Final Location Research Articles

Internal damage evolution of C/SiC composites in air at 1650 °C studied by in-situ synchrotron X-ray imaging

Carbon fibre reinforced silicon carbide (C/SiC) ceramic matrix composites have attracted considerable attention due to their exceptional properties and extensive potential applications as high-temperature structural materials. However, due to their complex structure and manufacturing defects, C/SiC composites exhibit intricate mechanical behaviour under thermal-mechanical-oxidative coupling environments. To date, systematic studies on the internal damage evolution and failure mechanisms of C/SiC composites under high-temperature oxidative environments are lacking. In this study, a combination of synchrotron X-ray micro-computed tomography (SR-μCT) and in-situ experiments under thermal-mechanical-oxidative coupling environments at room temperature and 1650 °C in air was used to characterize the internal microstructures and damage evolution processes of C/SiC composites at different loading levels. Additionally, the 3D strain fields during in-situ loading were quantitatively analysed using the Digital Volume Correlation (DVC) method. The findings underscore the substantial impact of oxidative damage on the mechanical response of C/SiC composites, particularly concerning tensile properties and fracture modes. At room temperature, severe delamination, fibre bundle pull-out and interfacial debonding occurred internally. Whereas, under high-temperature atmospheric conditions, severe fibre oxidation reactions occurred at the specimen edges, resulting in rapid porosity escalation. Crack initiation from surface defects followed by rapid inward propagation is observed. Moreover, while the strain distribution remains relatively uniform until fracture, a pronounced concentration of strain is evident near the fracture zones at room temperature, with an even greater concentration observed at 1650 °C. Notably, the region of concentrated strain within the 3D deformation field corresponds closely to the final fracture location, as revealed by quantitative DVC analysis.

Simulation of the Full‐Process Dynamics of Floating Vehicles Driven by Flash Floods

AbstractFlash flooding has become more prominent under climate change, threatening people's life and property. Post‐event investigations of recent events emphasize the role of floating debris, such as vehicles, in exacerbating damage. Few modeling methods and tools have been developed to simulate the full‐process dynamics of floating debris driven by large‐scale flood waves in real world. In this work, a fully coupled model is developed for simulating the full‐process interactive movements of vehicles driven by flash flood hydrodynamics, from entrainment, transport to deposition. The proposed coupled modeling system consists of a finite volume shock‐capturing hydrodynamic model solving the 2D shallow water equations and a 3D discrete element method (DEM) model. The proposed two‐way coupling approach estimates the hydrostatic and hydrodynamic forces acting on solid objects using the water depth and velocity predicted by the hydrodynamic model; the resulting counter forces on the fluid flow are then considered by adding extra source terms in the hydrodynamic model. A multi‐sphere method is further embedded in the DEM model to better represent vehicle shapes. New calculation modules are further implemented to represent the vehicle entrainment, contact and stopping motions. The coupled model is applied to reproduce a flash flood event hit Boscastle in the UK in 2004. Over 100 vehicles were moved and carried downstream by the highly transient flood flow. The model well predicts the hydrodynamics, interactive transport process and the final locations of vehicles. The proposed coupled model provides a new tool for simulating large‐scale flash flooding processes, including debris dynamics.

Improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

A 3-D Near-Field Source Localization Approach Based on the Combination of a Phase Interferometer, the Centroid Algorithm and the Perpendicular Foot Algorithm.

In this study, several 3-dimensional (3-D) parameter estimation and localization algorithms for wireless near-field (NF) sources are proposed employing the uniform circular array (UCA) structure. In the single-base-station case, the algebraic relation is demonstrated between the azimuth angle under the far-field (FF) assumption and the actual NF source firstly. Secondly, two groups of antenna pairs are selected with distances less than half the wavelength, which are called short baselines in the interferometer method. The foregoing short-baseline method is qualified to localize an NF source. In addition, a long-baseline method is also proposed with further research. Two groups of antenna pairs with distances greater than half the wavelength are selected as two long baselines. In the multiple-base-stations case, another two novel algorithms are also proposed. The first one is the centroid algorithm, which is based on the centroid calculation of three estimated source locations. And the second one is the perpendicular foot algorithm, which takes the perpendicular foot within three estimated source locations as the final positioning location. Simulation results illustrate that the proposed algorithms can achieve higher localization accuracy than the conventional 3-D Root MUSIC method. Moreover, the long-baseline method performs better than the short-baseline method. And it is also shown that the proposed perpendicular foot algorithm shows better performance than the proposed centroid algorithm.

