Search results for: wireless sensor networks

Authors: Sara Tokhi Arab, Tariq Salari, Ryozo Noguchi, Tofael Ahamed

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Land suitability analysis is essential for table grape cultivation in order to increase its production and productivity under the dry condition of Afghanistan. In this context, the main aim of this paper was to determine the suitable locations for vineyards based on satellite remote sensing and GIS (geographical information system) in Kabul Province of Afghanistan. The Landsat8 OLI (operational land imager) and thermal infrared sensor (TIRS) and shuttle radar topography mission digital elevation model (SRTM DEM) images were processed to obtain the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), land surface temperature (LST), and topographic criteria (elevation, aspect, and slope). Moreover, Jaxa rainfall (mm per hour), soil properties information are also used for the physical suitability of vineyards. Besides, socio-economic criteria were collected through field surveys from Kabul Province in order to develop the socio-economic suitability map. Finally, the suitable classes were determined using weighted overly based on a reclassification of each criterion based on AHP (Analytical Hierarchy Process) weights. The results indicated that only 11.1% of areas were highly suitable, 24.8% were moderately suitable, 35.7% were marginally suitable and 28.4% were not physically suitable for grapes production. However, 15.7% were highly suitable, 17.6% were moderately suitable, 28.4% were marginally suitable and 38.3% were not socio-economically suitable for table grapes production in Kabul Province. This research could help decision-makers, growers, and other stakeholders with conducting precise land assessments by identifying the main limiting factors for the production of table grapes management and able to increase land productivity more precisely.

Keywords: vineyards, land physical suitability, socio-economic suitability, AHP

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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: Abdullah Alnutayfat, Alexander Sutin

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One of the most important types of renewable energy resources is wind energy which can be produced by wind turbines. The blades of the wind turbine are exposed to the pressure of the harsh environment, which causes a significant issue for the wind power industry in terms of the maintenance cost and failure of blades. One of the reliable methods for blade inspection is the vibroacoustic structural health monitoring (SHM) method which examines information obtained from the structural vibrations of the blade. However, all vibroacoustic SHM techniques are based on comparing the structural vibration of intact and damaged structures, which places a practical limit on their use. Methods for nonlinear vibroacoustic SHM are more sensitive to damage and cracking and do not need to be compared to data from the intact structure. This paper presents the Vibro-Acoustic Modulation (VAM) method based on the modulation of high-frequency (probe wave) by low-frequency loads (pump wave) produced by the blade rotation. The blade rotation alternates bending stress due to gravity, leading to crack size variations and variations in the blade resonance frequency. This method can be used with the classical SHM vibration method in which the blade is excited by piezoceramic actuator patches bonded to the blade and receives the vibration response from another piezoceramic sensor. The VAM modification of this method analyzes the spectra of the detected signal and their sideband components. We suggest the VAM model as the simple mechanical oscillator, where the parameters of the oscillator (resonance frequency and damping) are varied due to low-frequency blade rotation. This model uses the blade vibration parameters and crack influence on the blade resonance properties from previous research papers to predict the modulation index (MI).

Keywords: wind turbine blades, damaged detection, vibro-acoustic structural health monitoring, vibro-acoustic modulation

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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: Yongquan Zhao, Bo Huang

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Spatial, Temporal, and Spectral Resolution (STSR) are three key characteristics of Earth observation satellite sensors; however, any single satellite sensor cannot provide Earth observations with high STSR simultaneously because of the hardware technology limitations of satellite sensors. On the other hand, a conflicting circumstance is that the demand for high STSR has been growing with the remote sensing application development. Although image fusion technology provides a feasible means to overcome the limitations of the current Earth observation data, the current fusion technologies cannot enhance all STSR simultaneously and provide high enough resolution improvement level. This study proposes a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which blends the high spatial resolution from the panchromatic image of Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multi-spectral image of Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high STSR images. The proposed HSTSFM contains three fusion modules: (1) spatial-spectral image fusion; (2) spatial-temporal image fusion; (3) temporal-spectral image fusion. A set of test data with both phenological and land cover type changes in Beijing suburb area, China is adopted to demonstrate the performance of the proposed method. The experimental results indicate that HSTSFM can produce fused image that has good spatial and spectral fidelity to the reference image, which means it has the potential to generate synthetic data to support the studies that require high STSR satellite imagery.

