Structural basis of PETISCO complex assembly during piRNA biogenesis in C. elegans.

Piwi-interacting RNAs (piRNAs) constitute a class of small RNAs that bind PIWI proteins and are essential to repress transposable elements in the animal germline, thereby promoting genome stability and maintaining fertility. C. elegans piRNAs (21U RNAs) are transcribed individually from minigenes as precursors that require 5' and 3' processing. This process depends on the PETISCO complex, consisting of four proteins: IFE-3, TOFU-6, PID-3, and ERH-2. We used biochemical and structural biology approaches to characterize the PETISCO architecture and its interaction with RNA, together with its effector proteins TOST-1 and PID-1. These two proteins define different PETISCO functions: PID-1 governs 21U processing, whereas TOST-1 links PETISCO to an unknown process essential for early embryogenesis. Here, we show that PETISCO forms an octameric assembly with each subunit present in two copies. Determination of structures of the TOFU-6/PID-3 and PID-3/ERH-2 subcomplexes, supported by in vivo studies of subunit interaction mutants, allows us to propose a model for the formation of the TOFU-6/PID-3/ERH-2 core complex and its functionality in germ cells and early embryos. Using NMR spectroscopy, we demonstrate that TOST-1 and PID-1 bind to a common surface on ERH-2, located opposite its PID-3 binding site, explaining how PETISCO can mediate different cellular roles.

PETISCO is a novel protein complex required for 21U RNA biogenesis and embryonic viability.

Piwi proteins are important for germ cell development in most animals. These proteins are guided to specific targets by small guide RNAs, referred to as piRNAs or 21U RNAs in Caenorhabditis elegans In this organism, even though genetic screens have uncovered 21U RNA biogenesis factors, little is known about how these factors interact or what they do. Based on the previously identified 21U biogenesis factor PID-1 (piRNA-induced silencing-defective 1), we here define a novel protein complex, PETISCO (PID-3, ERH-2, TOFU-6, and IFE-3 small RNA complex), that is required for 21U RNA biogenesis. PETISCO contains both potential 5' cap and 5' phosphate RNA-binding domains and interacts with capped 21U precursor RNA. We resolved the architecture of PETISCO and revealed a second function for PETISCO in embryonic development. This essential function of PETISCO is mediated not by PID-1 but by the novel protein TOST-1 (twenty-one U pathway antagonist). In contrast, TOST-1 is not essential for 21U RNA biogenesis. Both PID-1 and TOST-1 interact directly with ERH-2 using a conserved sequence motif. Finally, our data suggest a role for TOST-1:PETISCO in SL1 homeostasis in the early embryo. Our work describes a key complex for 21U RNA processing in C. elegans and strengthens the view that 21U RNA biogenesis is built on an snRNA-related pathway.

Intrinsically disordered protein PID-2 modulates Z granules and is required for heritable piRNA-induced silencing in the Caenorhabditis elegans embryo.

In Caenorhabditis elegans, the piRNA (21U RNA) pathway is required to establish proper gene regulation and an immortal germline. To achieve this, PRG-1-bound 21U RNAs trigger silencing mechanisms mediated by RNA-dependent RNA polymerase (RdRP)-synthetized 22G RNAs. This silencing can become PRG-1-independent and heritable over many generations, a state termed RNA-induced epigenetic gene silencing (RNAe). How and when RNAe is established, and how it is maintained, is not known. We show that maternally provided 21U RNAs can be sufficient for triggering RNAe in embryos. Additionally, we identify PID-2, a protein containing intrinsically disordered regions (IDRs), as a factor required for establishing and maintaining RNAe. PID-2 interacts with two newly identified and partially redundant eTudor domain-containing proteins, PID-4 and PID-5. PID-5 has an additional domain related to the X-prolyl aminopeptidase APP-1, and binds APP-1, implicating potential N-terminal proteolysis in RNAe. All three proteins are required for germline immortality, localize to perinuclear foci, affect size and appearance of RNA inheritance-linked Z granules, and are required for balancing of 22G RNA populations. Overall, our study identifies three new proteins with crucial functions in C. elegans small RNA silencing.

Clinical features and lymphocyte immunophenotyping analysis in primary immunodeficiency patients with non-transplant lymphoproliferative disorders.

