Search results for: predictive tracking

Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Ehsan Abshirini

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Rivers as one of the most important topographic factors have played a strategic role not only on the appearance of cities but they also affect the structure and morphology of cities. In this paper author intends to find out how a city in its physical network interacts with a river flowing inside. The pilot study is Angers, a city in western France, in which it is influenced by the Maine River. To this purpose space syntax method integrating with GIS is used to extract the properties of physical form of cities in terms of global and local integration value, accessibility and choice value. Simulating the state of absence of river in this city and comparing the result to the current state of city according to the effect of river on the morphology of areas located in different banks of river is also part of interest in this paper. The results show that although a river is not comparable to the city based on size and the area occupied by, it has a significant effect on the form of the city in both global and local properties. In addition, this study endorses that tracking the effect of river-cities and their interaction to rivers in a hybrid of space syntax and GIS may lead researchers to improve their interpretation of physical form of these types of cities.

Keywords: river-cities, Physical form, space syntax properties, GIS, topographic factor

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Authors: Samuel Onyekachi Chidi

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Nigeria today faces more internal threats stemming from ethnic and religious conflicts than external sources. This article seeks to examine the ethno-religious conflicts in Nigeria from 2015 to 2021 and their impact on national security. The research was guided by six objectives. The theoretical framework adopted for this study is Structural Conflict Theory, which provides an adequate explanation, a predictive rationale for the frequent occurrence of ethno-religious conflicts and a tendency to provide the necessary insight for their resolution. The results of the study revealed that there is a strong relationship between ethnicity, religion, conflict and national security and that the ethno-religious conflicts experienced in Nigeria have gross implications for national security. The study recommends that the secularity of the Nigerian state be restored and preserved and that the state of origin be removed and replaced by the state of residence in all our national documents, as this will reduce ethnic identity, which is in opposition to nationalism. Religious leaders, traditional rulers, the media and other stakeholders should support the government in its fight to reduce ethno-religious conflict by sensitizing its youth, preaching unity and peaceful coexistence, and discouraging the use of violence as a means of settling disputes between groups and individuals.

Keywords: ethnicity, religion, conflict, national security

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Authors: Beilei Xu, Wencheng Wu, Lei Lin, Rachel Melnyk, Ahmed Ghazi

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In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative cognitiveload-based feedback can also provide valuable insights during surgical training to optimize training efficiency and effectiveness. Various physiological measures have been evaluated for quantifying cognitive stress for different mental challenges. In this paper, we present a study using the cognitive stress measured by the task evoked pupillary response extracted from the time series eye-tracking measurements to predict task difficulties in a virtual reality based robotic surgery training environment. In particular, we proposed a differential-task-difficulty scale, utilized a comprehensive feature extraction approach, and implemented a multitask learning framework and compared the regression accuracy between the conventional single-task-based and three multitask approaches across subjects.

Keywords: surgical metric, task evoked pupillary response, multitask learning, TSFresh

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Authors: P. Singh, R. M. Banik

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Artificial neural network (ANN) was employed to optimize assay parameters viz., time, temperature, pH of reaction mixture, enzyme volume and substrate concentration of L-glutaminase from Bacillus cereus MTCC 1305. ANN model showed high value of coefficient of determination (0.9999), low value of root mean square error (0.6697) and low value of absolute average deviation. A multilayer perceptron neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model and its topology was obtained as 5-3-1 after applying Levenberg Marquardt (LM) training algorithm. The predicted activity of L-glutaminase was obtained as 633.7349 U/l by considering optimum assay parameters, viz., pH of reaction mixture (7.5), reaction time (20 minutes), incubation temperature (35˚C), substrate concentration (40mM), and enzyme volume (0.5ml). The predicted data was verified by running experiment at simulated optimum assay condition and activity was obtained as 634.00 U/l. The application of ANN model for optimization of assay conditions improved the activity of L-glutaminase by 1.499 fold.

Keywords: Bacillus cereus, L-glutaminase, assay parameters, artificial neural network

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Authors: Abdulaziz Afandi

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: neural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Antonello Troncone, Luigi Pugliese, Andrea Parise, Enrico Conte

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The present study deals with the landslides periodically reactivated by groundwater level fluctuations owing to rainfall. The main type of movement which generally characterizes these landslides consists in sliding with quite small-displacement rates. Another peculiar characteristic of these landslides is that soil deformations are essentially concentrated within a thin shear band located below the body of the landslide, which, consequently, undergoes an approximately rigid sliding. In this context, a simple method is proposed in the present study to forecast the movements of this type of landslides owing to rainfall. To this purpose, the landslide body is schematized by means of a two-block model. Some analytical solutions are derived to relate rainfall measurements with groundwater level oscillations and these latter, in turn, to landslide mobility. The proposed method is attractive for engineering applications since it requires few parameters as input data, many of which can be obtained from conventional geotechnical tests. To demonstrate the predictive capability of the proposed method, the application to a well-documented landslide periodically reactivated by rainfall is shown.

