Search results for: resilient networks

Authors: Jordan Georgiev Deliversky

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Fight against migrant smuggling has been put as a priority issues at the European Union policy agenda for more than a decade. The trafficked person, who has been targeted as the object of criminal exploitation, is specifically unique for human trafficking. Generally, the beginning of human trafficking activities is related to profit from the victim’s exploitation. The objective of this paper is to present measures that could result in the limitation of corruption mainly through analyzing the existing legislation framework against corruption in Europe. The analysis is focused on exploring the multiple origins of factors influencing migration processes in Europe, as corruption could be characterized as one of the most significant reasons for refugees to flee their countries. The main results show that law enforcement must turn the focus on the financing of the organized crime groups that are involved in migrant smuggling activities. Corruption has a significant role in managing smuggling operations and in particular when criminal organizations and networks are involved. Illegal migrants and refugees usually represent significant sources of additional income for officials involved in the process of boarding protection and immigration control within the European Union borders.

Keywords: corruption, influence, human smuggling, legislation, migration

Procedia PDF Downloads 329

Authors: Ahasanul Haque, Abdullah Sarwar, Khaliq Ahmed

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Networking is important among students to achieve better understanding. Social networking plays an important role in the education. Realizing its huge potential, various organizations, including institutions of higher learning have moved to the area of social networks to interact with their students especially through Facebook. Therefore, measuring the effectiveness of Facebook as a learning tool has become an area of interest to academicians and researchers. Therefore, this study tried to integrate and propose new theoretical and empirical evidences by linking the western idea of adopting Facebook as an alternative learning platform from a Malaysian perspective. This study, thus, aimed to fill a gap by being among the pioneering research that tries to study the effectiveness of adopting Facebook as a learning platform across other cultural settings, namely Malaysia. Structural equation modelling was employed for data analysis and hypothesis testing. This study findings have provided some insights that would likely affect students’ awareness towards using Facebook as an alternative learning platform in the Malaysian higher learning institutions. At the end, future direction is proposed.

Keywords: Learning Management Tool, social networking, education, Malaysia

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Authors: Ali Aghazadegan, Ali Shokria, Julia Mullarneya, Jon Tunnicliffe

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River channel confluences are dynamic systems with branching structures that exhibit a high degree of complexity both in natural and man-made open channel networks. Recent and past fields and modeling have investigated the river dynamics modeling of confluent based on a series of over-simplified assumptions (i.e. straight tributary channel with a bend with a 90° junction angle). Accurate assessment of such systems is important to the design and management of hydraulic structures and river engineering processes. Despite their importance, there has been little study of the hydrodynamics characteristics of river confluences, and the link between flow hydrodynamics and confluence morphodynamics in the confluence is still incompletely understood. This paper studies flow structures in confluences, morphodynamics and deposition patterns in 30 and 90 degrees confluences with different flow conditions. The results show that the junction angle is primarily the key factor for the determination of the confluence bed morphology and sediment pattern, while the discharge ratio is a secondary factor. It also shows that super elevation created by mixing flows is a key function of the morphodynamics patterns.

Keywords: helical flow, river confluence, bed morphology , secondary flows, shear layer

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Authors: Eric Kumar, Erica Cochran, Zhiang Zhang, Wei Liang, Ronak Mody

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The objective of this article was to create a modelling framework that exposes the marginal costs of shifting workloads across geographically distributed data-centers. Geographical distribution of internet services helps to optimize their performance for localized end users with lowered communications times and increased availability. However, due to the geographical and temporal effects, the physical embodiments of a service's data center infrastructure can vary greatly. In this work, we first identify that the sources of variances in the physical infrastructure primarily stem from local weather conditions, specific user traffic profiles, energy sources, and the types of IT hardware available at the time of deployment. Second, we create a traffic simulator that indicates the IT load at each data-center in the set as an approximator for user traffic profiles. Third, we implement a framework that quantifies the global level energy demands using building energy models and the traffic profiles. The results of the model provide a time series of energy demands that can be used for further life cycle analysis of internet services.

