Search results for: squared prediction risk

Authors: Debasish Chowdhury, Sunil Mekale, Sarvanamurthy Sakthivel, Sukhvinder Kaur, Rambabu Khambampati, Ashok Agarwal

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Introduction: The migrant intervention in India was initiated during the National AIDS Control Program (NACP) Phase-2 (2002-2007). HIV Sentinel surveillance Studies (HSS) conducted in 2012-13 indicated higher HIV prevalence among migrants (0.99%) compared to general populations (0.35%). Migrants continue to bear a heightened risk of HIV infection which results from the condition and structure of the migration process. USAID PHFI-PIPPSE project in collaboration with the National AIDS Control Organization (NACO) developed a unique system called Migrant Service Delivery System (MSDS) to capture migrants profile with respect to their risk profile and to provide tailor made services to them. Description: MSDS is a web-based system, designed and implemented to increase service uptake among migrants through evidence based planning. 110 destination migrants Targeted Intervention (TI) from 11 states were selected for study with varied target populations in terms of occupations; to understand occupation related risk behaviors among the migrants. Occupation wise registration data of high risk vulnerable migrants were analyzed through MSDS for the period April 2014–June 2016. Analysis was made on specific indicators among these occupational groups to understand the risk behavior and their vulnerability to HIV and STIs. Findings: Out of total HIV positive migrant’s workers (N= 847) enrolled in MSDS HIV rate is found to be highest among Auto-Rickshaw (18.66%) followed by Daily wage laborers (14.46%), Loom workers (10.73%), Industrial workers (10.04%) and Construction worker 7.93%. With 45.14% positivity, industrial workers are found to be most vulnerable to Sexually Transmitted Infections (STIs) (N=10057) among all occupational categories followed by loom workers (16.28%), Skilled worker (Furniture, Jeweler)-7.14%, daily wage laborers (5.45%). Conclusion: MSDS is an effective tool to assess migrants’ risk and their vulnerability to HIV for designing evidence informed program. This system calls for a replication across all destination TIs by NACO for differential strategies for different occupation groups to ensure better yield through scientific planning of intervention among high risk and high vulnerable migrants.

Keywords: migrants, migrant service delivery system, risk, vulnerability

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Authors: Bojan Najdenov, Danco Davcev

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Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control.

Keywords: traffic, congestion reduction, crowdsource, social networks, twitter, android

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Authors: Steven Walker

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This paper assesses recent data on the prevalence of poverty among children and young people diagnosed with mental health problems. The paper will demonstrate that the current hierarchy of risk factors for developing mental health problems needs adjusting to place poverty among the highest risk factors. Globally poverty is calculated to keep rising especially among less developed countries, and the post-Covid 19 economic recession in developed countries is set to rise. The experience of young people enduring Pandemic isolation is already being quantified and is expected to increase referrals for specialist intervention. Searches on several medical/psychological/social databases using keywords: poverty, children, mental illness were undertaken between 2018 and 2021. Worldwide, 700 million people still live in extreme poverty, half of whom are children. Children are physically and mentally disproportionately affected. Children who grow up impoverished lack the basic necessities they need to survive and thrive. 150 million children have been plunged into multidimensional poverty due to COVID-19. The poorest children are twice as likely to die in childhood than their wealthier peers. For those growing up in humanitarian crises such as Ukraine, the risks of deprivation and exclusion are magnified. In the world’s richest countries, one in seven children still live in poverty. Currently, one in four children in the European Union are at risk of falling into poverty. In Europe the impact of Brexit on the UK economy is predicted to reduce GDP by 5% in 2021 with a corresponding rise in poverty. According to the global charity Oxfam wealth inequality impacts levels of child abuse and affects women and girls worse and is a contributory factor in the risk of developing childhood mental illness. In the UK 2000 Foodbanks have opened since 2010, handing out 2 million food parcels annually, where there are currently 4 million children officially living in poverty. This research demonstrates that there is a strong association between families’ socio-economic circumstances and the chances that their children will experience mental illness. Evidence of this association is found repeatedly across developed countries. The paper will conclude by arguing that psychologists, psychiatrists, psychotherapists, social workers and CAMHS specialists need to place more importance on this critical socio-economic variable when assessing referred children and also advocate for political priorities in governments to reduce poverty and lower the risk of childhood mental illness.

Keywords: poverty, resilience, risk factor, socio economic, susceptibility

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Authors: Huy Q. Tran, Jungwon Huh, Kiseok Kwak, Choonghyun Kang

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Infrared thermography (IRT) technique has been proven to be a good method for nondestructive evaluation of concrete material. In the building, a broad range of applications has been used such as subsurface defect inspection, energy loss, and moisture detection. The purpose of this research is to consider the qualitative and quantitative performance of reinforced concrete deteriorations using active infrared thermography technique. An experiment of three different heating regimes was conducted on a concrete slab in the laboratory. The thermal characteristics of the IRT method, i.e., absolute contrast and observation time, are investigated. A linear relationship between the observation time and the real depth was established with a well linear regression R-squared of 0.931. The results showed that the absolute contrast above defective area increases with the rise of the size of delamination and the heating time. In addition, the depth of delamination can be predicted by using the proposal relationship of this study.