Lymphoproliferative disorders (LPD) comprise a heterogeneous group and are originally classified into the "Disease of immune dysregulation" category. Of 96 Taiwanese patients during 2003-2022, 31 (median 66, range 0.03-675 months) developed LPD, mainly including palpable lymphadenopathy (in 10 patients), intestinal lymphadenopathy associated with refractory inflammatory bowel disease (IBD in 8) and hepatosplenomegaly (in 7) during long-term follow-up (median 144, range 3-252 months). They distributed in the categories of antibody deficiency (2 CVID, 2 TTC37, PIK3CD, PIK3R1 and AICDA each), phagocyte (4 CYBB, 1 STAT1 and 1 IFNRG1), immune dysregulation (2 FOXP3, 2 XIAP and 2 HLH), combined immunodeficiencies (2 IL2RG; CD40L, ZAP70 and unknown each), syndromic features (2 STAT3-LOF, 1 WAS and 1 ATM) and three with anti-IFN-γ autoantibodies. An increased senescent (CD8 + CD57+) and CD21-low, disturbed transitional B (CD38 + IgM++), plasmablast B (CD38++IgM-), memory B (CD19 + CD27+) and TEMRA (CD27-IgD-) components were often observed in cross-sectional immunophenotyping and trended to develop LPD.

Novel Synonymous Variant in IL7R Causes Preferential Expression of the Soluble Isoform.

PURPOSE: The interleukin-7 receptor (IL-7R) is primarily expressed on lymphoid cells and plays a crucial role in the development, proliferation, and survival of T cells. Autosomal recessive mutations that disrupt IL-7Rα chain expression give rise to a severe combined immunodeficiency (SCID), which is characterized by lymphopenia and a T-B+NK+ phenotype. The objective here was to diagnose two siblings displaying the T-B+NK+ SCID phenotype as initial clinical genetic testing did not detect any variants in known SCID genes. METHODS: Whole genome sequencing (WGS) was utilized to identify potential variants causing the SCID phenotype. Splicing prediction tools were employed to assess the deleterious impact of the mutation. Polymerase Chain Reaction (PCR), Sanger sequencing, flow cytometry, and ELISA were then used to validate the pathogenicity of the detected mutation. RESULTS: We discovered a novel homozygous synonymous mutation in the IL7R gene. Our functional studies indicate that this variant is pathogenic, causing exon 6, which encodes the transmembrane domain, to be preferentially spliced out. CONCLUSION: In this study, we identified a novel rare synonymous mutation causing a loss of IL-7Rα expression at the cellular membrane. This case demonstrates the value of reanalyzing genetic data based on the clinical phenotype and highlights the significance of functional studies in determining the pathogenicity of genetic variants.

VIPPID: a gene-specific single nucleotide variant pathogenicity prediction tool for primary immunodeficiency diseases.

Distinguishing pathogenic variants from non-pathogenic ones remains a major challenge in clinical genetic testing of primary immunodeficiency (PID) patients. Most of the existing mutation pathogenicity prediction tools treat all mutations as homogeneous entities, ignoring the differences in characteristics of different genes, and use the same model for genes in different diseases. In this study, we developed a single nucleotide variant (SNV) pathogenicity prediction tool, Variant Impact Predictor for PIDs (VIPPID; https://mylab.shinyapps.io/VIPPID/), which was tailored for PIDs genes and used a specific model for each of the most prevalent PID known genes. It employed a Conditional Inference Forest model and utilized information of 85 features of SNVs and scores from 20 existing prediction tools. Evaluation of VIPPID showed that it had superior performance (area under the curve = 0.91) over non-specific conventional tools. In addition, we also showed that the gene-specific model outperformed the non-gene-specific models. Our study demonstrated that disease-specific and gene-specific models can improve SNV pathogenicity prediction performance. This observation supports the notion that each feature of mutations in the model can be potentially used, in a new algorithm, to investigate the characteristics and function of the encoded proteins.

Adaptive laboratory evolution in a novel parallel shaken pH-auxostat.

