Search results for: complex network approach

Authors: Debora P. Arce, Flavia J. Krsticevic, Marco R. Bertolaccini, Joaquín Ezpeleta, Estela M. Valle, Sergio D. Ponce, Elizabeth Tapia

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Small Heat Shock Proteins (sHSPs) are low molecular weight chaperones that play an important role during stress response and development in all living organisms. Fruit maturation and oxidative stress can induce sHSP synthesis both in Arabidopsis and tomato plants. RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. In the present work, we de novo assembled the Solanum lycopersicum transcriptome for three different maturation stages (mature green, breaker and red ripe). Differential gene expression analysis was carried out during tomato fruit development. We identified 12 sHSPs differentially expressed that might be involved in breaker and red ripe fruit maturation. Interestingly, these sHSPs have different subcellular localization and suggest a complex regulation of the fruit maturation network process.

Keywords: sHSPs, maturation, tomato, RNA-Seq, assembly

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

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Authors: M. Al-Jepoori, D. Bennett

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Teaching Object-Oriented Programming (OOP) as part of a Computing-related university degree is a very difficult task; the road to ensuring that students are actually learning object oriented concepts is unclear, as students often find it difficult to understand the concept of objects and their behavior. This problem is especially obvious in advanced programming modules where Design Pattern and advanced programming features such as Multi-threading and animated GUI are introduced. Looking at the students’ performance at their final year on a university course, it was obvious that the level of students’ understanding of OOP varies to a high degree from one student to another. Students who aim at the production of Games do very well in the advanced programming module. However, the students’ assessment results of the last few years were relatively low; for example, in 2016-2017, the first quartile of marks were as low as 24.5 and the third quartile was 63.5. It is obvious that many students were not confident or competent enough in their programming skills. In this paper, the reasons behind poor performance in Advanced OOP modules are investigated, and a suggested practice for teaching OOP based on a complex case study is described and evaluated.

Keywords: complex programming case study, design pattern, learning advanced programming, object oriented programming

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Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

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Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Chia-Chi Chang

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Most battery-driven wireless sensor devices will enter in sleep mode as soon as possible to extend the overall lifetime of a sensor network. It is necessary to turn off unnecessary radio and peripheral functions, especially the radio unit always consumes more energy than other components during wireless communication. The microcontroller is the most important part of the wireless sensor device. It is responsible for the manipulation of sensing data and communication protocols. The microcontroller always has different sleep modes, each with a different level of energy usage. The deeper the sleep, the lower the energy consumption. Most wireless sensor devices can only enter the sleep mode: the external low-frequency oscillator is still running to wake up the sleeping microcontroller when the sleep timer expires. In this paper, our sensor device can enter the extended sleep mode: none of the oscillator is running and the wireless sensor device has the nanoampere consumption and self-awaking ability. Finally, these wireless sensor devices were deployed in a smart home security network.

Keywords: wireless sensor network, battery-driven, sleep mode, home security

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Authors: Zahid Ullah, Atlas Khan

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The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

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Authors: Dina Mohamed Ayman, Rafieu Akin

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In an attempt to increase the female total entrepreneurial activities (TEA) within Egypt and Nigeria, this paper aims to investigate the challenges facing female entrepreneurs operating in Egypt, in relative to Nigeria. In this regard, both researchers undertook a qualitative approach due to the scarcity of the literature reviewed on the topic; in those particular countries, and as an in-depth comparative mode. Therefore, ten Egyptian entrepreneurs in relative to ten Nigerian entrepreneurs were in-depth investigated. The research findings prove that female entrepreneurs face complex problems for being both gender and country-specific. Regarding the gender-specific obstacles, the work/life imbalance due to the scarcity of child-care nurseries and the prevalence of the gender-role division while performing the house chores rather than the concept of co-operation, acted as a main source of cultural challenge because women are considered mostly as 'housewives'. However, interestingly, this specific gender-discrimination challenge is proven to have no grounded effect in terms of the business-establishment and daily dealings neither in Egypt nor Nigeria, as one of the sample exclaimed 'as long as you pay, then no gender difference is set on the table'. Other country-specific challenges facing female entrepreneurs, lied in, the aggregate weak entrepreneurial framework governing both countries, also, women faced the difficulty of access to financial institutions with collateral requirements that are usually "hardly to be met", besides, the absence of the "micro-credit-Grameen-banks" concept. As well, the scarcity of incubators and business training centers providing network, consultancy and well-trained workforce to female entrepreneurs constitute a major hurdle for women entrepreneurs operating in both countries. Finally, this paper will conclude the research by offering a set of public-policy recommendations to pave the way for females to choose self-employment as a career path.