Keywords: data-centers, energy, life cycle, network simulation

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Authors: Daewook Kim, Gilwon Yoon

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Intrabody communication (IBC) is a new way of transferring data using human body as a medium. Minute current can travel though human body without any harm. IBC can remove electrical wires for human area network. IBC can be also a secure communication network system unlike wireless networks which can be accessed by anyone with bad intentions. One of the IBC systems is based on frequency shift keying modulation where individual data are transmitted to the external devices for the purpose of secure access such as digital door lock. It was found that the quality of IBC data transmission was heavily dependent on ground configurations of electronic circuits. Reliable IBC transmissions were not possible when both of the transmitter and receiver used batteries as circuit power source. Transmission was reliable when power supplies were used as power source for both transmitting and receiving sites because the common ground was established through the grounds of instruments such as power supply and oscilloscope. This was due to transmission dipole size and the ground effects of floor and AC power line. If one site used battery as power source and the other site used the AC power as circuit power source, transmission was possible.

Keywords: frequency shift keying, ground, intrabody, communication, door lock

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Zhou Mo, Dennis Chow

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In the dynamic positioning system (DPS) for vessels, the reliable information transmission between each note basically relies on the wireless protocols. From the perspective of cluster-based routing pro-tocols for wireless sensor networks, the data fusion technology based on the sleep scheduling mechanism and remaining energy in network layer is proposed, which applies the sleep scheduling mechanism to the routing protocols, considering the remaining energy of node and location information when selecting cluster-head. The problem of uneven distribution of nodes in each cluster is solved by the Equilibrium. At the same time, Classified Forwarding Mechanism as well as Redelivery Policy strategy is adopted to avoid congestion in the transmission of huge amount of data, reduce the delay in data delivery and enhance the real-time response. In this paper, a simulation test is conducted to improve the routing protocols, which turns out to reduce the energy consumption of nodes and increase the efficiency of data delivery.

Keywords: DPS for vessel, wireless sensor network, data fusion, routing protocols

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Authors: Ali Borhani Manesh, Sirus Mohammadi

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Harmonic distortion is caused by nonlinear devices in the power system. A nonlinear device is one in which the current is not proportional to the applied voltage. Harmonic distortion is present to some degree on all power systems. Proactive monitoring of power quality disturbance levels by electricity utilities is vital to allow cost-effective mitigation when disturbances are perceived to be approaching planning levels and also to protect the security of customer installations. Ensuring that disturbance levels are within limits at the HV and EHV points of supply of the network is essential if satisfactory levels downstream are to be maintained. This paper presents discussion on a power quality monitoring campaign performed at the sub-transmission point of supply of a distribution network with the objective of benchmarking background disturbance levels prior to modifications to the substation and to ensure emissions from HV customers and the downstream MV networks are within acceptable levels. Some discussion on the difficulties involved in such a study is presented. This paper presents a survey of voltage and current harmonic distortion levels at transmission system in Kohgiloye and Boyrahmad. The effects of harmonics on capacitors and power transformers are discussed.

Keywords: power quality, harmonics, flicker, measurement, substation

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Authors: Chechen Liao, Sheng Yi Lin

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With the popularity of smartphone usage and the flourish of social networks, many people began to use the 'check-in' functions to share their location information and days of live and self-disclosure. In order to increase exposure and awareness, some stores provide discounts and other benefits to attract consumers to 'check-in' in their stores. The purpose of this study was to investigate whether personality traits, intrinsic motivations, extrinsic motivations, and privacy concerns would affect self-disclosure of location for consumers. Research data were collected from 407 individuals that have used Facebook check-in in Taiwan. This study used SmartPLS 2.0 structural equation modeling to validate the model. The results show that information sharing, information storage, enjoyment, self-presentation, get a feedback, economic reward, and keep up with trends had significant positive effects on self-disclosure. While extroversion and openness to use have significant positive effects on self-disclosure, conscientiousness and privacy concerns have significant negative effects on self-disclosure. The results of the study provide academic and practical implications for the future growth of location-based self-disclosure.

