cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19967767.v1
title: ENDOGENEITY IN STOCHASTIC PRODUCTION FRONTIER WITH ONE AND TWO-STEP MODELS: AN APPLICATION WITH MUNICIPAL DATA FROM THE BRAZILIAN AGRICULTURAL CENSUS

description: ABSTRACT Stochastic production frontier models are widely used in microeconometrics and, in the last decades, have been proven to be versatile in their range of applications. However, there are few studies concerning endogeneity in stochastic production frontier models. Here we present two stochastic production frontier models with endogenous variables based on the main distributions for the technical inefficiency. We also derive analytic gradient vectors to obtain the best performance at a reasonable computational time cost. The methodology presented here is based on one and two-step maximum likelihood estimation, allows for endogeneity and heteroscedasticity in relation to one or both error terms, and is implemented in R language. Finally, we illustrate an application with municipal data from the Brazilian agricultural census. The results show that capital dominates the production function, credit access and technical assistance are endogenous, and income concentration seems to impede productive inclusion through the more intensive use of technology.
DOI: 10.6084/m9.figshare.19967770
title: PROPOSITION OF A MATHEMATICAL PROGRAMMING MODEL FOR ALLOCATING HUMAN RESOURCES CONSIDERING MULTIPLE FACTORS AND USING DIFFERENT HEURISTICS

description: ABSTRACT Human Resource Allocation (HRA) can be defined as the way professionals are distributed across the organization’s tasks, given that each individual has his/her own set of characteristics, and that each task has specific needs. Thus, this paper puts forward a mathematical programming model for allocating human resources that considers employees’ formal qualifications and experience and the possibility of employees sharing tasks in each project. The proposed mathematical model was designed and implemented according to a set of heuristics based on a Greedy Search (GS), a Genetic Algorithm, a Cosine Pigeon- Inspired Optimizer and an Iterated Local Search (ILS), to solve small, medium and large random instances. Thus, it was verified which of the heuristics had the best performance according to certain indicators, such as resolution time and average quality of the solutions found. Finally, they were also compared with the optimal solution obtained for small and medium-sized instances, with the best average results to ILS, although these are not too far from those of the GS.
DOI: 10.6084/m9.figshare.19967770.v1
title: PROPOSITION OF A MATHEMATICAL PROGRAMMING MODEL FOR ALLOCATING HUMAN RESOURCES CONSIDERING MULTIPLE FACTORS AND USING DIFFERENT HEURISTICS

description: ABSTRACT Human Resource Allocation (HRA) can be defined as the way professionals are distributed across the organization’s tasks, given that each individual has his/her own set of characteristics, and that each task has specific needs. Thus, this paper puts forward a mathematical programming model for allocating human resources that considers employees’ formal qualifications and experience and the possibility of employees sharing tasks in each project. The proposed mathematical model was designed and implemented according to a set of heuristics based on a Greedy Search (GS), a Genetic Algorithm, a Cosine Pigeon- Inspired Optimizer and an Iterated Local Search (ILS), to solve small, medium and large random instances. Thus, it was verified which of the heuristics had the best performance according to certain indicators, such as resolution time and average quality of the solutions found. Finally, they were also compared with the optimal solution obtained for small and medium-sized instances, with the best average results to ILS, although these are not too far from those of the GS.
DOI: 10.6073/pasta/de251b9e292f0d892a7c291a55ed0edb
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 2 meteorological station: Daily average soil volumetric water content data: 2013 - ongoing

description: Daily Average of 30-minute Soil Volumetric Water Content data at 10 cm at CSIS Block-2. Collection of soil water content data at Jornada LTER Cross-scale Interactions Study (CSIS) blocks, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Soil volumetric water content is measured every 30 minutes at 10 cm depth at 3 locations at each of the 4 plots per block and averaged for the daily value. The locations in each plot are under bare soil, under perennial grass, and under shrub. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are recorded at an automated meteorological station installed at Jornada LTER CSIS Block-2 site. This is an ONGOING dataset.
DOI: 10.6084/m9.figshare.19967774
title: A NEW STRATEGY TO SOLVE LINEAR INTEGER PROBLEMS WITH SIMPLEX DIRECTIONS

description: ABSTRACT In this paper, we propose a new heuristic strategy to solve linear integer mathematical problems. The strategy begins by finding the optimal solution of the continuous associated problem and the simplex directions from that optimal solution. A total of n − m problems are generated and solved by the strategy, where m is the number of constraints and n is the number of variables of the problem. The best solution to those problems is the solution to the original problem. Several instance problems were randomly generated to validate the strategy, and our strategy finds good solutions in 80% of the instances. We also tested the proposed strategy with instances of the MIPLIB 2017. We compare a total of 39 instances and on average our strategy performed 47% fewer iterations than the solver, in 34 of 39 instances, the strategy found good solutions.
DOI: 10.6084/m9.figshare.19967774.v1
title: A NEW STRATEGY TO SOLVE LINEAR INTEGER PROBLEMS WITH SIMPLEX DIRECTIONS

