Minimization Strategy Research Articles

Carrier-Based PWM Strategy for Output Current Ripple Minimization in Multiphase Motor Drives

Carrier-Based PWM Strategy for Output Current Ripple Minimization in Multiphase Motor Drives

Featured minimally invasive therapeutic approach for chronic subdural hematoma: Embolization of middle meningeal artery - A narrative review.

Chronic subdural hematoma (c-SDH) is a frequent and serious neurological disease. It develops due to hemorrhage to the subdural space, mainly caused by head trauma. The middle meningeal artery (MMA) plays a critical role in the supply of blood to c-SDH. The decision on the type of treatment for c-SDH depends mainly on clinical and imaging evaluation. In cases in which patients are critically ill, the hematoma must be evacuated immediately. For this purpose, surgery is generally accepted as the mainstay of treatment. Among surgical techniques, twist-drill craniotomy, burr-hole craniotomy, and craniotomy are the three most used. The recurrence rate of c-SDH after surgery is an important problem with a rate of up to 30%. The technical success classification embolization of MMA (EMMA) has emerged as an effective and safe option for the treatment of c-SDH, especially those that recur. EMMA is commonly used as an adjunct to surgery or less frequently alone. The technical success of EMMA has been a promising minimal invasive strategy as an alternative or adjunctive therapy to surgical methods. Polyvinyl alcohol is the most widely used among various embolizing agents, including n-butyl cyanoacrylate, coil, and gelatin sponge. EMMA has been shown to prevent the formation or recurrence of c-SDH by eliminating blood flow to the subdural space. Complication rates are low. The large-scale comparative prospective will ensure efficacy and safety. This article aims to highlight the current information about EMMA in patients with c-SDH.

HSDNet: a poultry farming model based on few-shot semantic segmentation addressing non-smooth and unbalanced convergence.

Poultry farming is an indispensable part of global agriculture, playing a crucial role in food safety and economic development. Managing and preventing diseases is a vital task in the poultry industry, where semantic segmentation technology can significantly enhance the efficiency of traditional manual monitoring methods. Furthermore, traditional semantic segmentation has achieved excellent results on extensively manually annotated datasets, facilitating real-time monitoring of poultry. Nonetheless, the model encounters limitations when exposed to new environments, diverse breeding varieties, or varying growth stages within the same species, necessitating extensive data retraining. Overreliance on large datasets results in higher costs for manual annotations and deployment delays, thus hindering practical applicability. To address this issue, our study introduces HSDNet, an innovative semantic segmentation model based on few-shot learning, for monitoring poultry farms. The HSDNet model adeptly adjusts to new settings or species with a single image input while maintaining substantial accuracy. In the specific context of poultry breeding, characterized by small congregating animals and the inherent complexities of agricultural environments, issues of non-smooth losses arise, potentially compromising accuracy. HSDNet incorporates a Sharpness-Aware Minimization (SAM) strategy to counteract these challenges. Furthermore, by considering the effects of imbalanced loss on convergence, HSDNet mitigates the overfitting issue induced by few-shot learning. Empirical findings underscore HSDNet's proficiency in poultry breeding settings, exhibiting a significant 72.89% semantic segmentation accuracy on single images, which is higher than SOTA's 68.85%.

Control techniques of switched reluctance motors in electric vehicle applications: A review on torque ripple reduction strategies

<abstract> <p>As electric vehicles (EVs) continue to acquire prominence in the transportation industry, improving the outcomes and efficiency of their propulsion systems is becoming increasingly critical. Switched Reluctance Motors (SRMs) have become a compelling option for EV applications due to their simplicity, magnet-free design, robustness, and cost-effectiveness, making them an attractive choice for the growing EV market. Despite all these features and compared to other electrical machines, SRMs suffer from some restrictions, such as torque ripple and audible noise generation, stemming from their markedly nonlinear characteristics, which affect their productivity and efficiency. Therefore, to address these problems, especially the torque ripple, it is crucial and challenging to enhance the performance of the SRM drive system. This paper proposed a comprehensive review of torque ripple minimization strategies of SRMs in EV applications. It covered a detailed overview and categorized and compared many strategies, including two general categories of torque ripple mitigation encompassing optimization design topologies and control strategy developments. Then, focused on control strategy improvements and divided them into torque and current control strategies, including the sub-sections. In addition, the research also provided an overview of SRM fundamental operations, converter topologies, and excitation angle approaches. Last, a comparison between each method in torque control and current control strategies was listed, including the adopted method, features, and drawbacks.</p> </abstract>

