As academia in general, and research funders in particular, place ever greater importance on data as an output of research, so the value of good research data management practices becomes ever more apparent. In response to this, the Innovative Design and Manufacturing Research Centre (IdMRC) at the University of Bath, UK, with funding from the JISC, ran a project to draw up a data management planning regime. In carrying out this task, the ERIM (Engineering Research Information Management) Project devised a visual method of mapping out the data records produced in the course of research, along with the associations between them. This method, called Research Activity Information Development (RAID) Modelling, is based on the Unified Modelling Language (UML) for portability. It is offered to the wider research community as an intuitive way for researchers both to keep track of their own data and to communicate this understanding to others who may wish to validate the findings or re-use the data.

Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

Aim/Purpose: To monitor Sustainability Development Goals (SDG) established by the United Nations through the hierarchical architecture of informing systems Background: The paper discusses the case of Poland and its Gdansk region Contribution: The solution combines the big-picture of civilization with small-picture of a nation, regions, cities, and firms Findings: The presented solution can be implemented if the political will can be secured. Recommendations for Practitioners: Take the main idea of this paper and adapt to your local case. Recommendation for Researchers: Develop some prototypes of presented informing systems and test in your local environment Impact on Society: The success of the sustainability of globalizing society can be secured if the coherent informing systems can be applied to the planning, monitoring, and implementation of the UN's universal SDG. Future Research: Work on the modeling of costs and benefits of the presented solution.

Aim/Purpose: The first goal is to develop a decision support system for pricing and production amounts for a firm facing high levels of product returns. The second goal is to improve the management of the product returns process. Background: This study was conducted at a food importer and manufacturer in Israel facing a very high rate of product returns, much of which is eventually discarded. The firm’s products are commonly considered to be a low-cost generic alternative and are therefore popular among low-income families. Methodology: A decision support module was added to the plant’s business information system. The module is based on a supply chain pricing model and uses the sales data to infer future demand’s distribution. Ergonomic models were used to improve the design of the returns warehouse and the handling of the returns. Contribution: The decision support system allows to improve the plant’s pricing and quantity planning. Consequently, it reduced the size of product returns. The new design of the returns process is expected to improve worker’s productivity, reduces losses and results in safer outcomes. This study also demonstrates a successful integration and of a theoretical economical model into an information system. Findings: The results show the promise of incorporating pricing supply chain models into informing systems to achieve a practical business task. We were able to construct actual demand distributions from the data and offer actual pricing recommendations that reduce the number of returns while increasing potential profits. We were able to identify key deficiencies in the returns operations and added a module to the decisions support system that improves the returns management and links it with the sales and pricing modules. Finally, we produced a better warehouse design that supports efficient and ergonomic product returns handling. Recommendations for Practitioners: This work can be replicated for different suppliers, manufacturers and retailers that suffer from product returns. They will benefit from the reduction in returns, as well as the decrease in the losses associated with these returns. Recommendation for Researchers: It is worthwhile to research whether decision support systems can be applied to other aspects of the organizations’ operations. Impact on Society: Product returns is a lose-lose situation for producers, retailers and customers. Moreover, mismanagement of these returns is harmful for the environment and may result in the case of foods, in health hazards. Reducing returns and improving the handling improves sustainability and is beneficial for society. Future Research: The decision support system’s underlying pricing model assumes a specific business setting. This can be extended using other pricing models and applying them in a similar fashion to the current application.