description: ABSTRACT In this paper, we propose a new heuristic strategy to solve linear integer mathematical problems. The strategy begins by finding the optimal solution of the continuous associated problem and the simplex directions from that optimal solution. A total of n − m problems are generated and solved by the strategy, where m is the number of constraints and n is the number of variables of the problem. The best solution to those problems is the solution to the original problem. Several instance problems were randomly generated to validate the strategy, and our strategy finds good solutions in 80% of the instances. We also tested the proposed strategy with instances of the MIPLIB 2017. We compare a total of 39 instances and on average our strategy performed 47% fewer iterations than the solver, in 34 of 39 instances, the strategy found good solutions.
DOI: 10.6084/m9.figshare.19967777
title: INTEGER FORMULATIONS FOR THE INTEGRATED VEHICLE ROUTING PROBLEM WITH TWO-DIMENSIONAL PACKING CONSTRAINTS

description: ABSTRACT Two integer linear programming models are developed for the unrestricted vehicle routing problem with two-dimensional loading constraints. The first one is a complete model, and the other uses valid inequalities to guarantee that routes are connected and respect the two-dimensional loading constraints. The models are solved with a branch-and-cut algorithm. Computational experiments on benchmark instances showed the complete model has allowed optimal solutions for 5% of the instances, while the second model optimally solved 64% of the instances. Given the superior performance of the second model, we adapted it to handle the sequential variant of the problem, which is harder, and then optimal solutions were obtained for 46% of the instances within the given time limit. The second model compared with a branch-and-cut algorithm from the literature found identical or better solutions for all the instances.
DOI: 10.6084/m9.figshare.19967777.v1
title: INTEGER FORMULATIONS FOR THE INTEGRATED VEHICLE ROUTING PROBLEM WITH TWO-DIMENSIONAL PACKING CONSTRAINTS

description: ABSTRACT Two integer linear programming models are developed for the unrestricted vehicle routing problem with two-dimensional loading constraints. The first one is a complete model, and the other uses valid inequalities to guarantee that routes are connected and respect the two-dimensional loading constraints. The models are solved with a branch-and-cut algorithm. Computational experiments on benchmark instances showed the complete model has allowed optimal solutions for 5% of the instances, while the second model optimally solved 64% of the instances. Given the superior performance of the second model, we adapted it to handle the sequential variant of the problem, which is harder, and then optimal solutions were obtained for 46% of the instances within the given time limit. The second model compared with a branch-and-cut algorithm from the literature found identical or better solutions for all the instances.
DOI: 10.6084/m9.figshare.19967780
title: REPETITIVE ACCEPTANCE SAMPLING PLAN FOR LIFETIMES FOLLOWING A SKEW-GENERALIZED INVERSE WEIBULL DISTRIBUTION

description: ABSTRACT In this paper, we consider a skew-generalized inverse Weibull probability distribution for repetitive acceptance sampling plans based on truncated life tests with known shape parameter. The design parameters such as sample size and acceptance numbers are evaluated by considering the median life time of the test units as a quality parameter under the constraint of two risks, known as the producer’s risk and consumer’s risk at a certain level. We explained the proposed method with the help of tables for different values of the known parameter. The skew-generalized inverse Weibull distribution fits better a real data set considered than the generalized inverse Weibull distribution. Comparison between the proposed plan and the single sampling plan is presented.
DOI: 10.6084/m9.figshare.19967780.v1
title: REPETITIVE ACCEPTANCE SAMPLING PLAN FOR LIFETIMES FOLLOWING A SKEW-GENERALIZED INVERSE WEIBULL DISTRIBUTION

description: ABSTRACT In this paper, we consider a skew-generalized inverse Weibull probability distribution for repetitive acceptance sampling plans based on truncated life tests with known shape parameter. The design parameters such as sample size and acceptance numbers are evaluated by considering the median life time of the test units as a quality parameter under the constraint of two risks, known as the producer’s risk and consumer’s risk at a certain level. We explained the proposed method with the help of tables for different values of the known parameter. The skew-generalized inverse Weibull distribution fits better a real data set considered than the generalized inverse Weibull distribution. Comparison between the proposed plan and the single sampling plan is presented.
DOI: 10.6084/m9.figshare.19967782
title: RANKING OF A SET OF ACCOUNTS RECEIVABLE STRATEGIES IN A MEXICAN REGIONAL COMPANY BASED ON A MULTICRITERIA APPROACH

