Evaluation Of Competitiveness Research Articles

Low Resource Summarization using Pre-trained Language Models

With the advent of Deep Learning based Artificial Neural Networks models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly restricted to high-resource languages such as English and low-resource languages still suffer from a lack of available resources in terms of training datasets as well as models with even baseline evaluation results. Considering the limited availability of resources for low-resource languages, we propose a methodology for adapting self-attentive transformer-based architecture models (mBERT, mT5) for low-resource summarization, supplemented by the construction of a new baseline dataset (76.5k article, summary pairs) in a low-resource language Urdu. Choosing news (a publicly available source) as the application domain has the potential to make the proposed methodology useful for reproducing in other languages with limited resources. Our adapted summarization model urT5 with up to 44.78% reduction in size as compared to mT5 can capture contextual information of low resource language effectively with evaluation score (up to 46.35 ROUGE-1, 77 BERTScore) at par with state-of-the-art models in high resource language English (PEGASUS: 47.21, BART: 45.14 on XSUM Dataset) . The proposed method provided a baseline approach towards extractive as well as abstractive summarization with competitive evaluation results in a limited resource setup.

Construction and application of a core competitiveness evaluation index system for public health personnel training based on the CIPP model

BACKGROUND: With the change of human disease spectrum and the increase of public emergencies, the public’s demand for health services and health is gradually increasing and higher expectations are placed on public health personnel. OBJECTIVE: Research needs to establish a comprehensive system of evaluation indices for accurate assessment of the core competencies of public health personnel, to enhance their core competitiveness, and introduce novel approaches to evaluate talent development in the field of public health. METHODS: The study is based on the CIPP (Context Input Process Product) model and uses literature analysis, semi-structured interviews, and Delphi methods to construct an evaluation index system for the core competitiveness of public health talent cultivation. The entropy method is used to determine the weight of the model evaluation index. Finally, the improved Artistic Be Colony algorithm (ABC) is used to optimize the BP network, and apply it to evaluate the core competitiveness of public health talent cultivation. RESULTS: The improved BP network achieved the target accuracy within 11 iterations, with the optimal value observed after 16 iterations, producing an MSE (Mean Square Error) value of 10–13. The evaluation of the index system reported a 97% accuracy, and upon application to the university’s public health training programme, nearly 50% of students and teachers achieved core competitiveness quality scores above 90. CONCLUSIONS: The aforementioned method suggests that it can proficiently assess the fundamental competitiveness of training for public health personnel and offer guidance for future development in the domain of public health.

Exporting Beekeeping Products: Importance of Detailed Market Analysis and Brand Positioning

To strengthen the export potential of Georgian bees and intervene in international markets, it is necessary to make a comprehensive and profound analysis. The article discusses the influences of market segmentation, consumers’ preferences, international trading agreements, legal regulations, product certification, technological innovations and competition on the development of marketing and branding strategies, supporting the efficient positioning of the high-quality Georgian bee products on the international markets and increase in export. Competition evaluation is critical for determining market possibilities and positioning Georgian beekeeping on global markets. The physiological and qualitative functions of the Georgian mountain grey bee must be emphasized: its gentleness, excessive productivity, and resistance to ailments. The article additionally mentions the importance of a strategic method to boost export capability in integrating technological improvements and contemporary advertising strategies, which include digital technology and database analytics, which will have an instantaneous effect on increasing the charm of beekeeping products. Determining purchaser preferences, cooperation with international organizations and obtaining first-rate certificates are crucial for the global reputation of Georgian beekeeping manufacturers and gaining agreement with customers. Market analysis, contemporary branding, and dynamic marketing are considered the basis for the hit export of Georgian beekeeping products and their established order in international markets.

Partially ordered set evaluation of logistics competitiveness of coastal port cites considering carbon emissions

Aiming at the problem of logistics competitiveness of coastal cities, a logistics competitiveness evaluation method based on weighted and partially ordered set combinations is proposed. Carbon emissions and inhalable pollutant concentrations are included in the evaluation scope, and an evaluation index system for the logistics competitiveness of 17 coastal port cities is constructed. The results show that: (1) the competitiveness of the logistics industry in 17 coastal port cities in China has been continuously improved, and the catch-up effect of logistics industry development in coastal port cities such as Shenzhen, Ningbo and Qingdao is obvious. The competitiveness level of the logistics industry shows an obvious spatial imbalance. (2) The competitiveness of the logistics industry in Yingkou, Beibu Gulf and other coastal port cities is low, and the difference in competitiveness of the logistics industry in coastal port cities is the main reason for the overall imbalance. (3) The technological innovation, openness and economic development level of the city and the hinterland have a positive impact on the competitiveness of the logistics industry, and the level of economic development has the greatest contribution to the variance of the competitiveness of the logistics industry. The logistics competitiveness of 17 cities is ranked and classified. The leading cities have core diffusion effects and promote the development of the competitiveness of each city. Suggestions on improving the competitiveness of cities are conducive to the high-quality development of China’s logistics industry.

