Search results for: search data

Authors: Elham Kazemi

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Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem.

Keywords: Quadratic Assignment Problem (QAP), Discrete Cuckoo Optimization Algorithm (DCOA), meta-heuristic algorithms, optimization algorithms

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Authors: Hakim Benoumelghar

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The launch in 1999 of the open access Initiative (OAI) and the protocol for sharing metadata, OAI-PMH, in parallel with the provision of deposit platforms, open-source software, such as DSpace in 2002, which allow libraries to develop digital repositories and play a leading role in the open access movement, and by building institutional open archives alongside the theme. This study focuses on Algerian universities and their projects and platforms for digital repositories of theses and scientific papers and the possibilities of access to the university community to develop research and access to archives of scientific digital content offered by the scientific community. This contribution attempts to compare Algerian and foreign institutional deposits in developed countries in order to have development and perspectives to facilitate scientific research and give more possibilities to the scientific community in documentary matters.

Keywords: digital repository, repository software, university, algeria

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Authors: Sajid Ali, Dietrich Schröder

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The merging of internet and Web has created many disciplines and Web GIS is one these disciplines which is effectively dealing with the geospatial data in a proficient way. Web GIS technologies have provided an easy accessing and sharing of geospatial data over the internet. However, there is a single platform for easy and multiple accesses of the data lacks for the European Caribbean Association (Europaische Karibische Gesselschaft - EKG) to assist their members and other research community. The technique presented in this paper deals with designing of a geodatabase using PostgreSQL/PostGIS as an object oriented relational database management system (ORDBMS) for competent dissemination and management of spatial data and Web GIS by using OpenGeo Suite for the fast sharing and distribution of the data over the internet. The characteristics of the required design for the geodatabase have been studied and a specific methodology is given for the purpose of designing the Web GIS. At the end, validation of this Web based geodatabase has been performed over two Desktop GIS software and a web map application and it is also discussed that the contribution has all the desired modules to expedite further research in the area as per the requirements.

Keywords: desktop GISSoftware, European Caribbean association, geodatabase, OpenGeo suite, postgreSQL/PostGIS, webGIS, web map application

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Authors: Martin Arguelles Perez, Woosoon Yim, Alexander Barzilov

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High-fidelity radiation monitoring is an essential component in the enhancement of the situational awareness capabilities of the Department of Energy’s Office of Environmental Management (DOE-EM) personnel. In this paper, multiple units of unmanned aerial vehicles (UAVs) each equipped with a cadmium zinc telluride (CZT) gamma-ray sensor are used for radiation source localization, which can provide vital real-time data for the EM tasks. To achieve this goal, a fully autonomous system of multicopter-based UAV swarm in 3D tetrahedron formation is used for surveying the area of interest and performing radiation source localization. The CZT sensor used in this study is suitable for small-size multicopter UAVs due to its small size and ease of interfacing with the UAV’s onboard electronics for high-resolution gamma spectroscopy enabling the characterization of radiation hazards. The multicopter platform with a fully autonomous flight feature is suitable for low-altitude applications such as radiation contamination sites. The conventional approach uses a single UAV mapping in a predefined waypoint path to predict the relative location and strength of the source, which can be time-consuming for radiation localization tasks. The proposed UAV swarm-based approach can significantly improve its ability to search for and track radiation sources. In this paper, two approaches are developed using (a) 2D planar circular (3 UAVs) and (b) 3D tetrahedron formation (4 UAVs). In both approaches, accurate estimation of the gradient vector is crucial for heading angle calculation. Each UAV carries the CZT sensor; the real-time radiation data are used for the calculation of a bulk heading vector for the swarm to achieve a UAV swarm’s source-seeking behavior. Also, a spinning formation is studied for both cases to improve gradient estimation near a radiation source. In the 3D tetrahedron formation, a UAV located closest to the source is designated as a lead unit to maintain the tetrahedron formation in space. Such a formation demonstrated a collective and coordinated movement for estimating a gradient vector for the radiation source and determining an optimal heading direction of the swarm. The proposed radiation localization technique is studied by computer simulation and validated experimentally in the indoor flight testbed using gamma sources. The technology presented in this paper provides the capability to readily add/replace radiation sensors to the UAV platforms in the field conditions enabling extensive condition measurement and greatly improving situational awareness and event management. Furthermore, the proposed radiation localization approach allows long-term measurements to be efficiently performed at wide areas of interest to prevent disasters and reduce dose risks to people and infrastructure.