Keywords: entrepreneurship, female entrepreneurship, obstacles, framework conditions, culture, micro-credit

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Authors: Ashwaq Alsulami, Jianhua Shao

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Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.

Keywords: attributed community, attribute detection, community, social network

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Authors: Nassiri Khalid, El Hibaoui Abdelaaziz et Hajar Moha

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In this paper, we introduce and analyse a probabilistic distributed algorithm for a construction of a minimum spanning tree on network. This algorithm is based on the handshake concept. Firstly, each network node is considered as a sub-spanning tree. And at each round of the execution of our algorithm, a sub-spanning trees are merged. The execution continues until all sub-spanning trees are merged into one. We analyze this algorithm by a stochastic process.

Keywords: Spanning tree, Distributed Algorithm, Handshake Algorithm, Matching, Probabilistic Analysis

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Authors: Sunil Mittal, Sukhpreet Singh, Hardeep Kaur

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A simple voltammetric technique for on-line analysis of arsenite [As (III)] is reported. Owing to the affinity of As (III) with thiol group of proteins and enzymes, cysteine has been employed as reducing agent. The reduction study of As(III)-cysteine complex on indium tin oxide (ITO) electrode has been explored. The experimental parameters such as scan rate, cysteine concentration, pH etc. were optimized to achieve As (III) determination. The developed method provided dynamic linear range of detection from 0.1 to 1 mM with a detection limit of 0.1 mM. The method is applicable to environmental monitoring of As (III) from highly contaminated sources such as industrial effluents, wastewater sludge etc.

Keywords: arsenite, cysteine, linear sweep voltammetry, reduction

Procedia PDF Downloads 240

Authors: Alex Soares Castro, Matheus Manoel Teles de Menezes, Larissa Silva de Azevedo, Ana Carolina Caleffi Patelli, Osmair Vital de Oliveira, Aline Thais Bruni, Marcelo Firmino de Oliveira

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Considering the problem of the seizure of illicit drugs, as well as the development of electrochemical sensors using chemically modified electrodes, this work shows the study of the electrochemical activity of cocaine in carbon paste electrode chemically modified with Cu (II) trans 3-MeO salcn complex. In this context, cyclic voltammetry was performed on 0.1 mol.L⁻¹ KCl supporting electrolyte at a scan speed of 100 mV s⁻¹, using an electrochemical cell composed of three electrodes: Ag /AgCl electrode (filled KCl 3 mol.L⁻¹) from Metrohm® (reference electrode); a platinum spiral electrode, as an auxiliary electrode, and a carbon paste electrode chemically modified with Cu (II) trans 3-MeO complex (as working electrode). Two forms of cocaine were analyzed: cocaine hydrochloride (pH 3) and cocaine free base form (pH 8). The PM7 computational method predicted that the hydrochloride form is more stable than the free base form of cocaine, so with cyclic voltammetry, we found electrochemical signal only for cocaine in the form of hydrochloride, with an anodic peak at 1.10 V, with a linearity range between 2 and 20 μmol L⁻¹ had LD and LQ of 2.39 and 7.26x10-5 mol L⁻¹, respectively. The study also proved that cocaine is adsorbed on the surface of the working electrode, where through an irreversible process, where only anode peaks are observed, we have the oxidation of cocaine, which occurs in the hydrophilic region due to the loss of two electrons. The mechanism of this reaction was confirmed by the ab-inito quantum method.