Keywords: hybrid spatial-temporal-spectral fusion, high resolution synthetic imagery, least square regression, sparse representation, spectral transformation

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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: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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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

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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: Carmen Sica, Elena M. Scalisi, Sara Ignoto, Ludovica Palmeri, Martina Contino, Greta Ferruggia, Antonio Salvaggio, Santi C. Pavone, Gino Sorbello, Loreto Di Donato, Roberta Pecoraro, Maria V. Brundo

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Recently, a rise in the use of wireless internet technologies such as Wi-Fi and 5G routers/modems have been demonstrated. These devices emit a considerable amount of electromagnetic radiation (EMR), which could interact with the male reproductive system either by thermal or non-thermal mechanisms. The aim of this study was to investigate the direct in vitro influence of 5G radiation on sperm quality in Mytilus galloprovincialis, considered an excellent model for reproduction studies. The experiments at 27 GHz were conducted by using a no commercial high gain pyramidal horn antenna. To evaluate the specific absorption rate (SAR), a numerical simulation has been performed. The resulting incident power density was significantly lower than the power density limit of 10 mW/cm2 set by the international guidelines as a limit for nonthermal effects above 6 GHz. However, regarding temperature measurements of the aqueous sample, it has been verified an increase of 0.2°C, compared to the control samples. This very low-temperature increase couldn’t interfere with experiments. For experiments, sperm samples taken from sexually mature males of Mytilus galloprovincialis were placed in artificial seawater, salinity 30 + 1% and pH 8.3 filtered with a 0.2 m filter. After evaluating the number and quality of spermatozoa, sperm cells were exposed to electromagnetic fields a 27GHz. The effect of exposure on sperm motility and quality was evaluated after 10, 20, 30 and 40 minutes with a light microscope and also using the Eosin test to verify the vitality of the gametes. All the samples were performed in triplicate and statistical analysis was carried out using one-way analysis of variance (ANOVA) with Turkey test for multiple comparations of means to determine differences of sperm motility. A significant decrease (30%) in sperm motility was observed after 10 minutes of exposure and after 30 minutes, all sperms were immobile and not vital. Due to little literature data about this topic, these results could be useful for further studies concerning a great diffusion of these new technologies.

Keywords: mussel, spermatozoa, sperm motility, millimeter waves

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Authors: Ian Jones, Jonathan Griffiths

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Laser welding offers the potential for making very precise joints in plastics products, both in terms of the joint location and the amount of heating applied. These methods have allowed the production of complex products such as microfluidic devices where channels and structure resolution below 100 µm is regularly used. However, to date, the dimension of welds made using lasers has been limited by the focus spot size that is achievable from the laser source. Theoretically, the minimum spot size possible from a laser is comparable to the wavelength of the radiation emitted. Practically, with reasonable focal length optics the spot size achievable is a few factors larger than this, and the melt zone in a plastics weld is larger again than this. The narrowest welds feasible to date have therefore been 10-20 µm wide using a near-infrared laser source. The aim of this work was to prepare laser absorber tracks and channels less than 10 µm wide in PMMA thermoplastic using EB lithography followed by sealing of channels using laser welding to carry out welds with widths of the order of 1 µm, below the resolution limit of the near-infrared laser used. Welded joints with a width of 1 µm have been achieved as well as channels with a width of 5 µm. The procedure was based on the principle of transmission laser welding using a thin coating of infrared absorbent material at the joint interface. The coating was patterned using electron-beam lithography to obtain the required resolution in a reproducible manner and that resolution was retained after the transmission laser welding process. The joint strength was ratified using larger scale samples. The results demonstrate that plastics products could be made with a high density of structure with resolution below 1 um, and that welding can be applied without excessively heating regions beyond the weld lines. This may be applied to smaller scale sensor and analysis chips, micro-bio and chemical reactors and to microelectronic packaging.