An INS/UWB fusion localization scheme for wireless sensor network

Abstract As the demand for indoor services relying on location information grows, achieving precise indoor positioning is becoming an urgent issue to address. The ultra-wide band (UWB) accuracy can reach centimeter level, but is susceptible to non-line-of-sight (NLOS) effects. The inertial navigation system (INS) is not affected by the environment, but the error will diverge over time. Therefore, this paper focuses on the UWB/INS combined positioning system, which can combine the advantages of both. We propose a loosely-coupled INS/UWB indoor localization scheme based on factor graph optimization (FGO). Firstly, an agglomerative hierarchical clustering based NLOS mitigation method is applied to preprocess the UWB raw measurement data. The position estimates under UWB are then obtained using an improved extended Kalman filter to mitigate NLOS errors and provide more accurate location data for combined navigation. Then, we build the IMU preintegration factor and the zero-bias factor. Finally, FGO is used to fuse the information to get the final location. Designed experiments demonstrate the superiority of the algorithm. The simulation and experimental results show that the proposed scheme outperforms the comparison algorithms.

A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding

How to acquire the exact center of a circular sample is an essential task in object recognition. Present algorithms suffer from the high time consumption and low precision. To tackle these issues, we propose a novel circle center location algorithm based on sample density and adaptive thresholding. After obtaining circular contours through image pre-processing, these contours were segmented using a grid method to obtain the required coordinates. Based on the principle of three points forming a circle, a data set containing a large number of samples with circle center coordinates was constructed. It was highly probable that these circle center samples would fall within the near neighborhood of the actual circle center coordinates. Subsequently, an adaptive bandwidth fast Gaussian kernel was introduced to address the issue of sample point weighting. The mean shift clustering algorithm was employed to compute the optimal solution for the density of candidate circle center sample data. The final optimal center location was obtained by an iteration algorithm. Experimental results demonstrate that in the presence of interference, the average positioning error of this circle center localization algorithm is 0.051 pixels. Its localization accuracy is 64.1% higher than the Hough transform and 86.4% higher than the circle fitting algorithm.

Corrosion fatigue cracking behaviors of Q690qE high‐strength bridge steel as a weld joint in simulated marine environment

AbstractCorrosion fatigue behavior and mechanism of the weld joint of Q690qE high‐strength bridge steel was investigated in a simulated marine environment. It reveals the sub‐critical heat‐affected zone (SCHAZ) and coarse‐grained heat‐affected zone (CGHAZ) are the most vulnerable sites to corrosion fatigue cracking. The CGHAZ of the Q690qE steel weld joint was prone to corrosion fatigue initiation mainly because of micro‐galvanic corrosion between CGHAZ and weld metal (WM). The corrosion fatigue failure in SCHAZ mainly resulted from local stress/strain concentration due to weld softening. The final fracture location was determined by the competition between these two effects.

Sending a Signal: Evaluating the Impact of Program and Geographic Preference Signaling on the Internal Medicine Residency Match.

Background For over a decade, the number of residency applications has surged, a trend known as "application inflation." COVID-19 further intensified this trend, leading the Association of American Medical Colleges (AAMC) to addressthe issue by introducing a supplemental application in the 2021-2022 cycle, allowing programs to identify applicants with a connection to their program or geographic region. For the 2022-2023 cycle, the number of program signals increased from five to seven. The impact of the supplemental application and the increase in signals on the likelihood of an applicant matching with a programhas yet to be evaluated. Methods This retrospective cohort study evaluated the impact of program signaling and geographic preference on the matching likelihood in our internal medicine residency program. Data from MyERAS® and the Supplemental Application for 640 applicants who applied to our large, urban, university-based program in the Southeastern United States during the 2020-2021 and 2022-2023 application cycles were included. Using univariate and multivariate analysis, we examined the correlation between program signal, geographic preference, and final match location. Results Applicants who sent a program signal had nearly three-foldhigher odds of matching with our program. Geographic preference was numerically but not statistically associated with higher odds of matching. Both signaling a preference for matching with a program in an urban environment and couples matching correlated with decreased odds of matching with our program. Geography was an important predictor of match location as residing in our AAMC geographic region, our four-state area, and our specific state had increased odds of matching with our program. Conclusions Signaling our program was associated with increased odds of matching with our program. Geographic preferences were less predictive of a match with our program; however, they did predict the likelihood of a match at a program within that region. Future studies are needed to ensure external validity.

