Search Results (1,918)

This paper presents a comprehensive study on the dynamic modeling of distribution power systems with a focus on the integration of renewable energy sources (RESs) for stability analysis. Our research delves into the static and dynamic behavior of distribution systems, emphasizing the need for enhanced load modeling to mitigate planning and operational uncertainties. Using MATLAB/Simulink^®, we simulate four distinct study cases characterized by varying load types and levels of distributed generation (DG), particularly solar PV, under both balanced and unbalanced conditions. Our findings highlight the critical role of DG in influencing voltage stability, revealing that deviations in voltage and current during grid imbalances remain within acceptable limits. The study underscores the importance of DG-based inverters in maintaining grid stability through reactive power support and sets the stage for future research on microgrid simulations and battery storage integration to further enhance system stability and performance. Full article

Background. Overall, children and adolescents diagnosed with dyslexia or ADHD show an increased risk for psychological and mental health problems, and dyslexia and ADHD tend to coexist frequently. Thus, the main objective of this study was to examine psychological and mental health problems in dyslexia. Method. Participated 95 adolescents with dyslexia (DG), comorbid dyslexia + attention-deficit hyperactivity disorder, combined subtype (D + ADHD-CG), and a comparison group with typical development (TDCG). Self-reported measures of anxiety and depression, and parent and teacher versions of the Strengths and Difficulties Questionnaire (SDQ) were used. Results. Self-reports of internalizing problems showed that adolescents in the GD and D + ADHD-CG groups had more depression and stated anxiety problems with a very high percentage above the clinical cut-off point than the CG. Both the parent and teacher reports showed that the DG and D + ADHD-CG groups obtained higher mean values and a higher number of adolescents above the clinical cut-off of internalizing, externalizing, and total problems than the TDCG. The comorbid D + ADHD-CG group had the highest internalizing and externalizing problems. Conclusions. In conclusion, our findings indicate that the internalizing and externalizing problems experienced by adolescents with dyslexia and comorbid ADHD should be recognized early and treated promptly by education professionals. Full article

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators. Full article

The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods. Full article

Carotenoids are a class of highly hydrophobic compounds synthesized by plants in limited quantities. This study explores the potential for increasing the production yield of lycopene, a typical carotenoid compound, through engineered Escherichia coli. Given that lycopene biosynthesis occurs within microbial hosts and it is subsequently stored within lipid membranes, this study focuses on the impact of inducing membrane vesicles on lycopene yield by expressing monoglycosyldiacylglycerol synthase (MGS) or diglucosyldiacylglycerol synthase (DGS) from Acholeplasma laidlawii and inserting the upstream isopentenol utilization pathway (IUP) into the chromosome. The effect of MGS and DGS on lipid production in the cell was quantified. The results show that inserting the IUP into the chromosome increased the specific lycopene yield by 2.1-fold compared to the plasmid-based system when using a P_proD constitutive promoter and by 2.0-fold when using the inducible P_trc promoter. The expression of MGS and DGS resulted in a small increase of 31% and 33% (w/w) lipid content, respectively. When expressed in lycopene producing strains, the lycopene content decreased in the IUP strains but increased in the negative control strain expressing only the native MEP pathway from undetectable levels to 0.34 ± 0.08 mg/g. Full article

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller’s reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. Full article

Background: α-dystroglycanopathies are congenital muscular dystrophies in which genetic mutations cause the decrease or absence of a unique and complex O-linked glycan called matriglycan. This hypoglycosylation of O-linked matriglycan on the α-dystroglycan (α-DG) protein subunit abolishes or reduces the protein binding to extracellular ligands such as laminins in skeletal muscles, leading to compromised survival of muscle cells after contraction. Methods: Surrogate molecular linkers reconnecting laminin-211 and the dystroglycan β-subunit through bispecific antibodies can be engineered to improve muscle function in the α-dystroglycanopathies. This study reports the metabolic engineering of a novel glycofusion bispecific (GBi) antibody that fuses the mucin-like domain of the α-DG to the light chain of an anti-β-DG subunit antibody. Results: Transient HEK production with the co-transfection of LARGE1, the glycoenzyme responsible for the matriglycan modification, produced the GBi antibody only with a light matriglycan modification and a weak laminin-211 binding activity. However, when a sugar feed mixture of uridine, galactose, and manganese ion (Mn²⁺) was added to the culture medium, the GBi antibody produced exhibited a dramatically enhanced matriglycan modification and a much stronger laminin-binding activity. Conclusions: Further investigation has revealed that Mn²⁺ in the sugar feeds played a critical role in increasing the matriglycan modification of the GBi antibody, key for the function of the resulting bispecific antibody. Full article