Evaluation and impact factors of international competitiveness of China’s cobalt industry from the perspective of trade networks

In recent years, with the development of the new energy industry, the demand for cobalt as a raw material for power batteries has been increasing. However, China itself has a shortage of cobalt resources. Therefore, overcoming poor resource conditions and enhancing the international competitiveness of the cobalt industry have become urgent issues. This paper is based on global trade data on cobalt resources from 2007 to 2020. A panel regression model is constructed from the perspective of trade networks, and Entropy-Topsis is used to construct a comprehensive evaluation index system for the international competitiveness of critical nonferrous metals. This study empirically examines the impact of the trade network characteristics of cobalt resources on international competitiveness, assigns practical significance to trade network characteristic indicators, and analyses the overall competitiveness changes in the global cobalt industry chain and its upstream, midstream, and downstream sectors. The research findings reveal the following key points: (1) In recent years, the competitive focus of the cobalt industry chain in various countries has shifted from upstream and midstream to midstream and downstream, with increasingly fierce trade competition downstream, gradually tilting toward countries such as South Korea, Japan, and China. (2) Cobalt trade competition, which was initially characterized by competition among multiple countries, has gradually become more centralized and stable, with differences in the competitiveness of various countries occurring at different stages of the cobalt industry chain. (3) Network centrality and network heterogeneity both have a significant promoting effect on the international competitiveness of the industry, while network connectivity has a significant inhibitory effect on the improvement of international competitiveness.On this basis, the study also suggests some policy implications. The purpose of the study is to enhance the international competitiveness of China's cobalt industry from a trade perspective and to investigate the developments of cobalt trade between China and the rest of the world.

OTT 서비스 확산 이후 유료방송 VOD 이용의 결정 요인 분석

In order to establish a reasonable competitive policy after the rapid growth of OTT services, discussions on the competition situation and substitution between OTT services and Pay TV are active. This study aims to give implications for the future evaluation and policy of competition in the media market by looking at the factors affecting the expenditure of TV VOD services which have been decreasing in market size in recent years. Considering the characteristics of the data, this study analyzes two distinct effects on TV VOD expenditure, which are whether to spend on TV VOD and how much you spend on TV VOD, and data from 2021 to 2022 of the Korean Media Panel Survey were employed. First, the results of estimating the impact on whether to spend on TV VOD suggest that it was confirmed that subscribers who spend on OTT services are more likely to spend on TV VOD than non-OTT subscribers. In addition, IPTV households were more likely to spend on TV VODs than non-subscribers and CATV subscribers. As a result of estimating the factors affecting the amount of TV VOD expenditure, it was found that the longer OTT usage time, the higher TV VOD expenditure, confirming that a complementary relationship rather than a substitution between the two media services is still maintained.

Competition and Collaboration in Crowdsourcing Communities: What Happens When Peers Evaluate Each Other?

Crowdsourcing has evolved as an organizational approach to distributed problem solving and innovation. As contests are embedded in online communities and evaluation rights are assigned to the crowd, community members face a tension: They find themselves exposed to both competitive motives to win the contest prize and collaborative participation motives in the community. The competitive motive suggests they may evaluate rivals strategically according to their self-interest, the collaborative motive suggests they may evaluate their peers truthfully according to mutual interest. Using field data from Threadless on 38 million peer evaluations of more than 150,000 submissions across 75,000 individuals over 10 years and two natural experiments to rule out alternative explanations, we answer the question of how community members resolve this tension. We show that as their skill level increases, they become increasingly competitive and shift from using self-promotion to sabotaging their closest competitors. However, we also find signs of collaborative behavior when high-skilled members show leniency toward those community members who do not directly threaten their chance of winning. We explain how the individual-level use of strategic evaluations translates into important organizational-level outcomes by affecting the community structure through individuals’ long-term participation. Although low-skill targets of sabotage are less likely to participate in future contests, high-skill targets are more likely. This suggests a feedback loop between competitive evaluation behavior and future participation. These findings have important implications for the literature on crowdsourcing design, and the evolution and sustainability of crowdsourcing communities. Funding: This work was supported by the National Science Foundation [Grant IIS-1514283] and the U.S. Office of Naval Research [Grant N00014-17-1-2542]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.15163 .

