Laboratory Characterization of Unsteady Boundary Layers

The study of waves and their effects on mean flow and turbulence in natural water bodies is an important issue for applications in aquatic biology, coastal engineering, sediment transport and hydrodynamic of the lake. These waves result in the generation of an oscillatory (Stokes) boundary layer near the bottom of the water column. The goal of this study was to conduct various experiments that will be used to characterize the turbulence in unsteady boundary layers and help understand the relation between various flow variables (e.g. wave amplitude, frequency, water depth, turbulent kinetic energy, etc.). Using the research facilities provided, three different types of waves were generated. Turbulence characteristics of purely oscillatory waves from a large wave basin are analyzed for unsteadiness time scales. In a smaller water flume, data was obtained for mean currents alone as well as waves plus currents combined. For the latter scenario, the flow was decomposed into vectorial components and characterized for turbulent features. The results are compared to theoretical profiles derived by simplifying the Navier Stokes equation in each of the three experimental conditions and plotted using MATLAB. The obtained models have been applied to model turbulence enhancement for mussel clearance models in Great Lakes, with the potential for further modelling of natural environments. Moreover, there is vast scope of research in this area to understand how the surface roughness affects the effects of surface roughness on apparent roughness and boundary layer height in unsteady boundary layers

Characterizing Circular Supply Chain Practices in Industry 5.0 With Respect to Sustainable Manufacturing Operations

The current research investigated the significance of circular supply chain practices in Industry 5.0 with respect to their sustainable manufacturing operations. Through a comprehensive review of the literature, the current study identified key circular supply chain practices and their potential benefits for Industry 5.0. The findings indicated that closed-loop supply chains, sustainable sourcing, product design for circularity, and waste reduction may assist Industry 5.0 firms to achieve their sustainability objectives while enhancing the operational efficiency. Moreover, the study also highlighted the challenges associated with the implementation of circular supply chain practices including the necessity for collaboration among supply chain partners, investment in new technologies and infrastructure, and the development of new skills and capabilities. From a practical and managerial perspective, the implications suggest that firms aiming to adopt circular supply chain practices in Industry 5.0 should prioritize collaboration and coordination, make investments in new technologies and infrastructure, and foster the acquisition of new skills and capabilities. To complement this research, future studies could employ empirical research methods in order to validate the findings and recommendations as well as explore potential barriers to the implementation of circular supply chain practices in Industry 5.0

Characterizing Circular Supply Chain Practices in Industry 5.0 With Respect to Sustainable Manufacturing Operations

The current research investigated the significance of circular supply chain practices in Industry 5.0 with respect to their sustainable manufacturing operations. Through a comprehensive review of the literature, the current study identified key circular supply chain practices and their potential benefits for Industry 5.0. The findings indicated that closed-loop supply chains, sustainable sourcing, product design for circularity, and waste reduction may assist Industry 5.0 firms to achieve their sustainability objectives while enhancing the operational efficiency. Moreover, the study also highlighted the challenges associated with the implementation of circular supply chain practices including the necessity for collaboration among supply chain partners, investment in new technologies and infrastructure, and the development of new skills and capabilities. From a practical and managerial perspective, the implications suggest that firms aiming to adopt circular supply chain practices in Industry 5.0 should prioritize collaboration and coordination, make investments in new technologies and infrastructure, and foster the acquisition of new skills and capabilities. To complement this research, future studies could employ empirical research methods in order to validate the findings and recommendations as well as explore potential barriers to the implementation of circular supply chain practices in Industry 5.0

Computing the Parametric Geo-Accumulation and Ecological Risk Indices of Some Heavy Metals Along on, Charsadda-Peshawar Road, Pakistan

Charsadda to Peshawar road is characterized with diverse surrounding environment of residential settlements,industrial zones, commercial and agricultural sectors along with heavy traffic route which is contributing to heavy metalpollution. This study is focused on heavy metals: Cadmium (Cd), Chromium (Cr) and Lead (Pb) contribution to theatmospheric pollution level. The heavy metals pollution assessment is carried out by sample collection (soil dust samplesand two vegetation species Cyperus esculentus and Cynodon dactylon) from ten sites along the road which were analyzedby using atomic absorption spectrometry (AAS). Average values of pollution index (PI) as well as average value ofpollution load index (PLI) for Cr, Cd and Pb in case of Cyperus esculentus, Cynodon dactylon and dust were calculated.Geo-accumulation index of roadside dust for Cr, Cd and Pb were estimated along with ecological risk due to roadsidedust using potential ecological risk index (RI). The analyses of this study suggest that the indices for the Cd metal foundto be of more concern than Cr or Pb which correspond to middle or low level of pollution. Statistical analysis revealedthat the three metals had a weak to moderate relationship with one another indicating multiple and somewhat similarsources of pollution

Electrospun PVA/CuONPs/Bitter Gourd Nanofibers with Improved Cytocompatibility and Antibacterial Properties: Application as Antibacterial Wound Dressing

