Multidimensional Problem Research Articles

Twelve principles to assess in clinical approach to the older person

Approaching an older person (>60yrs) is full of challenges for a clinician. One of the major challenges is to treat multiple diseases in a single individual unlike in middle aged. The second challenge is effective communication skills, among which Listening is the most important, and third challenge is that the presentations of diseases have atypical symptoms. The principles discussed here are how to make a clinical approach simple, more practical, less time consuming, problem oriented and to be part of solutions. The approach in older person differs in view of multidimensional problems for which multidisciplinary team is required. The approach also consists of five dimensions namely physical, mental, economic, social and spiritual in a given elderly patient. The main goals should be to improve Functional capacity and provide good Quality of life. The most important factor encountered in geriatric practice that the elderly tend to owe the symptoms to ageing. In view of so many diversities, this article provides basic principles which may assist the clinician to have a holistic approach.

Matheuristic fixed set search applied to the multidimensional knapsack problem and the knapsack problem with forfeit sets

In this paper, we present a solution method for the multidimensional knapsack problem (MKP) and the knapsack problem with forfeit sets (KPFS) using a population-based matheuristic approach. Specifically, the learning mechanism of the fixed set search (FSS) metaheuristic is combined with the use of integer programming for solving subproblems. This is achieved by introducing a new ground set of elements that can be used for both the MKP and the KPFS that aim to maximize the information provided by the fixed set. The method for creating fixed sets is also adjusted to enhance the diversity of generated solutions. Compared to state-of-the-art methods for the MKP and the KPFS, the proposed approach offers an implementation that can be easily extended to other variants of the knapsack problem. Computational experiments indicate that the matheuristic FSS is highly competitive to best-performing methods from the literature. The proposed approach is robust in the sense of having a good performance for a wide range of parameter values of the method.

Education and Technology: Construction of a Distance Education System Beyond the COVID-19 Crisis

The COVID-19 pandemic has revealed a multidimensional problem. Added to the health crisis was the lack of social interaction, which directly affected education. Therefore, a number of strategies were required to compensate for the lack of face-to-face learning, considering the fact that most institutions had regulations and practices of that kind. This experience has established the need to develop a systemic approach, in which social interaction mediated by technology is clearly promoted. This work seeks to establish the general lines of future distance education models, considering the adaptations and opportunities that derive from it.

An adaptive binary quantum-behaved particle swarm optimization algorithm for the multidimensional knapsack problem

The multidimensional knapsack problem (MKP) is a classical combinatorial optimization problem with wide real-life applications. Binary quantum-behaved particle swarm optimization (BQPSO) algorithm is a popular heuristic algorithm used in binary optimization. While BQPSO exhibits strong global search capabilities, it is still prone to local optima due to particle aggregation. To address this issue, an adaptive BQPSO (ABQPSO) algorithm is proposed to solve the MKP efficiently. A hybrid encoding population initialization scheme is employed, leveraging specific knowledge of MKP to increase population diversity and improve search efficiency. Furthermore, ABQPSO uses a mapping strategy that converts continuous values into discrete values based on the average position of particles. An adaptive repair operator considering two pseudo-utility ratios is introduced to enable particles to explore different feasible regions, which dynamically adjusts current pseudo-utility ratios based on changes in the global best solution. A local search method is applied to guide particles towards convergence to the optimum. A local sparseness degree measurement and a diversity mechanism are utilized to avoid local optima. To evaluate the effectiveness of ABQPSO, it is compared against ten state-of-the-art algorithms using 168 MKP benchmark instances of varying scales. Experimental results reveal that ABQPSO outperforms the comparison algorithms, especially for large-scale problems, demonstrating better solution accuracy.

Identification of High and Low Voltage Ride-Through Control Parameters for Electromechanical Transient Modeling of Photovoltaic Inverter

The electromechanical transient model of a photovoltaic (PV) inverter’s high and low voltage ride-through has complex operating circumstances and a large number of control parameters, which makes parameter adjustment difficult. Furthermore, it is frequently challenging to identify a single set of control parameters that can successfully handle a variety of operating conditions. The Improved Differential Evolution Particle Swarm Optimization (IDEPSO) algorithm is proposed in this paper to provide a control parameter identification technique for high and low voltage ride-through that addresses these problems. Taking a 320kW PV inverter of a certain company as the research object, based on the specified current control strategy of high and low voltage ride-though, the parameters to be identified were determined by analyzing the influence of model parameters. Secondly, To enhance the algorithm’s capability to solve multidimensional optimization problems, convergence speed, and global search ability, the Differential Evolution (DE) algorithm’s search mechanism is incorporated into the Particle Swarm Optimization (PSO) algorithm, along with a non-fixed gradient inertia weight strategy for the algorithm’s inertia weight and the elite retention idea for the cross factor. Then, the objective function of the IDEPSO algorithm was built based on the concept of minimum deviation between simulation data and various groups of test data, and the significance of various working conditions was distinguished by weight division to improve the robustness of identification parameters. Finally, the identification parameters are imported into the PSASP program type 2 photovoltaic power station model, and the interval division and deviation calculation of the test data and simulation data are carried out. It is confirmed that the identification parameters meet the standards of the maximum variation permitted in GB/T 32892-2016 and are appropriate for a variety of working scenarios.

