Search Capacity Research Articles

Project Scheduling using Event Based Scheduler with ABC

Scheduling is the first and foremost step for every project implementation. Project scheduling could be a mechanism to communicate what tasks has to be compelled to get done and which resources are going to be allotted to finish those tasks in what timeframe. Project scheduling occurs during the planning phase of the project. The problem comprises the correct assignment of employees to the various tasks that frame a software project, casing in time and cost limitations. To accomplish this objective, this paper presents and discusses the EBS with ACO, FCM clustering and EBS with ABC and the conclusions are drawn from it. First, to schedule human resources to tasks we implement Event based scheduler with the Ant colony optimization algorithm (ACO) for probabilistic optimization, second, for fast scheduling we implemented Fuzzy c means clustering to assign similar data points of employees to clusters so that the searching space will be reduced. Third, for optimum scheduling we apply Artificial Bee Colony algorithm with Event Based Scheduler. Artificial bee colony (ABC) is an optimization algorithm based on stochastic calculation which has demonstrated good search capacities on numerous advancement issues. Based on these findings we briefly describe the scheduling with FCM-EBS with ABC prompt optimum values.

Are global R&D partnerships enough to increase a company's innovation performance? The role of search and integrative capacities

The number of global R&D partnerships has grown in recent times thanks to the wealth of cross-cultural knowledge and skills they provide to businesses, allowing firms to significantly differentiate their innovation processes and upgrade their innovation performance. But, in order to integrate and capitalize on external knowledge drawn through these international partnerships, companies also need to more effectively develop key internal capabilities. While the literature shows that absorptive capacity is critical in this process, only few studies successfully break up this concept into sub-capacities and analyze their specific impacts on firm's innovation performance, in particular in the specific and underdeveloped context of global R&D partnerships (GPs). This study addresses this research gap by empirically analyzing the interrelation between global R&D partnerships, search and integrative capacities, and innovation performance. We tested our hypotheses on a sample of 112 medium-sized Italian firms with established GPs, leveraging a Partial Least Square (PLS) Structural Equation Model (SEM). Our results suggest that knowledge drawn from GPs plays a vital role in the innovation processes of the analyzed firms, but only when combined with the development of both of the internal sub-capacities we investigated (namely search and integrative capacities) thus, demonstrating an indirect effect. Also, search capacity was found to have a stronger effect compared to integrative capacity, while also affecting the integrative capacity of the firm in question. The implications from a managerial perspective are also provided in order to stimulate debate on international R&D collaborations.

A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10−7 pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.

Rational approximation with cuckoo search algorithm for multifarious Painlevé type differential equations

Abstract This endeavor aims to present a new comprehensive technique to compute the approximate solutions of Painleve type differential equations (P-type-DE) with arbitrary order. The purpose is achieved by the idea of combining rational approximation with the cuckoo search algorithm (RACS). The cuckoo search algorithm (CSA) is mainly a meta-heuristic optimization technique inspired by the brood parasitism of cuckoo birds. It utilizes Levy flights for the searching procedure with few controlling variables, which makes its global search capacity extremely robust to optimize the objective function. Here, the key role of the objective function is played by the fitness function of P-type-DE, which is structured by a trial solution having unknown weights. Furthermore, a comparative study is also conducted among the calculated results, the exact and other reference solutions, which demonstrates the rightness and computational intelligence of RACS for the non-linear differential equations of arbitrary order.

A Novel Efficient Initial Access Method for 5G Millimeter Wave Communications Using Genetic Algorithm

Initial Access (IA) in millimeter wave (mmWave) communications, i.e., the mechanism by which the Base Station (BS) and the User Equipment (UE) establish a directional communication link, is a challenging problem. A significant delay can be incurred when the BS and the UE try to find the appropriate beam alignment for obtaining a directional link, and solving this problem efficiently has become an important research topic. In this work, we propose a new beam refinement method based on Genetic Algorithms (GAs) which is particularly advantageous when the number of antennas is large. In particular, we consider a scenario where both the BS and the UE are equipped with a single RF chain and multiple antennas, i.e., a single beam analog beamforming is considered at the BS and UE. The efficiency of the proposed method and the effect of several parameters, such as the number of transmit and receive antennas, codebook size, and transmission power are investigated. In the case of small parameters, it is shown that the proposed method achieves the same capacity of Exhaustive Search (ES) but with a much lower complexity. Moreover, in comparison with existing methods, our method has a superior performance in terms of capacity, outage probability, and power consumption.

Agricultural Information Needs of Rainfed Farmers: A Study from Telangana

A paradigm shift in rainfed agriculture can be obtained mainly through technological interventions. In this context, the role of extension and advisory services that are saddled with this responsibility should focus on novel approaches. Application of relevant information in agriculture sector brings positive impacts on farm productivity. Timely access to information on weather aspects, best farming practices, market information etc. help farmers to make correct decisions about what crops to plant, when to plant and where to sell their products. The best way to satisfy the rainfed farmers is to understand and consider their specific needs, constraints and capabilities properly, which helps to serve them in an efficient manner. A study has been undertaken to understand the different agricultural information needs of rainfed farmers in the Mahabubnagar district of Telangana state India. It is observed that farmers need various types of information related to agricultural activities and the major agricultural information needs of the farmers were found to be related to farm implements and machinery, water management, seed varieties, seed treatment, alternate crops and pest and disease management. In order to address these information needs, farmers were accessing a number of sources. It is important to enhance the information search capacity of the rainfed farmers to improve the farm productivity and income leading to enhanced livelihoods.

Total Scholarly Impact: Law Professor Citations in Non-Law Journals

This Article provides the first ranking of legal scholars and law faculties based on citations in non-law journals. Applying the methods, as much as possible, of the widely used Leiter-Sisk “Scholarly Impact Score,” which includes only citations in law publications, we calculate a “Interdisciplinary Scholarly Impact Score” from the non-law citations over a five-year period (2012-2018) to the work of tenured law faculty published in that period in non-law journals. We also provide the weighted scores for law faculty at the top 25 law schools as ranked by the US News rankings, a school-by-school ranking, and lists of the top five faculty by non-law citations at each school and of the top fifty scholars overall. The work of legal scholars outside of law journals is not trivial. Over 600 faculty members from the 25 schools in our cohort published almost 3,000 articles in non-law journals from 2012-2018, and those articles received close to 21,000 citations in non-law journals. The faculties that rank in the top ten based on weighted scores for Interdisciplinary Scholarly Impact using the Leiter-Sisk weighting method (2x the mean + the median) for all faculty with at least one publication in the study period are: Minnesota, Stanford, Yale, Duke, Cal-Irvine, Georgetown, Boston University, USC, Vanderbilt, and George Washington. The rankings, although subject to limitations similar to those faced by the law journal citation studies, demonstrate that it is possible with reasonable effort to include citations in both law and non-law journals in rankings of legal scholars and law school faculties. Legal scholars are cited in non-law journals for the work they publish in legal journals and, in many cases, for work they publish in non-law journals. Counting only their citations in law journals thus underestimates both the impact of their legal scholarship and their interdisciplinary impact. Non-law journals are widely read by law and policy scholars, scientists who influence legal scholarship, and policymakers, and publications and citations of legal scholars in non-law journals can be an indication of work that has transcended the conceptual frameworks, assumptions, or methods of legal research. Publications and citations in non-law journals thus provide an additional indication of the influence of legal scholars. Citations in non-law journals also provide an indication of the influence of legal scholars on the overall scholarly enterprise outside of law, and accounting for non-law citations in legal rankings can also encourage interdisciplinary scholarship. Scholars from non-law fields have made important contributions to legal scholarship, but the reverse should also be the case. Acceptance by other fields of legal scholars’ proposed legal reforms can play an important role in determining their success, which is made more likely when legal scholars are included in the work of other disciplines. For these reasons, we suggest in the Article that future evaluations of legal scholars’ work include both the Law Scholarly Impact Score and the new Interdisciplinary Scholarly Impact Score, or combine the two into a Total Scholarly Impact Score. Although there is some mismatch in the citation engine capacities and the time frames for our non-law journal citation study and the most recent Sisk et al. law journal citation study, a combination of the two can provide a rough approximation of the Total Scholarly Impact Score. The top ten law faculties based on this combined measure are: Yale, Harvard, Chicago, NYU, Stanford, Columbia, Duke, Cal-Berkeley, Pennsylvania, and Vanderbilt. The databases used in the law and non-law studies and their search capacities differ, making it difficult to develop a citation study method that captures all of a faculty members’ law and non-law publications and all citations to them in defined time frames. We are working to improve the non-law citation study database and search capacity. Following an introduction to the project, in Part I we discuss why accounting for legal scholars’ non-law publications and citations is important when assessing scholarly impact. Part II describes our methodology. Part III presents our results, and Part IV discusses the results.

A novel hybrid genetic algorithm for the location routing problem with tight capacity constraints

Location routing problem (LRP) is a popular and challenging topic in the field of logistic systems. LRP needs to address the depot location problem and vehicle routing problem at the same time. Till now, different LRP variants have been formulated to better meet realistic requirements. In this study, we focus on the capacitated LRP (CLRP) with tight capacity constraint on both depots and vehicles. To cope with the tight constraints, a hybrid genetic algorithm (HGA) is developed to search not only feasible solution space but also infeasible solution space. The proposed HGA combines the wide exploration capacity of GA, and the fast exploitation capacity of neighbourhood local search. To evolve GA for CLRP, solutions are represented by sets and sequences, and accordingly, a multi-sequence-based crossover is designed for the offspring generation. Moreover, a population management scheme is designed to facilitate GA’s evolution. Experiments are conducted on two benchmark sets and the results show that HGA is quite competitive with existing well-known CLRP algorithms on the classical instances. Furthermore, HGA is able to obtain quite a number of new best solutions on the real-life-like instances with tighter constraints.

An improved Simulated Annealing algorithm based on ancient metallurgy techniques

Simulated Annealing (SA) is a single-solution-based metaheuristic technique based on the annealing process in metallurgy. It is also one of the best-known metaheuristic algorithms due to its simplicity and good performance. Despite its interesting characteristics, SA suffers from several limitations such as premature convergence. On the other hand, Japanese swordsmithing refers to the manual-intensive process for producing high-quality bladed weapons from impure raw metals. During this process, Japanese smiths fold and reheat pieces of metal multiple times in order to eliminate impurities and defects. In this paper, an improved version of the SA algorithm is presented. In the new approach, a population of agents is considered. Each agent conducts a search strategy based on a modification of the SA scheme. The proposed algorithm modifies the original SA incorporating two new operators, folding and reheating, inspired by the ancient Japanese Swordsmithing technique. Under the new approach, the process of folding is conceived as a compression of the search space, while the reheating mechanism considers a reinitialization of the cooling process in the original SA scheme. With this inclusion, the new algorithm maintains the computational structure of the SA method but improving its search capacities. In order to evaluate its performance, the proposed algorithm is tested in a set of 28 benchmark functions, which include multimodal, unimodal, composite and shifted functions, and 3 real world optimization problems. The results demonstrate the high performance of the proposed method when compared to the original SA and other popular state-of-the-art algorithms.

Estimation of the hydraulic parameters of leaky aquifers based on pumping tests and coupled simulation/optimization: verification using a layered aquifer in Tianjin, China

Accurate estimates of aquifer parameters are necessary for effective groundwater management and for geotechnical engineering applications. Pumping tests may be employed to estimate the hydraulic conductivity in leaky aquifer/aquitard systems. This work introduces a hybrid algorithm with global search capacity (the Genetic algorithm, GA) and local search capacity—the Levenberg-Marquardt (LM) algorithm—coupled with a modified Neuman-Witherspoon solution for leaky aquifers to estimate the aquifer’s hydraulic parameters from pumping-test data. The GA is employed to determine the initial guesses of the aquifer parameter values. The optimal parameter values are then obtained with the LM algorithm, yielding a mixed GA/LM algorithm, herein named GALMA. Results show that the drawdown trends based on the estimated parameters agree well with measured drawdown. The proposed estimation algorithm identifies aquifer parameters with greater reliability than previous approaches. Verification of the GALMA is carried out based on three pumping tests in a layered aquifer in Tianjin, China, and on four historical case studies involving diverse hydrogeological settings. The excellent match between observed drawdown and GALMA-estimated parameters demonstrates the estimation accuracy and superior performance relative to previously reported estimation methods.