Keywords: check-in, extrinsic motivation, intrinsic motivation, personality trait, self-disclosure

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Authors: Heba M. Wagih, Hoda M. O. Mokhtar

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Social Network has recently played a significant role in both scientific and social communities. The growing adoption of social network applications has been a relevant source of information nowadays. Due to its popularity, several research trends are emerged to service the huge volume of users including, Location-Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. LBSNs applications are among the highly demanded applications that do not focus only on analyzing the spatiotemporal positions in a given raw trajectory but also on understanding the semantics behind the dynamics of the moving object. LBSNs are possible means of predicting human mobility based on users social ties as well as their spatial preferences. LBSNs rely on the efficient representation of users’ trajectories. Hence, traditional raw trajectory information is no longer convenient. In our research, we focus on studying human behavior trajectory which is the major pillar in location recommendation systems. In this paper, we propose an ontology design patterns with their underlying description logics to efficiently annotate human behavior trajectories.

Keywords: human behavior trajectory, location-based social network, ontology, social network

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Sancho-Esper Franco, Rodríguez Sánchez Carla, Sánchez Carolina, Orús-Sanclemente Carlos

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Water scarcity is a reality that affects many regions of the world and is aggravated by climate change and population growth. Saving water has become an urgent need to ensure the sustainability of the planet and the survival of many communities, where youth and social networks play a key role in promoting responsible practices and adopting habits that contribute to environmental preservation. This study analyzes the persuasion capacity of messages designed to promote pro-environmental behaviors among youth. Specifically, it studies how the efficacy (effectiveness) of the response (personal response efficacy/effectiveness) and the perception of distance from the source of the message influence the water-saving behavior of the audience. To do so, two communication frameworks are combined. First, the Construal Level Theory, which is based on the concept of "psychological distance", that is, people, objects or events can be perceived as psychologically near or far, and this subjective distance (i.e., social, temporal, or spatial) determines their attitudes, emotions, and actions. This perceived distance can be social, temporal, or spatial. This research focuses on studying the spatial distance and social distance generated by cultural differences between influencers and their audience to understand how cultural distance can influence the persuasiveness of a message. Research on the effects of psychological distance between influencers-followers in the pro-environmental field is very limited, being relevant because people could learn specific behaviors suggested by opinion leaders such as influencers in social networks. Second, different approaches to behavioral change suggest that the perceived efficacy of a behavior can explain individual pro-environmental actions. People will be more likely to adopt a new behavior if they perceive that they are capable of performing it (efficacy belief) and that their behavior will effectively contribute to solving that problem (personal response efficacy). It is also important to study the different actors (social and individual) that are perceived as responsible for addressing environmental problems. Specifically, we analyze to what extent the belief individual’s water-saving actions are effective in solving the problem can influence water-saving behavior since this individual effectiveness increases people's sense of obligation and responsibility with the problem. However, in this regard, empirical evidence presents mixed results. Our study addresses the call for experimental studies manipulating different subtypes of response effectiveness to generate robust causal evidence. Based on all the above, this research analyzes whether cultural distance (local vs. international influencer) and the perception of effectiveness of behavior (personal response efficacy) (personal/individual vs. collective) affect the actual behavior and the intention to conserve water of social network users. An experiment of 2 (local influencer vs. international influencer) x 2 (effectiveness of individual vs. collective response) is designed and estimated. The results show that a message from a local influencer appealing to individual responsibility exerts greater influence on intention and actual water-saving behavior, given the cultural closeness between influencer-follower, and the appeal to individual responsibility increases the feeling of obligation to participate in pro-environmental actions. These results offer important implications for social marketing campaigns that seek to promote water conservation.

Keywords: social marketing, influencer, message framing, experiment, personal response efficacy, water saving

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Authors: Gunasekaran Raja, Kottilingam Kottursamy, Rajakumar Arul, Ramkumar Jayaraman, Krithika Sairam, Lakshmi Ravi

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The synchronization server maintains a dynamically changing cache, which contains the data items which were requested and collected by the mobile node from the server. The order and presence of tuples in the cache changes dynamically according to the frequency of updates performed on the data, by the server and client. To synchronize, the data which has been modified by client and the server at an instant are collected, batched together by the type of modification (insert/ update/ delete), and sorted according to their update frequencies. This ensures that the DCASH (Dynamic Cache Synchronization Algorithm for Heterogeneous Reverse Y synchronizing Mobile Database Systems) gives priority to the frequently accessed data with high usage. The optimal memory management algorithm is proposed to manage data items according to their frequency, theorems were written to show the current mobile data activity is reverse Y in nature and the experiments were tested with 2g and 3g networks for various mobile devices to show the reduced response time and energy consumption.

