Search results for: PREDICT score

Authors: Evangeline Mbah, Margret Okeke, Agbo Joseph

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Agriculture was primarily a rural activity in Nigeria, but due to increasing demand for food and jobs for many urban dwellers, it became necessary for urban households to embark on farming as a means of improving household food security and additional income for economic empowerment. Urban agriculture serves as a veritable tool for poverty reduction among people living in urban areas mostly low-income earners and unemployed. The survey was conducted to identify key challenges encountered by households in Makurdi metropolis of Benue state, Nigeria who are engaged in urban agriculture. A well-structured questionnaire was used to collect data from a sample of respondents used for the study. Data were analyzed using frequency, percentage, mean score and standard deviation. Results show that a greater percentage (46.0%) of the respondents engaged in cultivation of leafy vegetable, 22.0% cultivated cassava, 21.0% planted sweet potato, 18.0% cultivated tomato while 56.0% reared poultry, 23.0% kept goat, among others. Sources of agricultural information indicated by the respondents were family members/relations (85.0%), friends/neighbours (73.0%), radio (68.0%), extension agents (57.0%), etc. Major challenges encountered by the respondents in urban agriculture include inadequate size of farmland (M= 2.72), lack of access to credit facilities (M= 2.63), lack of funds (M= 2.50), high cost of labour (M= 2.49), insecurity of lands (M= 2.46), theft of crops at maturity (M= 2.38), lack of farm inputs such as improved varieties of seeds, fertilizer and exotic breeds of livestock (M= 2.23), destruction of crops by stray farm animals (M= 1.96), among others. The study recommends that there is a need for adequate provision of farm inputs by the government at all levels at a subsidized rate in order to reduce the cost of production and enhance optimum productivity.

Keywords: urban, agriculture, household, challenges, Makurdi, Nigeria

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Authors: Y. Smirlis, G. Koronakos, S. Plitsos

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Shipbrokers assist the ship companies in chartering or selling and buying vessels, acting as intermediates between them and the market. They facilitate deals, providing their expertise, negotiating skills, and knowledge about ship market bargains. Their role is very important as it affects the profitability and market position of a shipping company. Due to their significant contribution, the shipping companies have to employ systematic procedures to evaluate the shipbrokers’ services in order to select the best and, consequently, to achieve the best deals. Towards this, in this paper, we consider shipbrokers as financial service providers, and we formulate the problem of evaluating and selecting shipbrokers’ services as a multi-criteria decision making (MCDM) procedure. The proposed methodology comprises a first normalization step to adjust different scales and orientations of the criteria and a second step that includes the mathematical model to evaluate the performance of the shipbrokers’ services involved in the assessment. The criteria along which the shipbrokers are assessed may refer to their size and reputation, the potential efficiency of the services, the terms and conditions imposed, the expenses (e.g., commission – brokerage), the expected time to accomplish a chartering or selling/buying task, etc. and according to our modelling approach these criteria may be assigned different importance. The mathematical programming model performs a comparative assessment and estimates for the shipbrokers involved in the evaluation, a relative score that ranks the shipbrokers in terms of their potential performance. To illustrate the proposed methodology, we present a case study in which a shipping company evaluates and selects the most suitable among a number of sale and purchase (S&P) brokers. Acknowledgment: This study is supported by the OptiShip project, implemented within the framework of the National Recovery Plan and Resilience “Greece 2.0” and funded by the European Union – NextGenerationEU programme.

Keywords: shipbrokers, multi-criteria decision making, mathematical programming, service-provider selection

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Authors: Salah Brahim Belakhdar, Tari Mohammed Amin, Rafai Abderrahmen, Amalsi Bilal

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Shear strength of sandy soils has been considered as the important parameter to study the stability of different civil engineering structures when subjected to monotonic, cyclic, and earthquake loading conditions. The proposed research investigated the effect of grading characteristics on the shear strength and mechanical behaviour of granular classes of sands mixed with salt in loose and dense states (Dr=15% and 90%). The laboratory investigation aimed at understanding the extent or degree at which shear strength of sand-silt mixture soil is affected by its gradation under static loading conditions. For the purpose of clarifying and evaluating the shear strength characteristics of sandy soils, a series of Casagrande shear box tests were carried out on different reconstituted samples of sand-silt mixtures with various gradations. The soil samples were tested under different normal stresses (100, 200, and 300 kPa). The results from this laboratory investigation were used to develop insight into the shear strength response of sand and sand-silt mixtures under monotonic loading conditions. The analysis of the obtained data revealed that the grading characteristics (D10, D50, Cu, ESR, and MGSR) have a significant influence on the shear strength response. It was found that shear strength can be correlated to the grading characteristics for the sand-silt mixture. The effective size ratio (ESR) and mean grain size ratio (MGSR) appear as pertinent parameters to predict the shear strength response of the sand-silt mixtures for soil gradation under study.