Aim/Purpose: In this study, the research proposes and experiments with a new model of collecting, storing, and analyzing big data on customer feedback in the tourism industry. The research focused on the Vietnam market. Background: Big Data describes large databases that have been “silently” built by businesses, which include product information, customer information, customer feedback, etc. This information is valuable, and the volume increases rapidly over time, but businesses often pay little attention or store it discretely, not centrally, thereby wasting an extremely large resource and partly causing limitations for business analysis as well as data. Methodology: The study conducted an experiment by collecting customer feedback data in the field of tourism, especially tourism in Vietnam, from 2007 to 2022. After that, the research proceeded to store and mine latent topics based on the data collected using the Topic Model. The study applied cloud computing technology to build a collection and storage model to solve difficulties, including scalability, system stability, and system cost optimization, as well as ease of access to technology. Contribution: The research has four main contributions: (1) Building a model for Big Data collection, storage, and analysis; (2) Experimenting with the solution by collecting customer feedback data from huge platforms such as Booking.com, Agoda.com, and Phuot.vn based on cloud computing, focusing mainly on tourism Vietnam; (3) A Data Lake that stores customer feedback and discussion in the field of tourism was built, supporting researchers in the field of natural language processing; (4) Experimental research on the latent topic mining model from the collected Big Data based on the topic model. Findings: Experimental results show that the Data Lake has helped users easily extract information, thereby supporting administrators in making quick and timely decisions. Next, PySpark big data processing technology and cloud computing help speed up processing, save costs, and make model building easier when moving to SaaS. Finally, the topic model helps identify customer discussion trends and identify latent topics that customers are interested in so business owners have a better picture of their potential customers and business. Recommendations for Practitioners: Empirical results show that facilities are the factor that customers in the Vietnamese market complain about the most in the tourism/hospitality sector. This information also recommends that practitioners reduce their expectations about facilities because the overall level of physical facilities in the Vietnamese market is still weak and cannot be compared with other countries in the world. However, this is also information to support administrators in planning to upgrade facilities in the long term. Recommendation for Researchers: The value of Data Lake has been proven by research. The study also formed a model for big data collection, storage, and analysis. Researchers can use the same model for other fields or use the model and algorithm proposed by this study to collect and store big data in other platforms and areas. Impact on Society: Collecting, storing, and analyzing big data in the tourism sector helps government strategists to identify tourism trends and communication crises. Based on that information, government managers will be able to make decisions and strategies to develop regional tourism, propose price levels, and support innovative programs. That is the great social value that this research brings. Future Research: With each different platform or website, the study had to build a query scenario and choose a different technology approach, which limits the ability of the solution’s scalability to multiple platforms. Research will continue to build and standardize query scenarios and processing technologies to make scalability to other platforms easier.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.

Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

Teaching object-oriented programming to students in an in-classroom environment demands well-thought didactic and pedagogical strategies in order to guarantee a good level of apprenticeship. To teach it on a completely distance learning environment (e-learning) imposes possibly other strategies, besides those that the e-learning model of Open University of Portugal dictates. This article analyses the behavior of the students of the 1st cycle in Computer Science while interacting with the object-oriented programming (OOP) discipline available to them on the Moodle platform. Through the evaluation of the level of interaction achieved in a group of relevant selected actions by the students, it is possible to identify their relevancy to the success of the programming learning process. Data was extracted from Moodle, numerically analyzed, and, with the use of some charts, behavior patterns of students were identified. This paper points out potential new approaches to be considered in e-learning in order to enhance programming learning results, besides confirming a high level of drop-out and a low level of interaction, thus finding no clear correlation between students’ success and the number of online actions (especially in forums), which reveals a possible failure of the main pillar on which the e-learning model relies.

The current rapid technological changes confront researchers of learning technologies with the challenge of evaluating them, predicting trends, and improving their adoption and diffusion. This study utilizes a data-driven discourse analysis approach, namely culturomics, to investigate changes over time in the research of learning technologies. The patterns and changes were examined on a corpus of articles published over the past decade (2006-2014) in the proceedings of Chais Conference for the Study of Innovation and Learning Technologies – the leading research conference on learning technologies in Israel. The interesting findings of the exhaustive process of analyzing all the words in the corpus were that the most commonly used terms (e.g., pupil, teacher, student) and the most commonly used phrases (e.g., face-to-face) in the field of learning technologies reflect a pedagogical rather than a technological aspect of learning technologies. The study also demonstrates two cases of change over time in prominent themes, such as “Facebook” and “the National Information and Communication Technology (ICT) program”. Methodologically, this research demonstrates the effectiveness of a data-driven approach for identifying discourse trends over time.