Keywords: mobile databases, synchronization, cache, response time

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Authors: Venkata Narayana Chejarla, Nafisa Parvez Shaik, Gopi Vara Prasad Marabathula, Deva Kumar Bejjam

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Yoga is an advanced, well-recognized method with roots in Indian philosophy. Yoga benefits both the body and the psyche. Yoga is a regular exercise that helps people relax and sleep better while also enhancing their balance, endurance, and concentration. Yoga can be learned in a variety of settings, including at home with the aid of books and the internet as well as in yoga studios with the guidance of an instructor. Self-learning does not teach the proper yoga poses, and doing them without the right instruction could result in significant injuries. We developed "Online Yoga Asana Trainer using Deep Learning" so that people could practice yoga without a teacher. Our project is developed using Tensorflow, Movenet, and Keras models. The system makes use of data from Kaggle that includes 25 different yoga poses. The first part of the process involves applying the movement model for extracting the 17 key points of the body from the dataset, and the next part involves preprocessing, which includes building a pose classification model using neural networks. The system scores a 98.3% accuracy rate. The system is developed to work with live videos.

Keywords: yoga, deep learning, movenet, tensorflow, keras, CNN

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Authors: Madhan Vinu, Yue-Chun Jiang, Yi-Feng Lin, Chia-Her Lin

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An effective approach to enhance the ethanol/water pervaporation of mixed matrix membranes prepared from three microporous aluminium based metal-organic frameworks (MOFs), [Al(OH)(BPDC)] (DUT-5), [Al(OH)(NDC)] (DUT-4) and [Al(OH)(BzPDC)] (CAU-8) have been synthesized by employing solvothermal reactions. Interestingly, all Al-MOFs showed attractive surface area with microporous 12.3, 10.2 and 8.0 Å for DUT-5, DUT-4 and CAU-8 MOFs which are confirmed through N₂ gas sorption measurements. All the microporous compounds are highly stable as confirmed by thermogravimetric analysis and temperature-dependent powder X-ray diffraction measurements. Furthermore, the synthesized microporous MOF particles of DUT-5, DUT-4, and CAU-8 were successfully incorporated into biological chitosan (CS) membranes to form DUT-5@CS, DUT-4@CS, and CAU-8@CS membranes. The different MOF loadings such as 0.1, 0.15, and 0.2 wt% in CS networks have been prepared, and the same were used to separate mixtures of water and ethanol at 25ºC in the pervaporation process. In particular, when 0.15 wt% of DUT-5 was loaded, MOF@CS membrane displayed excellent permeability and selectivity in ethanol/water separation than that of the previous literature. These CS based membranes separation through functionalized microporous MOFs reveals the key governing factors that are essential for designing novel MOF membranes for bioethanol purification.

Keywords: metal-organic framework, microporous materials, separation, chitosan membranes

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Authors: Ater Amogpai

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Hydropower and solar photovoltaic (PV) generation are crucial in sustainability and transitioning from fossil fuel to clean energy. Integrating renewable energy sources such as hydropower and solar photovoltaic (PV) into the distributed networks contributes to achieving energy balance, pollution mitigation, and cost reduction. Frequent power outages and a lack of load reliability characterize the current South Sudan electricity distribution system. The country’s electricity demand is 300MW; however, the installed capacity is around 212.4M. Insufficient funds to build new electricity facilities and expand generation are the reasons for the gap in installed capacity. The South Sudan Ministry of Energy and Dams gave a contract to an Egyptian Elsewedy Electric Company that completed the construction of a solar PV plant in 2023. The plant has a 35 MWh battery storage and 20 MW solar PV system capacity. The construction of Juba Solar PV Park started in 2022 to increase the current installed capacity in Juba City to 53 MW. The plant will begin serving 59000 residents in Juba and save 10,886.2t of carbon dioxide (CO2) annually.

