Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm.

With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

Development and Validation of a Prediction Model for Venous Thrombus Embolism (VTE) in Patients With Colorectal Cancer.

Cancer patients are at high risk of developing venous thromboembolism (VTE). The risk of VTE could be mitigated with the administration of prophylactic anticoagulants. Therefore, risk assessment models would be a useful tool in order to identify those patients who are at higher risk and will be benefited more by prophylactic anticoagulants. This study retrospectively examined 528 newly diagnosed colorectal cancer patients from January 2019 to January 2021. Specified logistic regression models were employed to screen the factors and establish prediction tools based on nomograms according to the final included variables. Discrimination, calibration, and clinical applicability were used to assess the performance of screening tools. In addition, internal verifications were conducted through 10-fold cross-verification, leave-one-out cross-validation, and Bootstrap verification. Four risk factors, closely related to the occurrence of VTE in colorectal cancer patients, were identified after univariate and multivariate logistic regression, including age, body mass index, activated partial thromboplastin time, and D-Dimer value. Besides, the risk assessment model named ABAD was built on the basis, displaying good discriminations and calibrations. The area under the curve was 0.705 (95% confidence interval [CI], 0.644 to 0.766). According to Hosmer-Lemeshow goodness-of-fit test, a good agreement between the predicted and observed VTE events in patients with newly-diagnosed gastrointestinal cancer was observed for χ2 = 6.864, P = .551. Internal validation was applied with a C-index of 0.669 in the 10-fold cross-verification, 0.658 in the leave-one-out cross verification and 0.684 in the bootstrap verification. We developed a prediction model called ABAD for newly diagnosed colorectal cancer patients, which can be used to predict the risk of VTE. After evaluation and internal verification, we believe that ABAD exhibited high predictive performance and availability and could be recommended.

Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM.

The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions.

Reactive oxygen species formation and its effect on CD4+ T cell-mediated inflammation.

Reactive oxygen species (ROS) are produced both enzymatically and non-enzymatically in vivo. Physiological concentrations of ROS act as signaling molecules that participate in various physiological and pathophysiological activities and play an important role in basic metabolic functions. Diseases related to metabolic disorders may be affected by changes in redox balance. This review details the common generation pathways of intracellular ROS and discusses the damage to physiological functions when the ROS concentration is too high to reach an oxidative stress state. We also summarize the main features and energy metabolism of CD4+ T-cell activation and differentiation and the effects of ROS produced during the oxidative metabolism of CD4+ T cells. Because the current treatment for autoimmune diseases damages other immune responses and functional cells in the body, inhibiting the activation and differentiation of autoreactive T cells by targeting oxidative metabolism or ROS production without damaging systemic immune function is a promising treatment option. Therefore, exploring the relationship between T-cell energy metabolism and ROS and the T-cell differentiation process provides theoretical support for discovering effective treatments for T cell-mediated autoimmune diseases.

Signaling pathways involved in the biological functions of dendritic cells and their implications for disease treatment.

The ability of dendritic cells (DCs) to initiate and regulate adaptive immune responses is fundamental for maintaining immune homeostasis upon exposure to self or foreign antigens. The immune regulatory function of DCs is strictly controlled by their distribution as well as by cytokines, chemokines, and transcriptional programming. These factors work in conjunction to determine whether DCs exert an immunosuppressive or immune-activating function. Therefore, understanding the molecular signals involved in DC-dependent immunoregulation is crucial in providing insight into the generation of organismal immunity and revealing potential clinical applications of DCs. Considering the many breakthroughs in DC research in recent years, in this review we focused on three basic lines of research directly related to the biological functions of DCs and summarized new immunotherapeutic strategies involving DCs. First, we reviewed recent findings on DC subsets and identified lineage-restricted transcription factors that guide the development of different DC subsets. Second, we discussed the recognition and processing of antigens by DCs through pattern recognition receptors, endogenous/exogenous pathways, and the presentation of antigens through peptide/major histocompatibility complexes. Third, we reviewed how interactions between DCs and T cells coordinate immune homeostasis in vivo via multiple pathways. Finally, we summarized the application of DC-based immunotherapy for autoimmune diseases and tumors and highlighted potential research prospects for immunotherapy that targets DCs. This review provides a useful resource to better understand the immunomodulatory signals involved in different subsets of DCs and the manipulation of these immune signals can facilitate DC-based immunotherapy.

