Search results for: advanced analytics

Authors: Aishrith Rao

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Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

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Authors: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith

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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.

Keywords: clustering, heterogeneous, stability, scalability, IoT, WSN

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Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: machine learning, text classification, NLP techniques, semantic representation

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Authors: Adrian Sebastian, Chris Gillespie

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We describe a case of diffuse rectal involvement with cap polyposis, manifesting with a protein-losing colopathy and occurring in the setting of advanced mechanical pelvic floor dysfunction. A 59-year-old male with a 5-year history of persistent excessive flatulence, defecatory difficulties, and diarrhea. He had extensive cap polyposis of the entire rectum endoscopically. His symptoms progressed to severe fecal incontinence with mucus leakage, pelvic pain, weight loss, and hypoalbuminemia. Clinical examination exhibited severe perineal descent, a large rectocele, poor anal squeeze, and a poor defecatory technique. After a trial of nonoperative therapies addressing his defecatory dysfunction, and Helicobacter pylori eradication, surgical resection was offered due to severe symptoms with ongoing incontinence and protein loss with no other reasonable options. A robotic abdominoperineal resection with a permanent colostomy was performed, followed by an uncomplicated recovery. Our observation of coexisting mechanical pelvic floor changes in this patient lends weight to the concept of a prolapse-related phenomenon in the pathophysiology of this rare condition.

Keywords: cap polyposis, pelvic dysfunction, fecal incontinence, case report

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Authors: Christo Nicholls

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The National Electricity Grid (NEG) in South Africa has been under strain for the last five years. This overburden of the NEG led Eskom (the State-Owned Entity responsible for the NEG) to implement a blunt methodology to assist them in reducing the maximum demand (MD) on the NEG, when required, called Loadshedding. The challenge of this methodology is that not only does it lead to immense technical issues with the distribution network equipment, e.g., transformers, due to the frequent abrupt off and on switching, it also has a broader negative fiscal impact on the distributors, as their key consumers (commercial & industrial) are now grid defecting due to the lack of Electricity Security Provision (ESP). This paper provides a pragmatic alternative methodology utilizing specific functionalities embedded within direct-connect single and three-phase Advanced Meter Infrastructure (AMI) Solutions deployed within the distribution network, in conjunction with a Multi-Agent Systems Based AI implementation focused on Automated Negotiation Peer-2-Peer trading. The results of this research clearly illustrate, not only does methodology provide a factual percentage contribution towards the NEG MD at the point of consideration, it also allows the distributor to leverage the real-time MD data from key consumers to activate complex, yet impact-measurable Demand Response (DR) programs.

Keywords: AI, AMI, demand response, multi-agent

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Authors: Shrikant Randhavane, Anjali Khambete

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Use of Pesticides is vital in attaining food security and protection from harmful pests and insects in living environment. Chlorpyrifos, an organophosphate pesticide is widely used worldwide for various purposes. Due to its wide use and applications, its residues are found in environmental matrices and persist in nature for long duration of time. This has an adverse effect on human, aquatic and living bodies. Use of different methodologies is need of an hour to treat such type of recalcitrant compound. The paper focuses on Hydrodynamic Cavitation (HC), a hybrid Advanced Oxidation Potential (AOP) method to degrade Chlorpyrifos in aqueous water. Obtained results show that optimum inlet pressure of 5 bars gave maximum degradation of 99.25% for lower concentration and 87.14% for higher concentration Chlorpyrifos solution in 1 hour treatment time. Also, with known initial concentrations, comparing treatment time with optimum pressure of 5 bars, degradation efficiency increases with Hydrodynamic Cavitation. The potential application of HC in removal of Chemical Oxygen Demand (COD) from real effluent with venturi as cavitating device reveals around 40% COD removal with 1 hour of treatment time.

Keywords: advanced oxidation potential, cavitation, chlorpyrifos, COD

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Authors: Shourav Chowdhury, Subrata Barua, K. M. Naimuddin, Imam Hassan Sajib, Md. Hasan, Shudipta Banik, Muna Das

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Traffic accidents stand as a leading cause of fatalities worldwide, significantly impacting global mortality rates. Accurate classification of road accidents through advanced technological solutions presents a crucial opportunity to revolutionize accident prevention and emergency response strategies. This paper presents an advanced deep-learning methodology customized for the classification of road accidents using CCTV surveillance footage. This real-time dataset, comprising approximately 18,000 frames, has been amassed, which is pivotal for enabling comprehensive research in this field. This substantial dataset is the foundation for these investigative efforts, providing a rich and diverse source for conducting an in-depth analysis of the features. It has achieved a remarkable accuracy of 97% on this dataset through the strategic utilization of transfer learning in conjunction with LSTM (Long short-term memory) techniques. This accomplishment underscores the efficacy of our approach, combining the strengths of transfer learning and LSTM models, resulting in a highly accurate classification system for road accident events.