Adaptive ECMS With Gear Shift Control by Grey Wolf Optimization Algorithm and Neural Network for Plug-In Hybrid Electric Buses

The Plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this paper proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working conditions are calculated by using the dynamic programming algorithm. The neural network is trained by these data in the offline part so that it can provide the appropriate gear-switching signal in time in the online model. For power-split control, this paper mainly selects an improved equivalent fuel consumption minimization strategy (ECMS) and uses the grey wolf optimization algorithm to iteratively solve the optimal equivalent factor. The proposed control strategy has not only been verified under various operating conditions but also verified by relevant experiments on the HIL platform. The results show that the proposed strategy has better fuel economy than ECMS and the rule-based strategy under the provided mixed operating conditions. Compared with the dynamic programming algorithm, the fuel consumption is only increased by 3.23%, but the problem of the curse of dimensionality is avoided.

Advanced Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles

This paper aims to develop an Advanced Equivalent Consumption Minimization Strategy (AECMS) for efficient fuel cell hybrid electric vehicles (FCHEV). The Equivalent Consumption Minimization Strategy (ECMS) is one of the most feasible solutions for hybrid electric vehicles as it has less complexity and low computational cost in making the optimal control decision. However, typical ECMS designs with fixed gain use could not enhance an optimal energy control when dealing with multi decisive factors for highly non-linear and uncertain systems like FCHEVs. To address this control challenge, the AECMS is developed as the combination of the ECMS and an adaptive equivalence factor to optimally split the energy flow between the fuel cell and battery in the FCHEV. First, a cost function is formulated based on two key objectives which are power consumptions of the fuel cell and battery, the equivalence factor and a set of soft and hard constraints, considering the number of fuel cell start/stop, fuel cell optimal operating range, state of charge (SoC) range, temperature ranges and power limits. Second, the adaptive equivalence factor is newly designed as the combination of states and dynamics of the fuel cell and battery and corresponding weighting factors. Third, fuzzy inferences are derived to adjust online the weighting factors in an adaptive manner according to the working conditions of the energy sources, subsequently improving the overall vehicle energy efficiency and maintaining the battery SoC while prolonging their lifetime. Numerical simulations using a FCHEV model have been carried out under UDDS and HWFET drive cycles to illustrate the superior performance of the AECMS against the traditional ECMS.

Fetal exposure to isotretinoin in Saudi Arabia: a multicenter real-world data analysis from 2015 to 2020.

Despite its high efficacy in treating severe acne, isotretinoin is associated with serious side effects, including teratogenicity. However, the extent of isotretinoin exposure during pregnancy in Saudi Arabia remains unknown. This study aims to quantify the extent of fetal exposure to isotretinoin in Saudi Arabia and to evaluate adherence to risk minimization measures approved by the Saudi Food and Drug Authority. Retrospective cohort study. This multicenter retrospective study included a cohort of 6233 women of childbearing ages (WCBAs) who had received isotretinoin therapy between 2015 and 2020. Exposure to isotretinoin use was ascertained from patients' electronic health records and was defined as any positive pregnancy test (urine or serum) or any diagnosis or procedure related to pregnancy occurring during the risk period. We defined the risk period starting from isotretinoin initiation until up to 30 days after the last prescription. We quantified the overall incidence proportion of fetal exposure to isotretinoin by dividing the number of pregnancy cases during the risk period by the total study sample of WCBAs. The cohort predominantly included young females (20-29 years), with a mean age of 24 years. Only 5% of the WCBAs used contraceptives, and 10% have a record of pregnancy testing. During the risk period, 34 pregnancies were identified, yielding a cumulative pregnancy incidence of 5.6 per 1000 WCBAs. Pregnancy outcomes for exposed women were about 5% of births had defects, while abortions accounted for 14.3% of pregnancies. Our investigation shows an alarming incidence of fetal exposure to isotretinoin in Saudi Arabia, substantially surpassing global estimates. These results underscore a critical need for enhanced interventions and robust risk minimization strategies tailored to the distinct challenges faced by the Saudi Arabian population.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of a Parallel Hybrid Electric Vehicle

