Simultaneous Consideration Research Articles

Myopia intervention and ultraviolet radiation related eye diseases: A narrative literature review.

There has been an increased understanding of the protective effect of two or more hours in high lux light on the development and progression of myopia. The aim of myopia management is to reduce the incidence of high myopia and sight-threatening myopic complications. Equally important are the sight-threatening complications of ultraviolet radiation (UVR) on the eye and adnexal structures. This review will analyze the literature for both these epidemics to help guide public health policy. Whilst increasing childhood high lux light exposure is important, consideration of a holistic eye health policy should ensure that UV eye diseases are also prevented. The advent of ultraviolet (UV) fluorescence photography has increased our understanding that significant UV eye damage occurs in childhood, with 81% of children aged 12-15 years having signs of UV eye damage. Hence, the need to reduce myopia and protect from UV-related eye diseases needs simultaneous consideration. Advocating for eye protection is important, particularly as the natural squint reflex is disabled with dark sunglasses lenses. The pathways UV reaches the eye need to be considered and addressed to ensure that sunglasses offer optimum UV eye protection. The design of protective sunglasses that simultaneously allow high lux light exposure and protect from UVR is critical in combating both these epidemics.

КОНЦЕПЦІЙНІ ЗАСАДИ УПРАВЛІННЯ КАПІТАЛОМ В СИСТЕМІ ЗАБЕЗПЕЧЕННЯ СТІЙКОСТІ БАНКІВ

The article examines the problems of managing banks and the country's banking system. Attention is focused on the issues of ensuring the financial stability of banks by means of effective and rational management of the capital of banking structures in Ukraine. The purpose of the study is to further develop the conceptual principles and provisions of capital management in the system of ensuring the stability of banks. The theoretical and methodological foundations of the stability of the banking system are summarized on the example of Ukraine. The position of the economic essence and significance of the stability of the banking system is characterized. Banking risks and their impact on the stability of the banking system are outlined. The methodical and applied provisions of managing the stability of the banking system of the state and banks in particular are highlighted. It has been proven that the financial stability of banks should be understood as their ability to withstand macroeconomic factors, fulfill obligations on time, ensure reliability and repayment of funds deposited by individuals and legal entities. It is shown that the biggest threat to the financial stability of the banking system is systemic risks (political, legal, social and general economic), which are formed as a result of changes in the strategic priorities of the socio-economic development of the state, an imperfectly formed organizational and economic mechanism, the inadequacy of the use of operational-tactical methods, means and measures of regulation of socio-economic processes and relations, which affects on the change of the political, social, demographic and economic situation. It has been established that an effective way to reduce systemic risk is the simultaneous consideration of its three main forms: spread risk; the risk of macroeconomic shocks; risk of imbalance.

Neutrosophic Evaluation of Health Promotion Programs for Resource Allocation

The efficient allocation of resources to health promotion and disease prevention programs in the field of healthcare is a multidimensional and highly sensitive challenge that requires the simultaneous consideration of multiple criteria and objectives. In this context, neutrosophic logic emerges as a promising tool capable of addressing the inherent uncertainty and imprecision in health decision-making. This article explores the application of neutrosophic logic in decision-making regarding the allocation of resources to health promotion and disease prevention programs, taking into account a wide range of criteria. The main objective of this research is to validate the importance of using neutrosophic logic in resource allocation for these programs, emphasizing its ability to handle ambiguity and uncertainty effectively and equitably. The results obtained have demonstrated that neutrosophic logic is an effective tool in decision-making in the field of public health.

Impact of OLTC equipped transformer operation on PV installation in urban distribution network

Presented paper addresses the problem of proper large-scale integration of photovoltaic (PV) systems in urban area distribution network (DN). A methodology for minimization of annual energy losses in DN by optimal placement of PV systems, which simultaneously considers rooftop PV potential and time dependent network operation, is presented. Optimal reactive power generation of PV systems and optimal setting of on-load tap changer (OLTC) equipped transformer in secondary substation are determined as well. Furthermore, proposed methodology enables evaluation of the impact of OLTC equipped transformer on the share of PV installation that urban DN can accommodate. Proposed methodology is based on the optimization tool called differential evolution, with the objective function set to minimize network’s annual energy losses, while preventing the voltage violations and thermal overloading of lines. Results of the case study performed on a real urban low voltage distribution network, with consideration of different scenarios of OLTC operation are presented. The results indicate that simultaneous consideration of chosen variables yields greater benefits to DN operation in terms of reduction of annual energy losses and greater PV penetration.