Keywords: mechanical behavior, silty sand, friction angle, cohesion, fines content

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Authors: Y. Zhang, J. Perez, S. Marnier

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To date, no study on goaltending butterfly motion has been completed in real conditions, during an ice hockey game or training practice, to the author's best knowledge. This motion, performed to save score, is unnatural, intense, and repeated. The target of this research activity is to identify representative biomechanical criteria for this goaltender-specific movement pattern. Determining specific physical parameters may allow to will identify the risk of hip and groin injuries sustained by goaltenders. Four professional or academic goalies were instrumented during ice hockey training practices with five inertial measurement units. These devices were inserted in dedicated pockets located on each thigh and shank, and the fifth on the lumbar spine. A camera was also installed close to the ice to observe and record the goaltenders' activities, especially the butterfly motions, in order to synchronize the captured data and the behavior of the goaltender. Each data recorded began with a calibration of the inertial units and a calibration of the fully equipped goaltender on the ice. Three butterfly motions were recorded out of the training practice to define referential individual butterfly motions. Then, a data processing algorithm based on the Madgwick filter computed hip and knee joints joint range of motion as well as angular specific angular velocities. The developed algorithm software automatically identified and analyzed all the butterfly motions executed by the four different goaltenders. To date, it is still too early to show that the analyzed criteria are representative of the trauma generated by the butterfly motion as the research is only at its beginning. However, this descriptive research activity is promising in its ecological assessment, and once the criteria are found, the tools and protocols defined will allow the prevention of as many injuries as possible. It will thus be possible to build a specific training program for each goalie.

Keywords: biomechanics, butterfly motion, human motion analysis, ice hockey, inertial measurement unit

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Authors: Çağan Alevkayali, Şermin Tağil

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Determination of soil pH surface distribution in urban areas is substantial for sustainable development. Changes on soil properties occur due to functions on performed in agriculture, industry and other urban functions. Soil pH is important to effect on soil productivity which based on sensitive and complex relation between plant and soil. Furthermore, the spatial variability of soil reaction is necessary to measure the effects of urbanization. The objective of this study was to explore the spatial variation of soil pH quality and the influence factors of human land use on soil Ph around Balikesir City using data for 2015 and Geographic Information Systems (GIS). For this, soil samples were taken from 40 different locations, and collected with the method of "Systematic Random" from the pits at 0-20 cm depths, because anthropologic sourced pollutants accumulate on upper layers of soil. The study area was divided into a grid system with 750 x 750 m. GPS was used to determine sampling locations, and Inverse Distance Weighting (IDW) interpolation technique was used to analyze the spatial distribution of pH in the study area and to predict the variable values of un-exampled places with the help from the values of exampled places. Natural soil acidity and alkalinity depend on interaction between climate, vegetation, and soil geological properties. However, analyzing soil pH is important to indirectly evaluate soil pollution caused by urbanization and industrialization. The result of this study showed that soil pH around the Balikesir City was neutral, in generally, with values were between 6.5 and 7.0. On the other hand, some slight changes were demonstrated around open dump areas and the small industrial sites. The results obtained from this study can be indicator of important soil problems and this data can be used by ecologists, planners and managers to protect soil supplies around the Balikesir City.

Keywords: Balikesir, IDW, GIS, spatial variability, soil pH, urbanization

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Authors: Nitin Pipralia, Amit Kmar Sinha, Gudrun de Boeck

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Atmospheric carbon dioxide (CO2) concentrations have been increasing since the beginning of the industrial revolution due to combustion of fossils fuel and many anthropogenic means. As the number of scenarios assembled by the International Panel on Climate Change (IPCC) predict a rise of pCO2 from today’s 380 μatm to approximately 900 μatm until the year 2100 and a further rise of up to 1900 μatm by the year 2300. A rise in pCO2 results in more dissolution in ocean surface water which lead to cange in water pH, This phenomena of decrease in ocean pH due to increase on pCO2 is ocean acidification is considered a potential threat to the marine ecosystems and expected to affect fish as well as calcerious organisms. The situation may get worste when the stress of salinity adds on, due to migratory movement of fishes, where fish moves to different salinity region for various specific activities likes spawning and other. Therefore, to understand the interactive impact of these whole range of two important environmental abiotic stresses (viz. pCO2 ranging from 380 μatm, 900 μatm and 1900 μatm, along with salinity gradients of 32ppt, 10 ppt and 2.5ppt) on the ecophysiologal performance of fish, we investigated various biological adaptive response in European sea bass (Dicentrarchus labrax), a model estuarine teleost. Overall, we hypothesize that effect of ocean acidification would be exacerbate with shift in ambient salinity. Oxygen consumption, ammonia metabolism, iono-osmoregulation, energy budget, ion-regulatory enzymes, hormones and pH amendments in plasma were assayed as the potential indices of compensatory responses.

