Multidimensional Problem Research Articles

UAV-fleet management for extended NextG emergency support infrastructure with QoS and cost aware

The recent developments in Unmanned Aerial Vehicles (UAV)-assisted wireless communications demonstrated the valuable contributions UAVs could offer to support communication infrastructures under duress. However, due to the complicated and costly operation management, the use of UAVs was extremely limited to fixed pre-arranged deployment or short-term ad-hoc service support. Unfortunately, fixed deployments are not suitable for emergencies such as unexpected failures or overhead; a situation ad-hoc deployment would serve much better. The main obstacle facing ad-hoc deployment models is the increased cost of operation and complicated power management. This paper presents a reliable self and mission-aware multi-hop UAV operation management to support communication networks in case of infrastructure failure or overload for extended time and distances. The formalized optimization offers a solution to the operation management challenges for ad-hoc UAV deployment as a balanced multi-objective, multi-dimensional convex-optimization problem. The paper also provides the pre-flying numerical analysis and parameters to enable optimal mission-oriented extension coverage as a solution guiding the presented UAV operation management framework. The optimization problem is simulated for a 5G-sub 6 cellular network to assess the proposed framework’s efficiency and effectiveness in a coverage extension situation. Results showed that the proposed framework could facilitate extended flight time, overlay network support, and stable service provisioning.

Estimation of extreme multivariate expectiles with functional covariates

The present article is devoted to the semi-parametric estimation of multivariate expectiles for extreme levels. The considered multivariate risk measures also include the possible conditioning with respect to a functional covariate, belonging to an infinite-dimensional space. By using the first order optimality condition, we interpret these expectiles as solutions of a multidimensional nonlinear optimum problem. Then the inference is based on a minimization algorithm of gradient descent type, coupled with consistent kernel estimations of our key statistical quantities such as conditional quantiles, conditional tail index and conditional tail dependence functions. The method is valid for equivalently heavy-tailed marginals and under a multivariate regular variation condition on the underlying unknown random vector with arbitrary dependence structure. Our main result establishes the consistency in probability of the optimum approximated solution vectors with a speed rate. This allows us to estimate the global computational cost of the whole procedure according to the data sample size. The finite-sample performance of our methodology is provided via a numerical illustration of simulated datasets.

A New Integral Function Algorithm for Global Optimization and Its Application to the Data Clustering Problem

The filled function method is an approach to finding global minimum points of multidimensional unconstrained global optimization problems. The conventional parametric filled functions have computational weaknesses when they are employed in some benchmark optimization functions. This paper proposes a new integral function algorithm based on the auxiliary function approach. The proposed method can successfully be used to find the global minimum point of a function of several variables. Some testing global optimization problems have been used to show the ability of this recommended method. The integral function algorithm is then implemented to solve the center-based data clustering problem. The results show that the proposed algorithm can solve the problem successfully.

Rethinking Solutions: Utilizing Islamic Ethics to Tackle Nigeria's Multifaceted Challenges

Nigeria, a country endowed with abundant natural resources, is plagued with multidimensional challenges that have hindered its progress and development. The challenges include poverty, corruption, insecurity, ethnic and religious conflicts, unemployment, poor infrastructure, and economic instability, among others. This study examines the potential of Islamic ethics as a solution to the multidimensional problems in Nigeria. Using a qualitative research approach, the study explores the ethical principles of Islam and how they can be applied in Nigeria to address the challenges facing the country. The findings of this study could contribute to the development of policies and strategies for sustainable development in Nigeria, providing a new perspective on how to address the challenges facing the country.

International bureaucrats’ attitudes toward global climate adaptation

The 2015 Paris Agreement has fueled debates about how the international bureaucrats driving international organizations’ engagement with climate adaptation ought to address adaptation challenges. While previous research has predominantly focused on the structural constraints in adaptation governance, this paper develops a distinct argument about the cognitive frames through which international bureaucrats view climate risks. The evidence comes from a survey among bureaucrats in three organizations that have engaged with adaptation to different extents: United Nations Environment, the United Nations High Commissioner for Refugees, and the World Health Organization. The results suggest that the majority of the surveyed bureaucrats view climate risks as a multidimensional problem. The evidence indicates that bureaucrats are more likely to view climate risks through multiple than through single issue frames, the more certain they perceive the knowledge about climate impacts in their issue area to be. By way of conclusion, the paper sketches broader implications for adaptation and international bureaucracy research.

