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This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

The purpose of this article is to (1) map the most important topics in the domain of sustainable business practices for entrepreneurial firms in emerging countries, (2) identify important terms in the various dimensions of sustainability in business and (3) identify the important practices and prioritize the practices.

This study has adopted a unique methodology that combines state-of-art scientometric analysis with the fuzzy nominal group technique (NGT) and fuzzy decision-making trial and evaluation laboratory (DEMATEL). Results obtained from the co-occurrence analysis in scientometrics were further mapped through NGT to obtain the list of the most important topics in the domain. The factors affecting sustainable business practices obtained through topic mapping were analyzed through fuzzy DEMATEL to obtain the cause-and-effect relation of variables.

The scale of firms, leadership, uncertainty, gender, country/location, education and tourism were found to be the factors affecting the sustainable business practices of entrepreneurial firms. The sustainable business practices for entrepreneurial firms were (1) innovation, (2) resilience, (3) policy, (4) business ethics and virtue ethics, (5) business model, (6) upcycling and value creation, (7) collaboration and (8) triple bottom line.

Policymakers in entrepreneurial firms, as well as other organizations, can implement the identified sustainable business practices to obtain optimum results and smooth functioning of the companies. The research framework obtained can be tested using exploratory methods.

Very few researchers have used the technique of scientometric analysis to identify the sustainable business practices of entrepreneurial firms, and to the best of the knowledge of the authors, no earlier researcher has attempted to use the technique of topic mapping, fuzzy NGT and fuzzy DEMATEL in combination.

While morality is ever-present in elections, scholars have yet to merge political public relations and Moral Foundations Theory. It is crucial to assess the complex morality present not only in social deduction, but also in political strategic communication. The current work aims to analyze the issue agendas and their relationships in the 2020 presidential campaign and assesses their moral strategy.

This study used a computer-assisted content analysis (N = 7,888) with each moral intuition coded from the Moral Foundations Dictionary. Datapoints included campaign tweets, Facebook posts, debate performances, remarks, news releases and nomination acceptance speeches. Coverage included articles from including The New York Times, Washington Post, Wall Street Journal, CNN and Fox News to assess both liberal and conservative media.

Candidates' issue and moral agendas were correlated with each other and with the media's agenda. Comparatively, the Biden campaign has stronger correlations when it came to connecting with issues, stakeholders and moral intuitions in the media agenda than the Trump campaign. For issues, the Biden campaign prioritized COVID-19 and the economy, while the Trump campaign prioritized the economy and crime. The candidates also had similar moral strategies.

This study suggests effectively leveraging organizational communications in democracies can support the transfer of object salience, moral attributes and networks to media coverage, public discourse and opponent messaging. It can also help achieve organizational goals by managing public image, reputation and expectations.

This work expands the literature by taking a pluralist moral psychology approach in assessing the salience and correlation of five moral intuitions: harm/care, fairness/reciprocity, ingroup/loyalty, authority/respect and purity/sanctity. This study serves as a springboard for examining morality's impact on political public relations.

Nature’s contribution to people (NCP) is a concept that specifically recognizes the relationship of “humans and the natural environment” similarly to concepts of “cultural landscapes” and consistent to “heritage.” These concepts are essential in incorporating a diverse range of stakeholders from different scales, which is important for policy and practice. The paper aims to review the existing peer-reviewed papers in the Philippines, using meta-analysis and systematic review. We addressed the significant interlinkages that help facilitate the transition and strengthen the correlation of cultural ecosystem services (CES) and NCP in a cultural landscape setting.

To distinguish and support claims for NCP from CES, we conducted a meta-analysis and systematic review based on the 30-existing peer-reviewed articles on the Philippine cultural ES in the lenses of “heritage” and “cultural landscape.”

The results generated a few numbers of the Philippine CES studies, yet it has increased cumulatively year by year consistent with previous international studies. We found that most studies are focused on topics associated with “Indigenous People, Ancestral Domains, Protected Areas/Landscapes, and Indigenous and Local Knowledge” on the linkages concurrently distinguishing NCP to CES, and may signify stronger economic valuation in uncovering the sociocultural dimensions of these scholarships through its relational values in the lenses of cultural landscape and heritage.

The authors limited the search to peer-reviewed journal articles published from online databases and did not consider Philippine University based and local publications to have a systematic review. This is to prevent underestimating the vast amount of CES literature and avoid gray literature that is not peer-reviewed; hence, being able to analyze and produce focused, yet, credible data.

In a generalizing perspective, NCP 1, 6, 8, 12, 13, 15, 16, 17 and 18 have the most realized positive correlations of the reporting categories to the Philippine CES studies. The majority of the context-specific perspective NCP has strong conceptual claims in the existing Philippine CES literature through the studies’ variables aside from NCP 3, 4, 5, 7, 8 and 11 that are still mostly in the nature of generalizing perspective.

