Search results for: statistical machine translation

Authors: Mustafa Metin Donma

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Circumference measurements are important because they are easily obtained values for the identification of the weight gain without determining body fat. They may give meaningful information about the varying stages of obesity. Besides, some formulas may be derived from a number of body circumference measurements to estimate body fat. Waist (WC), hip (HC) and neck (NC) circumferences are currently the most frequently used measurements. The aim of this study was to develop a formula derived from these three anthropometric measurements, each giving a valuable information independently, to question whether their combined power within a formula was capable of being helpful for the differential diagnosis of morbid obesity without metabolic syndrome (MetS) from MetS. One hundred and eighty seven children were recruited from the pediatrics outpatient clinic of Tekirdag Namik Kemal University Faculty of Medicine. The parents of the participants were informed about asked to fill and sign the consent forms. The study was carried out according to the Helsinki Declaration. The study protocol was approved by the institutional non-interventional ethics committee. The study population was divided into four groups as normal-body mass index (N-BMI), obese (OB), morbid obese (MO) and MetS, which were composed of 35, 44, 75 and 33 children, respectively. Age- and gender-adjusted BMI percentile values were used for the classification of groups. The children in MetS group were selected based upon the nature of the MetS components described as MetS criteria. Anthropometric measurements, laboratory analysis and statistical evaluation confined to study population were performed. Body mass index values were calculated. A circumference index, advanced Donma circumference index (ADCI) was introduced as WC*HC/NC. The statistical significance degree was chosen as p value smaller than 0.05. Body mass index values were 17.7±2.8, 24.5±3.3, 28.8±5.7, 31.4±8.0 kg/m2, for N-BMI, OB, MO, MetS groups, respectively. The corresponding values for ADCI were 165±35, 240±42, 270±55, and 298±62. Significant differences were obtained between BMI values of N-BMI and OB, MO, MetS groups (p=0.001). Obese group BMI values also differed from MO group BMI values (p=0.001). However, the increase in MetS group compared to MO group was not significant (p=0.091). For the new index, significant differences were obtained between N-BMI and OB, MO, MetS groups (p=0.001). Obese group ADCI values also differed from MO group ADCI values (p=0.015). A significant difference between MO and MetS groups was detected (p=0.043). The correlation coefficient value and the significance check of the correlation was found between BMI and ADCI as r=0.0883 and p=0.001 upon consideration of all participants. In conclusion, in spite of the strong correlation between BMI and ADCI values obtained when all groups were considered, ADCI, but not BMI, was the index, which was capable of differentiating cases with morbid obesity from cases with morbid obesity and MetS.

Keywords: anthropometry, body mass index, child, circumference, metabolic syndrome, obesity

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Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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Authors: Ismail Haddad, Djamel Herbadji, Aissa Belmeguenai, Selma Boumerdassi

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in this paper, we provide a novel approach for image encryption that employs the Fibonacci matrix and an enhanced fractional order chaotic map. The enhanced map overcomes the drawbacks of the classical map, especially the limited chaotic range and non-uniform distribution of chaotic sequences, resulting in a larger encryption key space. As a result, this strategy improves the encryption system's security. Our experimental results demonstrate that our proposed algorithm effectively encrypts grayscale images with exceptional efficiency. Furthermore, our technique is resistant to a wide range of potential attacks, including statistical and entropy attacks.

Keywords: image encryption, logistic map, fibonacci matrix, grayscale images

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Authors: Tomasz Robert Kuczerski

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The high complexity of the algorithm of the autonomous tandem detonator system creates an optimization problem due to the parallel operation of several machine states of the system. Many years of experience and classic analyses have led to a partially optimized model. Limitations on the energy resources of this class of autonomous systems make it necessary to search for more effective methods of optimisation. The use of the Quantum Approximate Optimization Algorithm (QAOA) in these studies shows the most promising results. With the help of multiple evaluations of several qubit quantum circuits, proper results of variable parameter optimization were obtained. In addition, it was observed that the increase in the number of assessments does not result in further efficient growth due to the increasing complexity of optimising variables. The tests confirmed the effectiveness of the QAOA optimization method.

Keywords: algorithm analysis, autonomous system, quantum optimization, tandem detonator

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Authors: Florin Leon

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This paper describes a simulator of traffic scenarios tailored to facilitate autonomous driving model training for urban environments. With the rising prominence of self-driving vehicles, the need for diverse datasets is very important. The proposed simulator provides a flexible framework that allows the generation of custom scenarios needed for the validation and enhancement of trajectory prediction algorithms. Its controlled yet dynamic environment addresses the challenges associated with real-world data acquisition and ensures adaptability to diverse driving scenarios. By providing an adaptable solution for scenario creation and algorithm testing, this tool proves to be a valuable resource for advancing autonomous driving technology that aims to ensure safe and efficient self-driving vehicles.

