Search results for: trans-european transport network

Authors: Jie Zhang, Chaomin Zhang

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Based on the network theory, this paper selects three Emerging-Market Multinationals Enterprises (EMNEs) as the research object and takes the typical overseas market opportunities constructed by them as the analysis unit to research the internal mechanism of overseas market opportunity construction of the EMNEs. The results show that: (1) EMNEs overseas market opportunity construction is a complex process, through the continuous interaction between enterprises and entities in the internal and external networks to achieve opportunity prototype, opportunity creation, and opportunity optimization in overseas markets. (2) Governments, foreign institutions and industry associations in the institutional network and competitors, partners, and customers in the commercial networks are the important entities in the construction of overseas market opportunities. Through the interaction of entity perception, relationship construction, and utilization, enterprises can obtain the necessary information, resources, and political asylum in the process of opportunity construction. (3) Organizations, project teams, and organizational sub-units within the enterprise are important internal entities for the construction of overseas market opportunities. Through the connection between different entities, they can achieve the circulation of resources within the organization and promote the opportunity construction of overseas markets. The research conclusions expand the relevant research on international opportunities and have inspiring and guiding significance for the expansion of EMNEs overseas markets.

Keywords: international (overseas) opportunities, opportunity construction, network entities, interaction, resource circulation

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Authors: Satyabrata Bera, Mintu Mondal

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Two-dimensional (2D) ferromagnetic materials have garnered significant attention due to their potential to host intriguing scientific phenomena such as the anomalous Hall effect, anomalous Nernst effect, and high transport spin polarization. This study focuses on the investigation of air-stable van der Waals(vdW) ferromagnets, FeGeTe₂ (FₙGT with n = 3, 4, and 5). Particular emphasis is placed on the Fe4GeTe2 (F4GT) compound, which exhibits a complex and fascinating magnetic behavior characterized by two distinct transitions: (i) paramagnetic (PM) to ferromagnetic (FM) around T C ∼ 270 K, and (ii) another spins reorientation transition (SRT) at T SRT ∼ 100 K . Scaling analysis of magnetocaloric effect confirms the second-order character of the ferromagnetic transition, while the same analysis at T SRT suggests that SRT is first-order phase transition. Moreover, the F4GT exhibits a large anomalous Hall conductivity (AHC), ∼ 490 S/cm at 2 K . The near-quadratic behavior of the anomalous Hall resistivity with the longitudinal resistivity suggests that a dominant AHC contribution arises from an intrinsic Berry curvature (BC) mechanism. Electronic structure calculations reveal a significant BC resulting from SOC-induced gapped nodal lines around the Fermi level, thereby giving rise to large AHC. Additionally, we reported exceptionally large anomalous Hall angle (≃ 10.6%) and Hall factor (≃ 0.22 V −1 ) values, the largest observed within this vdW family. The findings presented here, provide valuable insights into the fascinating magnetic and transport properties of 2D ferromagnetic materials, in particular, FₙGT family.

Keywords: 2D vdW ferromagnet, spin reorientation transition, anomalous hall effect, berry curvature

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Authors: Muhammad Akbar Hussain, Muhammad Mehdi Hussain, Waqar Haider

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There are tens of thousands of undetected and uncatalogued pieces of space debris in the Low Earth Orbit (LEO). They are not only difficult to be detected and tracked, their sheer number puts active satellites and humans in orbit around Earth into danger. With the entry of more governments and private companies into harnessing the Earth’s orbit for communication, research and military purposes, there is an ever-increasing need for not only the detection and cataloguing of these pieces of space debris, it is time to take measures to take them out and clean up the space around Earth. Current optical and radar-based Space Situational Awareness initiatives are useful mostly in detecting and cataloguing larger pieces of debris mainly for avoidance measures. Smaller than 10 cm pieces are in a relatively dark zone, yet these are deadly and capable of destroying satellites and human missions. A network of mobile observatories, connected to each other in real time and working in unison as a single instrument, may be able to detect small pieces of debris and achieve effective triangulation to help create a comprehensive database of their trajectories and parameters to the highest level of precision. This data may enable ground-based laser systems to help deorbit individual debris. Such a network of observatories can join current efforts in detection and removal of space debris in Earth’s orbit.

Keywords: space debris, low earth orbit, mobile observatories, triangulation, seamless operability

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Mayada Mazher, Ahmed Moustafa, Ahmed Abdellatif

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Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing.

