Desired System Research Articles

Aspects of Relevance of Hybrid Power Plants in Control and Stability of Weak Grids

This paper reviews the possible contributions of Hybrid Power Plants (HPPs) to support weak grids while maintaining the desired system stability. Moving towards a converter-dominant power system with less inherent inertia and distant connections to the nearest synchronous generator, frequency and voltage controls are becoming more critical to ensure the stability of the weak grid. In this regard, state-of-the-art literature is reviewed for frequency and voltage controllers in single-technology power plants, like wind and solar power plants. The contribution of this paper lies in providing a clear overview of available literature in terms of frequency and voltage control stages, regardless of the utilized control method. On the other hand, focus has been put on the increased utilization of HPPs to provide more flexibility, increased availability, and reduced variability through the combination of various sources, i.e., wind, solar, and storage. Furthermore, investigating the specific capabilities and challenges of HPPs, this review shows that very little literature has been conducted on voltage control using HPPs. Finally, the aspect of relevance of HPPs is discussed in the control and stability of modern power systems.

Modelling resilience dynamics for smart manufacturing systems: quantification and empirical analysis

ABSTRACT Smart manufacturing contributes to a better management of uncertainties and risks in today’s business environment, leading to a more resilient system. Despite its significance, the quantification of resilience in manufacturing environments is not well developed and is considered a pressing research gap. This study introduces a novel approach for assessing resilience through the development of a mathematical model for smart manufacturing systems. The sensitivity and applicability of the proposed model are investigated through simulation-based experimentation. The results demonstrate the level of resilience with the adaptability of smart manufacturing technologies. Interestingly, the trigger point derived from the simulation results is an indicator of the threshold limit for effective implementation of smart manufacturing technologies for the desired system resilience. The research provides a tool for decision-makers in industry that are considering investing in smart manufacturing solutions to increase their operational resilience. Decision-makers can design the deployment of smart manufacturing systems based on an informative trade-off to maintain the required resilience capabilities and achieve a competitive edge in the market.

Risk factors and simple scoring system for predicting postoperative nutritional status of Hirschsprung’s disease

BackgroundNutritional problem after surgery for Hirschprung’s disease (HSCR) was not optimistic. This study aimed to analyze the risk factors of postoperative undernutrition for patients with HSCR and establish a scoring system for predicting postoperative undernutrition.MethodsRetrospective review of 341 patients with HSCR who received Laparoscopic-assisted pull-through surgery in a tertiary-level pediatric hospital was conducted with assessments of clinical data. Univariate/multivariate Logistic regression analysis was used to identify independent factors of postoperative undernutrition, and establish a scoring system for predicting postoperative nutritional status based on the sum of adjusted odds ratios (ORs).ResultsThe postoperative undernutrition of 341 patients with HSCR was 29.9%. Multivariate Logistic regression analysis showed that non-breast feeding (mixed: OR = 6.116, artificial: OR = 12.00), preoperative undernutrition (risk of malnutrition: OR = 7.951, malnutrition: OR = 8.985), non-parental caregivers (OR = 3.164), long-segment HSCR (OR = 12.820), postoperative complications within 30 days (grade 1 ~ 2: OR = 2.924, Grade 3 ~ 4: OR = 6.249), and surgery for other systemic malformation (OR = 5.503) were risk factors for postoperative undernutrition (all p < 0.05), and scoring system was developed based on these determinants. The area under the receiver operator characteristic curve of the derivation sample was 0.887 (95% confidence interval [CI]: 0.839–0.934) and that of the validation sample was 0.846 (95% CI: 0.772 ~ 0.920) with the optimal cut-off value of 12; calibration curves of the derivation sample showed considerable predictive performance for postoperative undernutrition.ConclusionRisk factors identified affecting postoperative undernutrition should be taken seriously in patients with HSCR. We successfully developed a desirable scoring system to predict postoperative nutritional status, which might be helpful for clinical practice.