Combining vehicle routing and bin packing problem for vehicle routing planning: A case study of a chemical factory

In the logistics distribution process, effective transportation management is required to ensure quality and timely delivery. In addition, transportation must be more economical and faster. The case study company is a chemical plant, an industrial company that produces products for hygiene and cleaning. The delivery manager plans the transportation routes based only on her experience because no decision-making aids are available. In addition, the manager considers the shipping order from first to last when arranging the products on the transportation vehicles. The goods for the final delivery location are placed at the back and bottom of the delivery truck. This cannot guarantee that the transportation routes set by the manager are suitable. Therefore, this study aims to plan delivery routes, arrange items in trucks, and reduce transportation costs for a case study of a chemical factory by using vehicle route problems and container filling problems. Then, the program is developed in Python using the Saving Algorithm, the 3D First Fit Decreasing Algorithm, and the 3D Best Fit Algorithm. The results show that the program method can reduce the use of transportation vehicles from 19 to 14, which is 26.32%; reduce the total distance from 5,499.8 kilometers to 3,406.92 kilometers, which is 38.05%; reduce the wages of transportation vehicles from 36,666 to 24,993 baht, which is 31.84%; can increase the average total weight per vehicle from 961.22 to 1,319.71 kilograms, which is 37.30%; and increase the average space utilization per vehicle from 37.93% to 51.47%, which is 13.54%. This method can reduce costs and increase efficiency for the company. Finally, the developed program also supports inexperienced operations managers in making decisions when planning transportation routes and loading goods, Thereby saving working time.

Giant planet formation in the Solar System

The formation history of Jupiter has been of interest due to its ability to shape the solar system’s history. Yet little attention has been paid to the formation and growth of Saturn and the other giant planets. Here we explore through N-body simulations the implications of the simplest disc and pebble accretion model with steady-state accretion and an assumed ring structure in the disc at 5 AU on the formation of the giant planets in the solar system. We conducted a statistical survey of different disc parameters and initial conditions of the protoplanetary disc to establish which combination best reproduces the present outer solar system. We examined the effect of the initial planetesimal disc mass, the number of planetesimals and their size-frequency distribution slope, pebble accretion prescription and sticking efficiency on the likelihood of forming gas giants and their orbital distribution. The results reveal that the accretion sticking efficiency is the most sensitive parameter to control the final masses and number of giant planets. We have been unable to replicate the formation of all three types of giant planets in the solar system in a single simulation. The probability distribution of the final location of the giant planets is approximately constant in logr, suggesting there is a slight preference for formation closer to the Sun but no preference for more massive planets to form closer. The eccentricity distribution has a higher mean for more massive planets indicating that systems with more massive planets are more violent. We compute the average formation time for proto-Jupiter to reach 10 Earth masses to be 〈tc,J〉=1.1±0.3 Myr and for proto-Saturn 〈tc,S〉=3.3±0.4 Myr, while for the ice giants this increases to 〈tc,I〉=4.9±0.1 Myr. The formation timescales of the cores of the gas giants are distinct at >95% confidence, suggesting that they formed sequentially.

Decreased resting-state brain function in older adults predicts enlarged representational momentum.