Keywords: concrete building, infrared thermography, nondestructive evaluation, subsurface delamination

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Ravi Soni, Irfan Pathan, Manish Pande

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The current product development process needs simultaneous consideration of different physics. The performance of the product needs to be considered under both structural and fluid loads. Examples include ducts and valves where structural behavior affects fluid motion and vice versa. Simulation of fluid-structure interaction involves modeling interaction between moving components and the fluid flow. In these scenarios, it is difficult to calculate the damping provided by fluid flow because of dynamic motions of components and the transient nature of the flow. Abaqus Explicit offers general capabilities for modeling fluid-structure interaction with the Coupled Eulerian-Lagrangian (CEL) method. The Coupled Eulerian-Lagrangian technique has been used to simulate fluid spillage through fuel valves during dynamic closure events. The technique to simulate pressure drops across Eulerian domains has been developed using stagnation pressure. Also, the fluid flow is calculated considering material flow through elements at the outlet section of the valves. The methodology has been verified on Eaton products and shows a good correlation with the test results.

Keywords: Coupled Eulerian-Lagrangian Technique, fluid structure interaction, spillage prediction, stagnation pressure

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Authors: Ali Kassem, Mohamed Alsenbasy, Ahmed Nagaah

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Background: Diabetic foot is one of the often neglected complications of diabetes mellitus It was reported that patients of diabetic foot ulcer (DFU) have considerable morbidity and mortality. Due to arterial abnormalities, diabetic neuropathy, as well as the tendency to delayed wound healing, foot infection and or gangrene is relatively common in diabetic patients. Foot related problems are responsible for up to 50% of diabetic related hospital admissions. Aim of work: The aim of the present study is to assess the risk factors for DFU in diabetic patients attending Sohag University Hospitals (Upper Egypt) Material and methods: The present study includes 100 diabetic foot patients attending the diabetic outpatient clinic of Sohag University Hospitals. For all of the studied patients the following were done: Full medical history and clinical examination; thorough foot examination; Laboratory tests including: Blood glucose level, HBA1c, serum lipids and renal function tests, ECG and Echocardiography, Doppler study on the lower limbs. Results: Sixty eight percent of the affected patients were males versus 32 % female patients. All male patients and none of the female were smoker. Seventy nine percent of patients were living in rural areas versus 14 % in urban areas. Duration of diabetes was more than 12 years in 74%, less than 12 years in 26% of patients. Fifty percent of patients have associated hypertension, 46% have dyslipidemia, 18% have ischemic heart disease or old myocardial infarction and 8% have impaired renal function. History of previous foot ulcers was reported in 11 % and foot amputation in 2% of patients. Conclusion: Male gender, low socioeconomic status, smoking, long duration of diabetes, other cardiovascular risk factors particularly hypertension and previous history of foot ulceration are the major risk factors for diabetic foot in our locality.

Keywords: diabetic foot, diabetic neuropathy, foot gangrene, risk factors for diabetic complications

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Authors: Erwin Stolz, Hannes Mayerl, Anja Waxenegger, Wolfgang Freidl

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Research has often found poverty associated with adverse health outcomes, but it is unclear which (interplay of) mechanisms actually translate low economic resources into poor physical health. The goal of this study was to assess the impact of educational, material, psychosocial and behavioural factors in explaining the poverty-health association in old age. We analysed 28,360 observations from 11,390 community-dwelling respondents (65+) from the Survey of Health, Ageing and Retirement in Europe (SHARE, 2004-2013, 10 countries). We used multilevel growth curve models to assess the impact of combined income- and asset poverty risk on old age frailty index levels and trajectories. In total, 61.8% of the variation of poverty risk on frailty levels could be explained by direct and indirect effects, thereby highlighting the role of material and particularly psychosocial factors, such as perceived control and social isolation. We suggest strengthening social policy and public health efforts in order to fight poverty and its deleterious effects from early age on and to broaden the scope of interventions with regard to psychosocial factors.

Keywords: frailty, health inequality, old age, poverty

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Authors: Gayathri Pramod, Sheeja K. P.