Keywords: autophagy related proteins, integrated network analysis, macrophages polarization M1 and M2, tissue remodelling

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Authors: K. Hołda, D. Śliwa, K. Daniec, A. Nawrat

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This article was created as part of the developed master's thesis. It attempts to present a newly developed device, which will support the work of specialists dealing with broadly understood cybersecurity terms. The device is contrived to automate security tests. In addition, it simulates potential cyberattacks in the most realistic way possible, without causing permanent damage to the network, in order to maximize the quality of the subsequent corrections to the tested network systems. The proposed solution is a fully operational prototype created from commonly available electronic components and a single board computer. The focus of the following article is not only put on the hardware part of the device but also on the theoretical and applicatory way in which implemented cybersecurity tests operate and examples of their results.

Keywords: Raspberry Pi, ethernet, automated cybersecurity tests, ARP, DNS, backdoor, TCP, password sniffing

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Authors: Aulia Dwi Nastiti

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A number of studies and reports on terrorism have continuously addressed the role of internet and online activism to support terrorist and extremist groups. In particular, they stress on social media’s usage that generally serves for the terrorist’s propaganda as well as justification of their causes. While those analyses are important to understand how social media is a vital tool for global network terrorism, they are inadequate to explain the online communication patterns that enable terrorism acts. Beyond apparent online narratives, there is a deep cyber sphere where the very vein of terrorism movement lies. That is a hidden space in the internet called ‘darknet’. Recent investigations, particularly in Middle Eastern context, have shed some lights that this invisible space in the internet is fundamental to maintain the terrorist activities. Despite that, limited number of research examines darknet within the issue of terrorist movements in Indonesian context—where the country is considered quite a hotbed for extremist groups. Therefore, this paper attempts to provide an insight of darknet operation in Indonesian cases. By exploring how the darknet is used by the Indonesian-based extremist groups, this paper maps out communication patterns of terrorist groups on the internet which appear as an intermingled network. It shows the usage of internet is differentiated in different level of anonymity for distinctive purposes. This paper further argues that the emerging terrorist communication network calls for a more comprehensive counterterrorism strategy on the Internet.

Keywords: communication pattern, counterterrorism, darknet, extremist groups, terrorism

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Authors: Deborah Weidner, Melissa Morgera

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The model of care delivery in the Behavioral Health Network (BHN) is integrated across all five regions of Hartford Healthcare and thus spans the entirety of the state of Connecticut, with care provided in seven inpatient settings and over 30 ambulatory outpatient locations. While safety has been a core priority of the BHN in alignment with High Reliability practices, safety initiatives have historically been facilitated locally in each region or within each entity, with interventions implemented locally as opposed to throughout the network. To address this, the BHN introduced a network wide Safety Huddle during 2022. Launched in January, the BHN Safety Huddle brought together internal stakeholders, including medical and administrative leaders, along with executive institute leadership, quality, and risk management. By bringing leaders together and introducing a network-wide safety huddle into the way we work, the benefit has been an increase in awareness of safety events occurring in behavioral health areas as well as increased systemization of countermeasures to prevent future events. One significant discussion topic presented in huddles has pertained to environmental design and patient access to potentially dangerous items, addressing some of the most relevant factors resulting in harm to patients in inpatient and emergency settings for behavioral health patients. The safety huddle has improved visibility of potential environmental safety risks through the generation of over 15 safety alerts cascaded throughout the BHN and also spurred a rapid improvement project focused on standardization of patient belonging searches to reduce patient access to potentially dangerous items on inpatient units. Safety events pertaining to potentially dangerous items decreased by 31% as a result of standardized interventions implemented across the network and as a result of increased awareness. A second positive outcome originating from the BHN Safety Huddle was implementation of a recommendation to increase the emergency Narcan®(naloxone) supply on hand in ambulatory settings of the BHN after incidents involving accidental overdose resulted in higher doses of naloxone administration. By increasing the emergency supply of naloxone on hand in all ambulatory and residential settings, colleagues are better prepared to respond in an emergency situation should a patient experience an overdose while on site. Lastly, discussions in safety huddle spurred a new initiative within the BHN to improve responsiveness to assaultive incidents through a consultation service. This consult service, aligned with one of the network’s improvement priorities to reduce harm events related to assaultive incidents, was borne out of discussion in huddle in which it was identified that additional interventions may be needed in providing clinical care to patients who are experiencing multiple and/ or frequent safety events.