Keywords: ocean acidification, sea bass, pH climate change, salinity

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Authors: Rakan Alajmi, Rahaf Alnazzawi, Yara Aljefri, Abdullah Alafif, Ali Alraddadi, Awadh Alamri

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Background: The distribution of physicians in different specialties across Saudi Arabia is determined by the career choices of medical students. Dermatology residency program is one of the highly competitive programs here in Saudi Arabia. Assessing and understanding the factors perceived to be attractive in choosing dermatology will aid the directors of the specialty programs to plan for a more balanced workforce distribution to better suit the needs of the specialties. Aim: The aim of our study is to determine and assess the factors perceived to be significantly attractive when choosing dermatology as a future specialty. Methods: The study is a cross-sectional study conducted in King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia. A validated questionnaire was sent electronically to clinical year medical students. In addition to the questionnaire, gender, grade point average, preferred specialty, and other socio-demographic data were assessed. Results: A total of 121 clinical years medical students completed the questionnaire, 8 (6.6%) preferred dermatology as a specialty. 76 (62.8%) of the participants score a grade point average of more than 4.5 and 83 students (68.6%) chose their specialty during clinical years. The appeal of being a dermatologist (P= 0.047), the portrayal of different specialities in the media (P= 0.005), and the likelihood that dermatologists can influence patients’ lives (P=0.010) were shown to be significantly attractive factors. Conclusion: There are many factors that are affecting students’ choices when choosing a medical specialty. The appeal of being a dermatologist, the portrayal of different specialities in the media, and the likelihood that dermatologists can influence patients’ lives were shown to be significantly attractive factors when choosing dermatology as a future specialty. Recognizing medical students’ specialty perception will lead them to a proper specialty tailored to their needs.

Keywords: dermatology, career choice, medical specialties, student's perception

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Mustafa Salman, Nurcan Cetinkaya, Zehra Selcuk, Bugra Genc

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The objectives of the present study were to estimate the microbial-N flow to the small intestine and to predict the digestible organic matter intake (DOMI) in grazing Karayaka sheep based on urinary excretion of purine derivatives (xanthine, hypoxanthine, uric acid, and allantoin) by the use of spot urine sampling under field conditions. In the trial, 10 Karayaka sheep from 2 to 3 years of age were used. The animals were grazed in a pasture for ten months and fed with concentrate and vetch plus oat hay for the other two months (January and February) indoors. Highly significant linear and cubic relationships (P<0.001) were found among months for purine derivatives index, purine derivatives excretion, purine derivatives absorption, microbial-N and DOMI. Through urine sampling and the determination of levels of excreted urinary PD and Purine Derivatives / Creatinine ratio (PDC index), microbial-N values were estimated and they indicated that the protein nutrition of the sheep was insufficient. In conclusion, the prediction of protein nutrition of sheep under the field conditions may be possible with the use of spot urine sampling, urinary excreted PD and PDC index. The mean purine derivative levels in spot urine samples from sheep were highest in June, July and October. Protein nutrition of pastured sheep may be affected by weather changes, including rainfall. Spot urine sampling may useful in modeling the feed consumption of pasturing sheep. However, further studies are required under different field conditions with different breeds of sheep to develop spot urine sampling as a model.

Keywords: Karayaka sheep, spot sampling, urinary purine derivatives, PDC index, microbial-N, feed intake

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Authors: Jaydip De

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Urban parks are open places, green fields and riverside gardens usually maintained by public or private authorities, or eventually by both jointly; and utilized for a multidimensional purpose by the citizens. These parks are indeed the lung of urban centers. In urban socio-environmental setup, parks are the nucleus of social integration, community building, and physical development. In contemporary cities, these green places seem to perform as the panacea of congested, complex and stressful urban life. The alarmingly increasing urban population and the resultant congestion of high-rises are making life wearisome in neo-liberal cities. This has made the citizen always quest for open space and fresh air. In such a circumstance, the mere existence of parks is not capable of satisfying the growing aspirations. Therefore in this endeavour, a structured attempt is so made to empirically identify the utility, visitors’ satisfaction, and future needs through the cases of two urban parks of Kolkata Metropolitan Area, India. This study is principally based upon primary information collected through visitors’ perception survey conducted at the Chinsurah ground and Chandernagore strand. The correlation between different utility categories is identified and analyzed systematically. At the same time, indices like Weighted Satisfaction Score (WSS), Facility wise Satisfaction Index (FSI), Urban Park Satisfaction Index (UPSI) and Urban Park Necessity Index (UPNI) are advocated to quantify the visitors’ satisfaction and future necessities. It is explored that the most important utilities are passive in nature. Simultaneously, satisfaction levels of visitors are average, and their requirements are centred on the daily needs of the next generation, i.e., the children. Further, considering the visitors’ opinion planning measures are promulgated for holistic development of urban parks to revitalize sustainability of citified life.

Keywords: citified life, future needs, visitors’ satisfaction, urban parks, utility

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Authors: Adam Y. Sulaiman, Gerard I.Obasi, Roma Chang, Hussein Sayed Moghaieb, Ming J. Huang, Neil J. Hewitt

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Residential air-conditioning units are essential for quality indoor comfort in hot climate countries. Nevertheless, because of their non-renewable energy sources and the contribution of ecologically unfriendly working fluids, these units are a major source of CO2 emissions in these countries. The utilisation of sustainable technologies nowadays is essential to reduce the adverse effects of CO2 emissions by replacing conventional technologies. This paper investigates the feasibility of running an off-grid solar-powered air-conditioning bed unit using three low GWP refrigerants (R32, R290, and R600a) to supersede conventional refrigerants.A prototype air conditioning unit was built to supply cold air to a canopy that was connected to it. The assembled unit was designed to distribute cold air to a canopy connected to it. This system is powered by two 400 W photovoltaic panels, with battery storage supplying power to the unit at night-time. Engineering Equation Solver (EES) software is used to mathematically model the vapor compression cycle (VCC) and predict the unit's energetic and exergetic performance. The TRNSYS software was used to simulate the electricity storage performance of the batteries, whereas the IES-VE was used to determine the amount of solar energy required to power the unit. The article provides an analytical design guideline, as well as a comprehensible process system. Combining a renewable energy source to power an AC based-VCC provides an excellent solution to the real problems of high-energy consumption in warm-climate countries.