Anxiety disorder is one of the most common neuropsychiatric disorders, and affects many people’s daily activities. Although the pathogenesis and treatments of anxiety disorder have been studied for several decades, the underlying mechanisms remain elusive. Here, we provide evidence that olfactory stimuli with inhaled linalool or 2-phenylethanol decreased mouse anxiety-like behaviors and increased the activities of hippocampal dentate granule cells (DGCs). RNA-sequencing analysis identified retrograde endocannabinoid signaling, which is a critical pathway for mood regulation and neuron activation, is altered in the hippocampus of both linalool- and 2-phenylethanol-exposed mice. Further studies found that selective inhibition of endocannabinoid signaling by injecting rimonabant abolished the activation of DGCs and the anxiolytic effect induced by linalool or 2-phenylethanol. Together, these results uncovered a novel mechanism by which linalool or 2-phenylethanol decreases mouse anxiety-like behaviors and increases DG activity likely through activating hippocampal retrograde endocannabinoid signaling. Full article

In active distribution networks (ADNs), the extensive deployment of distributed generations (DGs) heightens system nonlinearity and non-stationarity, which can weaken fault characteristics and reduce fault detection accuracy. To improve fault detection accuracy in distribution networks, a method combining improved multivariate variational mode decomposition (IMVMD) and YOLOv10 network for active distribution network fault detection is proposed. Firstly, an MVMD method optimized by the northern goshawk optimization (NGO) algorithm named IMVMD is introduced to adaptively decompose zero-sequence currents at both ends of line sources and loads into intrinsic mode functions (IMFs). Secondly, considering the spatio-temporal correlation between line sources and loads, a dynamic time warping (DTW) algorithm is utilized to determine the optimal alignment path time series for corresponding IMFs at both ends. Then, the Markov transition field (MTF) transforms the 1D time series into 2D spatio-temporal images, and the MTF images of all lines are concatenated to obtain a comprehensive spatio-temporal feature map of the distribution network. Finally, using the spatio-temporal feature map as input, the lightweight YOLOv10 network autonomously extracts fault features to achieve precise fault-line selection. Experimental results demonstrate the robustness of the proposed method, achieving a fault detection accuracy of 99.88%, which can ensure accurate fault-line selection under complex scenarios involving simultaneous phase-to-ground faults at two points. Full article

Background/Objectives: Immune checkpoint blockade, particularly targeting the programmed cell death 1 (PD-1) receptor, is a promising strategy in cancer immunotherapy. The interaction between PD-1 and its ligands, PD-L1 and PD-L2, is crucial in immune evasion by tumors. Blocking this interaction with monoclonal antibodies like Nivolumab can restore anti-tumor immunity. This study aims to develop a stable expression system for Nivolumab-based anti-PD-1 in the Chinese Hamster Ovary (CHO) DG44 cell line using two different expression vector systems with various signal sequences. Methods: The heavy chain (HC) and light chain (LC) of Nivolumab were cloned into two expression vectors, pOptiVEC and pcDNA3.3. Each vector was engineered with two distinct signal sequences, resulting in the creation of eight recombinant plasmids. These plasmids were co-transfected into CHO DG44 cells in different combinations, allowing for the assessment of stable antibody production. Results: Both pOptiVEC and pcDNA3.3 vectors were successful in stably integrating and expressing the Nivolumab-based anti-PD-1 antibody in CHO DG44 cells. This study found that the choice of signal sequence significantly influenced the quantity of antibodies produced. The optimization of production conditions further enhanced antibody yield, indicating the potential for large-scale production. Conclusions: This study demonstrates that both pOptiVEC and pcDNA3.3 expression systems are effective for the stable production of Nivolumab-based anti-PD-1 in CHO DG44 cells. Signal sequences play a critical role in determining the expression levels, and optimizing production conditions can further increase antibody yield, supporting future applications in cancer immunotherapy. Full article

To improve the mechanization levels of red cluster pepper harvesting in hilly and mountainous regions of southwest China, a crawler-type harvester is developed to suit the local planting and growth characteristics of red cluster pepper and to facilitate mechanized picking, conveying, and collecting processes. The design, supported by theoretical calculations and structural analysis, includes detailed studies of the picking, conveying, and collecting mechanisms, as well as the hydraulic system. Factors affecting picking efficiency were identified. ADAMS simulation was used to determine the optimum rotational speed range for the spring-tooth roller by analyzing its trajectory. A prototype was then built and field tested with forward speed and the spring-tooth roller’s rotational speed as variables to assess impurity, damage, loss, and hanging rates. Data from these tests were analyzed using Design Expert software, which created a mathematical model relating the test indices to the two variables. Optimum parameters were identified, resulting in a harvester configuration that achieved an average productivity of 0.21 ha·h⁻¹ at a forward speed of 1.75 m·s⁻¹ and a roller rotational speed of 181 r·min⁻¹. The impurity rate was 26.7%, the loss rate was 6.1%, the damage rate was 2.3%, and the hanging rate was 4.2%, conforming to the industry standard DG/T 114-2019. This research provides a viable solution for mechanized harvesting of red cluster pepper in hilly and mountainous regions with small planting plots. Full article

Due to the consequences of fires, new and environmentally friendly firefighting techniques are constantly being sought. There are many methods of extinguishing flames around the world. One of them is a technique that uses acoustic waves for extinguishing, which can be seen as repeated sequences of molecular compression and dilation (acoustic waves transfer energy due to the movements of molecules and atoms). This research shows a new approach to the extinguishing of flames. In practice, the extinguishing capabilities of low-frequency modulated and unmodulated acoustic waves were tested on a laboratory station, the main component of which was a high-powered acoustic extinguisher (the nominal power was equal to 1700 W). A B&C 21DS115 woofer was applied as a sound source. A Rigol DG4102 and a Proel HPX2800 were used as an acoustic generator with a modulator and as a power amplifier, respectively. In this paper, the presented results are limited to extinguishing candle flames. The tests made it clear that flames can be extinguished using properly generated and directed acoustic waves. As the results indicate, it becomes possible to effectively extinguish flames with both low-frequency modulated and unmodulated acoustic waves, which brings many benefits. Full article

Recently, there has been fast-growing interest among researchers in discovering bioactive peptides from insects and evaluating their potential applications in livestock production. The present study aimed to assess the antioxidant properties and stability of fly maggot peptide (FMP) and its effects on Arbor Acres (AA) broilers’ oxidative stress induced by lipopolysaccharide (LPS) and hemocoagulase (HC). A total of 108 one-day-old AA broilers were randomly divided into six groups: CG (normal saline, basal diet), DG (LPS + HC, basal diet), VG (DG + vitamin C 50 ug/kg), LPG (DG + FMP 5 mg/kg), MPG (DG + FMP 15 mg/kg), and HPG (DG + FMP 25 mg/kg). The results showed that the addition of FMP to the diet promoted LPS+ HC-induced increases in average daily gain (ADG), total superoxide dismutase (T-SOD), total antioxidant capacity (T-AOC). Meanwhile, FMP regulated the intestinal morphology. Additionally, FMP decreased the increase in the contents of malondialdehyde (MDA), the relative weight of immune organs, interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α). In conclusion, this research suggested that the addition of FMP can relieve the LPS+ HC-induced oxidative stress of AA broilers and the recommended dose of FMP is 25 mg/kg. This study presents a theoretical foundation for the addition of an FMP supplement for the purpose of protecting broilers’ growth. Full article

Background: One defining feature of various aggressive cancers, including glioblastoma multiforme (GBM), is glycolysis upregulation, making its inhibition a promising therapeutic approach. One promising compound is 2-deoxy-d-glucose (2-DG), a d-glucose analog with high clinical potential due to its ability to inhibit glycolysis. Upon uptake, 2-DG is phosphorylated by hexokinase to 2-DG-6-phosphate, which inhibits hexokinase and downstream glycolytic enzymes. Unfortunately, therapeutic use of 2-DG is limited by poor pharmacokinetics, suppressing its efficacy. Methods: To address these issues, we synthesized novel halogenated 2-DG analogs (2-FG, 2,2-diFG, 2-CG, and 2-BG) and evaluated their glycolytic inhibition in GBM cells. Our in vitro and computational studies suggest that these derivatives modulate hexokinase activity differently. Results: Fluorinated compounds show the most potent cytotoxic effects, indicated by the lowest IC₅₀ values. These effects were more pronounced in hypoxic conditions. ¹⁹F NMR experiments and molecular docking confirmed that fluorinated derivatives bind hexokinase comparably to glucose. Enzymatic assays demonstrated that all halogenated derivatives are more effective HKII inhibitors than 2-DG, particularly through their 6-phosphates. By modifying the C-2 position with halogens, these compounds may overcome the poor pharmacokinetics of 2-DG. The modifications seem to enhance the stability and uptake of the compounds, making them effective at lower doses and over prolonged periods. Conclusions: This research has the potential to reshape the treatment landscape for GBM and possibly other cancers by offering a more targeted, effective, and metabolically focused therapeutic approach. The application of halogenated 2-DG analogs represents a promising advancement in cancer metabolism-targeted therapies, with the potential to overcome current treatment limitations. Full article

The evaluation of the analytical capabilities of a novel disposable flow cell for spectroscopic bioprocess monitoring is presented. The flow cell is presterilized and can be connected to any kind of bioreactor by weldable tube connections. It is clamped into a reusable holder, which is equipped with SMA-terminated optical fibers or an integrated light source and detection unit. This modular construction enables spectroscopic techniques like UV-Vis spectroscopy or turbidity measurements by scattered light for modern disposable bioreactors. A NIR scattering module was used for biomass monitoring in different cultivations. A high-cell-density fed-batch cultivation with Komagataella phaffii and a continuous perfusion cultivation with a CHO DG44 cell line were conducted. A high correlation between the sensor signal and biomass or viable cell count was observed. Furthermore, the sensor shows high sensitivity during low turbidity states, as well as a high dynamic range to monitor high turbidity values without saturation effects. In addition to upstream processing, the sensor system was used to monitor the purification process of a monoclonal antibody. The absorption module enables simple and cost-efficient monitoring of downstream processing and quality control measurements. Recorded absorption spectra can be used for antibody aggregate detection, due to an increase in overall optical density. Full article