Adaptive laboratory evolution (ALE) is a widely used microbial strain development and optimization method. ALE experiments, to select for faster-growing strains, are commonly performed as serial batch cultivations in shake flasks, serum bottles, or microtiter plates or as continuous cultivations in bioreactors on a laboratory scale. To combine the advantages of higher throughput in parallel shaken cultures with continuous fermentations for conducting ALE experiments, a new Continuous parallel shaken pH-auxostat (CPA) was developed. The CPA consists of six autonomous parallel shaken cylindrical reactors, equipped with real-time pH control of the culture medium. The noninvasive pH measurement and control are realized by biocompatible pH sensor spots and a programmable pump module, to adjust the dilution rate of fresh medium for each reactor separately. Two different strains of the methylotrophic yeast Ogataea polymorpha were used as microbial model systems for parallel chemostat and pH-auxostat cultivations. During cultivation, the medium is acidified by the microbial activity of the yeast. For pH-auxostat cultivations, the growth-dependent acidification triggers the addition of fresh feed medium into the reactors, leading to a pH increase and thereby to the control of the pH to a predetermined set value. By controlling the pH to a predetermined set value, the dilution rate of the continuous cultivation is adjusted to values close to the washout point, in the range of the maximum specific growth rate of the yeast. The pH control was optimized by conducting a step-response experiment and obtaining tuned PI controller parameters by the Chien-Hrones-Reswick (CHR) PID tuning method. Two pH-auxostat cultivations were performed with two different O. polymorpha strains at high dilution rates for up to 18 days. As a result, up to 4.8-fold faster-growing strains were selected. The increased specific maximum growth rates of the selected strains were confirmed in subsequent batch cultivations.

Maladaptive personality traits and older adult relationship satisfaction: A co-twin control approach to understanding associations.

OBJECTIVE: Maladaptive personality traits have been implicated in romantic relationship dissatisfaction, but the etiology of those links and the degree to which they extend to other types of relationships are unclear. The purpose of this study was to examine associations between maladaptive personality traits and satisfaction in various relationships using a co-twin control design to identify potential environmental contributions. METHOD: The sample consisted of 1340 older adult twin participants from the Minnesota Twin Registry (Mage = 70.3) that completed the Personality Inventory for DSM-5 Faceted Brief Form and Network of Relationships Inventory (Revised for Older Adults). RESULTS: Several maladaptive personality traits were phenotypically associated with relationship dissatisfaction, with detachment and negative affect having the largest effects. Further, within twin pair differences in detachment and negative affect were associated with greater relationship dissatisfaction, suggesting that observed associations were mediated partly by the unique environment, not solely the result of genetic and familial confounding. Both phenotypic and co-twin associations were strongest overall in the romantic partner relationship. CONCLUSION: These findings support the notion that maladaptive personality traits are implicated in interpersonal dysfunction across multiple domains.

Evolving trends in the management of pelvic inflammatory disease (PID) during SARS-CoV-2 pandemic: A multicenter retrospective cohort study.

BACKGROUND AND AIM: Pelvic inflammatory disease (PID) is usually managed by conservative treatment, but in selected cases, especially in the presence of a tubo-ovarian abscess (TOA), surgical management is a recognized treatment option. We compared the trends in managing PID and short-term outcomes before and during the SARS-CoV-2 pandemic. METHODS: This is a retrospective study performed in three Italian gynecological centers. We included patients admitted to hospital with a diagnosis of PID. Demographic characteristics, management, time to diagnosis, and time to treatment were compared before versus during the SARS-CoV-2 pandemic. RESULTS: One hundred nineteen PID patients were screened, eighty-one before the SARS-CoV-2 pandemic, and thirty-eight after the onset. At admission, leukocytosis (median 19.73 vs. 13.99 WBC/mm3, p-value = 0.02) was significantly higher in patients who underwent surgery after the onset of the pandemic. TOA incidence was higher in patients who underwent surgery during the SARS-CoV-2 pandemic, but the difference did not reach statistically significance (p = 0.06). The proportion of patients treated with surgery dropped to 26.3% after the onset from 46% of patients before the onset of pandemic (p = 0.03). Furthermore, a higher percentage of emergency surgical procedures on day 0 of hospital admission were performed after the onset of the pandemic (50% vs. 13.1%, p = 0.01). CONCLUSIONS: In this retrospective cohort study, we found that the SARS-CoV-2 pandemic influenced the clinical presentation and management of PID in favor of conservative treatment. Patients who underwent surgery during the SARS-CoV-2 pandemic had higher inflammatory markers.

