Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review.

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11-17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments.

In complex and dynamic environments, traditional pursuit-evasion studies may face challenges in offering effective solutions to sudden environmental changes. In this paper, a bio-inspired neural network (BINN) is proposed that approximates a pursuit-evasion game from a neurodynamic perspective instead of formulating the problem as a differential game. The BINN is topologically organized to represent the environment with only local connections. The dynamics of neural activity, characterized by the neurodynamic shunting model, enable the generation of real-time evasive trajectories with moving or sudden-change obstacles. Several simulation and experimental results indicate that the proposed approach is effective and efficient in complex and dynamic environments.

Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models.

This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.

Distributed Robust Learning-Based Backstepping Control Aided With Neurodynamics for Consensus Formation Tracking of Underwater Vessels.

This article addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Toward this end, graph theory is used to allow us to synthesize the distributed controller with a stability guarantee. Due to the fact that the parameter uncertainties only arise in the vessels' dynamic model, the backstepping control technique is then employed. Subsequently, to overcome the difficulties in handling time-varying and unknown systems, an online learning procedure is developed in the proposed distributed formation control protocol. Moreover, modeling errors, environmental disturbances, and measurement noises are considered and tackled by introducing a neurodynamics model in the controller design to obtain a robust solution. Then, the stability analysis of the overall closed-loop system under the proposed scheme is provided to ensure the robust adaptive performance at the theoretical level. Finally, extensive simulation experiments are conducted to further verify the efficacy of the presented distributed control protocol.

Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet.

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics-based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping-based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

Infrared and Visible Image Fusion Technology and Application: A Review.

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.

Enhanced Reduced-Order Extended State Observer for Motion Control of Differential Driven Mobile Robot.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

Novel Extended State Observer Design for Uncertain Nonlinear Systems via Refined Dynamic Event-Triggered Communication Protocol.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

A Variable Radius Side Window Direct SLAM Method Based on Semantic Information.

Simultaneous Localization and Mapping (SLAM) is a challenging and key issue in the mobile robotic fields. In terms of the visual SLAM problem, the direct methods are more suitable for more expansive scenes with many repetitive features or less texture in contrast with the feature-based methods. However, the robustness of the direct methods is weaker than that of the feature-based methods. To deal with this problem, an improved direct sparse odometry with loop closure (LDSO) is proposed, where the performance of the SLAM system under the influence of different imaging disturbances of the camera is focused on. In the proposed method, a method based on the side window strategy is proposed for preprocessing the input images with a multilayer stacked pixel blender. Then, a variable radius side window strategy based on semantic information is proposed to reduce the weight of selected points on semistatic objects, which can reduce the computation and improve the accuracy of the SLAM system based on the direct method. Various experiments are conducted on the KITTI dataset and TUM RGB-D dataset to test the performance of the proposed method under different camera imaging disturbances. The quantitative and qualitative evaluations show that the proposed method has better robustness than the state-of-the-art direct methods in the literature. Finally, a real-world experiment is conducted, and the results prove the effectiveness of the proposed method.

A Novel Rapid-Flooding Approach With Real-Time Delay Compensation for Wireless-Sensor Network Time Synchronization.

One-way-broadcast-based flooding time synchronization algorithms are commonly used in wireless-sensor networks (WSNs). However, the packet delay and clock drift pose a challenge to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome this, a rapid-flooding multibroadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this article. By using a rapid-flooding protocol, flooding latency of the referenced time information is significantly reduced in the RDC-RMTS. In addition, a new joint clock skew-offset maximum-likelihood estimation (MLE) is developed to obtain the accurate clock parameter estimations and the real-time packet delay estimation. Moreover, an innovative implementation of the RDC-RMTS is designed with an adaptive clock offset estimation. The experimental results indicate that the RDC-RMTS can easily reduce the variable delay and significantly slow the growth of by-hop error accumulation. Thus, the proposed RDC-RMTS can achieve accurate time synchronization in large-scale complex WSNs.

Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network.