Keywords: air-conditioning, refrigerants, PV panel, energy storages, VCC, exergy

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Yan Li, Qi-Fan Jia, Jie Zhou

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In today’s China, primary and secondary teachers are living a hard life with high pressure but low payment, which results in a sense of unfair and less satisfaction of life. However, teachers’ life satisfaction is a significant factor of their mental health and plays an important role in the development and progress of the society. This study was aimed to explore the effect of teachers’ perception of social justice on life satisfaction. 450 primary and secondary teachers from China were measured with life satisfaction scales, social justice scales, income satisfaction scale, job satisfaction scale, pressure condition scale, and major life event scale. Results showed their pressure is significantly higher than average, while life satisfaction, job satisfaction, income satisfaction and perceived social justice are lower. Hierarchical regression analysis showed that demographic variables, i.e., gender, age, education level and matric status, and factors related to occupation, i.e., professional title, school type and working hours per day cannot predict teachers’ life satisfaction. Teachers who had worked for 11-20 years had a lower life satisfaction compared to those with 1-5 years working experience. However, social status, monthly household income, income satisfaction, as well as job satisfaction were positively related to life satisfaction, whereas pressure condition was negatively related to it. After controlling for demographic factors and individual attitudes, social justice still had a positive effect on life satisfaction, among which distributive justice played a more important role than procedural justice. The suggestions on teachers’ condition in China and the implications for education reform to improve teachers’ mental health are discussed.

Keywords: life satisfaction, mental health, primary and secondary teachers, social justice

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Authors: Lina María Martínez Sánchez, Juliana Molina Valencia, Esteban Vallejo Agudelo, Daniel Gallego González, María Isabel Pérez Palacio, Juan Ricardo Gaviria García, María De Los Ángeles Rodríguez Gázquez, Gloria Inés Martínez Domínguez

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Alternative and complementary therapies constitute a vast and complex combination of interventions, philosophies, approaches, and therapies that acquire a holistic healthcare point of view, becoming an alternative for the treatment of patients with chronic pain. Objective: determine the characteristics of the use of alternative and complementary therapies in patients with chronic pain who consulted in a medical institution. Methodology: cross-sectional and descriptive study, with a population of patients that assisted to the outpatient consultation and met the eligibility criteria. Sampling was not conducted. A form was used for the collection of demographic and clinical variables and the Holistic Complementary and Alternative Medicine Questionnaire (HCAMQ) was validated. The analysis and processing of information was carried out using the SPSS program vr.19. Results: 220 people with chronic pain were included. The average age was 54.7±16.2 years, 78.2% were women, and 75.5% belonged to the socioeconomic strata 1 to 3. Musculoskeletal pain (77.7%), migraine (15%) and neuralgia (9.1%) were the most frequently types of chronic pain. 33.6% of participants have used some kind of alternative and complementary therapy; the most frequent were: homeopathy (14.5%), phytotherapy (12.7%), and acupuncture (11.4%). The total average HCAMQ score for the study group was 30.2±7.0 points, which shows a moderate attitude toward the use of complementary and alternative medicine. The highest scores according to the type of pain were: neuralgia (32.4±5.8), musculoskeletal pain (30.5±6.7), fibromyalgia (29.6±7.3) and migraine (28.5±8.8). The reliability of the HCAMQ was acceptable (Cronbach's α: 0.6). Conclusion: it was noted that the types of chronic pain and the clinical or therapeutic management of patients correspond to the data available in current literature. Despite the moderate attitude toward the use of these alternative and complementary therapies, one of every three patients uses them.

Keywords: chronic pain, complementary therapies, homeopathy, acupuncture analgesia

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Authors: M. L. Burnwal, P. Raychowdhury

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Earthquake leads to adverse effects on buildings resting on soft soils. Mitigating the response of shallow foundations on soft soil with different methods reduces settlement and provides foundation stability. Few methods such as the rocking foundation (used in Performance-based design), deep foundation, prefabricated drain, grouting, and Vibro-compaction are used to control the pore pressure and enhance the strength of the loose soils. One of the problems with these methods is that the settlement is uncontrollable, leading to differential settlement of the footings, further leading to the collapse of buildings. The present study investigates the utility of geosynthetics as a potential improvement of the subsoil to reduce the earthquake-induced settlement of structures. A steel moment-resisting frame building resting on loose liquefiable dry soil, subjected to Uttarkashi 1991 and Chamba 1995 earthquakes, is used for the soil-structure interaction (SSI) analysis. The continuum model can simultaneously simulate structure, soil, interfaces, and geogrids in the OpenSees framework. Soil is modeled with PressureDependentMultiYield (PDMY) material models with Quad element that provides stress-strain at gauss points and is calibrated to predict the behavior of Ganga sand. The model analyzed with a tied degree of freedom contact reveals that the system responses align with the shake table experimental results. An attempt is made to study the responses of footing structure and geosynthetics with unreinforced and reinforced bases with varying parameters. The result shows that geogrid reinforces shallow foundation effectively reduces the settlement by 60%.

