Generation Characteristics Research Articles

Comparative Analysis of In Vitro Pumps Used in Cardiovascular Investigations: Focus on Flow Generation Principles and Characteristics of Generated Flows

A comprehensive analysis of in vitro pumps used in cardiovascular research is provided in this review, with a focus on the characteristics of generated flows and principles of flow generations. The cardiovascular system, vital for nutrient circulation and waste removal, generates complex hemodynamics critical for endothelial cell function. Cardiovascular diseases (CVDs) could be caused by the disturbances in these flows, including aneurysms, atherosclerosis, and heart defects. In vitro systems simulate hemodynamic conditions on cultured cells in the laboratory to study and evaluate these diseases to advance therapies. Pumps used in these systems can be classified into contact and non-contact types. Contact pumps, such as piston and gear pumps, can generate higher flow rates, but they have a higher risk of contamination due to the direct interaction of pump with the fluid. Non-contact pumps, such as peristaltic and lab-on-disk centrifugal pumps, minimize contamination risks, but they are limited to lower flow rates. Advanced pumps including piezoelectric and I-Cor diagonal pumps are focused on improving the accuracy of flow replication and long-term stability. The operational principles, advantages, and some disadvantages of these pump categories are evaluated in this review, while providing insights for optimizing in vitro cardiovascular models and advancing therapeutic strategies against CVDs. The outcomes of the review elaborate the importance of selecting an appropriate pump system, to accurately replicate cardiovascular flow patterns.

The Clockwork Olive: Perspectives from the “American Shoulder”

The structure and the biological significance of the Olivinae fasciculate ornamentation known as “American Shoulder” (after its presence in the American olives) are investigated for around 20 Western Atlantic / Gulf of Mexico / Caribbean species. The applicability of the “Chevron Paradigm” by Tursch & Greifeneder (2001) to the subchannel area is questioned and contrary evidence is proposed. A robust correlation between lip thinning phases and the generation of the typical American Shoulder features is observed: their coincidence with rapid growth stages as reported by Strano (2017) is plausible but wasn’t established decisively in absence of destructive investigations. The issues of intraspecific and interspecific variability are addressed thanks to an extensive photographic coverage, and a new general description of the peculiar pattern emerges from the investigations. The initial identification of some specimens included in this study is questioned on the basis of their subchannel pattern, hinting at its possible use as an accessory character in species determination, and suggesting to include exhaustive descriptions of American Shoulder patterns in new nomenclatural acts involving the (sub)genus Americoliva.

Study on start-up characteristics of single piston free piston linear generator for small-scale compressed air energy storage system

This study presents a new idea of applying single piston free piston linear generator (FPLG) to small-scale compressed air energy storage (CAES) system. Firstly, a single piston FPLG prototype for CAES is built. Then, the thermodynamic, motion and output characteristics of FPLG during start-up process are studied based on the experimental data. The results show that the start-up process of single piston FPLG can be divided into three stages, and the order of achieving a steady state is movement and output > in-cylinder pressure > the in-cylinder temperature. The in-cylinder temperature increases in a spiral pattern, and the temperature difference between adjacent cycles at the same state point decreases. In the 0th cycle, when the free piston assembly (FPA) moves from top dead center (TDC) and bottom dead center (BDC), the peak current and power output are greater than the other cycles. The peak current and power output when FPA moves from TDC to BDC are greater than those when FPA moves from BDC to TDC. The maximum current and power output can reach 0.8 A and 62.25 W. At the beginning of the expansion stage, as the intake duration (tin) increases, the in-cylinder pressure increases first and then decreases. As the intake pressure (pin) increases, the in-cylinder pressure continues to increase. In addition, the in-cylinder temperature of working chamber A (TA) is lower than in-cylinder temperature of working chamber B (TB), and the temperature difference between TA and TB can reach 12.4 K. At higher pin condition, the intake point cannot be too close to the two ends and tin cannot be too long. At lower pin condition, the intake point cannot be too close to both ends and tin cannot be too short to ensure the system starts. The purpose of this paper is to provide valuable insights and guidance for the practical application of small-scale CAES system.

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech

Background and purposeDiagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. Materials and methodsMODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. ResultsThe HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. ConclusionsThe findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency.

