Search results for: information design

Authors: Dirk Bruland, Paulo Pinheiro, Ullrich Bauer

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Introduction: Over 3 million children, about one quarter of all students, experience at least one parent with mental disorder in Germany every year. Children of mentally-ill parents are at considerably higher risk of developing serious mental health problems. The different burden patterns and coping attempts often become manifest in children's school lives. In this context, schools can have an important protective function, but can also create risk potentials. In reference to Jorm, pupil-related teachers’ mental health literacy (Teacher-MHL) includes the ability to recognize change behaviour, the knowledge of risk factors, the implementation of first aid intervention, and seeking professional help (teacher as gatekeeper). Although teachers’ knowledge and increased awareness of this topic is essential, the literature provides little information on the extent of teachers' abilities. As part of a German-wide research consortium on health literacy, this project, launched in March for 3 years, will conduct evidence-based mental health literacy research. The primary objective is to measure Teacher-MHL in the context of pupil-related psychosocial factors at primary and secondary schools (grades 5 & 6), while also focussing on children’s social living conditions. Methods: (1) A systematic literature review in different databases to identify papers with regard to Teacher-MHL (completed). (2) Based on these results, an interview guide was developed. This research step includes a qualitative pre-study to inductively survey the general profiles of teachers (n=24). The evaluation will be presented on the conference. (3) These findings will be translated into a quantitative teacher survey (n=2500) in order to assess the extent of socio-analytical skills of teachers as well as in relation to institutional and individual characteristics. (4) Based on results 1 – 3, developing a training program for teachers. Results: The review highlights a lack of information for Teacher-MHL and their skills, especially related to high-risk-groups like children of mentally ill parents. The literature is limited to a few studies only. According to these, teacher are not good at identifying burdened children and if they identify those children they do not know how to handle the situations in school. They are not sufficiently trained to deal with these children, especially there are great uncertainties in dealing with the teaching situation. Institutional means and resources are missing as well. Such a mismatch can result in insufficient support and use of opportunities for children at risk. First impressions from the interviews confirm these results and allow a greater insight in the everyday school-life according to critical life events in families. Conclusions: For the first time schools will be addressed as a setting where children are especially "accessible" for measures of health promotion. Addressing Teacher-MHL gives reason to expect high effectiveness. Targeting professionals' abilities for dealing with this high-risk-group leads to a discharge for teacher themselves to handle those situations and increases school health promotion. In view of the fact that only 10-30% of such high-risk families accept offers of therapy and assistance, this will be the first primary preventive and health-promoting approach to protect the health of a yet unaffected, but particularly burdened, high-risk group.

Keywords: children of mentally ill parents, health promotion, mental health literacy, school

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Authors: Anna Ivanovskaya, Alexandra E. L. Overland, Swetha Chandrasekaran, Buddhinie S. Jayathilake

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Iron-based redox flow batteries (IRFB) are promising for grid-scale storage because of their low-cost and environmental safety. Earth-abundant iron can enable affordable grid-storage to meet DOE’s target material cost <$20/kWh and levelized cost for storage $0.05/kWh. In conventional redox flow batteries, energy is stored in external electrolyte tanks and electrolytes are circulated through the cell units to achieve electrochemical energy conversions. However, IRFBs are hybrid battery systems where metallic iron deposition at the negative side of the battery controls the storage capacity. This adds complexity to the design of a porous structure of 3D-electrodes to achieve a desired high storage capacity. In addition, there is a need to control parasitic hydrogen evolution reaction which accompanies the metal deposition process, increases the pH, lowers the energy efficiency, and limits the durability. To achieve sustainable operation of IRFBs, electrolyte pH, which affects the solubility of reactants and the rate of parasitic reactions, needs to be dynamically readjusted. In the present study we explore the impact of complexing agents on maintaining solubility of the reactants and find the optimal electrolyte conditions and battery operating regime, which are specific for IRFBs with additives, and demonstrate the robust operation.