John Robots, Thurgood Martian, and the Syntax Monster: A New Argument Against AI Judges

Abstract This paper argues that an AI judge is conceptually undesirable and not just something that lies beyond the state of the art in computer science. In a nutshell, even if an AI system could accurately predict how a good human judge would decide a particular case, its prediction would be the product of correlations between such factors as patterns of syntax in bodies of legal texts. This approach of AI systems is insufficient for basing their output on the sort of rationales that are expected of valid judicial decisions in any desirable legal system. Thus, by their very nature, AI systems are incapable of providing valid legal decisions in any such system.

Triplex event-triggered recursive quantizer-based networked control system under communication delay and packet dropouts

In light of network congestion, communication delay, and packet dropouts, a triplex event-triggered recursive quantizer-based networked control system (TERQNCS) is proposed in this paper. To constrain quantization error while alleviating network congestion, the recursive quantizer is constructed in the TERQNCS. Considering it is insufficient to adequately alleviate network congestion only with either the quantizer or the event trigger, a triplex event-triggered mechanism is designed through the recursive quantizer. For revealing the impacts of communication delay and packet dropouts, four Bernoulli random variables are adopted to model the network channels in the TERQNCS. Then, the model-based predictive controller is designed by virtue of the predictive states, estimated states, and Bernoulli-based model. The proposed TERQNCS can achieve the desired system performance under communication delay and packet dropouts while adequately alleviating network congestion and constraining quantization error. The stability of the TERQNCS is proved by theoretical analysis. Besides, the advantages of the TERQNCS are substantiated by a tunnel diode circuit networked control system.

Desired support system to eradicate urban homelessness: an exploratory descriptive study.

Background - The new increasing homeless lately consist of women, children, youth, the elderly and marginalized ethnic or migrant groups. Some of them are working and earn salaries, however, the income is not sufficient to live modestly. Purpose - The purpose of this research is to establish a desired support system to eradicate urban homelessness in the country. Design/methodology/approach - This is an exploratory descriptive method study which employed quantitative techniques. The study employed a social ecological model to investigate behavior of homeless via multiple levels of influences including intrapersonal, interpersonal, organizational, community and public policy. Surveys have been conducted on sixty-five homeless individuals. Findings - It was found that support systems and structures should be derived from the whole streams from families, communities, organizations and government. Employment opportunities, long-term shelters within the community places and highly demanded skills are needed to improve their living condition. Research limitations - This study is only focused on the socio-economic structures of the homeless in a capital city. Originality/value - This is an empirical research using a social ecological model for the homeless in the Kuala Lumpur area. Research on homeless study has received little attention and has yet to be fully explored.

Energy, exergy, exergy‐economic, and environmental evaluation of an optimized hybrid photovoltaic heat pump system with solar collector and PCM

AbstractNowadays, the use of hybrid systems has become very common all over the world. In this study, the aim is to minimize the use of grid energy to provide heating and cooling energy with the help of a hybrid heat pump equipped with a flat solar collector, phase change material (PCM), and photovoltaic (PV) panels. To achieve the best results, a numerical dynamic model consisting of different solar PV panels in three models, batteries, inverters, and hybrid heat pump along with collector and PCM has been modeled by solving Engineering Equation Solver (EES) and TRNSYS software. According to the proposed scenarios, multi‐objective optimization has been done to simultaneously improve the study answers in several sections by multi‐objective particle swarm optimization algorithm with MATLAB software. Also, economic and environmental optimization is also presented separately for comparing and reviewing solutions. The results of multi‐objective optimization show that the amount of lifecycle cost (LCC) when using polycrystalline panel is 21.26% lower than monocrystalline panel and 38.71% higher than thin film panel. As a result, according to the specific conditions and attitude, you can choose the desired system. Also, in the economic optimization, it was found that the best system is related to the polycrystalline panel, the volume of PCM used in the system is equal to 1 , the number of panels used is 18, and the minimum amount of LCC is $3929.08.

Rational Design of Fluorinated 2D Polymer Film Based on Donor-Accepter Architecture toward Multilevel Memory Device for Neuromorphic Computing.