Keywords: quality, safety, behavioral health, risk management

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Authors: Victor Lopez-Richard, Rafael Schio Wengenroth Silva, Fabian Hartmann

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Memcomputing is a computational paradigm that combines information processing and storage on the same physical platform. Key elements for this topic are devices with an inherent memory, such as memristors, memcapacitors, and meminductors. Despite the widespread emergence of memory effects in various solid systems, a clear understanding of the basic microscopic mechanisms that trigger them is still a puzzling task. We report basic ingredients of the theory of solid-state transport, intrinsic to a wide range of mechanisms, as sufficient conditions for a memristive response that points to the natural emergence of memory. This emergence should be discernible under an adequate set of driving inputs, as highlighted by our theoretical prediction and general common trends can be thus listed that become a rule and not the exception, with contrasting signatures according to symmetry constraints, either built-in or induced by external factors at the microscopic level. Explicit analytical figures of merit for the memory modulation of the conductance are presented, unveiling very concise and accessible correlations between general intrinsic microscopic parameters such as relaxation times, activation energies, and efficiencies (encountered throughout various fields in Physics) with external drives: voltage pulses, temperature, illumination, etc. These building blocks of memory can be extended to a vast universe of materials and devices, with combinations of parallel and independent transport channels, providing an efficient and unified physical explanation for a wide class of resistive memory devices that have emerged in recent years. Its simplicity and practicality have also allowed a direct correlation with reported experimental observations with the potential of pointing out the optimal driving configurations. The main methodological tools used to combine three quantum transport approaches, Drude-like model, Landauer-Buttiker formalism, and field-effect transistor emulators, with the microscopic characterization of nonequilibrium dynamics. Both qualitative and quantitative agreements with available experimental responses are provided for validating the main hypothesis. This analysis also shades light on the basic universality of complex natural impedances of systems out of equilibrium and might help pave the way for new trends in the area of memory formation as well as in its technological applications.

Keywords: memories, memdevices, memristors, nonequilibrium states

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Authors: Trude Tørset, María Díez Gutiérrez

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The west coast of Norway features numerous islands and fjords. Ferry services connect the roads when these features make the construction challenging. Currently, scientific effort is designated to assess potential ferry replacement projects along the European road E-39. The inconvenience of ferry dependency is imprecisely represented in the transport models, thus transport analyses of ferry replacement projects appear as guesstimates rather than reliable input to decision-making processes of such costly projects. Trips including ferry connections imply more inconvenient elements than just travel time and cost. The goal of this paper is to understand and explain the extra inconveniences associated to the dependency of the ferry. The first scientific approach is to identify the characteristics of the ferry travelers and their trips’ features, as well as whether the ferry represents an obstacle for some specific trip types. In doing so, a survey was conducted in 2011 in eight E-39 ferries and in 2013 in 18 ferries connecting different road categories. More than 20,000 passengers answered with their trip and socioeconomic characteristics. The travel patterns in the different ferry connections were compared. The analysis showed that the trip features differed based on the location of the ferry connections, yet independently of the road category. Additionally, the patterns were compared to the national travel survey to detect differences in the travel patterns due to the use of the ferry connections. The results showed that the share of commuting trips within the same travel time was lower if the ferry was part of the trip. The second scientific approach is to know how the different travelers perceive potential benefits for a ferry replacement project. In the 2011 survey, some of the questions were about the relevance of nine different benefits this project might bring. Travelers identified the better access to public services and job market as the most valuable benefits, followed by the reduced planning of the trip. In 2016, a follow-up survey in some of the ferry connections was carried out in order to investigate variations in travelers’ perceptions. The growing interest in ferry replacement projects might make travelers more aware of the potential benefits these would bring to their daily lives. This paper describes the travel behaviour of travelers using a ferry connection as part of their trips, as well as the potential inconveniences associated to these trips. The findings might provide valuable input to further development of transport models, concept evaluations and cost benefit analysis methods.

Keywords: ferry connections, ferry trip, inconvenience costs, travel behaviour

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Authors: H. El-Kamchouchi, Heba Gaber, Fatma Ahmed, Dalia H. El-Kamchouchi

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A digital signature is an electronic signature form used by an original signer to sign a specific document. When the original signer is not in his office or when he/she travels outside, he/she delegates his signing capability to a proxy signer and then the proxy signer generates a signing message on behalf of the original signer. The two parties must be able to authenticate one another and agree on a secret encryption key, in order to communicate securely over an unreliable public network. Authenticated key agreement protocols have an important role in building a secure communications network between the two parties. In this paper, we present a secure proxy signature scheme over an efficient and secure authenticated key agreement protocol based on factoring and discrete logarithm problem.