Keywords: accident, CCTV, footage, long short-term memory, surveillance

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Authors: Esther Friedlander, Philip Friedlander

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The use of immunotherapy that inhibits programmed death-1 (PD-1) or its ligand PD-L1 confers survival benefits in patients with non-small cell lung cancer (NSCLC). However, approximately 45% of patients experience primary treatment resistance, necessitating the development of strategies to improve efficacy. While the renin-angiotensin system (RAS) has systemic hemodynamic effects, tissue-specific regulation exists along with modulation of immune activity in part through regulation of myeloid cell activity, leading to the hypothesis that RAS inhibition may improve anti-PD-1/L-1 efficacy. A retrospective analysis was conducted that included 173 advanced solid tumor cancer patients treated at Valley Hospital, a community Hospital in New Jersey, USA, who were treated with a PD-1/L-1 inhibitor in a defined time period showing a statistically significant relationship between RAS pathway inhibition (RASi through concomitant treatment with an ACE inhibitor or angiotensin receptor blocker) and positive efficacy to the immunotherapy that was independent of age, gender and cancer type. Subset analysis revealed strong numerical benefit for efficacy in both patients with squamous and nonsquamous NSCLC as determined by documented clinician assessment of efficacy and by duration of therapy. A PUBMED literature search was now conducted to identify studies assessing the effect of RAS pathway inhibition on anti-PD-1/L1 efficacy in advanced solid tumor patients and compare these findings to those seen in the Valley Hospital retrospective study with a focus on NSCLC specifically. A total of 11 articles were identified assessing the effects of RAS pathway inhibition on the efficacy of checkpoint inhibitor immunotherapy in advanced cancer patients. Of the 11 studies, 10 assessed the effect on survival of RASi in the context of treatment with anti-PD-1/PD-L1, while one assessed the effect on CTLA-4 inhibition. Eight of the studies included patients with NSCLC, while the remaining 2 were specific to genitourinary malignancies. Of the 8 studies, two were specific to NSCLC patients, with the remaining 6 studies including a range of cancer types, of which NSCLC was one. Of these 6 studies, only 2 reported specific survival data for the NSCLC subpopulation. Patient characteristics, multivariate analysis data and efficacy data seen in the 2 NSLCLC specific studies and in the 2 basket studies, which provided data on the NSCLC subpopulation, were compared to that seen in the Valley Hospital retrospective study supporting a broader effect of RASi on anti-PD-1/L1 efficacy in advanced NSLCLC with the majority of studies showing statistically significant benefit or strong statistical trends but with one study demonstrating worsened outcomes. This comparison of studies extends published findings to the community hospital setting and supports prospective assessment through randomized clinical trials of efficacy in NSCLC patients with pharmacodynamic components to determine the effect on immune cell activity in tumors and on the composition of the tumor microenvironment.

Keywords: immunotherapy, cancer, angiotensin, efficacy, PD-1, lung cancer, NSCLC

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Authors: Masaumi Nakahara, Sou Watanabe, Hiromichi Ogi, Atsuhiro Shibata, Kazunori Nomura

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In the Japan Atomic Energy Agency, three types of experimental research, advanced reactor fuel reprocessing, radioactive waste disposal, and nuclear fuel cycle technology, have been carried out at the Chemical Processing Facility. The facility has generated high level radioactive liquid and solid wastes in hot cells. The high level radioactive solid waste is divided into three main categories, a flammable waste, a non-flammable waste, and a solid reagent waste. A plastic product is categorized into the flammable waste and molten with a heating mantle. The non-flammable waste is cut with a band saw machine for reducing the volume. Among the solid reagent waste, a used adsorbent after the experiments is heated, and an extractant is decomposed for its stabilization. All high level radioactive solid wastes in the hot cells are packed in a high level radioactive solid waste can. The high level radioactive solid waste can is transported to the 2nd High Active Solid Waste Storage in the Tokai Reprocessing Plant in the Japan Atomic Energy Agency.

