Search results for: academic speed and accuracy

Authors: Autumn Rooms Cypres, Barbara Driver

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The purpose of this study is to extend discussions about innovations and career politics. Research questions that grounded this effort were: How does an academic learn the unspoken rules of the academy? What happens politically to an academic’s career when their research speaks against the grain of society? Do professors perceive signals that it is time to move on to another institution or even to another career? Epistemology and Methods: This qualitative investigation was focused on examining perceptions of academics. Therefore an open-ended field study, based on Grounded Theory, was used. This naturalistic paradigm (Lincoln & Guba,1985) was selected because it tends to understand information in terms of whole, of patterns, and in relations to the context of the environment. The technique for gathering data was the process of semi-structured, in-depth interviewing. Twenty five academics across the United States were interviewed relative to their career trajectories and the politics and opportunities they have encountered in relation to their research efforts. Findings: The analysis of interviews revealed four themes: Academics are beholden to 2 specific networks of power that influence their sense of job security; the local network based on their employing university and the national network of scholars who share the same field of research. The fights over what counts as research can and does drift from the intellectual to the political, and personal. Academic were able to identify specific instances of shunning and or punishment from their colleagues related directly to the dissemination of research that spoke against the grain of the local or national networks. Academics identified specific signals from both of these networks indicating that their career was flourishing or withering. Implications: This research examined insights from those who persevered when the fights over what and who counts drifted from the intellectual to the political, and the personal. Considerations of why such drifts happen were offered in the form of a socio-political construct called Fit, which included thoughts on hegemony, discourse, and identity. This effort reveals the importance of understanding what professional capital is relative to job security. It also reveals that fear is an enmeshed and often unspoken part of the culture of Academia. Further research to triangulate these findings would be helpful within international contexts.

Keywords: politics, academia, job security, context

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Authors: Bryony Davies

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Many cell and tissue culture practices ignore the effects of gravity on cell biology, and little is known about how cell components may move in response to gravitational forces. Starfish oocytes provide an excellent model for interrogating the movement of cell components due to their unusually large size, ease of handling, and high transparency. Chromosomes from starfish oocytes can be visualised by microinjection of the histone-H2B-mCherry plasmid into the oocytes. The movement of the chromosomes can then be tracked by live-cell fluorescence microscopy. The results from experiments using these methods suggest that there is a replicable downward movement of centrally located chromosomes at a median velocity of 0.39 μm/min. Chromosomes nearer the nuclear boundary showed more restricted movement. Chromosome density and shape could also be altered by microinjection of restriction enzymes, primarily Alu1, before imaging. This was found to alter the speed of chromosome movement, with chromosomes from Alu1-injected nuclei showing a median downward velocity of 0.60 μm/min. Overall, these results suggest that there is a non-negligible movement of chromosomes in response to gravitational forces and that this movement can be altered by enzyme activity. Future directions based on these results could interrogate if this observed downward movement extends to other cell components and to other cell types. Additionally, it may be important to understand whether gravitational orientation and vertical positioning of cell components alter cell behaviour. The findings here may have implications for current cell culture practices, which do not replicate cell orientations or external forces experienced in vivo. It is possible that a failure to account for gravitational forces in 2D cell culture alters experimental results and the accuracy of conclusions drawn from them. Understanding possible behavioural changes in cells due to the effects of gravity would therefore be beneficial.

Keywords: starfish, oocytes, live-cell imaging, microinjection, chromosome dynamics

Procedia PDF Downloads 108

Authors: Jay P. Sipani, Riki H. Patel, Trushit Upadhyaya

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Today there is a demand to monitor environmental factors almost in all research institutes and industries and even for domestic uses. The analog data measurement requires manual effort to note readings, and there may be a possibility of human error. Such type of systems fails to provide and store precise values of parameters with high accuracy. Analog systems are having drawback of storage/memory. Therefore, there is a requirement of a smart system which is fully automated, accurate and capable enough to monitor all the environmental parameters with utmost possible accuracy. Besides, it should be cost-effective as well as portable too. This paper represents the Wireless Sensor (WS) data communication using DHT11, Arduino, SIM900A GSM module, a mobile device and Liquid Crystal Display (LCD). Experimental setup includes the heating arrangement of DHT11 and transmission of its data using Arduino and SIM900A GSM shield. The mobile device receives the data using Arduino, GSM shield and displays it on LCD too. Heating arrangement is used to heat and cool the temperature sensor to study its characteristics.

