Search results for: classification algorithms

Authors: Chao Ma

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In this paper, the evaluation criteria of authenticity evaluation system model are taken as the foundation so as to discuss the establishment problems about decentralization supervision system and mechanism of historical cultural town and village. The filtration of fitting towns and village's authenticity is conducted from the level, characteristic index and authentic assessment of evaluation model, thereby, supervising subject -interest related- coordinate organization can be taken as the venation in the management level, thus supervision mechanism of town and village's cultural inheritance can be combed, and the cultural inheritance management system and mechanism which is suitable to historical and cultural Chinese town and village will be provided. As the settlement with strong self-organizing characteristic, town and village don't recognize the management system as deeply as city. Therefore, it is necessary to establish town and village cultural evaluation system based on authenticity evaluation criteria. In this paper, authenticity evaluation system is established by taking this village's value evaluation criteria and protection as the cores, and the classification of participating options is beneficial to distribute local limited resources, protect hierarchically and accord with the local characters of town and village, build the evaluation system to run through the whole process of cultural inheritance, moreover, provide abundant information resources and make sure the value judgment criteria, thus supervision and management can be strengthened to effectively guard risk. By the above judgement and filtration of participating options, the management object with clear functions and supervision and coordination organization are established, thereby, the managerial logic of interest-related persons' decentralization can be clarified, evaluation system can be established, and the more targeted decentralization supervision system and mechanism of historical and cultural village will be built ultimately. Taking this method as a fundamental in cultural protection of town and village, not only can it be carried forward in the mass media, but also can cultivate the identity sense of indigenous people to come back historical and cultural villages, and resist the replacement of city culture.

Keywords: authenticity, rural culture, inheritance, supervision

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Authors: Ballari Bagchi

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The dynamism ingrained in the process of urban-rural integration is manifested in the emergence of rurban settlements, referring to areas that combine the characteristics of agricultural activities found in rural zones with those of suburban living areas and industrialised zones. The concept of rurbanisation refers to the idea of introducing urban conveniences and opportunities, to rural areas in an attempt to stem rural urban migration. In the backdrop of the worldwide problem of disharmonised urban-rural dependence and the associated problems in urban and rural areas, the present study seeks to explore the potentialities of few settlements having a blend of rural and urban characteristics in the urban field of Koch Bihar. The prime concern of the present paper is three-fold: (i) to identify the rurban centres, (ii) to analyse the spatial integration of these identified centres with the rural areas situated in the urban periphery, and (iii) to suggest the necessities to be introduced in these settlements. The methodology applied here includes rurban index, gravity model, and functional classification of rurban centres, correlation and regression analysis and cartographic representation of data collected through primary and secondary sources. The investigation has identified a number of settlements potentially viable to be termed as rurban centres which may render services to the other less equipped rural areas in all aspects of life and thereby would lessen the burden on Koch Bihar urban centre. The levels of infrastructure of these settlements should be such that it might even attract the urban population in a reverse direction. The villages belonging to the lower rung of these service settlements would require metalled road connection with these intermediate settlements in addition to their connection with the core town. That is to say, a proper policy needs to be adopted in this regard to furnish these settlements with required infrastructures for serving their own population as well as the population of other villages. As a consequence of that, the idea of a well-coordinated settlement hierarchy may emerge in future.

Keywords: Hinterland, rurban, settlement hierarchy, urban-rural integration

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Authors: Selim M. Khan, Aaron A. Goodarzi, Joshua M. Taron, Tryggve Rönnqvist

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Indoor air quality, a prime determinant of health, is strongly influenced by the presence of hazardous radon gas within the built environment. As a health issue, dangerously high indoor radon arose within the 20th century to become the 2nd leading cause of lung cancer. While the 21st century building metrics and human behaviors have captured, contained, and concentrated radon to yet higher and more hazardous levels, the issue is rapidly worsening in Canada. It is established that Canadians in the Prairies are the 2nd highest radon-exposed population in the world, with 1 in 6 residences experiencing 0.2-6.5 millisieverts (mSv) radiation per week, whereas the Canadian Nuclear Safety Commission sets maximum 5-year occupational limits for atomic workplace exposure at only 20 mSv. This situation is also deteriorating over time within newer housing stocks containing higher levels of radon. Deep machine learning (LSTM) algorithms were applied to analyze multiple quantitative and qualitative features, determine the most important contributory factors, and predicted radon levels in the known past (1990-2020) and projected future (2021-2050). The findings showed gradual downwards patterns in Sweden, whereas it would continue to go from high to higher levels in Canada over time. The contributory factors found to be the basement porosity, roof insulation depthness, R-factor, and air dynamics of the indoor environment related to human window opening behaviour. Building codes must consider including these factors to ensure adequate indoor ventilation and healthy living that can prevent lung cancer in non-smokers.