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of a Parallel Hybrid Electric Vehicle

Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles

The design of energy management strategy (EMS) that can be applied in real-time with nearly optimal fuel economy for connected plug-in hybrid electric vehicles (C-PHEVs) is an urgent need at present, especially in the context where the benefits of rapid development of intelligent transportation system are increasingly evident. Equivalent consumption minimization strategy (ECMS) is able to achieve the desired performance, on the premise that the equivalent factor (EF) is adjusted reasonably. To achieve this goal, a variable optimization domain-based cooperative EMS for C-PHEVs is proposed in this paper. First, an offline sub-cycle division method is designed, which is effectively utilized to construct a fitting function to output reference EF based on the latest driving information. Second, the theoretical relationship between EF and operating efficiency of the driving components is explored, and the variable optimization domain of EF is designed to avoid inefficient operation of engine. Third, the EF optimization is then simplified into a quadratic problem within the designed domain, which is rapidly solved online with the golden section search algorithm. Finally, the comparison work is conducted under two real-world driving cycles. The results exhibit that the proposed strategy improves the fuel economy of C-PHEV by 6.440 % and 7.936 % over that using the conventional adaptive ECMS (CA-ECMS), respectively. The average engine fuel consumption rates of the proposed strategy are significantly reduced compared with that of CA-ECMS. Moreover, the effectiveness of the proposed strategy is verified on the experiment platform, demonstrating its application prospects.

Optimized energy management and control strategy of photovoltaic/PEM fuel cell/batteries/supercapacitors DC microgrid system

The slow dynamics response of a PEMFC to high-level load variation must be solved. Consequently, it is necessary to integrate the DC microgrid with battery storage banks and ultracapacitors. To guarantee the DC microgrid components: PV array, PEMFC, battery bank, and supercapacitor work effectively; energy management strategies (EMSs) are essential. The EMS distributes the load with the PV array, PEMFC, lithium-ion battery, and supercapacitor considering high efficiency and low H2 consumption. An effective EMS using a recent Beluga Whale Optimization (BWO) was developed for sharing the load between the components of the DC microgrid. Sliding mode control (SMCS), classical equivalent consumption minimization strategy (ECMS) and other optimization algorithms such as White Shark Optimizer (WSO), Manta ray foraging optimization (MRFO), Harris Hawks Optimizer (HHO), soon eagle search (BES) and Artificial Hummingbird Algorithm (AHA) were considered for comparison with the suggested BWO. The results approved the superiority of the suggested ECMS-based BWO; the hydrogen consumption was reduced by 37.67 %, 46.4 %, 25.5 %, 32.58 %, 12.94 %, 12.1 %, and 9.67 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA, respectively. In addition, efficiency increased by 4.4 %, 13.49 %, 5.24 %, 10.51 %, 0.95 %, 4.52 %, and 0.32 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA, respectively.