Keywords: discrete logarithm, factoring, proxy signature, key agreement

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: S. K. Gupta, Richa Bansal

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In a deregulated power system structure, power producers, and customers share a common transmission network for wheeling power from the point of generation to the point of consumption. All parties in this open access environment may try to purchase the energy from the cheaper source for greater profit margins, which may lead to overloading and congestion of certain corridors of the transmission network. This may result in violation of line flow, voltage and stability limits and thereby undermine the system security. Utilities therefore need to determine adequately their Available Transfer Capability (ATC) to ensure that system reliability is maintained while serving a wide range of bilateral and multilateral transactions. This paper presents power transfer distribution factor based on AC load flow for the determination and enhancement of ATC. The study has been carried out for IEEE 24 bus Reliability Test System.

Keywords: available transfer capability, FACTS devices, power transfer distribution factors, electric

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Muhamad Aris Burhanudin, Angga Firmansyas, Bagus Jaya Santosa

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Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction.

Keywords: earthquake, fuzzy TOPSIS, neural network, tsunami

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Authors: Zhihao Zheng, Zhilin Wang, Linxin Liu

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In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications.

Keywords: knowledge graph, graph neural network, retrieval-augmented generation, NLP

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Authors: Farnaz Fotovvatikhah, Javad Akbari

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Test time of a test architecture is an important factor which depends on the architecture's delay and test patterns. Here a new architecture to store the test results based on network on chip is presented. In addition, simple analytical model is proposed to calculate link test time for built in self-tester (BIST) and external tester (Ext) in multiprocessor systems. The results extracted from the model are verified using FPGA implementation and experimental measurements. Systems consisting 16, 25, and 36 processors are implemented and simulated and test time is calculated. In addition, BIST and Ext are compared in terms of test time at different conditions such as at different number of test patterns and nodes. Using the model the maximum frequency of testing could be calculated and the test structure could be optimized for high speed testing.

Keywords: test, nano on-chip network, JTAG, modelling

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Authors: Tzay-An Shiau, Kuan-Lin Ho

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This study developed sustainability and quality of life indicators for urban passenger transportation by using Social Construction of Technology (SCOT). The initial indicators were proposed by referring to literatures and were summarized by using impact-based framework. Subsequently, the stakeholders were defined according to their interest, power and then classified into scientific, operational, policy making, policy monitoring and nonprofessional frames. The scientific frame consisted of nine scholars in transportation field. Ten representatives from Taipei Rapid Transit Corporation (TRTC), Taiwan Railways Administration (TRA) and bus operators were grouped into the operational frame. The policy making frame comprised of ten representatives from Department of Transportation, Taipei City Government (DOT, TCG), Department of Railways and Highways, Ministry of Transportation and Communication (DORH, MOTC), Directorate General of Highways, Ministry of Transportation and Communication (DGOH, MOTC) and Institute of Transportation, Ministry of Transportation and Communication (IOT, MOTC). The policy monitoring frame consisted of 15 representatives from Taipei City Councilor, legislator and reporter. The nonprofessional frame comprised of 72 Taipei citizens. The stakeholders were asked to evaluate the relative importance of indicators using Delphi survey method. Social construction of 14 transport sustainability indicators and 12 transport quality of life indicators were obtained.

Keywords: sustainability, quality of life, Social Construction of Technology (SCOT), stakeholder

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Authors: Adebowale Olukayode Efunnowo