Keywords: flow battery, iron-based redox flow battery, IRFB, energy storage, electrochemistry

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Authors: Joe Amalraj, M. Arunkumar

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Haze is an atmospheric phenomenon that signicantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. In this method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. This paper demonstrates the utility and effectiveness of a fusion-based technique for de-hazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications.

Keywords: single image de-hazing, outdoor images, enhancing, DSP

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Authors: Soumya Bhattacharjya, Avinandan Sahoo, Gaurav Datta

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Wind fragility analysis of chimney is often carried out disregarding temperature effect. However, the combined effect of wind and temperature is the most critical limit state for chimney design. Hence, in the present paper, an efficient fragility analysis for concrete chimney is explored under combined wind and temperature effect. Wind time histories are generated by Davenports Power Spectral Density Function and using Weighed Amplitude Wave Superposition Technique. Fragility analysis is often carried out in full Monte Carlo Simulation framework, which requires extensive computational time. Thus, in the present paper, an efficient adaptive metamodelling technique is adopted to judiciously approximate limit state function, which will be subsequently used in the simulation framework. This will save substantial computational time and make the approach computationally efficient. Uncertainty in wind speed, wind load related parameters, and resistance-related parameters is considered. The results by the full simulation approach, conventional metamodelling approach and proposed adaptive metamodelling approach will be compared. Effect of disregarding temperature in wind fragility analysis will be highlighted.

Keywords: adaptive metamodelling technique, concrete chimney, fragility analysis, stochastic extreme wind load, temperature effect

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Jasim Azhar, Morten Gjerde

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A city’s space is comprehended as a phenomenon that emerges from the ongoing negotiation between the constructed environment, urban processes, and bodily experience. Many spaces do not represent a static notion but are continually challenged and reconstituted. The ability to recognize those leftover spaces in the urban context is an integral part of an urban redevelopment process, where structured and layered approaches become useful in understanding to transform these spaces into places. Contemporary urban leftover spaces exist as a result of several factors and are present in every major city that often disrupts the flow of districts by creating visually unappealing places. These spaces can be designed, transformed and integrated so as to achieve environmental gains and social preferences. The paper explores how those small changes in visual quality of an urban leftover spaces in Wellington city influence a person’s experience significantly and its potential usage. These spaces can be seen as a catalyst for a change through an ecological sustainability’s framework. A creative and flexible design would lead to psychologically healthy places by improving the image of a city from within. The qualitative research is undertaken through the visual preference studies which will inform the planning initiatives by knowing what people feel about those visual changes in these leftover spaces. Those visual preferences can guide behavior and the emotional responses of different users for the redesign of those spaces with the meaningful attributes. The research is driven by the hypothesis that if the attributes are made visible, the likelihood of stimulating the interest of users should increase.

Keywords: leftover spaces, visual preferences, tactical urbanism, ecological sustainability

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Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

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Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

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Authors: Usman Yakubu, Muktar Haruna

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The paper examines the impact of the audit committee on the earning quality of the listed consumer goods sector in Nigeria. The study used data collected from annual reports and accounts of the 13 sampled companies for the periods 2007 to 2018. Data were analyzed by means of descriptive statistics to provide summary statistics for the variables; also, correlation analysis was carried out using the Pearson correlation technique for the correlation between the dependent and independent variables. Regression was employed using the Generalized Least Square technique since the data has both time series and cross sectional attributes (panel data). It was found out that the audit committee had a positive and significant influence on the earning quality in the listed consumer goods companies in Nigeria. Thus, the study recommends that competency and personal integrity should be the worthwhile attributes to be considered while constituting the committee; this could enhance the quality of accounting information. In addition to that majority of the committee members should be independent directors in order to allow a high level of independency to be exercised.