Keywords: radiation, unmanned aerial system(UAV), source localization, UAV swarm, tetrahedron formation

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Authors: Bylhah Mugotitsa, Jim Todd, Agnes Kiragga, Jay Greenfield, Evans Omondi, Lukoye Atwoli, Reinpeter Momanyi

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The INSPIRE network aims to build an open, ethical, sustainable, and FAIR (Findable, Accessible, Interoperable, Reusable) data science platform, particularly for longitudinal mental health (MH) data. While studies have been done at the clinical and population level, there still exists limitations in data and research in LMICs, which pose a risk of underrepresentation of mental disorders. It is vital to examine the existing longitudinal MH data, focusing on how FAIR datasets are. This landscape analysis aimed to provide both overall level of evidence of availability of longitudinal datasets and degree of consistency in longitudinal studies conducted. Utilizing prompters proved instrumental in streamlining the analysis process, facilitating access, crafting code snippets, categorization, and analysis of extensive data repositories related to depression, anxiety, and psychosis in Africa. While leveraging artificial intelligence (AI), we filtered through over 18,000 scientific papers spanning from 1970 to 2023. This AI-driven approach enabled the identification of 228 longitudinal research papers meeting inclusion criteria. Quality assurance revealed 10% incorrectly identified articles and 2 duplicates, underscoring the prevalence of longitudinal MH research in South Africa, focusing on depression. From the analysis, evaluating data and metadata adherence to FAIR principles remains crucial for enhancing accessibility and quality of MH research in Africa. While AI has the potential to enhance research processes, challenges such as privacy concerns and data security risks must be addressed. Ethical and equity considerations in data sharing and reuse are also vital. There’s need for collaborative efforts across disciplinary and national boundaries to improve the Findability and Accessibility of data. Current efforts should also focus on creating integrated data resources and tools to improve Interoperability and Reusability of MH data. Practical steps for researchers include careful study planning, data preservation, machine-actionable metadata, and promoting data reuse to advance science and improve equity. Metrics and recognition should be established to incentivize adherence to FAIR principles in MH research

Keywords: longitudinal mental health research, data sharing, fair data principles, Africa, landscape analysis

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Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

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Authors: Alicia Regodon Puyalto, Alfonso Garcia-Santos

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This article presents a method to analyze the use of indoor spaces based on data analytics obtained from inbuilt digital devices. The study uses the data generated by the in-place devices, such as smart locks, Wi-Fi routers, and electrical sensors, to gain additional insights on space occupancy, user behaviour, and comfort. Those devices, originally installed to facilitate remote operations, report data through the internet that the research uses to analyze information on human real-time use of spaces. Using an in-place Internet of Things (IoT) network enables a faster, more affordable, seamless, and scalable solution to analyze building interior spaces without incorporating external data collection systems such as sensors. The methodology is applied to a real case study of coliving, a residential building of 3000m², 7 floors, and 80 users in the centre of Madrid. The case study applies the method to classify IoT devices, assess, clean, and analyze collected data based on the analysis framework. The information is collected remotely, through the different platforms devices' platforms; the first step is to curate the data, understand what insights can be provided from each device according to the objectives of the study, this generates an analysis framework to be escalated for future building assessment even beyond the residential sector. The method will adjust the parameters to be analyzed tailored to the dataset available in the IoT of each building. The research demonstrates how human-centered data analytics can improve the future spatial design of indoor spaces.