Keywords: autonomous driving, car simulator, machine learning, model training, urban simulation environment

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Authors: Sait Ozsoy, Hacer Yasar Teke, Mustafa Dalgic, Cetin Ketenci, Ertugrul Gok, Kenan Karbeyaz, Azem Irez, Mesut Akyol

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Self-mutilating behavior or self-injury behavior (SIB) is defined as: intentional harm to one’s body without intends to commit suicide”. SIB cases are commonly seen in psychiatry and forensic medicine practices. Despite variety of SIB methods, cuts in the skin is the most common (70-97%) injury in this group of patients. Subjects with SIB have one or more other comorbidities which include depression, anxiety, depersonalization, and feeling of worthlessness, borderline personality disorder, antisocial behaviors, and histrionic personality. These individuals feel a high level of hostility towards themselves and their surroundings. Researches have also revealed a strong relationship between antisocial personality disorder, criminal behavior, and SIB. This study has retrospectively evaluated 6,599 autopsy cases performed at forensic medicine institutes of six major cities (Ankara, Izmir, Diyarbakir, Erzurum, Trabzon, Eskisehir) of Turkey in 2013. The study group consisted of all cases with SIB findings (psychopathic cuts, cigarette burns, scars, and etc.). The relationship between causes of death in the study group (SIB subjects) and the control group was investigated. The control group was created from subjects without signs of SIB. Mann-Whitney U test was used for age variables and Chi-square test for categorical variables. Multinomial logistic regression analysis was used in order to analyze group differences in respect to manner of death (natural, accident, homicide, suicide) and analysis of risk factors associated with each group was determined by the Binomial logistic regression analysis. This study used SPSS statistics 15.0 for all its statistical and calculation needs. The statistical significance was p <0.05. There was no significant difference between accidental and natural death among the groups (p=0.737). Also there was a unit increase in number of cuts in psychopathic group while number of accidental death decreased (95% CI: 0.941-0.993) by 0.967 times (p=0.015). In contrast, there was a significant difference between suicidal and natural death (p<0.001), and also between homicidal and natural death (p=0.025). SIB is often seen with borderline and antisocial personality disorder but may be associated with many psychiatric illnesses. Studies have shown a relationship between antisocial personality disorders with criminal behavior and SIB with suicidal behavior. In our study, rate of suicide, murder and intoxication was higher compared to the control group. It could be concluded that SIB can be used as a predictor of possibility of one’s harm to him/herself and other people.

Keywords: autopsy, cause of death, forensic science, self-injury behaviour

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Authors: Rajasekhar Mamilla, G. Janardhana, G. Anjan Babu

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The present research studies analyses the students’ satisfaction with university performance regarding the reliability dimension, ability of professors and staff to perform the promised services with quality to students in the post-graduate courses offered by Sri Venkateswara University in India. The research is done with the notion that the student compares the perceived performance with prior expectations. Customer satisfaction is seen as the outcome of this comparison. The sample respondents were administered with the schedule based on the stratified random technique for this study. Statistical techniques such as factor analysis, t-test and correlation analysis were used to accomplish the respective objectives of the study.

Keywords: satisfaction, reliability, service quality, customer

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Authors: Hongmin Deng, Qionghua Wang

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A modified two dimensional (2D) logistic map based on cross feedback control is proposed. This 2D map exhibits more random chaotic dynamical properties than the classic one dimensional (1D) logistic map in the statistical characteristics analysis. So it is utilized as the pseudo-random (PN) sequence generator, where the obtained real-valued PN sequence is quantized at first, then applied to radio frequency identification (RFID) communication system in this paper. This system is experimentally validated on a cortex-M₀ development board, which shows the effectiveness in key generation, the size of key space and security. At last, further cryptanalysis is studied through the test suite in the National Institute of Standards and Technology (NIST).

Keywords: chaos encryption, logistic map, pseudo-random sequence, RFID

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Authors: Javad Khaligh, Aghileh Heydari, Ali Akbar Heydari

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Mathematical epidemiological models for the spread of disease through a population are used to predict the prevalence of a disease or to study the impacts of treatment or prevention measures. Initial conditions for these models are measured from statistical data collected from a population since these initial conditions can never be exact, the presence of chaos in mathematical models has serious implications for the accuracy of the models as well as how epidemiologists interpret their findings. This paper confirms the chaotic behavior of a model for dengue fever and SI by investigating sensitive dependence, bifurcation, and 0-1 test under a variety of initial conditions.