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This study was carried out to assess the awareness and utilization of Social Network Tools (SNTs) among agricultural science students in Colleges of Education in Ogun State, Nigeria. Simple random sampling techniques were used to select 280 respondents from the study area. Descriptive statistics was used to describe the objectives while Pearson Product Moment Correlation was used to test the hypothesis. The result showed that the majority (71.8%) of the respondents were single, with a mean age of 20 years. Almost all (95.7%) the respondents were aware of Facebook and 2go as a Social Network Tools (SNTs) while 85.0% of the respondents were not aware of Blackplanet, LinkedIn, MyHeritage and Bebo. Many (41.1%) of the respondents had views that using SNTs can enhance extensive literature survey, increase internet browsing potential, promote teaching proficiency, and update on outcomes of researches. However, 51.4% of the respondents perceived that SNTs usage as what is meant for the lecturers/adults only while 16.1% considered it as mainly used by internet fraudsters. Findings revealed that about 50.0% of the respondents browsed Facebook and 2go daily while more than 80% of the respondents used Blackplanet, MyHeritage, Skyrock, Bebo, LinkedIn and My YearBook as the need arise. Major constraints to the awareness and utilization of SNTs were high cost and poor quality of ICTs facilities (77.1%), epileptic power supply (75.0%), inadequate telecommunication infrastructure (71.1%), low technical know-how (62.9%) and inadequate computer knowledge (61.1%). The result of PPMC analysis showed that there was an inverse relationship between constraints and utilization of SNTs at p < 0.05. It can be concluded that constraints affect efficient and effective utilization of SNTs in the study area. It is hereby recommended that management of colleges of education and agricultural institutes should provide good internet connectivity, computer facilities, and alternative power supply in order to increase the awareness and utilization of SNTs among students.

Keywords: awareness, utilization, social network tools, constraints, students

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Authors: Rafael Alexandre Dos Reis

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Research has shown a substantial increase in the participation of Conventionally Fuelled Vehicles (CFVs) in the urban transport modal split. The reasons for this unsustainable reality are multiple, from economic interventions to individual behaviour. The development and delivery of positive incentives for the adoption of more environmental-friendly modes of transport is an emerging strategy to help in tackling the problem of excessive use of conventionally fuelled vehicles. The efficiency of this approach, like other information-based schemes, can benefit from the knowledge of their potential impacts in theoretical constructs of multiple behaviour change theories. The goal of this research is to critically analyse theories of behaviour that are relevant to transport research and the impacts of positive incentives on the theoretical determinants of behaviour, strengthening the current body of evidence about the benefits of this approach. The main method to investigate this will involve a literature review on two main topics: the current theories of behaviour that have empirical support in transport research and the past or ongoing positive incentives programs that had an impact on car use reduction. The reviewed programs of positive incentives were the following: The TravelSmart®; Spitsmijden®; Incentives for Singapore Commuters® (INSINC); COMMUTEGREENER®; MOVESMARTER®; STREETLIFE®; SUPERHUB®; SUNSET® and the EMPOWER® project. The theories analysed were the heory of Planned Behaviour (TPB); The Norm Activation Theory (NAM); Social Learning Theory (SLT); The Theory of Interpersonal Behaviour (TIB); The Goal-Setting Theory (GST) and The Value-Belief-Norm Theory (VBN). After the revisions of the theoretical constructs of each of the theories and their influence on car use, it can be concluded that positive incentives schemes impact on behaviour change in the following manners: -Changing individual’s attitudes through informational incentives; -Increasing feelings of moral obligations to reduce the use of CFVs; -Increase the perceived social pressure to engage in more sustainable mobility behaviours through the use of comparison mechanisms in social media, for example; -Increase the perceived control of behaviour through informational incentives and training incentives; -Increasing personal norms with reinforcing information; -Providing tools for self-monitoring and self-evaluation; -Providing real experiences in alternative modes to the car; -Making the observation of others’ car use reduction possible; -Informing about consequences of behaviour and emphasizing the individual’s responsibility with society and the environment; -Increasing the perception of the consequences of car use to an individual’s valued objects; -Increasing the perceived ability to reduce threats to environment; -Help establishing goals to reduce car use; - iving personalized feedback on the goal; -Increase feelings of commitment to the goal; -Reducing the perceived complexity of the use of alternatives to the car. It is notable that the emerging technique of delivering positive incentives are systematically connected to causal determinants of travel behaviour. The preliminary results of the reviewed programs evidence how positive incentives might strengthen these determinants and help in the process of behaviour change.

