Currently, the World Wide Web is mostly composed of isolated and loosely connected "data islands". Connecting them together and retrieving only the information that is of interest to the user is the common Web usage process. Creating infrastructure that would support automation of that process by aggregating and integrating Web data in accordance to user's personal preferences would greatly improve today's Web usage. A significant part of Web data is available only through the login and password protected applications. As that data is very important for the usefulness of described process, proposed infrastructure needs to support authorized access to user's personal data. In this paper we propose a semantically enhanced Web portal that presents unique personalized user's entry to the domain-specific Web information. We also propose an identity management system that supports authorized access to the protected Web data. To verify the proposed solution, we have built Sweb - a semantically enhanced Web portal that uses proposed identity management system.

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

Small and Medium Enterprises (SMEs) face new challenges in the global market as customers require more complete and flexible solutions and continue to drastically reduce the number of suppliers. SMEs are trying to address these challenges through cooperation within collaborative enterprise networks (CENs). Human aspects constitute a fundamental issue in these networks as people, as opposed to organizations or Information Technology (IT) systems, cooperate. Since there is a lack of empirical studies on the role of human factors in IT-supported collaborative enterprise networks, this paper addresses the major human aspects encountered in this type of organization. These human aspects include trust issues, knowledge and know-how sharing, coordination and planning activities, and communication and mutual understanding, as well as their influence on the business processes of CENs supported by IT tools. This paper empirically proves that these aspects constitute key factors for the success or the failure of CENs. Two case studies performed on two different CENs in Switzerland are presented and the roles of human factors are identified with respect to the IT support systems. Results show that specific human factors, namely trust and communication and mutual understanding have to be well addressed in order to design and develop adequate software solutions for CENs.

A feature model (FM) defines the valid combinations of features, whose combinations correspond to a program in a Software Product Line (SPL). FMs may evolve, for instance, during refactoring activities. Developers may use a catalog of refactorings as support. However, the catalog is incomplete in principle. Additionally, it is non-trivial to propose correct refactorings. To our knowledge, no previous analysis technique for FMs is used for checking properties of general FM refactorings (a transformation that can be applied to a number of FMs) containing a representative number of features. We propose an efficient encoding of FMs in the Alloy formal specification language. Based on this encoding, we show how the Alloy Analyzer tool, which performs analysis on Alloy models, can be used to automatically check whether encoded general and specific FM refactorings are correct. Our approach can analyze general transformations automatically to a significant scale in a few seconds. In order to evaluate the analysis performance of our encoding, we evaluated in automatically generated FMs ranging from 500 to 2,000 features. Furthermore, we analyze the soundness of general transformations.

One important subject associated with personal authentication capabilities is the analysis of handwritten signatures. Among the many known techniques, algorithms based on linguistic formalisms are also possible. However, such techniques require a number of algorithms for intelligent image analysis to be applied, allowing the development of new solutions in the field of personal authentication and building modern security systems based on the advanced recognition of such patterns. The article presents the approach based on the usage of syntactic methods for the static analysis of handwritten signatures. The graph linguistic formalisms applied, such as the IE graph and ETPL(k) grammar, are characterised by considerable descriptive strength and a polynomial membership problem of the syntactic analysis. For the purposes of representing the analysed handwritten signatures, new hierarchical (two-layer) HIE graph structures based on IE graphs have been defined. The two-layer graph description makes it possible to take into consideration both local and global features of the signature. The usage of attributed graphs enables the storage of additional semantic information describing the properties of individual signature strokes. The verification and recognition of a signature consists in analysing the affiliation of its graph description to the language describing the specimen database. Initial assessments display a precision of the method at a average level of under 75%.

Patient information in health care systems mostly consists of textual data, and free text in particular makes up a significant amount of it. Information retrieval systems that concentrate on these text types have to deal with the different challenges these medical free texts pose to achieve an acceptable performance. This paper describes the evaluation of four different types of information retrieval strategies: keyword search, search performed by a medical domain expert, a semantic based information retrieval tool, and a purely statistical information retrieval method. The different methods are evaluated and compared with respect to its appliance in medical health care systems.