Keywords: radon, building metrics, deep learning, LSTM prediction model, lung cancer, canada, sweden

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Authors: Pankaj Dhiman, Simranjeet Kaur

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This research paper presents a comprehensive sentiment analysis of social media responses to videos on Facebook, YouTube, Twitter, and Instagram during the 2024 Indian general elections. The study focuses on the sentiment patterns of voters towards the National Democratic Alliance (NDA) and The Indian National Developmental Inclusive Alliance (INDIA) on these platforms. The analysis aims to understand the impact of social media on voter sentiment and its correlation with the election outcome. The study employed a mixed-methods approach, combining both quantitative and qualitative methods. With a total of 200 posts analysed during general election-2024 final phase, the sentiment analysis was conducted using natural language processing (NLP) techniques, including sentiment dictionaries and machine learning algorithms. The results show that NDA received significantly more positive sentiment responses across all platforms, with a positive sentiment score of 47% compared to INDIA's score of 38.98 %. The analysis also revealed that Twitter and YouTube were the most influential platforms in shaping voter sentiment, with 60% of the total sentiment score coming from these two platforms. The study's findings suggest that social media sentiment analysis can be a valuable tool for understanding voter sentiment and predicting election outcomes. The results also highlight the importance of social media in shaping public opinion and the need for political parties to engage effectively with voters on these platforms. The study's implications are significant, as they indicate that social media can be a key factor in determining the outcome of elections. The findings also underscore the need for political parties to develop effective social media strategies to engage with voters and shape public opinion.

Keywords: Indian Elections-2024, NDA, INDIA, sentiment analysis, social media, democracy

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Authors: Kefi Rahmadio, Filipus Armando Ginting, Richard Nainggolan

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This research shows the formation of the West Buru Basin and the potential utilization of this West Buru Basin as a geothermal potential. The research area is West Buru Island which is part of the West Buru Basin. The island is located in Maluku Province, with its capital city named Namlea. The island is divided into 10 districts, namely District Kepalamadan, Airbuaya District, Wapelau District, Namlea District, Waeapo District, Batabual District, Namrole District, Waesama District, Leksula District, and Ambalau District. The formation in this basin is Permian-Quarter. They start from the Formation Ghegan, Dalan Formation, Mefa Formation, Kuma Formation, Waeken Formation, Wakatin Formation, Ftau Formation and Leko Formation. These formations are composing this West Buru Basin. Determination of prospect area in the geothermal area with preliminary investigation stage through observation of manifestation, topographic shape and structure are found around prospect area. This is done because there is no data of earth that support the determination of prospect area more accurately. In Waepo area, electric power generated based on field observation and structural analysis, geothermal area of ​Waeapo was approximately 6 km², with reference to the SNI 'Classification of Geothermal Potential' (No.03-5012-1999), an area of ​​1 km² is assumed to be 12.5 MWe. The speculative potential of this area is (Q) = 6 x 12.5 MWe = 75 MWe. In the Bata Bual area, the geothermal prospect projected 4 km², the speculative potential of the Bata Bual area is worth (Q) = 4 x 12.5 MWe = 50 MWe. In Kepala Madan area, based on the estimation of manifestation area, there is a wide area of ​​prospect in Kepala Madan area about 4 km². The geothermal energy potential of the speculative level in Kepala Madan district is (Q) = 4 x 12.5 MWe = 50 MWe. These three areas are the largest geothermal potential on the island of West Buru. From the above research, it can be concluded that there is potential in West Buru Island. Further exploration is needed to find greater potential. Therefore, researchers want to explain the geothermal potential contained in the West Buru Basin, within the scope of West Buru Island. This potential can be utilized for the community of West Buru Island.

Keywords: West Buru basin, West Buru island, potential, Waepo, Bata Bual, Kepala Madan

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: drug addiction, artificial neural networks, multilayer perceptron (MLP), decision support system

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Authors: Alma Ramić, Mirjana Skočibušić, Renata Odžak, Tomica Hrenar, Ines Primožič

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In an attempt to identify a new class of antimicrobial agents, the antimicrobial potential of Cinchona alkaloid derivatives was evaluated. The bark of the Cinchona trees is the source of a variety of alkaloids, among which the best known are quinine, quinidine, cinchonine and cinchonidine. They are very useful as organocatalysts in stereoselective synthesis. On the other hand, quinine is traditionally used in the treatment of malaria. Furthermore, Cinchona alkaloids possess various analgesic, anti-inflammatory and anti–arrhythmic properties as well. In this work we present the synthesis of twenty quaternary derivatives of pseudo−enantiomeric Cinchona alkaloid derivatives to evaluate their antibacterial activity. Quaternization of quinuclidine moiety was carried out with groups diverse in their size. The structures of compounds were systematically modified to obtain drug-like properties with proper physical and chemical properties and avoiding toxophore. All compounds were prepared in good yields and were characterized by standard analytical spectroscopy methods (1D and 2D NMR, IR, MS). The antibacterial activities of all compounds were evaluated against series of recent clinical isolates of antibiotic susceptible Gram-positive and resistant Gram-negative pathogens by determining their zone of inhibition and minimum inhibitory concentrations. All compounds showed good to strong broad-spectrum activity, equivalent or better in comparison with standard antibiotics used. Furthermore, seven compounds exhibited significant antibacterial efficiency against Gram-negative isolates. To visualize the results, principal component analysis was used as an additional classification tool. Cytotoxicity of compounds with different cell lines in human cell culture was determined. Based on these results, substituted quaternary Cinchona scaffold can be considered as promising new class of antimicrobials and further investigations should be performed. Supported by Croatian Science Foundation, Project No 3775 ADESIRE.