Aim/Purpose: This study examines differences in credit and noncredit users’ learning and usage of the Plant Sciences E-Library (PASSEL, http://passel.unl.edu), a large international, open-source multidisciplinary learning object repository. Background: Advances in online education are helping educators to meet the needs of formal academic credit students, as well as informal noncredit learners. Since online learning attracts learners with a wide variety of backgrounds and intentions, it is important understand learner behavior so that instructional resources can be designed to meet the diversity of learner motivations and needs. Methodology: This research uses both descriptive statistics and cluster analysis. The descriptive statistics address the research question of how credit learners differ from noncredit learners in using an international e-library of learning objects. Cluster analysis identifies high and low credit/noncredit students based on their quiz scores and follow-up descriptive statistics to (a) differentiate their usage patterns and (b) help describe possible learning approaches (deep, surface, and strategic). Contribution: This research is unique in its use of objective, web-tracking data and its novel use of clustering and descriptive analytic approaches to compare credit and noncredit learners’ online behavior of the same educational materials. It is also one of the first to begin to identify learning approaches of the noncredit learner. Findings: Results showed that credit users scored higher on quizzes and spent more time on the online quizzes and lessons than did noncredit learners, suggesting their academic orientation. Similarly, high credit scorers spent more time on individual lessons and quizzes than did the low scorers. The most striking difference among noncredit learners was in session times, with the low scorers spending more time in a session, suggesting more browsing behavior. Results were used to develop learner profiles for the four groups (high/low quiz scorers x credit/noncredit). Recommendations for Practitioners: These results provide preliminary insight for instructors or instructional designers. For example, low scoring credit students are spending a reasonable amount of time on a lesson but still score low on the quiz. Results suggest that they may need more online scaffolding or auto-generated guidance, such as the availability of relevant animations or the need to review certain parts of a lesson based on questions missed. Recommendation for Researchers: The study showed the value of objective, web-tracking data and novel use of clustering and descriptive analytic approaches to compare different types of learners. One conclusion of the study was that this web-tracking data be combined with student self-report data to provide more validation of results. Another conclusion was that demographic data from noncredit learners could be instrumental in further refining learning approaches for noncredit learners. Impact on Society: Learning object repositories, online courses, blended courses, and MOOCs often provide learners the option of moving freely among educational content, choosing not only topics of interest but also formats of material they feel will advance their learning. Since online learning is becoming more prolific and attracts learners with a wide variety of backgrounds and intentions, these results show the importance of understanding learner behavior so that e-learning instructional resources can be designed to meet the diversity of learner motivations and needs. Future Research: Future research should combine web-tracking data with student self-report to provide more validation of results. In addition, collection of demographic data and disaggregation of noncredit student usage motivations would help further refining learning approaches for this growing population of online users.

This paper identifies areas in the design science research (DSR) subfield of the information systems (IS) discipline where a more detailed consideration of practitioner audiences of socio-technical design artifacts could improve current IS DSR research practice and proposes an initial conceptualization of these audiences. The consequences of not considering artifact audiences are identified through a critical appraisal of the current informing science lenses in the IS DSR literature. There are specific shortcomings in four areas: 1) treating practice stakeholders as a too homogeneous group, 2) not explicitly distinguishing between social and technical parts of socio-technical artifacts, 3) neglecting implications of the artifact abstraction level, and 4) a lack of explicit consideration of a dynamic or evolutionary fitness perspective of socio-technical artifacts. The findings not only pave the way for future research to further improve the conceptualization of artifact audiences, in order to improve the informing power – and thus, impact on practice and research relevance – of IS DSR projects; they can also help to bridge the theory-practice gap in other disciplines (e.g. computer science, engineering, or policy-oriented sociology) that seek to produce social and/or technical artifacts of practical relevance.

Aim/Purpose: In a world of rapidly expanding complexity and exponentially increasing data availability, IT-based knowledge management tools will be needed to manage and curate available information. This paper looks at a particular tool architecture that has been previously proposed: The Personal Knowledge Management System (PKMS). The specific focus is on how the proposed architecture conforms to design science principles that relate to how it is likely to evolve. Background: We first introduce some recent informing science and design science research frameworks, then examine how the PKMS architecture would conform to these. Methodology: The approach taken is conceptual analysis. Contribution: The analysis provides a clearer understanding of how the proposed PKMS would serve the diverse-client ambiguous-target (DCAT) informing scenario and how it could be expected to evolve. Findings: We demonstrate how the PKMS informing architecture can be characterized as a “social machine” that appears to conform to a number of principles that would facilitate its long-term evolution. Future Research: The example provided by the paper could serve as a model future research seeking to integrate design science and informing science in the study of IT artefacts.