Keywords: wireless communication, Arduino, DHT11, LCD, SIM900A GSM module, mobile phone SMS

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Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

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Authors: Michał Gęca, Konrad Pietrykowski, Grzegorz Barański

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The water pump in the compression-ignition internal combustion engine transports a hot coolant along a system of ducts from the engine block to the radiator where coolant temperature is lowered. This part needs to maintain a constant volumetric flow rate. Its power should be regulated to avoid a significant drop in pressure if a coolant flow decreases. The internal combustion engine cooling system uses centrifugal pumps for suction. The paper investigates 4 constructions of engine pumps. The pumps are from diesel engine of a maximum power of 75 kW. Each of them has a different rotor shape, diameter and width. The test stand was created and the geometry inside the all 4 engine blocks was mapped. For a given pump speed on the inverter of the electric engine motor, the valve position was changed and volumetric flow rate, pressure, and power were recorded. Pump speed was regulated from 1200 RPM to 7000 RPM every 300 RPM. The volumetric flow rates and pressure drops for the pump speeds and efficiencies were specified. Accordingly, the operations of each pump were mapped. Our research was to select a pump for the aircraft compression-ignition engine. There was calculated a pressure drop at a given flow on the block and radiator of the designed aircraft engine. The water pump should be lightweight and have a low power demand. This fact shall affect the shape of a rotor and bearings. The pump volumetric flow rate was assumed as 3 kg/s (previous AVL BOOST research model) where the temperature difference was 5°C between the inlet (90°C) and outlet (95°C). Increasing pump speed above the boundary flow power defined by pressure and volumetric flow rate does not increase it but pump efficiency decreases. The maximum total pump efficiency (PCC) is 45-50%. When the pump is driven by low speeds with a 90% closed valve, its overall efficiency drops to 15-20%. Acknowledgement: This work has been realized in the cooperation with The Construction Office of WSK "PZL-KALISZ" S.A." and is part of Grant Agreement No. POIR.01.02.00-00-0002/15 financed by the Polish National Centre for Research and Development.

Keywords: aircraft engine, diesel engine, flow, water pump

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Authors: Didier Auroux, Vladimir Groza

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This work is part of STEEP Marie-Curie ITN project, and it focuses on the identification of unknown parameters of the proposed generic Abrasive WaterJet Milling (AWJM) PDE model, that appears as an ill-posed inverse problem. The necessity of studying this problem comes from the industrial milling applications where the possibility to predict and model the final surface with high accuracy is one of the primary tasks in the absence of any knowledge of the model parameters that should be used. In this framework, we propose the identification of model parameters by minimizing a cost function, measuring the difference between experimental and numerical solutions. The adjoint approach based on corresponding Lagrangian gives the opportunity to find out the unknowns of the AWJM model and their optimal values that could be used to reproduce the required trench profile. Due to the complexity of the nonlinear problem and a large number of model parameters, we use an automatic differentiation software tool (TAPENADE) for the adjoint computations. By adding noise to the artificial data, we show that in fact the parameter identification problem is highly unstable and strictly depends on input measurements. Regularization terms could be effectively used to deal with the presence of data noise and to improve the identification correctness. Based on this approach we present results in 2D and 3D of the identification of the model parameters and of the surface prediction both with self-generated data and measurements obtained from the real production. Considering different types of model and measurement errors allows us to obtain acceptable results for manufacturing and to expect the proper identification of unknowns. This approach also gives us the ability to distribute the research on more complex cases and consider different types of model and measurement errors as well as 3D time-dependent model with variations of the jet feed speed.