Robot skill learning system of multi-space fusion based on dynamic movement primitives and adaptive neural network control

This article develops a robot skill learning system with multi-space fusion, simultaneously considering motion/stiffness generation and trajectory tracking. To begin with, surface electromyography (sEMG) signals from the human arm is captured based on the MYO armband to estimate endpoint stiffness. Gaussian Process Regression (GPR) is combined with dynamic movement primitive (DMP) to extract more skills features from multi-demonstrations. Then, the traditional DMP formulation is improved based on the Riemannian metric to encode the robot’s quaternions with non-Euclidean properties. Furthermore, an adaptive neural network (NN)-based finite-time admittance controller is designed to track the trajectory generated by the motion model and to reflect the learned stiffness characteristics. In this controller, a radial basis function neural network (RBFNN) is employed to compensate for the uncertainty of the robot dynamics. Finally, experimental validation is conducted using the ROKAE collaborative robot, confirming the effectiveness of the proposed approach. In summary, the presented framework is suitable for human-robot skill transfer method that require simultaneous consideration of position and stiffness in Euclidean space, as well as orientation on Riemannian manifolds.

Determining Compressed Concrete Element Limit States Based on the Widths and Depths of Cracks Caused by Transverse Deformations.

In the modern theory of compressed concrete elements, the most attention is paid to longitudinal deformations, whereas transverse ones are rarely considered and just within Poisson's coefficient limits (i.e., elastic concrete behavior in the transverse direction). However, transverse deformations significantly develop beyond the limits corresponding to Poisson's coefficient, where they lead to longitudinal crack initiation and development. In-depth experimental and numerical investigations of transverse deformations in the inelastic stage showed that it is necessary to consider crack propagation. The present study proposes simultaneous consideration of longitudinal and transverse deformations, as well as the appearance of cracks and their widths and depths. This allowed us to obtain a complete compressed concrete element behavior pattern at all performance stages in two types of limit states (based on longitudinal and transverse deformations). Consequently, new ultimate limit states by the depth and width of cracks caused by transverse deformations are proposed to be included in modern design practices and codes.

Simultaneous consideration of consumer preferences and seller revenue as a smart retail sales and management strategy

PurposeThe authors propose a dairy bundle, integrating strategies to jointly maximise producer revenue and consumer utility according to the latter's preferences.Design/methodology/approachAn algorithm based on a nested logit model identifies the bundle maximising producer revenue based on factors affecting consumer purchase behaviour. The data are drawn from a mall-intercept survey administered in Iran, with consumers stating a hypothetical choice among a comprehensive set of dairy products.FindingsDemographic characteristics and marketing mix elements significantly affect consumers' preferences. An algorithm based on the estimated dissimilarity parameter determines the best bundle of dairy products, simultaneously obtaining the highest utility and the highest expected revenue.Originality/valueConsumer preference and maximum producer or retail seller income are considered simultaneously. The bundling promotion strategy is widely used for food offerings and fresh foods and can be extended to other products.

Multi-level deep Q-networks for Bitcoin trading strategies

The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

A Bi‐Objective Stochastic Model of Locating‐Allocating‐Routing Relief and Rescue in Disaster Response Conditions: An Accelerated Benders Decomposition

Problem Statement. Proper and timely relief in the postdisaster phase is very important to minimize victims and casualties. It is necessary to assess the needs of the affected points and provide relief in the shortest possible time without wasting time. For this purpose, it is necessary and vital to determine the location of care centers, temporary accommodation centers, and routing to distribution vital and medical items. The Proposed Approach. In this paper, using a scenario‐based stochastic planning mathematical model, postdisaster relief is discussed. Also, attention has been paid to the distribution of vital items by using routing. Contributions. By using the epsilon constraint method, a strong efficient solution has been achieved for model’s objectives, and also a sensitivity analysis has been performed on some of the model’s parameters. Also, an accelerated stochastic benders decomposition algorithm is suggested to solve the problem modeled in this paper. To speed up the convergence of the solution algorithm, valid inequalities are introduced to get better quality lower bounds. Results. The results of the research show that the simultaneous consideration of the relief evacuation and distribution process improves the relief logistics process.

Host and parasite identity interact in scale-dependent fashion to determine parasite community structure.

Understanding the ecological assembly of parasite communities is critical to characterise how changing host and environmental landscapes will alter infection dynamics and outcomes. However, studies frequently assume that (a) closely related parasite species or those with identical life-history strategies are functionally equivalent, and (b) the same factors will drive infection dynamics for a single parasite across multiple host species, oversimplifying community assembly patterns. Here, we challenge these two assumptions using a naturally occurring host-parasite system, with the mussel Anodonta anatina infected by the digenean trematode Echinoparyphium recurvatum, and the snail Viviparus viviparus infected by both E. recurvatum and Echinostoma sp. By analysing the impact of temporal parasite dispersal, host species and size, and the impact of coinfection (moving from broader environmental factors to within-host dynamics), we show that neither assumption holds true, but at different ecological scales. The assumption that closely related parasites can be functionally grouped is challenged when considering dispersal to the host (i.e. larger scales), while the assumption that the same factors will drive infection dynamics for a single parasite across multiple host species is challenged when considering within-host interspecific competition (i.e. smaller scales). Our results demonstrate that host identity, parasite identity and ecological scale require simultaneous consideration in studies of parasite community composition and transmission.