Modeling and Control of a Two-Axis Stabilized Gimbal Based on Kane Method.

A two-axis stabilizing gimbal is a device that ensures a sensor is working properly on a moving platform. When classical mechanics (Newton-Euler and Lagrange) is employed to model a two-axis stable gimbal, its limitations can complicate the modeling process. To address this issue, a method for establishing a dynamic model for a two-axis stabilizing platform based on the Kane method is proposed in this paper. The Kane method offers the advantage of a simple model structure and computational efficiency. Initially, utilizing a generalized coordinate system, expressions of the generalized velocities, deflection velocities and angular velocities are derived. Subsequently, the generalized active forces and inertial forces acting on the two-axis stabilized gimbal are analyzed. Finally, by combining force and velocity with the Kane equation, the dynamic model of the two-axis stable platform is obtained, demonstrating the validity of the Kane method for establishing the two-axis stable platform model. To ensure the pointing accuracy stability of the two-axis stabilizing platform, a Novel Particle Swarm Optimization Proportion Integration Differentiation (NPSO-PID) controller is designed using the PSO algorithm. It is then simulated in MATLAB/Simulink and compared with a classical PID controller. Simulation results demonstrate that NPSO-PID exhibits superior object tracking performance compared to classical PID controllers and better optimization of control parameters compared to traditional PSO-PID controllers.

Efficient Cyberattack Detection Methods in Industrial Control Systems.

The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional-Integral-Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched. The first method verifies whether the value of the control signal sent to the control plant in the previous step is the actual value generated by the controller. The second method relies on detecting sudden, unusual changes in output variables, taking into account the inertial nature of dynamic plants. In the third method, a copy of the controller parameters is used to detect an attack on the controller's parameters implemented in the PLC. The fourth method uses the golden run in attack detection.

Liquid-Driven Microinjection System for Precise Fundus Injection.

Microinjection is usually applied to the treatment of some retinal disorders, such as retinal vein cannulation and displaced submacular hemorrhage. Currently, the microinjection procedure is usually performed by using the viscous fluid control of a standard vitrectomy system, which applies a fixed air pressure through foot pedal activation. The injection process with the fixed pressure is uncontrollable and lacks feedback, the high flow rate of the injected drug may cause damage to the fundus tissue. In this paper, a liquid-driven microinjection system with a flow sensor is designed and developed specifically for fundus injection. In addition, a PID sliding mode control (SMC) method is proposed to achieve precise injection in the injection system. The experimental results of fundus simulation injection demonstrate that the microinjection system meets the requirements of fundus injection and reduces the impact of the injection process on the fundus tissue.

Variable Universe Fuzzy-Proportional-Integral-Differential-Based Braking Force Control of Electro-Mechanical Brakes for Mine Underground Electric Trackless Rubber-Tired Vehicles.

Currently, the main solution for braking systems for underground electric trackless rubber-tired vehicles (UETRVs) is traditional hydraulic braking systems, which have the disadvantages of hydraulic pressure crawling, the risk of oil leakage and a high maintenance cost. An electro-mechanical-braking (EMB) system, as a type of novel brake-by-wire (BBW) system, can eliminate the above shortcomings and play a significant role in enhancing the intelligence level of the braking system in order to meet the motion control requirements of unmanned UETRVs. Among these requirements, the accurate control of clamping force is a key technology in controlling performance and the practical implementation of EMB systems. In order to achieve an adaptive clamping force control performance of an EMB system, an optimized fuzzy proportional-integral-differential (PID) controller is proposed, where the improved fuzzy algorithm is utilized to adaptively adjust the gain parameters of classic PID. In order to compensate for the deficiency of single-close-loop control and adjusting the brake gap automatically, a cascaded three-closed-loop control architecture with force/position switch technology is established, where a contact point detection method utilizing motor rotor angle displacement is proposed via experiments. The results of the simulation and experiments indicate that the clamping force response of the proposed multi-close-loop Variable Universe Fuzzy-PID (VUF-PID) controller is faster than the multi-closed-loop Fuzzy-PID and cascaded three-close-loop PID controllers. In addition, the chattering of braking force can be suppressed by 17%. This EMB system may rapidly and automatically finish the operation of the overall braking process, including gap elimination, clamping force tracking and gap recovery, which can obviously enhance the precision of the longitudinal motion control of UETRVs. It can thus serve as a BBW actuator of mine autonomous driving electric vehicles, especially in the stage of braking control.

