Search results for: data analyses

Authors: Saker Lilia, Youcef Mellouki, Lakhdar Sellami, Yacine Zerairia, Abdelhaid Zetili, Fatma Guahria, Fateh Kaious, Nesrine Belkhodja, Abdelhamid Mira

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Background: Sudden death is regarded as a suspicious demise necessitating autopsy, as stipulated by legal authorities. Chronic disseminated intravascular coagulation (DIC) is an acquired clinical and biological syndrome characterized by a severe and fatal prognosis, stemming from systemic, uncontrolled, diffuse coagulation activation. Irrespective of their origins, DIC is associated with a diverse spectrum of manifestations, encompassing minor biological coagulation alterations to profoundly severe conditions wherein hemorrhagic complications may take precedence. Simultaneously, microthrombi contribute to the development of multi-organ failures. Objective This study seeks to evaluate the role of autopsy in determining the causes of death. Materials and Methods: We present two instances of sudden death involving females who underwent autopsy at the Forensic Medicine Department of the University Hospital of Annaba, Algeria. These autopsies were performed at the request of the prosecutor, aiming to determine the causes of death and illuminate the exact circumstances surrounding it. Methods Utilized: Analysis of the initial information report; Findings from postmortem examinations; Histological assessments and toxicological analyses. Results: The presence of DIC was noted, affecting nearly all veins with distinct etiologies. Conclusion: For the establishment of a meaningful diagnosis: • Thorough understanding of the subject matter is imperative; • Precise alignment with medicolegal data is essential.

Keywords: chronic disseminated intravascular coagulation, sudden death, autopsy, causes of death

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Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

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The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

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Authors: Aidin Foroutan, David S. Wishart, Leluo L. Guan, Carolyn Fitzsimmons

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Maximizing efficiency and growth potential of beef cattle requires not only genetic selection (i.e. residual feed intake (RFI)) but also adequate nutrition throughout all stages of growth and development. Nutrient restriction during gestation has been shown to negatively affect post-natal growth and development as well as fertility of the offspring. This, when combined with RFI may affect progeny traits. This study aims to investigate the impact of selection for divergent genetic potential for RFI and maternal nutrition during early- to mid-gestation, on bull calf traits such as fertility and muscle development using multiple ‘omics’ approaches. Comparisons were made between High-diet vs. Low-diet and between High-RFI vs. Low-RFI animals. An epigenetics experiment on semen samples identified 891 biomarkers associated with growth and development. A gene expression study on Longissimus thoracis muscle, semimembranosus muscle, liver, and testis identified 4 genes associated with muscle development and immunity of which Myocyte enhancer factor 2A [MEF2A; induces myogenesis and control muscle differentiation] was the only differentially expressed gene identified in all four tissues. An initial metabolomics experiment on serum samples using nuclear magnetic resonance (NMR) identified 4 metabolite biomarkers related to energy and protein metabolism. Once all the biomarkers are identified, bioinformatics approaches will be used to create a database covering all the ‘omics’ data collected from this project. This database will be broadened by adding other information obtained from relevant literature reviews. Association analyses with these data sets will be performed to reveal key biological pathways affected by RFI and maternal nutrition. Through these association studies between the genome and metabolome, it is expected that candidate biomarker genes and metabolites for feed efficiency, fertility, and/or muscle development are identified. If these gene/metabolite biomarkers are validated in a larger animal population, they could potentially be used in breeding programs to select superior animals. It is also expected that this work will lead to the development of an online tool that could be used to predict future traits of interest in an animal given its measurable ‘omics’ traits.

Keywords: biomarker, maternal nutrition, omics, residual feed intake

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Fabrice Houngnandan, Celia Fery, Thomas Bockel, Julie Deter