Keywords: renewable energy, hydropower, solar energy, photovoltaic, South Sudan

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 127

Authors: N. David, H. O. Gao

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Air pollution is a serious environmental threat on a global scale and can cause harm to human health, morbidity and premature mortality. Reliable monitoring and control systems are therefore necessary to develop coping skills against the hazards associated with this phenomenon. However, existing environmental monitoring means often do not provide a sufficient response due to practical and technical limitations. Commercial microwave links that form the infrastructure for transmitting data between cell phone towers can be harnessed to map rain at high tempo-spatial resolution. Rainfall causes a decrease in the signal strength received by these wireless communication links allowing it to be used as a built-in sensor network to map the phenomenon. In this study, we point to the potential that lies in this system to indirectly monitor areas where air pollution is reduced. The relationship between pollutant wash-off and rainfall provides an opportunity to acquire important spatial information about air quality using existing cell-phone tower signals. Since the density of microwave communication networks is high relative to any dedicated sensor arrays, it could be possible to rely on this available observation tool for studying precipitation scavenging on air pollutants, for model needs and more.

Keywords: air pollution, commercial microwave links, rainfall, washout

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Authors: Arash Khosravi, Mohamad Nazir Ahmad

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Today’s economy is a knowledge-based economy in which knowledge is a crucial facilitator to individuals, as well as being an instigator of success. Due to the impact of globalization, universities face new challenges and opportunities. Accordingly, they ought to be more innovative and have their own competitive advantages. One of the most important goals of universities is the promotion of students as professional knowledge workers. Therefore, knowledge sharing and transferring at tertiary level between students and supervisors is vital in universities, as it decreases the budget and provides an affordable way of doing research. Knowledge-sharing impact factors can be categorized into three groups, namely: organizational, individual and technical factors. There are some individual barriers to knowledge sharing, namely: lack of time and trust, lack of communication skills and social networks. IT systems such as e-learning, blogs and portals can increase knowledge sharing capability. However, it must be stated that IT systems are only tools and not solutions. Individuals are still responsible for sharing information and knowledge. This paper proposes new research model to examine the effect of individual factors and organisational factors, namely: learning strategy, trust culture, supervisory support, as well as technological factor on knowledge sharing in a research supervision process at the University of Technology Malaysia.

Keywords: knowledge management, knowledge sharing, research supervision, knowledge transferring

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Authors: Edward Schwalb, Matthias Dehmer, Michael Schlenkrich, Farzaneh Taslimi, Ketron Mitchell-Wynne, Horen Kuecuekyan

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We present a generalized feature extraction approach for supporting Machine Learning (ML) algorithms which perform tasks similar to Finite-Element Analysis (FEA). We report results for estimating the Head Injury Categorization (HIC) of vehicle engine compartments across various impact scenarios. Our experiments demonstrate that models learned using features derived with a simple discretization approach provide a reasonable approximation of a full simulation. We observe that Decision Trees could be as effective as Neural Networks for the HIC task. The simplicity and performance of the learned Decision Trees could offer a trade-off of a multiple order of magnitude increase in speed and cost improvement over full simulation for a reasonable approximation. When used as a complement to full simulation, the approach enables rapid approximate feedback to engineering teams before submission for full analysis. The approach produces mesh independent features and is further agnostic of the assembly structure.

Keywords: mechanical design validation, FEA, supervised decision tree, convolutional neural network.