Modelling Well Location Between River and Second Contaminant Source in Riverbank Filtration Systems

Extracting water from pumping well near to river in riverbank filtration systems leads to the natural treatment for surface water. To increase the system efficiency, the well should be located at a distance from the river in which the well can produce high percentage of river water that satisfies the requirements of water quality. In case the well is located between river and a contaminant source, its location should be relocated to a suitable place between the two sources. In this article, an analytical solution is produced to specify the suitable distance of pumping well and polluted river in a riverbank filtration systems if there is a contaminant source to the present well. The analytical solution is obtained by using Green’s function. This approach is chosen because of its simplicity for solving multidimensional problems, and its flexibility to deal with arbitrary initial and boundary conditions. The model is compared with numerical one obtained by using MODFLOW software. The results indicate that the location obtained from proposed model is suitable for use to determine the well location. Additionally, the results show that increasing either the pumping rate or initial contaminant concentration leads to an increment to the distance between river well.

Devising a method to design supersonic nozzles of rocket engines by using numerical analysis methods

The object of research is supersonic nozzles of liquid-propellant rocket engines. The work considers the problem of the lack of an effective method for profiling the supersonic contour of the nozzle, which will generate maximum thrust. For its solution, a method is proposed, the essence of which is approximating the nozzle contour with a power-law polynomial and determining the values of its coefficients by solving a multidimensional minimization problem using numerical modeling methods. The expression for the axial component of the thrust with the opposite sign at the specified values of atmospheric pressure and radius at the nozzle section was chosen as the objective function in this paper. Using the proposed method, contours of optimal nozzles were obtained based on polynomials of powers 2, 3, and 4, which were compared with nozzles obtained by the generally accepted Rao method. The maximum value of the relative deviation modulus calculated during the comparison did not exceed 3 %, which allows us to assert the correctness of the obtained results. The existence of such a discrepancy is explained by the difference in the numerical modeling method used. In contrast to the method of characteristics common in similar problems, the method of finite volumes of the Godunov type was used in the work. This has made it possible to reduce the sensitivity of the calculation to initial and boundary conditions and make decisions regardless of the flow regime of combustion products. In addition, the use of the extended cells method for the integration of finite volumes at the boundary of the calculation domain significantly reduced the total time of solving the problem of profiling the contour of the optimal nozzle

Application of Yang homotopy perturbation transform approach for solving multi-dimensional diffusion problems with time-fractional derivatives

In this paper, we aim to present a powerful approach for the approximate results of multi-dimensional diffusion problems with time-fractional derivatives. The fractional order is considered in the view of the Caputo fractional derivative. In this analysis, we develop the idea of the Yang homotopy perturbation transform method (YHPTM), which is the combination of the Yang transform (YT) and the homotopy perturbation method (HPM). This robust scheme generates the solution in a series form that converges to the exact results after a few iterations. We show the graphical visuals in two-dimensional and three-dimensional to provide the accuracy of our developed scheme. Furthermore, we compute the graphical error to demonstrate the close-form analytical solution in the comparison of the exact solution. The obtained findings are promising and suitable for the solution of multi-dimensional diffusion problems with time-fractional derivatives. The main advantage is that our developed scheme does not require assumptions or restrictions on variables that ruin the actual problem. This scheme plays a significant role in finding the solution and overcoming the restriction of variables that may cause difficulty in modeling the problem.

Multidimensional high-temperature field measurement method for flame based on near infrared radiation spectrum of water vapor

The temperature distribution is an important parameter in the design and optimization of :various engine types, and this paper begins by analyzing the temperature sensitivities of different waveband combinations. Based on an evaluation of the effects of self-absorption phenomena on the temperature measurement accuracy, a multidimensional temperature measurement method based on the integrated ratio of the intensity of the water vapor broadband radiation spectra is proposed. To address the multidimensional temperature inversion problem, the practicality of a nonlinear reconstruction approach is evaluated, and a multiple linear iteration algorithm is proposed. In addition, validation experiments are performed in the form of methane/oxygen laminar flame temperature measurements in combination with parallel rotating beam spectroscopy, and the results show that the method exhibits temperature measurement resolution of 10 K. The method proposed and developed in this work provides a new experimental concept for high-temperature measurements and is expected to provide reliable multidimensional temperature data for combustion chambers and plumes in engine ground experiments.

