding characteristic time (WCT), determined as the ratio of laser spot diameter to the welding speed. In the steady stage of fiber laser keyhole welding, the keyhole formation process was a periodic motion of the laser beam on the front keyhole wall (FKW). The periodic drilling occurred because the laser beam was irradiated on the FKW, with the inclination angle fluctuating around the Brewster angle.

Numerous studies have discovered that chronic stress induces metabolic disorders by affecting iron and zinc metabolism, but the relationship between chronic stress and copper metabolism remains unclear. Here, we explore the influence of chronic corticosterone (CORT) exposure on copper metabolism and its regulatory mechanism in mice. Mice were treated with 100 μg/mL CORT in drinking water for a 4-week trial. We found that CORT treatment resulted in a significant decrease in plasma copper level, plasma ceruloplasmin activity, plasma and liver Cu/Zn-SOD activity, hepatic copper content, and liver metallothionein content in mice. CORT treatment led to the reduction in duodenal expression of copper transporter 1 (CTR1), duodenal cytochrome b (DCYTB), and ATPase copper-transporting alpha (ATP7A) at the mRNA and protein level in mice. CORT treatment activated nuclear glucocorticoid receptor (GR) and down-regulated CRT1 expression in Caco-2 cells, whereas these phenotypes were reversible by an antagonist of GR, RU486. Chromatin immunoprecipitation analysis revealed that GR bound to the Ctr1 promoter in Caco-2 cells. Transient transfection assays in Caco-2 cells demonstrated that the Ctr1 promoter was responsive to the CORT-activated glucocorticoid receptor, whereas mutation/deletion of the glucocorticoid receptor element (GRE) markedly impaired activation of the Ctr1 promoter. In addition, CORT-induced downregulation of Ctr1 promoter activity was markedly attenuated in Caco-2 cells when RU486 was added. These findings present a novel molecular target for CORT that down-regulates intestinal CTR1 expression via GR-mediated trans-repression in mice.

We propose a terahertz surface plasmon resonance sensor based on dielectric metagrating coupling to the spoof surface plasmon (SSP) mode on periodically grooved metal films. The well-designed silicon metagrating converts the normal incident to the necessary angle in the dielectric substrate exciting SSP with the transmission coupling between couplers and SSP metasurfaces. Using an all-dielectric metagrating as an external coupler, the tightly confined SSP mode can be excited within a small resonant cavity, causing the strong light–matter interaction. The proposed SSP dielectric meta-couplers will pave new routes for ultra-thin and compact sensing devices. The dielectric substrate thickness, the air gap distance between the substrate and the metal groove array, and metal groove gaps have remarkable influences on the characteristics of SSP sensing. The SSP sensing by metagrating coupling with the resonance frequency around 0.404 THz provides a high sensitivity of up to 335 GHz/RIU and a detection limit less than 0.0001 RIU with a frequency resolution of 33.5 MHz. Dielectric metagrating coupling SSP provides enormous potential for constructing ultra-sensitive and compact SSP sensors in the terahertz frequency region.

Raw point cloud data are often noisy, superfluous, and with topological defects, such as holes. These issues cause inaccurate geometric representation in 3D reconstruction. As a result, surface reconstruction from point cloud data is a highly challenging problem. In this paper, we address the aforementioned issues by taking advantage of the embedded information of segmentation, skeletonization, and user guidance. First, we pre-process the point cloud data with three steps, relocating each point, upsampling the point data, and optimizing normals to enhance the features and geometric details; second, a segmentation method converts the input cloud into separate parts; finally, we construct curve skeletons for each part and guide the surface reconstruction with minimal user interaction, where the parts of refined smooth shapes are fused to generate the final results. The comparison studies confirmed that the proposed method is able to produce state-of-the-art results in terms of preserving sharp features, handling missing data, and requiring minimal user intervention.

This paper proposes a novel method of optimizing locomotion controller for virtual characters, which is constructed with the deep neural network and learned with the assistance of existing motion data. The learning is accomplished with a progressive reward function by starting from a simple reward function and gradually imposing advanced requirements on the gait pattern. A multi-critic model is proposed to address the dynamically changing reward function by evaluating the individual goal in a separate fashion. This strategy proves effective in avoiding the local minima that are likely to occur when a set of weighted reward functions are introduced at the beginning of the optimization. The results show that the integration of motion data not only ensures the consistency between the synthetic and original motions but also accelerates the learning of network parameters. We demonstrate the application of our method to a variety of virtual characters (cheetah, hopper, 2-D walker, and 3-D humanoid) performing various tasks (walking, running, jumping, and traversing at different velocities and across uneven terrains).

