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This research proposes a low-cost system consisting of a hardware setup and a deep learning-based model to estimate broiler chickens' feed intake, utilizing audio signals captured by piezoelectric sensors. The signals were recorded 24/7 for 19 consecutive days. A subset of the raw data was chosen, and events were labeled in two classes, feed-pecking and non-pecking (including singing, anomaly, and silence samples). Next, the labeled data were preprocessed through a noise removal algorithm and a band-pass filter. Then, the spectrogram and the signal envelope were extracted from each signal and fed as inputs to a VGG-16-based convolutional neural network (CNN) with two branches for 1D and 2D feature extraction followed by a binary classification head to classify feed-pecking and non-pecking events. The model achieved 92% accuracy in feed-pecking vs. non-pecking events classification with an f1-score of 91%. Finally, the entire raw dataset was processed utilizing the developed model, and the resulting feed intake estimation was compared with the ground truth data from scale measures. The estimated feed consumption showed an 8 ± 7% mean percent error on daily feed intake estimation with a 71% R2 score and 85% Pearson product moment correlation coefficient (PPMCC) on hourly intake estimation. The results demonstrate that the proposed system estimates broiler feed intake at each feeder and has the potential to be implemented in commercial farms. [ABSTRACT FROM AUTHOR]

Solid-state-based microwave ovens are promising to mitigate the non-uniformity issue for their precise controlled microwave parameters. Multiphysics modeling is a useful tool for understanding complicated microwave heating processes. However, previous models using simple or manually measured oven geometry had challenges in accurately predicting the heating patterns. This study developed a 3-D scanning approach to characterize the accurate geometric details of the cavity and incorporate it in the multiphysics modeling of solid-state microwave heating. The effect of oven geometric details on modeling accuracy was evaluated for models using the simple box, manually measured, and 3-D scanned geometries at multiple microwave frequencies and port locations. A quantitative approach was also developed to replace the previously often-used qualitative approach to compare the spatial temperature profiles between the simulation and experiments. The Multiphysics-based models using 3-D scanned geometry showed significantly or considerably smaller RMSE values (1.57 to 4.11 °C) than the models with simple box geometry (1.73 to 6.33 °C) and manually measured geometry (1.48 to 4.66 °C) at most heating scenarios. The 3-D scanned approach can accurately incorporate the irregular geometric details of the oven cavity and can improve the prediction accuracy of microwave heating models for future food products and oven development. [ABSTRACT FROM AUTHOR]

Simple Summary: Monitoring drinking behavior allows producers to assess the health and well-being of their beef cattle. Changes in regular drinking behavior can serve as an indicator of potential health issues. Detecting these issues early on enables the application of timely interventions, mitigating the likelihood of severe complications and enhancing the prospects for prompt and efficient treatments. In the current study, we used computer vision techniques to study and analyze the drinking behavior of beef cattle. Two different camera positions were used to identify the drinking behavior. Our proposed method was able to successfully identify both the drinking behavior and drinking time of beef cattle. Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. The aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB camera and an ultrasonic sensor, was developed to record beef cattle drinking actions. We developed an algorithm for tracking the beef cattle's key body parts, such as head–ear–neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a long short-term memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validation purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet immediate needs and expand farmers' capability in monitoring animal health and well-being by identifying drinking behavior. [ABSTRACT FROM AUTHOR]

Simple Summary: Tracking the movements of chickens is important for understanding their well-being. Traditional methods for measuring chicken mobility are time-consuming and cannot provide real-time information. In this study, we, the researchers, used a combination of artificial intelligence methods and computer algorithms to track individual chickens. Using these methods, it was possible to detect and track individual chickens in two pens. We analyzed the data to see how far and how fast each chicken moved every hour and every day. Compared to manual measurements, the combined model provided more accurate measurements of the mobility of each chicken and the entire group, even when some chickens were hidden, or the detection was not perfect. This study may serve to effectively track indicators critical for broiler production performance and welfare. Mobility is a vital welfare indicator that may influence broilers' daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model's mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers' hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues. [ABSTRACT FROM AUTHOR]