In this article, a novel thruster information fusion fault diagnosis method for the deep-sea human occupied vehicle (HOV) is proposed. A deep belief network (DBN) is introduced into the multisensor information fusion model to identify uncertain and unknown, continuously changing fault patterns of the deep-sea HOV thruster. Inputs for the DBN information fusion fault diagnosis model are the control voltage, feedback current, and rotational speed of the deep-sea HOV thruster; and the output is the corresponding fault degree parameter ( s ), which indicates the pattern and degree of the thruster fault. In order to illustrate the effectiveness of the proposed fault diagnosis method, a pool experiment under different simulated fault cases is conducted in this study. The experimental results have proved that the DBN information fusion fault diagnosis method can not only diagnose the continuously changing, uncertain, and unknown thruster fault but also has higher identification accuracy than the information fusion fault diagnosis methods based on traditional artificial neural networks.

Circulating mitochondrial DNA is a proinflammatory DAMP in sickle cell disease.

The pathophysiology of sickle cell disease (SCD) is driven by chronic inflammation fueled by damage associated molecular patterns (DAMPs). We show that elevated cell-free DNA (cfDNA) in patients with SCD is not just a prognostic biomarker, it also contributes to the pathological inflammation. Within the elevated cfDNA, patients with SCD had a significantly higher ratio of cell-free mitochondrial DNA (cf-mtDNA)/cell-free nuclear DNA compared with healthy controls. Additionally, mitochondrial DNA in patient samples showed significantly disproportionately increased hypomethylation compared with healthy controls, and it was increased further in crises compared with steady-state. Using flow cytometry, structured illumination microscopy, and electron microscopy, we showed that circulating SCD red blood cells abnormally retained their mitochondria and, thus, are likely to be the source of the elevated cf-mtDNA in patients with SCD. Patient plasma containing high levels of cf-mtDNA triggered the formation of neutrophil extracellular traps (NETs) that was substantially reduced by inhibition of TANK-binding kinase 1, implicating activation of the cGAS-STING pathway. cf-mtDNA is an erythrocytic DAMP, highlighting an underappreciated role for mitochondria in sickle pathology. These trials were registered at www.clinicaltrials.gov as #NCT00081523, #NCT03049475, and #NCT00047996.

Anterolateral thigh free flaps and radial forearm free flaps in head and neck reconstruction: A 20-year analysis from a single institution.

BACKGROUND: Reconstruction after removal of a malignant tumor in the head and neck region is crucial for restoring tissue integrity, function, and aesthetics. We retrospectively analyzed patients who underwent intraoral reconstruction surgery using radial forearm free flaps (RFFF) and anterolateral thigh free flaps (ALT) at a single institution to provide more information supporting the choice of a reconstruction method after removal of head and neck cancer. METHODS: The charts of 708 patients who underwent head and neck reconstruction between 1998 and 2018 at the Department of Plastic and Reconstructive Surgery at our institution were retrospectively reviewed. Patients' age, sex, and history of radiation therapy, diabetes mellitus, and smoking were retrieved. The primary cancer site, types of defects, and complications were investigated. RESULTS: Overall, 473 and 95 patients underwent reconstruction surgery with RFFF and ALT, respectively. RFFF was more often used in patients with cancers of the pharynx, larynx, esophagus, or tonsil, while ALT was more frequently used in patients with cancers of the mouth floor with tonsil or tongue involvement. The proportion of patients undergoing ALT increased gradually. Flap failure and donor site morbidities did not show significant differences between the two groups. CONCLUSIONS: RFFF and ALT flaps resulted in similar outcomes in terms of flap survival and donor site morbidity. ALT can be an option for head and neck reconstruction surgery in patients with large and complex defects or for young patients who want to hide their donor site scars.

Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer.

