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... The study reviewed new trends that involve DL in dealing with cyber threats targeting IoT/cloud business models, while also acknowledging different methods have their limitations when adopted by industrial systems. Finally, based on their review of the literature, researchers suggest new ways to strengthen security using AI and DL within the cloud architecture in order to address research gaps in IoT-based cloud cybersecurity [28]. ...
Urban terminal logistics and distribution enterprises face the problems of duplicated network layouts, high costs, and inefficient urban environments. Recently, collaborative distribution using unmanned aerial vehicles (UAVs) and ground vehicles has been considered as a means of reducing costs and enhancing efficiency, thus overcoming the issues created by the high-density layout of enterprises’ relatively independent networks. This essentially involves constructing an air–ground collaborative common distribution network. To optimize the economic cost and distribution time of network operations, we established a site selection planning model for air–ground cooperative urban co-distribution centers and designed a solution method based on gray wolf optimization with K-means clustering. Taking the Wangsheren area of Jinan City, China, as an example, 15 UAV co-distribution centers and 13 vehicle co-distribution centers were identified. Although the average distribution cost of the network rose by 35–50% compared to traditional terminal distribution, the time saving was 80–85%, greatly improving the high-value-added service capacity. The end-distribution efficiency and customer satisfaction were also enhanced, which fully verifies the feasibility, validity, and applicability of the proposed model. Our approach can be applied to landing sites and the planning and optimization of large-scale commercial operations using logistics UAVs in urban areas.
The relationships between industrialization and urban regions, as well as technological advancements, innovations, and new work patterns and organizations, significantly impact the structuring of cities. Industrial sectors can become key drivers in shaping the form and macrostructure of a city over time. By examining and evaluating the existing system of structures and relations in the settlements in order to “estimate the land use differentiations” that may arise in the future and “to determine the spatial location of industrial facilities or sites,” a model has been developed. This model aims to predict potential “land use differentiations” and determine the “spatial location selection of industrial facilities or sites” in the future based on the analysis and evaluation of existing structures and relational systems in settlements. The model consists of the integration of the LUCAM, which is based on a cellular automata operating system, along with the “GIS-Based Integrated Industrial Area Location Selection Model,” which evaluates all data under the main headings of natural structure characteristics, socio-economic structure characteristics, and built environment characteristics using weighted overlay analysis. In this article, the general structure of the model developed within the scope of the research and İnegöl sampling are explained. The outputs of this model can be used for alternative separation for logistics centers, residential areas, secondary centers, reinforcement areas, and industrial sites in different settlements. In addition, in the research, a detailed model was designed by integrating decision-makers (central administrations, local administrations, registered users, NGOs) into the urban planning process. The proposed model can be used as a tool in designing transportation and other urban systems (distribution order of equipment areas, green area system, grading of centers, etc.).
Shelters are vital for affected people after a disaster because of the accommodation, safety, and security. In this paper, we analyze the shelter site selection criteria for disaster preparedness applying the best–worst method under interval type-2 fuzzy sets. The proposed approach utilizes the advantages of fewer pairwise comparisons with the best–worst method and better reflection of uncertainty with interval type-2 fuzzy sets. For this reason, the criteria are determined based on a literature review and the opinion of nine disaster experts. The experts have worked as disaster officers in a variety of locations, including Sivrice (Elazığ), Pazarcık, and Elbistan (Kahramanmaras) and Syrian refugee camps such as Nizip container city. In this step, 6 main criteria and 25 sub-criteria are evaluated using the proposed approach. According to the nine experts’ opinions, the most important main criterion is determined as proximity. Distribution center capacity, adequate distribution logistics personnel, available electricity, distance to settlement, and landslides and flooding are also determined as the five most important sub-criteria. For disaster preparedness, responsible organizations and managers should consider these important criteria for temporary shelter site selection.