Keywords: Abrasive Waterjet Milling, inverse problem, model parameters identification, regularization

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Authors: Choi Duckkyu, Choi Yong, Kang Taegyoung, Jun Hyeonjong, Choi Ilsu, Hyun Changsik

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This study was carried out to develop a soybean combine thresher that enables to reduce the damage rate of soybean threshing and the rate of unthreshing. The combine threshing cylinder was developed with 6 circular axis at each end and fixed with disc plates. It was attached to the prototype combine thresher. A combine thresher that has a cylinder with circular rod type threshing pegs was used for a comparative test. A series of comparative tests were conducted using dae-won soybean. The test of the soybean thresher was performed at the cylinder speeds of 210, 240, 270 and 300 rpm, and with the concave clearance of 10, 13 and 16 mm. The separating positions of soybean after threshing were researched on a separate box with 4 sections. The soybean positions of front, center, rear and rear outside, of 59.5%, 30.6%, 7.8% and 2.2% respectively, were obtained. At the cylinder speeds from 210 rpm to 300 rpm, the damage rate of soybean was increased from 0.1% to 4.2% correspondingly to speeds. The unthreshed rate of soybean under the same condition was increased from 0.9% to 4.1% correspondingly to speeds. 0.7% of the damage rate and 1.5% of the unthreshed rate was achieved at the cylinder speed of 240 rpm and with the concave clearance of 10 mm. For Daewon soybean, an optimum cylinder speed of 240 rpm and the concave clearance of 10 mm were identified. These results will be useful for the design, construction, and operation of soybean threshing harvesters.

Keywords: soybean harvest, combine threshing, threshing cylinder, optimum design

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Authors: Alirıza Kaleli, M. Akif Ceviz, Erdoğan Güner, Köksal Erentürk

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The cyclic variations in spark ignition engines occurring especially under specific engine operating conditions make the maximum pressure variable for successive in-cylinder pressure cycles. Minimization of cyclic variations has a great importance in effectively operating near to lean limit, or at low speed and load. The cyclic variations may reduce the power output of the engine, lead to operational instabilities, and result in undesirable engine vibrations and noise. In this study, spark timing is controlled in order to reduce the cyclic variations in spark ignition engines. Firstly, an ARMAX model has developed between spark timing and maximum pressure using system identification techniques. By using this model, the maximum pressure of the next cycle has been predicted. Then, self-tuning minimum variance controller has been designed to change the spark timing for consecutive cycles of the first cylinder of test engine to regulate the in-cylinder maximum pressure. The performance of the proposed controller is illustrated in real time and experimental results show that the controller has a reliable effect on cycle to cycle variations of maximum cylinder pressure when the engine works under low speed conditions.

Keywords: cyclic variations, cylinder pressure, SI engines, self tuning controller

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Authors: Antoine Lauvray, Fabien Poulhaon, Pierre Michaud, Pierre Joyot, Emmanuel Duc

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Additive Friction Stir Manufacturing (AFSM) is a new industrial process that follows the emergence of friction-based processes. The AFSM process is a solid-state additive process using the energy produced by the friction at the interface between a rotating non-consumable tool and a substrate. Friction depends on various parameters like axial force, rotation speed or friction coefficient. The feeder material is a metallic rod that flows through a hole in the tool. Unlike in Friction Stir Welding (FSW) where abundant literature exists and addresses many aspects going from process implementation to characterization and modeling, there are still few research works focusing on AFSM. Therefore, there is still a lack of understanding of the physical phenomena taking place during the process. This research work aims at a better AFSM process understanding and implementation, thanks to numerical simulation and experimental validation performed on a prototype effector. Such an approach is considered a promising way for studying the influence of the process parameters and to finally identify a process window that seems relevant. The deposition of material through the AFSM process takes place in several phases. In chronological order these phases are the docking phase, the dwell time phase, the deposition phase, and the removal phase. The present work focuses on the dwell time phase that enables the temperature rise of the system composed of the tool, the filler material, and the substrate and due to pure friction. Analytic modeling of heat generation based on friction considers as main parameters the rotational speed and the contact pressure. Another parameter considered influential is the friction coefficient assumed to be variable due to the self-lubrication of the system with the rise in temperature or the materials in contact roughness smoothing over time. This study proposes, through numerical modeling followed by experimental validation, to question the influence of the various input parameters on the dwell time phase. Rotation speed, temperature, spindle torque, and axial force are the main monitored parameters during experimentations and serve as reference data for the calibration of the numerical model. This research shows that the geometry of the tool as well as fluctuations of the input parameters like axial force and rotational speed are very influential on the temperature reached and/or the time required to reach the targeted temperature. The main outcome is the prediction of a process window which is a key result for a more efficient process implementation.