Development of a student intervarsity interprofessional healthcare team challenge to promote collaboration and teamwork

This curricular report describes the development and evaluation of an intervarsity competition entitled the All-Ireland Interprofessional Healthcare Challenge (AIPEC). Students from eight universities across the Republic of Ireland and Northern Ireland take part in this extracurricular event on an annual basis. In this manuscript, we report on the development of the case and on the findings of a student evaluation of the event following the 2022 event using the Assessment of the Interprofessional Team Collaboration Scale for Students (AITCS-II student version). Of the 34 students who participated in AIPEC 2022, responses were obtained from 24 (70.6 % response rate). Student feedback was overwhelmingly positive including positive agreement in all four AITCS-II subscales (partnership, coordination, cooperation and teamworking). Free text comments further endorse the event as a positive, collaborative student learning opportunity. In conclusion, our experience has shown the AIPEC to be a beneficial learning experience for students, with clear benefits in developing clinical knowledge and attitudes towards collaborative practice and teamworking.

Research on the Management of Sports Events in the Context of Sports Tourism

This article first analyzes the management significance of sports events in the context of sports tourism from the perspectives of increasing urban attractiveness, optimizing industrial structure, increasing employment opportunities, improving urban infrastructure, increasing economic benefits, and improving the quality of public services. Then it elaborates on management strategies for sports events in the context of sports tourism from the perspectives of competition management, information communication, cost control, risk control, logistics management, and evaluation management. Finally, it proposes management directions for sports events in the context of sports tourism from the perspectives of developing a whole-area tourism strategy, combining sports events with tourist attractions, improving the institutional guarantee of sports tourism, and strengthening the promotion and publicity of sports tourism.

Analysis of Trade Competitiveness of the World s Leading Textiles Exporters by Hybrid MCDM Methods

Purpose –This paper demonstrates the application of the IDOCRIW-based MABAC and MAIRCA methods in the evaluation of the trade competitiveness of the top fifteen textiles exporters in the world. Design/methodology/approach –The trade competitiveness index was used for assessing the trade competitiveness of exporters in the textile industry. The IDOCRIW method was used to obtain the weight of criteria. The analysis of the trade competitiveness of exporters was carried out using the new multi-criteria methods, namely the MABAC (Multi-Attributive Border Approximation area Comparison) and the MAIRCA (Multi-Atributive Ideal-Real Comparative Analysis). Findings –According to the results obtained by the IDOCRIW method, share in the world market (SWM) and net exports (NEX) were determined as the most and least important criteria with weight values of 0,3297 and 0,0816, respectively. After determining the criteria weight, the alternatives were ranked using the MABAC and MAIRCA methods. The results for both methods indicate that China, Italy, Luxembourg, Belgium, and Spain have the highest trade competitiveness, while Croatia, Ireland, the Republic of Cyprus, Malta, and Mexico have the lowest trade competitiveness in the textile industry. The findings demonstrated that MABAC and MAIRCA provided consistent solutions for ranking the alternatives. Additionally, the consistency and robustness of the results were tested using two different scenarios. The overall results revealed that China and Italy have the best trade competitiveness in the textile industry. Discussion –As expected, China and the European Unioun (EU) countries have the highest trade competitiveness in the world’s textile industry. It can also be stated the textile industry is strategic sector for both the EU countries and China.

Retraction Note: Network Attribute Analysis and Competitiveness Evaluation of Auto Parts Industry Cluster for e-Commerce Platform

Retraction Note: Network Attribute Analysis and Competitiveness Evaluation of Auto Parts Industry Cluster for e-Commerce Platform

Evaluation of Yield Advantages, Competitiveness and Economic Benefits of Pigeonpea (Cajanus cajan L.) Based Intercropping Systems Under Different Date of Sowing