Keywords: ab-initio computational method, analytical method, cocaine, Schiff base complex, voltammetry

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Authors: Mortez Alijani, Anas Osman

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Recently, the popularity of Unmanned aerial vehicles (UAVs) has skyrocketed amidst the unprecedented events and the global pandemic, as they play a key role in both the security and health sectors, through surveillance, taking test samples, transportation of crucial goods and spreading awareness among civilians. However, the process of designing and producing such aerial robots is suppressed by the internal and external constraints that pose serious challenges. Landing is one of the key operations during flight, especially, the autonomous landing of UAVs on a moving platform is a scientifically complex engineering problem. Typically having a successful automatic landing of UAV on a moving platform requires accurate localization of landing, fast trajectory planning, and robust control planning. To achieve these goals, the information about the autonomous landing process such as the intersection point, the position of platform/UAV and inclination angle are more necessary. In this study, the mathematical approach to this problem in the X-Y axis based on the inclination angle and position of UAV in the landing process have been presented. The experimental results depict the accurate position of the UAV, intersection between UAV and moving platform and inclination angle in the landing process, allowing prediction of the intersection point.

Keywords: autonomous landing, inclination angle, unmanned aerial vehicles, moving platform, X-Y axis, intersection point

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Authors: Zehra Sultan

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The study is based on the task-based language teaching approach which is found to be very effective in the EFL/ESL classroom. This approach engages learners to acquire the usage of authentic language skills by interacting with the real world through sequence of pedagogical tasks. The use of technology enhances the effectiveness of this approach. This study throws light on the historical background of TBLT and its efficacy in the EFL/ESL classroom. In addition, this study precisely talks about the implementation of this approach in the General Foundation Programme of Muscat College, Oman. It furnishes the list of the pedagogical tasks embedded in the language curriculum of General Foundation Programme (GFP) which are skillfully allied to the College Graduate Attributes. Moreover, the study also discusses the challenges pertaining to this approach from the point of view of teachers, students, and its classroom application. Additionally, the operational success of this methodology is gauged through formative assessments of the GFP, which is apparent in the students’ progress.

Keywords: task-based language teaching, authentic language, communicative approach, real world activities, ESL/EFL activities

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Authors: Jean Yves Trepanier, Sami Ammar, Sagnik Banik

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Lattice Boltzmann Method has been advantageous in simulating complex boundary conditions and solving for fluid flow parameters by streaming and collision processes. This paper includes the study of three different test cases in a confined domain using the method of the Lattice Boltzmann model. 1. An SRT (Single Relaxation Time) approach in the Lattice Boltzmann model is used to simulate Lid Driven Cavity flow for different Reynolds Number (100, 400 and 1000) with a domain aspect ratio of 1, i.e., square cavity. A moment-based boundary condition is used for more accurate results. 2. A Thermal Lattice BGK (Bhatnagar-Gross-Krook) Model is developed for the Rayleigh Benard convection for both test cases - Horizontal and Vertical Temperature difference, considered separately for a Boussinesq incompressible fluid. The Rayleigh number is varied for both the test cases (10^3 ≤ Ra ≤ 10^6) keeping the Prandtl number at 0.71. A stability criteria with a precise forcing scheme is used for a greater level of accuracy. 3. The phase change problem governed by the heat-conduction equation is studied using the enthalpy based Lattice Boltzmann Model with a single iteration for each time step, thus reducing the computational time. A double distribution function approach with D2Q9 (density) model and D2Q5 (temperature) model are used for two different test cases-the conduction dominated melting and the convection dominated melting. The solidification process is also simulated using the enthalpy based method with a single distribution function using the D2Q5 model to provide a better understanding of the heat transport phenomenon. The domain for the test cases has an aspect ratio of 2 with some exceptions for a square cavity. An approximate velocity scale is chosen to ensure that the simulations are within the incompressible regime. Different parameters like velocities, temperature, Nusselt number, etc. are calculated for a comparative study with the existing works of literature. The simulated results demonstrate excellent agreement with the existing benchmark solution within an error limit of ± 0.05 implicates the viability of this method for complex fluid flow problems.

Keywords: BGK, Nusselt, Prandtl, Rayleigh, SRT

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Authors: Lavinia B. Dulla

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The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data.