Результати проектування надзвукового сопла з подвійним розширенням для рідинного ракетного двигуна першого ступеня методами обчислювального аналізу

The subject of this study is dual-bell supersonic nozzles of liquid rocket engines. The goal is to design a dual-bell supersonic nozzle that will provide maximum thrust for a liquid rocket engine in the first stage of a launch vehicle by solving an optimization problem using numerical simulation methods. The goal of the study is to choose the exit pressures of each part of the dual-bell nozzle based on the known flight trajectory of the launch vehicle, set and solve the problem of optimizing the dual-bell nozzle contour, and assess the impact of the use of a dual-bell nozzle on the efficiency of a liquid-propellant rocket engine. The methods used are: numerical methods for solving the hyperbolic system of unsteady equations of gas dynamics and multidimensional minimization problems. The following results were obtained. The formulation and solution of the optimization problem of the contour of a dual-bell supersonic nozzle, considering the design limitations in the form of a fixed length of the nozzle, is carried out. By analyzing the flight path of the first stage of the launch vehicle, a first approximation of the nozzle contour was obtained. For further calculations, both sections were approximated by parabolas, the coefficients of which, together with the lengths of the sections, formed a vector of optimized parameters. An expression for the axial component of thrust with the opposite sign was used as the objective function to solve the minimization problem. Because of solving the optimization problem, a dual-bell nozzle contour was designed to provide maximum thrust. An assessment of the effectiveness of his work was also conducted. The calculated value of the average along the trajectory of the specific impulse when using a dual-bell nozzle was higher by 1.6% than when using a standard nozzle. Conclusions. The scientific novelty of the obtained results is as follows: the design of the dual-bell nozzle contour was performed by solving the problem of multidimensional optimization, taking into account the design constraints, and using computational analysis methods

Advanced Stochastic Approaches for Fredholm Integral Equations

Integral equations are of high applicability in different areas of applied mathematics, physics, engineering, geophysics, electricity and magnetism, kinetic theory of gases, quantum mechanics, mathematical economics, and queuing theory. That is why it is reasonable to develop and study efficient and reliable approaches to solve integral equations. For multidimensional problems the existing biased stochastic algorithms based on evaluation of finite number of integrals will suer more from the effect of high dimensionality, because they are based on quadrature points. So we need advanced unbiased algorithms for solving the multidimensional problem which is developed in this paper. A new unbiased stochastic method for solving multidimensional Fredholm integral equations of second kind is proposed and analysed. We compared the newly proposed unbiased algorithm with the old unbiased stochastic algorithm for the one dimensional problem and multidimensional problem.

The Therapy of Anxiety Disorders Around 1900. Hermann Oppenheim (1858-1919) and his Early Innovations

As a teacher and textbook author in psychiatry, Oppenheim initially received high national as well as international scientific recognition. In 1892, with his monograph on traumatic neurosis, which he regarded as the result of organic or molecular changes, his views became increasingly controversial and were met with rejection from the specialists of his time. mainly from German colleagues, probably not least because of his Jewish origin. Less historical attention, however, has been paid to Oppenheim's examination of the phenomenon of anxiety, which at his time was still a poorly elaborated pathological disorder. In his work, Oppenheim considered anxiety disorders to be an etiologically multifactorial disease and a syndrome in their own right. Oppenheim not only oriented himself towards common treatment methods such as dietetics or psychoanalysis but also considered the patient with his multi-dimensional problems as an individual deserving respect. What was more, he also applied self-developed psychotherapeutic treatment methods that show similarities with cognitive behavioral therapy that is predominantly used for anxiety disorders today. Oppenheim's work on anxiety disorders can be considered as highly innovative for his time.

On the Best Fitness Function for the WSN Lifetime Maximization: A Solution Based on a Modified Salp Swarm Algorithm for Centralized Clustering and Routing

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs are often limited by their energy resources, making energy efficiency a critical concern in designing WSNs. Clustering and multi-hop routing are effective methods for maximizing the lifetime of WSNs. The energy efficient clustering and routing problem in WSNs can be formulated as a multi-dimensional knapsack problem (d-kp). This formulation can generalize all clustering and routing protocols in regard to energy efficiency. In this paper, we investigate the hardness of the energy-efficient clustering and routing problem in WSNs based on the d-kp formulation and introduce a modified Salp Swarm Algorithm (SSA) as a method to solve this problem. SSA is a metaheuristic algorithm inspired by the behavior of salps in the ocean. Moreover, we prove that it is impossible to design a polynomial time fitness function to maximize a long-term metric such as FND (time of first node death), which is a commonly used metric for network lifetime. Our results have implications for the design of energy-efficient WSNs and call for the development of more efficient algorithms that can handle the complexity of this problem. furthermore, we evaluate our proposed algorithm using simulation and compare it to existing algorithms. Our results show that using SSA with a carefully designed fitness function based on the theoretical findings and a unique one-to-one routing protocol to mitigate the energy hole problem, outperforms many of the existing algorithms in terms of energy efficiency and network lifetime.