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Urban flooding has been seen rising in cities for the past few years. This rise in urban flooding is the result of increasing urbanization and increasing climate change. A resilient city design focuses on 'living with water'. This means that the city is capable of accommodating the floodwaters without having to risk any loss of lives or properties. The resilient city design incorporates green infrastructure, river edge treatment, open space design, etc. to form a city that functions as a whole for resilience. Sponge urbanism is a recent method for building resilient cities and is founded by China in 2014. Sponge urbanism is the apt method for resilience building for a tropical town like Aluva of Kerala. Aluva is a tropical town that experiences rainfall of about 783 mm per month during the rainy season. Aluva is an urbanized town which faces the risk of urban flooding and riverine every year due to the presence of Periyar River in the town. Impervious surfaces and hard construction and developments contribute towards flood risk by posing as interference for a natural flow and natural filtration of water into the ground. This type of development is seen in Aluva also. Aluva is designed in this research as a town that have resilient strategies of sponge city and which focusses on natural methods of construction. The flood susceptibility of Aluva is taken into account to design the spaces for sponge urbanism and in turn, reduce the flood susceptibility for the town. Aluva is analyzed, and high-risk zones for development are identified through studies. These zones are designed to withstand the risk of flooding. Various catchment areas are identified according to the natural flow of water, and then these catchment areas are designed to act as a public open space and as detention ponds in case of heavy rainfall. Various development guidelines, according to land use, is also prescribed, which help in increasing the green cover of the town. Aluva is then designed to be a completely flood-adapted city or sponge city according to the guidelines and interventions.

Keywords: climate change, flooding, resilient city, sponge city, sponge urbanism, urbanization

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: Jaimin Patel

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Cloud computing is one of the most sharp and important movement in various computing technologies. It provides flexibility to users, cost effectiveness, location independence, easy maintenance, enables multitenancy, drastic performance improvements, and increased productivity. On the other hand, there are also major issues like security. Being a common server, security for a cloud is a major issue; it is important to provide security to protect user’s private data, and it is especially important in e-commerce and social networks. In this paper, encryption algorithms such as Advanced Encryption Standard algorithms, their vulnerabilities, risk of attacks, optimal time and complexity management and comparison with other algorithms based on software implementation is proposed. Encryption techniques to improve the performance of AES algorithms and to reduce risk management are given. Secure Hash Algorithms, their vulnerabilities, software implementations, risk of attacks and comparison with other hashing algorithms as well as the advantages and disadvantages between hashing techniques and encryption are given.

Keywords: Cloud computing, encryption algorithm, secure hashing algorithm, brute force attack, birthday attack, plaintext attack, man in middle attack

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Authors: Balasaheb Bansode

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Diabetes is a very important component in non-communicable diseases. Diabetes ailment is a route of the multi-morbidities ailments. The South Indian states are almost completing the demographic transition in India. The study objectives present the prevalence of diabetes and its association with co-morbidities among the south Indian women. The study based on National Family Health Survey fourth round (NFHS) 4 conducted in 2015-16. The univariate, bivariate and multivariate analyses techniques have been used to find the association of risk factors and comorbidities with diabetics. The result reveals that the prevalence of diabetes is high among South Indian women. The study shows the women with diabetics have more chances to diagnose with hypertension and anemia comorbidities. The factors responsible for co-morbidities are changing the demographic situation, socioeconomic status, overweight and addict with substance use in South India. The awareness about diabetes prevention and management should be increased through health education, disease management programmes, trained peers and community health workers and community-based programmes.

Keywords: diabetes, risk factors, comorbidities, women

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Authors: I. L. Kim, J. Y. Lee, A. K. Tekile

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In consequence of the very high urbanization rate of Korea, the number of traffic noise damages in areas congested with population and facilities is steadily increasing. The current environmental noise levels data in major cities of the country show that the noise levels exceed the standards set for both day and night times. This research was about comparative analysis in search for optimal soundproof panel shape and design factor that can minimize sound reflection noise. In addition to the normal flat-type panel shape, the reflection noise reduction of swelling-type, combined swelling and curved-type, and screen-type were evaluated. The noise source model Nord 2000, which often provides abundant information compared to models for the similar purpose, was used in the study to determine the overall noise level. Based on vehicle categorization in Korea, the noise levels for varying frequency from different heights of the sound source (directivity heights of Harmonize model) have been calculated for simulation. Each simulation has been made using the ray-tracing method. The noise level has also been calculated using the noise prediction program called SoundPlan 7.2, for comparison. The noise level prediction was made at 15m (R1), 30 m (R2) and at middle of the road, 2m (R3) receiving the point. By designing the noise barriers by shape and running the prediction program by inserting the noise source on the 2nd lane to the noise barrier side, among the 6 lanes considered, the reflection noise slightly decreased or increased in all noise barriers. At R1, especially in the cases of the screen-type noise barriers, there was no reduction effect predicted in all conditions. However, the swelling-type showed a decrease of 0.7~1.2 dB at R1, performing the best reduction effect among the tested noise barriers. Compared to other forms of noise barriers, the swelling-type was thought to be the most suitable for reducing the reflection noise; however, since a slight increase was predicted at R2, further research based on a more sophisticated categorization of related design factors is necessary. Moreover, as swellings are difficult to produce and the size of the modules are smaller than other panels, it is challenging to install swelling-type noise barriers. If these problems are solved, its applicable region will not be limited to other types of noise barriers. Hence, when a swelling-type noise barrier is installed at a downtown region where the amount of traffic is increasing every day, it will both secure visibility through the transparent walls and diminish any noise pollution due to the reflection. Moreover, when decorated with shapes and design, noise barriers will achieve a visual attraction than a flat-type one and thus will alleviate any psychological hardships related to noise, other than the unique physical soundproofing functions of the soundproof panels.