Keywords: antibacterial efficiency, cinchona alkaloids, cytotoxicity, pseudo‐enantiomers

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

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Authors: Arpita Mondal, Aurobinda Routray, Sreeraj Puravankara, Rajashree Biswas

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The most difficult part of designing a battery management system (BMS) is battery modeling. A good battery model can capture the dynamics which helps in energy management, by accurate model-based state estimation algorithms. So far the most suitable and fruitful model is the equivalent circuit model (ECM). However, in real-time applications, the model parameters are time-varying, changes with current, temperature, state of charge (SOC), and aging of the battery and this make a great impact on the performance of the model. Therefore, to increase the equivalent circuit model performance, the parameter estimation has been carried out in the frequency domain. The battery is a very complex system, which is associated with various chemical reactions and heat generation. Therefore, it’s very difficult to select the optimal model structure. As we know, if the model order is increased, the model accuracy will be improved automatically. However, the higher order model will face the tendency of over-parameterization and unfavorable prediction capability, while the model complexity will increase enormously. In the time domain, it becomes difficult to solve higher order differential equations as the model order increases. This problem can be resolved by frequency domain analysis, where the overall computational problems due to ill-conditioning reduce. In the frequency domain, several dominating frequencies can be found in the input as well as output data. The selective frequency domain estimation has been carried out, first by estimating the frequencies of the input and output by subspace decomposition, then by choosing the specific bands from the most dominating to the least, while carrying out the least-square, recursive least square and Kalman Filter based parameter estimation. In this paper, a second order battery model consisting of three resistors, two capacitors, and one SOC controlled voltage source has been chosen. For model identification and validation hybrid pulse power characterization (HPPC) tests have been carried out on a 2.6 Ah LiFePO₄ battery.

Keywords: equivalent circuit model, frequency estimation, parameter estimation, subspace decomposition

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Authors: F. Fatoye, C. E. Mbada, T. Gebrye, A. O. Ogunsola, C. Fatoye, O. Oyewole

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Musculoskeletal disorders (MSDs) such as low back pain (LBP), cervical spondylosis (CSPD), sprain, osteoarthritis (OA), and post immobilization stiffness (PIS) have a major impact on individuals, health systems and society in terms of morbidity, long-term disability, and economics. This study estimated the direct and indirect costs of common MSDs in Osun State, Nigeria. A review of medical charts for adult patients attending Physiotherapy Outpatient Clinic at the Obafemi Awolowo University Teaching Hospitals Complex, Osun State, Nigeria between 2009 and 2018 was carried out. The occupational class of the patients was determined using the International Labour Classification (ILO). The direct and indirect costs were estimated using a cost-of-illness approach. Physiotherapy related health resource use, and costs of the common MSDs, including consultation fee, total fee charge per session, costs of consumables were estimated. Data were summarised using descriptive statistics mean and standard deviation (SD). Overall, 1582 (Male = 47.5%, Female = 52.5%) patients with MSDs population with a mean age of 47.8 ± 25.7 years participated in this study. The mean (SD) direct costs estimate for LBP, CSPD, PIS, sprain, OA, and other conditions were $18.35 ($17.33), $34.76 ($17.33), $32.13 ($28.37), $35.14 ($44.16), $37.19 ($41.68), and $15.74 ($13.96), respectively. The mean (SD) indirect costs estimate of LBP, CSPD, PIS, sprain, OA, and other MSD conditions were $73.42 ($43.54), $140.57 ($69.31), $128.52 ($113.46), sprain $140.57 ($69.31), $148.77 ($166.71), and $62.98 ($55.84), respectively. Musculoskeletal disorders contribute a substantial economic burden to individuals with the condition and society. The unacceptable economic loss of MSDs should be reduced using appropriate strategies. Further research is required to determine the clinical and cost effectiveness of strategies to improve health outcomes of patients with MSDs. The findings of the present study may assist health policy and decision makers to understand the economic burden of MSDs and facilitate efficient allocation of healthcare resources to alleviate the burden associated with these conditions in Nigeria.