With the increasing frequency of emergencies in recent years, the emergency response capacity of the emergency management system needs to be improved. Based on safety stock strategy, this paper proposes a multilevel siting model on the topic of mobile emergency response. We modeled the emergency response needs during emergencies by incorporating the population distribution of each region. The uncertainty of emergencies is modeled by aggregating the frequency of crises in each region over the past 20 years. The site selection model minimizes contingency logistics costs that include transshipment, deployment, inventory, and safety stock costs. In this paper, the IA (Immune Algorithm) is optimized to solve the constructed emergency site selection model. The experiments on the model were carried out with data from the area of Chongqing, Sichuan Province. The number of logistics centers and distribution storage warehouses was tested. The influence of safety stock strategy on the total cost of emergency logistics was analyzed. The research results found that the cost of safety stock is negatively related to the cost of transshipment. In addition, the total cost of emergency logistics has a lower bound. Adding distribution and storage warehouses does not further reduce the total emergency logistics cost.
In an emergency situation, fast and efficient logistics and distribution are essential for minimizing the impact of a disaster and for safeguarding property. When selecting a distribution center location, time satisfaction needs to be considered, in addition to the general cost factor. The improved jellyfish search algorithm (CIJS), which simulates the bionics of jellyfish foraging, is applied to solve the problem of an emergency logistics and distribution center site selection model considering time satisfaction. The innovation of the CIJS is mainly reflected in two aspects. First, when initializing the population, the two-level logistic map method is used instead of the original logistic map method to improve the diversity and uniform distribution of the population. Second, in the jellyfish search process, a Cauchy strategy is introduced to determine the moving distance of internal motions, which improves the global search capability and prevents the search from falling into local optimal solutions. The superiority of the improved algorithm was verified by testing 20 benchmark functions and applying them to site selection problems of different dimensions. The performance of the CIJS was compared to that of heuristic algorithms through the iterative convergence graph of the algorithm. The experimental results show that the CIJS has higher solution accuracy and faster solution speed than PSO, the WOA, and JS.
Featured Application
This paper studies the joint optimization of the threshold setting of large parcels, the location selection of multi-functional transit centers, and the allocation of terminal demand points under the implementation of multi-product (parcel) distribution by urban logistics enterprises, which is a new extension of the urban logistics site selection-path problem. Through this article, constructive suggestions are put forward for the future development of large-scale businesses in the logistics field, and other logistics companies can make a comprehensive assessment according to their own needs and conditions.
Abstract
Aiming at the problems of low resource utilization and high distribution cost of urban logistics enterprises, this paper introduces the threshold setting of large parcels, comprehensively considers the processing links of large parcels and standard parcels in loading, unloading, sorting, and other processing links, and constructs a logistics planning model with the type of multi-functional transit center as the variable and the total cost of the logistics system as the goal. Aiming at the shortcomings of the honey badger algorithm, three optimization strategies are used to improve the logistics model, and the effectiveness of the improved algorithm is verified by comparing with the CPLEX operation results. Based on the operation data of SF Jinzhou, this paper obtains the optimization results of large parcel threshold, multi-function transit center location layout, and terminal demand point allocation. From the results, the introduction of the threshold setting for large parcels has played a significant role in the joint optimization of multi-functional center location selection and terminal demand point allocation under multi-parcel distribution and provides theoretical data support for the existing urban logistics location problem.
The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies.
At a time of global epidemic control, the location of the medical logistics distribution center (MLDC) has an important impact on the operation of the entire logistics system to reduce the operating costs of the company, enhance the service quality and effectively control the COVID-19 on the premise of increasing the company’s profits. Thus, the research on the location of MLDC has important theoretical and practical application significance separately. Recently, the TODIM and VIKOR method has been used to solve multiple-attribute group decision-making (MAGDM) issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information. In this paper, we design the TODIM-VIKOR model to solve the MAGDM in PULT condition. Firstly, some basic concept of PULTSs is reviewed, and TODIM and VIKOR method are introduced. The extended TODIM-VIKOR model is proposed to tackle MAGDM problems under the PULTSs. At last, a numerical case study for medical logistics center site selection (MLCSS) is given to validate the proposed method.