The experiment conducted at the Department of Agronomy farm, VNMKV., Parbhani (MH), during the kharif seasons of 2016-2017 and 2017-2018, integrates key agro-environmental factors. The utilization of deep black (vertisol) soil with good drainage replicates prevalent soil conditions in the region, ensuring the relevance and applicability of the findings to tropical environments of Latin America [20,21]. The implementation of a split plot design with two replications, considering four sowing dates and seven cropping systems, enhances the experimental robustness, facilitating a comprehensive evaluation of the interactions between timing and cropping systems [6,22].The study's findings highlight the significance of sowing dates in maximizing pigeonpea equivalent yield and net monetary returns, with sowing within a week period after the regular commencement of the monsoon (D1) demonstrating superior performance. Furthermore, the pigeonpea + soybean intercropping system emerges as the most favorable [50], indicating its potential for enhancing agricultural productivity and economic returns in the region [17,19]. The study's insights into the competitiveness and economic benefits of different intercropping systems provide valuable guidance for agronomic management practices in tropical environments [50]. By identifying the superior performance of certain cropping systems, such as pigeonpea + soybean, and the influence of sowing dates on yield outcomes [51,45], the study informs farmers and agricultural practitioners on optimal strategies for maximizing productivity and profitability in pigeonpea cultivation [29,30].
 The observed behavior of component crops within intercropping systems underscores the complexity of crop interactions and highlights the need for tailored management approaches. The dominance of the niger crop in intercropping systems over the pigeonpea base crop emphasizes the importance of understanding crop dynamics for optimizing yield outcomes [5,18]. Additionally, the study's evaluation of different sowing dates contributes to enhancing climate resilience [14] in agricultural systems by identifying optimal timing strategies for mitigating climate variability effects on crop performance [7,37,13].

The OPS-SAT case: A data-centric competition for onboard satellite image classification

AbstractWhile novel artificial intelligence and machine learning techniques are evolving and disrupting established terrestrial technologies at an unprecedented speed, their adaptation onboard satellites is seemingly lagging. A major hindrance in this regard is the need for high-quality annotated data for training such systems, which makes the development process of machine learning solutions costly, time-consuming, and inefficient. This paper presents “the OPS-SAT case”, a novel data-centric competition that seeks to address these challenges. The powerful computational capabilities of the European Space Agency’s OPS-SAT satellite are utilized to showcase the design of machine learning systems for space by using only the small amount of available labeled data, relying on the widely adopted and freely available open-source software. The generation of a suitable dataset, design and evaluation of a public data-centric competition, and results of an onboard experimental campaign by using the competition winners’ machine learning model directly on OPS-SAT are detailed. The results indicate that adoption of open standards and deployment of advanced data augmentation techniques can retrieve meaningful onboard results comparatively quickly, simplifying and expediting an otherwise prolonged development period.

Study on the coordinated development of urban competitiveness and energy-carbon emission reduction in China

The urban competitiveness (UC) evaluation system is multidimensional and complex. This paper takes the simulated annealing (SA) model as the projection pursuit (PP) optimization to achieve a comprehensive assessment of competitiveness of 277 Chinese cities from 2011 to 2019, accompanied by energy saving and carbon-emission reduction (ESCER) as environmental measurements, to explore whether the two can meet the Porter hypothesis through coupling coordination degree (CCD). Further using spatiotemporal autocorrelation and obstacle degree model to uncover spatiotemporal features and interfering factors of coordinated development. Key findings include: (1) UC and ESCER show a slightly fluctuating upward trend during the research period, with apparent spatial variations. The eastern coastal region has a robust UC, while the less competitive central and western regions benefit from natural conditions, excelling in ESCER. (2) 87% of cities have achieved coordinated development between competitiveness and ESCER. Some coastal areas, often with a high CCD, are improving resource use efficiency and environmental benefits through economic agglomeration. From the perspective of the CCD collaboration network, the positive correlation accounts for about 85%, which reveals that most adjacent regions can cooperate on the road of coordinated development. (3) While differences exist in the coordinated development of UC-ESCER across various regions, social factors predominantly influence the obstacles affecting coordinated development. Specifically, a substantial barrier to the concordant progression of most cities is the number of patent applications, underscoring the pivotal role of innovation in aligning UC with ESCER.

A group decision framework for core competitiveness evaluation of small and medium-sized foreign trade enterprises under probabilistic linguistic term sets