Keywords: positive incentives, private car use reduction, sustainable behaviour, voluntary travel behaviour change

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Mohammad Ali Hasanzadeh, Thierry Van Elslander, Eddy Van De Voorde

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About 90 percent of world merchandise trade by volume being carried by sea. Maritime transport remains as back bone behind the international trade and globalization meanwhile all seaborne goods need using at least two ports as origin and destination. Amid seaborne traded cargos, container traffic is a prosperous market with about 16% in terms of volume. Albeit containerized cargos are less in terms of tonnage but, containers carry the highest value cargos amongst all. That is why efficient handling of containers in ports is very important. Accidents are the foremost causes that lead to port inefficiency and a surge in total transport cost. Having different port safety management systems (PSMS) in place, statistics on port accidents show that numerous accidents occur in ports. Some of them claim peoples’ life; others damage goods, vessels, port equipment and/or the environment. Several accident investigation illustrate that the most common accidents take place throughout transport operation, it sometimes accounts for 68.6% of all events, therefore providing a safer workplace depends on reducing collision risk. In order to quantify risks at the port area different variables can be used as exposure measurement. One of the main motives for defining and using exposure in studies related to infrastructure is to account for the differences in intensity of use, so as to make comparisons meaningful. In various researches related to handling containers in ports and intermodal terminals, different risk exposures and also the likelihood of each event have been selected. Vehicle collision within the port area (10-7 per kilometer of vehicle distance travelled) and dropping containers from cranes, forklift trucks, or rail mounted gantries (1 x 10-5 per lift) are some examples. According to the objective of the current research, three categories of accidents selected for collision risk assessment; fall of container during ship to shore operation, dropping container during transfer operation and collision between vehicles and objects within terminal area. Later on various consequences, exposure and probability identified for each accident. Hence, reducing collision risks profoundly rely on picking the right risk exposures and probability of selected accidents, to prevent collision accidents in container terminals and in the framework of risk calculations, such risk exposures and probabilities can be useful in assessing the effectiveness of safety programs in ports.

Keywords: container terminal, collision, seaborne trade, risk exposure, risk probability

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Authors: Sana Dridi, Jalel Aouissi, Rafla Attia, Taoufik Hermassi, Thouraya Sahli

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Soil erosion is an increasing issue in Mediterranean countries. In Tunisia, the capacity of dam reservoirs continues to decrease as a consequence of soil erosion. This study aims to predict sediment yield to enrich soil management practices using Soil and Water Assessment Tools model (SWAT) in the Siliana watershed (1041.6 km²), located in the northwest of Tunisia. A database was constructed using remote sensing and Geographical Information System. Climatic and flow data were collected from water resources directorates in Tunisia. The SWAT model was built to simulate hydrological processes and sediment transport. A sensitivity analysis, calibration, and validation were performed using SWAT-CUP software. The model calibration of stream flow simulations shows a good performance with NSE and R² values of 0.77 and 0.79, respectively. The model validation shows a very good performance with values of NSE and R² for 0.8 and 0.88, respectively. After calibration and validation of stream flow simulation, the model was used to simulate the soil erosion and sediment load transport. The spatial distributions of soil loss rate for determining the critical sediment source areas show that 63 % of the study area has a low soil loss rate less than 7 t ha⁻¹y⁻¹. The annual average soil loss rate simulated with the SWAT model in the Siliana watershed is 4.62 t ha⁻¹y⁻¹.

Keywords: water erosion, SWAT model, streamflow, SWATCUP, sediment yield

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Muhammad Qasim, Laura Minet

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Victoria (BC), Canada, is a major cruise ship destination, attracting over 600,000 tourists annually. Residents of the James Bay neighborhood, home to the Ogden Point cruise terminal, have expressed concerns about the impacts of cruise ship activity on local traffic, air pollution, and safety compliance. This study evaluates the effects of cruise ship-induced traffic in James Bay, focusing on traffic flow intensification, density surges, changes in traffic mix, and speeding violations. To achieve these objectives, traffic data was collected in James Bay during two key periods: May, before the peak cruise season, and August, during full cruise operations. Three Miovision cameras captured the vehicular traffic mix at strategic entry points, while nine traffic counters monitored traffic distribution and speeding violations across the network. Traffic data indicated an average volume of 308 vehicles per hour during peak cruise times in May, compared to 116 vehicles per hour when no ships were in port. Preliminary analyses revealed a significant intensification of traffic flow during cruise ship "hoteling hours," with a volume increase of approximately 10% per cruise ship arrival. A notable 86% surge in taxi presence was observed on days with three cruise ships in port, indicating a substantial shift in traffic composition, particularly near the cruise terminal. The number of tourist buses escalated from zero in May to 32 in August, significantly altering traffic dynamics within the neighborhood. The period between 8 pm and 11 pm saw the most significant increases in traffic volume, especially when three ships were docked. Higher vehicle volumes were associated with a rise in speed violations, although this pattern was inconsistent across all areas. Speeding violations were more frequent on roads with lower traffic density, while roads with higher traffic density experienced fewer violations, due to reduced opportunities for speeding in congested conditions. PTV VISUM software was utilized for fuzzy distribution analysis and to visualize traffic distribution across the study area, including an assessment of the Level of Service on major roads during periods before and during the cruise ship season. This analysis identified the areas most affected by cruise ship-induced traffic, providing a detailed understanding of the impact on specific parts of the transportation network. These findings underscore the significant influence of cruise ship activity on traffic dynamics in Victoria, BC, particularly during peak periods when multiple ships are in port. The study highlights the need for targeted traffic management strategies to mitigate the adverse effects of increased traffic flow, changes in traffic mix, and speed violations, thereby enhancing road safety in the James Bay neighborhood. Further research will focus on detailed emissions estimation to fully understand the environmental impacts of cruise ship activity in Victoria.

