Optimal Location Selection Research Articles

Towards advanced sustainable criteria for choosing the best site for collecting solar energy in cities using multi-criteria GIS

Solar energy has become a prominent and crucial source of clean energy due to the increasing global demand for electricity. There is a prevailing global trend towards the construction of solar farms as a means of energy generation. This study aims to assess multiple criteria encompassing urban, environmental, and social aspects in order to ascertain the optimal site for constructing solar farms. The evaluation primarily focuses on characteristics such as topography, solar radiation, accessibility, land use, and proximity to roads and power stations. In order to acquire the necessary spatial fit model, the data was evaluated utilizing a Geographic Information System (GIS) program, and the criteria were subsequently consolidated into an integrated geographic information system. A comprehensive review of the existing body of literature reveals that environmental factors, specifically solar radiation and aspect, play a crucial role in determining the suitable places for the establishment of solar energy collection projects (Merrouni et al. in Energy Procedia 49:2270-2279, 2013; McKinney in J Student Res Environ Sci Appalac 4:1-14, 2014). Furthermore, other analysis factors such as proximity to built-up regions, closeness to power lines, and proximity to roadways are taken into account (Hott et al. in GIS-based Spatial Analysis For LargeScale Solar Power And Transmission Line Issues: Case Study of Wyoming, U.S. In: Proceedings of the 41st American Solar Energy Society Meeting, 2012; Effat in Int J Adv Remote Sens GIS 2:205-220, 2013). These criteria have an impact on the cost of solar farms. The objective of this study was to assess several aspects influencing the selection of an optimal location for a solar energy farm, taking into consideration the aforementioned criteria. The study was carried out in New Aswan city, utilizing data obtained from the Urban Planning Authority within the Ministry of Housing and Urban Development, as well as the New Aswan City AuthorityI generated cartographic representations and compiled quantitative data, which informed our selection of places that met the established criteria through the utilization of a Geographic Information System (GIS) software. Upon the conclusion of the study, numerous locations that satisfied the established criteria were selected. The present study focuses on regions with high capacity, and the findings are depicted in the form of spatial and objective maps. One of the primary benefits associated with the utilization of this methodology lies in its versatility, as it can be implemented across several domains. Furthermore, a straightforward adjustment of the criteria facilitates its application in the selection of optimal locations for wind farms.

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented to display the optimal network of sensors and how the uncertainty in the inferred seismic moment tensor contracts. The scenario of full three-dimensional velocity models or unknown earthquake source locations is treated as nuisance uncertainty, contributing to the overall uncertainty without being the focus of the inversion. This is addressed using a consensus approach over a set of realizations of the nuisance parameter. We analyzed the resulting network of stations for the moment tensor inversion under model misspecification, which reflects realistic data-generating processes.

Application of Gradient Descent Optimization in Optimal Location Selection for Fire Stations in Newly Built Cities

Application of Gradient Descent Optimization in Optimal Location Selection for Fire Stations in Newly Built Cities

Selection of Optimal Technologies for Production of Goods: Space-System Approach

Despite the presence of a huge amount of research on various aspects related to the rationale for selection of optimal technologies, spatial aspects have traditionally remained unattended by scientists. Justification for selection of optimal technologies for the production and transportation of good and justification for selection of optimal location and capacity of the corresponding industries are interrelated tasks of the complex problem of optimizing the spatial and technological development of an economic sector within the relevant space. At first, based on the criterion of the availability of factors of production of the corresponding good, attractive production sites are identified and for each of them selection of place-based optimal production technology is justified. The developed systematic approach involves the stage of identifying locally optimal places and technologies of production and transportation for each sales market option according to the criterion of the minimum total costs of producing a good in the volume of demand of the corresponding sales market option and the costs of transporting this good to potential sales markets that form the evaluated market option sales in the amount of their demand. At the final stage, options for potential systemically optimal places and technologies, which are formed from locally optimal places and technologies, are compared. The option of potential systemically optimal locations and corresponding production and transportation technologies with minimal total costs for production and transportation is the best.