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Authors: Sylvie Michel, François Cocula

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Digital agriculture is an approach that exploits information and communication technologies. These encompass data acquisition tools like mobile applications, satellites, sensors, connected devices, and smartphones. Additionally, it involves transfer and storage technologies such as 3G/4G coverage, low-bandwidth terrestrial or satellite networks, and cloud-based systems. Furthermore, embedded or remote processing technologies, including drones and robots for process automation, along with high-speed communication networks accessible through supercomputers, are integral components of this approach. While farm-level adoption studies regarding digital agricultural technologies have emerged in recent years, they remain relatively limited in comparison to other agricultural practices. To bridge this gap, this study delves into understanding farmers' intention to adopt digital tools, employing the technology, organization, environment framework. A qualitative research design encompassed semi-structured interviews, totaling fifteen in number, conducted with key stakeholders both prior to and following the 2020-2021 COVID-19 lockdowns in France. Subsequently, the interview transcripts underwent thorough thematic content analysis, and the data and verbatim were triangulated for validation. A coding process aimed to systematically organize the data, ensuring an orderly and structured classification. Our research extends its contribution by delineating sub-dimensions within each primary dimension. A total of nine sub-dimensions were identified, categorized as follows: perceived usefulness for communication, perceived usefulness for productivity, and perceived ease of use constitute the first dimension; technological resources, financial resources, and human capabilities constitute the second dimension, while market pressure, institutional pressure, and the COVID-19 situation constitute the third dimension. Furthermore, this analysis enriches the TOE framework by incorporating entrepreneurial orientation as a moderating variable. Managerial orientation emerges as a pivotal factor influencing adoption intention, with producers acknowledging the significance of utilizing digital sales support tools to combat "greenwashing" and elevate their overall brand image. Specifically, it illustrates that producers recognize the potential of digital tools in time-saving and streamlining sales processes, leading to heightened productivity. Moreover, it highlights that the intent to adopt digital sales support tools is influenced by a market mimicry effect. Additionally, it demonstrates a negative association between the intent to adopt these tools and the pressure exerted by institutional partners. Finally, this research establishes a positive link between the intent to adopt digital sales support tools and economic fluctuations, notably during the COVID-19 pandemic. The adoption of sales support tools in agriculture is a multifaceted challenge encompassing three dimensions and nine sub-dimensions. The research delves into the adoption of digital farming technologies at the farm level through the TOE framework. This analysis provides significant insights beneficial for policymakers, stakeholders, and farmers. These insights are instrumental in making informed decisions to facilitate a successful digital transition in agriculture, effectively addressing sector-specific challenges.

Keywords: adoption, digital agriculture, e-commerce, TOE framework

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Authors: Guiheng Zhang, Wei Zhang, Jun Fu, Yudong Wang

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A 900 MHz three-stage SiGe power amplifier (PA) with high power gain is presented in this paper. Volterra Series is applied to analyze nonlinearity sources of SiGe HBT device model clearly. Meanwhile, the influence of operating current to IMD3 is discussed. Then a β-helper current mirror bias circuit is applied to improve linearity, since the β-helper current mirror bias circuit can offer stable base biasing voltage. Meanwhile, it can also work as predistortion circuit when biasing voltages of three bias circuits are fine-tuned, by this way, the power gain and operating current of PA are optimized for best linearity. The three power stages which fabricated by 0.18 μm SiGe technology are bonded to the printed circuit board (PCB) to obtain impedances by Load-Pull system, then matching networks are done for best linearity with discrete passive components on PCB. The final measured three-stage PA exhibits 21.1 dBm of output power at 1 dB compression point (OP1dB) with power added efficiency (PAE) of 20.6% and 33 dB power gain under 3.3 V power supply voltage.

Keywords: high gain power amplifier, linearization bias circuit, SiGe HBT model, Volterra series

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Authors: Joel Weymouth, Ramesh K. Karne, Alexander L. Wijesinha

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Bare Machine Internet is part of the Bare Machine Computing (BMC) paradigm. It is used in programming application ns to run directly on a device. It is software that runs directly against the hardware using CPU, Memory, and I/O. The software application runs without an Operating System and resident mass storage. An important part of the BMC paradigm is the Bare Machine Internet. It utilizes an Application Development model software that interfaces directly with the hardware on a network server and file server. Because it is “bare,” it is a powerful teaching and research tool that can readily display the internals of the network protocols, software, and hardware of the applications running on the Bare Server. It was also demonstrated that the bare server was accessible by laptop and by smartphone/android. The purpose was to show the further practicality of Bare Internet in Computer Engineering and Computer Science Education and Research. It was also to show that an undergraduate student could take advantage of a bare server with any device and any browser at any release version connected to the internet. This paper presents the Bare Web Server as an educational tool. We will discuss possible applications of this paradigm.

