Minimization Strategy Research Articles

Thiopurine Metabolite Shunting in Late Pregnancy Increases the Risk of Intrahepatic Cholestasis of Pregnancy in Women With Inflammatory Bowel Disease, and Can be Managed With Split Dosing.

The risk of intrahepatic cholestasis of pregnancy [ICP] is increased in thiopurine-exposed pregnancies. Thiopurine 'shunting', with a 6-methylmercaptopurine [MMP] to 6-thioguanine [TGN] ratio of >11, progresses over pregnancy, and may promote ICP development. We aimed to explore the association between thiopurine exposure and ICP, including the hypothesised impact of thiopurine shunting, and identify risk minimisation strategies. This prospective multicentre cohort study compared thiopurine and biologic monotherapy-exposed pregnant participants. Disease activity and obstetric outcome data, thiopurine metabolites, bile acids, and transaminases were obtained before conception, in each trimester, at delivery, and postpartum. Thiopurine dose management was at the discretion of the treating physician. Included were 131 thiopurine and 147 biologic monotherapy-exposed pregnancies. MMP/TGN ratio increased from preconception to third trimester [p <0.01], with approximately 25% of participants shunting in pregnancy. Second trimester split dosing led to a decrease in the median MMP/TGN ratio from 18 (interquartile range [IQR] 6-57) to 3 [IQR 2-3.5] at delivery [p = 0.04]. The risk of ICP was increased in thiopurine-exposed pregnancies (6.7% [7/105] vs 0% [0/112], p <0.001), with all ICP cases occurring in the setting of antenatal thiopurine shunting. Thiopurine dose increases (risk ratio [RR] 8.10, 95% confidence interval [CI] 1.88-34.85, p = 0.005) and shunting in third trimester [6.20, 1.21-30.73, p = 0.028] and at delivery [14.18, 1.62-123.9, p = 0.016] were associated with an increased risk of ICP. Thiopurine exposure is associated with an increased risk of ICP, particularly following dose increases antenatally and with shunting in late pregnancy. The latter may be effectively managed with split dosing, although further studies are warranted.

Tacrolimus-Induced Neurotoxicity After Transplant: A Literature Review.

Tacrolimus, a calcineurin inhibitor, is an immunosuppressant used globally to prevent rejection after organ transplantation. Although it significantly improves outcomes for solid organ transplant patients, it is associated with various side effects such as nephrotoxicity and neurotoxicity. Tacrolimus-induced neurotoxicity is frequently encountered in clinical practice and can present with a variety of symptoms that may occur even at therapeutic levels. Although tacrolimus-induced neurotoxicity is well documented, there is limited literature available on pharmacologic management. Twenty-eight case reports of tacrolimus-induced neurotoxicity were identified and analyzed in addition to other literature including reviews, retrospective studies, and animal model studies. The severity of cases of tacrolimus-induced neurotoxicity reported ranged from mild symptoms that could be managed with symptomatic treatment to conditions such as posterior reversible encephalopathy syndrome and chronic inflammatory demyelinating polyradiculoneuropathy that may require more immediate intervention. This information was utilized in addition to clinical experience to compile potential management options for prevention and treatment of neurotoxic adverse events. This review is limited by the utilization of primarily retrospective studies and case reports. The available literature on the subject is largely narrative and there are no guidelines on treatment of tacrolimus-induced neurotoxicity at the time of this research. This comprehensive review may guide further studies to investigate the pathophysiology of tacrolimus-induced neurotoxicity and to define patient-specific strategies for mitigation or minimization of neurotoxicity. This is especially important given that management of tacrolimus-induced neurotoxicity can include changes to immunosuppression that can result in an increased risk of rejection.

Knowledge-driven domain adaptation strategy for rotating machinery fault diagnosis under varying working condition

Due to the frequent switch of the working condition, fault diagnosis model for rotating machinery established on the training set (the source domain) cannot be effectively applied to the test set (the target domain). Traditional domain adaptation methods address this issue by performing feature alignment between the source and target domain, which ignores the positive guidance provided by prior knowledge. In this paper, we propose a prior knowledge-driven domain adaptation (PKDA) method for varying working condition fault diagnosis of rotating machinery, where a self-supervised learning framework is designed to integrate expert prior knowledge and structural prior knowledge. First, an expert prior knowledge guidance module is designed to extract features with physical significance. Then we align the features between the source and target domain by the max mean discrepancy metric. Besides, a progressive Shannon entropy minimization strategy is proposed to realize the feature distribution structure of intra-class compression and inter-class separation, which can effectively integrate the structural prior knowledge. In this way, PKDA can effectively utilize the prior knowledge to achieve better performance in varying working condition fault diagnosis tasks. The effectiveness of the proposed method is illustrated by an open-source rolling bearing fault dataset from Case Western Reserve University and an open-source gearbox fault dataset from Southeast University.