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

AbstractEdge caching at access points (APs) is a promising approach to alleviate the fronthaul burden and reduce user‐perceived delay. However, the edge caching placement is still challenging considering the coupling between caching and AP‐user association, limited fronthaul capacity, and multi‐AP deployment in the cell‐free (CF) massive MIMO systems. To this end, the authors establish a framework for the joint problem of AP‐user association and caching to minimize the content delivery delay which considers both cooperation delivery delay and radio access delay. It is an integer nonlinear programming problem and NP‐hard. The optimization problem is first decomposed into an AP‐user association sub‐problem and a caching placement sub‐problem to address this problem. A two‐stage matching algorithm is further proposed to achieve AP‐user association and a modified genetic algorithm to determine caching placement. A computationally efficient iterative algorithm is developed to solve the joint optimization problem. Finally, the global convergence and computational complexity of the proposed strategy are analyzed theoretically. Simulation results reveal that the proposed strategy can achieve better delivery delay performance than benchmark schemes.

A Novel Approach for a Predictive Online ECMS Applied in Electrified Vehicles Using Real Driving Data

To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system’s theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh.

Sensitivity and optimization analysis of pillar arrangement on the thermal–hydraulic performance of vapor chamber

A vapor chamber with pillar columns is imperative for heat management in high-powered electronic devices. Previous investigations presented a trade-off between the thermal transfer and flow pressure drop within the parameters of the pillar arrangement. However, the parameters of pillar arrangement at millimeter scale are not thoroughly considered, and this trade-off has yet to be addressed in the complete VC-P device. In this work, we optimized the parameters of pillar arrangement at millimeter scale within the complete VC-P, which was conducted by ANSYS Workbench with optiSLang. The multi-objective minimization strategy was the maximum temperature difference on the condenser surface (ΔTc,max), total thermal resistance (R), and total pressure drop (ΔPtotal). The three key parameters of optimizing pillar arrangement were the horizontal (X) and vertical (Y) pitch of pillar columns, and the diameter of pillar columns. First, a prediction model utilizing a response surface methodology derived from a central composite design was established. The results found that a critical pitch of pillar columns is approximately 15 mm. High sensitivity value (0.84) indicate that the diameter of pillar columns has a crucial impact on ΔTc,max and R. Using strength Pareto evolutionary algorithm 2, Part of these optimized VC-P exhibits reduce ΔPvc significant up to 40–60 %, while incurring a marginal increase in R and ΔTc,max higher less than 4–13 % and 1–8 % compared to the vapor chamber without pillar columns. Finally, the final optimal solution has been selected using the TOPSIS analysis method. The case study presented can offer valuable insights into the application of the same methodologies for the analysis of scenarios involving non-circular pillars.

SMEs tax minimization as shared responsibility

Prevailing criticism of tax minimization strategies is impacting contemporary business decisions. While extant research has focused on an alleged ‘moral’ shift among large accounting firms and corporates, little is known about tax responsibilities in the context of small and medium-sized enterprises (SMEs). Thus, we examine the following research question: How do SMEs and tax accountants perceive responsibility within the context of taxation? Our empirical investigation involves semi-structured interviews with accountants and their SME clients in Italy. Our findings point to an entanglement of legal, economic, ethical, and philanthropic responsibilities enacted in mundane tax strategy work. We propose a model of ‘shared responsibility’, aiming to clarify the complementary role of SMEs, tax accountants, and state actions in the curtailment of public funding, ultimately affecting the survival of all actors involved. Our work contributes to growing scholarship on business taxation as a form of social responsibility by emphasizing the shared nature of such responsibility, as well as showing how a current reliance on reputational risks to incentivise more responsible tax behaviour is ill suited to the SME context.

A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies.

Analisis Pengaruh Minimasi Pajak Dan Mekanisme Bonus Terhadap Transfer Pricing Dengan Komite Audit Sebagai Variabel Pemoderasi

This research aims to test and analyze the direct influence of tax minimization strategies, tunneling incentives, and bonus mechanisms on transfer pricing decisions. Meanwhile, the indirect influence involves the audit committee as a moderating factor, to test its influence on transfer pricing practices. The data used in this research comes from the Annual Reports of natural resource sector companies listed on the Indonesia Stock Exchange during the 2021-2022 period. A total of 185 companies used the purposive sampling method, producing a sample of 104 data which was analyzed using WarpPLS 8.0. Based on the research results, tax minimization has a direct influence on transfer pricing, while the bonus mechanism has no influence on transfer pricing and the audit committee cannot moderate the influence of both on transfer pricing. The implications of this research emphasize the importance of companies paying attention to their tax and transfer pricing policies as part of a general risk management and compliance strategy to prevent potential abuse or tax compliance risks.