The effect of supply- and demand-side subsidies on low-income renters’ housing outcomes: evidence from South Korea

In recent decades, there has been a global policy shift away from supply-side housing assistance towards demand-side cash subsidies. However, there has been insufficient empirical evidence on whether cash transfer has a critical advantage over the direct supply of subsidised housing in alleviating low-income renters’ immediate housing difficulties. This paper examines and compares the effects of supply- and demand-side housing subsidies on multidimensional housing problems among the lowest-income households in South Korea using longitudinal survey data. Our difference-in-differences analyses combined with propensity score matching demonstrate that public rental housing improves the recipients’ housing quality while it exacerbates housing cost burden and overcrowding. Meanwhile, the cash subsidy failed to enhance the recipients’ housing quality or lessen their housing cost burden, but it aggravated overcrowding. The study showed that different types of housing subsidies have different immediate housing outcomes, suggesting that the rationale for the ongoing global policy redirection towards demand-side subsidies needs to be reconsidered in the local context.

Sportsperson perceptions and involvement in reporting wildlife crimes

ABSTRACT The illegal killing of wildlife is a multi-dimensional problem. The detection and apprehension of violators is often reliant on citizen willingness to report violations to appropriate authorities. We conducted a statewide survey of sportspersons in Idaho to understand engagement with wildlife law enforcement, knowledge of violations, reporting of violations, and perceptions of the violation reporting process. Sportspersons who were more actively engaged in the outdoor community were more likely to have knowledge of wildlife crimes. Sportspersons reported that willingness to report known violations was influenced primarily by receiving education and information on the reporting process. Satisfaction with the reporting process varied but was highest when crimes were reported directly to wildlife officers. Our study provides insights that can increase public engagement with wildlife crime prevention programs, and guide enforcement decisions and actions when investigating wildlife crimes or apprehending violators.

Query-driven Qualitative Constraint Acquisition

Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. Recently, we introduced GEQCA-I, which stands for Generic Qualitative Constraint Acquisition, as a new active constraint acquisition method for learning qualitative constraints using qualitative queries. In this paper, we revise and extend GEQCA-I to GEQCA-II with a new type of query, universal query, for qualitative constraint acquisition, with a deeper query-driven acquisition algorithm. Our extended experimental evaluation shows the efficiency and usefulness of the concept of universal query in learning randomly-generated qualitative networks, including both temporal networks based on Allen’s algebra and spatial networks based on region connection calculus. We also show the effectiveness of GEQCA-II in learning the qualitative part of real scheduling problems.

Meet in the air: Distributed neighbor discovery in 3D networks with directional transceivers

We formulate and analyze a generic neighbor discovery problem in three-dimensional wireless networks with directional transceivers, where any pair of neighbor nodes in 3D Euclidean space need to steer their transceivers towards each other simultaneously to discover each other. This problem, termed as 3D directional neighbor discovery, arises in a variety of emerging networked systems such as flying ad hoc networks composed of drones and distributed space systems composed of spacecrafts. Compared to the omni-directional 2D neighbor discovery problem extensively investigated in the literature, the 3D directional neighbor discovery problem is intuitively more challenging. Motivated by this observation, we establish an algorithmic framework on the 3D directional neighbor discovery problem. We first mathematically formulate the problem and derive the worst-case discovery delay of any neighbor discovery algorithm. Guided by the performance limit, we then design distributed neighbor discovery algorithms achieving bounded and minimal worst-case discovery delay. We further demonstrate how our algorithmic framework can be generalized to solve generic multi-dimensional rendezvous problems. Extensive numerical analysis is then presented to empirically evaluate of our neighbor discovery algorithms.