1. Management of opioid use disorder: 2024 update to the national clinical practice guideline  CMAJ
2. New set of guidelines set to combat opioid usage  CTV News Toronto

1. Teenager in critical condition with Canada’s first human case of bird flu  The Guardian
2. B.C. teen with avian flu is in critical condition, provincial health officer says  CBC News
3. What do you do when a goose dies in your backyard, amid concerns about avian flu?  CityNews Kitchener
4. B.C. teen with Canada's first human case of avian flu in critical condition, Dr. Bonnie Henry says  CTV News Vancouver
5. B.C. teen diagnosed with bird flu in critical condition, with source of virus likely to remain unknown  The Globe and Mail

In the present scenario, the security of medical images is an important aspect in the field of image processing. Support vector machines (SVMs) are a supervised machine learning technique used in image classification. The roots of SVM are from statistical learning theory. It has gained excellent significance because of its robust, accurate, and very effective algorithm, even though it was applied to a small set of training samples. SVM can classify data into binary classification or multiple classifications according to the application's needs. Discrete wavelet transform (DWT) and singular value decomposition (SVD) transform techniques are utilised to enhance the image's security. In this paper, the image is first classified using SVM into ROI and RONI, and thereafter, to enhance the images diagnostic capabilities, the DWT-SVD-based hybrid watermarking technique is utilised to embed the watermark in the RONI region. Overall, our work makes a significant contribution to the field of medical image security by presenting a novel and effective solution. The results are evaluated using both perceptual and imperceptibility testing using PSNR and SSIM parameters. Different attacks were introduced to the watermarked image, which shows the efficacy and robustness of the proposed algorithm.

Physical Layer Security (PLS) and relay technology have emerged as viable methods for enhancing the security of wireless networks. Relay technology adoption enhances the extent of coverage and enhances dependability. Moreover, it can improve the PLS. Choosing relay and jammer nodes from the group of intermediate nodes effectively mitigates the presence of powerful eavesdroppers. Current methods for Joint Relay and Jammer Selection (JRJS) address the optimisation problem of achieving near-optimal secrecy. However, most of these techniques are not scalable for large networks due to their computational cost. Secrecy will decrease if eavesdroppers are aware of the relay and jammer intermediary nodes because beamforming can be used to counter the jammer. Consequently, this study introduces a multi-agent Q-learning-based PLS-enhanced secured joint relay and jammer in dual-hop wireless cooperative networks, considering the existence of several eavesdroppers. The performance of the suggested algorithm is evaluated in comparison to the current algorithms for secure node selection. The simulation results verified the superiority of the proposed algorithm.

Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

Physiological indicators are an important basis for reflecting the physiological health status of the human body and play an important role in medical practice. Association rules have also been one of the important research hotspots in recent years. This study aims to create a data mining system of association rules and artificial intelligence in biochemical indicators of sports training. This article uses Markov logic for network creation and system training, and tests whether the Markov logic network can be associated with the training system. The results show that the accuracy and recall rate obtained are about 90%, which shows that it is feasible to establish biochemical indicators of sports training based on Markov logic network, and the system has universal, guiding and constructive significance, ensuring that the construction of training system indicators will not go in the wrong direction.

Based on a survey of 2548 American adults conducted by Pew Research Center in 2021, this study finds that trust in the accuracy of news circulated on X (former Twitter) is positively correlated with following news sites on X, underscoring the crucial role of trust in news accuracy in shaping news-seeking behaviour. Trust in news accuracy also positively relates to political participation via X. Those who trust in news accuracy are more likely to perceive X as an effective tool for raising public awareness about political and social issues, as well as a positive force for democracy. However, exposure to misinformation weakens the connection between trust in news accuracy and users' perception about X as an effective tool for raising public awareness about political or social issues and as a positive driver for democracy.