Minimal intensity conditioning strategies for bone marrow failure: is it time for "preventative" transplants?

Hematopoietic cell transplantation (HCT) can cure blood dyscrasias and reduce the risk of hematologic cancers in patients with inherited bone marrow failure syndromes (IBMFS). However, because of its high mortality rate, HCT is generally reserved until patients with IBMFS manifest life-threatening cytopenias or myeloid malignancy, at which point outcomes are poor. Screening tests that accurately predict transformation and enable timely intervention are lacking. These unknowns and risks limit the use of HCT in patients with IBMFS, sometimes until significant disease-related sequelae have occurred. A major goal for IBMFS is to reduce cellular therapy-related complications to the point that earlier intervention can be considered before significant transfusion exposure, occurrence of comorbidities, or malignant transformation. In recent decades, disease-specific allogeneic HCT trials have yielded significant improvements in outcomes in IBMFS conditions, including Fanconi anemia and dyskeratosis congenita. This is in large part due to marked reductions in conditioning intensity to address the increased sensitivity of these patients to cytotoxic chemotherapy and radiation. The success of these approaches may also indicate an ability to leverage intrinsic fitness defects of hematopoietic stem and progenitor cells across IBMFS disorders. Now with advances in tracking somatic genetic evolution in hematopoiesis and tailored minimal intensity conditioning regimens, this question arises: is it time for preventative HCT for IBMFS?

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, and/or ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discusses various types of EMSs such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and mutative fuzzy logic controller (MFLC). It also discusses a systematic categorization of FCHEV topologies and delves into the unique characteristics of these topologies. Furthermore, it provides an in-depth comparative study of EMSs applied in FCHEVs, encompassing rule-based, optimization-based, and advanced learning-based approaches. However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This review aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as state of charge (SOC) and FC deterioration. This paper seeks a deeper comprehension of the recent advancements in EMSs for FCHEVs. It offers insights that can facilitate a more comprehensive grasp of the current state of research in this field, aiding researchers in staying up to date with the latest developments.

Trihalomethanes in developed and developing countries.

The reactions between natural organic matter, anthropogenic contaminants, ions, and disinfectants lead to the formation of disinfection by-products (DBPs) such as trihalomethanes (THMs) in drinking water. The formation of THMs is strongly related to the chlorination of water. The study's central objective was to compare the concentration of THMs in twenty developed and developing countries and their disinfection techniques. The THM concentration in 11 developed and 9 developing countries ranged from 0.5 µg/L (Germany) to 215 µg/L (Russia) and 3 µg/L (China) to 439.2 µg/L (Bangladesh), respectively. The developed country has partially succeeded in reducing THM concentration in drinking water, whereas significant steps are needed in developing countries to reduce the existing high THM concentration. The concentration of THMs in water varies among these countries because of the different water sources, water quality, environmental conditions, and efficiency of water treatment technologies. A meaningful relationship has been observed between the properties of water and the THM formation. The use of chemical disinfectants will result in new forms of DBPs that are undesirable due to their carcinogenic and mutagenic effects on human health. The DBP guidelines by various national and international agencies have helped to control and manage the THM concentration in drinking water. However, these regulatory standards are not continuously monitored. Therefore, the formation of these compounds should be prevented either by removing THMs forming precursors or by using an integrated approach for controlling THM formation by implementing advanced water treatment technology. Extensive research is desirable in domains like THM minimization strategies which are easy to deploy, scalable, and cost-effective.

FlexNet: A warm start method for deep reinforcement learning in hybrid electric vehicle energy management applications

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded ‘warm start’ method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications.