Competitive Approach Research Articles

Coauthorship Network Mining for Scholar Communication and Collaboration Path Recommendation

With the increasing penetration of interdisciplinary subjects, it is more difficult for researchers to complete a paper individually, showing that the division of labor can improve the level and efficiency of scientific research. Thus, collaboration among multiple scholars has become a trend in academic research. However, because the numbers of scholars and papers are increasing and cooperation between scholars has become more frequent in recent years, it is an increasingly challenging task to discover useful knowledge resources for researchers. Against the background of big data, how to help scholars quickly find interested target collaborators, encourage them to participate more actively in academic communication, and create high-quality achievements in scientific research has become a significant problem. Considering this challenge, this article proposes a framework of coauthorship strength, author contribution, and search (CCS，taking the first letter of the keyword), which is based on the coauthorship feature of Google Academics. In CSS, we combined the search algorithm to select the optimal connection path to help scholars find interested target scholars efficiently and to better solve practical application problems. Finally, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results of our approach show better search outcomes compared to other competitive approaches.

THREE-STEP INTERVIEW IN TEACHING SPEAKING

Three-Step Interview is a cooperative learning strategy that offers an alternative to the competitive approach of whole class question-answer, in which the teacher will provide a certain topic and students may ask questions in pair. The purpose of this research is to investigate students’ difficulties on implementing Three-Step Interview in speaking. This research used descriptive qualitative method. The tenth grade of IPA 3 in one Senior High School in Cimahi which consisted of 32 students were selected as the respondents of this research. Questionnaire and interview were used in order to obtain the data. The result of this research shows that most of the students feel difficulties in linguistic aspects, included vocabulary (94%), pronunciation (56%), and grammar (88%) out of 32 students. Meanwhile, there is no significance difficulties in implementing the technique, only 44% students who find difficulties in the second step. Keywords : Three-Step Interview, Teaching Speaking, Difficulty

Semi-solid processing of aluminum and magnesium alloys: Status, opportunity, and challenge in China

Owing to its low cost, short process and low energy consumption, semi-solid processing (SSP) of aluminum (Al) and magnesium (Mg) alloys has been considered as a competitive approach to fabricate complicated components with excellent performance. Over the past decade, significant progress has been achieved in deeply understanding the SSP process, the microstructure and performance of the fabricated components in China. This paper starts with a retrospective overview of some common slurry preparation methods, followed by presenting the performance and the underlying mechanisms of SSP fabricated alloys. Then, the mainstream opinions on the microstructure evolution and rheological flow behavior of semi-solid slurry are discussed. Subsequently, the general situation and some recent examples of industrial applications of SSP are presented. Finally, special attention is paid to the unresolved issues and the future directions in SSP of Al and Mg alloys in China.

Durability of an UHPFRC under mechanical and chloride loads

The high cost of UHPFRC is a limitation on the practical application in real construction projects. However, a very competitive UHPFRC approach is the hybrid structural elements, where thin layers of UHPFRC are employed to rehabilitate/strengthen damage cover concrete. New layers subjected to harsh conditions (loads and/or environmental) can eventually crack under service conditions, changing the local transport properties and thus, a faster ingress of detrimental substances occur, such as chlorides ions. Most of the studies on chloride penetration in UHPFRC have focused on determining the transport properties of sound, non-cracked specimens. Thus, an experimental campaign was carried out to assess chloride ingress in loaded and/or cracked UHPFRC and the effect of such ions on mechanical performance. Typical service cracks patterns were imposed on UHPFRC specimens and then exposed to wetting–drying cycles in a chloride solution. After 1-year chloride exposure, UHPFRC specimens were in good condition with no significant losses in flexural strength; however, stiffness might be affected. The chloride contents up to 20 mm depth were superior to the European standards critical chloride content. A minimum cover depth of 20 mm of new UHPFRC is recommended to protect a concrete substrate in hybrid structures for exposure classes XS3.

NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining

Nonoverlapping sequential pattern mining is an important type of sequential pattern mining (SPM) with gap constraints, which not only can reveal interesting patterns to users but also can effectively reduce the search space using the Apriori (anti-monotonicity) property. However, the existing algorithms do not focus on attributes of interest to users, meaning that existing methods may discover many frequent patterns that are redundant. To solve this problem, this article proposes a task called nonoverlapping three-way sequential pattern (NTP) mining, where attributes are categorized according to three levels of interest: strong, medium, and weak interest. NTP mining can effectively avoid mining redundant patterns since the NTPs are composed of strong and medium interest items. Moreover, NTPs can avoid serious deviations (the occurrence is significantly different from its pattern) since gap constraints cannot match with strong interest patterns. To mine NTPs, an effective algorithm is put forward, called NTP-Miner, which applies two main steps: support (frequency occurrence) calculation and candidate pattern generation. To calculate the support of an NTP, depth-first and backtracking strategies are adopted, which do not require creating a whole Nettree structure, meaning that many redundant nodes and parent–child relationships do not need to be created. Hence, time and space efficiency is improved. To generate candidate patterns while reducing their number, NTP-Miner employs a pattern join strategy and only mines patterns of strong and medium interest. Experimental results on stock market and protein datasets show that NTP-Miner not only is more efficient than other competitive approaches but can also help users find more valuable patterns. More importantly, NTP mining has achieved better performance than other competitive methods in clustering tasks. Algorithms and data are available at: https://github.com/wuc567/Pattern-Mining/tree/master/NTP-Miner .

Nanoparticle Size Threshold for Magnetic Agglomeration and Associated Hyperthermia Performance.

The likelihood of magnetic nanoparticles to agglomerate is usually estimated through the ratio between magnetic dipole-dipole and thermal energies, thus neglecting the fact that, depending on the magnitude of the magnetic anisotropy constant (K), the particle moment may fluctuate internally and thus undermine the agglomeration process. Based on the comparison between the involved timescales, we study in this work how the threshold size for magnetic agglomeration () varies depending on the K value. Our results suggest that small variations in K-due to, e.g., shape contribution, might shift by a few nm. A comparison with the usual superparamagnetism estimation is provided, as well as with the energy competition approach. In addition, based on the key role of the anisotropy in the hyperthermia performance, we also analyse the associated heating capability, as non-agglomerated particles would be of high interest for the application.

Improving Adherence of Infection Prevention Standards in Health Facilities: The Role of Competition Approach from Four Regions of Tanzania Mainland

Improving Adherence of Infection Prevention Standards in Health Facilities: The Role of Competition Approach from Four Regions of Tanzania Mainland

P-007: Exploring a safety switch in NKG2D and BCMA CAR NK-92MI immunotherapy

P-007: Exploring a safety switch in NKG2D and BCMA CAR NK-92MI immunotherapy

DETERMINANTS AND STRATEGIES OF MEDICAL TOURISM DEVELOPMENT BY TAKING THE SERVICE INTEGRATION AND TOURISM DESTINATION COMPETITIVENESS APPROACH (A Case Study Razavi Khorasan Province, Iran)

Introduction: Considering the significant position of medical tourism industry in global economy as well as Iran’s capabilities in this industry, the present research was conducted so as to compile the determinants of medical tourism development and present its respective strategies taking the approach of health village expansion and integrated service provision in Health Region No. 9 (Razavi Khorasan province). Methodology: This research was conducted by combining the qualitative and quantitative techniques. Data collection was done by content analysis followed by an interview and a survey administered to a number of key experts. Data were analyzed by MAXQDA-18, Expert Choice11 and Excel software and SOWT, fuzzy DEMATEL and AHP techniques. Findings: In this study, a framework of indexes was compiled and evaluated selected from different aspects taking the approach of service integration and destination competitiveness in the form of health villages. In the next phase, the status quo of Mashhad was categorized into 38 internal and 31 external factors to be prioritized. In the end, considering the identified factors and their interrelationships, the most important strategies were extracted and prioritized. Conclusions: To create a sustainable competitive market and attract more medical tourists, revealed that the respective authorities need to pay due attention to this issue proportionate to the indexes and strategies envisaged in the compilation, designing and implementation of policies and plans related to Mashhad’s medical tourism development in moving towards the creation of competitive advantage in an integrated way and promotion of medical tourism by focusing on the prioritization performed for the healthcare system.

Competitive market space in the context of the need to increase the competitiveness of the Russian economic system

The article considers the conceptual framework of the content essence of competition, the conditions for the formation of approaches for high competitiveness of business entities as the main element of the functioning of the market economy. The article analyzes the current state of the competitive market space in Russia. A comparative assessment of the state of the Russian competitive environment in 2016 and 2020 is carried out in the context of: determining the areas with the highest (and lowest) degree of influence on the competitive market, guidelines for strengthening competitive positions, positive effects of competition for business entities, etc. The key directions of increasing the competitiveness of business entities are formulated. An up-to-date set of methods for assessing competitiveness is presented. The advantages of healthy competition in the system of market relations for the growth of the welfare of economic entities, the economic development of the country and, accordingly, the improvement of the standard of living of the population are determined. In Russia, at present, the development and implementation of state programs for the development of small and medium-sized businesses in the regions is insufficient for the formation of an effective competitive market environment for the economy in the regions. While the development of healthy competitive relations allows the most effective impact on market mechanisms in the regions, and also contributes to the sustainable development of socio-economic, technical areas of the economy. And the key task of the state in this case is to provide opportunities for realizing the competitive potential of the region, that is, its readiness for effective competition and sustainable functioning of a certain competitive market environment. The formation of such an environment ensures the effectiveness of interregional and international relations of the region, which leads to positive effects in the socio-economic development of the state as a whole.

Experimental study on fast quenching of cryogenic LN2 flow through interior micro-fin tube

Future cryogenic vehicle requires a fast propellant fueling as well as a rapid pipeline quenching operation. In the present study, a novel interior micro-fin tube concept, with a series of cyclic fins arranged along the flow direction and has the size of 1 mm in both of fin height and fin width, is proposed to reach the purpose of cryogenic quenching acceleration. A cryogenic quenching test platform is established to evaluate the micro-fin effect. A straight tube and three micro-fin tubes, with fin intervals of 50, 100, and 200 mm, are fabricated and tested under different inlet Reynolds numbers (Re) conditions. The tube quenching cases covering inlet flow rate range of Re = 5000–45,000 are performed, and the results on temperature decrease, heat flux, and pressure drop are analyzed and compared. The test results demonstrate that the proposed micro-fin tube significantly reduces time cost in quenching process, and about 33–62% of time savings are reached for different Re conditions. The mechanism of micro-fin tube in facilitating quenching progress is that a radial velocity component within the annular vapor film could be induced by the micro-fin structure. This radial velocity acts on the liquid-vapor interface directly, which motivates interface fluctuation or tears the interface, and finally brings about an earlier liquid-wall contact at higher wall temperature. Moreover, it shows high heat flux appears immediately at the beginning of the quenching operation, and an approximately linear temperature decrease is experienced throughout the entire process. In addition, changing fin interval could affect the quenching rate under low Re conditions, but the fin interval influence decreases with the increase of Re. The test results show that the fin interval has slight influence on time cost when Re = 30,000. For a high-Re quenching event, a micro-fin tube with large fin interval could be suggested in the view of balancing consideration on heat transfer enhancement and flow resistance reduction. Compared to the surface treatment approaches, the proposed micro-fin structure tube provides a competitive approach in accelerating the quenching progress, and more work should be performed to optimize the micro-fin structure and the fin arrangement.

Analyzing students’ experience in programming with computational thinking through competitive, physical, and tactile games: the quadrilateral method approach

The lack of computational thinking (CT) skills can be one of the reasons why students find themselves having difficulties in writing a good program. Therefore, understanding how CT skills can be developed is essential. This research explores how CT skills can be developed for programming through competitive, physical, and tactile games. The CT elements in this research focus on four major programming concepts, which are decomposition, pattern recognition, abstraction, and algorithmic thinking. We have conducted game activities through several algorithms that include sorting, swapping, and graph algorithms and analyzed how the game affects the student experience (SX) in understanding the CT concept in those algorithms. We have applied the quadrilateral method approach to the data collection and analysis. The data was obtained through observation, interview/survey based on six SX criteria (attention, engagement, awareness, satisfaction, confidence, and performance), and performances of the conducted game activities were compared. The results of the quadrilation of the data collected show a positive impact on the SX, highlight the effectiveness of the competitive, physical, and tactile game approach proposed in this research towards programming and CT skills development.

QoS aware web service selection using orthogonal array learning on fruit fly optimization approach

PurposeThis paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.Design/methodology/approachIn this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.FindingsTo test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.Originality/valueIn this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

Hyperspectral Image Classification Based on Sparse Superpixel Graph

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

Dovish and Hawkish Influence in Distributive and Integrative Negotiations: The Role of (A)symmetry in Constituencies

Dovish and hawkish constituency pressures influence representative negotiations. Dovish constituency voices promote a collaborative and problem-solving approach, but can also allow for exploitation in negotiations. Hawkish voices encourage a competitive approach, but may leave value on the table. These dynamics are investigated in four experiments. In two interactive dyadic-negotiation experiments (Experiments 1 & 2; N = 186 and N = 220), we investigated how constituency pressures influenced outcomes in two negotiation settings (distributive and integrative). Representatives of dovish constituencies reached higher negotiation outcomes than representatives of hawkish constituencies, when facing a representative with a similar constituency (Experiment 1). However, when representatives with a dovish constituency met with representatives of a hawkish constituency, dovish representatives reached lower gains in both negotiation settings (Experiment 2). This hawkish advantage was replicated in two online scenario studies (Experiments 3 & 4; N = 248 and N = 319). There was no consistent empirical support for the role of a potential future interaction in eliciting representatives’ concessions (Experiment 1–3), however, an absence of accountability to constituents reduced representatives’ competitiveness, irrespective of whom they represented (Experiment 4). Theoretical and practical implications for labor relations, diplomacy, and business negotiations are discussed.