Keywords: economic burden, low back pain, musculoskeletal disorders, real-world

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Authors: Adel Francis

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Most of scheduling methods and software apply the critical path logic. This logic schedule activities, apply constraints between these activities and try to optimize and level the allocated resources. The extensive use of this logic produces a complex an erroneous network hard to present, follow and update. Planning and management building projects should tackle the coordination of works and the management of limited spaces, traffic, and supplies. Activities cannot be performed without the resources available and resources cannot be used beyond the capacity of workplaces. Otherwise, workspace congestion will negatively affect the flow of works. The objective of the space planning is to link the spatial and temporal aspects, promote efficient use of the site, define optimal site occupancy rates, and ensures suitable rotation of the workforce in the different spaces. The Chronographic scheduling modelling belongs to this category and models construction operations as well as their processes, logical constraints, association and organizational models, which help to better illustrate the schedule information using multiple flexible approaches. The model defined three categories of areas (punctual, surface and linear) and four different layers (space creation, systems, closing off space, finishing, and reduction of space). The Chronographical modelling is a more complete communication method, having the ability to alternate from one visual approach to another by manipulation of graphics via a set of parameters and their associated values. Each individual approach can help to schedule a certain project type or specialty. Visual communication can also be improved through layering, sheeting, juxtaposition, alterations, and permutations, allowing for groupings, hierarchies, and classification of project information. In this way, graphic representation becomes a living, transformable image, showing valuable information in a clear and comprehensible manner, simplifying the site management while simultaneously utilizing the visual space as efficiently as possible.

Keywords: building projects, chronographic modelling, CPM, critical path, precedence diagram, scheduling

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Authors: Waldir Roque, Gustavo Oliveira, Moises Santos, Tatiana Simoes

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Increasing the oil recovery factor of an oil reservoir has been a concern of the oil industry. Usually, the production placement zones are defined after some analysis of geological and petrophysical parameters, being the rock porosity, permeability and oil saturation of fundamental importance. In this context, the determination of hydraulic flow units (HFUs) renders an important step in the process of reservoir characterization since it may provide specific regions in the reservoir with similar petrophysical and fluid flow properties and, in particular, techniques supporting the placement of production zones that favour the tracing of directional wells. A HFU is defined as a representative volume of a total reservoir rock in which petrophysical and fluid flow properties are internally consistent and predictably distinct of other reservoir rocks. Technically, a HFU is characterized as a rock region that exhibit flow zone indicator (FZI) points lying on a straight line of the unit slope. The goal of this paper is to provide a trustful indication for oil production placement zones for the best-fit HFUs. The FZI cloud of points can be obtained from the reservoir quality index (RQI), a function of effective porosity and permeability. Considering log and core data the HFUs are identified and using the discrete rock type (DRT) classification, a set of connected cell clusters can be found and by means a graph centrality metric, the maximum closeness (MaxC) cell is obtained for each cluster. Considering the MaxC cells as production zones, an extensive analysis, based on several oil recovery factor and oil cumulative production simulations were done for the SPE Model 2 and the UNISIM-I-D synthetic fields, where the later was build up from public data available from the actual Namorado Field, Campos Basin, in Brazil. The results have shown that the MaxC is actually technically feasible and very reliable as high performance production placement zones.

Keywords: hydraulic flow unit, maximum closeness centrality, oil production simulation, production placement zone

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Authors: Ezat Vahidian, Babak Eshrati

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Introduction: Elder abuse, a very complex issue with diverse definitions and names, has been very slow to capture the public eye and public policy since it is manifested at many levels. It requires the involvement of different types of professionals. While elder abuse is not a new phenomenon, the speed of population ageing world-wide is likely to lead to an increase in its incidence and prevalence. Elder abuse has devastating consequences for older persons such as poor quality of life, psychological distress, and loss of property and security. It is also associated with increased mortality and morbidity. Elder abuse is a problem that manifests itself in both rich and poor countries and at all levels of society. Purpose: The purpose of this study is to determine the prevalence of elder abuse and effects of social factor on it in Markazi Province. Materials and methods: The society of the study was all of the elders in Markazi Province that were available by geographical address in the table of rural and urban household societies. The study was cross sectional and multi phases in sampling the first one was classification according rural and urban area and the second one was cluster sampling with equal cluster. Estimation of samples were 472 persons and increased by design effect to 1110 persons. Collection data was done by questionnaire and analyzed by SPSS and chi 2 exam. Results: This study showed 70 persons were abused (42/8% male and 57/2% female) mean of ages was 74/7 years. 64% were marred and 31% were widows. There were not any significant meaningful association between elder abuse and area of living (pv=0.299),occupation (p.v=0.104), education (pv=0.358) and age (P.value=0.104) there were significant meaningful association between physical impairment (pv=0.08), and movement impairment (P.value=0.008). Conclusion: Results verify that maltreatment occurred in the aged persons. Analysis of data indicated that elder abuse exist in every socioeconomic group with any context of education in urban area and rural area and in men and women. Prevalence of elder abuse was 6.3% (70 persons) that verify the data of developed countries with limited sample.