Keywords: bare machine computing, online research, network technology, visualizing network internals

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Authors: Seung Yoon Hyun, Hyeong Seung An, Myeong Ho Choi, Sung Hwan Bae, Yu Jong Sim

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In 2015, the southern part of the Korean Peninsula has 8.6 million poles, 1.25 million km power lines, and 2 million transformers, etc. It is the massive amount of distribution equipments which could cover a round-trip distance from the earth to the moon and 11 turns around the earth. These distribution equipments are spread out like capillaries and supplying power to every corner of the Korean Peninsula. In order to manage these huge power facility efficiently, KEPCO use DAS (Distribution Automation System) to operate distribution power system since 1997. DAS is integrated system that enables to remotely supervise and control breakers and switches on distribution network. Using DAS, we can reduce outage time and power loss. KEPCO has about 160,000 switches, 50%(about 80,000) of switches are automated, and 41 distribution center monitoring&control these switches 24-hour 365 days to get the best efficiency of distribution networks. However, the rapid increasing renewable energy sources become the problem in the efficient operation of distributed power system. (currently 2,400 MW, 75,000 generators operate in distribution power system). In this paper, it suggests the way to interconnect between renewable energy source and distribution power system.

Keywords: distribution, renewable, connect, DAS (Distribution Automation System)

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Authors: Mallikarjun B. Channappagoudar, Pallapa Venkataram

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The dynamic nature of Mobile adhoc network (MANET) due to mobility and disconnection of mobile nodes, leads to various problems in predicting the movement of nodes and their location information updation, for efficient interaction among the application specific nodes. Location management is one of the main challenges to be considered for an efficient service provision to the applications of a MANET. In this paper, we propose a location management protocol, for locating the nodes of a MANET and to maintain uninterrupted high-quality service for distributed applications by intelligently anticipating the change of location of its nodes. The protocol predicts the node movement and application resource scarcity, does the replacement with the chosen nodes nearby which have less mobility and rich in resources, with the help of both static and mobile agents, and maintains the application continuity by providing required network resources. The protocol has been simulated using Java Agent Development Environment (JADE) Framework for agent generation, migration and communication. It consumes much less time (response time), gives better location accuracy, utilize less network resources, and reduce location management overhead.

Keywords: mobile agent, location management, distributed applications, mobile adhoc network

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Authors: A. Al-Juboori, D. Wexler, H. Li, H. Zhu, C. Lu, A. McCusker, J. McLeod, S. Pannila, Z. Wang

Abstract:

Squats are a type railhead defect related to rolling contact fatigue (RCF) damage and are considered serious problem affecting a wide range of railway networks across the world. Squats can lead to partial or complete rail failure. Formation mechanics of squats on the surface of rail steel is still a matter of debate. In this work, structural and microstructural observations from ex-service damaged rail both confirms the phases present in white etching layer (WEL) regions and relationship between cracking in WEL and squat defect formation. XRD synchrotron results obtained from the top surfaces of rail regions containing both WEL and squat defects reveal that these regions contain both martensite and retained austenite. Microstructural analysis of these regions revealed the occurrence cracks extending from WEL down into the rail through the squat region. These findings obtained from field rail specimen support the view that WEL contains regions of austenite and martensitic transformation product, and that cracks in this brittle surface layer propagate deeper into the rail as squats originate and grow.

Keywords: squat, white etching layer, rolling contact fatigue, synchrotron diffraction

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Authors: Kuei-Ling Sun, Emily Chia-Yu Su

Abstract:

Allergy is a hypersensitive overreaction of the immune system to environmental stimuli, and a major health problem. These overreactions include rashes, sneezing, fever, food allergies, anaphylaxis, asthmatic, shock, or other abnormal conditions. Allergies can be caused by food, insect stings, pollen, animal wool, and other allergens. Their development of allergies is due to both genetic and environmental factors. Allergies involve immunoglobulin E antibodies, a part of the body’s immune system. Immunoglobulin E antibodies will bind to an allergen and then transfer to a receptor on mast cells or basophils triggering the release of inflammatory chemicals such as histamine. Based on the increasingly serious problem of environmental change, changes in lifestyle, air pollution problem, and other factors, in this study, we both collect allergens and non-allergens from several databases and use several machine learning methods for classification, including logistic regression (LR), stepwise regression, decision tree (DT) and neural networks (NN) to do the model comparison and determine the permutations of allergen amino acid’s sequence.

