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This paper aims to examine how organisational values affect diversity in terms of different competencies in rural family Small and Medium-sized Enterprises (SMEs). Recruiting a diverse workforce in rural family SMEs can be particularly difficult due to the prevalence of internal family values and the lack of available local specialised competencies. A deficiency of diversity in employment and competence acquisition and development can create problems, as it often prevents rural family SMEs from recruiting employees with a wide variety of qualifications and skills.

The study takes on a multi-case method of Swedish rural family SMEs, applying a qualitative content analysis approach. In total, 20 in-depth structured interviews are conducted with rural family SME owners and 2 industries were investigated and compared – the tourism and the manufacturing industries.

Rural family SMEs lack long-term employment strategies, and competence diversity does not appear to be a priority for rural family SMEs, as they often have prematurely decided who they will hire rather than what competencies are needed for their long-term business development. It is more important to keep the team of employees tight and the family spirit present than to include competence diversity and mixed qualifications in the employment acquisition and development.

Contrary to prior research, our findings indicate that rural family SMEs apply short-term competence diversity strategies rather than long-term prospects regarding competence acquisition and management, due to their family values and rural setting, which strictly narrows the selection of employees and competencies. Also, a general reluctance towards competence diversity is identified, which originates from the very same family values and rural context.

This study aims to investigate the potential of an empathetic mindset aimed at empowering undergraduate students to work toward sustainable development (SD), addressing both theoretical and practical dimensions.

A mixed quantitative and qualitative research method was used in this study. Cross-sectional quantitative survey data on students’ mindsets and actions toward SD was collected to examine the theoretical relationship between belief and behavior. Qualitative inquiry using focus-group interviews explored students’ on-site learning experiences.

This study provides evidence for the impact of an empathetic mindset on education for sustainable development (ESD). Results showed that students with a more empathetic mindset showed better attitudes and behaviors toward SD actions. Findings suggest that developing an empathetic mindset improves students’ attitudes toward taking substantial action to protect the environment.

This study introduces a novel perspective extending the application of empathetic mindset in ESD.

The purpose of this study is to discover what attributes of casino buffet restaurants are the most important for customers’ willingness to pay (WTP).

Choice-based conjoint analysis was used in this study to test seven attributes: food, price/value, real price, service, atmosphere, the number of reviews and user-generated star ratings. Sawtooth Software was used to do the conjoint analysis, and a series of significance t-tests were run to determine the significance of each attribute on WTP with Statistical Package for the Social Sciences (SPSS).

Based on a survey of 483 respondents who had visited a buffet at a casino within the last two years, this study found that food is ranked as the most significant attribute of a casino buffet restaurant, followed by real price and service quality.

Theoretically, this work is the first to the authors’ knowledge to apply the antecedents of behavioral intention to willingness-to-pay for niche restaurants. Practically, the results of this study will help casino buffet operators as they re-open after COVID-19. Future studies could collect data in the post-pandemic environment and examine WTP at casino buffets in different geographic locations.

This paper serves as a pilot study for the education of cultural landscape heritage conservation (CLHC) and review the preparedness of landscape architecture curricula in the Philippines for the CLHC specialization. It proposes the utilization of the “Geodesign” framework in formulating a developmental process and validation of the interrelationship and collaborative activity created by the thematic areas towards landscape heritage education and professionalization. The goal of the study is to create new possibilities for the profession through the study and professionalization of cultural landscapes, thus, raising awareness and significance of cultural heritage and heritage conservation in the lenses of the Philippine landscapes.

The research opted for literature reviews, comprehensive desktop reviews of the landscape architecture syllabi of higher education institutions (HEIs) and SWOT and PESTEL analyses as qualitative assessments, including stakeholder feedback discussions with the current four (4) HEIs, Philippine Association of Landscape Architects (PALA), Technical Committee for Landscape Architecture, Commission on Higher Education (TCLA CHED), Professional Regulation Commission–Board of Landscape Architecture (PRC–BOLA) and the ICOMOS IFLA International Scientific Committee on Cultural Landscapes, National Philippine Committee (Philippine NSCCL). The data were complemented by a quantitative assessment using Leopold and Lohani and Thann assessment matrix on importance (without considering magnitude), and for this study, it is the level of preparedness and integration.