Keywords: high level radioactive solid waste, advanced reactor fuel reprocessing, radioactive waste disposal, nuclear fuel cycle technology

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Authors: Zhiwen Pan, Jesus Pacheco, Salim Hariri, Yiqiang Chen, Bozhi Liu

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The Internet of Things (IoT) will lead to the development of advanced Smart Home services that are pervasive, cost-effective, and can be accessed by home occupants from anywhere and at any time. However, advanced smart home applications will introduce grand security challenges due to the increase in the attack surface. Current approaches do not handle cybersecurity from a holistic point of view; hence, a systematic cybersecurity mechanism needs to be adopted when designing smart home applications. In this paper, we present a generic intrusion detection methodology to detect and mitigate the anomaly behaviors happened in Smart Home Systems (SHS). By utilizing our Smart Home Context Data Structure, the heterogeneous information and services acquired from SHS are mapped in context attributes which can describe the context of smart home operation precisely and accurately. Runtime models for describing usage patterns of home assets are developed based on characterization functions. A threat-aware action management methodology, used to efficiently mitigate anomaly behaviors, is proposed at the end. Our preliminary experimental results show that our methodology can be used to detect and mitigate known and unknown threats, as well as to protect SHS premises and services.

Keywords: Internet of Things, network security, context awareness, intrusion detection

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Authors: Afrouz Yousefi, Mohtada Sadrzadeh

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The widespread presence of poly- and perfluoroalkyl substances (PFAS) poses a significant concern due to their ability to accumulate in living organisms and their persistence in the environment, thanks to their robust carbon-fluorine (C-F) bonds, which require substantial energy to break (485 kJ/mol). The prevalence of toxic PFAS compounds can be highly detrimental to ecosystems, wildlife, and human health. Ongoing efforts are dedicated to investigating methods for fully breaking down and eliminating PFAS from the environment. Among the various techniques employed, advanced oxidation processes have shown promise in completely breaking down emerging contaminants in wastewater. However, the drawback lies in the relatively slow reaction rates of these processes and the substantial energy input required, which currently impedes their widespread commercial adoption. We developed a hybrid process, comprising electro-Fenton as an advanced oxidation process and membrane distillation, to simultaneously degrade organic PFAS pollutants and extract pure water from the mixture. In this study, environmentally persistent perfluorooctanoic acid (PFOA), as an emerging contaminant, was used to study the effectiveness of the electro-Fenton/membrane distillation hybrid system. The PFOA degradation studies were conducted in two modes: electro-Fenton and electro-Fenton coupled with membrane distillation. High-performance liquid chromatography with ultraviolet detection (HPLC-UV), ion-chromatography (measuring fluoride ion concentration), total organic carbon (TOC) decay, mineralization current efficiency (MCE), and specific energy consumption (SEC) were evaluated for a single EF and hybrid EF-MD processes. In contrast to a single EF reaction, TOC decay improved significantly in the EF-MD process. Overall, the MCE of hybrid processes surpassed 100% while it remained under 50% for a single EF reaction. Calculations of specific energy consumption (SEC) demonstrated a substantial decrease of nearly one-third in energy usage when integrating the EF reaction with the MD process.

Keywords: water treatment, PFAS, membrane distillation, electro-Fenton, advanced oxidation

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Authors: Abdulrahman Aljabri, Essam R. I. Mahmoud, Hamad Almohamedi, Zhengyi Jiang

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Cold rolled thin strip has received intensive attention through technological and theoretical progress in the rolling process, as well as researchers have focused on its control during rolling as an essential parameter for producing thinner strip with good shape and profile. An advanced approach has been proposed to analysis the thin strip profile in cold rolling of pair roll crossing and shifting mill using Finite Element Analysis (FEA) with an ALE technique. The ALE (Arbitrary Lagrangian-Eulerian) techniques to enable more flexibility of the ALE technique in the adjustment of the finite element mesh, which provides a significant tool for simulating the thin strip under realistic rolling process constraint and provide accurate model results. The FEA can provide theoretical basis for the 3D model of controlling the strip shape and profile in thin strip rolling, and deliver an optimal rolling process parameter, and suggest corrective changes during cold rolling of thin strip.