Fluorine-containing 2D polymer (F-2DP) film is a desired system to regulate the charge transport in organic electronics but rather rarely reports due to the limited fluorine-containing building blocks and difficulties in synthesis. Herein, a novel polar molecule with antiparallel columnar stacking is synthesized and further embedded into an F-2DP system to control over the crystallinity of F-2DP film through self-complementary π-electronic forces. The donor-accepter-accepter'-donor' (D-A-A'-D') structure regulates the charge transportation efficiently, inducing multilevel memory behavior through stepwise charge capture and transfer processes. Thus, the device exhibits ternary memory behavior with low threshold voltage (Vth1 of 1.1V, Vth2 of 2.0V), clearly distinguishable resistance states (1:102:104) and ternary yield (83%). Furthermore, the stepwise formation of the charge complex endows the device with a wider range to regulate the conductive state, which allows its application in brain-inspired neuromorphic computing. Modified National Institute of Standards and Technology recognition can reach an accuracy of 86%, showing great potential in neuromorphic computing applications in the post-Moore era.

Long-Circulating and Brain-Targeted Liposomes Loaded with Isoliquiritigenin: Formation, Characterization, Pharmacokinetics, and Distribution.

Isoliquiritigenin (ISL) has excellent neuroprotective effects. However, its limitations, including poor solubility, low bioavailability, and low accumulation in the brain, restrict its clinical promotion. In this study, a novel type of ISL-loaded liposome (ISL-LP) modified with the brain-targeting polypeptide angiopep-2 was prepared to improve these properties. The zeta potential, morphology, particle size, encapsulation efficiency, drug loading, and in vitro release of ISL-LP were evaluated. The pharmacokinetics and tissue distribution of ISL and ISL-LP were also investigated. The results demonstrated that ISL-LP had an average particle size of 89.36 ± 5.04 nm, a polymer dispersity index of 0.17 ± 0.03, a zeta potential of -20.27 ± 2.18 mV, and an encapsulation efficiency of 75.04 ± 3.28%. The in vitro release experiments indicate that ISL-LP is a desirable sustained-release system. After intravenous administration, LPC-LP prolonged the circulation time of ISL in vivo and enhanced its relative brain uptake. In conclusion, ISL-LP could serve as a promising brain-targeting system for the treatment and prevention of central nervous system (CNS) disorders.

We Need a Combination of Approaches to Evaluate Health System Resilience Comment on "Re-evaluating Our Knowledge of Health System Resilience During COVID-19: Lessons From the First Two Years of the Pandemic".

Health system resilience has become a desirable health system attribute in the current permacrisis environment. The article by Saulnier and colleagues reviews the literature on health system resilience and refines the concept, pinpointing dimensions of resilience governance that have not reached consensus, or that are missing from the literature. In this commentary we complement the findings by discussing different conceptual frameworks for understanding resilience and introducing resilience testing, a method to assess health system resilience using a hypothetical shock scenario. Resilience testing is a mixed-methods approach that combines a review of existing data with a structured workshop, where health system experts collaboratively assess the resilience of their health system. The new method is proposed as a tool for policy-making, as the results can identify attributes of the current health system that may hinder or boost a resilient response to the next crisis.

Prediction of short circuit current of wind turbines based on artificial neural network model

The growth of renewable energy on a global scale is making significant strides in power plants. This is due to the increasing concern about climate change, the rising demand for electricity, and the necessity to reduce reliance on fossil fuels. Ensuring the successful integration of new energy resources into the existing network is just as crucial as it requires the system to be reliable and adaptable. For instance, wind energy, which is one of the renewable sources, has an intermittent nature that necessitates the ability to synchronize its actions to achieve the desired system performance. The objective of this study is to utilize a new neural network system to calculate the short circuit current of power plants. Specifically, the focus is on identifying and categorizing the short circuit faults that occur between the stator coils of the squirrel cage induction generator used in wind power generation. To achieve this, a system was developed to simulate turbine data. Subsequently, four feature extraction techniques and machine learning algorithms were employed to enable early detection of short circuit faults. The numerical results obtained from the simulation demonstrated the high efficiency and accuracy of the proposed model.