Long-range gene editing of LMNA

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Hartstichting (Dutch Heart Foundation) Background Mutations in the LMNA gene commonly affect the cardiac muscle tissue, which leads to the development of a severe dilated cardiomyopathy with life-threatening arrhythmias. The lack of response to conventional heart failure therapy in these patients indicates the urgent need for new therapies. Gene editing has the potential to become a curative treatment for these patients by correcting the genetic cause of the disease. Prime editing is a promising new gene editing technology, which uses a prime editing guide RNA (pegRNA) to target a specific genetic locus and creates edits using a reverse-transcriptase enzyme linked to a Cas9-nickase. Purpose Conventional prime-editing technology targets one mutation per pegRNA. However, with more than 600 disease-causing mutations described in LMNA, the feasibility of applying this technology to each mutation is limited. Therefore, we aim to target not a single, but a set of mutations with one single pegRNA. Methods The pegRNAs are optimised by selecting the optimal target regions, improving their stability and 3D structure. The in vitro editing efficiency is assessed using the fluoPEER reporter plasmid, transfected into HEK293T cells. Using flow cytometry, expression of mCherry fluorescent protein is detected following successful editing of the target mutation. Results Flow-cytometry analysis of treated cells revealed an editing efficiency of 12% with a long-range pegRNA targeting a full exon, compared to 15% with a conventional short pegRNA targeting the same mutation <10bp downstream of the Cas9 nick site. This editing efficiency using the same long-range pegRNA was retained at 9% when editing at a distant position >100bp downstream of the nick site. To increase the editing efficiency, we improved the stability of the pegRNAs by the addition of different 3’ structural motifs. This resulted in an increased editing efficiencies of 15% and 17% for nearby and distant mutations, respectively. Conclusion We show that it is possible to edit different mutations within a range of up to 120 bp with a single pegRNA. By optimisations of the pegRNA 3D structure and optimal selection of target locations we aim to further improve the editing efficiency of this system. This work could eventually provide a prime editing therapy able to cure disease in a subset of patients carrying different mutations in the same genetic region. For LMNA-associated diseases this would substantially reduce the number of pegRNAs that have to be developed to cover all mutations, greatly improving the feasibility of prime editing therapy.

Optimal Substation Placement: A Paradigm for Advancing Electrical Grid Sustainability

The critical importance of optimal substation placement intensifies as the world experiences sustained economic expansion and firmly pursues the decarbonization process. This paper develops an integrative approach to determining the optimal location for a new substation considering the evolving power framework. To this end, a projected 2% national load growth is taken into account, in accordance with the foresight of the Romanian authorities, emphasizing the need to place new substations to enhance the grid’s sustainability. Leveraging the Weibull distribution, a dataset is generated to simulate the anticipated load increase, starting from real power datasets in Romania. Two algorithms are designed for optimal substation positioning: geometric (center-of-gravity-based) and machine learning (K-means clustering). The primary comparison criterion is the minimization of power losses during energy distribution. The results reveal the machine learning approach (i.e., K-means clustering) as the superior alternative, attaining a 60% success rate in minimizing the power losses. However, acknowledging computational constraints, the concurrent utilization of both algorithms is advocated for optimal substation location selection, indicating a potential improvement in outcomes. This study emphasizes the critical need for advanced algorithms, stressing their role in mitigating power losses and optimizing energy utilization in response to evolving load patterns and sustainability goals.

Efficient fuzzy multi-criteria decision-making for optimal college location selection: A comparative study of min–max fuzzy TOPSIS approach

Ranking fuzzy values is often essential in fuzzy multi-criteria decision-making (FMCDM) systems since it is established on the comparison of hazy data pairs, even though the comparison needs temporal complexity. Then to provide a fuzzy MCDM approach under min–max operations for avoiding the fuzzy comparison. By using fuzzy TOPSIS’s min and max operations, the suggested method produces two positive and negative ideal solutions of each alternative. Here, before discussing the effectiveness of the matrices between the alternatives, first to analyze the weighted strength and weakness indices from both positive and negative indices. The results of the method have been described using a straightforward case study of a proper location for creating a college. The best location can be identified using this method based on various criteria. The outcomes are also compared with the suggested method for its effectiveness. Additionally, this problem analyzes the fuzzy TOPSIS approach in an interval-valued number for greater efficiency.