Keywords: epidemiological models, SEIR disease model, bifurcation, chaotic behavior, 0-1 test

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Authors: Johannes Gantner, Michael Held, Matthias Fischer

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The refurbishment of the building stock in terms of energy supply and efficiency is one of the major challenges of the German turnaround in energy policy. As the building sector accounts for 40% of Germany’s total energy demand, additional insulation is key for energy efficient refurbished buildings. Nevertheless the energetic benefits often the environmental and economic performances of insulation materials are questioned. The methods Life Cycle Assessment (LCA) as well as Life Cycle Costing (LCC) can form the standardized basis for answering this doubts and more and more become important for material producers due efforts such as Product Environmental Footprint (PEF) or Environmental Product Declarations (EPD). Due to increasing use of LCA and LCC information for decision support the robustness and resilience of the results become crucial especially for support of decision and policy makers. LCA and LCC results are based on respective models which depend on technical parameters like efficiencies, material and energy demand, product output, etc.. Nevertheless, the influence of parameter uncertainties on lifecycle results are usually not considered or just studied superficially. Anyhow the effect of parameter uncertainties cannot be neglected. Based on the example of an exterior wall the overall lifecycle results are varying by a magnitude of more than three. As a result simple best case worst case analyses used in practice are not sufficient. These analyses allow for a first rude view on the results but are not taking effects into account such as error propagation. Thereby LCA practitioners cannot provide further guidance for decision makers. Probabilistic analyses enable LCA practitioners to gain deeper understanding of the LCA and LCC results and provide a better decision support. Within this study, the environmental and economic impacts of an exterior wall system over its whole lifecycle are illustrated, and the effect of different uncertainty analysis on the interpretation in terms of resilience and robustness are shown. Hereby the approaches of error propagation and Monte Carlo Simulations are applied and combined with statistical methods in order to allow for a deeper understanding and interpretation. All in all this study emphasis the need for a deeper and more detailed probabilistic evaluation based on statistical methods. Just by this, misleading interpretations can be avoided, and the results can be used for resilient and robust decisions.

Keywords: uncertainty, life cycle assessment, life cycle costing, Monte Carlo simulation

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Authors: Abhishek Kumar

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X70 pipeline steel was electrochemically charged with hydrogen for different durations in order to find crack nucleation and propagation sites. After 3 hours charging, suitable regions for crack initiation and propagation were found. These regions were studied by OM, SEM, EDS and later Vicker hardness test was done. The results brought out that HIC cracks nucleated from regions rich of inclusions and further propagated through the segregation area of some elements, such as manganese, carbon, silicon and sulfur. It is worth-mentioning that all these potential sites for crack nucleation and propagation appeared at the centre of cross section of the specimens. Additionally, cracked area has harder phase than the non-cracked area which was confirmed by hardness test.

Keywords: X70 steel, morphology of inclusions, SEM/EDS/OM, simulation, statistical data

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Authors: Lazlo Fauth, Andreas Ligocki

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For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper, three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester, and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.

Keywords: predictive maintenance, condition monitoring, forest harvesting, forest engineering, oil data, hydraulic data

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Authors: Raja V., Chokkalingam B.

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For rough cutting, in many industries and educational institutions using carbon steels or HSS single point cutting tools in center lathe machine. In power hacksaw blades, only the cutter teeth region used to parting off the given material. The portions other than the teeth can be used as a single point cutting tool for rough turning and facing on soft materials. The hardness and Tensile strength of this used Power hacksaw blade is almost same as conventional cutting tools. In this paper, the effect of power hacksaw blades over conventional tool has been compared. Thickness of the blade (1.6 mm) is very small compared to its length and width. Hence, a special tool holding device is designed to hold the tool.

Keywords: hardness, high speed steels, power hacksaw blade, tensile strength

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Authors: Antti Nurminen, Avleen Malhi

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Significant progress in intelligent systems in combination with exceedingly wide application domains having machine learning as the core technology are usually opaque, non-intuitive, and commonly complex for human users. We use innovative IoT technology which monitors and analyzes moisture, humidity, luminosity and temperature levels to assist end users for optimization of environmental conditions for their houseplants. For plant health monitoring, we construct a system yielding the Normalized Difference Vegetation Index (NDVI), supported by visual validation by users. We run the system for a selected plant, basil, in varying environmental conditions to cater for typical home conditions, and bootstrap our AI with the acquired data. For end users, we implement a web based user interface which provides both instructions and explanations.