Simple Summary: Using automated approaches to investigate feeding behavior in broilers provides accurate, non-invasive, and large-scale data collection, real-time monitoring capabilities, and opportunities for advanced data analysis that would not be possible with manual observations. These benefits contribute to a better understanding of broilers' behavior for improving production efficiency and animal welfare, optimizing management practices, and promoting the profitability of poultry production. Hence, this study aimed to estimate the feeding time of individual broilers through an automated approach. First, the proposed algorithm detected the broilers' heads. Then, a Euclidean distance-based tracking algorithm tracked the detected heads. The developed algorithm can estimate the broiler's feeding time by identifying whether its head is inside the feeder area. The overall accuracy of each broiler's feeding time per visit to the feeding pan was 87.3%. Feeding behavior is one of the critical welfare indicators of broilers. Hence, understanding feeding behavior can provide important information regarding the usage of poultry resources and insights into farm management. Monitoring poultry behaviors is typically performed based on visual human observation. Despite the successful applications of this method, its implementation in large poultry farms takes time and effort. Thus, there is a need for automated approaches to overcome these challenges. Consequently, this study aimed to evaluate the feeding time of individual broilers by a convolutional neural network-based model. To achieve the goal of this research, 1500 images collected from a poultry farm were labeled for training the You Only Look Once (YOLO) model to detect the broilers' heads. A Euclidean distance-based tracking algorithm was developed to track the detected heads, as well. The developed algorithm estimated the broiler's feeding time by recognizing whether its head is inside the feeder. Three 1-min labeled videos were applied to evaluate the proposed algorithm's performance. The algorithm achieved an overall feeding time estimation accuracy of each broiler per visit to the feeding pan of 87.3%. In addition, the obtained results prove that the proposed algorithm can be used as a real-time tool in poultry farms. [ABSTRACT FROM AUTHOR]

Re is often used as a nonradioactive surrogate for Tc in borosilicate melts because both elements have similar valences, ionic radii, and coordination environments. Re solubility in high sodium borosilicate waste glasses is sensitive to melter redox conditions; at 1150°C the solubility is ∼4130 ppm under oxidizing atmospheres. Raman, X‐ray absorption spectroscopy, and X‐ray diffraction show that Re dissolved in waste glasses under oxidizing conditions is in perrhenate tetrahedra; under more reducing conditions, Re0 separates from the melt. From air to PO2$ {\rm P}_{{\rm O}_2} $∼ 10−4 atm, both Re7+ and Tc7+ are dominant species in borosilicate melts. However, Tc and Re behaviors deviate between PO2$ {\rm P}_{{\rm O}_2} $< 10−4 atm and PO2$ {\rm P}_{{\rm O}_2} $> 10−8 atm, where Tc4+ becomes dominant, whereas Re7+ remains stable. Unlike Tc4+, Re4+ is not observed in borosilicate melts at 1150°C and associated glasses. Near PO2$ {\rm P}_{{\rm O}_2} $= 10−8 atm, Tc4+ reduces to Tc0, whereas below PO2$ {\rm P}_{{\rm O}_2} $= 10−8 atm, Re7+ reduces to Re0. Re is found to be a good surrogate for Tc under oxidizing conditions (PO2$ {\rm P}_{{\rm O}_2} $> 10−4 atm) and under highly reducing conditions (PO2$ {\rm P}_{{\rm O}_2} $< 10−9 atm). Redox and solubility behaviors of Tc and Re are also sensitive to glass chemistry, such that the fair assessment of Re modeling Tc behavior should be glass specific. [ABSTRACT FROM AUTHOR]

The decline of natural pollinators necessitates the development of novel pollination technologies. In this work, we propose a drone-enabled autonomous pollination system (APS) that consists of five primary modules: environment sensing, flower perception, path planning, flight control, and pollination mechanisms. These modules are highly dependent upon each other, with each module relying on inputs from the other modules. In this paper, we focus on approaches to the flower perception, path planning, and flight control modules. First, we briefly introduce a flower perception method from our previous work to create a map of flower locations. With a map of flowers, APS path planning is defined as a variant of the Travelling Salesman Problem (TSP). Two path planning approaches are compared based on mixed-integer programming (MIP) and genetic algorithms (GA), respectively. The GA approach is chosen as the superior approach due to the vast computational savings with negligible loss of optimality. To accurately follow the generated path for pollination, we develop a convex optimization approach to the quadrotor flight control problem (QFCP). This approach solves two convex problems. The first problem is a convexified three degree-of-freedom QFCP. The solution to this problem is used as an initial guess to the second convex problem, which is a linearized six degree-of-freedom QFCP. It is found that changing the objective of the second convex problem to minimize the deviation from the initial guess provides improved physical feasibility and solutions similar to a general-purpose optimizer. The path planning and flight control approaches are then tested within a model predictive control (MPC) framework where significant computational savings and embedded adjustments to uncertainty are observed. Coupling the two modules together provides a simple demonstration of how the entire APS will operate in practice. [ABSTRACT FROM AUTHOR]