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Getting water cleaner and stopping global biodiversity loss requires indices to measure changes and evaluate the achievement of objectives. The endemic and protected seagrass species Posidonia oceanica is a biological indicator used to monitor the ecological quality of marine Mediterranean waters. One ecosystem index (EBQI), two biotic indices (PREI, Bipo), and several landscape indices, which measure the composition and configuration of the P. oceanica seagrass at the population scale have been developed. While the formers are measured at monitoring sites, the landscape indices can be calculated for the entire seabed covered by this ecosystem. This present work aims to search on the link between these indices and the best scale to be used in order to maximize this link. We used data collected between 2014 to 2019 along the French Mediterranean coastline to calculate EBQI, PREI, and Bipo at 100 sites. From the P. oceanica seagrass distribution map, configuration and composition indices around these different sites in 6 different grid sizes (100 m x 100 to 1000 m x 1000 m) were determined. Correlation analyses were first used to find out the grid size presenting the strongest and most significant link between the different types of indices. Finally, several models were compared basis on various metrics to identify the one that best explains the nature of the link between these indices. Our results showed a strong and significant link between biotic indices and the best correlations between biotic and landscape indices within the 600 m x 600 m grid cells. These results showed that the use of landscape indices is possible to monitor the health of seagrass beds at a large scale.

Keywords: ecological indicators, decline, conservation, submerged aquatic vegetation

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Authors: Sait Ali Uymaz, Gülay Tezel

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This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance.

Keywords: cuckoo search, particle swarm optimization, constrained optimization problems, penalty method

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Tas Yong Koh, Steve Rowlinson, Yuzhong Shen

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In construction safety management, safety climate has long been linked to workers' safety behaviors and performance. For this reason, safety climate concept and tools have been used as heuristics to diagnose a range of safety-related issues by some progressive contractors in Hong Kong and elsewhere. However, as a diagnostic tool, safety climate tends to treat the different components of the climate construct in a linear fashion. Safety management in construction projects, in reality, is a multi-faceted and multilevel phenomenon that resembles a complex system. Hence, understanding safety management in construction projects requires not only the understanding of safety climate but also the organizational-systemic nature of the phenomenon. Our involvement, diagnoses, and interpretations of a range of safety climate-related issues which culminated in the project’s sub-optimum safety performance in an infrastructure construction project have brought about such revelation. In this study, a range of data types had been collected from various hierarchies of the project site organization. These include the frontline workers and supervisors from the main and sub-contractors, and the client supervisory personnel. Data collection was performed through the administration of safety climate questionnaire, interviews, observation, and document study. The findings collectively indicate that what had emerged in parallel of the seemingly linear climate-based exploration is the exposition of the organization-systemic nature of the phenomenon. The results indicate the negative impacts of climate perceptions mismatch, insufficient work planning, and risk management, mixed safety leadership, workforce negative attributes, lapsed safety enforcement and resources shortages collectively give rise to the project sub-optimum safety performance. From the dynamic causation and multilevel perspective, the analyses show that the individual, group, and organizational levels issues are interrelated and these interrelationships are linked to negative safety climate. Hence the adoption of both perspectives has enabled a fuller understanding of the phenomenon of safety management that point to the need for an organizational-systemic intervention strategy. The core message points to the fact that intervention at an individual level will only meet with limited success if the risks embedded in the higher levels in group and project organization are not addressed. The findings can be used to guide the effective development of safety infrastructure by linking different levels of systems in a construction project organization.

Keywords: construction safety management, dynamic causation, multilevel analysis, safety climate

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Authors: Manh-Dung Ho, Van-Giap Pham, Van-Doanh Ho, Quang-Thien Tran, Tuan-Anh Tran

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The k0-factors and related nuclear data, i.e. the Q0-factors and effective resonance energies (Ēr) of the selected radionuclides which are used in the k0-based neutron activation analysis (k0-NAA), were critically reviewed to be integrated in the “k0-DALAT” software. The k0- and Q0-factors of some short-lived radionuclides: 46mSc, 110Ag, 116m2In, 165mDy, and 183mW, were experimentally determined at the Dalat research reactor. The other radionuclides selected are: 20F, 36S, 49Ca, 60mCo, 60Co, 75Se, 77mSe, 86mRb, 115Cd, 115mIn, 131Ba, 134mCs, 134Cs, 153Gd, 153Sm, 159Gd, 170Tm, 177mYb, 192Ir, 197mHg, 239U and 239Np. The reviewed data as compared with the literature data were biased within 5.6-7.3% in which the experimental re-determined factors were within 6.1 and 7.3%. The NIST standard reference materials: Oyster Tissue (1566b), Montana II Soil (2711a) and Coal Fly Ash (1633b) were used to validate the new reviewed data showing that the new data gave an improved k0-NAA using the “k0-DALAT” software with a factor of 4.5-6.8% for the investigated radionuclides.

Keywords: neutron activation analysis, k0-based method, k0 factor, Q0 factor, effective resonance energy

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Authors: Yan Liu

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The Gini coefficient indicates that the inequality of income distribution is rising in China. How individuals viewing the equality of current society is an important predicator of social turbulence. Perceptions of social justice may vary according to the social stratification. People usually use socioeconomic status to identify divisions between social stratifications. The objective of this study is to explore the potential influence of socioeconomic status on citizens’ perceptions of social justice in China. Socioeconomic status (SES) is usually reflected by either an SES indicator or a composite of three core dimensions: education, income and occupation. With data collected in the 2010 Chinese General Social Survey (CGSS), this study uses OLS regression analyses to examine the relationship between socioeconomic status (SES) and citizens’ perceptions of social justice. This study finds that most Chinese citizens believe that the current society is fair or more than fair. Socioeconomic status (SES) has a positive impact on citizens’ perceptions of social justice, which means individuals with higher indicator of socioeconomic status prefer to believe current society is fair. However, the three core dimensions which are used to measure socioeconomic status (SES) have different influences on perceptions of social justice: First, income helps enhance citizens’ sense of social justice. Second, education weakens citizens’ sense of social justice. Third, compared to the middle occupational status, people of both higher occupational status and lower occupational status have higher levels of perceptions of social justice. Though education creates a negative influence on perceptions of social justice, its effect is much weaker than that of income, which indicates income is a determining factor for enhancing people’s perceptions of social justice in China’s market society. Policy implications are discussed.

Keywords: education, income, occupation, perceptions of social justice, social stratification, socioeconomic status

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Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

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Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

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Authors: R. Shamsi, F. Sharifi

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In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose a multi-objective DEA-R model because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduce the efficiency score), an efficient decision-making unit (DMU) is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other cases, only the ratio of stochastic data may be available (e.g., the ratio of stochastic inputs to stochastic outputs). Thus, we provide a multi-objective DEA model without explicit outputs and prove that the input-oriented MOP DEA-R model in the invariable return to scale case can be replaced by the MOP-DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA-R, multi-objective programming, stochastic data, data envelopment analysis

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Authors: Sanjin Kršćanski, Josip Brnić

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Virtual Crack Closure Technique (VCCT) is a method used for calculating stress intensity factor (SIF) of a cracked body that is easily implemented on top of basic finite element (FE) codes and as such can be applied on the various component geometries. It is a relatively simple method that does not require any special finite elements to be used and is usually used for calculating stress intensity factors at the crack tip for components made of brittle materials. This paper studies applicability and accuracy of VCCT applied on standard metal specimens containing trough thickness crack, subjected to an in-plane bending load. Finite element analyses were performed using regular 4-node, regular 8-node and a modified quarter-point 8-node 2D elements. Stress intensity factor was calculated from the FE model results for a given crack length, using data available from FE analysis and a custom programmed algorithm based on virtual crack closure technique. Influence of the finite element size on the accuracy of calculated SIF was also studied. The final part of this paper includes a comparison of calculated stress intensity factors with results obtained from analytical expressions found in available literature and in ASTM standard. Results calculated by this algorithm based on VCCT were found to be in good correlation with results obtained with mentioned analytical expressions.

Keywords: VCCT, stress intensity factor, finite element analysis, 2D finite elements, bending

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Authors: Mehdi Razeghi

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Background: The purpose of this case series was to quantify gait to study muscular dystrophy disease. In this research, the quantitative differences between normal and waddling gaits were assessed by force plate analysis. Methods: Nineteen myopathy patients and twenty normal subjects serving as the control group participated in this research. In this study, quantitative analyses of gait have been used to investigate the differences between the mobility of normal subjects and myopathy patients. This study was carried out at the Iranian Muscular Dystrophy Association in Boali Hospital, Tehran, Iran, from October 2015 to July 2020. Patient data were collected from Iranian Muscular Dystrophy Association members. individuals signed an informed consent form approved by the ethics committee of the Azad University. All of the gait tests were performed using a Kistler force platform. Participants walked at a self-selected speed, barefoot, independently, and without assistive devices. Results: Our findings indicate that there were no significant differences between the patients and the control group in the anterior-posterior components of the ground reaction forces; however, there were considerable differences in the force components between the groups in the medial-lateral and vertical directions of the ground reaction force. In addition, there were significant differences in the time parameters between the groups in the vertical and medial-lateral directions.

Keywords: biomechanics, force plate analysis, gait disorder, ground reaction force, kinetic analysis, myopathy disease, rehabilitation engineering

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Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

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Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a simple user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud computing, data security, SAAS, PAAS, IAAS, Blowfish

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Authors: Emilio Galdeano-Gómez, Juan C. Pérez-Mesa, Laura Piedra-Muñoz, María C. García-Barranco, Jesús Hernández-Rubio

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The situation in which firms engage in simultaneous cooperation and competition with each other is a phenomenon known as co-opetition. This scenario has received increasing attention in business economics and management analyses. In the domain of supply chain networks and for small and medium-sized enterprises, SMEs, these strategies are of greater relevance given the complex environment of globalization and competition in open markets. These firms face greater challenges regarding technology and access to specific resources due to their limited capabilities and limited market presence. Consequently, alliances and collaborations with both buyers and suppliers prove to be key elements in overcoming these constraints. However, rivalry and competition are also regarded as major factors in successful internationalization processes, as they are drivers for firms to attain a greater degree of specialization and to improve efficiency, for example enabling them to allocate scarce resources optimally and providing incentives for innovation and entrepreneurship. The present work aims to contribute to the literature on SMEs’ internationalization strategies. The sample is constituted by a panel data of marketing firms from the Andalusian food sector and a multivariate regression analysis is developed, measuring variables of co-opetition and international activity. The hierarchical regression equations method has been followed, thus resulting in three estimated models: the first one excluding the variables indicative of channel type, while the latter two include the international retailer chain and wholesaler variable. The findings show that the combination of several factors leads to a complex scenario of inter-organizational relationships of cooperation and competition. In supply chain management analyses, these relationships tend to be classified as either buyer-supplier (vertical level) or supplier-supplier relationships (horizontal level). Several buyers and suppliers tend to participate in supply chain networks, and in which the form of governance (hierarchical and non-hierarchical) influences cooperation and competition strategies. For instance, due to their market power and/or their closeness to the end consumer, some buyers (e.g. large retailers in food markets) can exert an influence on the selection and interaction of several of their intermediate suppliers, thus endowing certain networks in the supply chain with greater stability. This hierarchical influence may in turn allow these suppliers to develop their capabilities (e.g. specialization) to a greater extent. On the other hand, for those suppliers that are outside these networks, this environment of hierarchy, characterized by a “hub firm” or “channel master”, may provide an incentive for developing their co-opetition relationships. These results prove that the analyzed firms have experienced considerable growth in sales to new foreign markets, mainly in Europe, dealing with large retail chains and wholesalers as main buyers. This supply industry is predominantly made up of numerous SMEs, which has implied a certain disadvantage when dealing with the buyers, as negotiations have traditionally been held on an individual basis and in the face of high competition among suppliers. Over recent years, however, cooperation among these marketing firms has become more common, for example regarding R&D, promotion, scheduling of production and sales.

Keywords: co-petition networks, international supply chain, maketing agrifood firms, SMEs strategies

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Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

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Authors: Poornima Gupta, Sadaf Siraj

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The study examined the organizational causes of organizational stress in women executives and entrepreneurs in India. This was done so that mediation strategies could be developed to combat the organizational stress experienced by them, in order to retain the female employees as well as attract quality talent. The data for this research was collected through the self- administered survey, from the women executives across various industries working at different levels in management. The research design of the study was descriptive and cross-sectional. It was carried out through a self-administered questionnaire filled in by the women executives and entrepreneurs in the NCR region. Multistage sampling involving stratified random sampling was employed. A total of 1000 questionnaires were distributed out of which 450 were returned and after cleaning the data 404 were fit to be considered for analyses. The overall findings of the study suggested that there were various job-related factors that induce stress. Fourteen factors were identified which were a major cause of stress among the working women by applying Factor analysis. The study also assessed the demographic factors which influence the stress in women executives across various industries. The findings show that the women, no doubt, were stressed by organizational factors. The mean stress score was 153 (out of a possible score of 196) indicating high stress. There appeared to be an inverse relationship between the marital status, age, education, work experience, and stress. Married women were less stressed compared to single women employees. Similarly, female employees 29 years or younger experienced more stress at work. Women having education up to 12th standard or less were more stressed compared to graduates and post graduates. Women who had spent more than two years in the same organization perceived more stress compared to their counterparts. Family size and income, interestingly, had no significant impact on stress. The study also established that the level of stress experienced by women across industries differs considerably. Banking sector emerged as the industry where the women experienced the most stress followed by Entrepreneurs, Medical, BPO, Advertising, Government, Academics, and Manufacturing, in that order. The results contribute to the better understanding of the personal and economic factors surrounding job stress and working women. It concludes that the organizations need to be sensitive to the women’s needs. Organizations are traditionally designed around men with the rules made by the men for the men. Involvement of women in top positions, decision making, would make them feel more useful and less stressed. The invisible glass ceiling causes more stress than realized among women. Less distinction between the men and women colleagues in terms of giving responsibilities, involvement in decision making, framing policies, etc. would go a long way to reduce stress in women.

Keywords: women, stress, gender in management, women in management

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Authors: Takahiro Togawa, Nguyen Huu Phuc, Shigeyuki Haruyama, Oke Oktavianty

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Our research focuses on a framework which analyses the relationship between product/process architecture, manufacturing organizational capability and manufacturing "implicit competitiveness" in order to improve manufacturing implicit competitiveness. We found that 1) there is a relationship between architecture-based manufacturing organizational capability and manufacturing implicit competitiveness, and 2) analysis and measures conducted in manufacturing organizational capability proved effective to improve manufacturing implicit competitiveness.

Keywords: implicit competitiveness, QCD, organizational capacity, architectural strategy

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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: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Neşe Alkan

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Purpose: The point that applied/clinical psychology, which is the practice and research discipline of the mental health field, has reached today can be summarized as the necessity of handling the psychological well-being of people from multiple perspectives and the goal of moving it to a higher level. Clients' subjective assessment of their own condition and wellbeing is an integral part of evidence-based interventions. There is a need for tools through which clients can evaluate the effectiveness of the psychotherapy/intervention performed with them and their contribution to the wellbeing and wellbeing of this process in a valid and reliable manner. The aim of this research is to meet this need, to test the reliability and validity of the index in Turkish, and explore its usability in the practices of both researchers and psychotherapists. Method: A total of 213 adults aged between 18-54, 69.5% working and 29.5% university students, were included in the study. Along with their demographic information, the participants were administered a set of scales: wellbeing, life satisfaction, spiritual satisfaction, shopping addiction, and loneliness, namely via an online platform. The construct validity of the wellbeing scale was tested with exploratory and confirmatory factor analyses, convergent and discriminant validity were tested with two-way full and partial correlation analyses and, measurement invariance was tested with one-way analysis of variance. Results: Factor analyzes showed that the scale consisted of six dimensions as it is in its original structure. The internal consistency of the scale was found to be Cronbach α = .82. Two-way correlation analyzes revealed that the wellbeing scale total score was positively correlated with general life satisfaction (r = .62) and spiritual satisfaction (r = .29), as expected. It was negatively correlated with loneliness (r = -.51) and shopping addiction (r = -.15). While the scale score did not vary by gender, previous illness, or nicotine addiction, it was found that the total wellbeing scale scores of the participants who had used antidepressant medication during the past year were lower than those who did not use antidepressant medication (F(1,204) = 7.713, p = .005). Conclusion: It has been concluded that the 12-item wellbeing scale consisting of six dimensions can be used in research and health sciences practices as a valid and reliable measurement tool. Further research which examines the reliability and validity of the scale in different widely used languages such as Spanish and Chinese is recommended.

Keywords: wellbeing, intervention effectiveness, reliability and validity, effectiveness

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Authors: T. Scattolin, E. Cavarzerani, F. Visentin, F. Rizzolio

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Organopalladium compounds have recently attracted attention for their high stability even under physiological conditions and, above all, for their remarkable in vitro cytotoxicity towards cisplatin-resistant cell lines. Among the organopalladium derivatives, those bearing at least one N-heterocyclic carbene ligand (NHC) and the Pd(II)-η³-allyl fragment have exhibited IC₅₀ values in the micro and sub-micromolar range towards several cancer cell lines in vitro and in some cases selectivity towards cancerous vs. non-tumorigenic cells. Herein, a selection of allyl and indenyl palladates were synthesized using a solvent-free method consisting of grinding the corresponding palladium precursors with different saturated and unsaturated azolium salts. All compounds have been fully characterized by NMR, XRD and elemental analyses. The intramolecular H, Cl interaction has been elucidated and quantified using the Voronoi Deformation Density scheme. Most of the complexes showed excellent cytotoxicity towards ovarian cancer cell lines, with I₅₀ values comparable to or even lower than cisplatin. Interestingly, the potent anticancer activity was also confirmed in a high-serous ovarian cancer (HGSOC) patient-derived tumoroid, with a clear superiority of this class of compounds over classical platinum-based agents. Finally, preliminary enzyme inhibition studies of the synthesized palladate complexes against the model TrxR show that the compounds have high activity comparable to or even higher than auranofin and classical Au(I) NHC complexes. Based on such promising data, further in vitro and in vivo experiments and in-depth mechanistic studies are ongoing in our laboratories.

Keywords: anticancer activity, palladium complexes, organoids, indenyl and allyl ligands

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Authors: Emine Akar

Abstract:

The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Arūnas Gudinavičius, Andrius Šuminas

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The analysis of scholarly journals formats is fragmented and needs to be studied from a point of view of scientific communication. While PDF is to the author’s best knowledge probably the most popular digital format of XXI century, but there are more formats available: HTML, EPUB, etc. Our aim is to analyze how these formats important to the readers and what is their contribution to scientific communication. We want to investigate how printed journals are still popular between scholars and does different formats are preferred between fields of science . In most cases, publishing of scientific journals are examined from a narrow perspective of a particular university science affair administrators or research funding institution. We believe that more data o n formats used in scholarly periodicals currently published in Lithuania as well as in Eastern Europe are needed. Science communication is often analyzed as a directed chain of information in the author-publisher-reader cycle. The paper is focusing on the publishing part of this chain. A distinction is made between formal and informal forms of scientific communication, which is relevant in today's context, when both forms of communication intertwine and complement each other. In our research, we will analyze formal documentary (formats of publication of scientific articles) communication - scientific information recorded in a certain medium and formatted in certain format (printed, PDF, HTML, EPUB, etc.). In our research, we will analyze the stage of publication of research results in scientific journals and their dissemination through specific publication formats. The paper is to systematize and analyze the various types of formats of scientific journal published in XXI century in Lithuania (small publishing market). The research analyses the case of small European country and presents publishing formats characteristics of the publication of scientific periodicals.

Keywords: scientific communication, scientific journals, publishing formats, reading

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Authors: Sandipika Gupta

Abstract:

Objective: Due to admirably low 30-day mortality rates for paediatric cardiac surgery, it is now pertinent to turn towards more intermediate-length outcomes such as morbidities closely associated with these surgeries. One such morbidity, major adverse events (MAE) comprises a group of adverse outcomes associated with paediatric cardiac surgery (e.g. cardiac arrest, major haemorrhage). Methods: This is a retrospective study that analysed the incidence and impact of MAE which was the primary outcome in the UK population. The data was collected in 5 centres between October 2015 and June 2017, amassing 3090 surgical episodes. The incidence and risk factors for MAE, were assessed through descriptive statistical analyses and multivariate logistic regression. The secondary outcomes of life status at 6 months and the length of hospital stay were also evaluated to understand the impact of MAE on patients. Results: Out of 3090 episodes, 134 (4.3%) had a postoperative MAE. The majority of the episodes were in: neonates (47%, P<0.001), high-risk cardiac diagnosis groups (20.1%, P<0.001), episodes with longer 5mes on the bypass (72.4%, P<0.001) and urgent surgeries (57.9%, P<0.001). Episodes reporting MAE also reported longer lengths of stay in hospital (29 days vs 9 days, P<0.001). Furthermore, patients experiencing MAE were at a higher risk of mortality at the 6-month life status check (mortality rates: 29.2% vs 2%, P<0.001).Conclusions: Key risk factors were identified. An important negative impact of MAE was found for patients. The identified risk factors could be used to profile and flag at-risk patients. Monitoring of MAE rates and closer investigation into the care pathway before and after individual MAEs in children’s heart units may lead to a reduction in these terrible events.

Keywords:

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Authors: Takahiro Nishigaki, Takashi Onoda

Abstract:

In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness.

Keywords: independent topic analysis, topic extraction, topic naming, web search engine

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Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

Abstract:

Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

Procedia PDF Downloads 132