SABO algorithm for optimum design of truss structures with multiple frequency constraints

In this study, a very recently developed intelligent algorithm called the self-adaptive bonomo optimizer (SABO) algorithm is implemented for the size and shape optimizations of the truss structures with the multiple frequency constraints. The bonomo optimizer (BO) algorithm is a recently developed metaheuristic optimization algorithm that imitates the social behavior and reproductive schemes of the bonobos and this algorithm is successfully applied to solve some challenging and highly nonlinear optimization problems. On the other hand, the BO algorithm suffers from the poor exploration and prematurely converges to the non-optimal solutions, especially when dealing with the multi-dimensional optimization problems. In order to overcome these shortcomings, an improved version of this algorithm, the so-called self-adaptive bonomo optimizer (SABO) algorithm, is utilized to optimize the truss structures subjected to the design constraints. The objective of this study is to minimize the weight of the truss structures with the multiple frequency constraints. Five well-known benchmark problems are considered to verify the robustness and reliability of the SABO algorithm. The design results obtained by the SABO algorithm are compared with those previously reported in the literature.

Relación entre la conciencia de madres y padres sobre el abuso sexual y las habilidades de los niños para decir «no»

IntroductionChild sexual abuse is a global and multidimensional social problem and causes devastating and permanent psychological, emotional, cognitive, behavioural, physical, sexual and interpersonal sequelae. This study examines the relationship between the ability to say «no» and parental awareness of sexual abuse in 4th grade primary school students. MethodsThe study was conducted between April 2022 and June 2022 in primary schools in the central district of a province in north-eastern Turkey. The sample consisted of 310 students enrolled in 4th grade and their parents. We collected the data through a personal information form, the ability to say «no» scale for children and the sexual abuse awareness scale for parents. ResultsThere was a weak positive correlation between the mean maternal scores of sexual abuse awareness and the mean scores of refusal and resistance in children (P˂.05), as well as a weak positive correlation between the mean paternal scores of sexual abuse awareness and the mean scores of refusal and resistance in children (P˂.05). ConclusionAs mothers’ and fathers’ awareness of sexual abuse myths and of teachings and actions to combat sexual abuse increased, the refusal of children also increased. Also, as fathers’ awareness of the signs of sexual abuse increased, children's refusal increased.

Multi-Compression Scale DNN Inference Acceleration based on Cloud-Edge-End Collaboration

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by model partitioning, which is particularly useful for intelligent scenarios that demand high accuracy and low latency. However, the dynamic nature of the edge environment and the diversity of end devices pose a significant challenge for DNN model partitioning strategies. Meanwhile, limited resources of the edge server make it difficult to manage resource allocation efficiently among multiple devices. In addition, most of the existing studies disregard the different service requirements of the DNN inference tasks, such as its high accuracy-sensitive or high latency-sensitive. To address these challenges, we propose a Multi-Compression Scale DNN Inference Acceleration (MCIA) based on cloud-edge-end collaboration. We model this problem as a mixed-integer multi-dimensional optimization problem, jointly optimizing the DNN model version choice, the partitioning choice, and the allocation of computational and bandwidth resources to maximize the tradeoff between inference accuracy and latency depending on the property of the tasks. Initially, we train multiple versions of DNN inference models with different compression scales in the cloud, and deploy them to end devices and edge server. Next, a deep reinforcement learning-based algorithm is developed for joint decision making of adaptive collaborative inference and resource allocation based on the current multi-compression scale models and the task property. Experimental results show that MCIA can adapt to heterogeneous devices and dynamic networks, and has superior performance compared with other methods.

Bi-level optimization configuration method for microgrids considering carbon trading and demand response

Under the background of the shortage of traditional energy and the increasingly serious environmental problems, this paper proposes a bi-level optimization configuration method for microgrids based on stepped carbon trading and price-based demand response. The stepped carbon trading mechanism is introduced into the planning model to clarify the impact of construction processes on land carbon emissions. Simultaneously, price-based demand response during system operation is introduced, guiding users to change the electricity consumption strategies to promote the consumption of renewable energy. Then, a bi-level optimal configuration framework is constructed. The upper level is optimized to allocate the capacity of the microgrid, with the lowest annual equivalent comprehensive cost as the objective function. The lower level is optimized for microgrid operation, with the minimum sum of the microgrid operation cost and carbon emission cost as the objective function. In view of the advantages of enhanced whale optimization algorithm (E-WOA) in solving multi-dimensional feature selection problems, E-WOA and CPLEX solver are combined to solve the bi-level optimization model for the first time. Simulation results show that after introducing a stepped carbon trading mechanism, the system carbon emissions decreased by 23.09%. Considering price-based demand response under the premise of introducing a stepped carbon trading mechanism, the total cost decreased by 3.57% and the carbon emissions decreased by 19.83%.