We reported an ultrabroadband mid-infrared (MIR) emission in the range of 1800 nm–3100 nm at room temperature (RT) from a Cr2+:ZnSe-doped chalcogenide glasses (ChGs) and studied the emission-dependent properties on the doping methods. A series of Cr2+:ZnSe/As40S57Se3 (in unit wt.%) glass-ceramics were prepared by hot uniaxial pressing (HUP) and melt-quenching methods, respectively. The glass-ceramics with MIR emission bands greater than 1000 nm were successfully prepared by both methods. The effects of matrix glass composition and grain doping concentration on the optical properties of the samples were studied. The occurrence state, morphology of the grains, and the microscopic elemental distributions were characterized using x-ray diffraction (XRD), scanning electron microscope (SEM), and energy dispersive spectrometer (EDS) analyses. [ABSTRACT FROM AUTHOR]

Due to the economic trends around the world and the strong support and guidance of relevant governments, the number of entrepreneurial projects has soared in China. It is necessary to construct a standard assessment system for entrepreneurial projects that is comprehensive and effective. This study extracted six dimensions and 23 criteria for evaluating entrepreneurial projects. This study applies the best and worst methods to obtain the weights of the dimensions and criteria. Then, the Technique for Order Preference by Similarity to an Ideal Solution method was used to evaluate the entrepreneurial projects in an entrepreneurial competition. The results show that team quality, the business model and entrepreneurship ability are the three most important dimensions for evaluating entrepreneurial projects. The improvement of the innovating ability, the training of the team and the value proposition are the three keys to successful implementation of the entrepreneurial projects. [ABSTRACT FROM AUTHOR]

The reaction of C60 and C70 with tert-Butyl hydroperoxide (TBHP) and characterization the compounds are discussed. The mass spectra suggest that the mixed peroxides so prepared may serve as precursors to fullerene multioxides with defined structures.

Mo-containing high-level nuclear waste borosilicate glasses were investigated as part of an effort to improve Mo loading while avoiding yellow phase crystallization. Previous work showed that additions of vanadium decrease yellow phase formation and increases Mo solubility. X-ray absorption spectroscopy (XAS) and Raman spectroscopy were used to characterize Mo environments in HLW borosilicate glasses and to investigate possible structural relationships between Mo and V. Mo XAS spectra for the glasses indicate isolated tetrahedral Mo 6+ O 4 with Mo-O distances near 1.75 Å. V XANES indicate tetrahedral V 5+ O 4 as the dominant species. Raman spectra show composition dependent trends, where Mo-O symmetrical stretch mode frequencies (ν 1 ) are sensitive to the mix of alkali and alkaline earth cations, decreasing by up to 10 cm −1 for glasses that change from Li + to Na + as the dominant network-modifying species. This indicates that MoO 4 tetrahedra are isolated from the borosilicate network and are surrounded, at least partly, by Na + and Li + . Secondary ν 1 frequency effects, with changes up to 7 cm −1 , were also observed with increasing V 2 O 5 and MoO 3 content. These secondary trends may indicate MoO 4 -MoO 4 and MoO 4 -VO 4 clustering, suggesting that V additions may stabilize Mo in the matrix with respect to yellow phase formation. [ABSTRACT FROM AUTHOR]

Inspired by the biosynthesis of heparan sulfate (HS), we present a direct epimerization approach to l-iduronic acid synthons (IdoA) from the corresponding C5 epimers, d-glucuronic acids (GlcA). With the best result, 95 % conversion was achieved. Weak intramolecular interactions dramatically affect the equilibrium constants of this thermodynamically governed process. Based on this approach, GlcA and IdoA donors required for the synthesis of fondaparinux were prepared in 14 steps, whereas over 20 steps were required previously. [ABSTRACT FROM AUTHOR]

This study in-situ modified a commercial nanofiltration membrane, NF90, through the concentration-polymerization-enhanced radical graft polarization method by applying two agents of 3-sulfopropyl methacrylate potassium salt (SPM) and 2-hydroxyethyl methacrylate (HEMA) with different dosages. Surface characterization revealed that the modified membranes became rougher and more hydrophilic compared with the pristine membrane. The modified membranes exhibited considerably enhanced separation performance with 5.8–19.6% higher NaCl rejection and 17.2–19.9% higher pharmaceuticals and personal care products (PPCPs) rejection than the pristine membrane. When treating the feedwater with high silica concentration, the modified membranes exhibited relatively less flux decline with high percentage of reversible fouling, especially the ones modified using a lower monomer concentration (0.01 M SPM and 0.01 M HEMA). Moreover, membrane modification enhanced the PPCP rejection (1.3–5.4%) after silica fouling by mitigating foulant deposition on the membrane surface. The fouling mechanism was confirmed to be intermediate blocking of membrane pores. Therefore, the in-situ modification technique with a low monomer concentration proved to be effective for mitigating silica fouling and improving PPCP rejection, which can be easily performed and cost-effective in practical application. [ABSTRACT FROM AUTHOR]