This empirical study investigates the mediating role of two components of psychological capital (PsyCap), namely self-efficacy and optimism, in the context of the relationship between transformational leadership (TL), work engagement (WE), and innovative work behaviour (IWB). The study was conducted among IT professionals with a minimum of three years of experience employed in Chennai, India. Data collection was executed using a Google Form, and both measurement and structural models were examined using SPSS 25.0 and AMOS 23.0. The findings of this study reveal several significant relationships. Firstly, transformational leadership (TL) demonstrates a robust positive association with work engagement (WE). Furthermore, work engagement (WE) positively correlates substantially with innovative work behaviour (IWB). Notably, the study underscores that two crucial components of psychological capital, specifically self-efficacy and optimism, mediate the relationship between transformational leadership (TL) and work engagement (WE). These findings carry valuable implications for IT company managers. Recognising that transformational leadership positively influences both work engagement and employees' innovative work behaviour highlights the pivotal role of leaders in fostering a productive and innovative work environment within IT organisations.

The purpose of this research is to investigate how genuine leaders impact the creativity and innovative behaviour (IWB) of information technology (IT) employees. It also examines the impact of perceived organisational support as a mediator in the correlations between authentic leadership as well as innovative behaviours. This study explores the influence of authentic leadership via the employee's IWB using aspects from social exchange theory as well as social cognitive theory. The data was collected from a sample of 487 employees of the IT sector in India. The partial least square method is applied to test the structural relationship of the research framework. Findings reveal that authentic leadership positively impact innovative work behaviour and perceived organisation support mediates authentic leadership and IWB. Additionally, when organisations and leaders support the employees and value their creative thinking then the employee replicates IWB in the organisation. The practical and theoretical implications are discussed.

This paper examines the growing aspect of perceive organisational politics (POPs) in organisations by understanding their employee engagement with mediating effect of emotional intelligence. This study is cross-sectional, wherein a survey is conducted on executives of different sectors holding strategic positions. The purposive sampling technique is applied to find the 117 most suitable executives for this survey. The survey is self-administered, and a questionnaire is used as an instrument with 43 measurement scale items adopted from previous similar studies. Construct's reliability and validity followed by PLS-SEM is performed using JASP statistical application. The result revealed that the dimensionality support and validation of POP based on a new set of measures centred on generalised beliefs of the application and abuse of power, infrastructure, credibility, choice making, and line-of-sight. In line with previous findings, the current findings also showed that POP works as a barrier to individual behavioural demand and can negatively affect work efficiency. Existence of perceive organisational politics due to the normative belief of the situation happing in the organisation, disengagement of employees, and also evaluates new empirical insight into the organisation by mediating emotional intelligence.

For a long time, high school students have been forced to practice selecting correct answers on college scholastic ability tests. Recently, it has been suggested that schools introduce biomimicry activities for STEM education to develop students' 21st century competency. However, there have been arguments about which system is more appropriate in terms of enhancing a student's competency development. Therefore, we evaluated neurological evidence of students' competency using fMRI scans taken during the selecting a correct answer for a biology question and during a biomimicry activity. Results showed that the repetitive practice of selecting correct responses limited a student's neurological activities to the brain network of the visual cortex and the front-parietal working memory cortex. However, the biomimicry activity simultaneously involved diverse prefrontal, parietal and temporal cortexes, and the putamen, limbic and cerebellum lobes. Therefore, this study proposes that the biomimicry activities could stimulate their coordinated brain development.

Immigrant students face a multitude of problems, among which are poor social adaptation and school integration. On the other hand, although digital narrations are widely used in education, they are rarely used for aiding students or for the resolution of complex problems. This study exploits the potential of digital narrations towards this end, by examining how the development and presentation of an autobiographical digital narration can assist immigrant students in overcoming their adaptation difficulties. For that matter, a female student presenting substantial problems was selected as the study’s subject. Data was collected from all the participating parties (subject, teacher, classmates) using a variety of tools, before, during, and after the intervention. It was found that through the digital narration she was able to externalize her thoughts and feelings and this, in turn, helped her in achieving a smoother integration in the school environment. In addition, the attitudes and perceptions of the other students for their foreign classmate were positively influenced. The intervention was short in duration and it did not require special settings. Hence, it can be easily applied and educators can consider using similar interventions. On the other hand, further research is recommended to establish the generalizability of the study’s findings.