With the development of China’s express delivery industry, the number of express packaging has proliferated, leading to many problems such as environmental pollution and resource waste. In this paper, the process of reverse logistics network design for express packaging recycling is given as an example in the M region, and a four-level network containing primary recycling nodes, recycling centers, processing centers, and terminals is established. A candidate node selection model based on the K-means algorithm is constructed to cluster by distance from 535 courier outlets to select 15 candidate nodes of recycling centers and processing centers. A node selection model based on the NSGA-II algorithm is constructed to identify recycling centers and processing centers from 15 candidate nodes with minimizing total cost and carbon emission as the objective function, and a set of Pareto solution sets containing 43 solutions is obtained. According to the distribution of the solution set, the 43 solutions are classified into I, II, and III categories. The results indicate that the solutions corresponding to Class I and Class II solutions can be selected when the recycling system gives priority to cost, Class II and Class III solutions can be selected when the recycling system gives priority to environmental benefits, and Class III solutions can be selected when the society-wide recycling system has developed to a certain extent. In addition, this paper also randomly selects a sample solution from each of the three types of solution sets, conducts coding interpretation for site selection, vehicle selection, and treatment technology selection, and gives an example design scheme.
Acadian forests of New Brunswick, Canada, are highly variable both in terms of species composition and quality. This is particularly accurate for hardwoods, for which value recovery is closely influenced by wood quality and a proper understanding of tree attributes. Therefore, based on several databases created between 2012 and 2021, the objectives of this study were to (1) characterize the stand-level distribution of species, size, form, and risk according to site factors and (2) determine the influence of different tree, stand, and site factors on the recovery of merchantable, sawlog, and veneer volumes. In total, 287,984 trees stemming from 9233 plots were analyzed for objective 1. For a subset of trees (743), tree, stand, and site attributes were also related to the product output of harvesting operations through a comprehensive inventory of standing trees and their associated products. Analyses were performed with linear and multinomial logistic regressions as well as factor analysis of mixed data (FAMD). Key results indicated that tree attributes and product recovery were significantly influenced by (1) environmental factors (soil, temperature, and precipitation), summarized by ecoregion in this study and (2) tree size and form. These findings suggest that the inclusion of site factors in supply planning could improve product recovery during forest operations.
Aquila chrysaetos and Aquila fasciata are two congeneric eagle species distributed in the Mediterranean region which are supposed to compete for similar breeding and foraging resources. In the present study, bioclimatic, topographic, and human-related habitat parameters were investigated for 64 and 75 nest sites of Golden and Bonelli’s eagles, respectively. The nests were found during fieldwork undertaken from 1995–2020. Overall, the habitat parameters that best discriminated nest site selection were associated with elevation, temperature, and land use with topographic variables being most powerful for niche separation. Univariate analysis, regression, and species distribution modeling identified a strong association of the species with altitude pinpointing the Golden eagle’s mountainous and continental character and the Bonelli’s eagle being a lowland and coastal species. Golden eagle nests were situated away from human settlements on steep cliffs in higher altitude areas with transitional woodland-shrub vegetation. In contrast Bonelli’s eagle nests were located on low-altitude warmer zones, closer to the coast and human settlements with more natural grasslands in their vicinity. The ecological niche separation of the two species was best described by altitude and temperature, though no clear-cut evidence was detected for their competitive exclusion. Inter-specific nearest neighboring distance was found statistically significant only for the Golden eagle which seems to be less tolerant in its co-existence with the Bonelli’s eagle. Conservation measures for both species should target territories under human pressure, though more research should focus on the species range use and habitat heterogeneity within overlapping territories.