Keywords: numerical model, additive manufacturing, friction, process

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Authors: Noura El-Ahmady El-Naggar

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Among the antitumour drugs, bacterial enzyme L-asparaginase has been employed as the most effective chemotherapeutic agent in pediatric oncotherapy especially for acute lymphoblastic leukemia. Glutaminase free L-asparaginase producing actinomycetes were isolated from soil samples collected from Egypt. Among them, a potential culture, strain NEAE-119, was selected and identified on the basis of morphological, cultural, physiological and biochemical properties, together with 16S rDNA sequence as Streptomyces olivaceus NEAE-119 and sequencing product(1509 bp) was deposited in the GenBank database under accession number KJ200342. The optimization of different process parameters for L-asparaginase production by Streptomyces olivaceus NEAE-119 using Plackett–Burman experimental design and response surface methodology was carried out. Fifteen nutritional variables (temperature, pH, incubation time, inoculum size, inoculum age, agitation speed, dextrose, starch, L-asparagine, KNO3, yeast extract, K2HPO4, MgSO4.7H2O, NaCl and FeSO4. 7H2O) were screened using Plackett–Burman experimental design. The most positive significant independent variables affecting enzyme production (temperature, inoculum age and agitation speed) were further optimized by the central composite face-centered design -response surface methodology. As a result, a medium of the following formula is the optimum for producing an extracellular L-asparaginase in the culture filtrate of Streptomyces olivaceus NEAE-119: Dextrose 3g, starch 20g, L-asparagine 10g, KNO3 1g, K2HPO4 1g, MgSO4.7H2O 0.1g, NaCl 0.1g, pH 7, temperature 37°C, agitation speed 200 rpm/min, inoculum size 4%, v/v, inoculum age 72 h and fermentation period 5 days.

Keywords: Streptomyces olivaceus NEAE-119, glutaminase free L-asparaginase, production, Plackett-Burman design, central composite face-centered design, 16S rRNA, scanning electron microscope

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Authors: Mohammed Qutishat, Omar Al-Omari, Kholoud Al-Damery, Mohammed Al-Qadiri

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Objective: The current study aims to explore the relationship between Night Eating Syndrome and the experiences of Fear of Missing out (FOMO) among college students in Oman. Methods: The study adopted a descriptive correlational design. The total sample was 366 based on defined inclusion criteria. The questionnaires were distributed over one month during the spring semester of 2020. We used a self-report instrument as a measurement tool to investigate the extents of the research phenomena, and it consists of two major sections: fear of missing out Questionnaires and Night Eating Questionnaire. Results: The respondents' age ranged between 18 and 30. The majority of the participants were female 76.7% (204), single 97.7% (266), in their third academic year 28.6% (76), live in –campus, 57.1% (152). The findings of this study showed that fear of missing out experiences are significantly correlated with age (P=.010), gender (P= .005), and daily sleeping hours (P= .007). However, night eating experiences are significantly associated with age (p=018), living arrangement (P= .017), and sleeping hours (P= .000). Conclusion: This article can define a limiting aspect of the relationship between fear of missing out and night eating behaviors. During academic life, students may find themselves overloaded and use their smartphones to do the simplest tasks they have, leading them to skip their meals frequently and interfere with their eating patterns and psychological function. Health awareness programs or the implementation of healthy eating standards and technology uses can be introduced for undergraduates.