A multi-criteria framework for electric vehicle charging location selection using double hierarchy preferences and unknown weights

The adverse effects of traditional vehicles on the environment increase the demand for clean vehicles, such as electric vehicles (EVs). The correct positioning of the charging points for such vehicles certainly promotes the acceptance and spread of EVs. Indeed, selecting optimal locations for electric vehicle charging stations (EVCS) is crucial for shaping a sustainable future. This study introduces an integrated methodology under a double hierarchy linguistic context with a criteria importance through an inter-criteria correlation (CRITIC) technique for experts' reliability determination, an attitudinal Cronbach's method for criteria weight estimation, and a novel multi-criteria technique considering the compromise ranking of alternatives from distance to ideal solution (CRADIS) formulation for optimal EVCS location selection. Based on the results, criteria such as service capability, ecological impact, land cost, and traffic density are the most crucial, with Manapparai, India, as the optimal location for a new EVCS construction. Further, a crucial finding is that the social dimension is substantial compared to the economy and environment dimensions for EVCS location selection. The novelty of the paper is that (i) uncertainty and expression of choices in natural language form for locations for EVCS are modeled effectively using a double hierarchy structure, (ii) experts' weights are obtained methodically by considering hesitation and interactions among experts, (iii) interdependencies among criteria and importance of experts are considered during criteria weight determination, and (iv) locations are ranked by not only considering criteria type but also resembles closely to the human-centric decision process. A detailed sensitivity analysis is further conducted to prove the proposed approach's effectiveness and stability. In the context of sustainable transportation, the work could contribute to the relevant literature through a powerful decision-making tool.

Optimal location selection of a casual-dining restaurant using a multi-criteria decision-making (MCDM) approach

Optimal location selection of a casual-dining restaurant using a multi-criteria decision-making (MCDM) approach

GIS-based approach for optimizing biowaste collection services in rural small sized municipalities

Directive (EU) 2018/851 of the European Parliament and of the Council establishes the obligation for Member States to ensure, by 31 December 2023, that biowaste is separated and recycled at source or collected selectively. As this is a responsibility of the municipalities, studies to evaluate the best solutions and to ensure the rationality of the investments to be made to fulfill these objectives is of particular importance. Bearing in mind the new municipality’s responsibility, this article aims to demonstrate the applicability of GIS in supporting planning and decision of a new service provided to the population. The solutions found must be economically sustainable and feasible for small size municipalities, especially for those with no formal biowaste management system implemented and with reduced resources. It is therefore proposed an approach to analyze vehicle route optimization for the selective collection and transportation of biowaste in markedly rural municipalities with a strong forestry component, equating solutions that allow their recovery, as well as the promotion of the management of forest spaces. The Network Analyst extension of the ArcGIS® software was used to answer questions related to travel time route optimization, optimal location selection and definition of service areas. The analysis performed allowed to identify and evaluate the main factors that minimize the costs associated with the undifferentiated and selective collection and transportation of biowaste in the study area (Oleiros-Amieira parish, Portugal). It was determined the need to distribute the service through 3 different routes, as well as optimizing the location of a biomass plant fed by forest remnants deposited in community containers spread across the study area. Developing simpler and cost-effective instruments for reducing waste transport costs for small forest owners and municipalities, such as the proposed one, is essential to ensure a successful implementation of Directive (EU) 2018/851.

Strategic Planning for Equitable RWIS Implementation: A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems (RWIS) station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables. Previous research primarily centered on single-variable analysis focusing on road surface temperature (RST). The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework. This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty. To demonstrate the effectiveness of this refinement, the authors applied the framework to Maine’s existing RWIS network, conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm. The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations. A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations. The location solutions generated have been adopted by MaineDOT for future implementations, attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