Representational momentum (RM) refers to the phenomenon in which an observer's judgment of the final location of a previously viewed moving target is often displaced forward in the direction of motion. This phenomenon is an adaptive mechanism that compensates for neural processing delays and is closely associated with visual cortex function. However, the impact of age-related decline of visual cortex function on the manifestations of RM remains unclear. The present study examined differences in the RM effect between older (N = 82) and younger adults (N = 74) using a cursor-positioning task. Additionally, resting-state functional magnetic resonance imaging was used to explore the potential neural substrates that underlie these differences, employing amplitude of low-frequency fluctuation (ALFF, reflecting the intensity of neural activity) and regional homogeneity (ReHo, reflecting the synchronization of neural activity) as indicators. Our findings indicate a significant increase in RM among older adults compared with younger adults. Neuroimaging data revealed a significant decrease in ALFF and ReHo within extensive regions of the visual cortex in older adults, validating age-related differences in this cortical area. More importantly, ALFF values in the bilateral visual area 3 and ReHo values in the bilateral visual area 2 in older adults exhibited a strong negative correlation with their RM effects. These results suggest that larger RM in older adults may be functional compensation for aging of the visual cortex. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Efficient Model for Waste Load and RouteOptimization

Urbanization frequently gives rise to substantial environmental issues, namely in waste management and water quality maintenance. Gross Pollutant Traps (GPTs) are essential in urban stormwater management as they effectively capture substantial pollutants before they enterthe centralwater bodies. Nevertheless, the irregular buildup of trash caused by fluctuating rainfall intensity hinders the effective transfer of garbage from GPTs to their ultimate disposal locations. This research presents a holistic approach toenhancing the efficiency of waste transportation by improving route and load planning. The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. We have created an optimization algorithm that usesthe forecast outcome from a prior research dataset. This algorithm is designed to efficiently plan the routes and loads for trucks responsible for transporting waste to its final disposal location. The optimization process consideredthe estimated amounts of garbage, the capacities of the vehicles, and the locations of the disposal sitesto reduce transportation expenses and save time. The system adaptively optimized routes using real-time data on the vehicle'sorigin and destination, ensuring effective allocation of resources and prompt garbage removal. Installingthis approach resulted in a substantial decrease in transportation expenses and enhanced compliance with waste pickup timetables. The integration of predictive modelingand route optimization is enhancing urban trash management. Accurate garbage quantity forecasts and optimized transportation logistics can enable municipalities to deploy resources more effectively, decrease operational costs, and improve environmental protection. We chose a subset of 7 days, equivalent to one week, from the projected dataset for our experiment.Subsequently, we conductednumerous trials involving various waste disposalfrequencies. The findings suggest that waste disposalevery four(4) days is the most advantageous approach. Still, itperforms similarlyto waste disposalevery three (3)days and has negligible environmental consequences. Hence, we select to execute the optimal solution for three(3) days, as it provides exceptional performancewhen consideringthe influence of natural pollution.

Spatial dispersion of post-feeding larvae of forensically important flies: Implications for medicolegal investigation

IntroductionCalliphoridae flies allow estimating the postmortem interval (PMI), as they are the first to arrive at a decomposing body. Its larvae feed on the carcass and then move away to pupate (post-feeding dispersal). Since the environment alters the physiology and behaviour of insects, the dispersal of larvae and the final location of pupae could be related to the place where the carcass is deposited. The aim of this work was to study the dispersal of post-feeding larvae in relation to environmental variables in northern Patagonia. Materials and methodsThe distribution of pupae in response to different illumination, temperature, and soil inclination conditions was evaluated. A circular box divided into quadrants was used for each treatment and a control. Post-feeding larvae were placed in the centre of each box, allowing them to disperse until they pupate. Differences in the number of pupae collected in each quadrant were evaluated by ×2 tests. ResultsAccording to the control, the dispersion of post-feeding larvae occurs randomly (p=.098). Regarding temperature, the larvae eluded the heat source (p<.01). The inclination treatments showed that larvae avoided the raised surface (p<.01). Concerning illumination conditions, no trend was observed (p=.41). ConclusionsKnowing the dispersal patterns of larvae, considering environmental variables, reduces search times and let infers the sectors with the highest probability of finding pupae. In this way, it is likely to estimate a more precise PMI in the framework of medico-legal investigations.