Keywords: settlement, shallow foundation, SSI, continuum FEM

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Authors: Baha-Aldeen S. Algmati, Ahmed R. Ballil

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Multiphase flows occur widely in many engineering and industrial processes as well as in the environment we live in. In particular, bubbly flows are considered to be crucial phenomena in fluid flow applications and can be studied and analyzed experimentally, analytically, and computationally. In the present paper, the dynamic motion of an air bubble rising within a column of liquid is numerically simulated using an open-source CFD modeling tool 'OpenFOAM'. An interface tracking numerical algorithm called MULES algorithm, which is built-in OpenFOAM, is chosen to solve an appropriate mathematical model based on the volume of fluid (VOF) numerical method. The bubbles initially have a spherical shape and starting from rest in the stagnant column of liquid. The algorithm is initially verified against numerical results and is also validated against available experimental data. The comparison revealed that this algorithm provides results that are in a very good agreement with the 2D numerical data of other CFD codes. Also, the results of the bubble shape and terminal velocity obtained from the 3D numerical simulation showed a very good qualitative and quantitative agreement with the experimental data. The simulated rising bubbles yield a very small percentage of error in the bubble terminal velocity compared with the experimental data. The obtained results prove the capability of OpenFOAM as a powerful tool to predict the behavior of rising characteristics of the spherical bubbles in the stagnant column of liquid. This will pave the way for a deeper understanding of the phenomenon of the rise of bubbles in liquids.

Keywords: CFD simulations, multiphase flows, OpenFOAM, rise of bubble, volume of fluid method, VOF

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Authors: Shweta Verma

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Background: Epilepsy is a chronic non-communicable central nervous system (CNS) disorder which affects a large population of all ages. Different classes of drugs are used for the treatment of this neurological disorder, but due to augmented drug resistance and side effects, these drugs become incompetent. Therefore, we design the synthesis of ten new derivatives of Phenytoin. The moiety of Phenytoin was hybridized with different phenols by using three step approach. The synthesized molecules were then investigated for different physicochemical parameters, such as Log P values using diverse software programs and to predict the potential to cross the blood-brain barrier. Objective: The Phenytoin derivatives were designed, synthesized, and characterized to meet the structural necessities indispensable for antiepileptic activity. Method: Firstly, the chloroacetylation of the 5,5-diphenyl hydantoin was carried out, and then various substituted phenols were added to it. The synthesized compounds were characterized and evaluated for antianxiety activity by elevated plus maze method and antiepileptic activity by using subcutaneous pentylenetetrazole (scPTZ) and maximal electroshock (MES) models and neurotoxicity. Result: The number of derivatives of 5,5-diphenyl hydantoin was developed and optimized. The number of parameters was optimized which reveal that the compound containing chloro group such as C3 and C6 showed imperative potential when compared with the standard drug Diazepam. Other compounds containing nitro and methyl group were also found to possess activity. Conclusion: It was summarized that the new compounds of 5,5-diphenyl hydantoin derivatives were synthesized. The results of the data show that the compound containing chloro group is more potent for CNS activity. The new compounds have the probability of being optimized further to engender new scaffolds to treat various CNS disorders.

Keywords: phenytoin, parameters, CNS activity, blood-brain barrier, Log P, CNS active

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Authors: Ghalia Shamlan, M. Denise Robertson, Adam Collins

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Appetite control (i.e. control of energy intake) is important for weight maintenance. Exercise contributes to the most variable component of energy expenditure (EE) but its impact is beyond the energy cost of exercise including physiological, behavioural, and appetite effects. Exercise is known to acutely influence effect appetite but evidence as to the independent effect of intensity is lacking. This study investigated the role of exercise intensity on appetite, energy intake (EI), appetite related hormone, fat utilisation and subjective measures of appetite. One hour after a standardised breakfast, 10 sedentary overweight volunteers. Subjects undertook either 8 repeated 60 second bouts of cycling at 95% VO2max (high intensity) or 30 minutes of continuous cycling, at a fixed cadence, equivalent to 50% of the participant’s VO2max (low intensity) in a randomised crossover design. Glucose, NEFA, glucagon-like peptide-1 (GLP-1) were measured fasted, postprandial, and pre and post-exercise. Satiety was assessed subjectively throughout the study using visual analogue scales (VAS). Ad libitum intake of a pasta meal was measured at the end (3-h post-breakfast). Interestingly, there was not significant difference in EE fat oxidation between HI and LI post-exercise. Also, no significant effect of high intensity (HI) was observed on the ad libitum meal, 24h and 48h EI post-exercise. However the mean 24h EI was 3000 KJ lower following HI than low intensity (LI). Despite, no significant differences in hunger score, glucose, NEFA and GLP-1 between both intensities were observed. However, NEFA and GLP-1 plasma level were higher until 30 min post LI. In conclusion, the similarity of EE and oxidation outcomes could give overweight individuals an option to choose between intensities. However, HI could help to reduce EI. There are mechanisms and consequences of exercise in short and long-term appetite control; however, these mechanisms warrant further explanation. These results support the need for future research in to the role of in regulation energy balance, especially for obese people.