Keywords: fuzzy regression approach, minimum fuzziness criterion, interval regression, prediction interval

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Authors: Manas Tripathi, Arunabha Mukhopadhyay

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In Cloud computing environment, cloud servers, sometimes may crash after receiving huge amount of request and cloud services may stop which can create huge loss to users of that cloud services. This situation is called Denial of Service (DoS) attack. In Distributed Denial of Service (DDoS) attack, an attacker targets multiple network paths by compromising various vulnerable systems (zombies) and floods the victim with huge amount of request through these zombies. There are many solutions to mitigate this challenge but most of the methods allows the attack traffic to arrive at Cloud Service Provider (CSP) and then only takes actions against mitigation. Here in this paper we are rather focusing on preventive mechanism to deal with these attacks. We analyze network topology and find most vulnerable paths beforehand without waiting for the traffic to arrive at CSP. We have used Dijkstra's and Yen’s algorithm. Finally, risk assessment of these paths can be done by multiplying the probabilities of attack for these paths with the potential loss.

Keywords: cloud computing, DDoS, Dijkstra, Yen’s k-shortest path, network security

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Authors: Ather Saeed, Arif Khan, Jeffrey Gosper

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Sensor devices are prone to errors and sudden node failures, which are difficult to detect in a timely manner when deployed in real-time, hazardous, large-scale harsh environments and in medical emergencies. Therefore, the loss of data can be life-threatening when the sensed phenomenon is not disseminated due to sudden node failure, battery depletion or temporary malfunctioning. We introduce a set of partial differential equations for localizing faults, similar to Green’s and Maxwell’s equations used in Electrostatics and Electromagnetism. We introduce a node organization and clustering scheme for self-stabilizing sensor networks. Green’s theorem is applied to regions where the curve is closed and continuously differentiable to ensure network connectivity. Experimental results show that the proposed GTFD (Green’s Theorem fault-detection and Self-stabilization) protocol not only detects faulty nodes but also accurately generates network stability graphs where urgent intervention is required for dynamically self-stabilizing the network.

Keywords: Green’s Theorem, self-stabilization, fault-localization, RSSI, WSN, clustering

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Authors: Violet Petit-Steeghs, Jochem J. R. Van Roon, Jacqueline E. W. Broerse

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Last decennia the retirement age in Europe is gradually increasing. As a result, people have to continue working for a longer period of time. Health problems due to increased sedentary behavior and mental conditions like burn-out, pose a threat in fulfilling employees’ working life. In order to stimulate the ability and willingness to work in the present and future, it is important to stay vital. Vitality is regarded in literature as a sense of energy, motivation and resilience. It is assumed that by increasing their vitality, employees will stay healthier and be more satisfied with their job, leading to a more sustainable employment and less absenteeism in the future. The aim of this project is to obtain insights into the experiences and barriers of employees, and specifically office workers, with regard to their vitality. These insights are essential in order to develop appropriate measures in the future. To get more insights in the experiences of office workers on their vitality, 8 focus group discussions were organized with 6-10 office workers from 4 different employers (an university, a national construction company and a large juridical and care service organization) in the Netherlands. The discussions were transcribed and analyzed via open coding. This project is part of a larger consortium project Provita2, and conducted in collaboration with University of Technology Eindhoven. Results showed that a range of interdependent factors form a complex network that influences office workers’ vitality. These factors can be divided in three overarching groups: (1) personal (2) organizational and (3) environmental factors. Personal intrinsic factors, relating to the office worker, comprise someone’s physical health, coping style, life style, needs, and private life. Organizational factors, relating to the employer, are the workload, management style and the structure, vision and culture of the organization. Lastly, environmental factors consist of the air, light, temperature at the workplace and whether the workplace is inspiring and workable. Office workers experienced barriers to improve their own vitality due to a lack of autonomy. On the one hand, because most factors were not only intrinsic but extrinsic, like work atmosphere or the temperature in the room. On the other hand, office workers were restricted in adapting both intrinsic as well as extrinsic factors. Restrictions to for instance the flexibility of working times and the workload, can set limitations for improving vitality through personal factors like physical activity and mental relaxation. In conclusion, a large range of interdependent factors influence the vitality of office workers. Office workers are often regarded to have a responsibility to improve their vitality, but are limitedly autonomous in adapting these factors. Measures to improve vitality should therefore not only focus on increasing awareness among office workers, but also on empowering them to fulfill this responsibility. A holistic approach that takes the complex mutual dependencies between the different factors and actors (like managers, employees and HR personnel) into account is highly recommended.