Keywords: rainfall, water level fluctuations, landslide mobility, two-block model

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving the army, moving convoys etc. The radar operator selects one of the promising targets into single target tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper, we present a technique using mathematical and statistical methods like fast fourier transformation (FFT) and principal component analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, FFT, principal component analysis, eigenvector, octave-notes, DSP

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Authors: Kouzi Katia

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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

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Authors: Ojo Oo, Akinde S. B., Kiilani A. O., Jayeola Jo, Jogbodo T. M., Ajani Ka, Olaniyan So, Adeagbo Oy, Bolarinwa Ra, Durosomo Ha, Sule W. F.

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Lockdown imposed to control SARS-CoV-2 transmission hampered malaria control services in Nigeria. Considering COVID-19 vaccination, we assessed Plasmodium falciparum (Pf) antigen and SARS-CoV-2 immunoglobulin-G (IgG) positivity among adults in Osogbo, Osun State, Nigeria. Consenting attendees of four Healthcare Centres were consecutively enrolled for blood sampling; relevant socio-demographic/behavioral/clinical/environmental data were collected with a questionnaire. Samples were tested, using commercial rapid test kits, for Pf antigen and SARS-CoV-2 IgG and results were analyzed using logistic regression. Participants' mean age was 40.99 years (n=200), and they were predominantly females (84.5%), traders/businessmen/women (86.0%), with self-reported receipt of COVID-19 vaccine from 123 (61.5%). Pf antigen positivity was 17.5% (95% CI: 12.23–22.77%) with age (p=0.004), marital status (p=0.004), report of stagnant water around the workplace (p=0.041) and bush around homes (p=0.008) being associated. SARS-CoV-2 IgG positivity was 56.5% (95% CI: 49.63–63.37%) with age (p=0.012) and receipt of COVID-19 vaccination (p=0.001) being associated. Although the vaccinated had a 22.8 times higher likelihood of IgG positivity, no factor was predictive of COVID-19 vaccine receipt. We report 17.5% Pf antigen positivity with four predictors, and 56.5% SARS-CoV-2 IgG positivity with two predictors.

Keywords: COVID-19, vaccine, IgG, Plasmodium falciparum, SARS-CoV-2

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Authors: S. Jursová, P. Pustějovská, S. Brožová, J. Bilík

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The paper deals with possibilities of interpretation of iron ore reducibility tests. It presents a mathematical model developed at Centre ENET, VŠB–Technical University of Ostrava, Czech Republic for an evaluation of metallurgical material of blast furnace feedstock such as iron ore, sinter or pellets. According to the data from the test, the model predicts its usage in blast furnace technology and its effects on production parameters of shaft aggregate. At the beginning, the paper sums up the general concept and experience in mathematical modelling of iron ore reduction. It presents basic equation for the calculation and the main parts of the developed model. In the experimental part, there is an example of usage of the mathematical model. The paper describes the usage of data for some predictive calculation. There are presented material, method of carried test of iron ore reducibility. Then there are graphically interpreted effects of used material on carbon consumption, rate of direct reduction and the whole reduction process.

Keywords: blast furnace technology, iron ore reduction, mathematical model, prediction of iron ore reduction

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Authors: Khatesiree Sripoothon, Usanee Sengpanich, Rattana Sittioum

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The objective of this study is to examine the factors influencing consumer purchasing decisions about healthy food. This model consists of two latent variables: Consumer Perception relating to NCDs and Consumer Perceived Product Value. The study was conducted in the northern provinces of Thailand, which are popular with tourists and have received support from the government for health tourism. A survey was used as the data collection method, and the questionnaire was applied to 385 tourists. An accidental sampling method was used to identify the sample. The statistics of frequency, percentage, mean, and structural equation model were used to analyze the data obtained. Additionally, all factors had a significant positive influence on healthy food purchasing decisions (p<0.01) and were predictive of healthy food purchasing decisions at 46.20 (R2=0.462). Also, these findings seem to underline a supposition that consumer perceptions of NCDs and perceived product value are key variables that strengthens the competitive effects of a healthy-friendly business entrepreneur. Moreover, reduce the country's public health costs for treating patients with the disease of NCDs in Thailand.