Keywords: elder abuse, education, occupation, area of living

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Authors: Rajashree Naik, Laxmi Kant Sharma

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Sambhar lake is the largest inland Salt Lake of India, declared as a Ramsar site on 23 March 1990. Due to high salinity and alkalinity condition its biodiversity richness is contributed by haloalkaliphilic flora and fauna along with the diverse land cover including waterbody, wetland, salt crust, saline soil, vegetation, scrub land and barren land which welcome large number of flamingos and other migratory birds for winter harboring. But with the gradual increase in the irrational salt extraction activities, the ecological diversity is at stake. There is an urgent need to assess the ecosystem. Advanced technology like remote sensing and GIS has enabled to look into the past, compare with the present for the future planning and management of the natural resources in a judicious way. This paper is a research work intended to present a vegetation in typical inland lake environment of Sambhar wetland using satellite data of NASA’s EO-1 Hyperion sensor launched in November 2000. With the spectral range of 0.4 to 2.5 micrometer at approximately 10nm spectral resolution with 242 bands 30m spatial resolution and 705km orbit was used to produce a vegetation map for a portion of the wetland. The vegetation map was tested for classification accuracy with a pre-existing detailed GIS wetland vegetation database. Though the accuracy varied greatly for different classes the algal communities were successfully identified which are the major sources of food for flamingo. The results from this study have practical implications for uses of spaceborne hyperspectral image data that are now becoming available. Practical limitations of using these satellite data for wetland vegetation mapping include inadequate spatial resolution, complexity of image processing procedures, and lack of stereo viewing.

Keywords: Algal community, NASA’s EO-1 Hyperion, salt-tolerant species, wetland vegetation mapping

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Authors: Liang-Jen Wang, Sung-Chou Li, Yuan-Ming Yeh, Sheng-Yu Lee, Ho-Chang Kuo, Chia-Yu Yang

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Background: Dysbiosis in the gut microbial community might be involved in the pathophysiology of attention deficit/hyperactivity disorder (ADHD). The fungal component of the gut microbiome, namely the mycobiota, is a hyperdiverse group of multicellular eukaryotes that can influence host intestinal permeability. This study therefore aimed to investigate the impact of fungal mycobiome dysbiosis and intestinal permeability on ADHD. Methods: Faecal samples were collected from 35 children with ADHD and from 35 healthy controls. Total DNA was extracted from the faecal samples, and the internal transcribed spacer (ITS) regions were sequenced using high-throughput next-generation sequencing (NGS). The fungal taxonomic classification was analysed using bioinformatics tools, and the differentially expressed fungal species between the ADHD and healthy control groups were identified. An in vitro permeability assay (Caco-2 cell layer) was used to evaluate the biological effects of fungal dysbiosis on intestinal epithelial barrier function. Results: The β-diversity (the species diversity between two communities), but not α-diversity (the species diversity within a community), reflected the differences in fungal community composition between ADHD and control groups. At the phylum level, the ADHD group displayed a significantly higher abundance of Ascomycota and significantly lower abundance of Basidiomycota than the healthy control group. At the genus level, the abundance of Candida (especially Candida albicans) was significantly increased in ADHD patients compared to the healthy controls. In addition, the in vitro cell assay revealed that C. albicans secretions significantly enhanced the permeability of Caco-2 cells. Conclusions: The current study is the first to explore altered gut mycobiome dysbiosis using the NGS platform in ADHD. The findings from this study indicated that dysbiosis of the fungal mycobiome and intestinal permeability might be associated with susceptibility to ADHD.

Keywords: ADHD, fungus, gut–brain axis, biomarker, child psychiatry

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing

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Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

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Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Kyle Yatich Terik, Collins Oduor

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The current healthcare system has made healthcare more accessible and efficient by the use of information technology through the implementation of computer algorithms that generate menus based on the diagnosis. While many systems just like these have been created over the years, their main objective is to help healthy individuals calculate their calorie intake and assist them by providing food selections based on a pre-specified calorie. That application has been proven to be useful in some ways, and they are not suitable for monitoring, planning, and managing hospital patients, especially that critical condition their dietary needs. The system also addresses a number of objectives, such as; the main objective is to be able to design, develop and implement an efficient, user-friendly as well as and interactive dietary management system. The specific design development objectives include developing a system that will facilitate a monitoring feature for users using graphs, developing a system that will provide system-generated reports to the users, dietitians, and system admins, design a system that allows users to measure their BMI (Body Mass Index), the system will also provide food template feature that will guide the user on a balanced diet plan. In order to develop the system, further research was carried out in Kenya, Nairobi County, using online questionnaires being the preferred research design approach. From the 44 respondents, one could create discussions such as the major challenges encountered from the manual dietary system, which include no easily accessible information of the calorie intake for food products, expensive to physically visit a dietitian to create a tailored diet plan. Conclusively, the system has the potential of improving the quality of life of people as a whole by providing a standard for healthy living and allowing individuals to have readily available knowledge through food templates that will guide people and allow users to create their own diet plans that consist of a balanced diet.