Transfer of Self‐Sustained Reactions between Thermally Coupled Reactive Material Elements

System engineering requires the implementation of assembly methods allowing the formation of functional elements according to a desired system design. This is possible by joining prefabricated elements on a desired place in the system or by forming functional materials at wanted locations. Both approaches need temperature treatment. A typical system in part consists of materials restricting the use of annealing procedures above 300 °C. For this reason, higher‐temperature operations have to be localized to the point of use and transferred to the next reactive element. A further requirement is the reduction of the thermal budget of the localized joining process implementing ultrashort heat treatment operations. Reactive metallic multilayer offers the combined possibility of implementing a localized heat source and the formation of a functional intermetallic alloy. The article demonstrates the feasibility of a heat and reaction transfer chain in a topological structured pattern consisting of localized reactive multilayer materials under conditions of their gasless combustion. For the used Ni/Al multilayer material system, the critical (obstacle thickness) dimensions for the reaction and heat transfer are determined using a high‐speed camera and pyrometer measurements.

Efficient voltage regulation: An RW-ARO optimized cascaded controller approach

This work introduces novel advancements in automatic voltage regulator (AVR) control, addressing key challenges and delivering innovative contributions. The primary motivation lies in enhancing AVR performance to ensure stable and reliable voltage output. A crucial innovation in this work is the introduction of the random walk aided artificial rabbits optimizer (RW-ARO). This novel optimization strategy incorporates a random walk approach, enhancing the efficiency of AVR control schemes. The proposed cascaded RPIDD2-PI controller, fine-tuned using the RW-ARO, stands out as a pioneering approach in the AVR domain. It demonstrates superior stability, faster response times, enhanced robustness, and improved efficiency compared to existing methods. Comparative analyses with established controller approaches reaffirm the exceptional performance of the proposed method. The new approach results in shorter rise times, quicker settling times, and minimal overshoot, highlighting its effectiveness and speed in achieving desired system responses. Moreover, the novel approach attains higher phase and gain margins, showcasing its superior performance in the frequency domain. The disturbance rejection and harmonic analysis are performed in order to demonstrate the efficacy of the proposed approach for potential real-world applications. The latter analyses further cement the superior capability of the proposed approach for the automatic voltage regulation.

Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.

Performance vs. lottery: department transfer system desired by employees

Purpose Among the nonfinancial rewards for employees are career development opportunities, for which an important measure is department transfers. This is because departments are unevenly popular, and assigning everyone to their desired department is difficult. However, research on department transfers is limited to improving the efficiency of human resources utilization from a management perspective, with employee perspectives being lacking. This study aims to fill this gap in the literature. Design/methodology/approach The authors conducted a randomized controlled trial in an online survey environment with 2,000 full-time employees in Japan. Through the lens of self-determination theory, the authors hypothesized that employees seek department transfers based on performance rather than department transfers based on luck (lottery). Findings The results supported the hypothesis. For performance-based department transfers, an acceptance probability of 30% was found to be appropriate. Furthermore, mid-career, male, staff-level employees, those with experience of changing jobs and those who are more satisfied with their current job were more likely to seek this system. Practical implications Corporate managers should note that employees emphasize self-determination regarding their careers, dislike relying based on luck and seek department transfers relying on merit-based performance. Originality/value To the best of the authors’ knowledge, this is the first study to determine the desirable system design for department transfers from the employee’s perspective and extend self-determination theory.

Efficient fuzzy methodology for congestion control in wireless sensor networks

In wireless sensor networks (WSNs), ensuring reliable and efficient communication between nodes requires effective congestion control. This study presents an innovative approach, called FSFG, which combines FCM fuzzy clustering, Fuzzy Inference System (FIS), Support Vector Regression (SVR), and Genetic Algorithm (GA) to address congestion in WSNs. FSFG consists of two phases: Structure Identification and Parameter Identification. In the first phase, fuzzy clustering techniques are employed to determine the desirability of outputs in different classes. Subsequently, support vector regressions are used to determine membership functions with a minimal error rate. In the parameter identification phase, the obtained rules from the FCM method are used to design the desired system, enabling the estimation of necessary outputs based on relevant inputs. Through simulations, the results demonstrate that the FSFG algorithm excels in congestion control, exhibiting a lower error rate and outperforming other algorithms in terms of delay and packet delivery rate.