Charging Type Selection for Electric Buses Using Interval-Valued Neutrosophic Decision Support Model

Using electricity to fuel buses has always been known as a green option for the public bus operations considering the recent regulations on environmental and climate concerns. Emission-free public transportation modes such as electric buses improve local air quality and reduce noise pollution and also contribute to achieving sustainability goals by developing a sustainable transportation system. Many projects have been carried out using different charging strategies for electric buses around the world. However, selecting the most appropriate charging strategy for the electric buses in the public transportation is a challenging and multidimensional decision-making problem for transportation management sector. Therefore, this article proposes a decision support model based on the interval-valued neutrosophic set which uses Shannon's entropy and mixed aggregation by comprehensive normalization technique to choose the most appropriate charging type for urban electric buses among alternatives such as opportunity, depot, inductive, and no shift. This article presents a real-life case study in Turkey to illustrate the applicability and advantages of the proposed model. Extensive sensitivity analysis and comparative analysis tests are conducted to show the robustness of the proposed methodology. The results show that depot charging is the most suitable charging strategy for Istanbul's bus operator system.

Pollution Effect of Used Engine Oil on Chemical Properties and some Heavy Metals of Sandy Soil

Used engine oil pollution is a serious multidimensional problem affecting all aspects of the environment leads to changes the physical, chemical and microbiological properties of the soil and this leads to the accumulation of nutrient and heavy metals in the soil and eventually transfers to plant. This research was carried out to study the effect of used engine oil pollution and incubation period on some chemical properties and heavy metals of the soil. Sandy soil contaminated with used engine oil levels of 0.0, 2.5, 5, 15, 30 and 60 ml. kg-. Soil samples were incubated for two and four weeks with the pollutant. The results showed that there was a significant effect of the two incubation periods and the levels of the pollutant in reducing the pH values in all samples. An effect of the incubation period and oil level factors in increasing the values of organic matter and cation exchange capacity factor was observed in the studied samples, and the effect was evident in the second incubation period. It was noted that there was a fluctuation in the concentrations of the studied heavy elements between significant and non-significant increases as a result of adding different levels of used engine oil.

The Role of Government in Reducing Stunting in Indonesia, Case Study: Optimization of BKKBN in Sukoharjo District

Indonesia is included in the list of countries with poor nutritional status, according to the World Health Organization (WHO). In 2019, the stunting rate in Indonesia was 27.6%. As a multidimensional problem, stunting requires cross-sectoral solutions. At the regional level, Indonesia has a Population and Family Planning Agency (BKKBN), which is specifically tasked with solving this problem. One area with a history of success in overcoming stunting in Indonesia is Sukoharjo Regency. This research examines the role of the government in Sukoharjo Regency, through the BKKBN, in making various efforts to reduce stunting rates. The research method is qualitative research with an empirical approach. Research data was collected through in-depth interviews, observation, and documentation, then analyzed using interactive analysis techniques. The research results found that the main steps taken were: deploying family cadres to socialize about gynecological health with prospective brides and grooms. The government utilizes technical and strategic regulations and carries out budgetary interventions to maximize the reduction in stunting rates.

构建乡城融合一体化多元主体扶贫体系的构想

With the continuous promotion of new urbanization, the multidimensional poverty problem of new urban residents is becoming increasingly prominent. Due to significant differences between the new citizen group and the old citizen group, farmer group, and migrant worker group, traditional poverty alleviation measures applicable to the old citizen, farmer group, and migrant worker group are inefficient when applied to the new citizen group. Therefore, it is necessary to explore new poverty alleviation measures that are suitable for the actual needs of new citizen groups. This measure emphasizes the integration of multiple resources through a flexible urbanization model, fully leveraging the roles of diverse entities such as cities, rural areas, governments, communities, and employers. It aims to build a poverty alleviation system that blocks the poverty of new urban groups through the construction of capacity systems, rights protection mechanisms, public service systems, and cost sharing mechanisms.