Keywords: earning quality, corporate governance, audit committee, financial reporting

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Authors: Aya Belal, Ahmed Maher Eltair, Maggie Ahmed Mashaly

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Geopolymer concrete is gaining recognition as a sustainable alternative to conventional Portland Cement concrete due to its environmentally friendly nature, which is a key goal for Smart City initiatives. It has demonstrated its potential as a reliable material for the design of structural elements. However, the production of Geopolymer concrete is hindered by batch-to-batch variations, which presents a significant challenge to the widespread adoption of Geopolymer concrete. To date, Machine learning has had a profound impact on various fields by enabling models to learn from large datasets and predict outputs accurately. This paper proposes an integration between the current drift to Artificial Intelligence and the composition of Geopolymer mixtures to predict their mechanical properties. This study employs Python software to develop machine learning model in specific Decision Trees. The research uses the percentage oxides and the chemical composition of the Alkali Solution along with the curing conditions as the input independent parameters, irrespective of the waste products used in the mixture yielding the compressive strength of the mix as the output parameter. The results showed 90 % agreement of the predicted values to the actual values having the ratio of the Sodium Silicate to the Sodium Hydroxide solution being the dominant parameter in the mixture.

Keywords: decision trees, geopolymer concrete, machine learning, smart cities, sustainability

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Marcin Zieliński, Marcin Dębowski, Mirosław Krzemieniewski

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This publication presents a device designed for mechanical fragmentation of plant substrate before methane fermentation. The device is equipped with a perforated rotary cylindrical drum coated with a thermal layer, connected to a substrate feeder and driven by a motoreducer. The drum contains ball- or cylinder-shaped weights of different diameters, while its interior is mounted with lateral permanent magnets with an attractive force ranging from 100 kg to 2 tonnes per m2 of the surface. Over the perforated rotary drum, an infrared radiation generator is mounted, producing 0.2 kW to 1 kW of infrared radiation per 1 m2 of the perforated drum surface. This design reduces the energy consumption required for the biomass destruction process by 10-30% in comparison to the conventional ball mill. The magnetic field generated by the permanent magnets situated within the perforated rotary drum promotes this process through generation of free radicals that act as powerful oxidants, accelerating the decomposition rate. Plant substrate shows increased susceptibility to biodegradation when subjected to magnetic conditioning, reducing the time required for biomethanation by 25%. Additionally, the electromagnetic radiation generated by the radiator improves substrate destruction by 10% and the efficiency of the process. The magnetic field and the infrared radiation contribute synergically to the increased efficiency of destruction and conversion of the substrate.

Keywords: biomass pretreatment, mechanical fragmentation, biomass, methane fermentation

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Authors: Bassem Boshra Thabet, Mohammed Ibrahim Elsabagh, Mohammad Adly Talaat

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The number of cellular mobile phone users has increased enormously worldwide over the last two decades. Consequently, the monitoring of the performance of the Mobile Network Operators (MNOs) in terms of network coverage and broadband signal strength has become vital for both of the MNOs and regulators. This monitoring helps telecommunications operators and regulators keeping the market playing fair and most beneficial for users. However, the adopted methodologies to facilitate this continuous monitoring process are still problematic regarding cost, effort, and reliability. This paper introduces My Operator Coverage (MOC) system that is using Internet of Things (IoT) concepts and tools to monitor the MNOs performance using a crowd-sourced real-time methodology. MOC produces robust and reliable geographical maps for the user-perceived quality of the MNOs performance. MOC is also meant to enrich the telecommunications regulators with concrete, and up-to-date information that allows for adequate mobile market management strategies as well as appropriate decision making.

Keywords: mobile performance monitoring, crowd-sourced applications, mobile broadband performance, cellular networks monitoring

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Authors: Laura Butabayeva, Svetlana Ismagulova, Gulbarshin Nogaibayeva, Maiya Temirbayeva, Aidana Zhussip