The aim of the present study was to evaluate the biomechanical mechanism of injuries of the thoracolumbar junction by the methods of a backward fall simulation experiment and finite element (FE) analysis (FEA). In the backward fall simulation experiment, one volunteer was selected to obtain the contact force data of the sacrococcygeal region during a fall. Utilizing the fall data, the FEA simulation of the backward fall process was given to the trunk FE model to obtain the stress status of local bone structures of the thoracolumbar junction during the fall process. In the fall simulation test, the sacrococcygeal region of the volunteer landed first; the total impact time was 1.14±0.58 sec, and the impact force was up to 4,056±263 N. The stress of thoracic (T)11 was as high as 42 MPa, that of the posterior margin and the junction of T11 was as high as 70.67 MPa, and that of the inferior articular process and the superior articular process was as high as 128 MPa. The average stress of T12 and the anterior margin of lumbar 1 was 25 MPa, and that of the endplate was as high as 21.7 MPa, which was mostly distributed in the back of the endplate and the surrounding cortex. According to the data obtained from the fall experiment as the loading condition of the FE model, the backward fall process can be simulated to improve the accuracy of FEA results. In the process of backward fall, the front edge of the vertebral body and the root of vertebral arch in the thoracolumbar junction are stress concentration areas, which have a greater risk of injury. [ABSTRACT FROM AUTHOR]

Current approaches to topdressing and weeding operations for rice cultivation present several disadvantages, such as poor precision, low efficiency, serious environmental pollution, and so on. This paper presents a row-controlled fertilizing–weeding machine to improve the precision of fertilizing and weeding operations and to reduce the heavy pollution associated with rice cultivation. A proportional–integral–derivative algorithm was adopted to realize accurate fertilization control, and an automatic driving system for agricultural machinery based on BeiDou navigation was used for accurate row-controlled operation. Accuracy testing and field experiments were carried out. The results show that the fertilization control system can stabilize the speed to within 0.55 s of the desired speed with a standard deviation of around 0.32 r·min−1. The row-controlled operation ensures the lateral deviation is within ± 5 cm at operating speeds below 5 km·h−1. The high uniformity and accuracy of fertilization meet agronomic requirements and rice cultivation standards, and the weeding performance is acceptable at working speeds below 5 km·h−1. [ABSTRACT FROM AUTHOR]

[Display omitted] • GCDs as substrate exhibit excellent SERS effect and catalytic properties. • GCDs can be further used for detection of glucose. • GCDs exhibit dual functional properties for chemical detection. The nanocomposites with highly synergistic effect show important potential application as nanozymes. Herein, the chain-like Au/carbon dots (CDs) (GCDs) nanocomposites were prepared by self-assembly method. The negatively charged Au nanoparticles (NPs) and positively charged CDs were connected by the electrostatic interaction. Then, the electron transfer between Au and CDs induces the strong catalytic effect of GCDs nanocomposites. The cross-linking reaction occurred between amino groups and carboxyl groups on the surface of CDs, which led to form the chain-like Au aggregation surrounded by carbon shells. By FDTD simulation, the aggregation of Au NPs may enhance the electromagnetic field so that the surface-enhanced Raman scattering (SERS) signal can be increased based on GCDs nanocomposites as substrate. Otherwise, the GCDs nanocomposites can also be used to catalyze the oxidation of colourless3,3',5,5′-tetramethylbenzidine (TMB) to blue oxTMB in the presence of H 2 O 2 , which displays the enhanced peroxidase-like activity compared with alone Au NPs or CDs. The obvious oxidation process of TMB molecules may be monitored by the change of SERS signal during the catalytic reaction. On this basis, GCDs nanocomposites can be further used for detection of glucose. The detection level of glucose is obtained as low as 5 × 10−7 M. Therefore, this provides a method to detect the glucose based on GCDs nanocomposites as an enzyme mimic. [ABSTRACT FROM AUTHOR]