Keywords: pair roll crossing, work roll shifting, strip shape and profile, finite element modeling

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Authors: Ebru Ozgur Guler, Huseyin Guler, Sera Sanli

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Social Progress Index (SPI) whose fundamentals have been thrown in the World Economy Forum is an index which aims to form a systematic basis for guiding strategy for inclusive growth which requires achieving both economic and social progress. In this research, it has been aimed to determine the relations among “Basic Human Needs” (BHN) (including four variables of ‘Nutrition and Basic Medical Care’, ‘Water and Sanitation’, ‘Shelter’ and ‘Personal Safety’) and “Opportunity” (OPT) (that is composed of ‘Personal Rights’, ‘Personal Freedom and Choice’, ‘Tolerance and Inclusion’, and ‘Access to Advanced Education’ components) dimensions of 2016 SPI for 138 countries which take place in the website of Social Progress Imperative by carrying out canonical correlation analysis (CCA) which is a data reduction technique that operates in a way to maximize the correlation between two variable sets. In the interpretation of results, the first pair of canonical variates pointing to the highest canonical correlation has been taken into account. The first canonical correlation coefficient has been found as 0.880 indicating to the high relationship between BHN and OPT variable sets. Wilk’s Lambda statistic has revealed that an overall effect of 0.809 is highly large for the full model in order to be counted as statistically significant (with a p-value of 0.000). According to the standardized canonical coefficients, the largest contribution to BHN set of variables has come from ‘shelter’ variable. The most effective variable in OPT set has been detected to be ‘access to advanced education’. Findings based on canonical loadings have also confirmed these results with respect to the contributions to the first canonical variates. When canonical cross loadings (structure coefficients) are examined, for the first pair of canonical variates, the largest contributions have been provided by ‘shelter’ and ‘access to advanced education’ variables. Since the signs for structure coefficients have been found to be negative for all variables; all OPT set of variables are positively related to all of the BHN set of variables. In case canonical communality coefficients which are the sum of the squares of structure coefficients across all interpretable functions are taken as the basis; amongst all variables, ‘personal rights’ and ‘tolerance and inclusion’ variables can be said not to be useful in the model with 0.318721 and 0.341722 coefficients respectively. On the other hand, while redundancy index for BHN set has been found to be 0.615; OPT set has a lower redundancy index with 0.475. High redundancy implies high ability for predictability. The proportion of the total variation in BHN set of variables that is explained by all of the opposite canonical variates has been calculated as 63% and finally, the proportion of the total variation in OPT set that is explained by all of the canonical variables in BHN set has been determined as 50.4% and a large part of this proportion belongs to the first pair. The results suggest that there is a high and statistically significant relationship between BHN and OPT. This relationship is generally accounted by ‘shelter’ and ‘access to advanced education’.

Keywords: canonical communality coefficient, canonical correlation analysis, redundancy index, social progress index

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Authors: Tsietsi J. Pilusa, Tumisang G. Seodigeng

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This study evaluates the benefits of advanced waste management practices in unlocking waste-to-energy opportunities within the solid waste industry. The key drivers of sustainable waste management practices, specifically with respect to packaging waste-to-energy technology options are discussed. The success of a waste-to-energy system depends significantly on the appropriateness of available technologies, including those that are well established as well as those that are less so. There are hard and soft interventions to be considered when packaging an integrated waste treatment solution. Technology compatibility with variation in feedstock (waste) quality and quantities remains a key factor. These factors influence the technology reliability in terms of production efficiencies and product consistency, which in turn, drives the supply and demand network. Waste treatment technologies rely on the waste material as feedstock; the feedstock varies in quality and quantities depending on several factors; hence, the technology fails, as a result. It is critical to design an advanced waste treatment technology in an integrated approach to minimize the possibility of technology failure due to unpredictable feedstock quality, quantities, conversion efficiencies, and inconsistent product yield or quality. An integrated waste-to-energy approach offers a secure system design that considers sustainable waste management practices.