Keywords: fear of missing out, night eating syndrome, smartphone, addiction

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: decision tree, feature selection, intrusion detection system, support vector machine

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Authors: Rei-Tang Tsai, Chih-Yang Wu, Chia-Yuan Chang, Ming-Ying Kuo

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This study aims to investigate the mixing behaviors of deionized (DI) water and carboxymethyl cellulose (CMC) solutions in C-shaped serpentine micromixers over a wide range of flow conditions. The flow of CMC solutions exhibits shear-thinning behaviors. Numerical simulations are performed to investigate the effects of the mean flow speed, fluid properties and geometry parameters on flow and mixing in the micromixers with serpentine channel of the same overall channel length. From the results, we can find the following trends. When fluid mixing is dominated by convection, the curvature-induced vortices enhance fluid mixing effectively. The mixing efficiency of a micromixer consisting of semicircular C-shaped repeating units with a smaller center-line radius is better than that of a micromixer consisting of major-segment repeating units with a larger center-line radius. The viscosity of DI water is less than the overall average apparent viscosity of CMC solutions, and so the effect of curvature-induced vortices on fluid mixing in DI water is larger than that in CMC solutions for the cases with the same mean flow speed.

Keywords: curved channel, microfluidics, mixing, non-newtonian fluids, vortex

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Authors: Dong Wu, Michael Grenn

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Managing software projects effectively is crucial for meeting deadlines, ensuring quality, and managing resources well. Traditional methods often struggle with predicting project timelines accurately due to uncertain schedules and complex data. This study addresses these challenges by combining time prediction algorithms with Large Language Models (LLMs). It makes use of real-world software project data to construct and validate a model. The model takes detailed project progress data such as task completion dynamic, team Interaction and development metrics as its input and outputs predictions of project timelines. To evaluate the effectiveness of this model, a comprehensive methodology is employed, involving simulations and practical applications in a variety of real-world software project scenarios. This multifaceted evaluation strategy is designed to validate the model's significant role in enhancing forecast accuracy and elevating overall management efficiency, particularly in complex software project environments. The results indicate that the integration of time prediction algorithms with LLMs has the potential to optimize software project progress management. These quantitative results suggest the effectiveness of the method in practical applications. In conclusion, this study demonstrates that integrating time prediction algorithms with LLMs can significantly improve the predictive accuracy and efficiency of software project management. This offers an advanced project management tool for the industry, with the potential to improve operational efficiency, optimize resource allocation, and ensure timely project completion.

Keywords: software project management, time prediction algorithms, large language models (LLMS), forecast accuracy, project progress prediction

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Authors: Harish K. Chandrawanshi, Mithun S. Rajput, Neelima Choure, Purnima Dey Sarkar, Shailesh Jain

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The present research study aimed to fabricate and evaluate biodegradable nanoparticles of aromatase inhibitor letrozole, intended for breast cancer therapy. Letrozole loaded poly(D,L-lactide-co-glycolide acid) nanoparticles were prepared by solvent evaporation method using dichlorometane as solvent (oil phase) and polyvinyl alcohol (PVA) as aqueous phase. Prepared nanoparticles were characterized by particle size, infrared spectra, drug loading efficiency, drug entrapment efficiency and in vitro release and also evaluated for in vivo anticancer activity. The high speed homogenizer was used to produce stable nanoparticles of mean size range 198.35 ± 0.04 nm with high entrapment efficiency (69.86 ± 2.78%). Percentage of drug and homogenization speed significantly influenced the particle size, entrapment efficiency and release (p<0.05). The nanoparticles show significant in vivo anticancer activity against Ehrlich ascites carcinoma in mice. The significant system sustained the release of letrozole drug effectively and further investigation could exhibit its potential usefulness in breast cancer therapy.