Keywords: in-place devices, IoT, human-centred data-analytics, spatial design

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Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

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With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

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This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: Dale Dzemydiene, Aurelija Burinskiene, Arunas Miliauskas, Kristina Ciziuniene

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Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Keywords: multi-objective, analysis, data flow, freight delivery, methodology

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Authors: Anna Demian, Levi Howard, L. Ng, Leslie Simon, Mark Dragon, A. Desai, Timothy Devlantes, W. David Freeman

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Introduction: Out-of-hospital cardiac arrest (OHCA) can carry a high mortality. For survivors, the most common complication is hypoxic-ischemic brain injury (HIBI). Rarely, lumbosacral spinal cord and/or other spinal cord artery ischemia can occur due to anatomic variation and variable mean arterial pressure after the return of spontaneous circulation. We present a case of an OHCA survivor who later woke up with bilateral leg weakness with preserved sensation (ASIA grade B, L2 level). Methods: We describe a clinical, radiographic, and laboratory presentation, as well as a National Library of Medicine (NLM) search engine methodology, characterizing incidence/prevalence of this entity is discussed. A 70-year-old male, a longtime smoker, and alcohol user, suddenly collapsed at a bar surrounded by friends. He had complained of chest pain before collapsing. 911 was called. EMS arrived, and the patient was in pulseless electrical activity (PEA), cardiopulmonary resuscitation (CPR) was initiated, and the patient was intubated, and a LUCAS device was applied for continuous, high-quality CPR in the field by EMS. In the ED, central lines were placed, and thrombolysis was administered for a suspected Pulmonary Embolism (PE). It was a prolonged code that lasted 90 minutes. The code continued with the eventual return of spontaneous circulation. The patient was placed on an epinephrine and norepinephrine drip to maintain blood pressure. ECHO was performed and showed a “D-shaped” ventricle worrisome for PE as well as an ejection fraction around 30%. A CT with PE protocol was performed and confirmed bilateral PE. Results: The patient woke up 24 hours later, following commands, and was extubated. He was found paraplegic below L2 with preserved sensation, with hypotonia and areflexia consistent with “spinal shock” or anterior spinal cord syndrome. MRI thoracic and lumbar spine showed a conus medullaris level spinal cord infarction. The patient was given IV steroids upon initial discovery of cord infarct. NLM search using “cardiac arrest” and “spinal cord infarction” revealed 57 results, with only 8 review articles. Risk factors include age, atherosclerotic disease, and intraaortic balloon pump placement. AoA (Artery of Adamkiewicz) anatomic variation along with existing atherosclerotic factors and low perfusion were also known risk factors. Conclusion: Acute paraplegia from anterior spinal cord infarction of the AoA territory after cardiac arrest is rare. Larger prospective, multicenter trials are needed to examine potential interventions of hypothermia, lumbar drains, which are sometimes used in aortic surgery to reduce ischemia and/or other neuroprotectants.

Keywords: cardiac arrest, spinal cord infarction, artery of Adamkiewicz, paraplegia

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Authors: Rihab Ben Lamine, Raoudha Ben Jemaa, Ikram Amous Ben Amor

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This paper proposes a graph planning technique for semantic adaptable Web Services composition. First, we use an ontology based context model for extending Web Services descriptions with information about the most suitable context for its use. Then, we transform the composition problem into a semantic context aware graph planning problem to build the optimal service composition based on user's context. The construction of the planning graph is based on semantic context aware Web Service discovery that allows for each step to add most suitable Web Services in terms of semantic compatibility between the services parameters and their context similarity with the user's context. In the backward search step, semantic and contextual similarity scores are used to find best composed Web Services list. Finally, in the ranking step, a score is calculated for each best solution and a set of ranked solutions is returned to the user.

Keywords: semantic web service, web service composition, adaptation, context, graph planning

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Authors: Amjad Khan

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The security of Vehicular ad hoc networking is of great importance as it involves serious life threats. Thus to provide secure communication amongst Vehicles on road, the conventional security system is not enough. It is necessary to prevent the network resources from wastage and give them protection against malicious nodes so that to ensure the data bandwidth availability to the legitimate nodes of the network. This work is related to provide a non conventional security system by introducing some constraints to minimize the DoS (Denial of services) especially data and bandwidth. The data packets received by a node in the network will pass through a number of tests and if any of the test fails, the node will drop those data packets and will not forward it anymore. Also if a node claims to be the nearest node for forwarding emergency messages then the sender can effectively identify the true or false status of the claim by using these constraints. Consequently the DoS(Denial of Services) attack is minimized by the instant availability of data without wasting the network resources.