Aim/Purpose: A literature review is often criticized for the absence of coherent construction, synthesis of topics, and well-reasoned analysis. A framework is needed for novice researchers to organize and present information obtained from the literature review. Background: Information and communication technologies advancement have yielded overwhelming information. The massive availability of information poses several challenges, including storage, processing, meaningful organization, and presentation for future consumption. Information System Researchers have developed frameworks, guidelines, and tools for gathering, filtering, processing, storing, and organizing information. Interestingly, information system researchers have vast information that needs meaningful organization and presentation to the research fraternity while conducting a literature review on a research topic. Methodology: This paper describes a framework called LACTiC (Location, Author, Continuum, Time, and Category) that we adapted from another framework called LATCH (Location, Alphabetical, Time, Category, and Hierarchy). LATCH was used to organize and present information on e-commerce websites for seamless navigation. We evaluated the LACTiC framework. Contribution: Information System Researchers can use the LACTiC framework to organize information obtained from literature review. Findings: The evaluation reveals that most researchers from information systems organize information obtained from the literature review category-wise, followed by continuum, author, time, and location. Recommendation for Researchers: Overall, the framework works well and can be helpful for researchers for an initial idea for organizing information obtained from the literature review. Future Research: To conceptualize the framework, the study was carried out using Information Systems related literature. To generalize the proposed framework, we may suggest that the study can be extended to other areas of business management, such as marketing, finance, operation, decision sciences, accounting, and economics.

Aim/Purpose: Parasocial relationships are today established on social media between influencers and their followers. While marketing effects are well-researched, little is known about the meaning of such relationships and the psychological mechanisms behind them. This study, therefore, explores the questions: “How do followers on Instagram interpret explicit fiction cues from influencers?” and “What does this reveal about the meaning of parasocial attachment?” Background: With a billion-dollar advertising industry and leading in influencing opinion, Instagram is a significant societal and economic player. One factor for the effective influence of consumers is the relationship between influencer and follower. Research shows that disclosing advertisements surprisingly does not harm credibility, and sometimes even leads to greater trustworthiness and, in turn, willingness to purchase. While such reverse dynamics are measurable, the mechanisms behind them remain largely unexplored. Methodology: The study follows an explorative approach with in-depth interviews, which are analyzed with Mayring’s content analysis under a reconstructive paradigm. The findings are discussed through the lens of critical psychology. Contribution: Firstly, this study contributes to the understanding of the communicative dynamics of influencer-follower communication alongside the reality-fiction-gap model, and, secondly, it contributes empirical insights through the analysis of 22 explorative interviews. Findings: The findings show (a) how followers rationalize fiction cues and justify compulsive decision-making, (b) how followers are vulnerable to influences, and (c) how parasocial attachment formation overshadows rational logic and agency. The findings are discussed with regard to mechanisms, vulnerabilities, rationalizations and cognitive bias, and the social self, as well as the ethics of influencer marketing and politics. Recommendation for Researchers: The contribution is relevant to relationship research, group dynamics and societal organizing, well-being, identity, and health perspectives, within psychology, sociology, media studies, and pedagogy to management. Future Research: Future research might seek to understand more about (a) quantifiable vulnerabilities, such as attachment styles, dispositions, and demographics, (b) usage patterns and possible factors of prevention, (c) cognitive and emotional mechanisms involved with larger samples, (d) the impact on relationships and well-being, and (e) possible conditions for the potential of parasocial attachment.