Keywords: breast cancer/therapy, letrozole, nanoparticles, PLGA

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Authors: Reza Salehi, Peter L. Dold, Yves Comeau

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The aim of the present study was to investigate the effect of a total of 6 operating parameters including pH (X1), temperature (X2), stirring speed (X3), chemical oxygen demand (COD) (X4), volatile suspended solids (VSS) (X5) and time (X6) on anaerobic orthophosphate release from enhanced biological phosphorus removal (EBPR) sludge. An 8-run Plackett Burman design was applied and the statistical analysis of the experimental data was performed using Minitab16.2.4 software package. The Analysis of variance (ANOVA) results revealed that temperature, COD, VSS and time had a significant effect with p-values of less than 0.05 whereas pH and stirring speed were identified as non-significant parameters, but influenced orthophosphate release from the EBPR sludge. The mathematic expression obtained by the first-order multiple linear regression model between orthophosphate release from the EBPR sludge (Y) and the operating parameters (X1-X6) was Y=18.59+1.16X1-3.11X2-0.81X3+3.79X4+9.89X5+4.01X6. The model p-value and coefficient of determination (R2) value were 0.026 and of 99.87%, respectively, which indicates the model is significant and the predicted values of orthophosphate release from the EBPR sludge have been excellently correlated with the observed values.

Keywords: anaerobic, operating parameters, orthophosphate release, Plackett-Burman design

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Authors: Yingjin Cui, Xue Bai, Serena Reese

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Student engagement and student interest during class instruction are important conditions for active learning. Engagement, which has an important relationship with learning motivation, influences students' levels of persistence in overcoming challenges. Lack of student engagement and absence from face-to-face lectures and tutorials, in turn, can lead to poor academic performance. However, keeping students motivated and engaged in the learning process in different instructional modes poses a significant challenge; students can easily become discouraged from attending lectures and tutorials across both online and face-to-face settings. Many factors impact students’ engagement in the learning process. If you want to keep students focused on learning, you have to invite them into the process of helping themselves by providing an active learning environment. Active learning is an excellent technique for enhancing student engagement and participation in the learning process because it provides means to motivate the student to engage themselves in the learning process through reflection, analyzing, applying, and synthesizing the material they learn during class. In this study, we discussed how to create an active learning class (both face-to-face and synchronous online) through engagement activities, including reflection, collaboration, screen messages, open poll, tournament, and transferring editing roles. These activities will provide an uncommon interactive learning environment that can result in improved learning outcomes. To evaluate the effectiveness of those engagement activities in the learning process, an experimental group and a control group will be explored in the study.

Keywords: active learning, academic performance, engagement activities, learning motivation

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Kehabtimer Shiferaw Kotiso, Getachew Hailemariam, Abiy Seifu Estifanos

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Introduction: Despite the highest share of low birth weight (LBW) for neonatal mortality and morbidity, predicting births with LBW for better intervention preparation is challenging. This study aims to predict LBW using a dataset encompassing 53,637 birth cohorts collected from 36 primary hospitals across seven regions in Ethiopia from February 2022 to June 2024. Methods: We identified ten explanatory variables related to maternal and neonatal characteristics, including maternal education, age, residence, history of miscarriage or abortion, history of preterm birth, type of pregnancy, number of livebirths, number of stillbirths, antenatal care frequency, and sex of the fetus to predict LBW. Using WEKA 3.8.2, we developed and compared seven machine learning algorithms. Data preprocessing included handling missing values, outlier detection, and ensuring data integrity in birth weight records. Model performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic curve (ROC AUC) using 10-fold cross-validation. Results: The results demonstrated that the decision tree, J48, logistic regression, and gradient boosted trees model achieved the highest accuracy (94.5% to 94.6%) with a precision of 93.1% to 93.3%, F1-score of 92.7% to 93.1%, and ROC AUC of 71.8% to 76.6%. Conclusion: This study demonstrates the effectiveness of machine learning models in predicting LBW. The high accuracy and recall rates achieved indicate that these models can serve as valuable tools for healthcare policymakers and providers in identifying at-risk newborns and implementing timely interventions to achieve the sustainable developmental goal (SDG) related to neonatal mortality.