Keywords: reflection noise, shaped noise barriers, sound proof panel, traffic noise

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Authors: Umer Malik, David Armstrong, Mark Ashworth, Alex Dregan, Veline L'Esperance, Lucy McDonnell, Mariam Molokhia, Patrick White

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Introduction: The microbiota lining epithelial surfaces is thought to play an important role in many human physiological functions including defense against pathogens and modulation of immune response. The microbiota is susceptible to disruption from external influences such as exposure to antibiotic medication. It is thought that antibiotic-induced disruption of the microbiota could predispose to pathogen overgrowth and invasion. We hypothesized that antibiotic use would be associated with increased risk of future infections. We carried out a systematic review of evidence of associations between antibiotic use and subsequent risk of community-acquired infections. Methods: We conducted a review of the literature for observational studies assessing the association between antibiotic use and subsequent community-acquired infection. Eligible studies were published before April 29th, 2016. We searched MEDLINE, EMBASE, and Web of Science and screened titles and abstracts using a predefined search strategy. Infections caused by Clostridium difficile, drug-resistant organisms and fungal organisms were excluded as their association with prior antibiotic use has been examined in previous systematic reviews. Results: Eighteen out of 21,518 retrieved studies met the inclusion criteria. The association between past antibiotic exposure and subsequent increased risk of infection was reported in 16 studies, including one study on Campylobacter jejuni infection (Odds Ratio [OR] 3.3), two on typhoid fever (ORs 5.7 and 12.2), one on Staphylococcus aureus skin infection (OR 2.9), one on invasive pneumococcal disease (OR 1.57), one on recurrent furunculosis (OR 16.6), one on recurrent boils and abscesses (Risk ratio 1.4), one on upper respiratory tract infection (OR 2.3) and urinary tract infection (OR 1.1), one on invasive Haemophilus influenzae type b (Hib) infection (OR 1.51), one on infectious mastitis (OR 5.38), one on meningitis (OR 2.04) and five on Salmonella enteric infection (ORs 1.4, 1.59, 1.9, 2.3 and 3.8). The effect size in three studies on Salmonella enteric infection was of marginal statistical significance. A further two studies on Salmonella infection did not demonstrate a statistically significant association between prior antibiotic exposure and subsequent infection. Conclusion: We have found an association between past antibiotic exposure and subsequent risk of a diverse range of infections in the community setting. Our findings provide evidence to support the hypothesis that prior antibiotic usage may predispose to future infection risk, possibly through antibiotic-induced alteration of the microbiota. The findings add further weight to calls to minimize inappropriate antibiotic prescriptions.

Keywords: antibiotic, infection, risk factor, side effect

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Authors: Anne C. Richer-De-Forges, Dominique Arrouays, Songchao Chen, Mercedes Roman Dobarco

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Most of the digital soil mapping (DSM) products rely on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs. However, many other observations (often qualitative, nominal, or ordinal) could be used as proxies of lab measurements or as input data for ML of PTF predictions. DSM and ML are briefly described with some examples taken from the literature. Then, we explore the potential of an ordinal qualitative variable, i.e., the hand-feel soil texture (HFST) estimating the mineral particle distribution (PSD): % of clay (0-2µm), silt (2-50µm) and sand (50-2000µm) in 15 classes. The PSD can also be measured by lab measurements (LAST) to determine the exact proportion of these particle-sizes. However, due to cost constraints, HFST are much more numerous and spatially dense than LAST. Soil texture (ST) is a very important soil parameter to map as it is controlling many of the soil properties and functions. Therefore, comes an essential question: is it possible to use HFST as a proxy of LAST for calibration and/or validation of DSM predictions of ST? To answer this question, the first step is to compare HFST with LAST on a representative set where both information are available. This comparison was made on ca 17,400 samples representative of a French region (34,000 km2). The accuracy of HFST was assessed, and each HFST class was characterized by a probability distribution function (PDF) of its LAST values. This enables to randomly replace HFST observations by LAST values while respecting the PDF previously calculated and results in a very large increase of observations available for the calibration or validation of PTF and ML predictions. Some preliminary results are shown. First, the comparison between HFST classes and LAST analyses showed that accuracies could be considered very good when compared to other studies. The causes of some inconsistencies were explored and most of them were well explained by other soil characteristics. Then we show some examples applying these relationships and the increase of data to several issues related to DSM. The first issue is: do the PDF functions that were established enable to use HSFT class observations to improve the LAST soil texture prediction? For this objective, we replaced all HFST for topsoil by values from the PDF 100 time replicates). Results were promising for the PTF we tested (a PTF predicting soil water holding capacity). For the question related to the ML prediction of LAST soil texture on the region, we did the same kind of replacement, but we implemented a 10-fold cross-validation using points where we had LAST values. We obtained only preliminary results but they were rather promising. Then we show another example illustrating the potential of using HFST as validation data. As in numerous countries, the HFST observations are very numerous; these promising results pave the way to an important improvement of DSM products in all the countries of the world.