Lihan TONG,Weijia LI,Qingxia YANG,Liyuan CHEN,Peng CHEN, Vol.E107-D, No.11, pp.1472-1475
We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.
Publication Date: 2024/11/01

The promise of a closer union between organizational and historical research has long been recognized. However its potential remains unfulfilled: the authenticity of theory development expected by organization studies and the authenticity of historical veracity required by historical research place exceptional conceptual and empirical demands on researchers. We elaborate the idea of historical organization studies, organizational research that draws extensively on historical data, methods and knowledge to promote historically informed theoretical narratives attentive to both disciplines. Building on prior research, we propose a typology of four differing conceptions of history in organizational research: history as evaluating, explicating, conceptualizing, and narrating. We identify five principles of historical organization studies - dual integrity, pluralistic understanding, representational truth, context sensitivity and theoretical fluency - and illustrate our typology holistically from the perspective of institutional entrepreneurship. We explore practical avenues for a creative synthesis, drawing examples from social movement research and micro-history. Historically informed theoretical narratives whose validity derives from both historical veracity and conceptual rigor, afford dual integrity that enhances scholarly legitimacy, enriching understanding of historical, contemporary and future-directed social realities.

The law of requisite variety is widely employed in management theorizing, and is linked with core strategy themes such as contingency and fit. We reflect upon requisite variety as an archetypal borrowed concept. We contrast its premises with insights from institutional and commitment literatures, draw propositions that set boundaries to its applicability, and review the ramifications of what we term "complexity misalignment." In this way, we contradict foundational assumptions of the law, problematize adaptation- and survival-centric views of strategizing, and theorize the role of human agency in variously complex regimes.

Labor codes have been voluntarily adopted and used by manufacturers in emerging economies for the past two decades, as a means of ensuring minimally acceptable or core labor standards for workers. However, far too little is known of the potential benefits from the voluntary adoption of labor codes to the manufacturer, and prior human resource management research has been virtually silent on the business implications of their use for emerging economy manufacturers participating in global supply chains. Drawing on previous work across multiple disciplines and proposing a framework that extends human resource management theory more explicitly and rigorously to the context of emerging economy manufacturing, I theorize and demonstrate that the voluntary adoption of a labor code may constitute an effective human resource investment in emerging economies in improving establishment-level employee outcomes and operational and financial performance. The hypotheses are tested using longitudinal data on a sample of apparel manufacturing plants in Sri Lanka. Implications of this study include providing insight into how to expand the scope and relevance of human resource management theory to better understand research and practice in emerging economies.

The Bangko Sentral ng Pilipinas is finalizing a regulatory framework to ensure the efficiency of clearing switch operations within the national payment system, particularly the automated clearing houses under the National Retail Payment System.

Prince Harry sparks frenzy because of his ‘terrorizing' plans for Christmas

Prince Harry’s terrifying effect on Christmas in 2024, for the Windsors has just become a point of conversation.

So much so that one expert has even stepped forward to offer his thoughts on the...

Amazing Grace is part of our Traditional Christian Music Library.

A doctor accused of criticizing the war in Ukraine in front of a patient was convicted Tuesday of spreading false information about the Russian military and sentenced to 5 1/2 years in prison, part of an unrelenting Kremlin crackdown on dissent.

American figure skater Ilia Malinin has been doing gymnastics off the ice much of his life, one of the many reasons why he can so effortlessly land the dazzling quadruple jumps that made him a world champion at just 19 years old last March.

A new report "A world that counts - Mobilising the Data Revolution for Sustainable Development" was recently published. The document points out the need for globally available and freely accessible data to monitor progress of Sustainable Development Goals (SDGs), to hold governments accountable and foster sustainable development. This issue needs to be solved in a timely manner, as the gaps between developed and developing countries, between information-rich and information-poor people are increasing. Furthermore, better integrated, timely and validated information can lead to better decision-making and real-time feedback to the citizens. However, still some challenges remain, and the report points out recommendations to overcome existing limitations (countries have poor data, data arrives too late, many issues are barely covered), e.g. through a global "Network of Data Innovation Networks" that connects both organizations and experts. 

The report was written by UN-IAEG, which is the United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development and is available online here: http://www.undatarevolution.org/wp-content/uploads/2014/11/A-World-That-Counts.pdf

XML markup of taxonomic research and specimen data is a valuable tool for structuring the incessantly accumulating biodiversity knowledge. It allows for the opportunity to collectively use the currently fragmented information for more detailed analysis.