Comparison of the Reaction Characteristics of Different Fuels in the Supercritical Multicomponent Thermal Fluid Generation Process

Supercritical multicomponent thermal fluid technology is a new technology with obvious advantages in offshore heavy oil recovery. However, there is currently insufficient understanding of the generation characteristics of the supercritical multicomponent thermal fluid, which is not conducive to the promotion and application of this technology. In order to improve the economic benefits and applicability of the supercritical multicomponent thermal fluid thermal recovery technology, this article reports on indoor supercritical multicomponent thermal fluid generation experiments and compares the reaction characteristics of different fuels in the supercritical multicomponent thermal fluid generation process. The research results indicate that the main components of the products obtained from the supercritical water–crude oil/diesel reaction are similar. Compared to the supercritical water–crude oil reaction, the total enthalpy value of the supercritical multicomponent thermal fluid generated by the supercritical water–diesel reaction is higher, and the specific enthalpy is lower. When the thermal efficiency of the boiler is the same, the energy equilibrium concentration of crude oil is lower than that of diesel. The feasibility of using crude oil instead of diesel to prepare supercritical multicomponent thermal fluids is analyzed from three aspects: reaction mechanism, economic benefits, and technical conditions. It is believed that using crude oil instead of diesel to prepare supercritical multicomponent thermal fluids has good feasibility.

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

A novel multi-model feature generation technique for suicide detection

Automated expert systems (AES) analyzing depression-related content on social media have piqued the interest of researchers. Depression, often linked to suicide, requires early prediction for potential life-saving interventions. In the conventional approach, psychologists conduct patient interviews or administer questionnaires to assess depression levels. However, this traditional method is plagued by limitations. Patients might not feel comfortable disclosing their true feelings to psychologists, and counselors may struggle to accurately predict situations due to limited data. In this context, social media emerges as a potentially valuable resource. Given the widespread use of social media in daily life, individuals often express their nature and mental state through their online posts. AES can efficiently analyze vast amounts of social media content to predict depression levels in individuals at an early stage. This study contributes to this endeavor by proposing an innovative approach for predicting suicide risks using social media content and machine learning techniques. A novel multi-model feature generation technique is employed to enhance the performance of machine learning models. This technique involves the use of a feature extraction method known as term frequency-inverse document frequency (TF-IDF), combined with two machine learning models: logistic regression (LR) and support vector machine (SVM). The proposed technique calculates probabilities for each sample in the dataset, resulting in a new feature set referred to as the probability-based feature set (ProBFS). This ProBFS is compact yet highly correlated with the target classes in the dataset. The utilization of concise and correlated features yields significant outcomes. The SVM model achieves an impressive accuracy score of 0.96 using ProBFS while maintaining a low computational time of 5.63 seconds even when dealing with extensive datasets. Furthermore, a comparison with state-of-the-art approaches is conducted to demonstrate the significance of the proposed method.

The Influence of Transformational Leadership and HEXACO Personality on Millennial Soldiers' Discipline in Indonesian Army (TNI AD)

This study examined the influence of transformational leadership style and HEXACO personality dimensions on the discipline of millennial soldiers in the Indonesian Army (TNI AD), focusing on leadership levels in Combat and Combat Support Battalions. The research utilized a mixed-method approach to explore how these variables interacted and contributed to enhancing soldier discipline. The findings indicated that both the transformational leadership style and the HEXACO personality dimensions significantly influenced soldier discipline. Furthermore, the study provided recommendations for applying an ideal leadership style that aligns with the unique cultural and generational characteristics in Indonesia. This research contributed to military leadership literature by offering new insights into human resource management within a military context, particularly in addressing the challenges posed by the millennial generation.

Fractional-order hybridly polarized vector beams: generation, focusing, propagation, and spatial self-phase modulation

As a class of integer-order vector beams, hybridly polarized vector beams (HPVBs) are widely used in focus shaping, femtosecond laser filamentation, linear and nonlinear polarization evolution, etc. Recently, fractional-order vector beams have gained widespread interest due to their more control parameters, rich photophysical properties, and novel nonlinear optical phenomena. In this work, we report the experimental generation, focusing and propagation characteristics, and spatial self-phase modulation (SSPM) phenomenon of fractional-order HPVBs. It is shown that during the focusing and propagation processes, the intensity pattern of fractional-order HPVBs develops from a near-Gaussian structure in the incident plane to an asymmetric structure in both the focal field and the far field. Meanwhile, their distributions of state of polarization (SoP) also evolve, although it remains a hybrid polarization distribution overall. When the focused fractional-order HPVBs pass through the nonlinear optical sample, the far-field self-diffraction intensity pattern displays an irregular concentric multi-ring structure with a hybrid polarization distribution. It is found that the nonlinear medium not only modulates the far-field intensity pattern of fractional-order HPVBs but also controls their SoP distribution. This symmetric breaking HPVB has potential application prospects in optical micro-manipulation, light-matter interaction, optical spin-orbit conversion, etc.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