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Over the years of independence, Kazakhstan has not only ratified international documents regulating the rights of children to Inclusive education, but also developed its own inclusive educational policy. Along with this, the state pays particular attention to high school students' preparedness for professional self-determination. However, a number of problematic issues in this field have been revealed, such as the lack of systemic mechanisms coordinating stakeholders’ actions in preparing schoolchildren for a conscious choice of in-demand profession, meeting their individual capabilities and special educational needs (SEN). The analysis of the state’s current situation indicates school graduates’ adaptation to the labor market does not meet existing demands of the society. According to the Ministry of Labor and Social Protection of the Population of the Republic of Kazakhstan, about 70 % of Kazakhstani school graduates find themselves difficult to choose a profession, 87 % of schoolchildren make their career choice under the influence of parents and school teachers, 90 % of schoolchildren and their parents have no idea about the most popular professions on the market. The results of the study conducted by KorlanSyzdykova in 2016 indicated the urgent need of Kazakhstani school graduates in obtaining extensive information about in- demand professions and receiving professional assistance in choosing a profession in accordance with their individual skills, abilities, and preferences. The results of the survey, conducted by Information and Analytical Center among heads of colleges in 2020, showed that despite significant steps in creating conditions for students with SEN, they face challenges in studying because of poor career guidance provided to them in schools. The results of the study, conducted by the Center for Inclusive Education of the National Academy of Education named after Y. Altynsarin in the state’s general education schools in 2021, demonstrated the lack of career guidance, pedagogical and psychological support for children with SEN. To investigate these issues, the further study was conducted to examine the state of students’ career guidance and socialization, taking into account their SEN. The hypothesis of this study proposed that to prepare school graduates for a conscious career choice, school teachers and specialists need to develop their competencies in early identification of students' interests, inclinations, SEN and ensure necessary support for them. The state’s 5 regions were involved in the study according to the geographical location. The triangulation approach was utilized to ensure the credibility and validity of research findings, including both theoretical (analysis of existing statistical data, legal documents, results of previous research) and empirical (school survey for students, interviews with parents, teachers, representatives of school administration) methods. The data were analyzed independently and compared to each other. The survey included questions related to provision of pedagogical support for school students in making their career choice. Ethical principles were observed in the process of developing the methodology, collecting, analyzing the data and distributing the results. Based on the results, methodological recommendations on students’ career guidance for school teachers and specialists were developed, taking into account the former’s individual capabilities and SEN.

Keywords: career guidance, children with special educational needs, inclusive education, Kazakhstan

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Authors: R. Bouhennache, T. Bouden, A. A. Taleb, A. Chaddad

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In this paper we propose a method to map the urban areas. The method uses an arithmetic calculation processed from the land features indexes and Tasseled cap transformation TC of multi spectral Thematic Mapper Landsat TM image. For this purpose the derived indexes image from the original image such SAVI the soil adjusted vegetation index, UI the urban Index, and EBBI the enhanced built up and bareness index were staked to form a new image and the bands were uncorrelated, also the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification approaches were first applied on the new image TM data using the reference spectra of the spectral library and subsequently the four urban, vegetation, water and soil land cover categories were extracted with their accuracy assessment.The urban features were represented using a logic calculation applied to the brightness, UI-SAVI, NDBI-greenness and EBBI- brightness data sets. The study applied to Blida and mentioned that the urban features can be mapped with an accuracy ranging from 92 % to 95%.

Keywords: EBBI, SAVI, Tasseled Cap Transformation, UI

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Authors: Saiyeed Jakaria Baksh Imran

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The earth is being endangered by many of the climate related issues today. The most serious issue for the world today is the global warming. Increase in Greenhouse gas (GHG) emissions post-industrial revolution period is the prime reason for global warming. Shipping is the fifth largest GHG emitting sector worldwide. The key reason for this is because, over 90% of the world trade is conducted through ocean as the ocean alone covers 70% of the earth surface. While the countries continue to develop, trade and commerce continue to increase between them simultaneously. However, there is no sign of reduction in GHG emission from shipping because of many concerned issues. Firstly, there is technological barrier for which ships cannot just become environment friendly immediately. Secondly, there is no alternative fuel available as well. Thirdly, there is no proper mechanism to measure how much ships emit as emission from ships vary according to the size, engine type and loading capacity of ships. The International Maritime Organization (IMO) being the governing body of the international shipping has implemented MARPOL Annex VI. However, the policy alone is not enough unless there is a proper data available regarding ship emissions, which the IMO is yet to figure out. This paper will present a critical analysis of existing IMO policies such as the Energy Efficiency Design Index (EEDI), Ship Energy Efficiency Management Plan (SEEMP), Data Collection System (SEEMP) and the IMO’s Initial Strategy on Reduction of Greenhouse Gas emissions from shipping. Also, the challenges exist in implementing such policies have been presented in the paper.

