3,595 research outputs found

    UMSL Bulletin 2023-2024

    Get PDF
    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

    Get PDF
    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the author’s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    Ciguatoxins

    Get PDF
    Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies

    International Academic Symposium of Social Science 2022

    Get PDF
    This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate

    Deep learning-powered vision-based energy management system for next-gen built environment

    Get PDF
    Heating, ventilation and air-conditioning (HVAC) systems provide thermally comfortable spaces for occupants, and their consumption is strongly related to how occupants utilise the building. The over- or under-utilisation of spaces and the increased adoption of flexible working hours lead to unnecessary energy usage in buildings with HVAC systems operated using static or fixed schedules during unoccupied periods. Demand-driven methods can enable HVAC systems to adapt and make timely responses to dynamic changes in occupancy. Approaches central to the implementation of a demand-driven approach are accurate in providing real-time information on occupancy, including the count, localisation and activity levels. While conventional occupancy sensors exist and can provide information on the number and location of occupants, their ability to detect and recognise occupancy activities is limited. This includes the operation of windows and appliances, which can impact the building’s performance. Artificial intelligence (AI) has recently become a critical tool in enhancing the energy performance of buildings and occupant satisfaction and health. Recent studies have shown the capabilities of AI methods, such as computer vision and deep learning in detecting and recognising human activities. The recent emergence of deep learning algorithms has propelled computer vision applications and performance. While several studies used deep learning and computer vision to recognise human motion or activity, there is limited work on integrating these methods with building energy systems. Such methods can be used to obtain accurate and real-time information about the occupants for assisting in the operation of HVAC systems. In this research, a demand-driven deep learning framework was proposed to detect and recognise occupancy behaviour for optimising the operation of building HVAC systems. The computer vision-based deep learning algorithm, convolutional neural network (CNN), was selected to develop the vision-based detector to recognise common occupancy activities such as sitting, standing, walking and opening and closing windows. A dataset consisting of images of occupants in buildings performing different activities was formed to perform the training the model. The trained model was deployed to an AI-powered camera to perform real-time detection within selected case study building spaces, which include university tutorial rooms and offices. Two main types of detectors were developed to show the capabilities of the proposed approach; this includes the occupancy activity detector and the window opening detector. Both detectors were based on the Faster R-CNN with Inception V2 model, which was trained and tested using the same approach. In addition, the influence of different parameters on the performance, such as the training data size, labelling method, and how real-time detection was conducted in different indoor spaces was evaluated. The results have shown that a single response 'people detector’ can accurately understand the number of people within a detected space. The ‘occupancy activity detector’ could provide data towards the prediction of the internal heat emissions of buildings. Furthermore, window detectors were formed to recognise the times when windows are opened, providing insights into the potential ventilation heat losses through this type of ventilation strategy employed in buildings. The information generated by the detector is then outputted as profiles, which are called Deep Learning Influence Profiles (DLIP). Building energy simulation (BES) was used to assess the potential impact of the use of detection and recognition methods on building performance, such as ventilation heat loss and energy demands. The generated DLIPs were inputted into the BES tool. Comparisons with static or scheduled occupancy profiles, currently used in conventional HVAC systems and building energy modelling were made. The results showed that the over- or under-estimation of the occupancy heat gains could lead to inaccurate heating and cooling energy predictions. The deep learning detection method showed that the occupancy heat gains could be represented more accurately compared to static office occupancy profiles. A difference of up to 55% was observed between occupancy DLIP and static heat gain profile. Similarly, the window detection method enabled accurate recognition of the opening and closing of windows and the prediction of ventilation heat losses. BES was conducted for various scenario-based cases that represented typical and/or extreme situations that would occur within selected case study buildings. Results showed that the detection methods could be useful for modulating heating and cooling systems to minimise building energy losses while providing adequate indoor air quality and thermal conditions. Based on the developed individual detectors, combined detectors were formed and also assessed during experimental tests and analysis using BES. The vision-based technique’s integration with the building control system was discussed. A heat gain prediction and optimisation strategy were proposed along with a hybrid controller that optimises energy use and thermal comfort. This should be further developed in future works and assessed in real building installations. This work also discussed the limitations and practical challenges of implementing the proposed technology. Initial results of survey-based questionnaires highlighted the importance of informing occupants about the framework approach and how DLIPs were formed. In all, preference is towards a less intrusive and effective approach that could meet the needs of optimising building energy loads for the next-gen built environment

    Verfassungsblatt: 2023/6

    Get PDF

    Graduate Catalog of Studies, 2022-2023

    Get PDF
    • …
    corecore