The paper brings forth to the conclusion that the landscape architecture curricula at the bachelor's degree level of the University of the Philippines – Diliman (UP – Diliman) in Quezon City and University of San Carlos (USC) in Cebu are prepared to integrate and/or consider updating their respective curriculum in accordance to the CLHC specialization. The curricula of Bulacan State University (BulSU) in Malolos, Bulacan, and the University of San Agustin (USA) in Iloilo may need to consider introducing courses related to the thematic areas to be able to create an area of basis for integration.

The study is initiated as part of the International Council on Monuments and Sites (ICOMOS) Philippines Internship programme during a master degree study wherein the research is bounded within the limit of its duration. Therefore, the study was able to only identify and chose the possible thematic areas and course concentrations for the specialization of CLHC; select, review and propose courses related to CLHC; screen courses from the landscape architecture curricula of the universities based on its course title, course information (if provided) and cross–checked with Government syllabi; and allocation of units and time per identified course as well as required prerequisite from thematic areas was not covered in this study.

The paper can be used as a tool to engage discussions with the PRC–BOLA in its development of the specialization of CLHC currently being planned. Study topics and themes identified can be the starting point of training programmes that can benefit students of the current four universities in the study and landscape architecture professionals alike. This will eventually translate to benefits to society as heritage conservation methodologies are developed by practitioners who can apply such knowledge to places of cultural and natural significance and develop learnings to concrete heritage laws and policies protecting landscapes.

This paper serves as a pilot study for the education and professionalization of CLHC in the Philippines. Significantly, the development of CLHC specialization in the Philippines shall open various opportunities in developing Philippine cultural landscape heritage conservationists trained at the local context.

Sorting is a very important algorithm to solve problems in computer science. The most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the data into subarrays and finally sorting them.

In this paper, the algorithm named Dual Parallel Partition Sorting (DPPSort) is analyzed and optimized. It consists of a partitioning algorithm named Dual Parallel Partition (DPPartition). The DPPartition is analyzed and optimized in this paper and sorted with standard sorting functions named qsort and STLSort which are quicksort, and introsort algorithms, respectively. This algorithm is run on any shared memory/multicore systems. OpenMP library which supports multiprocessing programming is developed to be compatible with C/C++ standard library function. The authors’ algorithm recursively divides an unsorted array into two halves equally in parallel with Lomuto's partitioning and merge without compare-and-swap instructions. Then, qsort/STLSort is executed in parallel while the subarray is smaller than the sorting cutoff.

In the authors’ experiments, the 4-core Intel i7-6770 with Ubuntu Linux system is implemented. DPPSort is faster than qsort and STLSort up to 6.82× and 5.88× on Uint64 random distributions, respectively.

The authors can improve the performance of the parallel sorting algorithm by reducing the compare-and-swap instructions in the algorithm. This concept can be used to develop related problems to increase speedup of algorithms.

The purpose of the study is to identify the latent topics from 9102 Web of Science (WoS) indexed research articles published in 2645 journals of the Sri Lankan authors from 1989 to 2021 by applying Latent Dirichlet Allocation to the abstracts. Dominant topics in the corpus of text, the posterior probability of different terms in the topics and the publication proportions of the topics were discussed in the article.

Abstracts and other details of the studied articles are collected from WoS database by the authors. Data preprocessing is performed before the analysis. “ldatuning” from the R package is applied after preprocessing of text for deciding subjects in light of factual elements. Twenty topics are decided to extract as latent topics through four metrics methods.