The location of a logistics center is very important in a logistics system, as the success of the location determines the whole logistics system’s structure, shape, and mode, and not only affects the logistics center’s own operating costs, performance, and future development, but also affects the operation of the entire logistics system. Therefore, the selection of the location for a logistics center has great significance for improving the efficiency of regional logistics and optimizing the structure of a logistics system. This study constructed a multi-factor constrained P-median site-selection model to optimize the locations of logistics centers to improve the efficiency of logistics and optimize the structure of the logistics system in a region. The results show that the optimal distribution of logistics center sites and the coverage of freight capacity demand derived from the particle swarm algorithm are more balanced than those derived by the other algorithm. Following the comparison of the results for the utility of the optimized layout points solved by the particle swarm algorithm and the immune genetic algorithm, it is concluded that the optimal fitness value obtained by the particle swarm algorithm is lower than the other. It is proven that the particle swarm algorithm of the P-median site-selection model under this multi-factor constraint has some reference value for the selection of the sites of multi-logistics centers.
The regular lockdown policy adopted in controlling the pandemic of COVID-19 has caused logistic disruptions in some areas that have a great impact on the living standards of residents and the production of enterprises. Given that the construction of emergency logistics centers is an effective solution, this paper takes the Yangtze River Delta Area (YRDA) of China as an example and discusses the site selection and material distribution of the emergency logistics centers in the region via a two-stage model. The first stage is the selection of candidate emergency logistics centers in the YRDA. A comprehensive evaluation index system is built with 4 primary and 15 secondary indexes to evaluate the logistic infrastructure capacity of the 41 cities in the YRDA. Further, through a principal component analysis, 12 cities are selected as candidate construction sites for emergency logistics centers. In the second stage, a biobjective site selection model with uncertain demand is established and calculated via the NSGA-II algorithm. According to the time sensitivity of emergency logistics, six cities are filtered from the optimal solution set, including Hefei, Hangzhou, Xuzhou, Wenzhou, Changzhou, and Shanghai, ensuring that all 41 cities are within their service scope.
Industrial activities induce various impacts on ecosystems that influence species richness and distribution. An effective way to assess the resulting impacts on biodiversity lies in vegetation mapping. Species classification achieved through supervised machine learning algorithms at the pixel level has shown promising results using hyperspectral images and multispectral, multitem-poral images. This study aims to determine whether airborne hyperspectral images with a high spatial resolution or phenological information obtained by spaceborne multispectral time series (Sentinel-2) are suitable to discriminate species and assess biodiversity in a complex impacted context. The industrial heritage of the study site has indeed induced high spatial heterogeneity in terms of stressors and species over a reduced scale. First, vegetation indices, derivative spectra, continuum removed spectra, and components provided by three feature extraction techniques, namely, Principal Component Analysis, Minimal Noise Fraction, and Independent Component Analysis, were calculated from reflectance spectra. These features were then analyzed through Sequential Floating Feature Selection. Supervised classification was finally performed using various machine learning algorithms (Random Forest, Support Vector Machines, and Regularized Logistic Regression) considering a probability-based rejection approach. Biodiversity metrics were derived from resulted maps and analyzed considering the impacts. Average Overall Accuracy (AOA) reached up to 94% using the hyperspectral image and Regularized Logistic Regression algorithm, whereas the time series of multispectral images never exceeded 72% AOA. From all tested spectral transformations, only vegetation indices applied to the time series of multispectral images increased the performance. The results obtained with the hyperspectral image degraded to the specifications of Sentinel-2 emphasize the importance of fine spatial and spectral resolutions to achieve accurate mapping in this complex context. While no significant difference was found between impacted and reference sites through biodiversity metrics, vegetation mapping highlighted some differences in species distribution .