In conclusion, our results imply that the previous and existing CES studies in the Philippines harbor more attributes presented by the NCP reporting categories. This is deemed more suitable, and may signify stronger economic valuation in uncovering the sociocultural dimensions of these scholarships through its relational values in the lenses of cultural landscape and heritage.

The purpose of this paper is to distinguish different types of sustainable digital entrepreneurs (SDEs) and explore their approaches toward enhancing organizational resilience.

Investigation of entrepreneur characteristics using Grounded Theory methodology; 12 semi-structured telephone interviews with (owner-)managers of digital-resilient small and medium-sized enterprises (SMEs) and start-ups in Germany; adaptation of a sustainability-digitalization-matrix for initial clustering; investigation of reoccurring patterns (within and between clusters) through variable-oriented content analysis; application of the capability-based conceptualization of organizational resilience for synthesis and extension.

First, the authors present a new typology of SDEs, including descriptions of the four main types (Process-Oriented System Thinker, Unconventional Strategist, Dynamic Visionary and Success-Oriented Opportunist). Second, the authors propose a conceptual framework with six success factors of organizational resilience. The framework accentuates the influence of SDEs on organizational culture and the macro-environment.

Digital sustainability and resilience are emerging management principles. The insights gained will allow (future) entrepreneurs to perform a self-assessment and replicate approaches toward enhancing SME resilience; for example, governing the co-creation of an organizational culture with a strong integrative view on sustainability and digitalization.

SMEs are characterized by high vulnerability and a reactive response to the disruptions caused by sustainability crises and digitalization. Blending sustainable and digital entrepreneurship at a micro-level, the authors identified the success factors underpinning organizational resilience that are associated with the characteristics of four types of SDEs.

Several manuscripts are adopting knowledge-based dynamic capabilities (KBDCs) as their main theoretical lens. However, these manuscripts lack consistent conceptualization and systematization of the construct. Consequently, the purpose of this study is to advance the understanding of KBDCs by clarifying the dominant concepts at the junction of knowledge management and dynamic capabilities domains, identifying which emerging themes are gaining traction with KBDCs scholars, demonstrating how the central thesis around KBDCs has evolved and explaining how can KBDCs scholars move towards finding a mutually agreed conceptualization of the field to advance empirical assessment.

The Clarivate Analytics Web of Science Core Collection database was used to extract 225 manuscripts that lie at the confluence of two promising management domains, namely, knowledge management and dynamic capabilities. A scientometric analysis including co-citation analysis, bibliographic coupling, keyword co-occurrence network analysis and text mining was conducted and integrated with a systematic review of results to facilitate an unstructured ontological discovery in the field of KBDCs.

The co-citation analysis produced three clusters of research at the junction of knowledge management and dynamic capabilities, whereas the bibliographic coupling divulged five themes of research that are gaining traction with KBDCs scholars. The systematic literature review helped to clarify each clusters’ content. While scientific mapping analysis explained how the central thesis around KBDCs has evolved, text mining and keyword analysis established how KBDCs emerge from the combination of knowledge management process capabilities and dynamic capabilities.

Minimal attention has been paid to systematizing the literature on KBDCs. Accordingly, KBDCs view has been investigated through complementary scientometric methods involving machine-based algorithms to allow for a more robust, structured, comprehensive and unbiased mapping of this emerging field of research.

The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.

This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.

Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.

The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

The literature on public procurement (PP) has increased significantly in recent years, and, to date, several reviews have been conducted to study this relevant subject. Nevertheless, a bibliometric analysis of the PP knowledge domain is still missing. To fill this knowledge gap, a bibliometric review is carried out to investigate the current state of PP research.

A total of 640 journal articles are selected from the Scopus database for the final analysis. The performance indicators of the literature are identified and explained through bibliometric analysis. Furthermore, the conceptual and intellectual structures are studied through a keyword co-occurrence network and bibliographic coupling.

The results of the review indicate that PP research has increased significantly in recent years. The top ten most productive journals, countries, authors and academic institutions are identified. The findings from the keyword co-occurrence network reveal six main research themes including innovation, corruption and green public procurement (GPP). By applying bibliographic coupling, the focus of PP research revolves around seven thematic areas: GPP, corruption, the role of small and medium-sized enterprises (SMEs) in PP, electronic PP, innovation, labour standards and service acquisition. The research potential of each thematic area is evaluated using a model based on maturity and recent attention (RA).

To the best of the authors' knowledge, this is the first study to successfully organise, synthesise and quantitatively analyse the development of the PP domain amongst a large number of publications on a large time scale.