Keywords: healthy food, perceived product value, perception of non-communicable diseases, purchasing decisions

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Authors: Hong-Yi Kuo

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Leadership is a key factor of improving workplace safety, and there have been abundant of studies which support the positive effects of appropriate leadership on employee safety performance in the western academic. However, little safety research focus on the Chinese leadership style like paternalistic leadership. To fill this gap, the resent study aims to examine the relationship between authoritarian leadership (one of the ternary mode in paternalistic leadership) and safety outcomes. This study makes hypothesis on different levels. First, on the group level, as an authoritarian leader regards safety value as the most important tasks, there would be positive effect on group safety outcomes through strengthening safety group norms by the emphasis on etiquette. Second, on the cross level, when a leader with authoritarian style has high priority on safety, employees may more obey the safety rules because of fear due to emphasis on absolute authority over the leader. Therefore, employees may show more safety performance and then increase individual safety outcomes. Survey data would be collected from 50 manufacturing groups (each group with more than 5 members and a leader) and a hierarchical linear modeling analysis would be conducted to analyze the hypothesis. Above the predictive result, the study expects to be a cornerstone of safety leadership research in the Chinese academic and practice.

Keywords: safety leadership, authoritarian leadership, group norms, safety behavior, supervisor safety priority

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Authors: Farzaneh Mohammadi Jouzdani, Vahid Javidroozi, Monica Mateo Garcia, Hanifa Shah

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The emergence of developing smart net-zero cities in recent years has attracted significant attention and interest from worldwide communities and scholars as a potential solution to the critical requirement for urban sustainability. This research-in-progress paper aims to investigate the development of smart net-zero cities to propose a digital transformation roadmap for smart net-zero cities with a primary focus on data. Employing systems thinking as an underpinning theory, the study advocates for the necessity of utilising a holistic strategy for understanding the complex interdependencies and interrelationships that characterise urban systems. The proposed methodology will involve an in-depth investigation of current data-driven approaches in the smart net-zero city. This is followed by utilising predictive analysis methods to evaluate the holistic impact of the approaches on moving toward a Smart net-zero city. It is expected to achieve systemic intervention followed by a data-focused and systemic digital transformation roadmap for smart net-zero, contributing to a more holistic understanding of urban sustainability.

Keywords: smart city, net-zero city, digital transformation, systems thinking, data integration, data-driven approach

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Authors: Yanto Budisusanto, Aulia Rachmawati

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Land navigation technology that is being developed is Google Street View to provide 360° street views, enabling the user to know the road conditions physically with the photo display. For climbers, especially beginners, detail information of climbing terrain is needed so climbers can prepare supplies and strategies before climbing. Therefore, we built a mountaineer guide application named Mountain Photo Sphere. This application displays a 360̊ panoramic view of mountain hiking trail and important points along the hiking path and its surrounding conditions. By combining panoramic photos 360̊ and tracking paths from coordinate data, a virtual tour will be formed. It is built using Java language and Android Studio. The hiking trail map composed by Google Maps API (Gaining access to google maps), Google GEO API (Gaining access to google maps), and OpenStreetMap API (Getting map files to be accessed offline on the Application). This application can be accessed offline so that climbers can use the application during climbing activities.

Keywords: google street view, panoramic photo 360°, mountain hiking, mountain photo sphere

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Brwa Abdulrahman Abubaker

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Recently, there has been a lot of interest in the difficult task of applying reinforcement learning to autonomous mobile robots. Conventional reinforcement learning (TRL) techniques have many drawbacks, such as lengthy computation times, intricate control frameworks, a great deal of trial and error searching, and sluggish convergence. In this paper, a modified Spiking Neural Network (SNN) is used to offer a distinct method for autonomous mobile robot learning and control in unexpected surroundings. As a learning algorithm, the suggested model combines dopamine modulation with spike-timing-dependent plasticity (STDP). In order to create more computationally efficient, biologically inspired control systems that are adaptable to changing settings, this work uses the effective and physiologically credible Izhikevich neuron model. This study is primarily focused on creating an algorithm for target tracking in the presence of obstacles. Results show that the SNN trained with three obstacles yielded an impressive 96% success rate for our proposal, with collisions happening in about 4% of the 214 simulated seconds.