The impacts of global warming on animals and their response to climate warming

This paper reviews on animals’ thermoregulations under climate change. There are aspects of animals’ thermoregulation that encompass behavior, physiology, and life history. Among these, behavioral thermoregulation is mostly studied and reported in the early years, which is usually about the strategic selection of optimal timing and locations, facilitating efficient evaporative and conduction heat dissipation, modifying their dietary preferences, and increasing shuttle behavior. The field of physiology involves hormonal regulation, such as secreting more glucocorticoids to achieve better balance control, changing body temperature to be more efficient in heat loss, energy allocation (e.g., metabolic rate) to select optimal strategy between growth and reproduction, and oxidative stress injury. In parallel, life history traits encompass rising growth rate, shrinking lifespan, changing body posture, and declining survival rate. Global warming is the main feature of climate change, with both of the average temperature rising and frequently heat waves occurring being the major characteristics simultaneously. Consequently, there come two scientific questions of this review: (1) What are the impacts of global warming on animals?(2) How do animals respond to climate change?

Addressing last-mile delivery challenges by using euclidean distance-based aggregation within spherical Fuzzy group decision-making

Optimal location selection plays a crucial role in enhancing logistic performance and addressing inefficiencies in last-mile delivery. Parcel lockers have emerged as an efficient solution in this regard. In the context of optimizing last-mile logistics in Dublin, Ireland, this research strives to identify the ideal locations for parcel lockers. It does so by pioneering the application of a unique decision-making model. To navigate the inherent uncertainties in decision-makers' preferences, the study harnesses the power of the Analytic Hierarchy Process (AHP) in Spherical Fuzzy environment with the Euclidean Distance-Based Aggregation Method (EDBAM) for the selection of parcel locker locations. The harmonious integration of these methodologies serves to mitigate the deterministic shortcomings typically associated with the AHP approach, presenting an innovative and robust solution for enhancing last-mile delivery efficiency. The outcomes of this research will provide valuable insights to policymakers in making informed strategic decisions regarding the selection of parcel locker locations in Dublin City.

The Effect of Economic and Infrastructure Factors on the Formation of Electric Vehicle Supply Chain and Optimal Location Selection: Korea-US FDI

Purpose - This study aims to present the changes and directions in the automotive supply chain in the face of changes in the global supply chain caused by external factors and the integration of industries resulting from internal factors. Assuming FDI in the Korean automotive industry in the US, this study analyzed the influential factors over the long term and derived the optimal location.
 Design/methodology - For this analysis, the characteristics and current status of the automotive industry are presented. Additionally, the study emphasizes the necessity and direction of change. The factors influencing Korea-US FDI in the automotive industry and the electric vehicle industry were analyzed through panel analysis. The optimal location from the perspective of distribution costs was selected using linear programming under the assumption that local demand will be replaced by local production in the future.
 Findings - This study found that the electric vehicle supply chain will change with the characteristics of the electric and electronic industries rather than with the traditional automotive industry. Additionally, in deriving the optimal location, the study emphasized the proximity to the consumption market.
 Originality/value - The analysis method and conclusions of this study not only present the influential factors and direction of FDI in the automotive industry but also can be applied to other industries. Moreover, the study provides practical and policy implications for industries and governments considering FDI in the US.

Optimization technique for optimal location selection based on medical image watermarking on healthcare system

The optimization algorithms mimic the process of natural evolution. In watermarking, appropriate positions to insert the watermark is identified by the image that covers. These positions represent the populations of genetic algorithms. The major drawback in genetic algorithm are that it may get stuck-up at a local optimum while moving towards the best global solution and hence the result is poor when compared to other local optimization techniques. The proposed work based on Bandelet based biogeography firefly hybrid algorithms. The Number of pixels, Intensity of the pixel and contrast are considered for watermarking. The redundancy is reduced by Bandelet and used to determine the best location to embed the information into an image both locally and globally. Results of these techniques are compared based on coefficient correlation, index structural similarity, and noise ratio from peak signal.