Keywords: black hole attack, grey hole attack, intransient traffic tempering, networking

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Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

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Authors: Abdalla Abdelnabi, Yousf Abushalah

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The G Formation is a major gas producing reservoir in the SM Field, eastern, Libya. It is called G limestone because it consists of shallow marine limestone. Well data and 3D-Seismic in conjunction with the results of a previous study were used to delineate the hydrocarbon reservoir of Middle Eocene G-Formation of SM Field area. The data include three-dimensional seismic data acquired in 2009. It covers approximately an area of 75 mi² and with more than 9 wells penetrating the reservoir. Seismic data are used to identify any stratigraphic and structural and features such as channels and faults and which may play a significant role in hydrocarbon traps. The well data are used to calculation petrophysical analysis of S field. The average porosity of the Middle Eocene G Formation is very good with porosity reaching 24% especially around well W 6. Average water saturation was calculated for each well from porosity and resistivity logs using Archie’s formula. The average water saturation for the whole well is 25%. Structural mapping of top and bottom of Middle Eocene G formation revealed the highest area in the SM field is at 4800 ft subsea around wells W4, W5, W6, and W7 and the deepest point is at 4950 ft subsea. Correlation between wells using well data and structural maps created from seismic data revealed that net thickness of G Formation range from 0 ft in the north part of the field to 235 ft in southwest and south part of the field. The gas water contact is found at 4860 ft using the resistivity log. The net isopach map using both the trapezoidal and pyramid rules are used to calculate the total bulk volume. The original gas in place and the recoverable gas were calculated volumetrically to be 890 Billion Standard Cubic Feet (BSCF) and 630 (BSCF) respectively.

Keywords: 3D seismic data, well logging, petrel, kingdom suite

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Authors: Kapil Pandey, Vishnu Goyal

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Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

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Authors: Fangfang Wang, Tianjing Wang, Leyi Fu, Feng Yun, Ningning Xie, Jue Zhou, Fan Qu

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Background: Ferroptosis, a recently discovered form of programmed cell death characterized by iron-dependent lipid peroxidation, may be linked to polycystic ovary syndrome (PCOS). Diseases marked by iron overload have been correlated with ferroptosis. Coincidently, investigations have revealed anomalies in iron metabolism among women with PCOS; however, there were inconsistencies in the evidence. Objective and Rationale: This review aimed to comprehensively explore the potential relationship between ferroptosis and PCOS by investigating the differences in iron metabolism among women with PCOS in comparison to a control group. Additionally, a narrative synthesis was provided on the past research status regarding the association between PCOS and ferroptosis. Methods: A systematic search of the literature was performed using PubMed, Embase, Web of Science from inception up to December 2022. Search terms relating to assisted PCOS, ferroptosis, and iron metabolism were used. PRISMA guidance was followed. RevMan 5.4 was utilized for conducting the meta-analysis, wherein the investigated outcomes included iron status (ferritin, iron, transferrin saturation) and a systemic iron-regulatory hormone (hepcidin). A narrative synthesis was performed to explore the correlation between PCOS and ferroptosis. Results: In the meta-analysis comprising a total of 16 studies, significant differences in serum ferritin levels between the PCOS group and the control group were observed (15 studies, standardized mean difference (SMD): 0.41, 95% CI: 0.22 to 0.59, P<0.01). This indicates elevated serum ferritin levels in PCOS patients compared to women without PCOS. The transferrin saturation in PCOS patients was significantly higher than that in the control group (3 studies, mean difference (MD): 4.39, 95% CI: 1.67 to 7.11, P<0.01). Regarding serum iron (6 studies, SMD: 0.05, 95% CI: -0.24 to 0.33, P=0.75) and serum hepcidin (4 studies, SMD: -0.44, 95% CI: -1.41 to 0.52, P=0.37), no statistically significant differences were observed between the PCOS group and the control group. Other studies have found that ferroptosis is involved in the occurrence and development of PCOS, offering valuable insights for guiding potential treatment measures and prognosis evaluation of PCOS. In addition, ferroptosis is involved in the miscarriage of PCOS-like rats; thus, controlling ferroptosis might improve pregnancy outcomes in PCOS. Conclusions: The observation of a significant elevation in serum ferritin and transferrin saturation levels in women with PCOS may suggest an underlying disturbance in iron metabolism, potentially inducing the activation of ferroptosis. Further research is imperative to elucidate the underlying pathophysiology, providing insights for potential preventive measures and therapeutic strategies. Limitation: There are some limitations as follows: First, due to limited extractable information, we excluded purely abstract publications and non-English publications. Second, the majority of original articles were case-control studies, making it difficult to determine the causal relationship between iron metabolism abnormalities and the onset of PCOS. Third, there is substantial heterogeneity in the definition of PCOS.