Keywords: digital soil mapping, improvement of digital soil mapping predictions, potential of using hand-feel soil texture, soil texture prediction

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Authors: Andreia Guimaraes Farnese, Mateus Fernandes Reu Urban, Leandro Procopio, Renato Zangaro, Regiane Albertini

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Senescence changes include postural balance, inferring the risk of falls, and can lead to fractures, bedridden, and the risk of death. Physical activity, e.g., cardiovascular exercises, is notable for improving balance due to brain cell stimulations, but fine coordination exercises also elevate cell brain metabolism. This study aimed to verify whether the elderly person who performs fine motor activity has a balance similar to that of those who practice physical activity. The subjects were divided into three groups according to the activity practice: control group (CG) with seven participants for the sedentary individuals, motor coordination group (MCG) with six participants, and activity practitioner group (PAG) with eight participants. Data comparisons were from the Berg balance scale, Time up and Go test, and stabilometric analysis. Descriptive statistical and ANOVA analyses were performed for data analysis. The results reveal that including fine motor activities can improve the balance of the elderly and indirectly decrease the risk of falls.

Keywords: balance, barapodometer, coordination, elderly

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Authors: S. Sushma, S. Balasubramanian, K. C. Latha, R. Sridhar

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Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and lacunarity contribute to assess breast cancer risk. Fractal Dimension represents the complexity while the lacunarity characterize the gap of a fractal dimension. In this paper, we present our result confirming that the lacunarity value resulted in algorithm using mammogram images states that level of lacunarity will be low when the Fractal Dimension value will be high.

Keywords: breast cancer, fractal dimension, image analysis, lacunarity, mammogram

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Authors: Chiung-Man Tsai, Chia-Jui Weng

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Leptin (LEP) and leptin receptor (LEPR) both play a crucial role in the mediation of physiological reactions and carcinogenesis and may serve as a candidate biomarker of oral cancer. The present case-control study aimed to examine the effects of single nucleotide polymorphisms (SNPs) of LEP -2548 G/A (rs7799039), LEPR K109R (rs1137100), and LEPR Q223R (rs1137101) with or without interacting to environmental carcinogens on the risk for oral squamous cell carcinoma (OSCC). The SNPs of three genetic allele, from 567 patients with oral cancer and 560 healthy controls in Taiwan were analyzed. All of The three genetic polymorphisms exhibited insignificant (P > .05) effects on the risk to have oral cancer. However, the patients with polymorphic allele of LEP -2548 have a significant low risk for the development of clinical stage (A/G, AOR = 0.670, 95% CI = 0.454–0.988, P < .05; A/G+G/G, AOR = 0.676, 95% CI = 0.467–0.978, P < .05) compared to patients with ancestral homozygous A/A genotype. Additionally, an interesting result was found that the impact of LEP -2548 G/A SNP on oral carcinogenesis in subjects without tobacco consumption (A/G, AOR=2.078, 95% CI: 1.161-3.720, p=0.014; A/G+G/G, AOR=2.002, 95% CI: 1.143-3.505, p=0.015) is higher than subjects with tobacco consumption. These results suggest that the genetic polymorphism of LEP -2548 G/A (rs7799039), LEPR K109R (rs1137100), and LEPR Q223R (rs1137101) were not associated with the susceptibility of oral cancer; SNP in LEP -2548 G/A showed a poor clinicopathological development of oral cancer; Population without tobacco consumption and with polymorphic LEP -2548 G/A gene may significantly increase the risk to have oral cancer.

Keywords: carcinogen, leptin, leptin receptor, oral squamous cell carcinoma, single nucleotide polymorphism

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Authors: Sonda Jallouli, Sameh Ghroubi, Salma Sakka, Abdelmoneem Yahia, Mohamed Habib Elleuch, Imen Ben Dhia, Chokri Mhiri, Omar Hammouda