Keywords: appetite, exercise, food intake, energy expenditure

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Authors: M. Reza Roshandel, Tannaz Aghaei Badr, Batoul Khoundabi, Sara C. Lewis, Soroush Rais-Bahrami, John Sfakianos, Reza Mehrazin, Ash K. Tewari

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Introduction: Prostate biopsy is the most reliable diagnostic method for choosing the appropriate management of prostate cancer. However, discrepancies between Gleason grade groups (GG) of different biopsies remain a significant concern. This study aims to assess the association of the radiological factors with GG discrepancies in patients with index Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, using radical prostatectomy (RP) specimens as the most accurate and informative pathology. Methods: This single-institutional retrospective study was performed on a total of 2289 consecutive prostate cancer patients with combined targeted and systematic prostate biopsy followed by radical prostatectomy (RP). The database was explored for patients with the index PI-RADS 3 lesions version 2 and 2.1. Cancers with PI-RADS 4 or 5 scoring were excluded from the study. Patient characteristics and radiologic features were analyzed by multivariable logistic regression. Number-density of lesions was defined as the number of lesions per prostatic volume. Results: Of the 151 prostate cancer cases with PI-RADS 3 index lesions, 27% and 17% had upgrades and downgrades at RP, respectively. Analysis of grade changes showed no significant associations between discrepancies and the number or the number density of PI-RADS 3 lesions. Moreover, the study showed no significant association of the GG changes with race, age, location of the lesions, or prostate volume. Conclusions: This study demonstrated that in PI-RADS 3 cancerous nodules, the chance of the pathology changes in the final pathology of RP specimens was low. Furthermore, having multiple PI-RADS 3 nodules did not change the conclusion, as the possibility of grade changes in patients with multiple nodules was similar to those with solitary lesions.

Keywords: prostate, adenocarcinoma, multiparametric MRI, Gleason score, robot-assisted surgery

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Authors: Kwaku Damoah

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The agricultural sector is characterized by continuous transformation, driven by factors such as demographic shifts, evolving consumer preferences, climate change, and migration trends. This dynamic environment presents complex challenges for key stakeholders including farmers, governments, and investors, who must navigate these changes to achieve optimal investment returns. To effectively predict market trends and uncover promising investment opportunities, a systematic, data-driven approach is essential. This paper introduces the Structural Evolution Index (SEI), a machine learning-based methodology. SEI is specifically designed to analyse long-term trends and forecast the potential of emerging agricultural products for investment. Versatile in application, it evaluates various agricultural metrics such as production, yield, trade, land use, and consumption, providing a comprehensive view of the evolution within agricultural markets. By harnessing data from the UN Food and Agricultural Organisation (FAOSTAT), this study demonstrates the SEI's capabilities through Comparative Exploratory Analysis and evaluation of international trade in agricultural products, focusing on Malaysia and Singapore. The SEI methodology reveals intricate patterns and transitions within the agricultural sector, enabling stakeholders to strategically identify and capitalize on emerging markets. This predictive framework is a powerful tool for decision-makers, offering crucial insights that help anticipate market shifts and align investments with anticipated returns.

Keywords: agricultural investment, algorithm, comparative exploratory analytics, machine learning, market trends, predictive analytics, structural evolution index

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Authors: Charles U. Eze, Joy Johnbest Egbo

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The study investigated the effect of Context-Based Instructional Strategy (CBIS) on students’ achievement in chemistry. CBIS was used as an experimental group and expository method (EM) as a control group, sources showed that students poor achievement in chemistry is from teaching strategy adopted by the chemistry teachers. Two research questions were answered, and two null hypotheses were formulated and tested. This strategy recognizes the need for student-centered, relevance of tasks and students’ voice; it also helps students develop creative and critical learning skills. A quasi-experimental (non-equivalent, pretest, posttest control group) design was adopted for the study. The population for the study comprised all senior secondary class one (SSI) students who were offering chemistry in co-education schools in Agbani Education zone. The instrument for data collection was a self-developed Basic Chemistry Achievement Test (BCAT). Relevant data were collected from a sample of SSI chemistry students using purposive random sampling techniques from two co-education schools in Agbani Education Zone of Enugu State, Nigeria. A reliability co-efficient was obtained for the instrument using Kuder-Richardson formula 20. Mean and standard deviation scores were used to answer the research questions while two-way analysis of covariance (ANCOVA) was used to test the hypotheses. The findings showed that the experimental group taught with context-based instructional strategy (CBIS) obtained a higher mean achievement score than the control group in the post BCAT; male students had higher mean achievement scores than their female counterparts. The difference was significant. It was recommended, among others, that CBIS should be given more emphasis in the training and re-training program of secondary school chemistry teachers.