Keywords: allergy, classification, decision tree, logistic regression, machine learning

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Authors: Hirseland Andreas, Kerschbaumer Lukas

Abstract:

In the case of Germany, there are about one million long-term unemployed – a figure that did not vary much during the past years. These long-term unemployed did not benefit from the prospering labor market while most short-term unemployed did. Instead, they are continuously dependent on welfare and sometimes precarious short-term employment, experiencing work poverty. Long-term unemployment thus turns into a main obstacle to become employed again, especially if it is accompanied by other impediments such as low-level education (school/vocational), poor health (especially chronical illness), advanced age (older than fifty), immigrant status, motherhood or engagement in care for other relatives. As can be shown by this current research project, in these cases the chance to regain employment decreases to near nil. Almost two-thirds of all welfare recipients have multiple impediments which hinder a successful transition from welfare back to sustainable and sufficient employment. Prospective employers are unlikely to hire long-term unemployed with additional impediments because they evaluate potential employees on their negative signaling (e.g. low-level education) and the implicit assumption of unproductiveness (e.g. poor health, age). Some findings of the panel survey “Labor market and social security” (PASS) carried out by the Institute of Employment Research (the research institute of the German Federal Labor Agency) spread a ray of hope, showing that unlikely does not necessarily mean impossible. The presentation reports on current research on these very scarce “success stories” of unlikely transitions from long-term unemployment to work and how these cases were able to perform this switch against all odds. The study is based on a mixed-method design. Within the panel survey (~15,000 respondents in ~10,000 households), only 66 cases of such unlikely transitions were observed. These cases have been explored by qualitative inquiry – in depth-interviews and qualitative network techniques. There is strong evidence that sustainable transitions are influenced by certain biographical resources like habits of network use, a set of informal skills and particularly a resilient way of dealing with obstacles, combined with contextual factors rather than by job-placement procedures promoted by Job-Centers according to activation rules or by following formal paths of application. On the employer’s side small and medium-sized enterprises are often found to give job opportunities to a wider variety of applicants, often based on a slow but steadily increasing relationship leading to employment. According to these results it is possible to show and discuss some limitations of (German) activation policies targeting the labor market and their impact on welfare dependency and long-term unemployment. Based on these findings, indications for more supportive small-scale measures in the field of labor-market policies are suggested to help long-term unemployed with multiple impediments to overcome their situation (e.g. organizing small-scale-structures and low-threshold services to encounter possible employers on a more informal basis like “meet and greet”).

Keywords: against-all-odds, mixed-methods, Welfare State, long-term unemployment

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Authors: Hardi Alunaza, Mauidhotu Rofiq

Abstract:

This research is attempted to describe the role of KontraS as track-6 on multi track diplomacy for conflict resolution in Myanmar in 2015. The researcher took the specific interest on multi track diplomacy and transnational advocacy concepts to analyze the phenomena. Furthermore, this essay is using the descriptive method with a qualitative approach. The data collection technique is literature study consisting of books, journals, and including data from the reliable website in supporting the explanation of this research. The result of this research is divided into two important points in explaining the role of KontraS in cases of human rights crisis in Myanmar. First, KontraS as human rights NGO in Indonesia was able to advocate against human rights violence that occurred in other countries by encouraging Indonesian Government to take part in the resolution of human rights issues affecting the Rohingya people in Burma. Also, KontraS take advantages of transnational advocacy networks as a form of politics and accountabilities responsibility of Non-Governmental Organization against human rights crisis in other countries.

Keywords: conflict resolution, human rights crisis, multi track diplomacy, transnational advocacy

Procedia PDF Downloads 302