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The pathophysiology, prevalence, and progression of MS are gender dependent. Indeed, the inflammation is more pronounced in women, but the neurodegeneration is more important in men. In addition, women have more sleep disorders while men suffer more from cognitive decline. These non-physical disorders can negatively affect postural balance and fall risk. However, no study has examined the difference between men and women in those physical parameters in MS. Our objective was to determine the effect gender difference on postural balance and fall risk in MS persons. Methods: Eight men and twelve women with relapsing remitting-MS participated in this study. The assessment includes a posturographic examination to assess static (with eyes opened (EO) and eyes closed (EC)) and dynamic (with EO) postural balance. Unipedal balance and fall risk were assessed by a clinical unipedal balance test and the Four Square Step Test, respectively. Sleep quality was assessed using Spiegel's questionnaire, and cognitive assessment was performed using the Montreal Cognitive Assessment (MoCA) and the Simple Reaction Time Test. Results: Compared to men, women showed an increase in CdPVm in static bipedal condition with EC (p=0.037; d=0.71) and a decrease in MoCA scores (p=0.028; d=1.06). No gender differences were found in the other tests. Discussion: Static postural balance was more impaired in women compared to men. This result could be explained by the more pronounced cognitive decline observed in women compared to men. Indeed, cognitive disorders have been shown to be predictive factors of postural balance impairment. Conclusion: women were less stable than men in the static condition, possibly due to their lower cognitive performance. This gender difference could be taken into account by therapists in training programs.

Keywords: multiple sclerosis, bipedal postural balance, fall risk, sleep disturbance, cognitive deficiency

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Authors: Mustapha Kamel Mihoubi, Mohamed Essadik Kerkar

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Probabilistic risk analysis models are used to provide a better understanding of the reliability and structural failure of works, including when calculating the stability of large structures to a major risk in the event of an accident or breakdown. This work is interested in the study of the probability of failure of concrete dams through the application of reliability analysis methods including the methods used in engineering. It is in our case, the use of level 2 methods via the study limit state. Hence, the probability of product failures is estimated by analytical methods of the type first order risk method (FORM) and the second order risk method (SORM). By way of comparison, a level three method was used which generates a full analysis of the problem and involves an integration of the probability density function of random variables extended to the field of security using the Monte Carlo simulation method. Taking into account the change in stress following load combinations: normal, exceptional and extreme acting on the dam, calculation of the results obtained have provided acceptable failure probability values which largely corroborate the theory, in fact, the probability of failure tends to increase with increasing load intensities, thus causing a significant decrease in strength, shear forces then induce a shift that threatens the reliability of the structure by intolerable values of the probability of product failures. Especially, in case the increase of uplift in a hypothetical default of the drainage system.

Keywords: dam, failure, limit-state, monte-carlo, reliability, probability, simulation, sliding, taylor

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Authors: Kasim Görenekli, Ali Gülbağ

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This study presents a comprehensive analysis of water consumption patterns in Kocaeli province, Turkey, utilizing various machine learning approaches. We analyzed data from 5,000 water subscribers across residential, commercial, and official categories over an 80-month period from January 2016 to August 2022, resulting in a total of 400,000 records. The dataset encompasses water consumption records, weather information, weekends and holidays, previous months' consumption, and the influence of the COVID-19 pandemic.We implemented and compared several machine learning models, including Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Particle Swarm Optimization (PSO) was applied to optimize hyperparameters for all models.Our results demonstrate varying performance across subscriber types and models. For official subscribers, Random Forest achieved the highest R² of 0.699 with PSO optimization. For commercial subscribers, Linear Regression performed best with an R² of 0.730 with PSO. Residential water usage proved more challenging to predict, with XGBoost achieving the highest R² of 0.572 with PSO.The study identified key factors influencing water consumption, with previous months' consumption, meter diameter, and weather conditions being among the most significant predictors. The impact of the COVID-19 pandemic on consumption patterns was also observed, particularly in residential usage.This research provides valuable insights for effective water resource management in Kocaeli and similar regions, considering Turkey's high water loss rate and below-average per capita water supply. The comparative analysis of different machine learning approaches offers a comprehensive framework for selecting appropriate models for water consumption prediction in urban settings.

Keywords: mMachine learning, water consumption prediction, particle swarm optimization, COVID-19, water resource management

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Authors: Ae Ra Han, Taek Hoo Lee, Sun Zoo Kim, Hwa Young Lee

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Introduction: Ovarian endometrioma is one of the important causes of poor ovarian reserve and up to half of them have recurred. However, the treatment for recurrence prevention has limited efficiency and repeated surgical management makes worsen the ovarian reserve. To find better management for recurrence prevention, we investigated risk factors and biomarkers for the recurrence of ovarian endometrioma. Methods: The medical records of women with the history of surgical dissection for ovarian endometrioma were collected. After exclusion of the cases with concurrent hysterectomy, been menopaused during follow-up, incomplete medical record, and loss of follow-up, a total of 134 women were enrolled. Immunohistochemical staining for progesterone receptor isoform B (PR-B) and nuclear factor kappa B (NFκB) was done with the fixed tissue blocks of their endometriomas which were collected at the time of surgery. Results: Severity of dysmenorrhea and co-existence of adenomyosis had significant correlation with recurrence of endometrioma. Increased PR-B (P = .041) and decreased NFκB (P = .036) immunoreactivity were found in recurrent group. Serum CA-125 level at the time of recurrence was higher than the highest level of CA-125 during follow-up in unrecurred group (55.6 vs. 21.3 U/mL, P = .014). Conclusion: We found that the severity of dysmenorrhea and coexistence of adenomyosis are risk factors for recurrence of ovarian endometrioma, and serial follow-up of CA-125 is effective to detect and prevent the recurrence. However, to determine the possibility of immunoreactivity of PR-B and NFκB as biomarkers for ovarian endometrioma, further studies of various races and large numbers with prospective design are needed.