UV communication cooperative formation UAV alliance capture algorithm.

The capture of target unmanned aerial vehicles (UAVs) by a UAV formation is one of the important and typical tasks in multi-UAV battlefield operations. In this paper, an ultraviolet (UV) light communication-assisted formation UAV alliance capture algorithm is proposed, which combines UV light communication technology with a capture algorithm. With the help of wireless UV light to assist UAV formation inter-UAV data confidentiality transmission and non-line-of-sight communication, the algorithm integrates the alliance generation algorithm with the region minimization strategy, solves the optimal alliance structure by using the dynamic programming method, and implements the aerial capture of the target UAVs by using the region minimization strategy, so as to complete the task of efficiently capturing multi-targets by the UAV formation in complex scenarios. Simulation comparisons were conducted between the region minimization strategy and the proposed UV communication-assisted formation UAV alliance capture algorithm. The results show that the proposed algorithm reduces energy consumption by 12.73% on average and decreases the average number of algorithm iterations by 27.49% during the UAV formation capture of multiple targets, which verifies its low energy consumption and high capture efficiency.

Online Lifecycle Operating Costs Minimization Strategy for Fuel Cell Buses Considering Power Sources Degradation

The high operating costs limit the popularity and development of fuel cell buses (FCBs). To enhance the lifecycle operating economy of FCBs, this article analyzes the impact of power sources degradation on operating costs and proposes an online operating costs minimization strategy, which integrates online state of health (SOH) estimation, online multiobjective optimization, and online adaptive adjustment into the control strategy framework. Specifically, the SOH of the power sources is estimated by cycle life models in real time. The hydrogen consumption, battery energy consumption, and power sources degradation are incorporated into the objective function and adjusted in real time according to the SOH. Under different SOH, the simulation results indicate that compared with the equivalent consumption minimization strategy, the proposed strategy can reduce fuel cell and battery degradation by 13.8% and 5%, and decrease operating costs by 5.3%. Moreover, the adaptability of the control strategy is enhanced throughout the full lifecycle.

Research on Energy Management Strategy of Fuel Cell Tractor Hybrid Power System

In recent years, more and more attention has been paid to fuel cell-based hybrid tractors. In order to optimize the global power distribution of tractors and further improve the fuel economy and fuel cell durability of the system, this paper designs an energy management strategy to maximize external energy efficiency based on fuel cell/lithium battery/supercapacitor hybrid tractors. This strategy aims to reduce the real-time hydrogen consumption of the system while maximizing the external energy output so as to reduce the impact of load randomness on the output power of the fuel cell. Under the typical ploughing conditions of the tractor, the simulation is compared with the state machine strategy and the equivalent hydrogen consumption minimization strategy. The results show that the proposed strategy meets the power requirements of a given ploughing condition, and compared with the two traditional strategy systems, the performance characteristics of auxiliary energy are more fully exerted. It reduces the burden on fuel cells and improves the durability of fuel cells. The hydrogen consumption of the system was reduced by 11.03 g and 16.54 g, respectively, improving the overall economy of the hybrid system.

Semisupervised 3D segmentation of pancreatic tumors in positron emission tomography/computed tomography images using a mutual information minimization and cross-fusion strategy.