Pathogenesis and treatment of Sjogren's syndrome: Review and update.

Sjogren's syndrome (SS) is a chronic autoimmune disease accompanied by multiple lesions. The main manifestations include dryness of the mouth and eyes, along with systemic complications (e.g., pulmonary disease, kidney injury, and lymphoma). In this review, we highlight that IFNs, Th17 cell-related cytokines (IL-17 and IL-23), and B cell-related cytokines (TNF and BAFF) are crucial for the pathogenesis of SS. We also summarize the advances in experimental treatment strategies, including targeting Treg/Th17, mesenchymal stem cell treatment, targeting BAFF, inhibiting JAK pathway, et al. Similar to that of SLE, RA, and MS, biotherapeutic strategies of SS consist of neutralizing antibodies and inflammation-related receptor blockers targeting proinflammatory signaling pathways. However, clinical research on SS therapy is comparatively rare. Moreover, the differences in the curative effects of immunotherapies among SS and other autoimmune diseases are not fully understood. We emphasize that targeted drugs, low-side-effect drugs, and combination therapies should be the focus of future research.

Construction of Intelligent Substation-Optimized Networking Communication Network Based on Source-Network-Load Interaction Environment.

Smart grids, which possess flexibility, cleanliness, safety, economy, and friendliness, have drawn a lot of attention from all over the world in an era of rapid social and economic development, power technology change, and energy and environmental constraints. However, there are still issues with the design, installation, and operation of smart substations, such as insufficient LAN integration, difficulty quantifying network performance, and inability to keep track of communication. The basic meaning and key technologies of substation communication standards are used as the research object in this paper, and the basic attributes of substation data flow, such as source, type, and function, are qualitatively analyzed. The mathematical model that is most closely fitted is 2.8% more effective. The research object is the topology of the process bus. Through the comparison of various solutions, including star topology, ring topology, bus topology, and mesh topology, the advantages and disadvantages of each topology networking scheme are revealed along with the particular functional requirements of the substation process layer. Further discussion is given to the crucial topology-related technologies of network congestion and flow control.

Microbial Diversity Characteristics of Areca Palm Rhizosphere Soil at Different Growth Stages.

The rhizosphere microflora are key determinants that contribute to plant health and productivity, which can support plant nutrition and resistance to biotic and abiotic stressors. However, limited research is conducted on the areca palm rhizosphere microbiota. To further study the effect of the areca palm's developmental stages on the rhizosphere microbiota, the rhizosphere microbiota of areca palm (Areca catechu) grown in its main producing area were examined in Wanning, Hainan province, at different vegetation stages by an Illumina Miseq sequence analysis of the 16S ribosomal ribonucleic acid and internal transcribed spacer genes. Significant shifts of the taxonomic composition of the bacteria and fungi were observed in the four stages. Burkholderia-Caballeronia-Paraburkholderia were the most dominant group in stage T1 and T2; the genera Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium were decreased significantly from T1 to T2; and the genera Acidothermus and Bacillus were the most dominant in stage T3 and T4, respectively. Meanwhile, Neocosmospora, Saitozyma, Penicillium, and Trichoderma were the most dominant genera in the stage T1, T2, T3, and T4, respectively. Among the core microbiota, the dominant bacterial genera were Burkholderia-Caballeronia-Paraburkholderia and Bacillus, and the dominant fungal genera were Saitozyma and Trichoderma. In addition, we identified five bacterial genera and five fungal genera that reached significant levels during development. Finally, we constructed the OTU (top 30) interaction network of bacteria and fungi, revealed its interaction characteristics, and found that the bacterial OTUs exhibited more extensive interactions than the fungal OTUs. Understanding the rhizosphere soil microbial diversity characteristics of the areca palm could provide the basis for exploring microbial association and maintaining the areca palm's health.