Keywords: GHG, IMO, EEDI, SEEMP, DCS, greenhouse gas, decarbonization, shipping

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Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

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Authors: Khaing Su Su Than, Hibino Yo

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Seismic evaluation is needed upon the buildings in Myanmar. Microtremor measurement was conducted in the main cities, Mandalay and Yangon. In order to evaluate the seismic properties of buildings currently under construction, seismic information was gathered for six buildings in Yangon city and four buildings in Mandalay city. The investigated buildings vary from 12m-80 m in height, and mostly public residence structures. The predominant period obtained from frequency results of the investigated buildings were given by horizontal to vertical spectral ratio (HVSR) for each building. The fundamental period results have been calculated in the form of Fourier amplitude spectra of translation along with the main structure. Based on that, the height (H)-period(T) relationship was observed as T=0.012H-0.017H in the buildings of Yangon and, observed the relationship as T=0.014H-0.019H in the buildings of Mandalay. The results showed that the relationship between height and natural period was slightly under the relationship T=0.02H that is used for Japanese reinforced concrete buildings, which indicated that the results depend on the properties and characteristics of materials used.

Keywords: HVSR, height-period relationship, microtremor, Myanmar earthquake, reinforced concrete structures

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Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

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The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.

Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG

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Authors: Ncoza Dlova, Tivani Mashamba-Thompson

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Garcinia livingstonei (Clusiaceae) extracts, morelloflavone- 7″- sulphate and sargaol were shown to be effective against hyper-pigmentation through inhibition of tyrosinase enzyme, in vitro . The aim of this study is to elucidate the structural mechanism through which morelloflavone- 7″- sulphate and sargaol binds human tyrosinase. Implementing a homology model to construct a tyrosinase model using the crystal structure of a functional unit from Octopus hemocyanin (PDB: 1JS8) as a reference template enabled us to create a human tyrosinase model. Molecular dynamics and binding free energy calculations were optimized to enable molecular dynamics simulation of the copper dependent inhibitors. Results show the importance of the hydrogen bond formation morelloflavone- 7″- sulphate and sargaol between compound and active site residues. Both complexes demonstrated the metallic coordination between compound and arginine residue as well as copper ions within the active site. The comprehensive molecular insight gained from this study should be vital in understanding the binding mechanism morelloflavone- 7″- sulphate and sargaol. Moreover, these results will assist in the design of novel of metal ion dependent enzyme inhibitors as potential anti-hyper-pigmentation disorder therapies.

Keywords: hyper-pigmentation disorders, dyschromia African skin, morelloflavone- 7″- sulphate, sagoal

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Authors: Ksenia Meshkova

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With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances.

Keywords: neural networks, computer vision, representation learning, autoencoders

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: Oualid Kherbach, Marian Liviu Mocan, Amine Ghoumrassi, Cristian Dumitrache

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The Globalization system and the development of economy, e-business, and introduction of new technologies formation create new challenges to all organizations particularly for small and medium enterprises (SMEs). Many studies on supply chain management (SCM) focus on large companies with universal operations employing high-stage information technology. These make a gap in the knowing of how SMEs use and practice supply chain management. In this screenplay, successful practices of supply chain management (SCM) can give SMEs an edge over their competitors. However, SMEs in Romania and Balkan countries face problems in SCM implementation and practices due to lack of resources and direction. The objectives of this research highlight the supply chain management practices of the small and medium enterprise strip in Romania and understand how SMEs manage and use SCM. This study Checks the potential existence of systematic differences between small businesses and medium-sized businesses with regard to supply chain management practices and the application of supply management has contributed to the improvement performance and increase the profitability of companies such as increasing the market share and improving the level of clients.