Ligand-binding properties of odorant-binding protein 6 in Athetis lepigone to sex pheromones and maize volatiles.

Athetis lepigone, a noctuid moth feeding on more than 30 different crops worldwide, has evolved a sophisticated, sensitive, and specific chemosensory system to detect and discriminate exogenous chemicals. Odorant-binding proteins (OBPs) are the most important agent in insect chemosensory systems to be explored as an alternative target for environmentally friendly approaches to pest management. To investigate the olfactory function of A. lepigone OBPs (AlepOBPs), AlepOBP6 was identified and expressed in Escherichia coli. The binding affinity of the recombinant OBP to 20 different ligands was then examined using a competitive binding approach. The results revealed that AlepOBP6 can bind to two sex pheromones and ten maize volatiles, and its conformation stability is pH dependent. We also carried out a structure-function study using different molecular approaches, including structure modeling, molecular docking, and a mutation functional assay to identify amino acid residues (M39, V68, W106, Q107, and Y114) involved in the binding of AlepOBP6 to both sex pheromones and maize volatiles in A. lepigone. These results suggest that AlepOBP6 is likely involved in mediating the responses of A. lepigone to sex pheromones and maize volatiles, which may play a pivotal function in mating, feeding, and oviposition behaviors. This study not only provides new insight into the binding mechanism of OBPs to sex pheromones and host volatiles in moths, but also contributes to the discovery of novel target candidates for developing efficient behavior disruptors to control A. lepigone in the future. © 2021 Society of Chemical Industry.

A Systematic Literature Review on Pricing Strategies in the Sharing Economy

As an emerging business model, the sharing economy has gained a large amount of academic attention; the pricing problem in the sharing economy has also been widely investigated. Aiming to capture the current state-of-the-art research on pricing strategies in the sharing economy and foreseeing directions for future research, this article conducts a systematic literature review and content analysis of 158 articles from the Scopus and Web of Science databases. As a result, first, this review proposes an extended definition covering B2C and C2C models and a model structure covering the entire supply chain, based on which 158 articles are categorized into nine sub-models covering 30 scenarios. Second, the general characteristics (i.e., research fields, time and journal distributions, research themes and scenarios) and technical details (i.e., theories, methodologies, approaches, models, and conclusions) of the 158 articles are reviewed and summarized by the pricing party, business mode, and scenario. Finally, this review proposes some future research directions of existing scenarios from the perspectives of information asymmetry, market competition, and empirical approaches, and discusses some extensions, including uninvestigated scenarios and COVID-19-related topics; correspondingly, this review suggests some analytical models and empirical approaches that can be employed to fill these gaps. The proposed research directions and corresponding approaches can be references for future research.

An overlapping sliding window and combined features based emotion recognition system for EEG signals

Purpose The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data. Design/methodology/approach Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features. Findings The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches. Originality/value The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach

The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches.

CIMask: Segmenting instances by class-specific semantic feature extraction and instance-specific attribute discrimination

Instance segmentation is one of the general but challenging tasks in computer vision. In this paper, we present a novel fast single-stage instance segmentation method, called CIMask, which is faster and more accurate than a few recent competitive approaches, including Mask R-CNN baselines. In our approach, the object mask generation is decoupled into class-specific semantic feature extraction and instance-specific attribute discrimination in parallel, responsible for generating powerful feature representation and identifying individuals, respectively. Then, the final masks are generated by distinguishing individuals with the same class-specific semantic. Furthermore, to better deal with the ambiguous samples in natural scenes, a center-aware sampling scheme for ground truth is introduced, which significantly improves the performance without adding extra parameters. Our comprehensive experiments on MS COCO dataset show the effectiveness of the proposed contributions, leading to state-of-the-art single-stage instance segmentation performance. The center-aware sampling scheme obtains a 1% steady performance gain on the Cityscapes dataset.