Antibacterial and cyto-compatible tricomponent composite electrospun nanofibers comprised of polyvinyl alcohol (PVA), copper II oxide nanoparticles (CuONPs), and Momordica charantia (bitter gourd, MC) extract were examined for their potential application as an effective wound dressing. Metallic nanoparticles have a wide range of applications in biomedical engineering because of their excellent antibacterial properties; however, metallic NPs have some toxic effects as well. The green synthesis of nanoparticles is undergoing development with the goal of avoiding toxicity. The aim of adding Momordica charantia extract was to reduce the toxic effects of copper oxide nanoparticles as well as to impart antioxidant properties to electrospun nanofibers. Weight ratios of PVA and MC extract were kept constant while the concentration of copper oxide was optimized to obtain good antibacterial properties with reduced toxicity. Samples were characterized for their morphological properties, chemical interactions, crystalline structures, elemental analyses, antibacterial activity, cell adhesion, and toxicity. All samples were found to have uniform morphology without any bead formation, while an increase in diameters was observed as the CuO concentration was increased in nanofibers. All samples exhibited antibacterial properties; however, the sample with CuO concentration of 0.6% exhibited better antibacterial activity. It was also observed that nanofibrous mats exhibited excellent cytocompatibility with fibroblast (NIH3T3) cells. The mechanical properties of nanofibers were slightly improved due to the addition of nanoparticles. By considering the excellent results of nanofibrous mats, they can therefore be recommended for wound dressing applications

Leveraging deep learning and big data to enhance computing curriculum for industry-relevant skills: a Norwegian case study

Computer science graduates face a massive gap between industry-relevant skills and those learned at school. Industry practitioners often counter a huge challenge when moving from academics to industry, requiring a completely different set of skills and knowledge. It is essential to fill the gap between the industry's required skills and those taught at varsities. In this study, we leverage deep learning and big data to propose a framework that maps the required skills with those acquired by computing graduates. Based on the mapping, we recommend enhancing the computing curriculum to match the industry-relevant skills. Our proposed framework consists of four layers: data, embedding, mapping, and a curriculum enhancement layer. Based on the recommendations from the mapping module, we made revisions and modifications to the computing curricula. Finally, we perform a case study of the Norwegian IT jobs market, where we make recommendations for data science and software engineering-related jobs. We argue that by using our proposed methodology and analysis, a significant enhancement in the computing curriculum is possible to help increase employability, student satisfaction, and smart decision-making

Crossing linguistic barriers: authorship attribution in Sinhala texts

Authorship attribution involves determining the original author of an anonymous text from a pool of potential authors. The author attribution task has applications in several domains, such as plagiarism detection, digital text forensics, and information retrieval. While these applications extend beyond any single language, existing research has predominantly centered on English, posing challenges for application in languages such as Sinhala due to linguistic disparities and a lack of language processing tools. We present the first comprehensive study on cross-topic authorship attribution for Sinhala texts and propose a solution that can effectively perform the authorship attribution task even if the topics within the test and training samples differ. Our solution consists of three main parts: (i) extraction of topic-independent stylometric features, (ii) generation of a small candidate author set with the help of similarity search, and (iii) identification of the true author. Several experimental studies were carried out to demonstrate that the proposed solution can effectively handle real-world scenarios involving a large number of candidate authors and a limited number of text samples for each candidate author

AGI-P: A Gender Identification Framework for Authorship Analysis Using Customized Fine-Tuning of Multilingual Language Model

In this investigation, we propose a solution for the author’s gender identification task called AGI-P. This task has several real-world applications across different fields, such as marketing and advertising, forensic linguistics, sociology, recommendation systems, language processing, historical analysis, education, and language learning. We created a new dataset to evaluate our proposed method. The dataset is balanced in terms of gender using a random sampling method and consists of 1944 samples in total. We use accuracy as an evaluation measure and compare the performance of the proposed solution (AGI-P) against state-of-the-art machine learning classifiers and fine-tuned pre-trained multilingual language models such as DistilBERT, mBERT, XLM-RoBERTa, and Multilingual DEBERTa. In this regard, we also propose a customized fine-tuning strategy that improves the accuracy of the pre-trained language models for the author gender identification task. Our extensive experimental studies reveal that our solution (AGI-P) outperforms the well-known machine learning classifiers and fine-tuned pre-trained multilingual language models with an accuracy level of 92.03%. Moreover, the pre-trained multilingual language models, fine-tuned with the proposed customized strategy, outperform the fine-tuned pre-trained language models using an out-of-the-box fine-tuning strategy. The codebase and corpus can be accessed on our GitHub page at: https://github.com/mumairhassan/AGI-

Vision based intelligent traffic light management system using Faster R-CNN

Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real-time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real-time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R-CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state-of-the-art methodologies

Sustainable Irrigation Management for Higher Yield

Sustainable irrigation is sensible application of watering to plants in agriculture, landscapes that aids in meeting current survival and welfare needs. Sustainable irrigation management can help with climate change adaptation, labor, energy savings, and the production of higher-value and yield of crops to achieve zero hunger in water-scarce world. To ensure equal access to water and environmental sustainability, investments in expanded and enhanced irrigation must be matched by improvements in water governance. Sustainable irrigation must be able to cope with water scarcity, and be resilient to other resource scarcities throughout time in context of energy and finance. The themes and SDGs related to clean water, water resources sustainability, sustainable water usage, agricultural and rural development are all intertwined in the concept of “sustainable irrigation for higher yield.” Sustainable irrigation management refers to the capability of using water in optimum quantity and quality on a local, regional, national, and global scale to meet the needs of humans and agro-ecosystems at present and in the future to sustain life, protect humans and biodiversity from natural and human-caused disasters which threaten life to exist. Resultantly higher yields will ensure food security