The pilot research presented here explores the classroom use of Emerging Literacy in Mathematics (ELM) software, a research-based bilingual interactive multimedia instructional tool, and its potential to develop emerging numeracy skills. At the time of the study, a central theme of early mathematics curricula, Number Concept, was fully developed. It was broken down into five mathematical concepts including counting, comparing, adding, subtracting and decomposing. Each of these was further subdivided yielding 22 online activities, each building in a level of complexity and abstraction. In total, 234 grade one students from 12 classes participated in the two-group post-test study that lasted about seven weeks and for which students in the experimental group used ELM for about 30 minutes weekly. The results for the final sample of 186 students showed that ELM students scored higher on the standardized math test (Canadian Achievement Test, 2008) and reported less boredom and lower anxiety as measured on the Academic Emotions Questionnaire than their peers in the control group. This short duration pilot study of one ELM theme holds great promise for ELM’s continued development.

The paper describes the development of four learning modules that focus on technical details of how a person’s privacy might be compromised in real-world scenarios. The paper shows how students benefited from the addition of hands-on learning experiences of privacy and data protection to the existing information technology courses. These learning modules raised students’ awareness of potential breaches of privacy as a user as well as a developer. The demonstration of a privacy breach in action helped students to design, configure, and implement technical solutions to prevent privacy violations. The assessment results demonstrate the strength of the technical approach.

Aim/Purpose: Business analytics is a cross-functional field that is important to implement for a college and has emerged as a critically important core component of the business curriculum. It is a difficult task due to scheduling concerns and limits to faculty and student resources. This paper describes the process of creating a central video repository to serve as a platform for just in time teaching and the impact on student learning outcomes. Background: Industry demand for employees with analytical knowledge, skills, and abilities requires additional analytical content throughout the college of business curriculum. This demand needs other content to be added to ensure that students have the prerequisite skills to complete assignments. Two pedagogical approaches to address this issue are Just-in-Time Teaching (JiTT) and scaffolding, grounded in the Vygoskian concept of “Zone of Proximal Development. Methodology: This paper presents a case study that applies scaffolding and JiTT teaching to create a video repository to add business analytics instruction to a curriculum. The California Critical Thinking Skills Test (CCTST) and Major Field Test (MFT) scores were analyzed to assess learning outcomes. Student and faculty comments were considered to inform the results of the review. Contribution: This paper demonstrates a practical application of scaffolding and JiTT theory by outlining the process of using a video library to provide valuable instructional resources that support meaningful learning, promote student academic achievement, and improve program flexibility. Findings: A centrally created library is a simple and inexpensive way to provide business analytics course content, augmenting standard content delivery. Assessment of learning scores showed an improvement, and a summary of lessons learned is provided to guide implications. Recommendations for Practitioners: Pedagogical implications of this research include the observation that producing a central library of instructor created videos and assignments can help address knowledge and skills gaps, augment the learning of business analytics content, and provide a valuable educational resource throughout the college of business curriculum. Recommendation for Researchers: This paper examines the use of scaffolding and JiTT theories. Additional examination of these theories may improve the understanding and limits of these concepts as higher education evolves due to the combination of market forces changing the execution of course delivery. Impact on Society: Universities are tasked with providing new and increasing skills to students while controlling the costs. A centrally created library of instructional videos provides a means of delivering meaningful content while controlling costs. Future Research: Future research may examine student success, including the immediate impact of videos and longitudinally using video repositories throughout the curriculum. Studies examining the approach across multiple institutions may help to evaluate the success of video repositories. Faculty acceptance of centrally created video libraries and assignments should be considered for the value of faculty recruiting and use in the classroom. The economic impact on both the university and students should be evaluated.