Optimal location selection for a distributed hybrid renewable energy system in rural Western Australia: A data mining approach

The adverse effects of coal and gas energy production with the subsequent rapid increase in energy consumption emphasize the importance for Australia to adopt more renewable energy sources to counteract these dismissive contributions to climate change. This work presents a data mining approach for optimally selecting the best locations for installing a distributed hybrid renewable energy generation system for rural regions in Western Australia. The K-Means and K-Medoids clustering algorithms were used to divide the constructed dataset into clusters. In total, 69 locations were selected for the overall dataset, proceeding with the filtering process. The returned cluster data were graphically rendered on a Western Australia map for the region. The clustering algorithms were evaluated using the Dunn index, such that K-Means performed to a higher degree than K-Medoids, given our dataset's nature. After passing the generated clusters to HOMER software to generate the potential wind and solar energy output for each centroid, K-Medoids produced a set of locations that generated higher solar and wind energy on average. However, due to the reduced internal validation, K-Medoids might not be as valuable as K-Means, it does not cluster the data points very well, and within-cluster location energy requirements are not considered in our study.

An effective digital audio watermarking using a deep convolutional neural network with a search location optimization algorithm for improvement in Robustness and Imperceptibility

Watermarking is the advanced technology utilized to secure digital data by integrating ownership or copyright protection. Most of the traditional extracting processes in audio watermarking have some restrictions due to low reliability to various attacks. Hence, a deep learning-based audio watermarking system is proposed in this research to overcome the restriction in the traditional methods. The implication of the research relies on enhancing the performance of the watermarking system using the Discrete Wavelet Transform (DWT) and the optimized deep learning technique. The selection of optimal embedding location is the research contribution that is carried out by the deep convolutional neural network (DCNN). The hyperparameter tuning is performed by the so-called search location optimization, which minimizes the errors in the classifier. The experimental result reveals that the proposed digital audio watermarking system provides better robustness and performance in terms of Bit Error Rate (BER), Mean Square Error (MSE), and Signal-to-noise ratio. The BER, MSE, and SNR of the proposed audio watermarking model without the noise are 0.082, 0.099, and 45.363 respectively, which is found to be better performance than the existing watermarking models.

Site Selection for Sustainable Wind-Solar Hybrid Energy Harvesting Plant

The world’s energy landscape is undergoing rapid transformation as societies strive towards renewable and sustainable power sources in response to the need for energy security and climate change. Depletion of fossil fuels and increasing focus on environmental sustainability have been significant drivers in this global shift towards renewables. Among the promising solutions, solar-wind hybrid energy systems have gained attention, capitalizing on the complementary nature of solar and wind resources. However, selecting the optimal site for these hybrid plants is crucial for their successful implementation and efficiency. This research introduces a comprehensive methodology for site selection, seamlessly integrating the Delphi method and Analytic Hierarchy Process (AHP). The Delphi method harnesses expert consensus to distil essential criteria, while AHP offers a structured framework to evaluate and prioritize these criteria. The results showed successful utilization of the AHP and Delphi techniques for evaluating the optimal location selection among different cities for solar-wind hybrid energy harnessing in the northwest region of Pakistan. The key factors for selection of optimal location were identified through expert feedback and analysis. Among the selected key main criteria, resource availability and infrastructure and socio-economic factors emerged as the top-ranked criteria in this study. The results of this study hold significant relevance and should be duly considered for solar-wind power project site selection, while also offering opportunities for further enhancements in the future.

PPOLQ: Privacy-Preserving Optimal Location Query With Multiple-Condition Filter in Outsourced Environments

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.

Theoretical Study and Systematic Design of Multiport Wire Antenna

This work presents a systematic study of multi-port wire antennas (MPWA) as a step toward reconfigurable radiation apertures, an essential component in modern wireless systems. We predict the surface current distribution (SCD) of the multi-port structure and derive a closed-form relation for the radiation pattern of the aperture as a function of its input port excitations. Based on this, we present a design procedure for optimal selection of port locations and their respective input excitations with a desired radiation pattern in mind. Finally, we develop a computationally efficient method to derive the impedance matrix of the multi-port aperture all without the need for full-wave simulation. As an example, we use this method to design a 15-port mm-wave antenna. We demonstrate that for a selected aperture size and port locations and only by adjusting the phases and amplitudes of excitation, we can reconfigure the MPWA to synthesize a desired beam at any frequency within the 20-40GHz range. There is good agreement between results predicted by this method compared to those from EM-based simulations, enabling an efficient and scalable approach for designing multi-port radiators.