Keywords: explainable artificial intelligence, intelligent agent, IoT, NDVI

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Authors: Liang Zhou, Qinke Sun

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Taking the Yangtze River Economic Belt as an example, using long-term nighttime lighting data from DMSP/OLS from 1992 to 2012, support vector machine classification (SVM) was used to quantitatively extract urban built-up areas of economic belts, and spatial analysis of expansion intensity index, standard deviation ellipse, etc. was introduced. The model conducts detailed and in-depth discussions on the strength, direction, and type of the expansion of the middle and lower reaches of the economic belt and the key node cities. The results show that: (1) From 1992 to 2012, the built-up areas of the major cities in the Yangtze River Valley showed a rapid expansion trend. The built-up area expanded by 60,392 km², and the average annual expansion rate was 31%, that is, from 9615 km² in 1992 to 70007 km² in 2012. The spatial gradient analysis of the watershed shows that the expansion of urban built-up areas in the middle and lower reaches of the river basin takes Shanghai as the leading force, and the 'bottom-up' model shows an expanding pattern of 'upstream-downstream-middle-range' declines. The average annual rate of expansion is 36% and 35%, respectively. 17% of which the midstream expansion rate is about 50% of the upstream and downstream. (2) The analysis of expansion intensity shows that the urban expansion intensity in the Yangtze River Basin has generally shown an upward trend, the downstream region has continued to rise, and the upper and middle reaches have experienced different amplitude fluctuations. To further analyze the strength of urban expansion at key nodes, Chengdu, Chongqing, and Wuhan in the upper and middle reaches maintain a high degree of consistency with the intensity of regional expansion. Node cities with Shanghai as the core downstream continue to maintain a high level of expansion. (3) The standard deviation ellipse analysis shows that the overall center of gravity of the Yangtze River basin city is located in Anqing City, Anhui Province, and it showed a phenomenon of reciprocating movement from 1992 to 2012. The nighttime standard deviation ellipse distribution range increased from 61.96 km² to 76.52 km². The growth of the major axis of the ellipse was significantly larger than that of the minor axis. It had obvious east-west axiality, in which the nighttime lights in the downstream area occupied in the entire luminosity scale urban system leading position.

Keywords: urban space, support vector machine, spatial characteristics, night lights, Yangtze River Economic Belt

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Authors: Han-Woong Ahn, In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

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It is important to accurately identify machine parameters for direct vector control. To obtain the parameter values, traditional methods can be used such as no-load and rotor locked tests. However, there are many differences between values obtained from the traditional tests and actual values. In addition, there are drawbacks that additional equipment and cost are required for the experiment. Therefore, it is hard to temporary operation to estimate induction motor parameters. Therefore, this paper deals with the estimation algorithm of induction motor parameters without a motor operation and the measurement from additional equipment such as sensors and dynamometer. The validity and usefulness of the estimation algorithm considering inverter nonlinearity is verified by comparing the conventional method with the proposed method.

Keywords: induction motor, parameter, off-line estimation, inverter nonlinearity

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Authors: B.J. Khoza, B. Kembo

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Global socioeconomic developments and ever-growing technological advancements of the art and design industry indicate the pivotal importance of lifelong learning. There exists a discrepancy between competencies, personal ambition, and workplace requirements. There are few , if at all, institutions of higher learning in South Africa which offer Short Learning Programmes (SLP) in Art and Design Education. Traditionally, Art and Design education is delivered face to face via a hands-on approach. In this way the enduring perception among educators is that art and design education does not lend itself to online delivery. Short Learning programmes (SLP) are a concentrated approach to make revenue and lure potential prospective students to embark on further education study, this is often of weighted value to both students and employers. SLPs are used by Higher Education institutions to generate income in support of the core academic programmes. However, there is a gap in terms of the translation of art and design studio pedagogy into SLPs which provide quality education, are adaptable and delivered via a blended mode. In our paper, we propose a conceptual framework drawing on secondary research to analyse existing research to SLPs for arts and design education. We aim to indicate a new dimension to the process of using a design-based research approach for short learning programmes in art and design education. The study draws on a conceptual framework, a qualitative analysis through the lenses of Herrington, McKenney, Reeves and Oliver (2005) principles of the design-based research approach. The results of this study indicate that design-based research is not only an effective methodological approach for developing and deploying arts and design education curriculum for 1st years in Higher Education context but it also has the potential to guide future research. The findings of this study propose that the design-based research approach could bring theory and praxis together regarding a common purpose to design context-based solutions to educational problems.