Keywords: DMS, dietitian, patient, administrator

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Authors: Fuyuan Ma, Yuhan Wang, Junhe Zhang, Ying Wang

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Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks.

Keywords: source identification, ordinary differential equations, label propagation, complex networks

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Guo Yu, Haoming Ma, Peiru Zhou

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BACKGROUND: In addition to hyperglycemia, hypoglycemia, and glycemic variability, a decrease in the time in the targeted blood glucose range (TIR) may be associated with an increased risk of death for critically ill patients. However, the relationship between the TIR and mortality may be influenced by the presence of diabetes and glycemic variability. METHODS: A total of 998 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The TIR is defined as the percentage of time spent in the target blood glucose range of 3.9–10.0 mmol/L within 24 hours. The relationship between TIR and in-hospital in diabetic and non-diabetic patients was analyzed. The effect of glycemic variability was also analyzed. RESULTS: The binary logistic regression model showed that there was a significant association between the TIR as a continuous variable and the in-hospital death of severely ill non-diabetic patients (OR=0.991, P=0.015). As a classification variable, TIR≥70% was significantly associated with in-hospital death (OR=0.581, P=0.003). Specifically, TIR≥70% was a protective factor for the in-hospital death of severely ill non-diabetic patients. The TIR of severely ill diabetic patients was not significantly associated with in-hospital death; however, glycemic variability was significantly and independently associated with in-hospital death (OR=1.042, P=0.027). Binary logistic regression analysis of comprehensive indices showed that for non-diabetic patients, the C3 index (low TIR & high CV) was a risk factor for increased mortality (OR=1.642, P<0.001). In addition, for diabetic patients, the C3 index was an independent risk factor for death (OR=1.994, P=0.008), and the C4 index (low TIR & low CV) was independently associated with increased survival. CONCLUSIONS: The TIR of non-diabetic patients during ICU hospitalization was associated with in-hospital death even after adjusting for disease severity and glycemic variability. There was no significant association between the TIR and mortality of diabetic patients. However, for both diabetic and non-diabetic critically ill patients, the combined effect of high TIR and low CV was significantly associated with ICU mortality. Diabetic patients seem to have higher blood glucose fluctuations and can tolerate a large TIR range. Both diabetic and non-diabetic critically ill patients should maintain blood glucose levels within the target range to reduce mortality.

Keywords: severe disease, diabetes, blood glucose control, time in targeted blood glucose range, glycemic variability, mortality

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Authors: Mohammad Hossain Mohammadi, Saif Al Ameri, Sana Ziaei, Jinane Mounsef

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Automated enumeration of the number of hand fingers is widely used in several motion gaming and distance control applications, and is discussed in several published papers as a starting block for hand recognition systems. The automated finger enumeration technique should not only be accurate, but also must have a fast response for a moving-picture input. The high performance of video in motion games or distance control will inhibit the program’s overall speed, for image processing software such as Matlab need to produce results at high computation speeds. Since an automated finger enumeration with minimum error and processing time is desired, a comparative study between two finger enumeration techniques is presented and analyzed in this paper. In the pre-processing stage, various image processing functions were applied on a real-time video input to obtain the final cleaned auto-cropped image of the hand to be used for the two techniques. The first technique uses the known morphological tool of skeletonization to count the number of skeleton’s endpoints for fingers. The second technique uses a radial distance method to enumerate the number of fingers in order to obtain a one dimensional hand representation. For both discussed methods, the different steps of the algorithms are explained. Then, a comparative study analyzes the accuracy and speed of both techniques. Through experimental testing in different background conditions, it was observed that the radial distance method was more accurate and responsive to a real-time video input compared to the skeletonization method. All test results were generated in Matlab and were based on displaying a human hand for three different orientations on top of a plain color background. Finally, the limitations surrounding the enumeration techniques are presented.

Keywords: comparative study, hand recognition, fingertip detection, skeletonization, radial distance, Matlab

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Authors: Victoria Bila, Iryna Ventskivska, Oleksandra Zahorodnia