Accurate segmentation of pancreatic cancer tumors using positron emission tomography/computed tomography (PET/CT) multimodal images is crucial for clinical diagnosis and prognosis evaluation. However, deep learning methods for automated medical image segmentation require a substantial amount of manually labeled data, making it time-consuming and labor-intensive. Moreover, addition or simple stitching of multimodal images leads to redundant information, failing to fully exploit the complementary information of multimodal images. Therefore, we developed a semisupervised multimodal network that leverages limited labeled samples and introduces a cross-fusion and mutual information minimization (MIM) strategy for PET/CT 3D segmentation of pancreatic tumors. Our approach combined a cross multimodal fusion (CMF) module with a cross-attention mechanism. The complementary multimodal features were fused to form a multifeature set to enhance the effectiveness of feature extraction while preserving specific features of each modal image. In addition, we designed an MIM module to mitigate redundant high-level modal information and compute the latent loss of PET and CT. Finally, our method employed the uncertainty-aware mean teacher semi-supervised framework to segment regions of interest from PET/CT images using a small amount of labeled data and a large amount of unlabeled data. We evaluated our combined MIM and CMF semisupervised segmentation network (MIM-CMFNet) on a private dataset of pancreatic cancer, yielding an average Dice coefficient of 73.14%, an average Jaccard index score of 60.56%, and an average 95% Hausdorff distance (95HD) of 6.30 mm. In addition, to verify the broad applicability of our method, we used a public dataset of head and neck cancer, yielding an average Dice coefficient of 68.71%, an average Jaccard index score of 57.72%, and an average 95HD of 7.88 mm. The experimental results demonstrate the superiority of our MIM-CMFNet over existing semisupervised techniques. Our approach can achieve a performance similar to that of fully supervised segmentation methods while significantly reducing the data annotation cost by 80%, suggesting it is highly practicable for clinical application.

Immediate Processing of Sentences in Language Communication

Sentence processing is a fundamental and critical area of inquiry within the field of language processing, as it plays an essential role in facilitating effective communication. The present study focuses on the immediate processing of sentences in English language communication. The article begins by providing an overview of the basic principles underlying sentence processing. It then delves into the various parsing strategies, including the late closure strategy and the minimal attachment strategy. The modular and interactive models of sentence processing are also presented and discussed. Additionally, the relationship between sentence processing and meaning is explored, with an examination of the role of factors such as presupposition and plausibility in language communication process. Finally, the role of prosody in language processing is expounded upon. The paper draws several conclusions regarding the nature of immediate sentence processing in language communication.

Strategies for Sustainable Construction Waste Minimization in the Modern Era

Strategies for Sustainable Construction Waste Minimization in the Modern Era

Model Based Optimization of Energy Consumption in Milk Evaporators

This work explores five falling film evaporator (FFE) simulation approaches combined with energy consumption minimization strategies, namely Mechanical Vapor Recompression and Thermal Vapor Recompression (MVR and TVR, respectively). Global system analysis and advanced dynamic optimization strategies are then investigated to minimize steam consumption, the cost of steam, and the total annualized cost and to maximize product yield. The results indicate that higher TVR discharge pressures, or MVR compression ratios, along with higher feed temperatures, enhance evaporation but increase operational costs. The most economical option includes three evaporator effects with TVR to achieve 50% product dry mass content. However, for a 35% dry mass content, MVR becomes cost-effective with an 11% reduction in unit electricity prices or a simultaneous 7% drop in electricity prices and a 5% increase in gas-based steam prices. Furthermore, switching from milk powder production to milk concentrates leads to an annual cost reduction ranging from 10.8 to 44%. Additionally, a forecasted 20% (or more) reduction in biomass-based steam cost can lead to lower annual expenditure compared with the nominal NG-based steam case. Regarding the total annualized cost, for a new plant design, optimization strategies lead to a 9–45% reduction in the total cost depending on the case under consideration.

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

Analysis of electrotherapy in patients with implantable cardioverter-defibrillator for primary prevention of sudden cardiac death according to remote monitoring data

Aim: To determine the factors associated with development of justified and inappropriate implantable cardioverter-defibrillator (ICD) activations in patients at high risk of sudden cardiac death based on the remote monitoring systemMaterial and Methods. We analyzed remote monitoring data from 2014 to 2022 in 132 patients with ICD for primary prevention of sudden cardiac death (SCD). Patients were divided into 2 groups 1 – patients with persistent paroxysmal tachyarrhythmias; 2 – patients without persistent paroxysms of ventricular tachycardia (VT) / ventricular fibrillation (VF). In case of inappropriate shocks, the causes and possible predictors of their development were analyzed.Results. Of 132 patients, 62 (46.9%) patients appeared to have persistent VT/VF. It was found that the probability of detecting persistent paroxysms of VT/VF decreased with a history of revascularization (p = 0.030) and increased in the absence of amiodarone therapy (p = 0.012), with increasing age (p = 0.035), with decreasing left ventricular ejection fraction (LVEF) less than 35% (p = 0.016). 71 arrhythmic episodes (17.9%) in 27 (20.4%) patients were considered as false detection of tachyarrhythmias. Analyzing the causes of inappropriate electrotherapy, different types of supraventricular tachyarrhythmias in the zone of VT detection (85.9%), including atrial fibrillation (25.4%), in smaller percentage of cases there was discovered T-wave detection 4.2%, noise on the electrode – 2.8%, 7.1% – double counter due to the operation of the cardiac contractility modulation device. When analyzing factors associated with the development of false detection, reliable results were obtained with regard to the presence of a history of atrial fibrillation (p = 0.036), implanted single-chamber ICD (p = 0.028).Conclusion. The development of persistent ventricular tachyarrhythmias was noted in 47% of patients with ICD as a part of primary prevention of sudden cardiac death, and 20.4% had the development of inappropriate detection and electrotherapy. Predictors of their occurrence have been identified, which can be used as development of strategies for shock minimization.