The accuracy limit of chemical shift predictions for species in aqueous solution.

Interpreting NMR experiments benefits from first-principles predictions of chemical shifts. Reaching the accuracy limit of theory is relevant for unambiguous structural analysis and dissecting theoretical approximations. Since accurate chemical shift measurements are based on using internal reference compounds such as trimethylsilylpropanesulfonate (DSS), a detailed comparison of experimental with theoretical data requires simultaneous consideration of both target and reference species ensembles in the same solvent environment. Here we show that ab initio molecular dynamics simulations to generate liquid-state ensembles of target and reference compounds, including explicitly their short-range solvation environments and combined with quantum-mechanical solvation models, allows for predicting highly accurate 1H (∼0.1-0.5 ppm) and aliphatic 13C (∼1.5 ppm) chemical shifts for aqueous solutions of the model compounds trimethylamine N-oxide (TMAO) and N-methylacetamide (NMA), referenced to DSS without any system-specific adjustments. This encompasses the two peptide bond conformations of NMA identified by NMR. The results are used to derive a general-purpose guideline set for predictive NMR chemical shift calculations of NMA in the liquid state and to identify artifacts of force field models. Accurate predictions are only obtained if a sufficient number of explicit water molecules is included in the quantum-mechanical calculations, disproving a purely electrostatic model of the solvent effect on chemical shifts.

Simultaneous consideration of the accident and terror risks for hazardous materials transportation

Simultaneous consideration of the accident and terror risks for hazardous materials transportation

Simultaneous Consideration of System Design and Post-Production Support Network Decisions in the Context of a Performance-Based Contract

Simultaneous Consideration of System Design and Post-Production Support Network Decisions in the Context of a Performance-Based Contract

Optimum design of planetary gear set using multi-objective optimization considering mass, power loss and transmission error

In this study, a multi-objective optimal design of a planetary gear set was performed considering mass, power loss, and transmission error. In particular, the results were analyzed from a novel perspective. The implemented multi-objective optimal design methodology minimizes the mass, power loss, and vibration with respect to seven macro-geometry variables. The peak-to-peak static transmission error (PPSTE) of each gear pair corresponds to the excitation source of the planetary gear set, and reducing the PPSTE helps minimize vibration. Thus, the PPSTE was calculated using the time-varying mesh stiffness obtained through an analytical model. The non-dominated sorting genetic algorithm-III (NSGA-III) was implemented as a multi-objective optimization algorithm, and the criteria importance through the inter-criteria correlation method was implemented for quantitative comparison purposes regarding the combination of objective functions. The results showed that obtaining an optimal macro-geometry design for a planetary gear set exhibiting good performance required simultaneous consideration of the mass, power loss, and vibration. Furthermore, the results confirmed that a versatile optimization algorithm, such as NSGA-III, should be used to capture the discontinuous solution distribution owing to various constraints of the planetary gear set.

Bring more than green? The impact of green human resource management on hospitality employees' organizational citizenship behaviors

AbstractHotels are implementing green human resource management to achieve sustainable development in response to increasingly stringent environmental policies and to meet stakeholder expectations. This study aims to elucidate the impact of green human resource management (GHRM) on organizational citizenship behaviors (OCBs) implemented by hospitality employees in organizational, interpersonal, and customer contexts, respectively. Specifically, a mediation model with dual moderation from employees' personality, perceptual and cognitive perspectives simultaneously is proposed, which supported by organizational support theory, social identity theory and social cognitive theory. Structural equation modeling is used to evaluate 440 data collected from the Chinese hospitality industry based on conditional sampling method. This paper finds that GHRM can promote OCBs towards the organization and customers and perceived CSR has a mediating effect. Employees' environmental value positively moderates the promotion of perceived CSR through GHRM implementation, and the proactive personality positively moderates the relationship between perceived CSR and OCBs towards the organization and customers. The original value of the study lies in the simultaneous consideration of the three aspects of employees' cognition, perception, and personality, expanding the research perspective of GHRM and discovering the benefits beyond green that this green management model can bring. For hotel managers, this study helps them understand that GHRM brings hotels more than just green. Hotels should focus on matching potential employees' attributes with the organizational culture during recruitment and on improving employees' environmental value and proactive personality level during their subsequent training.