Concerning the limit of logistics node expansion in the current the Belt and Road Railway System to macroscopic and qualitative description, this paper analyzes the problem of the optimal logistics node selection of The Belt and Road Railway System. This paper puts forward the Belt and Road Railway System logistics node center site Container Maintenance model based on the principal component and the FP_growth network, analyzes the optimal railway system logistics node center location, and adopts expansion of the practical the Belt and Road Railway System as an example to conduct simulation analysis of the model algorithm. Results suggest that the model algorithm put forward in this paper can efficiently confirm the optimal railway system logistics node center location, match logistics nodes for practical demands in accordance with the hierarchical structure of logistics nodes and find the optimal logistics center, distribution center and freight transfer center. This paper summarizes researches about expansion of logistics nodes of The Belt and Road Railway System, and successfully analyzes the expansion of the optimal logistics nodes of The Belt and Road Railway System through the hierarchical structure of the practical logistics nodes of The Belt and Road Railway System and combining cluster results of the FP growth network and the comprehensive rating obtained through the principal component. The simulation analysis results of this paper can efficiently find out the optimal railway system logistics node center location, and provide a new analysis perspective for the academic study on the center Container Maintenance for the expansion of relevant logistics nodes of The Belt and Road Railway System.
Species distribution models (SDMs) represent a widely acknowledged tool to identify priority areas on the basis of occurrence data and environmental factors. However, high levels of topographical and climatic micro-variation are a hindrance to reliably modelling the distribution of narrow-endemic species when based on classic occurrence and climate datasets. Here, we used high-resolution environmental variables and occurrence data obtained from dedicated field studies to produce accurate SDMs at a local scale. We modelled the potential current distribution of 23 of the 25 rarest species from Mount Kaala, a hotspot of narrow-endemism in New Caledonia, using occurrence data from two recent sampling campaigns, and eight high-resolution (10 m and 30 m) environmental predictors in a Species Distribution Modelling framework. After a first sampling operation, we surveyed six additional areas containing, overall, 13 of the 20 species modelled at this stage, to validate our projections where the highest species richness levels were predicted. The ability of our method to define conservation areas was largely validated with an average 84% of predicted species found in the validation areas, and additional data collected enabling us to model three more species. We therefore identified the areas of highest conservation value for the whole of Mount Kaala. Our results support the ability of SDMs based on presence-only data such as MaxEnt to predict areas of high conservation value using fine-resolution environmental layers and field-collected occurrence data in the context of small and heterogeneous systems such as tropical islands.
Over evolutionary time, pathogen challenge shapes the immune phenotype of the host to better respond to an incipient threat. The extent and direction of this selection pressure depends on the local pathogen composition, which is in turn determined by biotic and abiotic features of the environment. However, little is known about adaptation to local pathogen threats in wild animals. The Gentoo penguin (Pygoscelis papua) is a species complex that lends itself to the study of immune adaptation because of its circumpolar distribution over a large latitudinal range, with little or no admixture between different clades. In this study, we examine the diversity in a key family of innate immune genes - the Toll-like receptors (TLRs) - across the range of the Gentoo. The three TLRs that we investigated present varying levels of diversity, with TLR4 and TLR5 greatly exceeding the diversity of TLR7. We present evidence of positive selection in TLR4 and TLR5, which points to pathogen-driven adaptation to the local pathogen milieu. Finally, we demonstrate that two positively selected co-segregating sites in TLR5 are sufficient to alter the responsiveness of the receptor to its bacterial ligand, flagellin. Taken together, these results suggest that Gentoo penguins have experienced distinct pathogen-driven selection pressures in different environments, which may be important given the role of the Gentoo as a sentinel species in some of the world's most rapidly changing environments.