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles.

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

Guía metodológica de empoderamiento estudiantil desde la gestión formativa socio-cultural y profesional en la Universidad de Tahoua, Níger

The debates about the formation of professionals in Africa in general but particularly in Niger are always asking about how to form authentic professionals, competent and engaged with the context that they belong to, in a way that they could be capable to concrete socio cultural and economic affective transformations. At the University of Tahoua after the review of all the different formative programs of education, legal, and economic majors through the observation techniques, surveys and focus groups, are revealed shortcomings related with the student's capacity to ideal with the researches that allow the cultural transformations, in function of the contextual necessities. Since there this project exposes the methodological guide to boost the search capacities of all the students from the university, the guide of the application allowed to the investigated people to confront the problems previously mentioned in order to propose viable and affective solutions. The results show significant advances in the professional job and in their rooted relationship to the membership context. Keywords : Professional empowerment, identity, sociocultural, methodological guide, development and contextual transformation.

Managing external knowledge search: the multiple and contingent roles of absorptive capacity

ABSTRACTNowadays, it is widely accepted that the search for external knowledge is critical to firm innovation performance. However, a framework to systematically demonstrate what factors interactively influence a firm’s openness in innovation search is still in lack. In this study, taking a perspective of absorptive capacity for innovation search, we develop an integrative framework with three-way interaction to explore how absorptive capacity, financial slack, and industrial competition shape a firm’s external knowledge search behaviour. GLS regression is adopted to examine the model with a balanced 10-year panel dataset of 298 manufacturing firms who publicly traded in the USA. The results reveal that both direct and indirect effects of absorptive capacity on external knowledge search are contingent upon the degree of industrial competition, and will be diminished for firms embedded in highly competitive industries. Directions for future research and managerial implications are further discussed.

A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization.

Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.

Quantum‐Behaved Particle Swarm Optimization Algorithm Based on the Two‐Body Problem

The present study proposes an improved Quantum-behaved particle swarm optimization algorithm based on the two-body problem model (QTPSO) for solving the problem that other quantum-behaved particle swarm optimization algorithms easily converge on local optimal solutions when solving complex nonlinear problems. In the proposed QTPSO algorithm, particles are categorised as core particles and edge particles. Once the position of the core particle is determined, the edge particle appears in the vicinity of the attractor exhibiting a high probability, and the attractor is obtained through the random weighted sum of the core particle and the optimal mean position. Through simulation of the motion of these two particles by applying the interaction of the particles in the two-body problem, this mechanism not only improves the diversity of the population, but also enhances the local search capacity. To validate the proposed algorithm, three groups of experimental results were obtained to compare the proposed algorithm with other swarm intelligence algorithms. The experimental results indicate the superiority of the QTPSO algorithm.

Ternary content-addressable memory with MoS2 transistors for massively parallel data search

Ternary content-addressable memory (TCAM) is specialized hardware that can perform in-memory search and pattern matching for data-intensive applications. However, achieving TCAMs with high search capacity, good area efficiency and good energy efficiency remains a challenge. Here, we show that two-transistor–two-resistor (2T2R) transition metal dichalcogenide TCAM (TMD-TCAM) cells can be created by integrating single-layer MoS2 transistors with metal-oxide resistive random-access memories (RRAMs). The MoS2 transistors have very low leakage currents and can program the RRAMs with exceptionally robust current control, enabling the parallel search of very large numbers of data bits. These TCAM cells also exhibit remarkably large resistance ratios (R-ratios) of up to 8.5 × 105 between match and mismatch states. This R-ratio is comparable to that of commercial TCAMs using static random-access memories (SRAMs), with the key advantage that our 2T2R TCAMs use far fewer transistors and have zero standby power due to the non-volatility of RRAMs. By integrating two-dimensional MoS2 transistors with metal-oxide resistive random-access memories, two-transistor–two-resistor ternary content-addressable memory cells can be created, which could be used to search large amounts of data in parallel.