Research on Gate Opening Control Based on Improved Beetle Antennae Search.

To address the issues of sluggish response and inadequate precision in traditional gate opening control systems, this study presents a novel approach for direct current (DC) motor control utilizing an enhanced beetle antennae search (BAS) algorithm to fine-tune the parameters of a fuzzy proportional integral derivative (PID) controller. Initially, the mathematical model of the DC motor drive system is formulated. Subsequently, employing a search algorithm, the three parameters of the PID controller are optimized in accordance with the control requirements. Next, software simulation is employed to analyze the system's response time and overshoot. Furthermore, a comparative analysis is conducted between fuzzy PID control based on the improved beetle antennae search algorithm, and conventional approaches such as the traditional beetle antennae search algorithm, the traditional particle swarm algorithm, and the enhanced particle swarm algorithm. The findings indicate the superior performance of the proposed method, characterized by reduced oscillations and accelerated convergence compared to the alternative methods.

Design of Mixed-Mode Analog PID Controller with CFOAs.

The design of a mixed-mode proportional-integral-derivative (PID) controller circuit using current-feedback operational amplifiers (CFOAs) as active components is proposed. With the same circuit topology, the proposed configuration of three CFOAs, four resistors, and two capacitors is capable of performing the PID controller in each of the following four modes: voltage mode, trans-admittance mode, current mode, and trans-impedance mode. Numerous mathematical analyses are conducted to determine the controller's performance under both ideal and non-ideal conditions. Additionally, the mixed-mode second-order lowpass filter is suggested and also used to examine the workability of the proposed mixed-mode PID controller in a feedback control structure. The proposed PID controller is implemented with the commercially available IC-type CFOA AD844, and the simulation results are presented to illustrate the functionality of the controller and its closed-loop control system. According to the findings, the total power consumption of the proposed PID controller is 0.348 W, with symmetrical supply voltages of ±9 V. It also has a temperature variation of less than 0.2% over the AD844's usable range. Monte Carlo statistical analysis results revealed that the gain responses of the controller exhibited a deviation of no more than 7.72% from the theoretical value. The controlled filter in a closed-loop control system has a 43% faster rise time and peak time than the uncontrolled filter in all four modes of operation. It also has a steady-state error less than 0.2 mV for voltage responses and 0.72 µA for current responses.

Optimized Design of EdgeBoard Intelligent Vehicle Based on PP-YOLOE.

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system's ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles' operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model's performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms.

Low-Cost Plant-Protection Unmanned Ground Vehicle System for Variable Weeding Using Machine Vision.

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles-25%, 50%, 75%, and 100%-showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system.

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm.

Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions.

Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System.

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of -2.5 m/s2 within a deviation of ±0.1 m/s2, compared with the deviation of 0.11 m/s2 of fuzzy PID and the deviation of 0.13 m/s2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery.

Transient Behavior and Control of Polyethylene Production in a Fluidized Bed Reactor Utilizing Population Balance Model.

In this study, a fluidized bed reactor for polyethylene production was employed using a dry mode approach, where the recycle stream may contain components of a nature that cannot be condensed through standard cooling. To analyze the behavior of the fluidized bed reactors during the copolymerization of ethylene with butene, a dynamic population balance model was employed. The study includes sensitivity analyses through computer simulations to examine the variations in reactor temperature, molecular weights, catalyst feed rate, and monomer/comonomer concentrations in the fluidized bed reactor. It is noteworthy that the reactor exhibits instability under normal operational conditions and is sensitive to changes in the catalyst feed rate and coolant temperature of the heat exchanger. The findings also highlight challenges such as temperature fluctuations above the polymer melting point. This underscores the importance of implementing a temperature control system to prevent issues like reactor shutdown due to elevated temperatures. Dynamic instabilities were observed under specific circumstances and were successfully controlled using Proportional Integral Derivative (PID) control strategies. The population balance model is essential for understanding the complexity of transient polymerization reactions. It enables researchers to simulate and optimize polymerization processes by utilizing the detailed kinetics of the reaction.