Fasciola gigantica: Ultrastructural localisation of neoblast recruitment in somatic tissues during growth and development in the hepatic parenchyma of experimentally infected mice

Application of ‘omics’ technology, and advances in in vitro methods for studying the growth of Fasciola hepatica, have highlighted the central role of migrating neoblasts in driving forward development and differentiation towards the adult-like form. Neoblast populations present molecular heterogeneity, morphological variation and changes associated with recruitment of these stem cells into their final tissue locations. However, terminal differentiation towards function, has received much less attention than has been the case for the free-living Platyhelminths. An actively replicating neoblast population, comprising cells with heterochromatic nuclei consistent with regulation of gene expression, has been identified in the parenchyma of juvenile Fasciola gigantica migrating in the liver of experimentally infected mice. In some of these cells, early cytoplasmic differentiation towards myocyte function was noted. Neoblasts have also been identified close to, and incorporated in, the subtegumental zone, the gastrodermis and the excretory ducts. In these locations, progressive morphological differentiation towards terminal function has been described. This includes the appearance of specific progenitors of type-1, type-2 and type-3 tegumental cells, the latter possibly contributing to tegumental spine development. ‘Cryptic’ surface molecular differentiation is postulated to account for recognition and ‘docking’ of migrating neoblasts with their final site for terminal differentiation.

Upper end of the central canal of the human spinal cord: Quantitative anatomical study and 3D modeling

AbstractThe upper end of the central canal of the human spinal cord has been repeatedly implicated in the pathogenesis of various diseases, yet its precise normal position in the medulla oblongata and upper cervical spinal cord remains unclear. The purpose of this study is to describe the anatomy of the upper end of the central canal with quantitative measurements and a three‐dimensional (3D) model. Seven formalin‐embalmed human brainstems were included, and the central canal was identified in serial axial histological sections using epithelial membrane antigen antibody staining. Measurements included the distances between the central canal (CC) and the anterior medullary fissure (AMF) and the posterior medullary sulcus (PMS). The surface and perimeter of the CC and the spinal cord were calculated, and its anterior–posterior and maximum lateral lengths were measured for 3D modeling. The upper end of the CC was identified in six specimens, extending from the apertura canalis centralis (ACC) to its final position in the cervical cord. Positioned on the midline, it reaches its final location approximately 15 mm below the obex. No specimen showed canal dilatation, focal stenosis, or evidence of syringomyelia. At 21 mm under the ACC in the cervical cord, the median distance from the CC to the AMF was 3.14 (2.54–3.15) mm and from the CC to the PMS was 5.19 (4.52–5.43) mm, with a progressive shift from the posterior limit to the anterior third of the cervical spinal cord. The median area of the CC was consistently less than 0.1 mm2. The upper end of the CC originates at the ACC, in the posterior part of the MO, and reaches its normal position in the anterior third of the cervical spinal cord less than 2 cm below the obex. Establishing the normal position of the upper end of this canal is crucial for understanding its possible involvement in cranio‐cervical junction pathologies.

Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining

This study proposes a new prescriptive analytics method that aims to provide decision-makers with a systematic and objective approach to identify suitable locations, considering the spatial distribution of different types of restaurants. The method comprises of two main components: spatial co-location pattern mining and locationGCN, where locationGCN is based on graph convolutional network (GCN). The spatial co-location pattern mining is utilized to capture the spatial correlation of specific restaurant to determine the candidate location selection range. The locationGCN is designed to further screen out final suitable location ranges for the specific restaurant type. A case study using restaurant data from Xiamen Island collected from Dianping.com is conducted. The empirical results demonstrate that the algorithm achieves an accuracy of 74.88%, precision of 63.59%, and recall of 77.48%. Results indicate that the proposed approach can provide suitable location recommendations for specific types of restaurants based on existing restaurant distribution information.