Keywords: emerging markets, evaluation tool, interventions, waste treatment technologies

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Abdullah Alhajoj, Bassam Alowaiesh

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This work aims to reduce the risks of discharging olive mill waste directly to the environment without treatment in Sakaka City, KSA. The organic loads expressed by chemical oxygen demand (COD) and biological oxygen demand (BOD) of the produced wastewater (OMWW) as well as the solid waste (OMW) were evaluated. The wastes emitted from the three-phase centrifuge decanters was found to be higher than that emitted from the two-phase centrifuge decanters. The olive mill wastewater (OMWW) was treated using advanced oxidation combined with filtration treatment. The results indicated that the concentration of COD, BOD, TSS, oil and grease and phenol was reduced by using complex sand filtration from 72150, 21660 10256, 36430, and 1470 mg/l to 980, 421, 58, 68, and 0.35 mg/l for three-phase OMWW and from 150562, 17955, 15325, 19658 and 2153 mg/l to 1050, 501, 29, 0.75, and 0.29 mg/l, respectively. While, by using modified trickling filter (packed with the neck of waste plastic bottles the concentration of the previously mentioned parameters was reduced to 1190, 570, 55, 0.85, and 0.3 mg/l, respectively. This work supports the application of such treatment technique for reducing the environmental threats of olive mill waste effluents in Saudi Arabia.

Keywords: two-phase, three-phase, olive mill, olive oil, waste treatment, filtration, advanced oxidation, waste plastic bottles

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Authors: Mary Luna Mancao Ninal

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This study aimed to determine the performance of the kindergarten teachers and its relation to pupils’ achievement in different learning areas in the Division of Kabankalan City. Using the standardized assessment and evaluation of the Department of Education secondary data, 100 kinder teachers and 2901 kinder pupils were investigated to determine the performance of the kindergarten teachers based on their Competency–Based Performance Appraisal System for Teachers and the periodic assessment of kinder pupils collected as secondary data. Weighted mean, Pearson–r, chi-square, Analysis of Variance were used in the study. Findings revealed that the kindergarten teacher respondents were 26-31 years old and most of them were female and married; they spent teaching for two years and less and passed the Licensure Examination for Teachers. They were very satisfactory as to instructional competences, school, and home and community involvement, personal, social, and professional characteristics. It also revealed that performance of the kindergarten pupils on their period of assessment shows that they were slightly advanced in their development. It also shows that domain as to performance of the kindergarten pupils were average overall development. Based on the results, it is recommended that Kindergarten teacher must augment their educational qualification and pursue their graduate studies and must develop the total personality of the children for them to achieve high advanced development to become productive individual.

Keywords: performance, kindergarten teacher, learning areas, professional, pupil

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Authors: Ming-Jong Tsai, T. M. Wu, R. C. Chu

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The purpose of this study is to develop an Advanced linear calibration Technique for air flow sensing by using CTA-based Hot wire Anemometry. It contains a host PC with Human Machine Interface, a wind tunnel, a wind speed controller, an automatic data acquisition module, and nonlinear calibration model. To improve the fitting error by using single fitting polynomial, this study proposes a Multiple three-order Polynomial Fitting Method (MPFM) for fitting the non-linear output of a CTA-based Hot wire Anemometry. The CTA-based anemometer with built-in fitting parameters is installed in the wind tunnel, and the wind speed is controlled by the PC-based controller. The Hot-Wire anemometer's thermistor resistance change is converted into a voltage signal or temperature differences, and then sent to the PC through a DAQ card. After completion measurements of original signal, the Multiple polynomial mathematical coefficients can be automatically calculated, and then sent into the micro-processor in the Hot-Wire anemometer. Finally, the corrected Hot-Wire anemometer is verified for the linearity, the repeatability, error percentage, and the system outputs quality control reports.

Keywords: flow rate sensing, hot wire, constant temperature anemometry (CTA), linear calibration, multiple three-order polynomial fitting method (MPFM), temperature compensation

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Authors: Maryam Kalkatechi

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Nowadays, parametric design, scientific analysis, and digital fabrication are dominant. Many architectural practices are increasingly seeking to incorporate advanced digital software and fabrication in their projects. Proposing an erudite design process that combines digital and practical aspects in a strong frame within the method was resulted from the dissertation research. The digital aspects are the progressive advancements in algorithm design and simulation software. These aspects have assisted the firms to develop more holistic concepts at the early stage and maintain collaboration among disciplines during the design process. The erudite design process enhances the current design processes by encouraging the designer to implement the construction and architecture knowledge within the algorithm to make successful design processes. The erudite design process also involves the ongoing improvements of applying the new method of 3D printing in construction. This is achieved through the ‘data-sketches’. The term ‘data-sketch’ was developed by the author in the dissertation that was recently completed. It accommodates the decisions of the architect on the algorithm. This paper introduces the erudite design process and its components. It will summarize the application of this process in development of the ‘3D printed construction unit’. This paper contributes to overlaying the academic and practice with advanced technology by presenting a design process that transfers the dominance of tool to the learned architect and encourages innovation in design processes.