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The goal of the study: To study the distribution of patients of the Kiyv Perinatal Center according to the Robson system and compare it with world data. Materials and methods: a comparison of the distribution of patients of Kiyv Perinatal center according to the Robson system for 2 periods - the first quarter of 2019 and 2020. For each group, 3 indicators were analyzed - the share of this group in the overall structure of patients of the Perinatal Center for the reporting period, the frequency of abdominal delivery in this group, as well as the contribution of this group to the total number of abdominal delivery. Obtained data were compared with those of the WHO in the guidelines for the implementation of the Robson system in 2017. Results and its discussion: The distribution of patients of the Perinatal Center into groups in the Robson classification is not much different from that recommended by the author. So, among all women, patients of group 1 dominate; this indicator does not change in dynamics. A slight increase in the share of group 2 (6.7% in 2019 and 9.3% - 2020) was due to an increase in the number of labor induction. At the same time, the number of patients of groups 1 and 2 in the Perinatal Center is greater than in the world population, which is determined by the hospitalization of primiparous women with reproductive losses in the past. The Perinatal Center is distinguished from the world population and the proportion of women of group 5 - it was 5.4%, in the world - 7.6%. The frequency of caesarean section in the Perinatal Center is within limits typical for most countries (20.5-20.8%). Moreover, the dominant groups in the structure of caesarean sections are group 5 (21-23.3%) and group 2 (21.9-22.9%), which are the reserve for reducing the number of abdominal delivery. In group 2, certain results have already been achieved in this matter - the frequency of cesarean section in 2019 here amounted to 67.8%, in the first quarter of 2020 - 51.6%. This happened due to a change in the leading method of induction of labor. Thus, the Robson system is a convenient and affordable tool for assessing the structure of caesarean sections. The analysis showed that, in general, the structure of caesarean sections in the Perinatal Center is close to world data, and the identified deviations have explanations related to the specialization of the Center.

Keywords: cesarian section, Robson system, Kyiv Perinatal Center, labor induction

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

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Authors: Mohamed M. Mashaly, Ahmed M. F. El Shiwi

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Background: Shoulder impingement syndrome is an encroachment of subacromial tissues, rotator cuff, subacromial bursa, and the long head of the biceps tendon, as a result of narrowing of the subacromial space. Activities requiring repetitive or sustained use of the arms over head often predispose the rotator cuff tendon to injury. Purpose: To compare between Combined effect therapeutic exercises and Shockwave therapy versus therapeutic exercises and phonophoresis in the treatment of shoulder impingement syndrome. Methods: Thirty patients diagnosed as shoulder impingement syndrome stage II Neer classification due to mechanical causes. Patients were randomly distributed into two equal groups. The first group consisted of 15 patients with a mean age of (45.46+8.64) received therapeutic exercises (stretching exercise of posterior shoulder capsule and strengthening exercises of shoulder muscles) and shockwave therapy (6000 shocks, 2000/session, 3 sessions, 2 weeks apart, 0.22mJ/mm^2) years. The second group consisted of 15 patients with a mean age of 46.26 (+ 8.05) received same therapeutic exercises and phonophoresis (3 times per week, each other day, for 4 consecutive weeks). Patients were evaluated pretreatment and post treatment for shoulder pain severity, shoulder functional disability, shoulder flexion, abduction and internal rotation motions. Results: Patients of both groups showed significant improvement in all the measured variables. In between groups difference the shock wave group showed a significant improvement in all measured variables than phonophoresis group. Interpretation/Conclusion: Combined effect of therapeutic exercises and shock wave were more effective than therapeutic exercises and phonophoresis on decreasing shoulder pain severity, shoulder functional disability, increasing in shoulder flexion, abduction, internal rotation in patients with shoulder impingement syndrome.

Keywords: shoulder impingement syndrome, therapeutic exercises, shockwave, phonophoresis

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Authors: Devanshu Gupta, Kailash Chandra, Priyanka Das, Joyjit Ghosh

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Beetles, in the insect order Coleoptera are the most species-rich group on this planet today. They represent about 40% of the total insect diversity of the world. With a considerable range of landform types including significant mountain ranges, deserts, fertile irrigational plains, and hilly forested areas, India is one of the mega-diverse countries and includes more than 0.1 million faunal species. Despite having rich biodiversity, the efforts to catalogue the beetle diversity of the extant species/taxa reported from India have been less. Therefore, in this paper, the information on the beetle fauna of India is provided based on the data available with the museum collections of Zoological Survey of India and taxa extracted from zoological records and published literature. The species were listed with their valid names, synonyms, type localities, type depositories, and their distribution in states and biogeographic zones of India. The catalogue also incorporates the bibliography on Indian Coleoptera. The exhaustive species inventory, prepared by us include distributional records from Himalaya, Trans Himalaya, Desert, Semi-Arid, Western Ghats, Deccan Peninsula, Gangetic Plains, Northeast, Islands, and Coastal areas of the country. Our study concludes that many of the species are still known from their type localities only, so there is need to revisit and resurvey those collection localities for the taxonomic evaluation of those species. There are species which exhibit single locality records, and taxa-specific biodiversity assessments are required to be undertaken to understand the distributional range of such species. The primary challenge is taxonomic identifications of the species which were described before independence, and the type materials are present in overseas museums. For such species, taxonomic revisions of the different group of beetles are required to solve the problems of identification and classification.