OBJECTIVE: Some excellent prognostic models based on survival analysis methods for breast cancer have been proposed and extensively validated, which provide an essential means for clinical diagnosis and treatment to improve patient survival. To analyze clinical and follow-up data of 12119 breast cancer patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, we developed a gradient boosting algorithm, called EXSA, by optimizing survival analysis of XGBoost framework for ties to predict the disease progression of breast cancer. METHODS: EXSA is based on the XGBoost framework in machine learning and the Cox proportional hazards model in survival analysis. By taking Efron approximation of partial likelihood function as a learning objective for ties, EXSA derives gradient formulas of a more precise approximation. It optimizes and enhances the ability of XGBoost for survival data with ties. After retaining 4575 patients (3202 cases for training, 1373 cases for test), we exploit the developed EXSA method to build an excellent prognostic model to estimate disease progress. Risk score of disease progress is evaluated by the model, and the risk grouping and continuous functions between risk scores and disease progress rate at 5- and 10-year are also demonstrated. RESULTS: Experimental results on test set show that the EXSA method achieves competitive performance with concordance index of 0.83454, 5-year and 10-year AUC of 0.83851 and 0.78155, respectively. CONCLUSION: The proposed EXSA method can be utilized as an effective method for survival analysis. SIGNIFICANCE: The proposed method in this paper can provide an important means for follow-up data of breast cancer or other disease research.

Consensus Affinity Graph Learning for Multiple Kernel Clustering.

Significant attention to multiple kernel graph-based clustering (MKGC) has emerged in recent years, primarily due to the superiority of multiple kernel learning (MKL) and the outstanding performance of graph-based clustering. However, many existing MKGC methods design a fat model that poses challenges for computational cost and clustering performance, as they learn both an affinity graph and an extra consensus kernel cumbersomely. To tackle this challenging problem, this article proposes a new MKGC method to learn a consensus affinity graph directly. By using the self-expressiveness graph learning and an adaptive local structure learning term, the local manifold structure of the data in kernel space is preserved for learning multiple candidate affinity graphs from a kernel pool first. After that, these candidate affinity graphs are synthesized to learn a consensus affinity graph via a thin autoweighted fusion model, in which a self-tuned Laplacian rank constraint and a top- k neighbors sparse strategy are introduced to improve the quality of the consensus affinity graph for accurate clustering purposes. The experimental results on ten benchmark datasets and two synthetic datasets show that the proposed method consistently and significantly outperforms the state-of-the-art methods.

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone.

Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we do not know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the gray level co-occurrence matrixes (GLCMs) of the face mask's micro-photos. Next, a three-result detection system is accomplished by using K Nearest Neighbor (KNN) algorithm. The results of validation experiments show that our system can reach an accuracy of 82.87% (measured by macro-measures) on the testing dataset. The precision of Type I 'normal use' and the recall of type III 'not recommended' reach 92.00% and 92.59%. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.

Manic episode in patient with bipolar disorder and recent multiple sclerosis diagnosis: A case report.

INTRODUCTION/RATIONALE: Multiple sclerosis (MS) is associated with a higher prevalence of mood and psychiatric disorders, such as bipolar disorder (BD). While mania is most often associated with BD, MS can also induce manic symptoms. However, it is crucial to distinguish which condition is causing mania since medical management is different based on its etiology. Herein, we report a case of a manic episode in a middle-aged female with a prolonged history of BD who received a recent diagnosis of MS 1 year ago. PATIENT CONCERNS: A 56-year-old female presented with an episode of mania and psychosis while receiving a phenobarbital taper for chronic lorazepam use. She had a prolonged history of bipolar type 1 disorder and depression. She showed optic neuritis and was diagnosed with MS a year prior. DIAGNOSES: The patient was diagnosed with BD-induced mania based on the absence of increased demyelination compared to previous MRI and lack of new focal or lateralizing neurologic findings of MS. INTERVENTIONS: Lithium was given for mood stabilization and decreased dosage of prior antidepressant medication. Risperidone was given for ongoing delusions. OUTCOMES: After 8 days of hospitalization, patient's mania improved but demonstrated atypical features and ongoing delusions. She was discharged at her request to continue treatment in an outpatient setting. CONCLUSION/LESSON: In BD patients with an episode of mania, MS should be included in the differential, since both conditions can cause manic symptoms. The origin of mania should be delineated through a detailed neurological exam, neuroimaging, and thorough patient-family psychiatric history for appropriate clinical treatment.