Keywords: polycystic ovary syndrome, ferroptosis, iron metabolism, systematic review and meta-analysis

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Authors: M. Fikriansyah Wicaksono, Rafael Arief Budiman, M. Very Setiawan

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Currently, a wide range of innovative mobile library (M-Library) service is provided for the users in the library. The M-Library service is an innovation that aims to bring the collections of the library to users who currently use their smartphone so often. With M-Library services, it is expected that the users can fulfill their information needs more conveniently and practically. This study aims to find out how users use M-Library services provided by UGM library. This study applied a quantitative approach to investigate how to use the application M-Library. The Technology Acceptance Model (TAM) theory is applied to perform the analysis in terms of perceived usefulness, perceived ease of use, attitude towards behavior, behavioral intention and actual system usage. The results show that overall the users found that the M-Library application is useful to meet their information needs. Such as facilitate user to access e-resources, search UGM library collections, online booking collections, and reminder for returning book.

Keywords: m-library, mobile library services, technology acceptance, library of UGM

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Authors: Hailye Tekleselassie

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Edge computing is a spread computing outline that transports initiative applications closer to data sources such as IoT devices or local edge servers, and possible happenstances would skull the action of new technologies. However, this investigation was attained to investigation the consciousness of technology and communications organization workers and computer users who support the service cloud. Surveys were used to achieve these objectives. Surveys were intended to attain these aims, and it is the functional using survey. Enquiries about confidence are also a key question. Problems like data privacy, integrity, and availability are the factors affecting the company’s acceptance of the service cloud.

Keywords: IoT, data, security, edge computing

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Authors: Amirhossein Mohajerzadeh, Abolghasem Mohajerzadeh

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In this paper, target parameter is estimated with desirable precision in hierarchical wireless sensor networks (WSN) while the proposed algorithm also tries to prolong network lifetime as much as possible, using efficient data collecting algorithm. Target parameter distribution function is considered unknown. Sensor nodes sense the environment and send the data to the base station called fusion center (FC) using hierarchical data collecting algorithm. FC builds underlying phenomena based on collected data. Considering the aggregation level, x, the goal is providing the essential infrastructure to find the best value for aggregation level in order to prolong network lifetime as much as possible, while desirable accuracy is guaranteed (required sample size is fully depended on desirable precision). First, the sample size calculation algorithm is discussed, second, the average queue length based on M/M[x]/1/K queue model is determined and it is used for energy consumption calculation. Nodes can decrease transmission cost by aggregating incoming data. Furthermore, the performance of the new algorithm is evaluated in terms of lifetime and estimation accuracy.

Keywords: aggregation, estimation, queuing, wireless sensor network

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Authors: Cuitao Zhang, Xiongwen He

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According to the new requirements of the future spacecraft, such as networking, modularization and non-cable, this paper studies the CCSDS wireless communications standards, and focuses on the low data-rate wireless communications for spacecraft monitoring and control. The application fields and advantages of wireless communications are analyzed. Wireless communications technology has significant advantages in reducing the weight of the spacecraft, saving time in spacecraft integration, etc. Based on this technology, a scheme for spacecraft data system is put forward. The corresponding block diagram and key wireless interface design of the spacecraft data system are given. The design proposal of the wireless node and information flow of the spacecraft are also analyzed. The results show that the wireless communications scheme is reasonable and feasible. The wireless communications technology can meet the future spacecraft demands in networking, modularization and non-cable.