Keywords: erudite, data-sketch, algorithm design in architecture, design process

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Authors: Ayanda P. Deliwe, Storm B. Watson

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The COVID-19 pandemic has disrupted the established practices of higher education institutions (HEIs). Most higher education institutions worldwide had to shift from traditional face-to-face to online learning. The online environment and new online tools are disrupting the way in which higher education is presented. Furthermore, the structures of higher education institutions have been impacted by rapid advancements in information and communication technologies. Emerging technologies should not be viewed in a negative light because, as opposed to the traditional curriculum that worked to create productive and efficient researchers, emerging technologies encourage creativity and innovation. Therefore, using technology together with traditional means will enhance teaching and learning. Emerging technologies in higher education not only change the experience of students, lecturers, and the content, but it is also influencing the attraction and retention of students. Higher education institutions are under immense pressure because not only are they competing locally and nationally, but emerging technologies also expand the competition internationally. Emerging technologies have eliminated border barriers, allowing students to study in the country of their choice regardless of where they are in the world. Higher education institutions are becoming indifferent as technology is finding its way into the lecture room day by day. Academics need to utilise technology at their disposal if they want to get through to their students. Academics are now competing for students' attention with social media platforms such as WhatsApp, Snapchat, Instagram, Facebook, TikTok, and others. This is posing a significant challenge to higher education institutions. It is, therefore, critical to pay attention to emerging technologies in order to see how they can be incorporated into the classroom in order to improve educational quality while remaining relevant in the work industry. This study aims to understand how emerging technologies have been utilised at Nelson Mandela University in presenting teaching and learning activities since April 2020. The primary objective of this study is to analyse how academics are incorporating emerging technologies in their teaching and learning activities. This primary objective was achieved by conducting a literature review on clarifying and conceptualising the emerging technologies being utilised by higher education institutions, reviewing and analysing the use of emerging technologies, and will further be investigated through an empirical analysis of the use of emerging technologies at Nelson Mandela University. Findings from the literature review revealed that emerging technology is impacting several key areas in higher education institutions, such as the attraction and retention of students, enhancement of teaching and learning, increase in global competition, elimination of border barriers, and highlighting the digital divide. The literature review further identified that learning management systems, open educational resources, learning analytics, and artificial intelligence are the most prevalent emerging technologies being used in higher education institutions. The identified emerging technologies will be further analysed through an empirical analysis to identify how they are being utilised at Nelson Mandela University.

Keywords: artificial intelligence, emerging technologies, learning analytics, learner management systems, open educational resources

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: James Wapyer, Simon A. Kawagle

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The current downturn in the metal and oil prices has significantly affected the mineral and petroleum sectors of Papua New Guinea. The sectors have not grown substantially in the last three years compared to previous years. Resources of several projects have not been proved up as well as feasibility studies not undertaken on advanced projects. In the 2012-2015 periods, however, development licences for four projects have been granted - the Solwara-1 project in the Manus Basin, the Woodlark project, the Crater Mountains project and the Stanley gas-condensate project. There has been some progress on three advanced projects – Frieda River copper-gold porphyry, Mount Kare gold, and the Wafi-Golpu projects. The oilfields are small by world standard but have been high rates of production. The developments of liquefied natural gas projects are progressing well and the first LNG project with ExxonMobil and partners shipped its first cargo in May 2014, the second with Total and partners involving Elk-Antelope gas-condensate fields is in its development stage, and the third with Horizon Oil and partners involving gas fields in the western Papuan basin is in the planning stage. Significantly, in the years 2012-2015, the country has exported liquefied natural gas, nickel, cobalt and chromium, and has granted exploration licences for iron-sands and coal measures for the first time.