Equivalent Consumption Minimization Strategy for Full-Electric Ship Energy Management with Multiple Objectives

Optimal energy management is still a challenge in full-electric vessels. New degrees of flexibility in the energy management resulting from the load sharing between multiple, heterogenous power sources lead to a suboptimal solution using rule-based control. Therefore, advanced control strategies present a solution to the challenge of finding the optimal control input for a nonlinear multi-objective power and energy problem in sufficient time. As additional benefit, advanced control allows to incorporate multiple objectives in the optimization such as minimization of several emissions, operational costs, and component degradation. Equivalent Consumption Minimization Strategy (ECMS) is a strategy for instantaneous optimization, which is promising for applications in vessels with a high degree of uncertainty in the load profile. It incorporates multiple objectives by assigning equivalent cost factors in the cost function, allowing a flexible expansion of the control problem. In this paper, we present a novel ECMS-based control strategy for a full-electric vessel with the ability to react flexibly to changing mission conditions. First, we define the objectives for the control problem, in this study \ce{CO2} production, hazardous emission production, fuel consumption, energy cost, and the degradation of the battery. Second, we develop a pareto-front approach for a-posteriori definition of the equivalent cost factors. To showcase energy consumption reduction, we use a benchmark control based on state-of-the-art control strategies. A full-electric case study vessel with high uncertainty in the load profile is chosen to evaluate the proposed controller. Several different load profiles are generated and tested to evaluate the performance of the ECMS controller in dealing with different types of loads. The results will demonstrate the effectiveness of the proposed novel control strategy in reducing energy consumption while minimizing other hazardous emission outputs and preserving the health of the battery.

Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving

This study proposes a predictive equivalent consumption minimization strategy (P-ECMS) that utilizes velocity prediction and considers various dynamic constraints to mitigate fuel cell degradation assessed using a dedicated sub-model. The objective is to reduce fuel consumption in real-world conditions without prior knowledge of the driving mission. The P-ECMS incorporates a velocity prediction layer into the Energy Management System. Comparative evaluations with a conventional adaptive-ECMS (A-ECMS), a standard ECMS with a well-tuned constant equivalence factor, and a rule-based strategy (RBS) are conducted across two driving cycles and three fuel cell dynamic restrictions (|di/dt|max≤ 0.1, 0.01, and 0.001 A/cm2s). The proposed strategy achieves H2 consumption reductions ranging from 1.4% to 3.0% compared to A-ECMS, and fuel consumption reductions of up to 6.1% when compared to RBS. Increasing dynamic limitations lead to increased H2 consumption and durability by up to 200% for all tested strategies.

Optimal Evolution Strategy for Continuous Strategy Games on Complex Networks via Reinforcement Learning.

This article presents an optimal evolution strategy for continuous strategy games on complex networks via reinforcement learning (RL). In the past, evolutionary game theory usually assumed that agents use the same selection intensity when interacting, ignoring the differences in their learning abilities and learning willingness. Individuals are reluctant to change their strategies too much. Therefore, we design an adaptive strategy updating framework with various selection intensities for continuous strategy games on complex networks based on imitation dynamics, allowing agents to achieve the optimal state and a higher cooperation level with the minimal strategy changes. The optimal updating strategy is acquired using a coupled Hamilton-Jacobi-Bellman (HJB) equation by minimizing the performance function. This function aims to maximize individual payoffs while minimizing strategy changes. Furthermore, a value iteration (VI) RL algorithm is proposed to approximate the HJB solutions and learn the optimal strategy updating rules. The RL algorithm employs actor and critic neural networks to approximate strategy changes and performance functions, along with the gradient descent weight update approach. Meanwhile, the stability and convergence of the proposed methods have been proved by the designed Lyapunov function. Simulations validate the convergence and effectiveness of the proposed methods in different games and complex networks.