The Effect of Employee Engagement on Employee Performance by Moderation of Generational Characteristics of Employees in Private Agencies

Employee engagement is an important issue that is the main concern of the organization's leaders in managing its employees. The changing characteristics of the employee generation are becoming a specific challenge for human resource management. Employees who are engaged with their work will exert all their abilities, efforts, and thoughts and significantly contribute, where the engagement and personal characteristics possessed by employees in carrying out their work can directly impact their performance. This study aimed to analyze employee engagement's effect on employee performance by moderating the generational characteristics of private agency employees in the City of Padang, West Sumatra. Data was collected through questionnaires distributed to 100 employee respondents working in private institutions in the city of Padang, with purposive sampling technique. Then, the data is processed using the Partial Least Square-Structural Equation Modeling (PLS-SEM) method. The results of this study indicate that employee engagement generational characteristics (Y and Z generation) have a positive and significant effect on employee performance. Generational characteristics (Y and Z generation) also positively and significantly impact employee engagement. Generational characteristics do not moderate the relationship between employee involvement and employee performance, but directly employee engagement and employee performance can be improved with generational characteristic variables. Organizational leaders should pay attention to employee engagement and the characteristics of the generation of employees and can make it a potential to improve employee performance.

Enhanced Study of CO2 Hydrate Formation in Marine Oil–Gas Based on Additive Effect

During marine oil–gas extraction, significant amounts of carbon dioxide (CO2) gas are often produced. Effectively separating these associated CO2 gases during extraction has become a critical technical challenge. Therefore, this paper aims to enhance the efficiency of CO2 hydrate-based capture technology and conduct relevant research. The goal is to increase the driving force for hydrate formation by combining the traditional thermodynamic additive TBAB with pressure modulation and to improve the hydrate formation rate through the use of multiple kinetic promoters. This paper presents the initial investigation into the effect of the thermodynamic accelerator tetrabutylammonium bromide (TBAB) on the characteristics of CO2 hydrate formation. The promotion effects of TBAB solutions with varying mass concentrations (3%, 5%, 7.5%, 10%) and reaction pressures (3 MPa, 4 MPa) were subjected to a systematic analysis, and the optimal conditions were identified as 4 MPa and a 5 wt% TBAB concentration. Subsequently, the impact of combining TBAB with kinetic promoters (SDS, nano Al2O3, L-methionine, L-leucine) on CO2 hydrate generation characteristics was further investigated. In this paper, the effect of a single promoter on the generation characteristics of CO2 hydrate was investigated, and the efficient carbon trapping ability of the complex promoter was verified, which provides theoretical support for the application of CO2 trapping technology using the hydrate method.

Coach perspectives on coach-athlete relationships and characteristics of Generation Z academy level rugby union players from South Africa.

The coach-athlete relationship is not merely a by-product of the coaching process but serves as its foundation. Coaches today must align their approaches with the characteristics (character, strengths, and growth areas) of a new generation of athletes, commonly referred to as Generation Z (Gen Z). Born between 1995 and 2012, Gen Z's grew up in a digital age, which shaped their character and behaviour. The purpose of the study was to explore coach-athlete relationships, the characteristics of Gen Z athletes that influence this relationship, and the process of building healthy coach-athlete relationships, from the perspectives of coaches. The study used a qualitative methodology to elicit the perspectives of 11 male rugby union coaches (M ± SD age: 42.09 ± 7.56 years; with 12.83 ± 3.48 years of coaching experience), through semi-structured individual online interviews. We developed the interview script from Jowett's Coach-Athlete Relationship Questionnaire (CART-Q). The interviews were analysed using Merriam and Tisdell's six-step process. The analysis yielded six super themes (and 17 themes). 1. Coach perspectives on coaching Gen Z players (developmental facilitators and debilitators, Covid-19's effect on development). 2. Closeness between coaches and athletes [loving and caring, getting closer, trusting, (not) respecting some players]. 3. Commitment (personally and towards the team, sacrifices). 4. Complementarity (goal setting, collectively working towards common goals). 5. Co-orientation (communicating, reciprocal relationship views). 6. Developing the coach-athlete relationship (approaches thereto, practical steps). Various facets of the coach-athlete relationship are interlinked and shaped by the characteristics and behaviours of this generation of athletes. Practical recommendations are offered on how to cultivate the coach-athlete relationship on emotional, cognitive, and behavioural levels.