Keywords: occupational health, perspectives office workers, sustainable employment, vitality at work, work & wellbeing

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Authors: Jared Oluoch

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Connected vehicles are equipped with wireless sensors that aid in Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication. These vehicles will in the near future provide road safety, improve transport efficiency, and reduce traffic congestion. One of the challenges for connected vehicles is how to ensure that information sent across the network is secure. If security of the network is not guaranteed, several attacks can occur, thereby compromising the robustness, reliability, and efficiency of the network. This paper discusses existing security mechanisms and unique properties of connected vehicles. The methodology employed in this work is exploratory. The paper reviews existing security solutions for connected vehicles. More concretely, it discusses various cryptographic mechanisms available, and suggests areas of improvement. The study proposes a combination of symmetric key encryption and public key cryptography to improve security. The study further proposes message aggregation as a technique to overcome message redundancy. This paper offers a comprehensive overview of connected vehicles technology, its applications, its security mechanisms, open challenges, and potential areas of future research.

Keywords: VANET, connected vehicles, 802.11p, WAVE, DSRC, trust, security, cryptography

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Authors: Fang-Bo Yu, Li-Bo Guan, Sheng-Dao Shan

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Biogas slurry was mixed with six commercial fungicides and screening against 11 phytopathogens was carried out. Results showed that inhibition of biogas slurry was different for the test strains and no significant difference between treatments of Didymella bryoniae, Fusarium oxysporum f. sp. vasinfectum, Aspergillus niger, Rhizoctonia cerealis, F. graminearum and Septoria tritici was observed. However, significant differences were found among Penicillium sp., Botrytis cinerea, Alternaria sonali, F. oxysporum F. sp. melonis and Sclerotinia sclerotiorum. The approach described here presents a promising alternative to current manipulation although some issues still need further examination. This study could contribute to the development of sustainable agriculture and better utilization of biogas slurry.

Keywords: anaerobic digestion, biogas slurry, phytopathogen, sustainable agriculture

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian

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To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.

Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven

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Authors: Ahmed Tarek, Ahmed Alveed

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Vehicle routing problem is a well-known re-search avenue in computing. Modern vehicle routing is more focused with the GPS-based coordinate system, as the state-of-the-art vehicle, and trucking systems are equipped with digital navigation. In this paper, a new two dimensional coordinate-based algorithm for addressing the vehicle routing problem for a supply chain network is proposed and explored, and the algorithm is compared with other available, and recently devised heuristics. For the algorithms discussed, which includes the pro-posed coordinate-based search heuristic as well, the advantages and the disadvantages associated with the heuristics are explored. The proposed algorithm is studied from the stand point of a small supermarket chain delivery network that supplies to its stores in four different states around the East Coast area, and is trying to optimize its trucking delivery cost. Minimizing the delivery cost for the supply network of a supermarket chain is important to ensure its business success.

Keywords: coordinate-based optimal routing, Hamiltonian Circuit, heuristic algorithm, traveling salesman problem, vehicle routing problem

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Authors: Ziara Carmelli G. Tan, Irene Carmelle S. Tan