description: ABSTRACT Account receivable can help companies expand sales and reduce inventory; however, bad debt in account receivable is not uncommon, and it dramatically occupies company funds. Therefore, the study on the problems existing in managing the account receivable of a company is necessary. For this reason, the selection of a final accounts receivable strategy needs to consider at least two factors of importance: decision-maker preferences and fuzzy preference information in the decision criteria. In this paper, a multicriteria approach for ranking a set of accounts receivable strategies in a company is proposed, and the case of a Mexican regional company is presented. The proposed model considers all the above factors. The approach uses the ELECTRE III method to obtain a recommendation per the decision maker’s preferences. The results showed that the most preferred alternative is visiting an account receivable executive at home and agreement calls. Insights gained from this applied research had practical implications for the decision- maker, including a better understanding of the problem and its value, reflected in a credit management department setup.
DOI: 10.6084/m9.figshare.19967782.v1
title: RANKING OF A SET OF ACCOUNTS RECEIVABLE STRATEGIES IN A MEXICAN REGIONAL COMPANY BASED ON A MULTICRITERIA APPROACH

description: ABSTRACT Account receivable can help companies expand sales and reduce inventory; however, bad debt in account receivable is not uncommon, and it dramatically occupies company funds. Therefore, the study on the problems existing in managing the account receivable of a company is necessary. For this reason, the selection of a final accounts receivable strategy needs to consider at least two factors of importance: decision-maker preferences and fuzzy preference information in the decision criteria. In this paper, a multicriteria approach for ranking a set of accounts receivable strategies in a company is proposed, and the case of a Mexican regional company is presented. The proposed model considers all the above factors. The approach uses the ELECTRE III method to obtain a recommendation per the decision maker’s preferences. The results showed that the most preferred alternative is visiting an account receivable executive at home and agreement calls. Insights gained from this applied research had practical implications for the decision- maker, including a better understanding of the problem and its value, reflected in a credit management department setup.
DOI: 10.6073/pasta/21d504ba3ab7540017626d95b5410734
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 2 meteorological station: Daily summary data: 2013 - ongoing

description: This data package contains hourly air temperature, relative humidity, wind speed, wind direction, and precipitation summary data from Jornada Basin LTER Cross Scale Interactions Study (CSIS) Block-2 met station. Air temperature, relative humidity, wind speed, wind direction, and precipitation are measured and calculated at an automated meteorological station connected to a 900 MHz spread spectrum wireless network. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Measurements are aggregated at intervals of 5, 30, 60 minutes and daily, with precipitation additionally provided with 1 second aggregation during precipitation events. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Precipitation is not measured at Block 1 and Block 12 because of proximity to Block 2 and Block 13, respectively . This is an ONGOING dataset.
DOI: 10.6084/m9.figshare.19967789
title: BIBLIOMETRIC STUDIES ON MULTI-CRITERIA DECISION ANALYSIS (MCDA) METHODS APPLIED IN MILITARY PROBLEMS

description: ABSTRACT Military issues have great relevance worldwide since they affect the security and sovereignty of nations. In this context, the application of Multicriteria Decision Analysis (MCDA) methods is important because accurate decision-making is the deciding factor for success, which can reduce expenses and increase defense capacity. This paper aims to present a literature review on the main applications of MCDA in the military area, considering the tactical, operational and strategic spheres. The methodology includes a bibliometric study and literature review of documents from the Scopus and Web of Science databases. The bibliometric study identified the document type, language, year of publication, authors, author network, author’s publications, affiliation, keyword clusters, the field of knowledge, country and the main applied MCDA methods in military problems. The literature review allows us to verify that, as well as in other areas of knowledge, the Analytic Hierarchy Process (AHP) is the most applied MCDA method in the military area.
DOI: 10.6084/m9.figshare.19967789.v1
title: BIBLIOMETRIC STUDIES ON MULTI-CRITERIA DECISION ANALYSIS (MCDA) METHODS APPLIED IN MILITARY PROBLEMS

description: ABSTRACT Military issues have great relevance worldwide since they affect the security and sovereignty of nations. In this context, the application of Multicriteria Decision Analysis (MCDA) methods is important because accurate decision-making is the deciding factor for success, which can reduce expenses and increase defense capacity. This paper aims to present a literature review on the main applications of MCDA in the military area, considering the tactical, operational and strategic spheres. The methodology includes a bibliometric study and literature review of documents from the Scopus and Web of Science databases. The bibliometric study identified the document type, language, year of publication, authors, author network, author’s publications, affiliation, keyword clusters, the field of knowledge, country and the main applied MCDA methods in military problems. The literature review allows us to verify that, as well as in other areas of knowledge, the Analytic Hierarchy Process (AHP) is the most applied MCDA method in the military area.
DOI: 10.6073/pasta/2f49209fe7a7bc389930c7af8be23fb5
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 3 meteorological station: 30-minute summary data: 2017 - ongoing