Keywords: exploration, mineral, Papua New Guinea, petroleum

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Authors: Maria Torres

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After the acknowledgment of contrastive analysis, Pit Coder’s establishment of error analysis revolutionized the way instructors analyze and examine students’ writing errors. One question that relates to error analysis with speakers of a first language, in this case, Spanish, who are learning a second language (English), is the type of errors that these learners make along with the causes of these errors. Many studies have looked at the way the native tongue influences second language acquisition, but this method does not take into account other possible sources of students’ errors. This paper examines writing samples from an advanced ESL class whose first language is Spanish at non-profit organization, Learning Quest Stanislaus Literacy Center. Through error analysis, errors in the students’ writing were identified, described, and classified. The purpose of this paper was to discover the type and origin of their errors which generated appropriate treatments. The results in this paper show that the most frequent errors in the advanced ESL students’ writing pertain to interlanguage and a small percentage from an intralanguage source. Lastly, the least type of errors were ones that originate from negative transfer. The results further solidify the idea that there are other errors and sources of errors to account for rather than solely focusing on the difference between the students’ mother and target language. This presentation will bring to light some strategies and techniques that address the issues found in this research. Taking into account the amount of error pertaining to interlanguage, an ESL teacher should provide metalinguistic awareness of the students’ errors.

Keywords: error analysis, ESL, interlanguage, intralangauge

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Authors: Samuel Ato Dadzie

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The objective of this study is to examine the influence of eclectic paradigm on motivational strategy of foreign subsidiaries in Ghana. This study uses binary regression model, and the analysis was based on 75 manufacturing investments made by MNEs from different countries in 1994–2008. The results indicated that perceived market size increases the probability of foreign firms undertaking a market seeking (MS) in Ghana, while perceived cultural distance between Ghana and foreign firm’s home countries decreased the probability of foreign firms undertaking an market seeking (MS) foreign direct investment (FDI) in Ghana. Furthermore, extensive international experience decreases the probability of foreign firms undertaking a market seeking (MS) foreign direct investment (FDI) in Ghana. Most of the studies done by earlier researchers were based on the advanced and emerging countries and offered support for the theory, which was used in generalizing the result that multinational corporations (MNCs) normally used the theory regarding investment strategy outside their home country. In using the same theory in the context of Ghana, the result does not offer strong support for the theory. This means that MNCs that come to Sub-Sahara Africa cannot rely much on eclectic paradigm for their motivational strategies because prevailing economic conditions in Ghana are different from that of the advanced and emerging economies where the institutional structures work.

Keywords: foreign subsidiary, motives, Ghana, foreign direct investment

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Authors: Thinh Huu Nguyen, Shahab Sharfaei, Jindřich Soukup

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In the pursuit of reducing production costs, offshoring has been a major trend throughout global value chains for many decades. However, with the rise of advanced technologies, new opportunities to automate their production are changing the motivation of multinational firms to go offshore. Instead, many firms are working to relocate their offshored activities from developing economies back to their home countries. This phenomenon, known as reshoring, has recently garnered much attention as it becomes clear that automation in advanced countries might have major implications not only on their own economies but also through international trade on the economy of low-income countries, including their labor market outcomes and their comparative advantages. Thus, while using robots to substitute human labor may lower the relative costs of producing at home, it has the potential to decrease employment and demand for exports from developing economies through reshoring. In this paper, we investigate the recent literature to provide a further understanding of the relationships between robotization and the reshoring of production. Moreover, we analyze the impact of robot adoption on international trade in both developed and emerging markets. Finally, we identify the research gaps and provide avenues for future research in international economics. This study is a part of the project funded by the Internal Grant Agency (IGA) of the Faculty of Business Administration, Prague University of Economics and Business.

Keywords: automation, robotization, reshoring, international trade

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: Omar Khan, Shana Krstevska, Edwin George, Veronica Gorden, Fen Bao, Christina Caon, NP-C, Carla Santiago, Imad Zak, Navid Seraji-Bozorgzad