It is observed that medical science, agriculture, research and development and chemistry-related topics dominate the subject categories as a whole. “Irrigation” and “mortality and health care” have a significant growth in the publication proportion from 2019 to 2021. For the most occurring latent topics, it is seen that terms like “activity” and “acid” carry higher posterior probability.

Topic models permit us to rapidly and efficiently address higher perspective inquiries without human mediation and are also helpful in information retrieval and document clustering. The unique feature of this study has highlighted how the growth of the universe of knowledge for a specific country can be studied using the LDA topic model.

This study will create an incentive for text analysis and information retrieval areas of research. The results of this paper gave an understanding of the writing development of the Sri Lankan authors in different subject spaces and over the period. Trends and intensity of publications from the Sri Lankan authors on different latent topics help to trace the interests and mostly practiced areas in different domains.

The lack of conceptual approaches for organizing and expressing capabilities, usage and impact of intelligent machines (IMs) in work settings is an obstacle to moving beyond isolated case examples, domain-specific studies, 2 × 2 frameworks and expert opinion in discussions of IMs and work. This paper's purpose is to illuminate many issues that often are not addressed directly in research, practice or punditry related to IMs. It pursues that purpose by presenting an integrated approach for identifying and organizing important aspects of analysis and evaluation related to IMs in work settings. 

This paper integrates previously published ideas related to work systems (WSs), smart devices and systems, facets of work, roles and responsibilities of information systems, interactions between people and machines and a range of criteria for evaluating system performance.

Eight principles outline a straightforward and flexible approach for analyzing and evaluating IMs and the WSs that use them. Those principles are based on the above ideas.

This paper provides a novel approach for identifying design choices for situated use of IMs. The breadth, depth and integration of this approach address a gap in existing literature, which rarely aspires to this paper’s thoroughness in combining ideas that support the description, analysis, design and evaluation of situated uses of IMs.

The purpose of this study is to reveal empirical facts that literacy and marketing strategies effectively impact people using Sharia pawn products during the COVID-19 pandemic because of the pressure of capital needs and financial difficulties.

This study used an exploratory qualitative research approach through semi-structured interviews with six partners of different productive ages and social, economic and educational backgrounds. After data reduction, presentation, description and validation, this paper develops the theory and presents it as a qualitative thematic analysis.

The findings of this study revealed that those who had socialised Sharia pawnshops had a low literacy level. Nonetheless, the socialisation of Sharia pawnshops increases literacy and public understanding of pawnshop products in Sharia pawnshops. This issue arises because of the COVID-19 pandemic, which makes it impossible for Sharia pawnshops to hold an event or socialise.

The authenticity of this study proves that literacy and marketing strategies can increase public awareness of Sharia pawn products during the COVID-19 pandemic.

The study aims to evaluate PubMed publications on ChatGPT or artificial intelligence (AI) involvement in scientific or medical writing and investigate whether ChatGPT or AI was used to create these articles or listed as authors.

This scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A PubMed database search was performed for articles published between January 1 and November 29, 2023, using appropriate search terms; both authors performed screening and selection independently.

From the initial search results of 127 articles, 41 were eligible for final analysis. Articles were published in 34 journals. Editorials were the most common article type, with 15 (36.6%) articles. Authors originated from 27 countries, and authors from the USA contributed the most, with 14 (34.1%) articles. The most discussed topic was AI tools and writing capabilities in 19 (46.3%) articles. AI or ChatGPT was involved in manuscript preparation in 31 (75.6%) articles. None of the articles listed AI or ChatGPT as an author, and in 19 (46.3%) articles, the authors acknowledged utilizing AI or ChatGPT.

Researchers worldwide are concerned with AI or ChatGPT involvement in scientific research, specifically the writing process. The authors believe that precise and mature regulations will be developed soon by journals, publishers and editors, which will pave the way for the best usage of these tools.

This scoping review expressed data published on using AI or ChatGPT in various scientific research and writing aspects, besides alluding to the advantages, disadvantages and implications of their usage.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.