A new research paper, published in theBiodiversity Data Journal, demonstrates how XML markup using GoldenGATE can address the challenges presented by unstructured legacy data, like those presented in the widely used PDF format. The paper demonstrates how structured primary biodiversity data can be extracted from such legacy sources and aggregated with and jointly queried with data from other Darwin Core-compatible sources, to present a visualization of these data that can communicate key information contained in biodiversity literature.

Specimen data in taxonomic literature are among the highest quality primary biodiversity data. Innovative cybertaxonomic journals such as the Biodiversity Data Journal are using workflows that preserve the data's structure and semantic specificity and disseminate electronic content to aggregators and other users that makes these data reusable.

Such structure however is lost in traditional taxonomic publishing and currently, access to that resource is cumbersome, especially for non-specialist data consumers.

The question is: how do you manage this vast distributed repository of knowledge about biodiversity to make it easily available reusable for future research?

To answer this challenge this project queried XML structured articles published in Biodiversity Data Journal along with historical taxonomic literature marked up using GoldenGATE, and represents the results as a series of standard charts. XML structured documents are maintained by the Swiss NGO Plazi and are freely available online.

In such form, data associated with specimens becomes much more valuable as it can reveal key information about a particular species, and even about the scientists who investigate them. Charts indicate at a glance, for example, what time of year and elevation range a species is likely to be found at, useful information if you want to search for it in the field.

Our accumulated biodiversity knowledge includes an estimated 2-3 billion specimens in natural history collections and 500 million pages of printed text. These are the data we need to answer questions that are relevant to our world today, like setting conservation priorities and anticipating the effects of climate change on biodiversity and ecosystem functions that affect the lives of people.

"In short, we have half a billion pages worth of biodiversity knowledge and are just learning how to query it. The real power comes when data from many articles are combined, queried, and reused for new purposes. Potential applications span the scientific, policy, and public spheres. When we all have better access to the information that already exists in the global corpus of biodiversity literature, this helps us do a better job of exploring what we don't know and wisely applying what we do." explains the lead author Dr Jeremy Miller, Naturalis Biodiversity Center.

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This project was supported by pro-iBiosphere and EU BON, two FP-7 (European Union Seventh Framework Programme, 2007-2013) grants (No 312848 and 308454).

Amazing Piano Stairs From Around The World 1. Piano Stairs in Stockholm, Sweden blendingpoint.com “Piano Stairs” is an interactive playful musical stairway installation created into ... Read more

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Colonization in Africa was much more than a land grab. It was a project to replace — and even erase — local cultures. To label them inferior. Music, arts, literature and of course language. In other words, it permeated everything. So how do you undo that? How do you unlearn what you’ve been forced to learn?

In this hour, produced in partnership with the Consortium of Humanities Centers and Institutes (CHCI) and Africa is a Country — we learn what it means to decolonize the mind.

Original Air Date: March 20, 2021

Guests: 

Adom Getachew — Simon Gikandi — Ngugi wa Thiong’o

Interviews In This Hour: 

Reckon with the Past To Decolonize the Future — Reclaiming the Hidden History of Blackness — Never Write In The Language of the Colonizer

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GUT HEALTH  Dr. Don Colbert maintains that people can take control of their health. As he likes to put it, “Your genetics may load the gun, but lifestyle pulls the trigger.” Our choices can change our genetics, he adds. Dr. Colbert is often asked why the gastrointestinal (GI) tract is so important. He says it’s because it’s considered the “second brain” due its complex neural network. “The gut-brain axis is a fascinating and intricate connection that highlights the two-way communication between...

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CertainTeed, a leading North American manufacturer of building materials, today announced the launch of Terminus Transitions and Terminus Glazing Channels as new product extensions to the company’s Terminus Perimeter Trim line. The Terminus Perimeter Trim line is a premier suspension system accessory that allows for easy installation of suspended ceilings with exposed edges, including clouds, peninsulas and islands.

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