Keywords: globalization, small and medium enterprises, supply chain management, practices

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Authors: Teerapon Pirom, Ura Pancharoen

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Amoxicillin is an antibiotic which is widely used to treat various infections in both human beings and animals. However, when amoxicillin is released into the environment, it is a major problem. Amoxicillin causes bacterial resistance to these drugs and failure of treatment with antibiotics. Liquid membrane is of great interest as a promising method for the separation and recovery of the target ions from aqueous solutions due to the use of carriers for the transport mechanism, resulting in highly selectivity and rapid transportation of the desired metal ions. The simultaneous processes of extraction and stripping in a single unit operation of liquid membrane system are very interesting. Therefore, it is practical to apply liquid membrane, particularly the HFSLM for industrial applications as HFSLM is proved to be a separation process with lower capital and operating costs, low energy and extractant with long life time, high selectivity and high fluxes compared with solid membranes. It is a simple design amenable to scaling up for industrial applications. The extraction and recovery for (Amoxicillin) through the hollow fiber supported liquid membrane (HFSLM) using aliquat336 as a carrier were explored with the experimental data. The important variables affecting on transport of amoxicillin viz. extractant concentration and operating time were investigated. The highest AMOX- extraction percentages of 85.35 and Amoxicillin stripping of 80.04 were achieved with the best condition at 6 mmol/L [aliquat336] and operating time 100 min. The extraction reaction order (n) and the extraction reaction rate constant (kf) were found to be 1.00 and 0.0344 min-1, respectively.

Keywords: aliquat336, amoxicillin, HFSLM, kinetic

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Authors: I. P. Ogbuewu, I. F. Etuk, V. U. Odoemelam, I. C. Okoli, M. U. Iloeje

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The effect of dietary ginger rhizome meal on libido and semen characteristics of post-pubertal rabbit bucks was investigated in an experiment that lasted for 12 weeks. Thirty-six post-pubertal bucks were randomly assigned to 4 dietary groups of 9 rabbits each in a completely randomized design. Four experimental diets were formulated to contain ginger rhizome meal at 0 g/kg feed (BT0), 5g/kg feed (BT5), 10 g/kg feed (BT10), and 15g/kg feed (BT15) were fed ad libitum to the experimental animals. Results revealed that semen colour changed from cream milky to milky. Data on semen pH and sperm concentration were similar (p>0.05) among the dietary groups. Semen volume for the bucks in BT0 (0.64 mL) and BT5 (0.60 mL) groups were significantly (p<0.05) higher than those in BT10 (0.44 mL) and BT15 (0.46 mL) groups. Total spermatozoa concentration value was significantly (p<0.05) higher in BT0 and BT5 groups than those in BT10 and BT15 groups. Sperm motility and percent live sperm declined (p<0.05) progressively among the treatment groups. Percent dead sperm were significantly (p<0.05) lower for bucks in BT0 group than in BT10 and BT15 groups. Reaction time had a dose-dependent increase; however, the observed difference was not significant (p>0.05). These results indicate that the inclusion of ginger rhizome meal at 5-15g per kg feed in ration for post-pubertal rabbit bucks could cause mild depressive effect on semen production and quality.

Keywords: rabbits, semen, libido, ginger

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Authors: Giulio Mangano, Alberto De Marco, Giovanni Zenezini

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The concept of City Logistics (CL) has emerged to improve the impacts of last mile freight distribution in urban areas. In this paper, a System Dynamics (SD) model exploring the dynamics of the diffusion of a ICT platform for CL management across different populations is proposed. For the development of the model two sources have been used. On the one hand, the major diffusion variables and feedback loops are derived from a literature review of existing diffusion models. On the other hand, the parameters are represented by the value propositions delivered by the platform as a response to some of the users’ needs. To extract the most important value propositions the Business Model Canvas approach has been used. Such approach in fact focuses on understanding how a company can create value for her target customers. These variables and parameters are thus translated into a SD diffusion model with three different populations namely municipalities, logistics service providers, and own account carriers. Results show that, the three populations under analysis fully adopt the platform within the simulation time frame, highlighting a strong demand by different stakeholders for CL projects aiming at carrying out more efficient urban logistics operations.