Structural damage detection based on transmissibility functions with unsupervised domain adaptation

Deep learning (DL) has been used for structural damage detection by training neural network models with a large amount of data. These trained models perform well when the test samples are from the data with an identical distribution of the training data. The distributions are always different due to numerical modelling errors and operational environmental varieties, and the data for damage scenarios is difficult to be obtained in practice. A novel method based on the joint maximum discrepancy and adversarial discriminative domain adaptation (JMDAD) for structural damage detection without labelled measurement data has been developed to address the above issues. To reduce the influence of external excitations in practice, the transmissibility function of measured structural responses is used for structural damage detection. The proposed network includes a feature generator, two classifiers and one discriminator. Firstly, the feature generator and domain discriminator are to extract features and merge their distributions at the domain level to overcome the issue of insufficient data from the target real structure. Secondly, the generator and two classifiers are optimised to align their distributions at the class level using the classification discrepancy between the two classifiers. As a result, the damage-sensitive features are extracted and aligned to eliminate modelling errors and operational environmental varieties between the source and target structures. Three case studies have been conducted in this paper, e.g., one case between two building structures with different storeys, another case between the numerical and experimental structures subjected to random and earthquake excitations and the application of Canton Tower. The results show that the proposed method is much robust to the measurement noise and operational environment and the structural damage is identified accurately without labelled measurement data from the target structure in operational environments.

Simulation and Experimental Study on Heat Transfer Performance of Bionic Structure-Based Battery Liquid Cooling Plate

This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm × 20 mm × 3 mm, and the negative pole size is 22 mm × 20 mm × 3 mm. Experimental testing of the Li-ion battery’s heat generation model parameters, in conjunction with bionic structure and micro-channel features, has led to the development of this innovative cooling system. The traditional bionic liquid cooling plate’s structure is often singular; however, the flow path of the liquid cooling plate designed in this paper is based on the combination of the distribution of human blood vessel branches and the structure of insect wing veins. The external dimension of the liquid cooling plate is 152 mm × 100 mm × 6 mm (length × width × height). Utilizing numerical simulation and thermodynamic principles, we analyzed the heat transfer efficacy of the bionic liquid cooling module for power batteries. Specifically, we investigated the impact of varying coolant flow rates and the contact radius between flow channels on the thermal performance of the bionic battery modules. Our findings indicate that a liquid flow rate of 0.6 m/s achieves a stable maximum surface temperature and temperature differential across the bionic battery liquid cooling module, with a relatively low overall system power consumption, suggesting room for further enhancement of heat transfer performance. By augmenting the contact radius between flow channels, we observed an initial increase in the maximum surface temperature, temperature differential, and inlet–outlet pressure differential at a flow rate of 0.2 m/s. However, at flow rates equal to or exceeding 0.4 m/s, these parameters stabilized across different design Scenarios. Notably, the pump power consumption remained consistent across various scenarios and flow rates. This study’s outcomes offer valuable insights for the development of liquid-cooled battery thermal management systems that are energy-efficient and offer superior heat transfer capabilities.

Vnímanie vybraných možností financovania v start-upoch a korporáciách generáciou Z

The current generation of young people is growing up in an era that is strongly influenced by digital technologies and the Internet, characterized by fears of stereotypes, but also by flexibility, non-conflict and temporary work. From this point of view, regular work in a company can appear undynamic, and starting one's own business is becoming more and more popular. The characteristics of generation Z can also greatly influence the perception of ways of financing businesses and the resulting form of business itself. The presented scientific article is focused on the perception of selected methods of financing, which fundamentally differ in start-ups and in other types of business. The aim of the article is, based on a questionnaire survey, to analyze and compare the perception of selected methods of financing in companies by generation Z with regard to the financing of start-ups. The results assume the differentiation of the preferences of generation Z in individual areas and the determination of whether this generation is more inclined to traditional or alternative or risky forms of financing.

Generation and geochemical characteristics of acid rock drainage (ARD) in Barton Peninsula, King George Island (KGI), maritime, Antarctica

