PurposePartial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.Design/methodology/approachA systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.FindingsThe study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.Originality/valueThis research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.
Farmers are facing serious plant protection issues and phytosanitary risks, in particular in the tropics. Such issues are food insecurity, lower income in traditional low-input agroecosystems, adverse effects of pesticide use on human health and on the environment in intensive systems and export restrictions due to strict regulations on quarantine pests and limits on pesticide residues. To provide more and better food to populations in both the southern and northern hemispheres in a sustainable manner, there is a need for a drastic reduction in pesticide use while keeping crop pest and disease damage under control. This can be achieved by breaking with industrial agriculture and using an agroecological approach, whose main pillar is the conservation or introduction of plant diversity in agroecosystems. Earlier literature suggest that increasing vegetational biodiversity in agroecosystems can reduce the impact of pests and diseases by the following mechanisms: (1) resource dilution and stimulo-deterrent diversion, (2) disruption of the spatial cycle, (3) disruption of the temporal cycle, (4) allelopathy effects, (5) general and specific soil suppressiveness, (6) crop physiological resistance, (7) conservation of natural enemies and facilitation of their action against aerial pests and (8) direct and indirect architectural/physical effects. Here we review the reported examples of such effects on a broad range of pathogens and pests, e.g. insects, mites, myriapods, nematodes, parasitic weeds, fungi, bacteria and viruses across different cropping systems. Our review confirms that it is not necessarily true that vegetational diversification reduces the incidence of pests and diseases. The ability of some pests and pathogens to use a wide range of plants as alternative hosts/reservoirs is the main limitation to the suppressive role of this strategy, but all other pathways identified for the control of pests and disease based on plant species diversity (PSD) also have certain limitations. Improving our understanding of the mechanisms involved should enable us to explain how, where and when exceptions to the above principle are likely to occur, with a view to developing sustainable agroecosystems based on enhanced ecological processes of pest and disease control by optimized vegetational diversification.
The aim of this study was to investigate the impact of eBPF technology on the performance of network solutions in Kubernetes clusters. Two configurations were compared: a traditional iptables-based setup and eBPF based solution via the Cilium networking plugin. Performance tests were conducted, measuring throughput, latency, CPU usage, and memory consumption under unloaded and loaded conditions. The results indicate that the traditional configuration achieved higher throughput and lower latency in unloaded scenarios. However, under load, the eBPF-enabled cluster demonstrated advantages, including reduced CPU and memory usage and slightly improved latency. This study highlights the potential of eBPF as an efficient technology for Kubernetes environments, particularly in scenarios demanding high performance and resource efficiency.
Information technology, Electronic computers. Computer science
In the 21st century, digital technology has become integral to daily life, significantly impacting the skills and knowledge of undergraduate students. This research aims to develop a Semantic Web for learning digital technology in the 21st century by employing ontology techniques to enhance the efficiency of information retrieval. The system is designed to offer flexible learning, adaptable to students' needs, and focuses on categorizing content into three main classes and twelve subclasses. These classes define relationships using four object properties to connect main classes, subclasses, and instances, and four data type properties to link instances with data and relationships between digital technologies. This approach clarifies information and makes it more relevant for undergraduate students. Despite the advantages of ontology techniques in improving information retrieval and recommendation processes, challenges remain due to the complexity of constructing data relationships and establishing rules for data storage and retrieval. Effectively managing semantic data requires specialized knowledge to ensure accurate and efficient outcomes. The ontology knowledge base primarily consists of digital technology, innovation, and digital skills. Based on evaluations by three experts, the Semantic Web for digital technology learning in the 21st century, developed using ontology techniques, was rated at a very good level (\bar{x} = 4.52, S.D. = 0.19). The system's performance was also validated, showing precision at 96.25%, recall at 92.08%, and an F-measure of 95.29%, indicating its effectiveness in supporting learning through digital technology.