Keywords: spiking neural network, spike-timing-dependent plasticity, dopamine modulation, reinforcement learning

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Authors: Saurav Dhawan, Sanchit Bansal

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Background T-MACS is a computer-based decision aid that ‘rules in’ and ‘rules out’ ACS using a combination of the presence or absence of six clinical features with only one biomarker measured on arrival: hs-cTnT. T-MACS had 99.3% negative predictive value and 98.7% sensitivity for ACS, ‘ruling out’ ACS in 40% of patients while ‘ruling in’ 5% at the highest risk. We aim at benchmarking the use of T-MACS which could help to conserve healthcare resources, facilitate early discharges, and ensure safe practice. Methodology Randomized retrospective data collection (n=300) was done from ED electronic records across 3 hospital sites within MFT over a period of 2 months. Data was analysed and compared by percentage for the usage of T-MACS, number of admissions/discharges, and in days for length of stay in hospital. Results MRI A&E had the maximum compliance with the use of T-MACS in the trust at 66%, with minimum admissions (44%) and an average length of stay of 1.825 days. NMG A&E had an extremely low compliance rate (8 %), with 75% admission and 3.387 days as the average length of stay. WYT A&E had no TMACS recorded, with a maximum of 79% admissions and the longest average length of stay at 5.07 days. Conclusion All three hospital sites had a RAG rating of ‘RED’ as per the compliance levels. The assurance level was calculated as ‘Very Limited’ across all sites. There was a positive correlation observed between compliance with TMACS and direct discharges from ED, thereby reducing the average length of stay for patients in the hospital.

Keywords: ACS, discharges, ED, T-MACS

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Authors: Sahar Heidary, Ramin Ghasemi Shayan

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By the development of new coronavirus (2019-nCoV) pneumonia, chest high-resolution computed tomography (HRCT) has been one of the main investigative implements. To realize timely and truthful diagnostics, defining the radiological features of the infection is of excessive value. The purpose of this impression was to consider the imaging demonstrations of early-stage coronavirus disease 2019 (COVID-19) and to run an imaging base for a primary finding of supposed cases and stratified interference. The right prophetic rate of HRCT was 85%, sensitivity was 73% for all patients. Total accuracy was 68%. There was no important change in these values for symptomatic and asymptomatic persons. These consequences were besides free of the period of X-ray from the beginning of signs or interaction. Therefore, we suggest that HRCT is a brilliant attachment for early identification of COVID-19 pneumonia in both symptomatic and asymptomatic individuals in adding to the role of predictive gauge for COVID-19 pneumonia. Patients experienced non-contrast HRCT chest checkups and images were restored in a thin 1.25 mm lung window. Images were estimated for the existence of lung scratches & a CT severity notch was allocated separately for each patient based on the number of lung lobes convoluted.

Keywords: COVID-19, radiology, respiratory diseases, HRCT

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Neziha Jaouedi, Noureddine Boujnah, Mohamed Salim Bouhlel

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In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database.

Keywords: feautures extraction, human action classifier, wavelet neural network, beta wavelet

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, artificial neural network, kinect, stereotypical motor movements

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Authors: Marcell Serra de Almeida Martins, Benedito de Souza Ribeiro Neto, Gerson Lima Serejo, Carlos Gustavo Resque Dos Santos

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Indoor positioning systems use sensors such as Bluetooth, ZigBee, and Wi-Fi, as well as cameras for image capture, which can be fixed or mobile. These computer vision-based positioning approaches are low-cost to implement, mainly when it uses a mobile camera. The present study aims to create a design of a fiducial marker for a low-cost indoor localization system. The marker is tape-shaped to perform a continuous reading employing two detection algorithms, one for greater distances and another for smaller distances. Therefore, the location service is always operational, even with variations in capture distance. A minimal localization and reading algorithm were implemented for the proposed marker design, aiming to validate it. The accuracy tests consider readings varying the capture distance between [0.5, 10] meters, comparing the proposed marker with others. The tests showed that the proposed marker has a broader capture range than the ArUco and QRCode, maintaining the same size. Therefore, reducing the visual pollution and maximizing the tracking since the ambient can be covered entirely.