Wind farm site selection using GIS-based mathematical modeling and fuzzy logic tools: a case study of Burundi

Introduction: The electricity generated from nuclear plants and petroleum-based products has a negative influence on the environment as a whole. It has shown the utility to search out and promote the utilization of renewable, environmentally friendly, and sustainable energy sources such as solar, wind, and geothermal. Nowadays, Wind energy resource has quickly emerged as the world’s fastest-growing energy source.Methods: However, the selection of the most suitable places for developing a wind farm is a crucial challenge that can be seen as a problem of site selection, which involves numerous conflicting variables. Therefore, it is classified as an MCDM (multi-criteria decision-making) problem. The main objective of this research is to determine the best locations in Burundi for the installation of wind farms. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. This study considers several key factors when determining the optimal location for a wind farm. These factors include wind speed, slope, proximity to the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map.Results and Discussion: The obtained results indicate that 20.91% of the whole study area is suitable nevertheless, only 1.96% is tremendously suitable for wind turbine placement. The western part of Burundi is the optimal area for constructing a wind farm, and the most is in Lake Tanganyika.

Spot position scheme on a quadrant detector for a spaceborne laser communication system

The paper proposes a spot positioning method based on a four-quadrant detector for the limited computing power and memory of spaceborne laser communication, in which the adaptive interpolation segmentation (AIS) algorithm is used to fit the theoretical position curve. The algorithm uses linear operations though the fitting process and the simulated result indicates that it has higher positioning accuracy in the center area of the quadrant detector. A spot receiving and positioning system was built for experimentation and the final location of the spot was calculated. The positioning error is analyzed to evaluate the performance of the whole system. It is shown that the positioning accuracy is highest in the stable communication area of the system. In result, the scheme achieves high accuracy with simple operations, which is more suitable for spaceborne laser communication systems to release more performance for communication.

A hybrid convolutional neural network-transformer method for received signal strength indicator fingerprinting localization in Long Range Wide Area Network

In recent years, low-power wide area networks (LPWANs), particularly Long-Range Wide Area Network (LoRaWAN) technology, are increasingly being adopted into large-scale Internet of Things (IoT) applications thanks to having the ability to offer cost-effective long-range wireless communication at low-power. The need to provide location-stamped communications to IoT applications for meaningful interpretation of physical measurements from IoT devices has increased demand to incorporate location estimation capabilities into LoRaWAN networks. Fingerprint-based localization methods are increasingly becoming popular in LoRaWAN networks because of their relatively high accuracy compared to range-based localization methods. This work proposes hybrid convolutional neural networks (CNNs)-transformer fingerprinting method to localize a node in a LoRaWAN network. CNNs are adopted to complement the strengths of the Transformer by adding the ability to capture local features from input data and consequently allow the Transformer, through the attention mechanism, to effectively learn global dependencies from the input data. Specifically, the proposed method works by first learning the local location features from the input data using the CNNs and passing the resulting information to the transformer encoder to learn global features from the input data. The output of the transformer encoder is then concatenated with information learned at the local level and then passed through the regressor for the final location estimation. With a localization performance of 290.71 m mean error achieved, the proposed method outperformed similar state-of-the-art works in the literature evaluated on the same publicly available LoRaWAN dataset.

Suitability Index for the Placement of Solar Plants Based on Inequality Measurements and on Satellite Images

The selection of a certain location for the placement of a solar facility depends on the solar resource availability, which is generally assessed though exceedance probabilities. However, the choice of the specific exceedance probability is arbitrary and the assessment will be different depending on the choice taken. Furthermore, exceedance probabilities do not reflect seasonal variability, which affects radiation availability. Therefore, in this work we present a new index, the suitability index based on Theil (SIT), which allows us to assess with a single value the degree of suitability of a site for installing a solar plant. Obtained from the Theil index, it considers the availability of the resource and its seasonal variability, based as it is on the proportion of the given radiation in each month. As we will see, the new index is clearly more sensitive to the amount of radiation expressed in terms of the 50th percentile than to the variability, as given by the interquartile range. This is a quality to be pondered since scarcity of radiation will always be a greater disadvantage for a solar installation than high variability. The results obtained in the study, grounded in the application of satellite images, show that the index adequately reflects the radiation characteristics in the study area. The territory is broken into areas associated with such characteristics through a cluster analysis, so that geographical and economic elements can be considered when choosing the final location for a solar installation. Furthermore, the new index may include the effects of energy storage during the months in which a certain demand is exceeded.