Aim/Purpose: This paper explores the potential value of critical thinking in computer science education and discusses strategies for its integration across the curriculum. Background: As technology rapidly evolves and becomes increasingly integrated into society, there is a growing need for computer science graduates who can think critically about the ethical, societal, and technical implications of their work. Methodology: This study employs a conceptual analysis approach, reviewing existing literature on critical thinking in computer science education and synthesising insights from various sources. The analysis focuses on identifying challenges in implementing critical thinking instruction and proposing practical solutions. Contribution: This paper provides an overview of the current discourse on integrating critical thinking into computer science curricula. It explores the distinction between critical thinking and computational thinking, discusses various pedagogical approaches, and offers insights into potential challenges of implementation. Findings: The paper identifies six key challenges in embedding critical thinking into computer science education. It proposes initial steps to address these challenges, including curriculum redesign, innovative assessment methods, and faculty development strategies. Recommendations for Practitioners: Educators should adopt a balanced approach that complements technical education with critical thinking exercises, using diverse teaching methods such as dialogue-based teaching and authentic instruction. Recommendation for Researchers: Future research should focus on empirical studies to assess the effectiveness of the proposed solutions and develop standardised tools for evaluating critical thinking skills in computer science. Impact on Society: By enhancing critical thinking skills in computer science education, we can produce graduates who are better equipped to address complex technological challenges and their societal implications. Future Research: Further investigation is needed into the most effective pedagogical approaches for teaching critical thinking in computer science, with a focus on multidisciplinary perspectives.

Numerous scandals due to unethical decisions occur despite the growing use of decision support systems (DSS). Several scholars recommend incorporating ethical attributes along with business requirements in DSS design. However, little guidance exists to indicate which ethical attributes to include and the importance ethical attributes should be given in comparison to business requirements. This study addresses this deficiency by identifying ethical requirements to integrate in DSS design drawn from the business ethics literature. This study conducted a large-scale empirical survey with information technology decision-makers to examine the relative importance of DSS fit with ethical and business requirements as well as the appropriate balance of those requirements on perceived DSS performance. The results show that decision makers perceive better DSS performance when the ethical and business requirements align with its organisation's beliefs than from ethical or business requirements alone.

To better explore the application of unsupervised learning neural networks in psychological interventions for college students, this study investigates the relationships among latent psychological variables from the perspective of neural networks. Firstly, college students' psychological crisis and intervention systems are analysed, identifying several shortcomings in traditional psychological interventions, such as a lack of knowledge dissemination and imperfect management systems. Secondly, employing the Human-Computer Interaction (HCI) approach, a structural equation model is constructed for unsupervised learning neural networks. Finally, this study further confirms the effectiveness of unsupervised learning neural networks in psychological interventions for college students. The results indicate that in psychological intervention for college students. Additionally, the weightings of the indicators at the criterion level are calculated to be 0.35, 0.27, 0.19, 0.11 and 0.1. Based on the results of HCI, an emergency response system for college students' psychological crises is established, and several intervention measures are proposed.

The traditional numerical simulation method of financial fluctuation cycle does not focus on the study of non-linear financial fluctuation but has problems such as high numerical simulation error and long time. To solve this problem, this paper introduces the non-linear equation of motion to optimise the numerical simulation method of financial fluctuation cycle. A comprehensive analysis of the components of the financial market, the establishment of a financial market network model and the acquisition of relevant financial data under the support of the model. Based on the collection of financial data, set up financial volatility index, measuring cycle, the financial wobbles, to establish the non-linear equations of motion, the financial wobbles, the influence factors of the financial volatility cycle as variables in the equation of motion, through the analysis of different influence factors under the action of financial volatility cycle change rule, it is concluded that the final financial fluctuation cycle, the results of numerical simulation. The simulation results show that, compared with the traditional method, the numerical simulation of the proposed method has high precision, low error and short time, which provides relatively accurate reference data for the stable development of regional economy.

In order to improve the mental health level of college students, a data mining based mental health education strategy for college students is proposed. Firstly, analyse the characteristics of data mining and its potential value in mental health education. Secondly, after denoising the mental health data of college students using wavelet transform, data mining methods are used to identify the psychological crisis status of college students. Finally, based on the psychological crisis status of college students, measures for mental health education are proposed from the following aspects: building a psychological counselling platform, launching psychological health promotion activities, establishing a psychological support network, strengthening academic guidance and stress management. The example analysis results show that after the application of the strategy in this article, the psychological health scores of college students have been effectively improved, with an average score of 93.5 points.