Ungulates inhabiting arid ecosystems are reliant on productive forb and shrub communities during summer months to meet nutritional demands for survival and reproduction. In the western United States, expansion of woodland vegetation into shrub‐dominated communities and the potential loss of habitat are of concern with regard to animal populations reliant on robust sagebrush shrub and forb vegetation. We used mixed‐effects logistic regression to model resource selection of female mule deer (Odocoileus hemionus) during the summers of 2005–2008. We considered selection at two spatial scales, and during three daily activity periods (night, crepuscular, and mid‐day) corresponding to resting and foraging. We evaluated habitat variables related to vegetation type, site productivity, terrain attributes, and fine‐scale abundance of pinyon–juniper cover. Those attributes allowed us to account for variation in site productivity and animal behaviors that could obscure the true relationship between density of pinyon–juniper woodland and habitat use by mule deer. We detected observable differences in habitat use by mule deer among the three activity periods with significant differences in movement rates. Deer were most active during crepuscular periods corresponding with foraging activity, and least active mid‐day when temperatures were highest. Summer habitat consisted of sites with high potential productivity, greater shrub abundance, and greater proximity to riparian areas. Deer avoided high levels of tree cover (>40% coverage) at all spatial and temporal scales, and in general selection declined with increasing pinyon–juniper cover, particularly during foraging periods. Nonetheless, mule deer selected areas with low to intermediate tree cover (10% to 40%) during resting periods and avoided areas of productive shrub and forb vegetation and riparian areas, when surrounded by denser stands of pinyon–juniper cover. These results emphasize the importance of productive shrub and forb vegetation to mule deer inhabiting semi‐arid regions and suggest that low levels of tree cover are beneficial, especially during resting periods. Nevertheless, dense pinyon–juniper cover was generally avoided even in areas of high site productivity. These relationships lend support to the concern that increases in large, dense stands of pinyon–juniper reduce availability of summer habitat and alter patterns of resource selection for mule deer, a shrub‐dependent ungulate.
With the rapid rise of the Internet, China’s e-commerce has also flourished. The development of e-commerce has led to an increase in the volume of logistics and distribution. The further development of e-commerce has also placed higher demands on the timeliness of logistics and distribution. The competition of e-commerce companies has shifted from the competition between business models to the competition between logistics services. The scientific and rational distribution site selection planning is the prerequisite and guarantee for the efficient operation of logistics distribution network. To balance the contradiction between logistics distribution speed and distribution cost has become the key to competition among e-commerce companies. This paper analyzes the current network structure and distribution mode of e-commerce logistics city distribution, and analyzes and discusses the problems existing in current e-commerce logistics city distribution. Furthermore, the bi-level programming is studied. According to the characteristics of the bi-level programming problem, the genetic algorithm flow suitable for bi-level programming is proposed. The bi-level programming model of urban distribution service network site selection with limited lines is proposed. Through the verification of the genetic algorithm in this paper, the proposed method can plan a reasonable service site location layout and distribution models and path selection. The results show that the average daily fuel cost can be reduced by 37.6%, and the transportation distance and fuel cost can be optimized best.
Background:
Mitochondria play a key role in the balance of energy and heat production, and therefore the mitochondrial genome is under natural selection by environmental temperature and food availability, since starvation can generate more efficient coupling of energy production. However, selection over mitochondrial DNA (mtDNA) genes has usually been evaluated at the population level. We sequenced by NGS 12 mitogenomes and with four published genomes, assessed genetic variation in ten penguin species distributed from the equator to Antarctica. Signatures of selection of 13 mitochondrial protein-coding genes were evaluated by comparing among species within and among genera (Spheniscus, Pygoscelis, Eudyptula, Eudyptes and Aptenodytes). The genetic data were correlated with environmental data obtained through remote sensing (sea surface temperature [SST], chlorophyll levels [Chl] and a combination of SST and Chl [COM]) through the distribution of these species.
Results:
We identified the complete mtDNA genomes of several penguin species, including ND6 and 8 tRNAs on the light strand and 12 protein coding genes, 14 tRNAs and two rRNAs positioned on the heavy strand. The highest diversity was found in NADH dehydrogenase genes and the lowest in COX genes. The lowest evolutionary divergence among species was between Humboldt (Spheniscus humboldti) and Galapagos (S. mendiculus) penguins (0.004), while the highest was observed between little penguin (Eudyptula minor) and Adélie penguin (Pygoscelis adeliae) (0.097). We identified a signature of purifying selection (Ka/Ks < 1) across the mitochondrial genome, which is consistent with the hypothesis that purifying selection is constraining mitogenome evolution to maintain Oxidative phosphorylation (OXPHOS) proteins and functionality. Pairwise species maximum-likelihood analyses of selection at codon sites suggest positive selection has occurred on ATP8 (Fixed-Effects Likelihood, FEL) and ND4 (Single Likelihood Ancestral Counting, SLAC) in all penguins. In contrast, COX1 had a signature of strong negative selection. ND4 Ka/Ks ratios were highly correlated with SST (Mantel, p-value: 0.0001; GLM, p-value: 0.00001) and thus may be related to climate adaptation throughout penguin speciation.