Keywords: endometriosis, recurrence, biomarker, risk factor

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Karolina Patora

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This article investigates liquidity risk management by banks, which has gained significant importance since the global financial crisis of 2008. The issue is of particular interest for countries like Poland, in which foreign capital plays a dominant role. Such an ownership structure poses certain risks to the local banking sector, which faces an increased probability of the withdrawal of funding or assets’ transfers abroad in case of a crisis. Both these factors can have a detrimental influence on the liquidity position of foreign-owned banks and hence negatively affect the financial stability of the whole banking sector. The aim of this study is to evaluate the impact of a dominating share of foreign investors in the Polish banking sector on the liquidity position of commercial banks. The study hypothesizes that the ownership structure of the Polish banking sector, in which there are banks predominantly controlled by foreign investors, does not pose a threat to the liquidity position of Polish banks. A supplementary research hypothesis is that the liquidity risk profile of foreign-owned banks differs from that of domestic banks. The sample consists of 14 foreign-owned banks and 5 domestic banks owned by local investors, which together constitute approximately 87% of the banking sector’s assets. The data covers the period of 2004–2014. The results of the regression models show no evidence of significant differences in terms of the dynamics of changes of the liquidity buffers between the foreign-owned and domestic banks, although the signs of the coefficients might suggest that the foreign-owned banks were decreasing the holdings of liquid assets at a slower pace over the examined period, compared to the domestic banks. However, no proof of the statistical significance of these findings has been found. The supplementary research hypothesis that the liquidity risk profile of foreign-controlled banks differs from that of domestic banks was rejected.

Keywords: foreign-owned banks, liquidity position, liquidity risk, financial stability

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: Fagner O. Serrano, Danielle R. Bouchard, Todd A. Duhame

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Walking cadence (steps/min) is an effective way to prescribe exercise so an individual can reach a moderate intensity, which is recommended to optimize health benefits. To our knowledge, there is no study on the required walking cadence to reach a moderate intensity for people that present chronic conditions or risk factors for chronic conditions such as Cardiovascular Diseases (CVD). The objectives of this study were: 1- to identify the walking cadence needed for people at risk of CVD to a reach moderate intensity, and 2- to develop and test an equation using clinical variables to help professionals working with individuals at risk of CVD to estimate the walking cadence needed to reach moderate intensity. Ninety-one people presenting a minimum of two risk factors for CVD completed a medically supervised graded exercise test to assess maximum oxygen consumption at the first visit. The last visit consisted of recording walking cadence using a foot pod Garmin FR-60 and a Polar heart rate monitor, aiming to get participants to reach 40% of their maximal oxygen consumption using a portable metabolic cart on an indoor flat surface. The equation to predict the walking cadence needed to reach moderate intensity in this sample was developed as follows: The sample was randomly split in half and the equation was developed with one half of the participants, and validated using the other half. Body mass index, height, stride length, leg height, body weight, fitness level (VO2max), and self-selected cadence (over 200 meters) were measured using objective measured. Mean walking cadence to reach moderate intensity for people age 64.3 ± 10.3 years old at risk of CVD was 115.8  10.3 steps per minute. Body mass index, height, body weight, fitness level, and self-selected cadence were associated with walking cadence at moderate intensity when evaluated in bivariate analyses (r ranging from 0.22 to 0.52; all P values ≤0.05). Using linear regression analysis including all clinical variables associated in the bivariate analyses, body weight was the significant predictor of walking cadence for reaching a moderate intensity (ß=0.24; P=.018) explaining 13% of walking cadence to reach moderate intensity. The regression model created was Y = 134.4-0.24 X body weight (kg).Our findings suggest that people presenting two or more risk factors for CVD are reaching moderate intensity while walking at a cadence above the one officially recommended (116 steps per minute vs. 100 steps per minute) for healthy adults.