Keywords: context-based instructional strategy, expository strategy, student-centered

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Mirco Tarozzi, Giacomo Pignagnoli, Andrea Benedetti

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Structural systems are often subjected to degradation processes due to different kind of phenomena like unexpected loadings, ageing of the materials and fatigue cycles. This is true especially for bridges, in which their safety evaluation is crucial for the purpose of a design of planning maintenance. This paper discusses the experimental evaluation of the stiffness reduction from frequency changes due to uniform damage scenario. For this purpose, a 1:4 scaled bridge has been built in the laboratory of the University of Bologna. It is made of concrete and its cross section is composed by a slab linked to four beams. This concrete deck is 6 m long and 3 m wide, and its natural frequencies have been identified dynamically by exciting it with an impact hammer, a dropping weight, or by walking on it randomly. After that, a set of loading cycles has been applied to this bridge in order to produce a uniformly distributed crack pattern. During the loading phase, either cracking moment and yielding moment has been reached. In order to define the relationship between frequency variation and loss in stiffness, the identification of the natural frequencies of the bridge has been performed, before and after the occurrence of the damage, corresponding to each load step. The behavior of breathing cracks and its effect on the natural frequencies has been taken into account in the analytical calculations. By using a sort of exponential function given from the study of lot of experimental tests in the literature, it has been possible to predict the stiffness reduction through the frequency variation measurements. During the load test also crack opening and middle span vertical displacement has been monitored.

Keywords: concrete bridge, damage detection, dynamic test, frequency shifts, operational modal analysis

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Authors: Reda Abdel Azim

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Gas conning phenomenon considered one of the prevalent matter in oil field applications as it significantly affects the amount of produced oil, increase cost of production operation and it has a direct effect on oil reservoirs recovery efficiency as well. Therefore, evaluation of such phenomenon and study the reservoir mechanisms that may strongly affect invading gas to the producing formation is crucial. Gas conning is a result of an imbalance between two major forces controlling the oil production: gravitational and viscous forces especially in naturally fractured reservoirs where the capillary pressure forces are negligible. Once the gas invading the producing formation near the wellbore due to large producing oil rate, the oil gas contact will change and such reservoirs are prone to gas conning. Moreover, the oil volume expected to be produced requires the use of long horizontal perforated well. This work presents a numerical simulation study to predict and propose solutions to gas coning in naturally fractured oil reservoirs. The simulation work is based on discrete fractures and permeability tensors approaches. The governing equations are discretized using finite element approach and Galerkin’s least square technique (GLS) is employed to stabilize the equation solutions. The developed simulator is validated against Eclipse-100 using horizontal fractures. The matrix and fracture properties are modelled. Critical rate, breakthrough time and GOR are determined to be used in investigation of the effect of matrix and fracture properties on gas coning. Results show that fracture distribution in terms of diverse dip and azimuth has a great effect on conning occurring. In addition, fracture porosity, anisotropy ratio, and fracture aperture.

Keywords: gas conning, finite element, fractured reservoirs, multiphase

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Authors: S. Potgieter-Vermaak, N. Bain, A. Brown, K. Shaw

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It is well-known from public news media that air pollution is a health hazard and is responsible for early deaths. The quantification of the relationship between air quality and health is a probing question not easily answered. It is known that airborne particulate matter (APM) <2.5µm deposits in the tracheal and alveoli zones and our research probes the possibility of quantifying pulmonary injury by linking reactive oxygen species (ROS) in these particles to DNA damage. Currently, APM mass concentration is linked to early deaths and limited studies probe the influence of other properties on human health. To predict the full extent and type of impact, particles need to be characterised for chemical composition and structure. APMs are routinely analysed for their bulk composition, but of late analysis on a micro level probing single particle character, using micro-analytical techniques, are considered. The latter, single particle analysis (SPA), permits one to obtain detailed information on chemical character from nano- to micron-sized particles. This paper aims to provide a snapshot of studies using data obtained from chemical characterisation and its link with in-vitro studies to inform on personal health risks. For this purpose, two studies will be compared, namely, the bioaccessibility of the inhalable fraction of urban road dust versus total suspended solids (TSP) collected in the same urban environment. The significant influence of metals such as Cu and Fe in TSP on DNA damage is illustrated. The speciation of Hg (determined by SPA) in different urban environments proved to dictate its bioaccessibility in artificial lung fluids rather than its concentration.

Keywords: air pollution, human health, in-vitro studies, particulate matter

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Authors: Shian-Ling Keng, Onn Siong Yim, Poh San Lai, Soo Chong Chew, Anne Chong, Richard Ebstein

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Research has demonstrated a positive association between mindfulness meditation and physical health. Little work, however, has examined the association between trait mindfulness and leukocyte telomere length (LTL), an emerging marker of cellular aging. The present study aimed to examine whether facets of trait mindfulness are correlated with longer LTL in a Singaporean Han Chinese sample and whether these facets may mediate the association between psychological symptoms and LTL. 158 adults (mean age = 27.24 years) completed measures assessing trait mindfulness and psychological symptoms (i.e., depression and stress) and provided blood samples for analyses of LTL using qPCR. Multiple regression analyses were conducted to assess the association between facets of trait mindfulness and LTL. Bootstrapping-based mediational analyses were run to examine the role of trait mindfulness as a mediator of the association between psychological symptoms and LTL. Of five facets of trait mindfulness (describe, act with awareness, observe, nonreactivity, and nonjudging), nonreactivity was significantly associated with LTL, after controlling for the effects of age, gender, and education, β = .21, p = .006. Further, there was a trend for overall trait mindfulness, β = .15, p = .06, and nonjudging, β = .13, p = .095, to each predict longer LTL. Nonreactivity significantly mediated the association between depression and LTL, BCa 95% CI [-.004, -.0004], p=.03, as well as the association between stress and LTL, BCa 95% CI [-.004, -.0004], p=.04. The results provide preliminary evidence for a positive association between selected facets of trait mindfulness and slower cellular aging, indexed by LTL. The findings suggest that individuals who are high on equanimity may experience slower aging at the cellular level, presumably through engaging in more effective coping mechanisms and modulation of stress. The findings also highlight the role of nonreactivity as a potential mechanism that underlies the association between LTL and psychological symptoms.