Since the reform and opening up, China’s foreign trade has developed rapidly. The import and export trade volume has doubled, and the team of foreign trade enterprises is constantly expanding. After China’s accession to the WTO and the liberalization of foreign trade management rights, a group of small and medium-sized foreign trade enterprises have grown up in recent years and are becoming a new force in the development of China’s foreign trade industry. So, how to make these enterprises grow and grow in the international market competition, and focus on cultivating the core competitiveness of small and medium-sized foreign trade enterprises, has become a strategic issue related to the overall development of foreign trade. Recently, COmbinative Distance-based ASsessment (CODAS) method computes the Euclidean distances as the primary measure and Hamming distances as the secondary measure to assess alternatives based on predetermined attribute. Moreover, the probabilistic linguistic term sets (PLTSs) are effective tool for depicting uncertainty of the multiple attribute group decision making (MAGDM) problems. The core competitiveness evaluation of small and medium-sized foreign trade enterprises is a classical MAGDM. In this paper, the CODAS method is extended to the MAGDM with PLTSs. Firstly, the definition of PLSs is introduced. Then, on the basis of the classical CODAS method, the PL-CODAS method is proposed to cope with MAGDM under PLTSs and its significant characteristic is that it can fully consider PLED and PLHD. Finally, a practical example for core competitiveness evaluation of small and medium-sized foreign trade enterprises is given to verify the developed approach and some comparative analysis was also given to verify the PL-CODAS approach.

Collegiate student-athlete mental health symptom trajectories across a competitive season: do training load and resilience matter?

ABSTRACT Student-athlete mental health is an important and complex issue necessitating examination of variables that may explain changes in key outcomes during training and competition periods. Further, in-season changes in mental health may be predicted by student-athlete training load or psychological resilience and should be examined further. This longitudinal study assessed changes in mental health symptoms of depression, anxiety, and subjective well-being across a competitive season and examined potential interactive effects of resilience and training load in predicting mental health symptoms over time. Participants were 160 NCAA Division I student-athletes between 18–23 years old (mean age = 19.9 ± 1.3). We measured mental health symptoms and resilience at 3 timepoints (across 12 weeks) throughout a competitive season with psychometric questionnaires. Daily training loads were also collected and then used to calculate average acute to chronic workload ratios (ACWR). Positive mental health symptoms did not change across student-athletes; whereas, negative symptoms changed over time with the highest levels of symptoms occurring in the middle of the season. Resilience was positively associated with subjective well-being and negatively associated with anxiety and depression across the study period. ACWR was not associated with depression or anxiety symptom scores but a significant interaction between ACWR and resilience was found for subjective well-being symptoms. Specifically, resilience buffered against ACWR’s negative effect on positive mental health. Results suggest the importance of student-athlete resilience during the competition season and evaluation of depression and anxiety at pre-season with mid-season re-evaluation.

Deterministic attribute selection for isolation forest

Modern data mining techniques have been gained importance in recent years. In particular, anomaly detection algorithms, applied in key sectors of information technology, have been growing in popularity. One of the efficient and fast algorithms is Isolation Forest. The method consists of two separated stages: Forest formation and evaluation of elements. The first stage relies on forming a forest of isolation trees. Each tree is built in the same manner according to drawn samples and random divisions of data attributes. In this study, an innovative deterministic attribute selection method is proposed, maintaining its random value. New ideas based on imbalance, clustering, and a dispersion of values through non-linear transformation of elements are introduced and thoroughly analyzed. These novel anomaly detection approaches are applied to 25 real datasets, as well as our own artificially generated databases. The Area Under the ROC Curve and the Area Under the PR Curve are used as a measure of the outliers classification quality. The results of the numerical experiment have proven high efficiency and competitive evaluation speed of the proposals in comparison to other Isolation Forest-based approaches, as well as several other popular techniques.

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Patents on Risk Evaluation and Mitigation Strategies for Prescription Drugs and Generic Competition

This cross-sectional study identifies the prevalence of patents on risk evaluation and mitigation strategies and their association with delaying generic competition.

A novel regret-rejoice cross-efficiency approach for energy stock portfolio optimization

As climate change intensifies, more and more people realize that it is necessary and urgent to optimize energy structure through constructing energy asset portfolios. However, most of existing literature on energy asset portfolio models neglects real market restrictions, and the related portfolio performance evaluation approaches ignore the impact of the psychological factors of decision makers (DMs) on evaluation results, which limit their applications. To overcome these limitations, we first present a mean-WMCVaR (MWMC) model for energy stock portfolio selection with real features. Then, we employ the proposed MWMC model and its closely related works to yield a set of investment schemes and determine their characteristic index values including mean, WMCVaR, carbon intensity and ESG. Subsequently, we present a novel regret-rejoice cross-efficiency (NRRCE) approach to evaluate the performance of these investment schemes, and then make a comparison with the ones of the other seven competitive evaluation approaches by using an empirical example. Empirical results show that the proposed MWMC model can yield an optimal investment scheme with a better trade-off between economic and ecological benefits, and our NRRCE approach can effectively provide reasonable and reliable evaluation results for different investment schemes.