Keywords: cruise ship tourism, air quality, traffic violations, transport dynamics, pollution

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Authors: Eugene Wong, Felix Chan, Linsey Chen, Joey Cheung

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The widespread use of technologies and cyber/digital means for complex maritime operations have led to a sharp rise in global cyber-attacks. They have generated an increasing number of liability disputes, insurance claims, and legal proceedings. An array of antiquated case law, regulations, international conventions, and obsolete contractual clauses drafted in the pre-technology era have become grossly inadequate in addressing the contemporary challenges. This paper offers a critique of the ambiguity of cybersecurity liabilities under the obligation of seaworthiness entailed in the Hague-Visby Rules, which apply either by law in a large number of jurisdictions or by express incorporation into the shipping documents. This paper also evaluates the legal and technological criteria for assessing whether a vessel is properly equipped with the latest offshore technologies for navigation and cargo delivery operations. Examples include computer applications, networks and servers, enterprise systems, global positioning systems, and data centers. A critical analysis of the carriers’ obligations to exercise due diligence in preventing or mitigating cyber-attacks is also conducted in this paper. It is hoped that the present study will offer original and crucial insights to policymakers, regulators, carriers, cargo interests, and insurance underwriters closely involved in dispute prevention and resolution arising from cybersecurity liabilities.

Keywords: seaworthiness, cybersecurity, liabilities, risks, maritime, transport

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Authors: N. G. Margiani, I. G. Kvartskhava, G. A. Mumladze, Z. A. Adamia

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Chemical doping with different elements and compounds at various amounts represents the most suitable approach to improve the superconducting properties of bismuth-based superconductors for technological applications. In this paper, the influence of partial substitution of Sr(BO₂)₂ for SrO on the phase formation kinetics and transport properties of (Bi,Pb)-2223 HTS has been studied for the first time. Samples with nominal composition Bi_1.7Pb_0.3Sr_2-xCa₂Cu₃O_y[Sr(BO₂)₂]_x, x=0, 0.0375, 0.075, 0.15, 0.25, were prepared by the standard solid state processing. The appropriate mixtures were calcined at 845 ^oC for 40 h. The resulting materials were pressed into pellets and annealed at 837 ^oC for 30 h in air. Superconducting properties of undoped (reference) and Sr(BO₂)₂-doped (Bi,Pb)-2223 compounds were investigated through X-ray diffraction (XRD), resistivity (ρ) and transport critical current density (J_c) measurements. The surface morphology changes in the prepared samples were examined by scanning electron microscope (SEM). XRD and J_c studies have shown that the low level Sr(BO₂)₂ doping (x=0.0375-0.075) to the Sr-site promotes the formation of high-T_c phase and leads to the enhancement of current carrying capacity in (Bi,Pb)-2223 HTS. The doped sample with x=0.0375 has the best performance compared to other prepared samples. The estimated volume fraction of (Bi,Pb)-2223 phase increases from ~25 % for reference specimen to ~70 % for x=0.0375. Moreover, strong increase in the self-field J_c value was observed for this dopant amount (J_c=340 A/cm²), compared to an undoped sample (J_c=110 A/cm²). Pronounced enhancement of superconducting properties of (Bi,Pb)-2223 superconductor can be attributed to the acceleration of high-T_c phase formation as well as the improvement of inter-grain connectivity by small amounts of Sr(BO₂)₂ dopant.