Keywords: depression, mindfulness, stress, telomere length

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Authors: Novee Lor C. Leyso, Maylin C. Palatino

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Under-five malnutrition continues to persist in the Philippines, particularly in the island Province of Marinduque, with prevalence of some forms of malnutrition even worsening in recent years. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon as key in analyzing patterns of geographic variation, identification of community-appropriate programs and interventions, and focused targeting on high-risk areas. Using data from a province-wide household-based census conducted in 2014–2016, this study aimed to determine and evaluate spatial clusters of under-five malnutrition, across the province and within each municipality at the individual level using household location. Malnutrition was defined as weight-for-age z-score that fall outside the 2 standard deviations from the median of the WHO reference population. The Kulldorff’s elliptical spatial scan statistic in binomial model was used to locate clusters with high-risk of malnutrition, while adjusting for age and membership to government conditional cash transfer program as proxy for socio-economic status. One large significant cluster of under-five malnutrition was found southwest of the province, in which living in these areas at least doubles the risk of malnutrition. Additionally, at least one significant cluster were identified within each municipality—mostly located along the coastal areas. All these indicate apparent geographical variations across and within municipalities in the province. There were also similarities and disparities in the patterns of risk of malnutrition in each cluster across municipalities, and even within municipality, suggesting underlying causes at work that warrants further investigation. Therefore, community-appropriate programs and interventions should be identified and should be focused on high-risk areas to maximize limited government resources. Further studies are also recommended to determine factors affecting variations in childhood malnutrition considering the evidence of spatial clustering found in this study.

Keywords: Binomial model, Kulldorff’s elliptical spatial scan statistic, Philippines, under-five malnutrition

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Authors: Tai Yuan Yu, Pei-Jen Wang

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Oil-free turbomachinery is considered one of the critical technologies for future green power generation systems as rotor machinery systems. Oil-free technology allows clean, compact, and maintenance-free working, and gas foil bearings, abbreviated as GFBs, are important for the technology. Since the first applications in the auxiliary power units and air cycle machines in the 1970s, obvious improvement has been created to the computational models for dynamic rotor behavior. However, many technical issues are still poorly understood or remain unsolved, and some of those are thermal management and the pattern of how pressure will be distributed in bearing clearance. This paper presents a three-dimensional, abbreviated as 3D, fluid-structure interaction model of single pad foil bearings and three pad foil bearings to predict bearing working behavior that researchers could compare characteristics of those. The coupling analysis model involves dynamic working characteristics applied to all the gas film and mechanical structures. Therefore, the elastic deformation of foil structure and the hydrodynamic pressure of gas film can both be calculated by a finite element method program. As a result, the temperature distribution pattern could also be iteratively solved by coupling analysis. In conclusion, the working fluid state in a gas film of various pad forms of bearings working characteristic at constant rotational speed for both can be solved for comparisons with the experimental results.

Keywords: fluid-structure interaction, multi-physics simulations, gas foil bearing, oil-free, transient thermo-hydrodynamic

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Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

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Authors: Marlene Rosa, Susana Lopes

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There are few studies that explore the potential of board games as a performance measure, despite it can be an interesting strategy in the context of frailty populations. In fact, board games are immersive strategies than can inhibit the pressure of being evaluated. This study aimed to test the ability of gamed-base strategies to assess executive function in elderly population. Sixteen old participants were included: 10 with affected executive functions (G1 – 85.30±6.00 yrs old; 10 male); 6 with executive functions with non-clinical important modifications (G2 - 76.30±5.19 yrs old; 6 male). Executive tests were assessed using the Frontal Assessment Battery (FAB), which is a quick-applicable cognitive screening test (score<12 means impairment). The board game used in this study was the TATI Hand Game, specifically for training rhythmic coordination of the upper limbs with multiple cognitive stimuli. This game features 1 table grid, 1 set of Single Game cards (to play with one hand); Double Game cards (to play simultaneously with two hands); 1 dice to plan Single Game mode; cards to plan the Double Game mode; 1 bell; 2 cups. Each participant played 3 single game cards, and the following data were collected: (i) variability in time during board game challenges (SD); (ii) number of errors; (iii) execution speed (sec). G1 demonstrated: high variability in execution time during board game challenges (G1 – 13.0s vs G2- 0.5s); a higher number of errors (1.40 vs 0.67); higher execution velocity (607.80s vs 281.83s). These results demonstrated the potential of implementing board games as a functional assessment strategy in geriatric care. Future studies might include larger samples and statistical methodologies to find cut-off values for impairment in executive functions during performance in TATI game.

Keywords: board game, aging, executive function, evaluation

Procedia PDF Downloads 140