Least mean square based adaptive control of active power filter

The proposed control scheme is based on the least mean square (LMS) algorithm. The LMS algorithm is employed to estimate the necessary reference tracking current for the active power filter (APF). The proposed control scheme aims to enhance the dynamic response of the APF and minimize steady-state error. The weights of the LMS technique are calculated based on the estimated current of the APF. This algorithm is employed to minimize the error difference between the desired system output and its actual output, known as the mean square error (MSE). The estimated weights are utilized to modify the reference current weights, enabling them to follow the intended current of the APF. The online adaption of the LMS method involves the real-time adjustment of the weights. The performance of the LMS-based APF control is evaluated through a simulation study in MATLAB/Simulink, where it is compared with the conventional control method.

Matching workloads ro systems with deep reinforcement learning

Along with the evolution of computer microarchitecture over the years, the number of dies, cores, and embedded multi-die interconnect bridges has grown. Optimizing the workload running on a central processing unit (CPU) to improve the computer performance has become a challenge. Matching workloads to systems with optimal system configurations to achieve the desired system performance is an open challenge in both academic and industrial research. In this paper, we propose two reinforcement learning (RL) approaches, a deep reinforcement learning (DRL) approach and an evolutionary deep reinforcement learning (EDRL) approach, to find an optimal system configuration for a given computer workload with a system performance objective. The experimental results demonstrate that both approaches can determine the optimal system configuration with the desired performance objective. The comparison studies illustrate that the DRL approach outperforms the standard RL approaches. In the future, these DRL approaches can be leveraged in system performance auto-tuning studies.

Monolithic three-dimensional tier-by-tier integration via van der Waals lamination.

Two-dimensional (2D) semiconductors have shown great potential for monolithic three-dimensional (M3D) integration due to their dangling-bonds-free surface and the ability to integrate to various substrates without the conventional constraint of lattice matching1-10. However, with atomically thin body thickness, 2D semiconductors are not compatible with various high-energy processes in microelectronics11-13, where the M3D integration of multiple 2D circuit tiers is challenging. Here we report an alternative low-temperature M3D integration approach by van der Waals (vdW) lamination of entire prefabricated circuit tiers, where the processing temperature is controlled to 120 °C. By further repeating the vdW lamination process tier by tier, an M3D integrated system is achieved with 10 circuit tiers in the vertical direction, overcoming previous thermal budget limitations. Detailed electrical characterization demonstrates the bottom 2D transistor is not impacted after repetitively laminating vdW circuit tiers on top. Furthermore, by vertically connecting devices within different tiers through vdW inter-tier vias, various logic and heterogeneous structures are realized with desired system functions. Our demonstration provides a low-temperature route towards fabricating M3D circuits with increased numbers of tiers.

A simulation-based approach for resilience assessment of process system: A case of LNG terminal system

System resilience denotes the capacity to uphold desired system performance in the face of disruptions. Evaluating the resilience of a process system necessitates a thorough consideration of the intricate interplay between its components and the pivotal role of process parameters in reflecting the repercussions of disruptions on the system. This paper introduces an integrated methodology that takes into account component interactions and leverages process data for the resilience assessment of a process system. The proposed methodology comprises four key components: system structure analysis, disruption impacts analysis, process simulation, and resilience assessment. Firstly, the system structure is meticulously scrutinized using a P-graph model. This analysis encompasses the assessment of the significance and interplay of components, as well as the evaluation of how component failures affect the system's overall processes. Secondly, a Markov model is devised to examine the state transition process of components and quantifies the maintenance time needed for failed components. Subsequently, a simulation model is formulated to acquire real-time process parameters in the presence of disruptive events. Finally, the system's performance response function (PRF) is derived from the normalization of these process parameters. Building upon this foundation, a resilience assessment is conducted with a focus on the PRF. To illustrate the effectiveness of this methodology, an LNG terminal system is employed as an exemplar.