Keywords: microchannels, polymer, EB lithography, laser welding

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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

Procedia PDF Downloads 145

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

Abstract:

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: Dehni Abdellatif, Lounis Mourad

Abstract:

The new approach of applied spatial geostatistics in materials sciences, agriculture accuracy, agricultural statistics, permitted an apprehension of managing and monitoring the water and groundwater qualities in a relationship with salt-affected soil. The anterior experiences concerning data acquisition, spatial-preparation studies on optical and multispectral data has facilitated the integration of correction models of electrical conductivity related with soils temperature (horizons of soils). For tomography apprehension, this physical parameter has been extracted from calibration of the thermal band (LANDSAT ETM+6) with a radiometric correction. Our study area is Oran region (Northern West of Algeria). Different spectral indices are determined such as salinity and sodicity index, the Combined Spectral Reflectance Index (CSRI), Normalized Difference Vegetation Index (NDVI), emissivity, Albedo, and Sodium Adsorption Ratio (SAR). The approach of geostatistical modeling of electrical conductivity (salinity), appears to be a useful decision support system for estimating corrected electrical resistivity related to the temperature of surface soils, according to the conversion models by substitution, the reference temperature at 25°C (where hydrochemical data are collected with this constraint). The Brightness temperatures extracted from satellite reflectance (LANDSAT ETM+) are used in consistency models to estimate electrical resistivity. The confusions that arise from the effects of salt stress and water stress removed followed by seasonal application of the geostatistical analysis in Geographic Information System (GIS) techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt-affected soil.

Keywords: geostatistical modelling, landsat, brightness temperature, conductivity

Procedia PDF Downloads 422

Authors: Mallikarjun B. Channappagoudar, Pallapa Venkataram

Abstract:

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

Procedia PDF Downloads 364

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

Procedia PDF Downloads 299

Authors: Winncy Du, Jeremy Nguyen, Harpinder Dhillon, Reinardus Justin Halim, Clayton Haske, Trent Hughes, Marissa Ortiz, Rozy Saini

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Stroke often causes death or cerebro-vascular (CV) brain damage. Most patients with CV brain damage lost their motor control on their limbs. This paper focuses on developing a reliable, safe, and non-invasive EEG-based robot-assistant stroke rehabilitation system to help stroke survivors to rapidly restore their motor control functions for their limbs. An electroencephalogram (EEG) recording device (EPOC Headset) and was used to detect a patient’s brain activities. The EEG signals were then processed, classified, and interpreted to the motion intentions, and then converted to a series of robot motion commands. A six-axis articulated robot (AdeptSix 300) was employed to provide the intended motions based on these commends. To ensure the EEG device, the computer, and the robot can communicate to each other, an Arduino microcontroller is used to physically execute the programming codes to a series output pins’ status (HIGH or LOW). Then these “hardware” commends were sent to a 24 V relay to trigger the robot’s motion. A lookup table for various motion intensions and the associated EEG signal patterns were created (through training) and installed in the microcontroller. Thus, the motion intention can be direct determined by comparing the EEG patterns obtaibed from the patient with the look-up table’s EEG patterns; and the corresponding motion commends are sent to the robot to provide the intended motion without going through feature extraction and interpretation each time (a time-consuming process). For safety sake, an extender was designed and attached to the robot’s end effector to ensure the patient is beyond the robot’s workspace. The gripper is also designed to hold the patient’s limb. The test results of this rehabilitation system show that it can accurately interpret the patient’s motion intension and move the patient’s arm to the intended position.

Keywords: brain waves, EEG sensor, motion control, robot-assistant stroke rehabilitation

Procedia PDF Downloads 359

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

Procedia PDF Downloads 278

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 294