Keywords: checklist, taxonomy, museum collections, biogeographic zones

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Authors: Mahmoud Huleihel, George Abu-Aqil, Manal Suleiman, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman

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Urinary tract infections (UTIs) are considered to be the most common bacterial infections worldwide, which are caused mainly by Escherichia (E.) coli (about 80%). Klebsiella pneumoniae (about 10%) and Pseudomonas aeruginosa (about 6%). Although antibiotics are considered as the most effective treatment for bacterial infectious diseases, unfortunately, most of the bacteria already have developed resistance to the majority of the commonly available antibiotics. Therefore, it is crucial to identify the infecting bacteria and to determine its susceptibility to antibiotics for prescribing effective treatment. Classical methods are time consuming, require ~48 hours for determining bacterial susceptibility. Thus, it is highly urgent to develop a new method that can significantly reduce the time required for determining both infecting bacterium at the species level and diagnose its susceptibility to antibiotics. Fourier-Transform Infrared (FTIR) spectroscopy is well known as a sensitive and rapid method, which can detect minor molecular changes in bacterial genome associated with the development of resistance to antibiotics. The main goal of this study is to examine the potential of FTIR spectroscopy, in tandem with machine learning algorithms, to identify the infected bacteria at the species level and to determine E. coli susceptibility to different antibiotics directly from patients' urine in about 30minutes. For this goal, 1600 different E. coli isolates were isolated for different patients' urine sample, measured by FTIR, and analyzed using different machine learning algorithm like Random Forest, XGBoost, and CNN. We achieved 98% success in isolate level identification and 89% accuracy in susceptibility determination.

Keywords: urinary tract infections (UTIs), E. coli, Klebsiella pneumonia, Pseudomonas aeruginosa, bacterial, susceptibility to antibiotics, infrared microscopy, machine learning

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Authors: Anshu Gupta, Charu Gupta

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Background: Punjab, a border state in India has achieved notoriety world over for its drug abuse problem. People right from school kids to elderly are hooked to drugs. This pattern of substance abuse is prevalent in both cities and villages alike. Excess of younger population in India has further aggravated the situation. It is feared that the benefits of India’s economic growth may well be negated by the rising substance abuse especially in this part of the country. It is quite evident that the pattern of substance abuse tends to change over time which is an impediment in the formulation of effective strategies to tackle this issue. Aim: Purpose of the study was to ascertain the change in the pattern of drug abuse for two consecutive years in the out patient department (OPD) population. Method: The study population comprised of all the patients reporting for deaddiction to the psychiatry outpatient department over a period of twelve months for two consecutive years. All the patients were evaluated by the International Classification of Diseases; 10 criteria for substance abuse/dependence. Results: A considerably high prevalence of substance abuse was present in the Indian population. In general, there was an increase in prevalence from first to the second year, especially among the female population. Increase in prevalence of substance abuse appeared to be more prominent among the younger age group of both the sexes. A significant increase in intravenous drug abuse was observed. Peer pressure and parental imitation were the major factors fueling substance abuse. Precipitation or fear of withdrawal symptoms was the major factor preventing abstinence. Substance abuse had a significant effect on the health and interpersonal relations of these patients. Summary/Conclusion: Drug abuse and addiction are on the rise throughout India. Changing cultural values, increasing economic stress and dwindling supportive bonds appear to be leading to initiation of substance abuse. Need of the hour is to formulate a comprehensive strategy to bring about an overall reduction in the use of drugs.

Keywords: deaddiction, peer pressure, parental imitation, substance abuse/dependance

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Authors: Dragan Zivotic, Mirjana Ilic, Aleksandra Perovic, Predrag Gavrilovic

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As with the provision of other services, the service quality perception is one of the key factors that the modern manager must pay attention to. Countries in which the state regulation is in transition also have specific features in providing fitness services. Identification of the dimensions in which the most significant different service quality perception between different types of fitness centers, enables managers to profile the offer according to the wishes and expectations of users. The aim of the paper was the comparison of the quality of services perception in the field of fitness in Serbia between three categories of fitness centers: the privately owned centers, the publicly owned centers, and the Public-private partnership centers. For this research 350 respondents of both genders (174 men and 176 women) were interviewed, aged between 18 and 68 years, being beneficiaries of fitness services for at least 1 year. Administered questionnaire with 100 items provided information about the 15 basic areas in which they expressed the service quality perception in the gym. The core sample was composed of 212 service users in private fitness centers, 69 service users in public fitness centers and 69 service users in the public-private partnership. Sub-samples were equal in representation of women and men, as well as by age and length of use of fitness services. The obtained results were subject of univariate analysis with the Kruskal-Wallis non-parametric analysis of variance. Significant differences between the analyzed sub-samples were not found solely in the areas of rapid response and quality outcomes. In the multivariate model, the results were processed by backward stepwise discriminant analysis that extracted 3 areas that maximize the differences between sub-samples: material and technical basis, secondary facilities and coaches. By applying the classification function 93.87% of private centers services users, 62.32% of public centers services users and 85.51% of the public-private partnership centers users of services were correctly classified (total 86.00%). These results allow optimizing the allocation of the necessary resources in profiling offers of a fitness center in order to optimally adjust it to the user’s needs and expectations.

Keywords: fitness, quality perception, management, public ownership, private ownership, public-private partnership, discriminative analysis

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