Keywords: low birth weight, machine learning, classification, neonatal mortality, Ethiopia

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Authors: Cromwell F. Gopo, Joy L. Picar

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This study aimed to predict the employability of senior high school graduates for S.Y. 2018- 2019 in the Davao del Norte Division through quantitative research design using the descriptive status and predictive approaches among the indicated parameters, namely gender, school type, academics, academic award recipient, skills, values, and strand. The respondents of the study were the 33 secondary schools offering senior high school programs identified through simple random sampling, which resulted in 1,530 cases of graduates’ secondary data, which were analyzed using frequency, percentage, mean, standard deviation, and binary logistic regression. Results showed that the majority of the senior high school graduates who come from large schools were females. Further, less than half of these graduates received any academic award in any semester. In general, the graduates’ performance in academics, skills, and values were proficient. Moreover, less than half of the graduates were not employed. Then, those who were employed were either contractual, casual, or part-time workers dominated by GAS graduates. Further, the predictors of employability were gender and the Information and Communications Technology (ICT) strand, while the remaining variables did not add significantly to the model. The null hypothesis had been rejected as the coefficients of the predictors in the binary logistic regression equation did not take the value of 0. After utilizing the model, it was concluded that Technical-Vocational-Livelihood (TVL) graduates except ICT had greater estimates of employability.

Keywords: employability, senior high school graduates, Davao del Norte, Philippines

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

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Authors: Adewole A. Ayoade, Marshall R. Sweatt, John P. H. Steele, Qi Han, Khaled Al-Wahedi, Hamad Karki, William A. Yearsley

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Understanding the behavioral characteristics of sensors is a crucial step in fusing data from several sensors of different types. This paper introduces a practical, real-time approach to integrate heterogeneous sensor data to achieve higher accuracy than would be possible from any one individual sensor in localizing a mobile robot. We use this approach in both indoor and outdoor environments and it is especially appropriate for those environments like oil and gas refineries due to their sparse and featureless nature. We have studied the individual contribution of each sensor data to the overall combined accuracy achieved from the fusion process. A Sequential Update Extended Kalman Filter(EKF) using validation gates was used to integrate GPS data, Compass data, WiFi data, Inertial Measurement Unit(IMU) data, Vehicle Velocity, and pose estimates from Fiducial marker system. Results show that the approach can enable a mobile robot to navigate autonomously in any environment using a priori information.

Keywords: inspection mobile robot, navigation, sensor fusion, sequential update extended Kalman filter

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Authors: Pitimanus Bunlue

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The objectives of this study were to identify the unique characteristics of Salaya Old market, Phutthamonthon, Nakhon Pathom and develop the effective video media to promote the homeland awareness among local people and the characteristic features of this community were collectively summarized based on historical data, community observation, and people’s interview. The acquired data were used to develop a media describing prominent features of the community. The quality of the media was later assessed by interviewing local people in the old market in terms of content accuracy, video, and narration qualities, and sense of homeland awareness after watching the video. The result shows a 6-minute video media containing historical data and outstanding features of this community was developed. Based on the interview, the content accuracy was good. The picture quality and the narration were very good. Most people developed a sense of homeland awareness after watching the video also as well.

Keywords: audio-visual, creating homeland awareness, Phutthamonthon Nakhon Pathom, research and development

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Authors: Aishik Deb, Rituparna Sinha

Abstract:

We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

Abstract:

Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: M. Fowsia, Dominic Xavier Fernando, Vinojitha, Rahamath Juliyana

Abstract:

Aerofoil is a primary element to be designed during the initial phase of creating any new aircraft. It is the component that forms the cross-section of the wing. The wing is used to produce lift force that balances the weight which is acting downwards. The lift force is created due to pressure difference over the top and bottom surface which is caused due to velocity variation. At sub-sonic velocities, for a real fluid, we obtain a smooth flow of air over both the surfaces. In this era of high speed travel, commercial aircraft that can travel faster than speed of sound barrier is required. However transonic velocities cause the formation of shock waves which can cause flow separation over the top and bottom surfaces. In the transonic range, shock waves move across the top and bottom surfaces of the aerofoil, until both the shock waves merge into a single shock wave that is formed near the leading edge of theaerofoil. In this paper, a transonic aerofoil is designed and its aerodynamic properties at different velocities in the Transonic range (M = 0.8; 0.9; 1; 1.1; 1.2) are studied with the help of CFD. The Pressure and Velocity distributions over the top and bottom surfaces of aerofoil are studied and the variations of shock patterns, at different velocities, are analyzed. The analysis can be used to determine the effect of drag divergence on the lift created by the aerofoil.