Keywords: Consultative Committee for Space Data Systems (CCSDS) standards, information flow, non-cable, spacecraft, wireless communications

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Authors: Shrey Sharma, Gunjan Kumar Verma

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High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.

Keywords: inversion, limitations, optimization, resistivity

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Authors: Wang Yang

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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map

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Authors: Estela Mayra de Moura Vianna, Thiago José Tavares Ávila, Bruno Morais Silva, Diego Henrique Bezerra, Paulo Henrique Gomes Silva, Alan Pedro da Silva

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The Brazilian Federal Constitution defines the access to information as a crucial right of the citizen and the Law on Access to Public Information, which regulates this right. Accordingly, the Fiscal Responsibility Act, 2000, amended in 2009 by the “Law of Transparency”, began demanding a wider disclosure of public accounts for the society, including electronic media for public access. Thus, public entities began to create "Transparency Portals," which aim to gather a diversity of data and information. However, this information, in general, is still published in formats that do not simplify understanding of the data by citizens and that could be better especially available for audit purposes. In this context, a proposal of ontology about Brazilian Transparency Portal can play a key role in how these data will be better available. This study aims to identify and implement in ontology, the data model about Transparency Portal ecosystem, with emphasis in activities that use these data for some applications, like audits, press activities, social government control, and others.

Keywords: audit, government transparency, ontology, public sector

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Authors: Fakhreddin El Wali Elalaoui, Maatouk Mustapha

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The problem of global climate change isbecoming more and more serious. Therefore, there is a growing interest in renewable energy sources to minimize the impact of this phenomenon. Environmental policies are changing in different countries, including Morocco, with a greater focus on the integration and development of renewable energy projects. The purpose of this paper is to evaluate the potential of solar power plants in Morocco based on two technologies: concentrated solar power (CSP) and photovoltaics (PV). In order to perform an accurate search, we must follow a certain method to select the correct criteria. Four selection criteria were retained: climate, topography, location, and water resources. AnalyticHierarchy Process (AHP) was used to calculate the weight/importance of each criterion. Once obtained, weights are applied to the map for each criterion to produce a final ranking that ranks regions according to their potential. The results show that Morocco has strong potential for both technologies, especially in the southern region. Finally, this work is the first in the field to include the whole of Morocco in the study area.

Keywords: PV, Csp, solar energy, GIS

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Authors: Loh Fu Quan, Fong Zi Heng, Burra Venkata Durga Kumar

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Cloud computing has completely transformed the way many businesses operate. Traditionally, confidential data of a business is stored in computers located within the premise of the business. Therefore, a lot of business capital is put towards maintaining computing resources and hiring IT teams to manage them. The advent of cloud computing changes everything. Instead of purchasing and managing their infrastructure, many businesses have started to shift towards working with the cloud with the help of a cloud service provider (CSP), leading to cost savings. However, it also introduces security risks. This research paper focuses on the security risks that arise during data migration and user authentication in cloud computing. To overcome this problem, this paper provides a comprehensive framework that includes Transport Layer Security (TLS), user authentication, security tokens and multi-level data encryption. This framework aims to prevent authorized access to cloud resources and data leakage, ensuring the confidentiality of sensitive information. This framework can be used by cloud service providers to strengthen the security of their cloud and instil confidence in their users.

Keywords: Cloud computing, Cloud security, Cloud security issues, Cloud security framework

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Authors: Lam Hong Lan, Gabor Sarlos

Abstract:

It is hard to search for best practices in promotion for local fashion brands in Vietnam as the industry is still very young. Local fashion start-ups have grown quickly in the last five years, thanks in part to the internet and social media. However, local designer/owners can face a huge challenge when competing with international brands in the Vietnamese market – and few local case studies are available for guidance. In response, this paper studied how local small- to medium-sized enterprises (SMEs) promote to their target customers in order to compete with international brands. Knowledge of both successful and unsuccessful approaches generated by this study is intended to both contribute to the academic literature on local fashion in Vietnam as well as to help local designers to learn from and improve their brand-building strategy. The primary study featured qualitative data collection via semi-structured depth interviews. Transcription and data analysis were conducted manually in order to identify success factors that local brands should consider as part of their promotion strategy. Purposive sampling of SMEs identified five designers in Ho Chi Minh City (the biggest city in Vietnam) and three designers in Hanoi (the second biggest) as interviewees. Participant attributes included: born in the 1980s or 1990s; familiar with internet and social media; designer/owner of a successful local fashion brand in the key middle market and/or mass market segments (which are crucial to the growth of local brands). A secondary study was conducted using social listening software to gather further qualitative data on what were considered to be successful or unsuccessful approaches to local fashion brand promotion on social media. Both the primary and secondary studies indicated that local designers had maximized their promotion budget by using owned media and earned media instead of paid media. Findings from the qualitative interviews indicate that internet and social media have been used as effective promotion platforms by local fashion start-ups. Facebook and Instagram were the most popular social networks used by the SMEs interviewed, and these social platforms were believed to offer a more affordable promotional strategy than traditional media such as TV and/or print advertising. Online stores were considered an important factor in helping the SMEs to reach customers beyond the physical store. Furthermore, a successful online store allowed some SMEs to reduce their business rental costs by maintaining their physical store in a cheaper, less central city area as opposed to a more traditional city center store location. In addition, the small comparative size of the SMEs allowed them to be more attentive to their customers, leading to higher customer satisfaction and rate of return. In conclusion, this study found that these kinds of cost savings helped the SMEs interviewed to focus their scarce resources on producing unique, high-quality collections in order to differentiate themselves from international brands. Facebook and Instagram were the main platforms used for promotion and brand-building. The main challenge to this promotion strategy identified by the SMEs interviewed was to continue to find innovative ways to maximize the impact of a limited marketing budget.

Keywords: Vietnam, SMEs, fashion brands, promotion, marketing, social listening

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Samia Kouki

Abstract:

Solving NP-hard combinatorial optimization problems by exact search methods, such as Branch-and-Bound, may degenerate to complete enumeration. For that reason, exact approaches limit us to solve only small or moderate size problem instances, due to the exponential increase in CPU time when problem size increases. One of the most promising ways to reduce significantly the computational burden of sequential versions of Branch-and-Bound is to design parallel versions of these algorithms which employ several processors. This paper describes a parallel Branch-and-Bound algorithm called GALB for solving the classical permutation flowshop scheduling problem as well as its implementation on a Grid computing infrastructure. The experimental study of our distributed parallel algorithm gives promising results and shows clearly the benefit of the parallel paradigm to solve large-scale instances in moderate CPU time.

Keywords: grid computing, permutation flow shop problem, branch and bound, load balancing

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Authors: Eyob Seife, Tesfalem Teshome, Bereket Seyoum, Behailu Getachew, Yohans Demis

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Developing countries continue to face preventable communicable diseases, such as vaccine-preventable diseases. The Expanded Programme on Immunization (EPI) was established by the World Health Organization in 1974 to control these diseases. Health data use is crucial in decision-making, but ensuring data quality remains challenging. The study aimed to assess the accuracy ratio, timeliness, and quality index of regular immunization programme data in the Birkod district of the Somali Region, Ethiopia. For poor data quality, technical, contextual, behavioral, and organizational factors are among contributors. The study used a quantitative cross-sectional design conducted in September 2022GC using WHO-recommended data quality self-assessment tools. The accuracy ratio and timeliness of reports on regular immunization programmes were assessed for two health centers and three health posts in the district for one fiscal year. Moreover, the quality index assessment was conducted at the district level and health facilities by trained assessors. The study found poor data quality in the accuracy ratio and timeliness of reports at all health units, which includes zeros. Overreporting was observed for most facilities, particularly at the health post level. Health centers showed a relatively better accuracy ratio than health posts. The quality index assessment revealed poor quality at all levels. The study recommends that responsible bodies at different levels improve data quality using various approaches, such as the capacitation of health professionals and strengthening the quality index components. The study highlighted the need for attention to data quality in general, specifically at the health post level, and improving the quality index at all levels, which is essential.

Keywords: Birkod District, data quality, quality index, regular immunization programme, Somali Region-Ethiopia

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