Abstract Metal–organic frameworks (MOFs) are of great interest as potential electrochemically active materials. However, few studies have been conducted into understanding whether control of the shape and components of MOFs can optimize their electrochemical performances due to the rational realization of their shapes. Component control of MOFs remains a significant challenge. Herein, we demonstrate a solvothermal method to realize nanostructure engineering of 2D nanoflake MOFs. The hollow structures with Ni/Co- and Ni-MOF (denoted as Ni/Co-MOF nanoflakes and Ni-MOF nanoflakes) were assembled for their electrochemical performance optimizations in supercapacitors and in the oxygen reduction reaction (ORR). As a result, the Ni/Co-MOF nanoflakes exhibited remarkably enhanced performance with a specific capacitance of 530.4 F g−1 at 0.5 A g−1 in 1 M LiOH aqueous solution, much higher than that of Ni-MOF (306.8 F g−1) and ZIF-67 (168.3 F g−1), a good rate capability, and a robust cycling performance with no capacity fading after 2000 cycles. Ni/Co-MOF nanoflakes also showed improved electrocatalytic performance for the ORR compared to Ni-MOF and ZIF-67. The present work highlights the significant role of tuning 2D nanoflake ensembles of Ni/Co-MOF in accelerating electron and charge transportation for optimizing energy storage and conversion devices.

We propose an automatic method to identify people who are potentially-infected by droplet-transmitted diseases. This high-risk group of infection was previously identified by conducting large-scale visits/interviews, or manually screening among tons of recorded surveillance videos. Both are time-intensive and most likely to delay the control of communicable diseases like influenza. In this paper, we address this challenge by solving a multi-tasking problem from the captured surveillance videos. This multi-tasking framework aims to model the principle of Close Proximity Interaction and thus infer the infection risk of individuals. The complete workflow includes three essential sub-tasks: (1) person re-identification (REID), to identify the diagnosed patient and infected individuals across different cameras, (2) depth estimation, to provide a spatial knowledge of the captured environment, (3) pose estimation, to evaluate the distance between the diagnosed and potentially-infected subjects. Our method significantly reduces the time and labor costs. We demonstrate the advantages of high accuracy and efficiency of our method. Our method is expected to be effective in accelerating the process of identifying the potentially infected group and ultimately contribute to the well-being of public health.

Divalent metal transporter 1 (DMT1) is a key transporter of iron uptake and delivering in human and animals. However, post-transcriptional regulation of DMT1 is poorly understood. In this study, bioinformatic algorithms (TargetScan, PITA, miRanda, and miRDB) were applied to predict, screen, analyze, and obtain microRNA-16 family members (miR-16, miR-195, miR-497, and miR-15b) targeting DMT1, seed sequence and their binding sites within DMT1 3′ untranslated region (3′ UTR) region. As demonstrated by dual-luciferase reporter assays, luciferase activity of DMT1 3′ UTR reporter was impaired/enhanced when microRNA-16 family member over-expression plasmid/its inhibitor was transfected to HCT116 cells. Corroboratively, co-transfection of microRNA-16 family member over-expression plasmid and DMT1 3′ UTR mutant reporter repressed the luciferase activity in HCT116 cells. In addition, over-expression microRNA-16 family member augmented its expression and diminished DMT1 protein expression in HCT116 cells. Interestingly, tail vein injection of miR-16 assay revealed reduced plasma iron levels, higher miR-16 expression, and lower DMT1 protein expression in the duodenum of mice. Taken together, we provide evidence that microRNA-16 family (miR-16, miR-195, miR-497, and miR-15b) is confirmed to repress intestinal DMT1 expression in vitro and in vivo, which will give valuable insight into post-transcriptional regulation of DMT1.

It is well-known that hepatic iron dysregulation, which is harmful to health, can be caused by stress. The aim of the study was to evaluate chronic variable stress (CVS) on liver damage, hepatic ferrous iron deposition and its molecular regulatory mechanism in rats. Sprague Dawley rats at seven weeks of age were randomly divided into two groups: a control group (Con) and a CVS group. CVS reduces body weight, but increases the liver-to-body weight ratio. The exposure of rats to CVS increased plasma aspartate aminotransferase (AST), alkaline phosphatase (ALP) and hepatic malondialdehyde (MDA) levels, but decreased glutathione peroxidase (GSH-Px) activity, resulting in liver damage. CVS lowered the total amount of hepatic iron content, but induced hepatic Fe(II) accumulation. CVS up-regulated the expression of transferrin receptor 1 (TFR1) and ZRT/IRT-like protein 14 (ZIP14), but down-regulated ferritin and miR-181 family members. In addition, miR-181 family expression was found to regulate ZIP14 expression in HEK-293T cells by the dual-luciferase reporter system. These results indicate that CVS results in liver damage and induces hepatic Fe(II) accumulation, which is closely associated with the up-regulation of ZIP14 expression via the miR-181 family pathway.