Modeling barriers to adoption of digitization in supply chains using FTOPSIS and its impact on sustainability TBL

PurposeThis article aims at - exploring and prioritizing the barriers to adoption of digitization in supply chains (SCs), categorizing them into sustainability triple bottom line (STBL) based upon their direct impact and suggesting digital technologies to address each barrier.Design/methodology/approachA five-phase methodology is used which consists of an exploration of 44 barriers to the adoption of digitization in SCs, analysis of 44 barriers for mean, standard deviation and Cronbach alpha based on questionnaire-based feedback of 25 experts, extraction of 10 most significant barriers through 05 experts, followed by categorization of the barriers into STBL referring to their direct impact on STBL, prioritization of ten barriers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution and recommendation of digital technologies to address each barrier.FindingsWhile all the barriers considered in this study significantly impede the adoption of digitization in SCs, lack of top management commitment (B1) is found to be most crucial while lack of culture toward use of information and communication technology required for digitization (B3) has minimum impact. Large investment in digital infrastructure (B6), difficulty in integration of cyber physical systems (CPSs) on varied platforms (B8) and lack of experts having knowledge of digital technologies (B2) are equally important barriers requiring more attention while adopting digitization in SCs.Research limitations/implicationsThis study is mainly based on feedback from 25 seasoned experts; a wider cross section of experts will give more insight.Practical implicationsThe outcomes are very significant for organizations looking to adopt digitization in their SCs. Simultaneous consideration to all the barriers becomes impractical hence prioritization of same will be useful for the SC managers to benchmark their preparedness and decide strategies for the adoption of digitization with due consideration toward the impact of barriers on STBL. The digital technologies recommended will further aid in planning the digital strategies to address each barrier.Originality/valueA unique approach to explore, analyze, prioritize and categorize the barriers to adoption of digitization in SCs is used to provide a deeper understanding of factors deterring the same. It implies that a supportive top management along with systematic allocation of finances plays a crucial role. The importance of availability of digital experts for integrating CPSs on a single platform is also highlighted. The digital technologies recommended will further assist the organizations toward adoption of digitization in SCs with due consideration to STBL.

MATHEMATICAL MODEL AND OPTIMIZATION ALGORITHM ACCORDING TO THE CRITERIA OF MINIMUM CONTACT STRESS FOR GEARS WITH CONVEX-CONCAVE CONTACT

Reducing the mass and dimensions of gears is an actual task of modern mechanical engineering. One of the perspective ways to solve it is the use of gearing with a convex-concave contact of the teeth. The study is devoted to the development of mathematical model and algorithm for the optimal design of cylindrical gears with convex-concave contact of the working surfaces. Optimality criteria: minimum contact stresses taking into account design, geometrical and technological constraints. An objective function is constructed for the case of minimizing contact stresses: contact stresses σH in the mesh must take the minimum possible value when all constraints are met. Variables planning are defined. These are pressure angle at the pole αС, the curvature radius at the upper part of contact path rkh, and the curvature radius at the lower part of contact path rkd. A method for solving the problem of optimal design is chosen – probing the space of design parameters was chosen from all the variety. The points of the LPτ-sequence are used as test points. The algorithm for optimal design of gear has been developed. They take into account the constructive, technical and technological features. They also allow to reduce the error of the calculations due to the error-control of the calculations. The main stages of the algorithm are as follows: setting input data (numerical constraints on design variables, gear parameters and its load); generation of LPτ-sequence for variables planning with simultaneous consideration of their numerical constraints; checking of functional constraints; additional verification calculations (if necessary) of the gearing contact strength; formation of an array of possible solutions; searching of the best solution variant (the test point corresponding to the minimum value of the objective function) by sorting the array. Keywords: gear, convex-concave contact, optimization, objective function, variables planning, algorithm

CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

Modified constitutive models for Inconel 718 considering current density and temperature in electrically assisted forming process

Electrically Assisted Forming (EAF) technology has obvious advantages in material forming. To develop an effective constitutive model considering electrical effects, room temperature and electrically assisted quasi-static uniaxial tensile tests were conducted using ultrathin nickel-based superalloy plates with a thickness of 0.25 mm. The research focused on the two most widely recognized effects: the Joule thermal and the electric athermal effects. The mechanism of current action can be divided into two scenarios: one considering the Joule thermal effect only, and the other considering both effects simultaneously. Two basic constitutive models, namely the Modified-Hollomon model and the Johnson-Cook (J-C) model, were selected to be optimized through the classification of two different situations, and four optimized constitutive models were proposed. It was found that the J-C model with simultaneous consideration of the Joule thermal effect and electric athermal effect had the best prediction effect by comparing the results of these four models. Finally, the accuracy of the optimization model was verified by finite element simulation of the electrically assisted stretching optimization model. The results show that the constitutive model can effectively predict the temperature effect caused by the Joule heat effect and the athermal effect of current on the material.

A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing

Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.