Evaluation of Three Common Green Building Materials Using ELECTRE Method

This research paper examines various options for replacing halogenated flame retardants with non-halogenated green construction material choices. Cellulosic construction materials like bamboo do not form compounds with conventional flame retardants. Due to a number of circumstances, including the minimal effectiveness of the flame-retardant activity, they may thus be an unnecessary component of the substrate and simply peel off. Utilizing an additive that comes into direct contact with at least one component of the "green building material" will provide long-lasting flame retardancy since the flame retardant will become a structurally integral element of the substrate. The response mechanisms of various treatment approaches are also discussed in this research to ensure that non-halogenated flame retardants effectively protect sustainable building materials like wood and bamboo. This paper demonstrates how various ELECTRE approaches may be used to choose effective tactics that take into account both technical and human behavioral barriers using a typical case study inside an organization. The impact of impedance from each system subsystem is investigated to assure the dependability of the selected method. When employee participation is a deciding element in the multidimensional strategic planning problem, a comparison of a range of compensated and non-compensatory models reveals that the models may produce less. Resistance strategies; However, ELECTRE shows very reasonable sensitivity. The alternatives are Gladstone, Port Augusta, Collie and Tarong. the evaluation parameters are Construction cost, Procurement cost, Waste reduction, GHG emissions, Cement replacement and Self-consolidating. The Final Result of Net superior value and rank the Gladstone is in fourth rank, The Port Augusta is in second rank, The Collie is in third rank, The Tarong is in first rank of the Net Inferior Value and Rank the Tarong is in fourth rank, The Gladstone is in first rank, The Port Augusta is in Second rank, The Collie is in third rank.

From Ambiguity to Clarity: Unraveling the Power of Similarity Measures in Multi-Polar Interval-Valued Intuitionistic Fuzzy Soft Sets

The utilization of similarity measures in the context of multi-polar interval-valued intuitionistic fuzzy soft sets (mPIVIFSS) is a significant theoretical approach that offers a valuable structure for tackling intricate situations that are characterized by imprecise information and uncertainty. Nevertheless, these issues frequently entail conditions that are not limited in scope and exhibit a considerable level of uncertainty in scenarios involving multiple dimensions, hence presenting substantial difficulties. The objective of this research study is to provide a comprehensive understanding of similarity measures in the context of mPIVIFSS. Additionally, we will examine several operations, such as complement, subset, union, intersection, AND, OR, and De-Morgan’s Laws, as they relate to mPIVIFSS. This study not only includes theoretical debates but also explores practical applications. These efforts contribute to the improvement of decision-making, pattern recognition, and data analysis in settings characterized by ambiguous and uncertain information, hence emphasizing the importance of similarity measures in efficiently addressing multi-dimensional problems.

Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory

The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.

Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems

Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods.

Traffic network analysis via multidimensional split variational inequality problem with multiple output sets

<abstract><p>In this study, we introduce and investigate a new class of split inverse problems, comprising a multidimensional parameter of evolution, which we call the multidimensional split variational inequality problem with multiple output sets. To demonstrate its applicability, we formulate the equilibrium flow of multidimensional traffic network models for an arbitrary number of locations. We define a multidimensional split Wardrop condition with multiple output sets and establish its equivalence with the formulated equilibrium flow of multidimensional traffic network models. We then establish the existence and uniqueness of equilibria for our proposed model. In addition, we propose a method for solving the introduced problem. We then validate our results using some numerical experiments.</p></abstract>

Políticas De Información Para Democratizar El Conocimiento: três casos en Argentina

This contribution is based on the premise that inequality in Latin America and the Caribbean is a multidimensional problem that further deepened existing gaps. Faced with this situation, the democratization of knowledge is a fundamental strategy to addressinequality, and its democratic and equitable access must be guaranteed by coherent and comprehensive State policies. It analyzes three programs and public policies that emerged in Argentina in the first decades of the 21st century, which guarantee access to and use of information and the democratization of knowledge: the Conectar Igualdad Program; the Open Access Digital Repositories law, and the Access to Public Information law. Finally, it reflects on the contributions that Library and Information Science professionals can make to strengthen these guidelines and effectively contribute to the construction of more inclusive, balanced and democratic societies.

Marginal Bayesian Posterior Inference using Recurrent Neural Networks with Application to Sequential Models.

In Bayesian data analysis, it is often important to evaluate quantiles of the posterior distribution of a parameter of interest (e.g., to form posterior intervals). In multi-dimensional problems, when non-conjugate priors are used, this is often difficult generally requiring either an analytic or sampling-based approximation, such as Markov chain Monte-Carlo (MCMC), Approximate Bayesian computation (ABC) or variational inference. We discuss a general approach that reframes this as a multi-task learning problem and uses recurrent deep neural networks (RNNs) to approximately evaluate posterior quantiles. As RNNs carry information along a sequence, this application is particularly useful in time-series. An advantage of this risk-minimization approach is that we do not need to sample from the posterior or calculate the likelihood. We illustrate the proposed approach in several examples.