Experimental benchmark control problem for multi-axial real-time hybrid simulation

Advancing RTHS methods to readily handle multi-dimensional problems has great potential for enabling more advanced testing and synergistically using existing laboratory facilities that have the capacity for such experimentation. However, the high internal coupling between hydraulics actuators and the nonlinear kinematics escalates the complexity of actuator control and boundary condition tracking. To enable researchers in the RTHS community to develop and compare advanced control algorithms, this paper proposes a benchmark control problem for a multi-axial real-time hybrid simulation (maRTHS) and presents its definition and implementation on a steel frame excited by seismic loads at the base. The benchmark problem enables the development and validation of control techniques for tracking both translation and rotation degrees of freedom of a plant that consists of a steel frame, two hydraulic actuators, and a steel coupler with high stiffness that couples the axial displacements of the hydraulic actuators resulting in the required motion of the frame node. In this investigation, the different components of this benchmark were developed, tested, and a set of maRTHS were conducted to demonstrate its feasibility in order to provide a realistic virtual platform. To offer flexibility in the control design process, experimental data for identification purposes, finite element models for the reference structure, numerical, and physical substructure, and plant models with model uncertainties are provided. Also, a sample example of an RTHS design based on a linear quadratic Gaussian controller is included as part of a computational code package, which facilitates the exploration of the tradeoff between robustness and performance of tracking control designs. The goals of this benchmark are to: extend existing control or develop new control techniques; provide a computational tool for investigation of the challenging aspects of maRTHS; encourage a transition to multiple actuator RTHS scenarios; and make available a challenging problem for new researchers to investigate maRTHS approaches. We believe that this benchmark problem will encourage the advancing of the next-generation of controllers for more realistic RTHS methods.

Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model

Automotive businesses often delegate logistical tasks to third-party logistics (3PLs) service providers to acquire a competitive edge in the dynamic market. Nevertheless, selecting the most suitable third-party logistics (3PL) partner is a multifaceted undertaking that needs careful evaluation of several criteria and alternatives. This research aims to introduce an integrated grey Multiple Criteria Decision Making (MCDM) framework for automotive businesses to deal with the multidimensional 3PL selection decision problem. This framework incorporates an enhanced Preference Selection Index (PSI), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and Mixed Aggregation by Comprehensive Normalization Technique (MACONT). The LOPCOW-G and grey PSI (PSI-G) methods extract the criterion weights, whereas the MACONT-G method ranks the alternatives. The suggested framework's practicality is shown by conducting a case study about evaluating and selecting a third-party logistics (3PLs) provider. The findings indicate that the parameters of significant importance are “skilled workforce (0.0977),” “financial strength (0.0901),” and “IT-IS competence (0.0839).” Furthermore, TPL4 has been recognized as the most optimum option with a value of 0.4797. The MACONT-G model is as well compared against other grey MCDM techniques to assess the validity of the proposed model. The Pearson correlation coefficient between MACONT-G and the other models based on grey sets is 0.958, suggesting a significant and positive link. Furthermore, it is worth noting that a sensitivity analysis has been conducted to validate the accuracy and reliability of the created framework. In conclusion, this study has identified managerial and policy implications that might assist policymakers and executives in effectively evaluating 3PL providers.

The Learning Collaboratory: developing and evaluating public health students' skills while promoting community health.

Complex and continuous developments in health and healthcare require innovative changes in programs that educate public health scientists and professionals. Public health change agents need critical competencies to confront today and tomorrow's leading problems including leadership, communication, interprofessional practice, and systems thinking. Public Health training programs teach competencies through their applied field experience and culminating project, typically late in the program, and often implemented in isolation from peers and faculty. Objectives and skills do not always align closely with community-based program needs. Students pursuing a degree in science in public health need to deeply comprehend multi-dimensional and interconnected systemic problems and communicate with diverse stakeholders across disciplines to produce relevant community-engaged research. The University of Miami Public Health Learning Collaboratory (LC) was established to transform the learning experience of public health master's students by providing opportunities to develop necessary core skills for effective public health practice early in their training, while applying these skills to address real-world public health needs in the community. Spanning an average of 3 semesters, the LC promotes student involvement in collaborative and impactful capstone and thesis projects. Practice-based teaching and service learning are central approaches to teaching cross-cutting competencies of leadership, communication, problem solving, collaboration, and systems thinking in public health. Significant to the approach is the engagement of previous cohorts of senior students to teach back to junior students, further integrating concepts learned. Long term alumni feedback recognized strengths of the program, including its structure, teamwork & collaboration, critical thinking & problem solving, guidance, nurture & support, teaching back, and content & curriculum. Community partners agreed the LC prepared students to practice in the field of public health. The LC is a promising model for master's level public health education and community application, given the opportunities it provides to strengthen and integrate students' public health skills in a supportive environment, and enhance the transferability and sustainability of student and faculty's community public health work.