Clinical Decision Support Intervention and Time to Imaging in Older Patients with Traumatic Brain Injury.

BACKGROUND: Traumatic brain injury (TBI) is the leading cause of elderly trauma admissions. Previous research identified that each minute delay to TBI diagnosis was associated with a 2% mortality increase, delaying treatment to older patients (age ≥70 years) who do not meet trauma activation criteria. A TBI protocol and clinical decision support intervention (CDS-I) were developed to reduce time to imaging in older patients with head trauma not meeting trauma activation criteria. STUDY DESIGN: An emergency department (ED) head CT protocol and CDS-I were developed and implemented to facilitate rapid imaging of older patients. Patients age ≥ 70 years, with TBI and receiving anticoagulation, met inclusion criteria. The primary outcomes measure was time from ED arrival to head CT imaging comparing before (PRE: January 1, 2016 to December 31, 2016) vs after (POST: August 1, 2018 to April 3, 2019) protocol implementation. Negative binomial regression models evaluated the association of intervention on time to imaging. LOWESS (locally weighted scatterplot smoothing) was used to evaluate the association of intervention on mortality over time. RESULTS: The study examined 451 patients (269 PRE and 182 POST). Positive head CTs were seen in 78 (17.3%), and 57 of 78 (73%) patients had a Glasgow Coma Scale > 13. POST-intervention decreased time to head CT from 56 to 27 minutes (interquartile range [IQR] PRE: 32 to 93 to POST:16 to 44, p < 0.001) and POST-intervention patients had reduced hospital length of stay (incidence rate ratio [IRR] 0.83, 95% CI 0.72 to 0.86, p = 0.01). CONCLUSIONS: A significant proportion of older patients receiving anticoagulation, but not meeting trauma activation criteria, had positive CT findings. Implementation of a rapid triage protocol with CDS-I reduced time to imaging and may reduce mortality in the highest-risk populations.

Global reconstruction reduces the uncertainty of oceanic nitrous oxide emissions and reveals a vigorous seasonal cycle.

Assessment of the global budget of the greenhouse gas nitrous oxide ([Formula: see text]O) is limited by poor knowledge of the oceanic [Formula: see text]O flux to the atmosphere, of which the magnitude, spatial distribution, and temporal variability remain highly uncertain. Here, we reconstruct climatological [Formula: see text]O emissions from the ocean by training a supervised learning algorithm with over 158,000 [Formula: see text]O measurements from the surface ocean-the largest synthesis to date. The reconstruction captures observed latitudinal gradients and coastal hot spots of [Formula: see text]O flux and reveals a vigorous global seasonal cycle. We estimate an annual mean [Formula: see text]O flux of 4.2 ± 1.0 Tg N[Formula: see text], 64% of which occurs in the tropics, and 20% in coastal upwelling systems that occupy less than 3% of the ocean area. This [Formula: see text]O flux ranges from a low of 3.3 ± 1.3 Tg N[Formula: see text] in the boreal spring to a high of 5.5 ± 2.0 Tg N[Formula: see text] in the boreal summer. Much of the seasonal variations in global [Formula: see text]O emissions can be traced to seasonal upwelling in the tropical ocean and winter mixing in the Southern Ocean. The dominant contribution to seasonality by productive, low-oxygen tropical upwelling systems (>75%) suggests a sensitivity of the global [Formula: see text]O flux to El Niño-Southern Oscillation and anthropogenic stratification of the low latitude ocean. This ocean flux estimate is consistent with the range adopted by the Intergovernmental Panel on Climate Change, but reduces its uncertainty by more than fivefold, enabling more precise determination of other terms in the atmospheric [Formula: see text]O budget.

An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control.

During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m3/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved.