Search results for: model for identification of attributes quality

Authors: Mauricio Gracia, Luis Leal, Bharat Verma

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Computational fluid dynamics (CFD) has become nowadays an important tool in the process of hydrodynamic design of modern ships. CFD is used to model any phenomena related to fluid flow in a control volume like a ship or any offshore structure in the sea. In the present study, the current force drag coefficients for a Colombian Navy Frigate in deep and shallow water are estimated through the application of CFD. The study shows the process of simulating the ship current drag coefficients using the CFD simulations method, which is conducted using STAR-CCM+ software package. The Almirante Padilla class Frigate ship scale model is investigated. The results show the ship current drag coefficient calculated considering a current speed of 1 knot with a 90° drift angle for the full-scale ship. Predicted results were compared against the current drag coefficients published in the Lloyds register OCIMF report. It is shown that the simulation results agree fairly well with the published results and that STAR-CCM+ code can predict current drag coefficients.

Keywords: CFD, current draft coefficient, STAR-CCM+, OCIMF, Bollard pull

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Authors: Elham Ahmadi, Roshaali Khaturia, Pardis Sahraei, Mohammad Niyayesh, Omid Fatahi Valilai

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Nowadays, Information Technology (IT) is changing the way traditional enterprise management concepts work. One of the most dominant IT achievements is the Blockchain Technology. This technology enables the distributed collaboration of stakeholders for their interactions while fulfilling the security and consensus rules among them. This paper has focused on the application of Blockchain technology to enhance one of traditional inventory management models. The Vendor Managed Inventory (VMI) has been considered one of the most efficient mechanisms for vendor inventory planning by the suppliers. While VMI has brought competitive advantages for many industries, however its centralized mechanism limits the collaboration of a pool of suppliers and vendors simultaneously. This paper has studied the recent research for VMI application in industries and also has investigated the applications of Blockchain technology for decentralized collaboration of stakeholders. Focusing on sustainability issue for total supply chain consisting suppliers and vendors, it has proposed a Blockchain based VMI conceptual model. The different capabilities of this model for enabling the collaboration of stakeholders while maintaining the competitive advantages and sustainability issues have been discussed.

Keywords: vendor managed inventory, VMI, blockchain technology, supply chain planning, sustainability

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

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Authors: L. Rashidian, M. Rajabali

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Climate changes have unpredictable and costly effects on water resources of various basins. The impact of atmospheric phenomena on human life and the environment is so significant that only knowledge of management can reduce its consequences. In this study, using LARS.WG model and down scaling of general circulation climate model HADCM-3 and according to the IPCC scenarios, including series A1b, A2 and B1, we simulated data from 2010 to 2040 in order to using them for long term forecasting of climate parameters of the Caspian Sea and its impact on sea level. Our research involves collecting data on monthly precipitation amounts, minimum and maximum temperature and daily sunshine hours, from meteorological organization for Caspian Sea coastal station such as Gorgan, Ramsar, Rasht, Anzali, Astara and Ghaemshahr since their establishment until 2010. Considering the fact that the fluctuation range of water level in the Caspian Sea has various ups and downs in different times, there is an increase in minimum and maximum temperature for all the mentioned scenarios, which will last until 2040. Overall, the amount of rainfall in cities bordering the Caspian Sea was studied based on the three scenarios, which shows an increase in the amount. However, there will be a decrease in water level of the Caspian Sea till 2040.

Keywords: IPCC, climate change, atmospheric circulation, Caspian Sea, HADCM3, sea level

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Robert McGrath, Zara Qureshi

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A innovative leadership philosophy that is proposed herein is distinctly more humane than most leadership approaches Concerned Leadership. The central idea to this approach is to consider the whole person that comes to work; their professional skills and talents, as well as any personal, emotional challenges that could be affecting productivity and effectiveness at work. This paper explores Concerned Leadership as an integration of the two conceptual models areas examined in this paper –(1) leaderful organizations and practices, as well as (2) organizational culture, and defines leadership in the context of Mental Health and Wellness in the workplace. Leaderful organizations calls for organizations to implement leaderful practice. Leaderful practice is when leadership responsibility and decision-making is shared across all team members and levels, versus only delegated to top management as commonly seen. A healthy culture thrives off key aspects such as acceptance, employee pride, equal opportunity, and strong company leadership. Concerned Leadership is characterized by five main components: Self-Concern, Leaderful Practice, Human Touch, Belonging, and Compassion. As scholars and practitioners conceptualize leadership in practice, the present model seeks to uphold the dignity of each organizational member, thereby having the potential to transform workplaces and support all members.

Keywords: leadership, mental health, reflective practice, organizational culture

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Khaled Ayfi

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In industrial oxygen systems at high temperature and high pressure, contamination by solid particles is one of the principal causes of ignition hazards. Indeed, gas can sweep away particles, generated by corrosion inside the pipes or during maintenance operations (welding residues, careless disassembly, etc.) and produce accumulations at places where the gas velocity decrease. Moreover, in such an environment rich in oxygen (oxidant), particles are highly reactive and can ignite system walls more actively and at higher temperatures. Oxidation based thermal effects are responsible for mechanical properties lost, leading to the destruction of the pressure equipment wall. To deal with this problem, a numerical analysis is done regarding a sample representative of a wall subjected to pressure and temperature. The validation and analysis are done comparing the numerical simulations results to experimental measurements. More precisely, in this work, we propose a numerical model that describes the thermomechanical behavior of thin metal disks under pressure and subjected to laser heating. This model takes into account the geometric and material nonlinearity and has been validated by the comparison of simulation results with experimental measurements.

Keywords: ignition, oxygen, numerical simulation, thermomechanical behavior

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Authors: Oyvind Nordvik, Peder O. L. Heggdal, Jonas Brannstrom, Flemming Vassbotn, Anne Kari Aarstad, Hans Jorgen Aarstad

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Background: Hearing Loss (HL) is a condition that may affect people in all stages of life, but the prevalence increases with age, mostly because of age-related HL, generally referred to as presbyacusis. As human speech is related to relatively high frequencies, even a limited hearing loss at high frequencies may cause impaired speech intelligibility. Being diagnosed with, treated for and living with a chronic condition such as HL, must for many be a disabling and stressful condition that put ones coping resources to test. Stress is a natural part of life and most people will experience stressful events or periods. Chronic diseases, such as HL, are risk factor for distress in individuals, causing anxiety and lowered mood. How an individual cope with HL may be closely connected to the level of distress he or she is experiencing and to personality, which can be defined as those characteristics of a person that account for consistent patterns of feelings, thinking, and behavior. Thus, as to distress in life, such as illness or disease, available coping strategies may be more important than the challenge itself. The same line of arguments applies to level of experienced health-related quality of life (HRQoL). Aim: The aim of this study was to investigate the relationship between distress, HRQoL, reported hearing loss, personality and coping in patients with HL. Method: 158 adult (aged 18-78 years) patients with HL, referred for hearing aid (HA) fitting at Haukeland University Hospital in western Norway, participated in the study. Both first-time users, as well as patients referred for HA renewals were included. First-time users had been pre-examined by an ENT-specialist. The questionnaires were answered before the actual HA fitting procedure. The pure-tone average (PTA; frequencies 0.5, 1, 2, and 4 kHz) was determined for each ear. The Eysenck personality inventory, neuroticism and lie scales, the Theoretically Originated Measure of the Cognitive Activation Theory of Stress (TOMCATS) measuring active coping, hopelessness and helplessness, as well as distress (General Health Questionnaire (GHQ) - 12 items) and the EORTC Quality of Life Questionnaire general part were answered. In addition, we used a revised and shortened version of the Abbreviated Profile of Hearing Aid Benefit (APHAB) as a measure of patient-reported hearing loss. Results: Significant correlations were determined between APHAB (weak), HRQoL scores (strong), distress scores (strong) on the one side and personality and choice of coping scores on the other side. As measured by stepwise regression analyses, the distress and HRQoL scores were scored secondary to the obtained personality and coping scores. The APHAB scores were as determined by regression analyses scored secondary to PTA (best ear), level of neuroticism and lie score. Conclusion: We found that reported employed coping style, distress/HRQoL and personality are closely connected to each other in this patient group. Patient-reported HL was associated to hearing level and personality. There is need for further investigations on these questions, and how these associations may influence the clinical context.

Keywords: coping, distress, hearing loss, personality

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Authors: Nadia Belu, Laurenţiu Mihai Ionescu, Agnieszka Misztal

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The automotive industry is one of the most important industries in the world that concerns not only the economy, but also the world culture. In the present financial and economic context, this field faces new challenges posed by the current crisis, companies must maintain product quality, deliver on time and at a competitive price in order to achieve customer satisfaction. Two of the most recommended techniques of quality management by specific standards of the automotive industry, in the product development, are Failure Mode and Effects Analysis (FMEA) and Control Plan. FMEA is a methodology for risk management and quality improvement aimed at identifying potential causes of failure of products and processes, their quantification by risk assessment, ranking of the problems identified according to their importance, to the determination and implementation of corrective actions related. The companies use Control Plans realized using the results from FMEA to evaluate a process or product for strengths and weaknesses and to prevent problems before they occur. The Control Plans represent written descriptions of the systems used to control and minimize product and process variation. In addition Control Plans specify the process monitoring and control methods (for example Special Controls) used to control Special Characteristics. In this paper we propose a computer-aided solution with Genetic Algorithms in order to reduce the drafting of reports: FMEA analysis and Control Plan required in the manufacture of the product launch and improved knowledge development teams for future projects. The solution allows to the design team to introduce data entry required to FMEA. The actual analysis is performed using Genetic Algorithms to find optimum between RPN risk factor and cost of production. A feature of Genetic Algorithms is that they are used as a means of finding solutions for multi criteria optimization problems. In our case, along with three specific FMEA risk factors is considered and reduce production cost. Analysis tool will generate final reports for all FMEA processes. The data obtained in FMEA reports are automatically integrated with other entered parameters in Control Plan. Implementation of the solution is in the form of an application running in an intranet on two servers: one containing analysis and plan generation engine and the other containing the database where the initial parameters and results are stored. The results can then be used as starting solutions in the synthesis of other projects. The solution was applied to welding processes, laser cutting and bending to manufacture chassis for buses. Advantages of the solution are efficient elaboration of documents in the current project by automatically generating reports FMEA and Control Plan using multiple criteria optimization of production and build a solid knowledge base for future projects. The solution which we propose is a cheap alternative to other solutions on the market using Open Source tools in implementation.

Keywords: automotive industry, FMEA, control plan, automotive technology

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Authors: Pomila Sharma

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The rapid urbanization in India was not accompanied by the establishment of waste water treatment facility at similar and same pace. The inland fresh water ecosystem is increasingly subjected to great stress from various human activities. Jalmahal Lake is located in Jaipur city of Rajasthan state; the lake was constructed about 400 years ago and surrounded by hills. The lake was approximately 139 hectare in full spread and has catchment area of 23.5 sq. kilometer. Out of the total catchment area approximate 40% falls inside dense urban area of Jaipur city. During the showers, the treated and untreated waste waters and runoff waters get mixed and enter the lake through the various influx channels, and the lake water quality gets affected by the inflow of waste water. The main objective of this work was to use the Moringa oleifera seeds as a natural adsorbent for the treatment of wastewater in lake. Moringa oleifera is a tropical, multipurpose tree whose seeds contain high-quality edible oil 40% by weight and water soluble, non-toxic protein that act as an effective coagulant for the removal of organic matter in water and waste water treatment. Laboratory Jar test procedure had been used for coagulation studies; an experiment runs using lake water. Water extracts/powder of Moringa seed applied to treat polluted water of lake. In present study various doses of Moringa oleifera seed coagulant viz. 100 mg/L, 200 mg/L, and 400 mg/L were taken and checked for the efficiency dose on treated and untreated polluted water. Turbidity and color removal is one of the important steps in a waste water treatment processes. The results indicate significant reduction in turbidity and color. Standard plate count was significantly reduced fecal coliform levels too. All parameters were reduced with the increased dose of Moringa oleifera. It was clear from the study Moringa oleifera seed was shown to be a potential bio-coagulant, for treatment of sewage laden polluted water in the lake.

Keywords: coagulant, Moringa oleifera, plate count, turbidity, wastewater

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

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Authors: Jia-Lien Hsu, Chiung-Wen Chang

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Research articles may follow a simple and succinct structure of organizational patterns, called move. For example, considering extended abstracts, we observe that an extended abstract usually consists of five moves, including Background, Aim, Method, Results, and Conclusion. As another example, when publishing articles in PubMed, authors are encouraged to provide a structured abstract, which is an abstract with distinct and labeled sections (e.g., Introduction, Methods, Results, Discussions) for rapid comprehension. This paper introduces a method for computational analysis of move structures (i.e., Background-Purpose-Method-Result-Conclusion) in abstracts and introductions of research documents, instead of manually time-consuming and labor-intensive analysis process. In our approach, sentences in a given abstract and introduction are automatically analyzed and labeled with a specific move (i.e., B-P-M-R-C in this paper) to reveal various rhetorical status. As a result, it is expected that the automatic analytical tool for move structures will facilitate non-native speakers or novice writers to be aware of appropriate move structures and internalize relevant knowledge to improve their writing. In this paper, we propose a Bayesian approach to determine move tags for research articles. The approach consists of two phases, training phase and testing phase. In the training phase, we build a Bayesian model based on a couple of given initial patterns and the corpus, a subset of CiteSeerX. In the beginning, the priori probability of Bayesian model solely relies on initial patterns. Subsequently, with respect to the corpus, we process each document one by one: extract features, determine tags, and update the Bayesian model iteratively. In the testing phase, we compare our results with tags which are manually assigned by the experts. In our experiments, the promising accuracy of the proposed approach reaches 56%.

Keywords: academic English writing, assisted writing, move tag analysis, Bayesian approach

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Authors: Yun Zuo, Wenju Li

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Shenzhen is one of China’s gates to the world. What was once a fishing village is now a metropolis of more than 10 million people. Because of its unprecedented pace of development, it also brings a serious of issues, such as the self-renewal of the city. In the paper, we use Sungang-Quingshuihe(SQ) as an example. SQ is one of the oldest districts in the east of Shenzhen. Nowadays, SQ faces many challenges. This is because once the logistics area has been slowly disappear, the new identity will be replaced. As a result, we are to minimize damages to the city in transforming process by seeking for a new design strategy. In the meantime, we think that each district in a city has its own role forming the whole city together. Therefore, a district transformation is functionally-oriented and for improving city quality in focus.

Keywords: old district, renewal strategy, public space, sustainable development

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Authors: Khatesiree Sripoothon, Usanee Sengpanich, Rattana Sittioum

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The objective of this study is to examine the factors influencing consumer purchasing decisions about healthy food. This model consists of two latent variables: Consumer Perception relating to NCDs and Consumer Perceived Product Value. The study was conducted in the northern provinces of Thailand, which are popular with tourists and have received support from the government for health tourism. A survey was used as the data collection method, and the questionnaire was applied to 385 tourists. An accidental sampling method was used to identify the sample. The statistics of frequency, percentage, mean, and structural equation model were used to analyze the data obtained. Additionally, all factors had a significant positive influence on healthy food purchasing decisions (p<0.01) and were predictive of healthy food purchasing decisions at 46.20 (R2=0.462). Also, these findings seem to underline a supposition that consumer perceptions of NCDs and perceived product value are key variables that strengthens the competitive effects of a healthy-friendly business entrepreneur. Moreover, reduce the country's public health costs for treating patients with the disease of NCDs in Thailand.

Keywords: healthy food, perceived product value, perception of non-communicable diseases, purchasing decisions

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Authors: H. Aljabiry, L. Bailey, S. Young

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Qatar is a desert with an average temperature 37⁰C, reaching over 40⁰C during summer. Precipitation is uncommon and mostly in winter. Qatar depends on desalination for drinking water and on groundwater and recycled water for irrigation. Water consumption and network leakage per capita in Qatar are amongst the highest in the world; re-use of treated wastewater is extremely limited with only 14% of treated wastewater being used for irrigation. This has led to the country disposing of unwanted water from various sources in lagoons situated around the country, causing concern over the possibility of environmental pollution. Accordingly, our hypothesis underpinning this research is that the quality and quantity of water in lagoons is having an impact on the groundwater reservoirs in Qatar. Lagoons (n = 14) and wells (n = 55) were sampled for both summer and winter in 2018 (summer and winter). Water, adjoining soil and plant samples were analysed for multiple elements by Inductively Coupled Plasma Mass Spectrometry. Organic and inorganic carbon were measured (CN analyser) and the major anions were determined by ion chromatography. Salinization in both the lagoon and the wells was seen with good correlations between Cl⁻, Na⁺, Li, SO₄, S, Sr, Ca, Ti (p-value < 0.05). Association of heavy metals was observed of Ni, Cu, Ag, and V, Cr, Mo, Cd which is due to contamination from anthropological activities such as wastewater disposal or spread of contaminated dust. However, looking at each elements none of them exceeds the Qatari regulation. Moreover, gypsum saturation in the system was observed in both the lagoon and wells water samples. Lagoons and the water of the well are found to be of a saline type as well as Ca²⁺, Cl⁻, SO₄²⁻ type evidencing both gypsum dissolution and salinization in the system. Moreover, Maps produced by Inverse distance weighting showed an increasing level of Nitrate in the groundwater in winter, and decrease chloride and sulphate level, indicating recharge effect after winter rain events. While E. coli and faecal bacteria were found in most of the lagoons, biological analysis for wells needs to be conducted to understand the biological contamination from lagoon water infiltration. As a conclusion, while both the lagoon and the well showed the same results, more sampling is needed to understand the impact of the lagoons on the groundwater.

Keywords: groundwater quality, lagoon, treated wastewater, water management, wastewater treatment, wetlands

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Authors: Misbah Fatma, Anam Saiyeda

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This analysis charts the introduction and expansion of research into the filter bubble phenomena over the last 10 years using a large dataset of academic publications. This bibliometric study demonstrates how interdisciplinary filter bubble research is. The identification of key authors and organizations leading the filter bubble study sheds information on collaborative networks and knowledge transfer. Relevant papers are organized based on themes including algorithmic bias, polarisation, social media, and ethical implications through a systematic examination of the literature. In order to shed light on how these patterns have changed over time, the study plots their historical history. The study also looks at how research is distributed globally, showing geographic patterns and discrepancies in scholarly output. The results of this bibliometric analysis let us fully comprehend the development and reach of filter bubble research. This study offers insights into the ongoing discussion surrounding information personalization and its implications for societal discourse, democratic participation, and the potential risks to an informed citizenry by exposing dominant themes, interdisciplinary collaborations, and geographic patterns. In order to solve the problems caused by filter bubbles and to advance a more diverse and inclusive information environment, this analysis is essential for scholars and researchers.

Keywords: bibliometric analysis, social media, social networking, algorithmic personalization, self-selection, content moderation policies and limited access to information, recommender system and polarization

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Authors: Nwachukwu M. A., Nwachukwu J. I., Anyanwu J., Emeka U., Okorondu J., Acholonu C.

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Actualization of the 17 sustainable development goals (SDGs) conceived by the United Nations in 2015 is a global challenge that may not be feasible in sub-Saharan Africa by the year 2030, except universities play a committed role. This is because; there is a need to educate the people about the concepts of sustainability and sustainable development in the region to make the desired change. Here is a sensitization paper with a model of intervention and curricular planning to allow advancement in understanding and knowledge of SDGs. This Model Center for Sustainability Studies (MCSS) will enable partnerships with institutions in Africa and in advanced nations, thereby creating a global network for sustainability studies not found in sub-Saharan Africa. MCSS will train and certify public servants, government agencies, policymakers, entrepreneurs and personnel from organizations, and students on aspects of the SDGs and sustainability science. There is a need to add sustainability knowledge into environmental education and make environmental education a compulsory course in higher institutions and a secondary school certificate exam subject in sub-Saharan Africa. MCSS has 11 training modules that can be replicated anywhere in the world.

Keywords: sustainability, higher institutions, training, SDGs, collaboration, sub-Saharan Africa

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Authors: Zafar Mahmood, Surayya Naz, Nazir Shah Khattak

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The degree of freedom relates to our capability to observe or model the energy paths within the system. Higher the number of energy paths being modeled leaves to us a higher degree of freedom, but increasing the time and modeling complexity rendering it useless for today’s world’s need for minimum time to market. Since the number of residuals that can be uniquely isolated are dependent on the number of independent outputs of the system, increasing the number of sensors required. The examples of discrete position sensors that may be used to form an array include limit switches, Hall effect sensors, optical sensors, magnetic sensors, etc. Their mechanical design can usually be tailored to fit in the transitional path of an STME in a variety of mechanical configurations. The case studies into multi-sensor system were carried out and actual data from sensors is used to test this generic framework. It is being investigated, how the proper modeling of limit switches as timing sensors, could lead to unified and neutral residual space while keeping the implementation cost reasonably low.

Keywords: low-cost limit sensors, fault diagnostics, Single Throw Mechanical Equipment (STME), parameter estimation, parity-space

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Authors: Nouha Ben Salah, Mohamed Ali Omri

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This paper treats the association between disclosure level and the cost of equity capital in Tunisian’slisted firms. This relation is tested by using two models. The first is used for testing this relation directly by regressing firm specific estimates of cost of equity capital on market beta, firm size and a measure of disclosure level. The second model is used for testing this relation by introducing information asymmetry as mediator variable. This model is suggested by Baron and Kenny (1986) to demonstrate the role of mediator variable in general. Based on a sample of 21 non-financial Tunisian’s listed firms over a period from 2000 to 2004, the results prove that greater disclosure is associated with a lower cost of equity capital. However, the results of indirect relationship indicate a significant positive association between the level of voluntary disclosure and information asymmetry and a significant negative association between information asymmetry and cost of equity capital in contradiction with our previsions. Perhaps this result is due to the biases of measure of information asymmetry.

Keywords: cost of equity capital, voluntary disclosure, information asymmetry, and Tunisian’s listed non-financial firms

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Marco Furinghetti, Alberto Pavese, Michele Rinaldi

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Concave Surface Slider devices have been more and more used in real applications for seismic protection of both bridge and building structures. Several research activities have been carried out, in order to investigate the lateral response of such a typology of devices, and a reasonably high level of knowledge has been reached. If radial analysis is performed, the frictional force is always aligned with respect to the restoring force, whereas under bidirectional seismic events, a bi-axial interaction of the directions of motion occurs, due to the step-wise projection of the main frictional force, which is assumed to be aligned to the trajectory of the isolator. Nonetheless, if non-linear time history analyses have to be performed, standard codes provide precise rules for the definition of an averagely spectrum-compatible set of accelerograms in radial conditions, whereas for bidirectional motions different combinations of the single components spectra can be found. Moreover, nowadays software for the adjustment of natural accelerograms are available, which lead to a higher quality of spectrum-compatibility and to a smaller dispersion of results for radial motions. In this endeavor a simplified design procedure is defined, for building structures, base-isolated by means of Concave Surface Slider devices. Different case study structures have been analyzed. In a first stage, the capacity curve has been computed, by means of non-linear static analyses on the fixed-base structures: inelastic fiber elements have been adopted and different direction angles of lateral forces have been studied. Thanks to these results, a linear elastic Finite Element Model has been defined, characterized by the same global stiffness of the linear elastic branch of the non-linear capacity curve. Then, non-linear time history analyses have been performed on the base-isolated structures, by applying seven bidirectional seismic events. The spectrum-compatibility of bidirectional earthquakes has been studied, by considering different combinations of single components and adjusting single records: thanks to the proposed procedure, results have shown a small dispersion and a good agreement in comparison to the assumed design values.

Keywords: concave surface slider, spectrum-compatibility, bidirectional earthquake, base isolation

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Authors: M. Kosandi, V. Susanti, N. I. Subono, E. Kartini

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This paper discusses the risk of glorification trap in combating human trafficking, as it is shown in the case of Indonesia. Based on a research on Indonesian combat against trafficking in 2017-2018, this paper shows the tendency of misinterpretation and misapplication of the Indonesian anti-trafficking law into misusing the law for glorification, to create an image of certain extent of achievement in combating human trafficking. The objective of this paper is to explain the persistent occurrence of human trafficking crimes despite the significant progress of anti-trafficking efforts of Indonesian government. The research was conducted in 2017-2018 by qualitative approach through observation, depth interviews, discourse analysis, and document study, applying the three-dimensional model for analyzing human trafficking in the source country. This paper argues that the drive for glorification of achievement in the combat against trafficking has trapped Indonesian government in the loop of misinterpretation, misapplication, and misuse of the anti-trafficking law. In return, the so-called crime against humanity remains high and tends to increase in Indonesia.

Keywords: human trafficking, anti-trafficking policy, transnational crime, source country, glorification trap

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Authors: Shereif. H. Mahmoud

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Egypt has limited water resources, and it will be under water stress by the year 2030. Therefore, Egypt should consider natural and non-conventional water resources to overcome such a problem. Rain harvesting is one solution. This Paper presents a geographic information system (GIS) methodology - based on decision support system (DSS) that uses remote sensing data, filed survey, and GIS to identify potential RWH areas. The input into the DSS includes a map of rainfall surplus, slope, potential runoff coefficient (PRC), land cover/use, soil texture. In addition, the outputs are map showing potential sites for RWH. Identifying suitable RWH sites implemented in the ArcGIS model environment using the model builder of ArcGIS 10.1. Based on Analytical hierarchy process (AHP) analysis taking into account five layers, the spatial extents of RWH suitability areas identified using Multi-Criteria Evaluation (MCE). The suitability model generated a suitability map for RWH with four suitability classes, i.e. Excellent, Moderate, Poor, and unsuitable. The spatial distribution of the suitability map showed that the excellent suitable areas for RWH concentrated in the northern part of Egypt. According to their averages, 3.24% of the total area have excellent and good suitability for RWH, while 45.04 % and 51.48 % of the total area are moderate and unsuitable suitability, respectively. The majority of the areas with excellent suitability have slopes between 2 and 8% and with an intensively cultivated area. The major soil type in the excellent suitable area is loam and the rainfall range from 100 up to 200 mm. Validation of the used technique depends on comparing existing RWH structures locations with the generated suitability map using proximity analysis tool of ArcGIS 10.1. The result shows that most of exiting RWH structures categorized as successful.

Keywords: rainwater harvesting (RWH), geographic information system (GIS), analytical hierarchy process (AHP), multi-criteria evaluation (MCE), decision support system (DSS)

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Authors: Desiree Vandenberghe, Johan Albrecht

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Non-communicable diseases (NCDs) are responsible for the vast majority of deaths in the European Union (EU) and represent a large share of total health care spending. A future increase in this health and financial burden is likely to be driven by population ageing, lifestyle changes and technological advances in medicine. Without adequate prevention measures, this burden can severely threaten population health and economic development. To tackle this challenge, a correct assessment of the current burden of NCDs is required, as well as a projection of potential increases of this burden. The contribution of this paper is to offer perspective on the evolution of the NCD burden towards the future and to give an indication of the potential of prevention policy. A Non-Homogenous, Semi-Markov model for the EU was constructed, which allowed for a projection of the cost burden for the four main NCDs (cancer, cardiovascular disease, chronic respiratory disease and diabetes mellitus) towards 2030 and 2050. This simulation is done based on multiple baseline scenarios that vary in demand and supply factors such as health status, population structure, and technological advances. Finally, in order to assess the potential of preventive measures to curb the cost explosion of NCDs, a simulation is executed which includes increased efforts for preventive health care measures. According to the Markov model, by 2030 and 2050, total costs (direct and indirect costs) in the EU could increase by 30.1% and 44.1% respectively, compared to 2015 levels. An ambitious prevention policy framework for NCDs will be required if the EU wants to meet this challenge of rising costs. To conclude, significant cost increases due to Non-Communicable Diseases are likely to occur due to demographic and lifestyle changes. Nevertheless, an ambitious prevention program throughout the EU can aid in making this cost burden manageable for future generations.

Keywords: non-communicable diseases, preventive health care, health policy, Markov model, scenario analysis

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Authors: Sandesh Yadav

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Earthquake constitutes one of the most terrible natural hazards which often turn into a disaster or causing extensive devastation and loss of human lives and their properties. In the year 2015, Nepal experienced the most devastating earthquakes on 25th April, 2015 and 12th May, 2015 respectively. Several villages, towns, human constructions and their properties, lives were completely damaged. The hazardous effect of Nepal earthquakes depends not only on their magnitude of Richter Scale on intensity alone, but also on so many factors, such as geology of earth crust (lithology, elasticity, soil condition, permissible stress, rock structures etc.). The unscientifically and non-seismically designed buildings resulted in huge loss of life and property. Further, the loss due to earthquake can be grouped into three broad categories namely agriculture sector (loss of livestock, poultry and food stocks), industrial sector (mainly brick production industry) and infrastructural sector (transportation infrastructure). The present research study begins with the tracing of Geological history of earthquakes in Nepal along with identification of causes of Nepal earthquakes, 2015. Secondly, research study identifies the extent of tremors of earthquakes of 2015 in Nepal and surrounding areas along with their sphere of impact. Thirdly, the research study tries to assess the agricultural loss, industrial loss and infrastructural loss due to earthquakes in Nepal. Lastly, the research study ends with the various recommendations and suggestions in order to minimize the loss due to earthquakes in the future.

Keywords: earthquake, richter scale, sphere of impact, tremors

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Salman Akrokayan

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Synthetic cDNA (ScDNA) of granulocyte colony-stimulating factor (G-CSF) was constructed using a DNA synthesizer with the aim to increase its expression level. 5' end of the ScDNA of G-CSF coding region was modified by decreasing the GC content without altering the predicted amino acids sequence. The identity of the resulting protein from ScDNA was confirmed by the highly specific enzyme-linked immunosorbent assay. In conclusion, a synthetic G-CSF cDNA in combination with the recombinant DNA protocol offers a rapid and reliable strategy for synthesizing the target protein. However, the commercial utilization of this methodology requires rigorous validation and quality control.

Keywords: synthetic cDNA, recombinant G-CSF, cloning, gene expression

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Authors: Neelam Patel

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Farmers in peri-urban areas of developing countries depend on wastewater for Irrigation but with great environmental and health hazards. Since, irrigation with wastewater is growing in the developing countries but its suitability to environment and other health factors should be checked. Metal pollution is a very serious issue these days, various neuro, physical and mental disorders are prevailing due to the metal pollution. Waste water contaminated with heavy metals got accumulated in the soil and then bioaccumulated in the vegetables irrigated with waste water. A 3-year field experiment on cauliflower has been done by using wastewater with two different methods of irrigation i.e. Drip and Flood irrigation and checked the impact on the cauliflower and soil quality. Heavy metals (Cr, Cu, Ni, Zn and Pb) have been studied in wastewater used for the irrigation and their accumulation in the soil and vegetable was studied. The study reveals that the concentration of heavy metals increases by 100 times from initial in soil. After 3 years, the concentration of Copper(41 ppm) Chromium(39.4 ppm) Lead(62.2ppm) Zinc(100.5 ppm) and Nickel(75.7 ppm) in Flood irrigated soil while in Drip irrigated soil , Copper (36.4 ppm) Chromium(36.8 ppm) Lead(53.7 ppm) Zinc(70.3 ppm) and Nickel (53.9 ppm). In vegetable, the wastewater irrigated shows an increase in the concentration of metals with the time and the accumulation of Nickel (6.98ppm), Lead (30.18 ppm) and Zinc (55.83 ppm) in drip irrigated while in flood irrigated, Nickel (30.58 ppm), Lead (73.95ppm) Zinc (93.50 ppm) and Copper (54.58 ppm) in edible part of cauliflower which is above the permissible limits suggested by different international agencies. On other hand, the nutrients content i.e. Nitrogen, Phosphorus and Potassium in soil was increased in concentration with time. The study pointed out that the metal contaminated waste water consisting the nutrients in it but also heavy metals which causes health issues in human. While the increase in concentration of nutrients in the soil indirectly helpful to the farmers economically by restricting the use of fertilizers. But the metal pollution directly affects the health of human being. The different method of irrigation suggested that the drip irrigated vegetable acquired less metal then the flood one and is a better combo with the waste water for the irrigation.

Keywords: drip irrigation, heavy metals, metal contamination, waste water

Procedia PDF Downloads 305