СУТЬ ТА ПРИНЦИПИ ПРОЦЕСУ УПРАВЛІННЯ ПОДАТКОВИМИ РИЗИКАМИ СУБ’ЄКТІВ ГОСПОДАРЮВАННЯ

The constant change in the rules of the game in the field of taxation, the frank fiscal attitude of the controlling authorities towards taxpayers, the complexity of the legislation and the increase in the amount of tax obligations – all this requires business entities to take measures to manage their tax risks. During the last decades, the importance of this issue is only growing. Even trying to strictly follow the norms of tax legislation, business cannot always count on a constructive dialogue with tax authorities. Tax requirements around the world are increasing at a rapid pace, tax authorities are significantly intensifying their efforts in the field of tax collection, making significant investments in data, technology, and qualified specialists. In this regard, a change in the thinking of business entities can be observed: the tax minimization strategy is replaced by a new tax risk minimization strategy. It should be the basis of the modern process of tax risk management of business entities, which is becoming especially relevant today. To effectively manage your tax risks, you need to know both the theoretical and practical aspects of this process. The purpose of the article is to reveal the essence of the process of tax risk management of economic entities, to define its constituent elements and principles, to classify forms of tax risk management, to find possible ways to reduce the impact of external and internal factors of tax risks. The theoretical and methodological basis of this scientific research is analysis, synthesis, induction, deduction, analogy, abstraction, concretization and comparison. The article concludes that the theoretical and practical aspects of the process of tax risk management of business entities are mainly based on the general concept of risk management. In particular, this concerns the purpose of this process and the typical composition of its elements. And the specificity of tax risk management in comparison with other financial risks is the need to have deep professional knowledge in the field of accounting and taxation and the application of such risk management methods that arise from the main factors that can cause the emergence of tax risks.

Optimal velocity planning based on the solution of the Euler-Lagrange equations with a neural network based velocity regression

Trajectory optimization is a complex process that includes an infinite number of possibilities and combinations. This work focuses on a particular aspect of the trajectory optimization, related to the optimization of a velocity along a predefined path, with the aim of minimizing power consumption. To tackle the problem, a functional formulation and minimization strategy is developed, by means of the Euler-Lagrange equation. The minimization is later performed using a neural network approach. The strategy is deemed Lagrange-Net, as it is based on the minimization of the energy functional, by the means of Lagrange's equation and neural network approximations.

Increasing Client Fraud Risk Disclosure with Minimization Techniques

SYNOPSIS Auditing standards require auditors to conduct fraud inquiries as part of their risk assessment process. During fraud inquiries, auditors frequently ask client personnel directly about “fraud, alleged fraud, or suspected fraud,” using standard language taken from AS 2110. However, guidance from the Center for Audit Quality (CAQ) suggests that avoiding the word “fraud” results in greater disclosure regarding fraud risk. Although untested, the CAQ’s conjecture is supported by literature in psychology and criminology, which suggests that minimizing the seriousness of the offense (i.e., minimization) increases disclosure. This study examines two minimization strategies. The first involves replacing the word “fraud” with “questionable behavior.” The second employs social comparison to increase the perceived frequency that clients report misconduct to auditors. Experimental results suggest that either strategy increases client reporting intentions relative to a control condition using the language prescribed by AS 2110. Our findings may help auditors conduct more effective fraud inquiries. Data Availability: Data are available from the authors. JEL Classifications: M42; M48.

Enhanced Power Injection Strategies for Voltage Sag Minimization: Integrating the Maximum Apparent Power of the Converter and the Thévenin Equivalent

Enhanced Power Injection Strategies for Voltage Sag Minimization: Integrating the Maximum Apparent Power of the Converter and the Thévenin Equivalent

Exact and heuristic max-plus strategies for makespan minimization in permutation flow shops with time-window constraints

We consider permutation flow shops with time-window constraints, which consist of several jobs to be processed in a sequence of machines in the same order, where no job overtaking is allowed, and with restrictions on the timing of the operations. The investigated class of permutation flow shops is larger than the one typically considered in scheduling literature. This paper introduces two strategies, based on previous results in the max-plus algebra, to obtain a schedule minimizing the makespan: an exact branch and bound method, and a heuristics inspired from receding-horizon control. Simulations demonstrate the advantages and limitations of the proposed approaches.