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.

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.

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.

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.

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.

Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.

Near-infrared (NIR) spectral sensors can deliver the spectral response of light absorbed by materials. Data analysis technology based on NIR sensors has been a useful tool for quality identification. In this paper, an improved deep convolutional neural network (CNN) with batch normalization and MSRA (Microsoft Research Asia) initialization is proposed to discriminate the tobacco cultivation regions using data collected from NIR sensors. The network structure is created with six convolutional layers and three full connection layers, and the learning rate is controlled by exponential attenuation method. One-dimensional kernel is applied as the convolution kernel to extract features. Meanwhile, the methods of L2 regularization and dropout are used to avoid the overfitting problem, which improve the generalization ability of the network. Experimental results show that the proposed deep network structure can effectively extract the complex characteristics inside the spectrum, which proves that it has excellent recognition performance on tobacco cultivation region discrimination, and it also demonstrates that the deep CNN is more suitable for information mining and analysis of big data.

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.

Postoperative Cancer Surveillance Following Oncoplastic Surgery with Latissimus Dorsi Flap: a Matched Case-Control Study.

BACKGROUND: The latissimus dorsi (LD) myocutaneous flap is a widely used local option in oncoplastic surgery for avoiding breast deformities; however, concerns exist regarding its influence in monitoring recurrence. In this study, we evaluated the impact of this flap on postoperative cancer surveillance. METHODS: Each patient receiving oncoplastic surgery with LD flap after partial mastectomy were matched in age, cancer stage, and body mass index with patients receiving partial mastectomy alone. Twenty-nine patients with the oncoplastic LD flap received 99 mammograms and 139 ultrasonograms, while 29 patients with partial mastectomy alone underwent 92 mammograms and 129 ultrasonograms. Mammographic and ultrasonographic findings were classified by Breast Imaging Reporting and Data System (BI-RADS) category and reviewed. Any recommendations for additional evaluation and recurrence were documented. RESULTS: During an average follow-up period of 44 months, although the oncoplastic group demonstrated more newly developed benign calcifications (control 14% vs. oncoplastic 41%; p = 0.019) on mammography, the percentage of recall for additional imaging in category 0, and the short-interval follow-up in category 3, was not different between the control and oncoplastic group. Regarding ultrasonography, BI-RADS category was also not different between the two groups; however, the control group showed more fluid collections than the oncoplastic group (control 21% vs. oncoplastic 0%; p = 0.023). One case of local recurrence was observed in the control group. CONCLUSION: Although there was an increase in benign calcifications in the oncoplastic group, there were no additional abnormal findings requiring further intervention. We concluded that the LD flap for oncoplastic surgery does not interfere with cancer surveillance, and even decreases the rate of fluid collection.

Early Imaging Associated With Improved Survival in Older Patients With Mild Traumatic Brain Injuries.

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of trauma-related death and disability. Computed tomography (CT) imaging of the head is essential for diagnosis of intracranial hemorrhage. This study aimed to identify optimal time to imaging and its impact on mortality for older patients with mild TBIs. MATERIALS AND METHODS: State-wide quality collaborative data were used from level I-II trauma centers. Inclusion criteria were ICD-9/10 codes for head trauma, age ≥50, admission/emergency department Glasgow Coma Scale ≥14, injury severity score ≤20, nonfull trauma activation, and head CT imaging time between 5 and 90 min of arrival. Locally weighted scatterplot smoothing plot data were used to dichotomize patients into early and late head CT imaging cohorts. Multivariable logistic regression and negative binomial models were used to evaluate the effect of early verses late head CT on clinical outcomes. The primary outcome was in-hospital mortality. RESULTS: Mortality nadired at 35 min. Each 1-min delay in CT imaging resulted in a 2% increase in mortality (P = 0.002). Early patients had significantly reduced in-hospital mortality (P = 0.03), shorter emergency department length of stay (P < 0.001), and were more likely to receive fresh frozen plasma within 4 h if anticoagulated (P = 0.03). Teaching, high-volume, and level 2 trauma centers were all less likely to provide early head CTs (all P < 0.05). CONCLUSIONS: Delay in head CT imaging in the setting of potential mild TBI was associated with an increase in mortality. A delay in diagnosis cascades into delays in delivery of therapeutic interventions. Head CT within 35 min should be evaluated as a quality metric for older patients with mild TBI.

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches.

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer's control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results' robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.