Keywords: bismuth-based superconductor, critical current density, phase formation, Sr(BO₂)₂ doping

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Authors: Aleksei Samarin, Artyom Nazarenko, Valentin Malykh

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Image enhancement is becoming more and more popular, especially on mobile devices. Nowadays, it is a common approach to enhance an image using a convolutional neural network (CNN). Such a network should be of significant size; otherwise, a possibility for the artifacts to occur is overgrowing. The existing large CNNs are computationally expensive, which could be crucial for mobile devices. Another important flaw of such models is they are poorly interpretable. There is another approach to image enhancement, namely, the usage of predefined filters in combination with the prediction of their applicability. We present an approach following this paradigm, which outperforms both existing CNN-based and filter-based approaches in the image enhancement task. It is easily adaptable for mobile devices since it has only 47 thousand parameters. It shows the best SSIM 0.919 on RANDOM250 (MIT Adobe FiveK) among small models and is thrice faster than previous models.

Keywords: universal filter, image enhancement, neural networks, computer vision

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Authors: Katharina Eberhardt, Florian Klaus Kaiser, Frank Schultmann

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Last-mile distribution is one of the most critical parts of a disaster relief operation. Various uncertainties, such as infrastructure conditions, resource availability, and fluctuating beneficiary demand, render last-mile distribution challenging in disaster relief operations. The need to balance critical performance criteria like response time, meeting demand and cost-effectiveness further complicates the task. The occurrence of disasters cannot be controlled, and the magnitude is often challenging to assess. In summary, these uncertainties create a need for additional flexibility, agility, and preparedness in logistics operations. As a result, strategic planning and efficient network design are critical for an effective and efficient response. Furthermore, the increasing frequency of disasters and the rising cost of logistical operations amplify the need to provide robust and resilient solutions in this area. Therefore, we formulate a scenario-based bi-objective optimization model that integrates pre-positioning, allocation, and distribution of relief supplies extending the general form of a covering location problem. The proposed model aims to minimize underlying logistics costs while maximizing demand coverage. Using a set of disruption scenarios, the model allows decision-makers to identify optimal network solutions to address the risk of disruptions. We provide an empirical case study of the public authorities’ emergency food storage strategy in Germany to illustrate the potential applicability of the model and provide implications for decision-makers in a real-world setting. Also, we conduct a sensitivity analysis focusing on the impact of varying stockpile capacities, single-site outages, and limited transportation capacities on the objective value. The results show that the stockpiling strategy needs to be consistent with the optimal number of depots and inventory based on minimizing costs and maximizing demand satisfaction. The strategy has the potential for optimization, as network coverage is insufficient and relies on very high transportation and personnel capacity levels. As such, the model provides decision support for public authorities to determine an efficient stockpiling strategy and distribution network and provides recommendations for increased resilience. However, certain factors have yet to be considered in this study and should be addressed in future works, such as additional network constraints and heuristic algorithms.

Keywords: humanitarian logistics, bi-objective optimization, pre-positioning, last mile distribution, decision support, disaster relief networks

Procedia PDF Downloads 79

Authors: Saeid Eshghi, Neeraj Saxena, Abdulmajeed Alsultan

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Carbon dioxide (CO₂) alongside other gas emissions in the atmosphere cause a greenhouse effect, resulting in an increase of the average temperature of the planet. Transportation vehicles are among the main contributors of CO₂ emission. Stationary vehicles with initiated motors produce more emissions than mobile ones. Intersections with traffic lights that force the vehicles to become stationary for a period of time produce more CO₂ pollution than other parts of the road. This paper focuses on analyzing the CO₂ produced by the traffic flow at Anzac Parade Road - Barker Street intersection in Sydney, Australia, before and after the implementation of Light rail transport (LRT). The data are gathered during the construction phase of the LRT by collecting the number of vehicles on each path of the intersection for 15 minutes during the evening rush hour of 1 week (6-7 pm, July 04-31, 2018) and then multiplied by 4 to calculate the flow of vehicles in 1 hour. For analyzing the data, the microscopic simulation software “VISSIM” has been used. Through the analysis, the traffic flow was processed in three stages: before and after implementation of light rail train, and one during the construction phase. Finally, the traffic results were input into another software called “EnViVer”, to calculate the amount of CO₂ during 1 h. The results showed that after the implementation of the light rail, CO₂ will drop by a minimum of 13%. This finding provides an evidence that light rail is a sustainable mode of transport.

Keywords: carbon dioxide, emission modeling, light rail, microscopic model, traffic flow

Procedia PDF Downloads 143