Keywords: city logistics, simulation, system dynamics, business model

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Authors: Teresa Azevedo PerdicoúLis, Paulo Lopes Dos Santos

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The conceptualisation of the problem of network optimisation as a noncooperative game sets up a holistic interactive approach that brings together different network features (e.g., com-pressor stations, sources, and pipelines, in the gas context) where the optimisation objectives are different, and a single optimisation procedure becomes possible without having to feed results from diverse software packages into each other. A mathematical model of this type, where independent entities take action, offers the ideal modularity and subsequent problem decomposition in view to design a decentralised algorithm to optimise the operation and management of the network. In a game framework, compressor stations and sources are under-stood as players which communicate through network connectivity constraints–the pipeline model. That is, in a scheme similar to tatonnementˆ, the players appoint their best settings and then interact to check for network feasibility. The devolved degree of network unfeasibility informs the players about the ’quality’ of their settings, and this two-phase iterative scheme is repeated until a global optimum is obtained. Due to network transients, its optimisation needs to be assessed at different points of the control interval. For this reason, the proposed approach to optimisation has two stages: (i) the first stage computes along the period of optimisation in order to fulfil the requirement just mentioned; (ii) the second stage is initialised with the solution found by the problem computed at the first stage, and computes in the end of the period of optimisation to rectify the solution found at the first stage. The liability of the proposed scheme is proven correct on an abstract prototype and three example networks.

Keywords: connectivity matrix, gas network optimisation, large-scale, noncooperative game, system decomposition

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Authors: Lela Pintarić, Iva Rezić, Ana Vrsalović Presečki

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Considering an enormous increase of the use of metal nanoparticles with the exactly defined characteristics, the main goal of this research was to found the optimal and environmental friendly method of their synthesis. The synthesis of the inorganic core-shell nanoparticles was optimized as a model. The core-shell nanoparticles are composed of the enzyme core belted with the metal ions, oxides or salts as a shell. In this research, enzyme urease was the core catalyst and the shell nanoparticle was made of silver. Silver nanoparticles are widespread utilized and some of their common uses are: as an addition to disinfectants to ensure an aseptic environment for the patients, as a surface coating for neurosurgical shunts and venous catheters, as an addition to implants, in production of socks for diabetics and athletic clothing where they improve antibacterial characteristics, etc. Characteristics of synthesized nanoparticles directly depend on of their size, so the special care during this optimization was given to the determination of the size of the synthesized nanoparticles. For the purpose of the above mentioned optimization, sixteen experiments were generated by the Design of Experiments (DoE) method and conducted under various temperatures, with different initial concentration of the silver nitrate and constant concentration of the urease of two separate manufacturers. Synthesized nanoparticles were analyzed by the Nanoparticle Tracking Analysis (NTA) method on Malvern NanoSight NS300. Results showed that the initial concentration of the silver ions does not affect the concentration of the synthesized silver nanoparticles neither their size distribution. On the other hand, temperature of the experiments has affected both of the mentioned values.

Keywords: core-shell nanoparticles, optimization, silver, urease

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Authors: Roseline Chizoba Onuoha, Rose Ngozi Uzoka