Orientation-Insensitive Multi-Antenna Reader for Wireless Biomedical Applications.

This paper presents a multi-antenna external reader system that enables orientation insensitive communication with implantable medical devices (IMDs) for wireless biomedical applications. The proposed system consists of a circular array with six loop antennas. The antenna placement and orientations are determined by analyzing the near-field magnetic field variations of the loop antenna. The proposed system is first simulated using HFSS electromagnetic simulation software. Our simulations show that the received power at the proposed external reader with six antennas only varies about 5 dB for any given orientation of the implanted antenna, which is highly significant compared to the 20-35 dB variation with a single external antenna. Here, we select the antenna which provides the largest coupling between the IMD to receive/transmit signals. A prototype of the proposed multi-antenna external reader is then implemented using custom-designed PCBs that interconnect loop antennas, transceiver ICs, and commercially-available circuit components. A custom PCB with a miniaturized loop antenna is used to emulate an implantable device. Based on measurement results, the received power in the external reader only varies about 3 dB when the miniaturized antenna rotates with respect to the x-axis. These measurements show good agreement with the simulated reader.

Towards Magnetic Field Gradient-Based Imaging and Control of In-Body Devices.

This papers reports a magnetic field gradient-based imaging system for in-body devices which takes inspiration from the localization principles of magnetic resonance imaging. By applying three orthogonal magnetic field gradients, the location of a device inside the body can be determined by measuring the magnetic fields in the device and transmitting this information to an external reader. The proposed system consists of one pair of Helmholtz coils and two pairs of saddle coils and is capable of generating the three orthogonal gradient fields. To emulate an implantable device, a miniature sensor module was designed using off-the-shelf components and semi-passive UHF RFID. The proposed localization system produces magnetic field gradients up to 187.4 G/m while consuming 1 A and achieves an average localization error of 80 µm.

Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors.

As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation of the temperature distribution, which provides the possibility to distinguish the fault type of reciprocating compressors by differentiating the distribution using infrared thermal imaging. In this paper, three types of common fault are laboratory experimented in an uncontrolled temperature environment. The temperature distribution signals of a reciprocating compressor are captured by a non-contact infrared camera remotely in the form of heat maps during the experimental process. Based on the temperature distribution under baseline condition, temperature fields of six main components were selected via Hue-Saturation-Value (HSV) image as diagnostic features. During the experiment, the average grayscale values of each component were calculated to form 6-dimension vectors to represent the variation of the temperature distribution. A computational efficient multiclass support vector machine (SVM) model is then used for classifying the differences of the distributions, and the classification results demonstrate that the average temperatures of six main components aided by SVM is a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 99%.

Programmable ExG biopotential front-end IC for wearable applications.

This paper presents a configurable CMOS integrated circuit front-end for the recording of a wide range of biopotentials (ExG). The system offers a choice between a single-differential or double-differential recording channel topology, wide continuously adjustable gain range (37-66 dB), selectable CMOS or BJT input stages, offset compensation, differential and buffered single-ended voltage output. Measured results from a prototype manufactured in 0.35 µm CMOS technology are presented. Practical recording examples of the electrocardiogram (ECG) and electromyogram (EMG) confirm its operation. The chip consumes between 110 and 324 µW depending on configuration, occupies a core area of 0.16 mm(2), achieves a CMRR > 97 dB , and 21 nV/√Hz input-referred noise. The chip is suited for combination with a microcontroller in long-term wearable physiological sensing applications.

[Determination of Pb and Cd in atomospheric particulates by flame atomic absorption spectrometry coupled with on-line flow injection pretreatment with ultrasonic leaching].

In this paper, the method for extracting the metals from the filter membrane of the atomospheric particulates with ultrasonic leaching was reported. The dissolution conditions of kinds and acidity as well as the interference conditions were studied. The method of determination Pb and Cd of the atomospheric particulates by flame atomic absorption spectrometry coupled with on-line flow injection preconcentration was proved to be rapid and accuracy. The recoveries are more than 97%. The relative standard deviation of six samples are less than 2.6%.