Keywords: cardiovascular disease, moderate intensity, older adults, walking cadence

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Authors: H. Ju, M. Jones, G. D. Mishra

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Introduction: Limited longitudinal studies have examined the relationship between BMI and dysmenorrhea, resulting in mixed results. This study aims to investigate the long-term association between BMI and dysmenorrhea. Methods: 9,688 women from Australian Longitudinal Study on Women’s Health (ALSWH), a prospective population-based cohort study, were followed for 13 years. Data were collected through self-reported questionnaires repeatedly on all variables, including dysmenorrhea, weight and height. The longitudinal association between dysmenorrhea and BMI or BMI transition (change of BMI categories between two successive surveys) was investigated by generalized estimating equations. Results: When the women were aged 22 to 27 years, approximately 11% were obese, 7% underweight, and 25% reported dysmenorrhea. Over the study period, the prevalence of obesity doubled whereas that of underweight declined substantially. The prevalence of dysmenorrhea remained relatively stable. Compared to women with a normal weight, significantly higher odds of reporting dysmenorrhea were detected for both women who were underweight (odds ratio (OR) 1.25, 95% confidence interval (CI) 1.09, 1.43) and obese (OR 1.20, 95% CI 1.10, 1.31). Being overweight was not associated with increased risk of dysmenorrhea. Compared to women who remained at normal weight or overweight over time, significant risk was detected for women who: remained underweight or obese (OR 1.35, 95% CI 1.23, 1.49), were underweight but became normal or overweight (OR 1.29, 95% CI 1.11, 1.50), became underweight (OR 1.24, 95% CI 1.01, 1.52). However, the higher risk among obese women disappeared when they lost weight and became normal weight or overweight (OR 1.07, 95% CI 0.87, 1.30). Conclusions: A U-shaped association was revealed between dysmenorrhea and BMI, revealing higher risk of dysmenorrhea for both underweight and obese women. Further, the risk disappeared when obese women lost weight and acquired a healthier BMI. However obesity certainly poses a greater burden of disease from the public health perspective, thus requires greater effort to tackle the increasing problem at the population level. It is important to maintain a healthy weight over time for women to enjoy a better reproductive health.

Keywords: body mass index, dysmenorrhea, obesity, painful period, underweight

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Authors: Gerald H. Artisen, Miah Joy O Awing, Elydia O. Ayat, Rachel L. Ayangwa, Zeah D. Baggas, Aspen S. Baguiyac, Delight D. Baguiyac, Kristine G. Bakidan, Nemesis B. Bakidan, Ketly B. Balanggao, Rhea G. Bala-Is, Hope Lulet A. Lomioan

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The study investigated the prevalence and risk factors associated with postpartum depression among mothers in the Pasil, Kalinga, contributing to a better understanding of the mental health challenges faced by this specific population. This research utilized a cross-sectional descriptive study to assess postpartum depression prevalence and identify contributing factors in Pasil, utilizing a quantitative approach and collecting data on sociodemographic characteristics, obstetric data, and the Edinburgh Postnatal Depression Scale. The study concluded that probable depression can be seen among mothers in the Pasil, which resulted in a risk of suicidality with a percentage of 40.08. Additionally, most of the respondents are aged 28–32, married, have a college degree, are unemployed, have a monthly income of 1,000–5,000, are female, have hypertension, gave birth naturally, have two children, have a planned pregnancy, and are currently breastfeeding. Lastly, the study found that mothers in Pasil who have unplanned pregnancies under obstetric factors are at high risk of developing postpartum depression, with a p-value below the 0.05 level. The study recommends barangay health professionals develop initiatives to inform aspiring mothers about postpartum depression (PPD) and resources to help them adjust to motherhood. It also recommends frequent check-ins with new mothers to identify special healthcare needs. Programs should be independently funded by LGUs, and support from family and relatives is recommended to prevent PPD.

Keywords: maternal health, postpartum depression, mothers, Pasil

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Authors: Kalpana Rajouriya, Ajay Taneja

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Particulates are ubiquitous in the air environment and cause serious threats to human beings, such as lung cancer, COPD, and Asthma. Particulates mainly arise from industrial effluent, vehicular emission, and other anthropogenic activities. In the glass industrial city Firozabad, real-time monitoring of size segregated Particulate Matter (PM) and black carbon was done by Aerosol Black Carbon Detector (ABCD) and GRIMM portable aerosol Spectrometer at two different sites in which one site is urban and another is rural. The average mass concentration of size segregated PM during the study period (March & April 2022) was recorded as PM10 (223.73 g/m⁻³), PM5.0 (44.955 g/m⁻³), PM2.5 (59.275 g/m⁻³), PM1.0 (33.02 g/m⁻³), PM0.5 (2.05 g/m⁻³), and PM0.25 (2.99 g/m⁻³). The highest concentration of BC was found in Urban due to the emissions from diesel engines and wood burning, while NO2 was highest at the rural sites. The average concentrations of PM10 (6.08 and 2.73 times) PM2.5 exceeded the NAAQS and WHO guidelines. Particulate Matter deposition and health risk assessment was done by MPPD and USEPA model to know about the particulate matter toxicity in industrial residents. Health risk assessment results showed that Children are most likely to be affected by exposure of PM10 and PM2.5 and may have various non-carcinogenic and carcinogenic diseases. Deposition results inferred that the sensitive exposed population, especially 9 years old children, have high PM deposition as well as visualization and may be at risk of developing health-related problems from exposure to size-segregated PM. They will be discussed during presentation.

Keywords: particulate matter, black carbon, NO2, deposition of PM, health risk

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