Keywords: multiscale recognition, indoor localization, tape-shaped marker, fiducial marker

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Authors: Murat Colak, Berkay Cakir Saridogan

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Since the dawn of modern corporations, almost every single employee has been working in the same loop, which contains three basic steps: going to work, providing the needs for the work, and getting back home. Only a small amount of people were able to break that standard and live outside the box. As the 2019 pandemic hit the Earth and most companies shut down their physical offices, that loop had to change for everyone. This means that the old management styles had to be significantly re-arranged to the "work from home" type of business methods. The methods include online conferences and meetings, time and task tracking using algorithms, globalization of the work, and, most importantly, remote working. After the global epidemic started, even the tech giants were concerned. Now, it can be seen those technology companies have an incredible step-up in their shares compared to the other companies because they know how to manage such situations even better than every other industry. This study aims to take the old traditional management styles in big companies and compare them with the post-covid methods (2019-2022). As a result of this comparison made using the annual reports and shared statistics, this study aims to explain why the winners of this crisis are the technology companies.

Keywords: Covid-19, technology companies, business models, remote work

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Authors: Ejoke U. P., Smitha Dev., Madwuke Ann, DuPlessis E. D.

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The study examines the relationship between psychological capital (PsyCap) and social well-being among international faculty members in the United Arab Emirates (UAE). The UAE has become a significant destination for global academic talent, yet challenges related to social integration, acceptance, and overall well-being persist among its international faculty. The study focuses on the predictive role of PsyCap, encompassing hope, efficacy, resilience, and optimism, in determining various dimensions of social well-being, including social integration, acceptance, contribution, actualization, and coherence. Additionally, the research investigates the potential moderating or mediating effects of institutional support and Faculty Job-Status position on the relationship between PsyCap and social well-being. Through structural equation modeling, we found that institutional support mediated the positive relationship between PsyCap and SWB and the permanent Faculty job-status position type strengthens the relationship between PsyCap and SWB. Our findings uncover the pathways through which PsyCap influences the social well-being outcomes of international faculty in the UAE. The findings will contribute to the development of tailored interventions and support systems aimed at enhancing the integration experiences and overall well-being of international faculty within the UAE academic community. Thus, fostering a more inclusive and thriving academic environment in the UAE.

Keywords: faculty job-status, institutional-faculty, psychological capital, social well-being, UAE

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Authors: Nieves M. C. Talavera-Prieto, Abel G. M. Ferreira, António T. G. Portugal, Rui J. Moreira, Jaime B. Santos

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The knowledge of biodiesel density over large ranges of temperature and pressure is important for predicting the behavior of fuel injection and combustion systems in diesel engines, and for the optimization of such systems. In this study, cottonseed oil was transesterified into biodiesel and its density was measured at temperatures between 288 K and 358 K and pressures between 0.1 MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg.m^-3. Experimental pressure-volume-temperature (pVT) cottonseed data was used along with literature data relative to other 18 biodiesels, in order to build a database used to test the correlation of density with temperarure and pressure using the Goharshadi–Morsali–Abbaspour equation of state (GMA EoS). To our knowledge, this is the first that density measurements are presented for cottonseed biodiesel under such high pressures, and the GMA EoS used to model biodiesel density. The new tested EoS allowed correlations within 0.2 kg•m-3 corresponding to average relative deviations within 0.02%. The built database was used to develop and test a new full predictive model derived from the observed linear relation between density and degree of unsaturation (DU), which depended from biodiesel FAMEs profile. The average density deviation of this method was only about 3 kg.m-3 within the temperature and pressure limits of application. These results represent appreciable improvements in the context of density prediction at high pressure when compared with other equations of state.

Keywords: biodiesel density, correlation, equation of state, prediction

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Authors: Mário Ernesto Sitoe, Orlando Zacarias

Abstract:

University education is influenced by several factors that range from the adoption of strategies to strengthen the whole process to the academic performance improvement of the students themselves. This work uses data mining techniques to develop a predictive model to identify students with a tendency to evasion and retention. To this end, a database of real students’ data from the Department of University Admission (DAU) and the Department of Mathematics and Informatics (DMI) was used. The data comprised 388 undergraduate students admitted in the years 2009 to 2014. The Weka tool was used for model building, using three different techniques, namely: K-nearest neighbor, random forest, and logistic regression. To allow for training on multiple train-test splits, a cross-validation approach was employed with a varying number of folds. To reduce bias variance and improve the performance of the models, ensemble methods of Bagging and Stacking were used. After comparing the results obtained by the three classifiers, Logistic Regression using Bagging with seven folds obtained the best performance, showing results above 90% in all evaluated metrics: accuracy, rate of true positives, and precision. Retention is the most common tendency.