Conclusions:
These results identify mtDNA candidate genes under selection which could be involved in broad-scale adaptations of penguins to their environment. Such knowledge may be particularly useful for developing predictive models of how these species may respond to severe climatic changes in the future.
To mitigate losses from public health emergencies, promptly choosing an emergency medical supply distribution center is essential. The success of rescues hinges on the center’s location. Location decision for such centers is fraught with uncertainty and risk, complicating the task for decision makers (DMs). Often, DMs operate under bounded rationality in urgent situations. In response, this paper introduces a novel fuzzy multicriteria decision-making approach, integrating interval-valued intuitionistic fuzzy numbers, interval intuitionistic trapezoidal fuzzy numbers, not-in-my-backyard (NIMBY) effect, and cumulative prospect theory, to address the uncertainty and risk in decision-making. The method begins by introducing the NIMBY effect to construct the evaluation criteria system of emergency medical supply distribution centers. Next, the alternative under the evaluation criteria is evaluated by fuzzy linguistic terms and interval intuitionistic trapezoidal fuzzy numbers. The interval-valued intuitionistic fuzzy numbers are used to determine the weight of each DM by calculating the relative scores of advantage and disadvantage. The criteria weight is determined via a combination of fuzzy network analysis and the entropy weight method. Cumulative prospect theory is then employed to rank site selections, factoring in DM risk preferences. The approach is tested on an emergency medical supply distribution center location for sensitivity and comparative analysis, proving its efficacy and stability. This research presents an innovative fuzzy model for evaluating emergency medical supply distribution center site selection in uncertain conditions, suggesting the need to incorporate multiple dimensions such as realistic criteria systems, uncertain decision information, and variable environments in such decision-making processes.
Purpose
This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.
Design/methodology/approach
This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.
Findings
The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.
Originality/value
This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.
Few observations exist for the Kisatchie painted crayfish (Faxonius maletae), and little is known about its distribution, habitat use, or population structure. The lack of basic ecological data has led to a “Data Deficient” designation, a category of conservation concern that is often overlooked for management actions. This work provides updated data and practical management suggestions for similarly data deficient species.
Comprehensive (i.e., beyond known collection sites) crayfish surveys paired with habitat assessments across northeast Texas were conducted. Among 74 survey sites in ten counties within and adjacent to known F. maletae range, eight total crayfish species were encountered within 35 of these sites (= 47.3% of total). Thirteen F. maletae individuals were collected across seven sites (9.5%). Notably, two individuals were found where the species had not been previously observed.
Stepwise multiple logistic regression indicated that conductivity and availability of exposed root masses positively related to F. maletae presence, although neither factor was statistically significant on its own. Neither nonmetric multidimensional scaling nor canonical correspondence analysis identified patterns between F. maletae presence and the presence of other crayfish species.
Overall, F. maletae is geographically rare and occurs in exceptionally low abundance, challenging the ability to understand drivers of its distribution. However, given the depth of this survey, we can confidently say that F. maletae is no longer “Data Deficient” but instead has compelling evidence of a limited range and small population size. The Data Deficient versus rarity debate has wide-reaching impacts on the conservation of aquatic taxa; species that are data deficient require heightened research focus in general, whereas rare organisms require targeted management to protect specific habitats. Furthermore, we suggest future management efforts focus on maintaining current populations through active monitoring to inform potential habitat suitability of new areas for protection.