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Background: Trichotillomania (TTM) and Major Depressive Disorder (MDD) are two psychiatric conditions that frequently co-occur, presenting a significant challenge for treatment due to their complex interplay. TTM involves repetitive hair-pulling, leading to noticeable hair loss and distress, while MDD is characterized by persistent low mood and loss of interest or pleasure, leading to dysfunctionality. This case report examines the intricate relationship between TTM and MDD in a young adult female, emphasizing the need for a comprehensive, multifaceted therapeutic approach to address both disorders effectively. Case Presentation: The patient is a 21-year-old female college student and youth church leader who presented with chronic hair-pulling and depressive symptoms. Her premorbid personality was marked by low self-esteem and a strong need for external validation. Despite her academic and social responsibilities and achievements, she struggled with managing her emotional distress, which was exacerbated by her family dynamics and her role within her church community. Her hair-pulling and mood symptoms were particularly triggered by self-esteem threats and feelings of inadequacy. She was diagnosed with Trichotillomania, Scalp and Major Depressive Disorder. Intervention/Management: The patient’s treatment plan was comprehensive, incorporating both pharmacological and non-pharmacological interventions. Initial pharmacologic management was Fluoxetine 20mg/day up, titrated to 40mg/day with no improvement; hence, shifted to Escitalopram 20mg/day and started with N-acetylcysteine 600mg/day with noted significant improvement in symptoms. Psychotherapeutic strategies played a crucial role in her treatment. These included supportive-expressive psychodynamic psychotherapy, which helped her explore and understand underlying emotional conflicts. Cognitive-behavioral techniques were employed to modify her maladaptive thoughts and behaviors. Grief processing was integrated to help her cope with significant losses. Family therapy was done to address conflicts and collaborate with the treatment process. Psychoeducation was provided to enhance her understanding of her condition and to empower her in her treatment journey. A suicide safety plan was developed to ensure her safety during critical periods. An interprofessional approach, which involved coordination with the Dermatology service for co-management, was also a key component of her treatment. Outcome: Over the course of 15 therapy sessions, the patient demonstrated significant improvement in both her depressive symptoms and hair-pulling behavior. Her active engagement in therapy, combined with pharmacological support, facilitated better emotional regulation and a more cohesive sense of self. Her adherence to the treatment plan, along with the collaborative efforts of the interprofessional team, contributed to her positive outcomes. Discussion: This case underscores the significance of addressing both TTM and its comorbid conditions to achieve effective treatment outcomes. The intricate interplay between TTM and MDD in the patient’s case highlights the importance of a comprehensive treatment plan that includes both pharmacological and psychotherapeutic approaches. Supportive-expressive psychodynamic psychotherapy, Cognitive-behavioral techniques, and Family therapy were particularly beneficial in addressing the complex emotional and behavioral aspects of her condition. The involvement of an interprofessional team, including dermatology co-management, was crucial in providing holistic care. Future practice should consider the benefits of such a multidisciplinary approach to managing complex cases like this, ensuring that both the psychological and physiological aspects of the disorders are adequately addressed.

Keywords: cognitive-behavioral therapy, interprofessional approach, major depressive disorder, psychodynamic psychotherapy, trichotillomania

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Authors: L. Janská, J. Kubrický

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The article describes a research focused on the influence of the information and communication technology (ICT) on the pupils' learning. The investigation deals with the influences that distinguish between the group of pupils influenced by ICT and the group of pupils not influenced by ICT. The group influenced by ICT should evince a different approach in number of areas (in managing of two and more activities at once, in a quick orientation and searching for information on the Internet, in an ability to quickly and effectively assess the data sources, in the assessment of attitudes and opinions of the other users of the network, in critical thinking, in the preference to work in teams, in the sharing of information and personal data via the virtual social networking, in insisting on the immediate reaction on their every action etc.).

Keywords: ICT influence, digital natives, pupil´s learning

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Authors: Meron Okbandrias

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The South African formal economy is rule-based economy, unlike most African and Asian markets. It has a highly developed financial market. In such a market, foreign migrants have dominated the small or spaza shops that service the poor. They are highly competitive and capture significant market share in South Africa. This paper analyses the factors that assisted the foreign migrants in having a competitive age. It does that by interviewing Somali, Bangladesh, and Ethiopian shop owners in Cape Town analysing the data through a narrative analysis. The paper also analyses the 2019 South African consumer report. The three migrant nationalities mentioned above dominate the spaza shop business and have significant distribution networks. The findings of the paper indicate that family, ethnic, and nationality based network, in that order of importance, form bases for a relationship-based business network that has trust as its mainstay. Therefore, this network ensures the pooling of resources and abiding by certain principles outside the South African rule-based system. The research identified practises like bulk buying within a community of traders, sharing information, buying from a within community distribution business, community based transportation system and providing seed capital for people from the community to start a business is all based on that relationship-based system. The consequences of not abiding by the rules of these networks are social and economic exclusion. In addition, these networks have their own commercial and social conflict resolution mechanisms aside from the South African justice system. Network theory and relationship based systems theory form the theoretical foundations of this paper.