This study deals with the generation, geochemical characteristics, and environmental impacts of acid rock drainage (ARD), a global environmental problem, on the Barton Peninsula. To elucidate the governing processes and to assess the environmental hazards of ARD, we present chemical data from lakes, ponds, and creeks with a wide range of pH values. We also provide mineralogical and geochemical compositions of sediments and bedrocks. Compared to weak-acidic and neutral waters, waters that display typical characteristics of ARD with low pH (3.7 to 4.2), high sulfate (46 to 92 mg/L), and Fe (0.8 to 16.5 mg/L) occur in the northern tip of the peninsula. Acidic waters with the highest cation (e.g., K, Na, Si, and Ca) and anion (e.g., SO4) compositions indicate ARD-enhanced rock weathering in the peninsula. Consistently, quantifying of chemical weathering degree yields the highest chemical index of alteration (CIA) and the mafic index of alteration (MIA) with the lowest ICV values for sediments from the acidic waters. Enrichment factors (EFs) calculated for As, Co, Cd, Cu, Pb, Zn, and Ni indicate severe to minor enrichment for As and Pb metals, respectively in the acidic water-associated sediments. Heavy metal concentrations of acidic waters also display the highest values for the peninsula, with Fe, Cu, and Cd metals exceeding the chronic aquatic toxicity limit (CAT). Therefore, geochemical records of acidic waters and sediments, especially lakes, may help in tracing the long-term environmental impacts of ARD, while sediments obtained from the weak acidic and near-neutral waters, together with water chemistry data, may provide a better representative composition of the bedrocks with neutralizing potential. The data presented here may contribute to predicting the source/s, and extent of future ARD generation in the peninsula, which is likely to be enhanced by increased chemical weathering due to climate warming.

High-precision and lightweight small-target detection algorithm for low-cost edge intelligence

The proliferation of edge devices driven by advancements in Internet of Things (IoT) technology has intensified the challenge of achieving high-precision small target detection, as it demands extensive computational resources. This amplifies the conflict between the need for precise detection and the requirement for cost-efficiency across numerous edge devices. To solve this problem, this paper introduces an enhanced target detection algorithm, MSGD-YOLO, built upon YOLOv8. The Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module is enhanced through the integration of the Ghost module and dynamic convolution, resulting in a more lightweight architecture while enhancing feature generation. Additionally, Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) replaces Spatial Pyramid Pooling Fast (SPPF) to expand the receptive field, optimizing multi-level feature aggregation for improved performance. Furthermore, a novel Multi-Scale Ghost Convolution (MSGConv) and Multi-Scale Generalized Feature Pyramid Network (MSGPFN) are introduced to enhance feature fusion and integrate multi-scale information. Finally, four optimized dynamic convolutional detection heads are employed to capture target features more accurately and improve small target detection precision. Evaluation on the VisDrone2019 dataset shows that compared with YOLOv8-n, MSGD-YOLO improves mAP@50 and mAP@50–95 by 14.1% and 11.2%, respectively. In addition, the model not only achieves a 16.1% reduction in parameters but also attains a processing speed of 24.6 Frames Per Second (FPS) on embedded devices, thereby fulfilling real-time detection requirements.

A piezoelectric oscillator for low-speed wind energy collection

ABSTRACT The exploration of low-speed wind harvesters is key to harnessing wind energy and enabling self-powered Internet of Things (IoT) sensor networks. In this paper, a piezoelectric oscillator (APO) for low-speed wind energy collection (B-WEC) is proposed and applied to low-speed wind energy harvesting. The magnetic model and power generation characteristics of the harvester are formulated, the magnetic torque is simulated, and the performance under different parameter combinations is tested experimentally. The results show that the APO rotation reduces the magnetic resistive torque by 26.00%. The highest peak-to-peak voltage is 92.35 V for an APO magnet diameter is 8 mm and the angle of the stopper (γ) is 60°, which is 1258.09% higher than fixed (γ is non-existent). Moreover, it has a maximum power of 0.56 mW at 4 m/s, and the optimally matched resistance is 200 kΩ. The harvester can light up about 150 LEDs. The B-WEC designed in this paper can effectively reduce the driving wind speed of piezoelectric wind energy harvesters and can be used as a power source for self-powered networked sensor networks. This study provides a new solution idea for the exploitation of wind energy.

Dynamic Determination Method of Line Drop Compensator Parameters for Voltage Regulators Based on Mixture of Experts Using Real‐Time Information

In this paper, a method for determining line drop compensator (LDC) parameters for step voltage regulators and on‐load tap changers is proposed to avoid voltage violations in distribution systems with voltage fluctuations due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators need to effectively solve voltage problems caused by the widespread installation of distributed energy resources with the existing voltage regulators to construct an efficient and rational infrastructure. Our focus was on improving the method for determining the LDC parameters for the existing voltage regulators based on LDC control. A mechanism to dynamically change the LDC parameters depending on the situations in the distribution system was developed by using a mixture of experts, one of the machine learning techniques, and real‐time voltage and power flow information from information technology switches. The power flow calculations were performed using a distribution system model constructed based on the topology, loads, and generation characteristics of an actual distribution system in the Hokuriku region. The effectiveness of the proposed method was evaluated in terms of its improved effect on PV and EV hosting capacity, as well as the impact on the frequency of tap operations of voltage regulators. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.