Keywords: evasion and retention, cross-validation, bagging, stacking

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Authors: Eduardo Rodrigues, Gisela Mendes, José M. Torres, José E. Sousa

Abstract:

In the sequence of the liberalisation of the electricity sector a progressive engagement of consumers has been considered and targeted by sector regulatory policies. With the objective of promoting market competition while protecting consumers interests, by transferring some of the upstream benefits to the end users while reaching a fair distribution of system costs, different market models to value consumers’ demand flexibility at the energy community level are envisioned. Local Energy and Flexibility Markets (LEFM) involve stakeholders interested in providing or procure local flexibility for community, services and markets’ value. Under the scope of DOMINOES, a European research project supported by Horizon 2020, the local market concept developed is expected to: • Enable consumers/prosumers empowerment, by allowing them to value their demand flexibility and Distributed Energy Resources (DER); • Value local liquid flexibility to support innovative distribution grid management, e.g., local balancing and congestion management, voltage control and grid restoration; • Ease the wholesale market uptake of DER, namely small-scale flexible loads aggregation as Virtual Power Plants (VPPs), facilitating Demand Response (DR) service provision; • Optimise the management and local sharing of Renewable Energy Sources (RES) in Medium Voltage (MV) and Low Voltage (LV) grids, trough energy transactions within an energy community; • Enhance the development of energy markets through innovative business models, compatible with ongoing policy developments, that promote the easy access of retailers and other service providers to the local markets, allowing them to take advantage of communities’ flexibility to optimise their portfolio and subsequently their participation in external markets. The general concept proposed foresees a flow of market actions, technical validations, subsequent deliveries of energy and/or flexibility and balance settlements. Since the market operation should be dynamic and capable of addressing different requests, either prioritising balancing and prosumer services or system’s operation, direct procurement of flexibility within the local market must also be considered. This paper aims to highlight the research on the definition of suitable DR models to be used by the Distribution System Operator (DSO), in case of technical needs, and by the retailer, mainly for portfolio optimisation and solve unbalances. The models to be proposed and implemented within relevant smart distribution grid and microgrid validation environments, are focused on day-ahead and intraday operation scenarios, for predictive management and near-real-time control respectively under the DSO’s perspective. At local level, the DSO will be able to procure flexibility in advance to tackle different grid constrains (e.g., demand peaks, forecasted voltage and current problems and maintenance works), or during the operating day-to-day, to answer unpredictable constraints (e.g., outages, frequency deviations and voltage problems). Due to the inherent risks of their active market participation retailers may resort to DR models to manage their portfolio, by optimising their market actions and solve unbalances. The interaction among the market actors involved in the DR activation and in flexibility exchange is explained by a set of sequence diagrams for the DR modes of use from the DSO and the energy provider perspectives. • DR for DSO’s predictive management – before the operating day; • DR for DSO’s real-time control – during the operating day; • DR for retailer’s day-ahead operation; • DR for retailer’s intraday operation.

Keywords: demand response, energy communities, flexible demand, local energy and flexibility markets

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Authors: Sandeep Pandey, Nimra Habib, Ranjana Singh, Abbas Ali Mahdi

Abstract:

Leukemia is a malignancy of blood that mainly affects children and young adults; while advancement in the early diagnosis will have the potential to improve the outcome of diseases. A wide range of disease including leukemia shows inflammatory signals in their pathogenesis. In a pilot study conducted in our laboratory, 52 people were screened, of which 26 had leukemia and 26 were free from any kind of malignancy. We performed the estimation of the inflammatory cytokine Interleukin-8 and it was found significantly raised in all the leukemia patients concerning healthy volunteers who participated in the study. Flow cytometry had been performed for the confirmation of leukemia and further genomic, and proteomic, analyses of the sample revealed that IL-8 levels showed a positive correlation in patients with leukemia. The results had shown constitutive secretion of interleukin-8 by leukemia cells. So, our finding demonstrated that IL-8 is considered to have a role in the pathogenesis of leukemia, and quantification of IL-8 levels in leukemia conditions might be more useful and feasible in the clinical setting for the prediction of drug responses where it may represent a putative target for innovative diagnostic toward effective therapeutic approaches. However, further research explorations in this area are needed that include a greater number of patients with all different forms of leukemia, and estimating their IL-8 levels may hold the key for the additional predictive values on the recurrence of leukemia and its prognosis.

Keywords: T-ALL, IL-8, leukemia pathogenesis, cancer therapeutics

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