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Objective: To quantify cellular injury in the substantia nigra (SN) in patients with Parkinson's disease (PD) and to examine the effect of rasagiline of tissue injury in the SN in patients with PD. Background: N-acetylaspartate (NAA) quantified with MRS is a reliable marker of neuronal metabolic function. Fractional anisotropy (FA) and mean diffusivity (MD) obtained with DTI, characterize tissue alignment and integrity. Rasagline, has been shown to exert anti-apototic effect. We applied these advanced MRI techniques to examine: (i) the effect of rasagiline on cellular injury and metabolism in patients with early PD, and (ii) longitudinal changes seen over time in PD. Methods: We conducted a prospective longitudinal study in patients with mild PD, naive to dopaminergic treatment. The imaging protocol included multi-voxel proton-MRS and DTI of the SN, acquired on a 3T scanner. Scans were performed at baseline and month 3, during which the patient was on no treatment. At that point, rasagiline 1 mg orally daily was initiated and MRI scans are were obtained at 6 and 12 months after starting rasagiline. The primary objective was to compare changes during the 3-month period of “no treatment” to the changes observed “on treatment” with rasagiline at month 12. Age-matched healthy controls were also imaged. Image analysis was performed blinded to treatment allocation and period. Results: 25 patients were enrolled in this study. Compared to the period of “no treatment”, there was significant increase in the NAA “on treatment” period (-3.04 % vs +10.95 %, p= 0.0006). Compared to the period of “no treatment”, there was significant increase in following 12 month in the FA “on treatment” (-4.8% vs +15.3%, p<0.0001). The MD increased during “no treatment” and decreased in “on treatment” (+2.8% vs -7.5%, p=0.0056). Further analysis and clinical correlation are ongoing. Conclusions: Advanced MRI techniques quantifying cellular injury in the SN in PD is a feasible approach to investigate dopaminergic neuronal injury and could be developed as an outcome in exploratory studies. Rasagiline appears to have a stabilizing effect on dopaminergic cell loss and metabolism in the SN in PD, that warrants further investigation in long-term studies.

Keywords: substantia nigra, Parkinson's disease, MRI, neuronal loss, biomarker

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Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Marthese Azzopardi, Liberato Camilleri

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This research was performed to investigate the study habits and level of difficulty perceived by post-secondary students in Biology at Advanced-level topics after completing their first year of study. At the end of a two-year ‘sixth form’ course, Maltese students sit for the Matriculation and Secondary Education Certificate (MATSEC) Advanced-level biology exam as a requirement to pursue science-related studies at the University of Malta. The sample was composed of 23 students (16 taking Chemistry and seven taking some ‘Other’ subject at the Advanced Level). The cohort comprised seven males and 16 females. A questionnaire constructed by the authors, was answered anonymously during the last lecture at the end of the first year of study, in May 2016. The Chi square test revealed that gender plays no effect on the various study habits (c² (6) = 5.873, p = 0.438). ‘Reading both notes and textbooks’ was the most common method adopted by males (71.4%), whereas ‘Writing notes on each topic’ was that mostly used by females (81.3%). The Mann-Whitney U test showed no significant difference in the study habits of students and the mean assessment mark obtained at the end of the first year course (p = 0.231). Statistical difference was found with the One-ANOVA test when comparing the mean assessment mark obtained at the end of the first year course when students are clustered by their Secondary Education Certificate (SEC) grade (p < 0.001). Those obtaining a SEC grade of 2 and 3 got the highest mean assessment of 68.33% and 66.9%, respectively [SEC grading is 1-7, where 1 is the highest]. The Friedman test was used to compare the mean difficulty rating scores provided for the difficulty of each topic. The mean difficulty rating score ranges from 1 to 4, where the larger the mean rating score, the higher the difficulty. When considering the whole group of students, nine topics out of 21 were perceived as significantly more difficult than the other topics. Protein synthesis, DNA Replication and Biomolecules were the most difficult, in that order. The Mann-Whitney U test revealed that the perceived level of difficulty in comprehending Biomolecules is significantly lower for students taking Chemistry compared to those not choosing the subject (p = 0.018). Protein Synthesis was claimed as the most difficult by Chemistry students and Biomolecules by those not studying Chemistry. DNA Replication was the second most difficult topic perceived by both groups. The Mann-Whitney U test was used to examine the effect of gender on the perceived level of difficulty in comprehending various topics. It was found that females have significantly more difficulty in comprehending Biomolecules than males (p=0.039). Protein synthesis was perceived as the most difficult topic by males (mean difficulty rating score = 3.14), while Biomolecules, DNA Replication and Protein synthesis were of equal difficulty for females (mean difficulty rating score = 3.00). Males and females perceived DNA Replication as equally difficult (mean difficulty rating score = 3.00). Discovering the students’ study habits and perceived level of difficulty of specific topics is vital for the lecturer to offer guidance that leads to higher academic achievement.

Keywords: biology, perceived difficulty, post-secondary, study habits

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