Keywords: design education, design-based research, digital literacies, multi-literacies, short learning programme

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Authors: Silvia Gastaldello, Maria Grillo, Luca Tassoni, Claudio Maran, Stefano Balbo

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Antioxidants are nowadays attractive not only for the countless benefits to the human and animal health, but also for the perspective of use as food preservative instead of synthetic chemical molecules. In this study, the radical scavenging activity of six protein extracts from pulse and oleaginous seeds was evaluated. The selected matrices are Pisum sativum (yellow pea from two different origins), Carthamus tinctorius (safflower), Helianthus annuus (sunflower), Lupinus luteus cv Mister (lupin) and Glycine max (soybean), since they are economically interesting for both human and animal nutrition. The seeds were grinded and proteins extracted from 20mg powder with a specific vegetal-extraction kit. Proteins have been quantified through Bradford protocol and scavenging activity was revealed using DPPH assay, based on radical DPPH (2,2-diphenyl-1-picrylhydrazyl) absorbance decrease in the presence of antioxidants molecules. Different concentrations of the protein extract (1, 5, 10, 50, 100, 500 µg/ml) were mixed with DPPH solution (DPPH 0,004% in ethanol 70% v/v). Ascorbic acid was used as a scavenging activity standard reference, at the same six concentrations of protein extracts, while DPPH solution was used as control. Samples and standard were prepared in triplicate and incubated for 30 minutes in dark at room temperature, the absorbance was read at 517nm (ABS30). Average and standard deviation of absorbance values were calculated for each concentration of samples and standard. Statistical analysis using t-students and p-value were performed to assess the statistical significance of the scavenging activity difference between the samples (or standard) and control (ABSctrl). The percentage of antioxidant activity has been calculated using the formula [(ABSctrl-ABS30)/ABSctrl]*100. The obtained results demonstrate that all matrices showed antioxidant activity. Ascorbic acid, used as standard, exhibits a 96% scavenging activity at the concentration of 500 µg/ml. At the same conditions, sunflower, safflower and yellow peas revealed the highest antioxidant performance among the matrices analyzed, with an activity of 74%, 68% and 70% respectively (p < 0.005). Although lupin and soybean exhibit a lower antioxidant activity compared to the other matrices, they showed a percentage of 46 and 36 respectively. All these data suggest the possibility to use undervalued edible matrices as antioxidants source. However, further studies are necessary to investigate a possible synergic effect of several matrices as well as the impact of industrial processes for a large-scale approach.

Keywords: antioxidants, DPPH assay, natural matrices, vegetal proteins

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Authors: Nikhil Byanna, Abdolghani Ebrahimi, Diego Klabjan

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How does one objectively measure the performance of an individual offensive lineman in the NFL? The existing literature proposes various measures that rely on subjective assessments of game film, but has yet to develop an objective methodology to evaluate performance. Using a variety of statistics related to an offensive lineman’s performance, we develop a framework to objectively analyze the overall performance of an individual offensive lineman and determine specific linemen who are overvalued or undervalued relative to their salary. We identify eight players across the 2013-2014 and 2014-2015 NFL seasons that are considered to be overvalued or undervalued and corroborate the results with existing metrics that are based on subjective evaluation. To the best of our knowledge, the techniques set forth in this work have not been utilized in previous works to evaluate the performance of NFL players at any position, including offensive linemen.

Keywords: offensive lineman, player performance, NFL, machine learning

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Authors: Akash Amalan, Rui Wang, Yanqi Qiao, Emmanouil Panaousis, Kaitai Liang

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Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled General Adversarial Networks (GANs) to benefit from the rich distributed training data while preserving privacy. However, in a non-IID setting, current federated GAN architectures are unstable, struggling to learn the distinct features, and vulnerable to mode collapse. In this paper, we propose an architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse, and instability for non-IID datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e., high inception score) on average over 20 clients compared to baseline FLGAN.

Keywords: federated learning, generative adversarial network, inference attack, non-IID data distribution

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Authors: Jing Jin

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The incorporation of artificial intelligence (AI) and machine learning (ML) in aircraft manufacturing and aerospace supply chains leads to the generation of a substantial amount of data among various tiers of suppliers and OEMs. Identifying the high-quality information challenges decision-makers. The application of AI/ML models necessitates access to 'high-quality' information to yield desired outputs. However, the process of information sharing introduces complexities, including distortion through various communication channels and biases introduced by both human and AI entities. This phenomenon significantly influences the quality of information, impacting decision-makers engaged in configuring supply chain systems. Traditionally, distorted information is categorized as 'low-quality'; however, this study challenges this perception, positing that distorted information, contributing to stakeholder goals, can be deemed high-quality within supply chains. The main aim of this study is to identify and evaluate the dimensions of information quality crucial to the UK aerospace supply chain. Guided by a central research question, "What information quality dimensions are considered when defining information quality in the UK aerospace supply chain?" the study delves into the intricate dynamics of information quality in the aerospace industry. Additionally, the research explores the nuanced impact of information distortion on stakeholders' decision-making processes, addressing the question, "How does the information distortion phenomenon influence stakeholders’ decisions regarding information quality in the UK aerospace supply chain system?" This study employs deductive methodologies rooted in positivism, utilizing a cross-sectional approach and a mono-quantitative method -a questionnaire survey. Data is systematically collected from diverse tiers of supply chain stakeholders, encompassing end-customers, OEMs, Tier 0.5, Tier 1, and Tier 2 suppliers. Employing robust statistical data analysis methods, including mean values, mode values, standard deviation, one-way analysis of variance (ANOVA), and Pearson’s correlation analysis, the study interprets and extracts meaningful insights from the gathered data. Initial analyses challenge conventional notions, revealing that information distortion positively influences the definition of information quality, disrupting the established perception of distorted information as inherently low-quality. Further exploration through correlation analysis unveils the varied perspectives of different stakeholder tiers on the impact of information distortion on specific information quality dimensions. For instance, Tier 2 suppliers demonstrate strong positive correlations between information distortion and dimensions like access security, accuracy, interpretability, and timeliness. Conversely, Tier 1 suppliers emphasise strong negative influences on the security of accessing information and negligible impact on information timeliness. Tier 0.5 suppliers showcase very strong positive correlations with dimensions like conciseness and completeness, while OEMs exhibit limited interest in considering information distortion within the supply chain. Introducing social network analysis (SNA) provides a structural understanding of the relationships between information distortion and quality dimensions. The moderately high density of ‘information distortion-by-information quality’ underscores the interconnected nature of these factors. In conclusion, this study offers a nuanced exploration of information quality dimensions in the UK aerospace supply chain, highlighting the significance of individual perspectives across different tiers. The positive influence of information distortion challenges prevailing assumptions, fostering a more nuanced understanding of information's role in the Industry 4.0 landscape.

Keywords: information distortion, information quality, supply chain configuration, UK aerospace industry

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Authors: H. M. Liman, Y. M. Suleiman A. A. David

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Poor air quality often considered one of the greatest environmental threats facing the world today is caused majorly by the emission of carbon monoxide into the atmosphere. The principal air pollutant is carbon monoxide. One prominent source of carbon monoxide emission is the transportation sector. Not much was known about the emission levels of carbon monoxide, the primary pollutant from the road transportation in the study area. Therefore, this study assessed the levels of carbon monoxide emission from road transportation in the Minna, Niger State. The database shows the carbon monoxide data collected. MSA Altair gas alert detector was used to take the carbon monoxide emission readings in Parts per Million for the peak and off-peak periods of vehicular movement at the road intersections. Their Global Positioning System (GPS) coordinates were recorded in the Universal Transverse Mercator (UTM). Bar chart graphs were plotted by using the emissions level of carbon dioxide as recorded on the field against the scientifically established internationally accepted safe limit of 8.7 Parts per Million of carbon monoxide in the atmosphere. Further statistical analysis was also carried out on the data recorded from the field using the Statistical Package for Social Sciences (SPSS) software and Microsoft excel to show the variance of the emission levels of each of the parameters in the study area. The results established that emissions’ level of atmospheric carbon monoxide from the road transportation in the study area exceeded the internationally accepted safe limits of 8.7 parts per million. In addition, the variations in the average emission levels of CO between the four parameters showed that morning peak is having the highest average emission level of 24.5PPM followed by evening peak with 22.84PPM while morning off peak is having 15.33 and the least is evening off peak 12.94PPM. Based on these results, recommendations made for poor air quality mitigation via carbon monoxide emissions reduction from transportation include Introduction of the urban mass transit would definitely reduce the number of traffic on the roads, hence the emissions from several vehicles that would have been on the road. This would also be a cheaper means of transportation for the masses and Encouraging the use of vehicles using alternative sources of energy like solar, electric and biofuel will also result in less emission levels as the these alternative energy sources other than fossil fuel originated diesel and petrol vehicles do not emit especially carbon monoxide.

Keywords: carbon monoxide, climate change emissions, road transportation, vehicular

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Leen Sulaimani, Maryam Hafiz, Naba Ali, Roba Alsharif

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One of Amazon’s most crucial online systems is artificial intelligence. Amazon would not have a worldwide successful online store, an easy and secure way of payment, and other services if it weren’t for artificial intelligence and machine learning. Amazon uses AI to expand its operations and enhance them by upgrading the website daily; having a strong base of artificial intelligence in a worldwide successful business can improve marketing, decision-making, feedback, and more qualities. Aiming to have a rational AI system in one’s business should be the start of any process; that is why Amazon is fortunate that they keep taking care of the base of their business by using modern artificial intelligence, making sure that it is stable, reaching their organizational goals, and will continue to thrive more each and every day. Artificial intelligence is used daily in our current world and is still being amplified more each day to reach consumer satisfaction and company short and long-term goals.

Keywords: artificial intelligence, Amazon, business, customer, decision making

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Authors: Eva Šmelová, Alena Berčíková

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The issue of school maturity and readiness of a child for a successful start of compulsory education is one of the long-term monitored areas, especially in the context of education and psychology. In the context of the curricular reform in the Czech Republic, the issue has recently gained importance. Analyses of research in this area suggest a lack of a broader overview of indicators informing about the current level of children’s school maturity and school readiness. Instead, various studies address partial issues. Between 2009 and 2013 a research study was performed at the Faculty of Education, Palacký University Olomouc (Czech Republic) focusing on children’s maturity and readiness for compulsory education. In this study, social skills were of marginal interest; the main focus was on the mental area. This previous research is smoothly linked with the present study, the objective of which is to identify the level of school maturity and school readiness in selected characteristics of social skills as part of the adaptation process after enrolment in compulsory education. In this context, the following research question has been formulated: During the process of adaptation to the school environment, which social skills are weakened? The method applied was observation, for the purposes of which the authors developed a research tool – record sheet with 11 items – social skills that a child should have by the end of preschool education. The items were assessed by first-grade teachers at the beginning of the school year. The degree of achievement and intensity of the skills were assessed for each child using an assessment scale. In the research, the authors monitored a total of three independent variables (gender, postponement of school attendance, participation in inclusive education). The effect of these independent variables was monitored using 11 dependent variables. These variables are represented by the results achieved in selected social skills. Statistical data processing was assisted by the Computer Centre of Palacký University Olomouc. Statistical calculations were performed using SPSS v. 12.0 for Windows and STATISTICA: StatSoft STATISTICA CR, Cz (software system for data analysis). The research sample comprised 115 children. In their paper, the authors present the results of the research and at the same time point to possible areas of further investigation. They also highlight possible risks associated with weakened social skills.

Keywords: compulsory education, curricular reform, educational diagnostics, pupil, school curriculum, school maturity, school readiness, social skills

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Authors: Titanilla Komenda, Viktorio Malisa

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In this paper, the influence of diversity-related factors on the design of collaborative scenarios is analysed. Based on the evaluation, a framework for simulating human-robot-collaboration is presented that considers both human factors as well as the overall system performance. The implementation of the model is shown on a real-life scenario from industry and validated in terms of traceability, safety and physical limitations. By comparing scenarios that consider diversity with those only meeting system performance, an overall understanding of individually adapted human-robot-collaborative workspaces is reached. A diversity-related guideline for human-robot-collaborations provides a summary of the research and aids in optimizing future applications. Finally, limitations and future amendments of the model are discussed.

Keywords: diversity, human-machine system, human-robot collaboration, simulation

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Authors: Jiahao Tian, Michael D. Porter

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Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval-censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of the week. This approach presented here provides accurate intensity estimates and can also uncover day-of-week clusters that share the same intensity patterns. Anticipating where and when crimes might occur is a key element to successful policing strategies. However, this task is complicated by the presence of interval-censored data. The censored data refers to the type of data that the event time is only known to lie within an interval instead of being observed exactly. This type of data is prevailing in the field of criminology because of the absence of victims for certain types of crime. Despite its importance, the research in temporal analysis of crime has lagged behind the spatial component. Inspired by the success of solving crime-related problems with a statistical approach, we propose a statistical model for the temporal intensity estimation of crime with censored data. The model is built on Poisson regression and has special penalty terms added to the likelihood. An EM algorithm was derived to obtain maximum likelihood estimates, and the resulting model shows superior performance to the competing model. Our research is in line with the smart policing initiative (SPI) proposed by the Bureau Justice of Assistance (BJA) as an effort to support law enforcement agencies in building evidence-based, data-driven law enforcement tactics. The goal is to identify strategic approaches that are effective in crime prevention and reduction. In our case, we allow agencies to deploy their resources for a relatively short period of time to achieve the maximum level of crime reduction. By analyzing a particular area within cities where data are available, our proposed approach could not only provide an accurate estimate of intensities for the time unit considered but a time-variation crime incidence pattern. Both will be helpful in the allocation of limited resources by either improving the existing patrol plan with the understanding of the discovery of the day of week cluster or supporting extra resources available.

Keywords: cluster detection, EM algorithm, interval censoring, intensity estimation

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Authors: B. Tirpude, V. Surwade, P. Murkey, P. Wankhade, S. Meena

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There is no statistical data to establish variation in epiphyseal fusion in girls in central India population. This significant oversight can lead to exclusion of persons of interest in a forensic investigation. Epiphyseal fusion of proximal end of humerus in eighty females were analyzed on radiological basis to assess the range of variation of epiphyseal fusion at each age. In the study, the X ray films of the subjects were divided into three groups on the basis of degree of fusion. Firstly, those which were showing No Epiphyseal Fusion (N), secondly those showing Partial Union (PC), and thirdly those showing Complete Fusion (C). Observations made were compared with the previous studies.

Keywords: epiphyseal union, shoulder joint, proximal end of humerus

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Authors: Vinay Patil, Swapnil Kekade, Ashish Supare, Vinayak Pawar, Shital Jadhav, Rajkumar Singh

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In recent days, bainitic steel is used in automobile and non-automobile sectors due to its high strength. Bainitic steel is difficult to machine because of its high hardness, hence in this paper machinability of bainitic steel is studied by using Taguchi design of experiments (DOE) approach. Convectional turning experiments were done by using L16 orthogonal array for three input parameters viz. cutting speed, depth of cut and feed. The Taguchi method is applied to study the performance characteristics of machining parameters with surface roughness (Ra), cutting force and tool wear rate. By using Taguchi analysis, optimized process parameters for best surface finish and minimum cutting forces were analyzed.

Keywords: conventional turning, Taguchi method, S/N ratio, bainitic steel machining

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Authors: Sana Rezig, Yosr Ben Mlik, Mounir Jaouadi, Foued Khoffi, Slah Msahli, Bernard Durand

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Increasing interest in environmental concerns, natural fibers are once again being considered as reinforcements for polymer composites. The main objective of this study is to explore another natural resource, Typha fiber; which is renewable without production cost and available abundantly in nature. The aim of this study was to study the flexural properties of composite resin with and without reinforcing Typha leaf and stem fibers. The specimens were made by the hand-lay-up process using polyester matrix. In our work, we focused on the effect of various treatment conditions (sea water, alkali treatment and a combination of the two treatments), as a surface modifier, on the flexural properties of the Typha fibers reinforced polyester composites. Moreover, weight ratio of Typha leaf or stem fibers was investigated. Besides, both fibers from leaf and stem of Typha plant were used to evaluate the reinforcing effect. Another parameter, which is reinforcement structure, was investigated. In fact, a first composite was made with air-laid nonwoven structure of fibers. A second composite was with a mixture of fibers and resin for each kind of treatment. Results show that alkali treatment and combined process provided better mechanical properties of composites in comparison with fiber treated by sea water. The fiber weight ratio influenced the flexural properties of composites. Indeed, a maximum value of flexural strength of 69.8 and 62,32 MPa with flexural modulus of 6.16 and 6.34 GPawas observed respectively for composite reinforced with leaf and stem fibers for 12.6 % fiber weight ratio. For the different treatments carried out, the treatment using caustic soda, whether alone or after retting seawater, show the best results because it improves adhesion between the polyester matrix and the fibers of reinforcement. SEM photographs were made to ascertain the effects of the surface treatment of the fibers. By varying the structure of the fibers of Typha, the reinforcement used in bulk shows more effective results as that used in the non-woven structure. In addition, flexural strength rises with about (65.32 %) in the case of composite reinforced with a mixture of 12.6% leaf fibers and (27.45 %) in the case of a composite reinforced with a nonwoven structure of 12.6 % of leaf fibers. Thus, to better evaluate the effect of the fiber origin, the reinforcing structure, the processing performed and the reinforcement factor on the performance of composite materials, a statistical study was performed using Minitab. Thus, ANOVA was used, and the patterns of the main effects of these parameters and interaction between them were established. Statistical analysis, the fiber treatment and reinforcement structure seem to be the most significant parameters.

Keywords: flexural properties, fiber treatment, structure and weight ratio, SEM photographs, Typha leaf and stem fibers

Procedia PDF Downloads 415