The research on citizens' color preference is an important prerequisite for the scientific formulation of urban color planning. Color preferences are normally identified over the Internet or through questionnaire surveys and adjective descriptions. But the analysis has its limitations due to the uncertainty of color descriptions. Based on the quantitative analysis of the three attributes of hue, blackness, and chromaticness, this article obtains the inherent law of color preference of citizens, and compares it with the results of color adjective preference done by our research group before, which can supplement the accuracy of adjective description, so as to provide a reference for the color positioning of urban color planning. This article obtains the three attributes of color samples chosen by the public in the form of questionnaires as well as color sample selections. Among which 36 representative sites in Shanghai were selected according to the six types of land use. Adopting a more detailed method of dividing nine color series, viz. yellow (Y), red‐yellow (YR), red (R), red‐blue (RB), blue (B), blue‐green (BG), green (G), green‐yellow (GY), and the neutrals (WS). Moreover, chi‐square test and multiple logistic regression model were used to analyze the color preference factors and explore the relationship between citizen attributes and the three attributes of color sample selection. Research findings are as follows: (a) The citizens' favorite colors of urban architecture center round red, high whiteness, and mid‐low chromaticness. (b) The main factors that influence citizens' hue preference include gender, age, and monthly income; the main factors that influence citizens' blackness preference include: questionnaire distribution points, the frequency of coming to Shanghai, and whether to go abroad; gender and age are the main factors that affect citizens' chromaticness preference. (c) Compared with the previous studies on adjective selection among citizens, it is found that the effect of gender, age, monthly income, and educational background on color preference is similar; differences are in the occupational types, frequency of coming to Shanghai, and whether travels abroad.
Representative samples of biota collected relative to environmental gradients are important for measuring present distributions and predicting shifts in distribution of taxa in response to climate change or reduced river connectivity. Based on river ecology theory and established measures of species diversity, we present a method to identify suitable river segments for sampling and monitoring changes in taxon diversity. Alpha and beta diversities of selected aquatic macroinvertebrates were assessed in seven South African rivers. Data were drawn from historical and field samples and represented longitudinal species patterns down longitudinal river axes. Representative sampling sites were identified using a logistic regression model to predict the probability of site pairs that were more than 50% similar as a function of up-/downstream distance. Alpha diversities peaked in the upper third of river lengths; beta diversities showed predictable exponential decay rates down river axes up to and excluding the start of estuarine conditions. Application of the model to a 370-km long river indicated that 14 sites should be selected for sampling to capture overall biodiversity patterns. Additional factors, such as confluences tributaries, which influence alpha diversity at sites, are identified and incorporated into site selection.
The mixture recycling of reclaimed asphalt pavement (RAP) has become a widely used strategy in road maintenance. The article aimed at acquiring the optimum solutions to quantity and location distribution of RAP stockpile logistic centers over a regional highway network using a multi-factor analysis approach to further promote the development of asphalt recycling technology. Firstly, the economic radius of RAP stockpile logistic centers was calculated to determine the optimal number of RAP stockpile logistic centers according to the density of road network in the target region. Then, the proper location of each RAP stockpile logistic center was investigated employing ArcGIS buffer analysis and Floyd algorithm, with three factors taken into consideration (environment impacts, RAP material yield and consumption of each road in highway network, and the economic transport distance). Finally, upon the selection of each RAP stockpile logistic center site, the economic benefits were evaluated through cost-benefit analysis (CBA). In a case study based on the data of Jiangsu province expressway network, it suggested that an establishment of 13 RAP stockpile logistic centers will meet the recycling maintenance need of asphalt pavement over the whole highway network in Jiangsu province, contributing to a 6-year net maintenance benefit of about 70 million CNY.
Hopfield artificial neural network-based optimization method for selecting nodes of fresh agricultural products international logistics network
- N Hu
Hu N. Hopfield artificial neural network-based
optimization method for selecting nodes of fresh
agricultural products international logistics network.
Pakistan Journal of Agricultural Sciences.
2023;60(3):473-83. DOI: 10.21162/pakjas/23.101
Site selection for biogenic reef restoration in offshore environments: The Natura 2000 area Borkum Reef Ground as a case study for native oyster restoration. Aquatic Conservation-Marine and Freshwater Ecosystems
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