description: This data package contains 30-minute air temperature, relative humidity, and wind summary data from Jornada Basin LTER Cross Scale Interactions Study (CSIS) Block-1 met station. Average air temperature, relative humidity, wind speed, wind direction, and precipitation are measured and calculated at an automated meteorological station connected to a 900 MHz spread spectrum wireless network. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Measurements are aggregated at intervals of 5, 30, 60 minutes and daily, with precipitation additionally provided with 1 second aggregation during precipitation events. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Precipitation is not measured at Block 1 and Block 12 because of proximity to Block 2 and Block 13, respectively. This is an ONGOING dataset.
DOI: 10.6084/m9.figshare.19967886
title: Covariate-assisted community detection in multi-layer networks

description: Communities in multi-layer networks consist of nodes with similar connectivity patterns across all layers. This article proposes a tensor-based community detection method in multi-layer networks, which leverages available node-wise covariates to improve community detection accuracy. This is motivated by the network homophily principle, which suggests that nodes with similar covariates tend to reside in the same community. To take advantage of the node-wise covariates, the proposed method augments the multi-layer network with an additional layer constructed from the node similarity matrix with proper scaling, and conducts a Tucker decomposition of the augmented multi-layer network, yielding the spectral embedding vector of each node for community detection. Asymptotic consistencies of the proposed method in terms of community detection are established, which are also supported by numerical experiments on various synthetic networks and two real-life multi-layer networks.
DOI: 10.6084/m9.figshare.19967886.v1
title: Covariate-assisted community detection in multi-layer networks

description: Communities in multi-layer networks consist of nodes with similar connectivity patterns across all layers. This article proposes a tensor-based community detection method in multi-layer networks, which leverages available node-wise covariates to improve community detection accuracy. This is motivated by the network homophily principle, which suggests that nodes with similar covariates tend to reside in the same community. To take advantage of the node-wise covariates, the proposed method augments the multi-layer network with an additional layer constructed from the node similarity matrix with proper scaling, and conducts a Tucker decomposition of the augmented multi-layer network, yielding the spectral embedding vector of each node for community detection. Asymptotic consistencies of the proposed method in terms of community detection are established, which are also supported by numerical experiments on various synthetic networks and two real-life multi-layer networks.
DOI: 10.5281/zenodo.6471678
title: Datasets of the manuscript "Optical manipulation of Rashba-split 2-Dimensional Electron Gas"

description: You can find here the two TimeResolved ARPES datasets used in the paper titled "Optical manipulation of Rashba-split 2-Dimensional Electron Gas". https://www.nature.com/articles/s41467-022-30742-5 Data are in Igor Pro binary format. RawData_1 is the dataset used in Fig.3. The x scaling has been converted from angle to momentum and the energy is rescaled with respect to the chemical potential at negative delays. The Time Delay scaling is found in timewave_1. Similarly, RawData_2, RawData_20 and timewave_2 are the datasets used in Fig.2 of the paper. This x scale in both datasets has been converted from angle to momentum. The y-axes in RawData_20 is the kinetic energy. The y-axes in RawData_2 is the energy rescaled to the chemical potential at each time delay. The chemical potential is obtained by fitting the topological surface state around its Fermi momentum.
DOI: 10.5281/zenodo.6471677
title: Datasets of the manuscript "Optical manipulation of Rashba-split 2-Dimensional Electron Gas"

description: You can find here the two TimeResolved ARPES datasets used in the paper titled "Optical manipulation of Rashba-split 2-Dimensional Electron Gas". https://www.nature.com/articles/s41467-022-30742-5 Data are in Igor Pro binary format. RawData_1 is the dataset used in Fig.3. The x scaling has been converted from angle to momentum and the energy is rescaled with respect to the chemical potential at negative delays. The Time Delay scaling is found in timewave_1. Similarly, RawData_2, RawData_20 and timewave_2 are the datasets used in Fig.2 of the paper. This x scale in both datasets has been converted from angle to momentum. The y-axes in RawData_20 is the kinetic energy. The y-axes in RawData_2 is the energy rescaled to the chemical potential at each time delay. The chemical potential is obtained by fitting the topological surface state around its Fermi momentum.
DOI: 10.35002/dhbd-1978
title: Damage Prevention Regulation Contravention Reports (formerly know as Unauthorized Activity Reports)
DOI: 10.5281/zenodo.6608763
title: Deep learning for ecology and evolution

description: Supplementary table with references on deep learning in ecology and evolution. Includes reference information with URLs and abstracts.
DOI: 10.5281/zenodo.6478770
title: Deep learning for ecology and evolution

description: Supplementary table with references on deep learning in ecology and evolution. Includes reference information with URLs and abstracts.