In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect.

The research goal is to solve the problems of low accuracy and long time existing in traditional teaching reform performance evaluation methods, a performance evaluation method of teaching reform based on hierarchical multi-task deep learning is proposed. Under the principle of constructing the evaluation index system, the evaluation indicator system should be constructed. The weight of the evaluation index is calculated through the analytic hierarchy process, and the calculation result of the evaluation weight is taken as the model input sample. A hierarchical multi-task deep learning model for teaching reform performance evaluation is built, and the final teaching reform performance score is obtained. Through relevant experiments, it is proved that compared with the experimental comparison method, this method has the advantages of high evaluation accuracy and short time, and can be further applied in relevant fields.

In order to improve the classification accuracy and online allocation accuracy of teaching resources and shorten the allocation time, this paper proposes a new online allocation method of college ideological and political curriculum teaching resources based on differential search algorithm. Firstly, the feedback parameter model of teaching resources cleaning is constructed to complete the cleaning of teaching resources. Secondly, according to the results of anti-interference consideration, the linear feature extraction of ideological and political curriculum teaching resources is carried out. Finally, the online allocation objective function of teaching resources for ideological and political courses is constructed, and the differential search algorithm is used to optimise the objective function to complete the online allocation of resources. The experimental results show that this method can accurately classify the teaching resources of ideological and political courses, and can shorten the allocation time, with the highest allocation accuracy of 97%.

The EU 'General Data Protection Regulation' (GDPR) marked the most important step towards reforming data privacy regulation in recent years, as it has brought about significant changes in data process in various sectors, ranging from healthcare to banking and beyond. Various concerns have been raised, and as a consequence of these, certain parts of the text of the GDPR itself have already started to become questionable due to rapid technological progress, including, for example, the use of information technology, automatisation processes and advanced algorithms in individual decision-making activities. The road to GDPR compliance by all European Union members may prove to be a long one and it is clear that only time will tell how GDPR matters will evolve and unfold. In this paper, we aim to offer a review of the practical, ethical and constitutional aspects of the new regulation and examine all the controversies that the new technology has given rise to in the course of the regulation's application.

The patient must follow a good diet to lessen the risk of health conditions. The body needs vitamins, minerals, and nutrients for illness prevention. When the human body does not receive the right amount of nutrients, nutritional disorders can develop, which can cause a number of different health issues. Chronic diseases like diabetes and hypertension can be brought on by dietary deficiencies. The human body receives the nutrients from a balanced diet to function properly. This research has a prototype that enables patients to find nutritious food according to their health preferences. It suggests meals based on their preferences for nutrients such as protein, fibre, high-fibre, low-fat, etc., and diseases such as pregnancy and diabetes. The process implements the recommendation based on the patient's profile (content-relied, K-NN), recommendation relied on patients with similar profiles, and recommendation based on the patient's past or recent activity.

Chempound is a new generation repository architecture based on RDF, semantic dictionaries and linked data. It has been developed to hold any type of chemical object expressible in CML and is exemplified by crystallographic experiments and computational chemistry calculations. In both examples, the repository can hold >50k entries which can be searched by SPARQL endpoints and pre-indexing of key fields. The Chempound architecture is general and adaptable to other fields of data-rich science.

This study investigates the nexus among the commodities: bitcoin, copper, gold, silver, crude oil, and iron ore. Previous studies on establishing the plausibility and the dynamic nexus among commodities are rare. This research attempts to fill this gap. This study investigates whether there are long-term and short-term links between commodities for the period 2010-2022 by applying the bounds testing method to co-integration and ECM, built using an ARDL model and establishing both short-term and long-term relationships among the economic variables analysed. The ECM confirmed the presence of some co-integration relationship for all the variables, both in the short and long term. A strong correlation was discovered among the commodities, which were greatly influenced by their lagged values. The results of this study provides an opportunity for policymakers and researchers to understand the nature of the relationship between the analysed variables and further support the development of new policies for economic sustainability.