A grouping particle swarm optimizer

Due to the lack of global search capacity, most evolutionary or swarm intelligence based algorithms show their inefficiency when optimizing multi-modal problems. In this paper, we propose a grouping particle swarm optimizer (GPSO) to solve this kind of problem. In the proposed algorithm, the swarm consists of several groups. For every several iterations, an elite group is constructed and used to replace the worst one. The thought of grouping is helpful for improving the diversity of the solutions, and then enhancing the global search ability of the algorithm. In addition, we apply a simple mutation operator to the best solution so as to help it escape from local optima. The GPSO is compared with several variants of particle swarm optimizer (PSO) and some state-of-the-art evolutionary algorithms on CEC15 benchmark functions and three practical engineering problems. As demonstrated by the experimental results, the proposed GPSO outperforms its competitors in most cases.

Infection by the microsporidium of Clado Nosema/Vairimorpha in pupal parasitoids.

The sugarcane borer, Diatraea saccharalis is one of the hosts more used for parasitoid pupal multiplication in Brazil. The parasitoids pupal of Trichospilus diatraeae and Palmistichus elaeisis are generalist natural enemies with potential to suppress populations of diverse families of lepidopteran pests. The success in the utilization of these natural enemies in the field is directly related to the capacity of search of the host, this capacity might be affected by the presence of the pathogens. In this context, the aim of this essay was to detect the presence of intracellular parasites of Phylum Microsporidia. These pathogens may cause morphological and behavioral alterations. The presence of infection was verified by microscopy and was confirmed by amplification of region small subunit (SSU) of ribosomal RNA using universal primers for microsporidia of Nosema sp. The purified PCR products were submitted to sequencing, and the sequences that had been obtained were edited and aligned with the sequences in a Genbank database. In this way, it was possible to verify the presence of intracellular parasites in T. diatraeae, P. elaeisis and D. saccharalis pertaining to Clade Nosema/Vairimorpha. However, this is the first one report about detection of the microsporidia in the parasitoids T. diatraeae and P. elaeisis.

Simultaneous Multilateral Search

This paper studies simultaneous multilateral search (SMS) in over-the-counter markets: When searching, a customer simultaneously contacts several dealers and trades with the one offering the best quote. Higher search intensity (how often one can search) improves welfare, but higher search capacity (how many dealers one can contact) or higher transparency about dealer inventories might be harmful. When the market is in distress, customers might inefficiently favor bilateral bargaining (BB) over SMS. Such preference for BB speaks to the sluggish adoption of SMS trading, like request-for-quote protocols, in over-the-counter markets. Furthermore, a market-wide shift to SMS may not be socially optimal.

CP-ABSE: A Ciphertext-Policy Attribute-Based Searchable Encryption Scheme

Searchable encryption provides an effective mechanism that achieves secure search over encrypted data. A popular application model of searchable encryption is that a data owner stores encrypted data to a server and the server can effectively perform keyword-based search over encrypted data according to a query trapdoor submitted by a data user, where the owner’s data and the user’s queries are kept secret in the server. Recently, many searchable encryptions have been proposed to achieve better security and performance, provide secure data updatable feature ( dynamics ), and search results verifiable capability ( verifiability ). However, most of the existing works endow the data user an unlimited search capacities and do not consider a data user’s search permissions. In practical application, granting search privileges for data users is a very important measure to enforce data access control. In this paper, we propose an attribute-based searchable encryption scheme by leveraging the ciphertext-policy attribute-based encryption technique. Our scheme allows the data owner to conduct a fine-grained search authorization for a data user. The main idea is that a data owner encrypts an index keyword under a specified access policy, if and only if, a data user’s attributes satisfy the access policy, the data user can perform search over the encrypted index keyword. We provide the detailed correctness analyses, performance analyses, and security proofs for our scheme. The extensive experiments demonstrate that our proposed scheme outperforms the similar work CP-ABKS proposed by Zheng on many aspects.