Keywords: transonic aerofoil, cfd, drag divergence, shock formation, viscous flow

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Authors: Ana B. Vivas, Antonia Ypsilanti, Aristea I. Ladas, Angeles F. Estevez

Abstract:

Mild Cognitive Impairment (MCI) is considered an intermediate stage between normal and pathological aging, as a substantial percentage of people diagnosed with MCI converts later to dementia of the Alzheimer’s type. Memory is of the first cognitive processes to deteriorate in this condition. In the present study we employed the differential outcomes procedure (DOP) to improve visuospatial memory in a group of participants with MCI. The DOP requires the structure of a conditional discriminative learning task in which a correct choice response to a specific stimulus-stimulus association is reinforced with a particular reinforcer or outcome. A group of 10 participants with MCI, and a matched control group had to learn and keep in working memory four target locations out of eight possible locations where a shape could be presented. Results showed that participants with MCI had a statistically significant better terminal accuracy when a unique outcome was paired with a location (76% accuracy) as compared to a non differential outcome condition (64%). This finding suggests that the DOP is useful in improving working memory in MCI patients, which may delay their conversion to dementia.

Keywords: mild cognitive impairment, working memory, differential outcomes, cognitive process

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

Abstract:

Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Boy Subirosa Sabarguna, Aria Kekalih, Irzan Nurman

Abstract:

Introduction: Software in the form of Clinical Decision Support System could help students in understanding the mind set of decision-making in diagnosis and treatment at the stage of general practitioners. This could accelerate and ease the learning process which previously took place by using books and experience. Method: Gather 1000 members of the National Medical Multimedia Digital Community (NM2DC) who use the “flowchart of diagnosis and treatment” software, and analyse factors related to: display, speed in learning, convenience in learning, helpfulness and usefulness in the learning process, by using the Likert Scale through online questionnaire which will further be processed using percentage. Results and Discussions: Out of the 1000 members of NM2DC, apparently: 97.0% of the members use the software and 87.5% of them are students. In terms of the analysed factors related to: display, speed in learning, convenience in learning, helpfulness and usefulness of the software’s usage, the results indicate a 90.7% of fairly good performance. Therefore, the “Flowchart of Diagnosis and Treatment” software has helped students in understanding the decision-making of diagnosis and treatment. Conclusion: the use of “Flowchart of Diagnosis and Treatment” software indicates a positive role in helping students understand decision-making of diagnosis and treatment.

Keywords: usage, software, diagnosis and treatment, medical education

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Authors: Uwaruile Austin Obuh

Abstract:

This research work focuses on the use of computers in improving the academic performance of students in mathematics in Benin City, Edo State. To guide this study, two research questions were raised, and two corresponding hypotheses were formulated. A total of one hundred and twenty (120) respondents were randomly selected from four schools in the city (60 boys and 60 girls). The instrument employed for the collation of data for the study was the multiple-choice test items on geometry (MCTIOG), drawn from past senior school certificate examinations (SSCE) questions. The instrument was validated by an expert in mathematics and measurement and evaluation. The data obtained from the pre and post-test were analysed using the mean, standard deviation, and T-test. The study revealed a non-significant difference between the experimental and control group in the pre-test, and the two groups were found to be the same before treatment began. The study also revealed that the experimental group performed better than the control group. One can, therefore, conclude that the use of computers for mathematics instruction has improved the performance of students in Geometry. Therefore, the hypothesis was rejected. The study finally revealed that there was no significant difference between the boys and girls taught mathematics using a computer. Therefore, the hypothesis which states there will be no significant difference in the performance of boys and girls taught mathematics using the computer was not rejected. Consequent upon the findings of this study, a number of recommendations were postulated that would enhance the performance of teachers in the use of computer-aided instruction.

Keywords: computer, teaching, learning, mathematics

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