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This study aimed at investigating perceived effect of physical exercise on healthy well-being of pregnant mothers in Imo state. The study was guided by three research questions and three null hypotheses tested at 0.05 level of significance. The study was a quasi-experimental non-equivalent control group design involving pre and post tests. A sample of 92 pregnant women drawn from a total population of 922 registered pregnant women in ten randomly selected health centers in Imo State through multistage sampling technique was used. A 41 item structured instrument titled Physical Exercise Pregnancy Test (PEPT) was used for the study. The PEPT was validated by three experts from measurement and evaluation, educational psychology and health education. Crombach Alpha method was used to determine the reliability of Physical Exercise Pregnancy Test (PEPT) and reliability index of 0.82 was obtained. Mean and standard deviation were used to answer the research questions; while Analysis of Covariance (ANCOVA) was used in analyzing the hypotheses. Findings of the study revealed that physical exercise affects physical, social and emotional wellbeing scores of pregnant women. The study also indicated that intervention using physical exercise significantly enhanced healthy well-being scores of pregnant mothers who were exposed to physical exercise than those who received conventional health talks; Location has no significant interaction effect on the mean well-being scores of pregnant women via PEPT. Among recommendations made were that pregnant women should participate in physical exercise.

Keywords: educational psychology, Imo state, Physical exercise, pregnant women

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Authors: Mahadzirah Mohamad, Morliyati Mohammad, Nor Azman Mat Ali, Zainudin Awang

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Globally, youth substance abuse has been identified as the problem that causes substantial damage not only to individuals, but also to families and communities. In addition, substance abuse youths have become unproductive resources that would play lesser roles in the nation’s development. The increasing trend of substance abuse among youths has raised a lot of concern among various quarters in Malaysia. It has also been reported that Malay youths are the majority group involved in substance abuse. However, it was noted that life satisfaction had been found to be an important mitigating factor that addressed substance abuse. The objectives of the study were twofold: firstly, to ascertain the effect of life satisfaction on substance abuse among Malay youth. Secondly, to identify the role of delinquency on the relationship between life satisfaction and substance abuse. This study adopted a cross-sectional research design. Self-administered questionnaires were distributed to 500 Malay youths at the youth programmes using a two-step sampling technique: area sampling and systematic sampling. The research hypotheses were tested using Structural Equation Modelling. The findings of the study revealed that there is no significance relationship between life satisfaction and substance abuse. There is a significant inverse relationship between life satisfaction and delinquency. Moreover, delinquency has a positive significant influence on substance abuse. The use of Bootstrapping analysis proved that delinquency plays a full mediating role in the relationship between life satisfaction and substance abuse. This study suggested that life satisfaction has no effect on youth substance abuse. In order to reduce substance abuse, efforts should be undertaken to reduce delinquency behaviour by increasing youth life satisfaction.

Keywords: delinquency, life satisfaction, substance abuse, youth

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Authors: T. Noichl, M. Cramer, G. E. Dlugosch, I. Hosenfeld

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Teachers are exposed to a variety of stresses in their work context. These can have a negative impact on physical and psychological well-being. The online training ‘Better Living! Self-care for teachers’ is based on the training ‘Better Living! Self-care for mental health professionals’, which has been proven to be effective over a period of 3 years. The training for teachers is being evaluated for its effectiveness between October 2021 and March 2023 in a study funded by the German Federal Ministry of Education and Research. The aim of the training is to promote self-care and mindfulness among participants and thereby to foster well-being. The concept of self-care was already mentioned in antiquity and was also named as an imperative by philosophers such as Socrates and Epictetus. In the absence of a universal understanding of self-care today, the following definition was developed within the research group: Self-care is 1) facing oneself in a loving and appreciative way, 2) taking one's own needs seriously, and 3) actively contributing to one's own well-being. The study is designed as a randomized wait-control group repeated-measures design with 4 (treatment group) resp. 6 (wait-control group) measurement points. Central dependent variables are self-care, mindfulness, stress, and well-being. To assess the long-term effectiveness of training participation, these constructs are surveyed at the beginning and the end of the training as well as five weeks and one year later. Based on the results of the evaluation with mental health professionals, it is expected that participation will lead to an increase in subjective well-being, self-care, and mindfulness. The first results of the evaluation study are presented and discussed with regard to the effectiveness of the training among teachers.

Keywords: longitudinal intervention study, mindfulness, self-care, teachers’ mental health, well-being

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