Keywords: migrant, spaza shops, relationship-based system, South Africa

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Authors: Rejab Hajlaoui, Tarek Moulahi, Hervé Guyennet

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In a highway scenario, the vehicle speed can exceed 120 kmph. Therefore, any vehicle can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To ensure an effective stability in vehicular ad hoc networks with minimum broadcasting storm, we have developed a weighted algorithm based on the k-medoids clustering algorithm (WKCA). Indeed, the number of clusters and the initial cluster heads will not be selected randomly as usual, but considering the available transmission range and the environment size. Then, to ensure optimal assignment of nodes to clusters in both k-medoids phases, the combined weight of any node will be computed according to additional metrics including direction, relative speed and proximity. Empirical results prove that in addition to the convergence speed that characterizes the k-medoids algorithm, our proposed model performs well both AODV-Clustering and OLSR-Clustering protocols under different densities and velocities in term of end-to-end delay, packet delivery ratio, and throughput.

Keywords: communication, clustering algorithm, k-medoids, sensor, vehicular ad hoc network

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Authors: M. Ciminello, I. Dimino, S. Ameduri, A. Concilio

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Improving aircraft's efficiency is one of the key elements of Aeronautics. Modern aircraft possess many advanced functions, such as good transportation capability, high Mach number, high flight altitude, and increasing rate of climb. However, no aircraft has a possibility to reach all of this optimized performance in a single airframe configuration. The aircraft aerodynamic efficiency varies considerably depending on the specific mission and on environmental conditions within which the aircraft must operate. Structures that morph their shape in response to their surroundings may at first seem like the stuff of science fiction, but take a look at nature and lots of examples of plants and animals that adapt to their environment would arise. In order to ensure both the controllable and the static robustness of such complex structural systems, a monitoring network is aimed at verifying the effectiveness of the given control commands together with the elastic response. In order to achieve this kind of information, the use of FBG sensors network is, in this project, proposed. The sensor network is able to measure morphing structures shape which may show large, global displacements due to non-standard architectures and materials adopted. Chord -wise variations may allow setting and chasing the best layout as a function of the particular and transforming reference state, always targeting best aerodynamic performance. The reason why an optical sensor solution has been selected is that while keeping a few of the contraindication of the classical systems (like cabling, continuous deployment, and so on), fibre optic sensors may lead to a dramatic reduction of the wires mass and weight thanks to an extreme multiplexing capability. Furthermore, the use of the ‘light’ as ‘information carrier’, permits dealing with nimbler, non-shielded wires, and avoids any kind of interference with the on-board instrumentation. The FBG-based transducers, herein presented, aim at monitoring the actual shape of adaptive trailing edge. Compared to conventional systems, these transducers allow more fail-safe measurements, by taking advantage of a supporting structure, hosting FBG, whose properties may be tailored depending on the architectural requirements and structural constraints, acting as strain modulator. The direct strain may, in fact, be difficult because of the large deformations occurring in morphing elements. A modulation transducer is then necessary to keep the measured strain inside the allowed range. In this application, chord-wise transducer device is a cantilevered beam sliding trough the spars and copying the camber line of the ATE ribs. FBG sensors array position are dimensioned and integrated along the path. A theoretical model describing the system behavior is implemented. To validate the design, experiments are then carried out with the purpose of estimating the functions between rib rotation and measured strain.

Keywords: fiber optic sensor, morphing structures, strain sensor, shape reconstruction

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Authors: Alireza Ghaffari, Hassan Saghi

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Project management is more complex than managing the day-to-day affairs of an organization. When the project dimensions are broad and multiple projects have to be monitored in different locations, the integrated management becomes even more complicated. One of the main concerns of project managers is the integrated project management, which is mainly rooted in the lack of accurate and accessible information from different projects in various locations. The collection of dispersed information from various parts of the network, their integration and finally the selective reporting of this information is among the goals of integrated information systems. It can help resolve the main problem, which is bridging the information gap between executives and senior managers in the organization. Therefore, the main objective of this study is to design and implement an important subset of a project management information system in order to successfully control the cost of construction projects so that its results can be used to design raw software forms and proposed relationships between different project units for the collection of necessary information.

Keywords: financial performance, cost subsystem, PMIS, project management

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