Problem statement. The integration of artificial intelligence (AI) into the field of education has become one of the key factors transforming pedagogical activities worldwide. The proliferation of generative AI tools (ChatGPT, DeepSeek, GigaChat) is accompanied by numerous discussions about their impact on the learning process and teachers’ professional activities. Among the main challenges highlighted in the global academic literature are: 1) the lack of unified attitudes towards AI use; 2) insufficient digital literacy among participants in the educational process; and 3) ethical and long-term risks of applying AI in education. The aim of this study is to explore future teachers’ attitudes towards the use of generative AI in solving professional tasks and to determine the impact of additional training on their perception of AI tools. Methodology. The empirical study involved 32 students pursuing a pedagogical profile. Surveys were conducted before and after completing an elective course on the use of AI in teachers’ professional activities. Methods included self-assessment (attitude survey), analysis of survey data, and statistical processing of results using the Student’s t-test to assess the significance of changes in future teachers’ attitudes towards AI. Results. The significance of additional training for improving future teachers’ attitudes towards AI has been confirmed. It was found that generative AI is perceived most positively in text generation tasks, while tasks involving assignment grading and generating video and audio materials inspire the least trust. The training helped reduce negative perceptions and improved the attitude towards using AI in solving professional tasks. Conclusion. The findings confirm the need for targeted training for future teachers in the fundamentals of AI to minimize negative aspects and ensure effective use of the technology. The developed principles could form the basis for creating educational disciplines and professional development courses, enabling more rational and safe applications of AI in education.
Virtual Mental Health Assistants (VMHAs) are utilized in health care to provide patient services such as counseling and suggestive care. They are not used for patient diagnostic assistance because they cannot adhere to safety constraints and specialized clinical process knowledge (ProKnow) used to obtain clinical diagnoses. In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We also introduce a new dataset of diagnostic conversations guided by safety constraints and ProKnow that healthcare professionals use (ProKnow-data). We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively (ProKnow-algo). We demonstrate the limitations of using state-of-the-art large-scale language models (LMs) on this dataset. ProKnow-algo incorporates the process knowledge through explicitly modeling safety, knowledge capture, and explainability. As computational metrics for evaluation do not directly translate to clinical settings, we involve expert clinicians in designing evaluation metrics that test four properties: safety, logical coherence, and knowledge capture for explainability while minimizing the standard cross entropy loss to preserve distribution semantics-based similarity to the ground truth. LMs with ProKnow-algo generated 89% safer questions in the depression and anxiety domain (tested property: safety). Further, without ProKnow-algo generations question did not adhere to clinical process knowledge in ProKnow-data (tested property: knowledge capture). In comparison, ProKnow-algo-based generations yield a 96% reduction in our metrics to measure knowledge capture. The explainability of the generated question is assessed by computing similarity with concepts in depression and anxiety knowledge bases. Overall, irrespective of the type of LMs, ProKnow-algo achieved an averaged 82% improvement over simple pre-trained LMs on safety, explainability, and process-guided question generation. For reproducibility, we will make ProKnow-data and the code repository of ProKnow-algo publicly available upon acceptance.
The study aimed to analyze the relationship between emotional intelligence on employee engagement, job satisfaction, and work-life balance. In addition, analyze the relationship of work-life balance on employee engagement, job satisfaction, and job performance, as well as the effect of performance on career development for employees of one of the world-class tire supplier companies in Suryacipta Industrial Estate, Karawang. The data processing method used structural equation modeling (SEM). The results showed that there is a significant effect of emotional intelligence on employee engagement and work-life balance, but does not have a significant effect on job satisfaction. Work-life balance also has a significant effect on employee engagement, job satisfaction, and job performance. In addition, job performance has a significant effect on career development. The work-life balance of employees needs to be considered by the organization so that employees can carry out two very different roles effectively to increase job performance. Future research is expected to be able to use a more specific questionnaire statement on emotional intelligence variables so that it can provide more representative results.