Search results for: sensors networks

Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Kimberley Kershaw, Denis Hyams-Ssekasi

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Although there is a wealth of information about refugees and Asylum seekers, Refugee job opportunities continue to be one of the most hotly contested areas and less researched within the social sciences. Refugees are a vital asset in the society due to their experiences, skills, and competences. However, society perceives them differently, and as such, their prior lived experiences are often underutilised. This research study gleans from the work conducted during the Refugee Employment Support Clinic delivered for 12 weeks within a University setting in the North West of England. The study is conducted using three perspectives, refugees, students, and researchers, allowing for identification of the challenges encountered by the refugees concerning job opportunities. Through the utilisation of the qualitative research method, the study has found that refugees experience a wide range of issues unrelated to their skills, prior experience, and education but rather due to the red tapes connected to their legal identity labelling. Refugees struggle to build reliable employment networks that appreciate and acknowledge their capabilities and talents, impacting their ability to navigate the labour market and classism. Notably, refugees are misunderstood within their new societies, and little care is taken to understand the unique struggles they face with respect to securing paid work in their industry or field of work due to their lack of experience in the UK. Unlike other European countries, it is evident that the UK has no strategic approach to enhancing the chances of paid or voluntary work for refugees. A clinic like this provided lenses for comprehending how refugees can be better supported with employment related opportunities. By creating a safe and conducive platform for honest and open discussion about employment and through collaborative approaches with local community agencies, doors were opened for social and professional networks to be built. The study concluded that there is a need for local communities and education establishments to be more aware of the prevailing challenges and in a position to support at all stages of their asylum claim in order for the perceptions of distrust and uncertainty around refugees to be minimised.

Keywords: refugees, employment, community, classism, education

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Authors: Hyun Chul Koag, Hyun-Sik Ahn

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In this paper, we propose a design approach for the safety mechanism of an actuator used in a commercial vehicle’s EPS system. As the number of electric/electronic system in a vehicle increases, the importance of the functional safety has been receiving much attention. EPS(Electric Power Steering) systems for commercial vehicles require large power than passenger vehicles, and hence, dual motor can be applied to get more torque. We show how to formulate the development process for the design of hardware and software of an EPS system using dual motors. A lot of safety mechanisms for the processor, sensors, and memory have been suggested, however, those for actuators have not been fully researched. It is shown by metric analyses that the target ASIL(Automotive Safety Integrated Level) is satisfied in the point of view of hardware of EPS controller.

Keywords: safety mechanism, functional safety, commercial vehicles, electric power steering

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Authors: Raunak Munjal, Mohammad Akif Ali, Prithiv Raj

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This study investigates the relative effectiveness of various flight controllers for drone obstacle avoidance. To assess ultrasonic sensors' performance in real-time obstacle detection, they are integrated with ESP32 and Arduino Nano controllers. The study determines which controller is most effective for this particular application by analyzing important parameters such as accuracy (mean absolute error), standard deviation, and mean distance range. Furthermore, the study explores the possibility of incorporating state-driven algorithms into the Arduino Nano configuration to potentially improve obstacle detection performance. The results offer significant perspectives for enhancing sensor integration, choosing the best flight controller for obstacle avoidance, and maybe enhancing drones' general environmental navigation ability.

Keywords: ultrasonic distance measurement, accuracy and consistency, flight controller comparisons, ESP32 vs arduino nano

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Authors: Kolesnik Nadezda A.

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The research aims to elaborate inter-organizational network relationships management model to maximize value co-creation. We propose a network management framework that requires evaluation of network partners with respect to their position and role in network; and elaboration of appropriate relationship development strategy with partners in network. Empirical research and approval is based on the case study method, including structured in-depth interviews with the companies from b2b market.

Keywords: inter-organizational networks, value co-creation, model, B2B market

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Authors: Rasim Alguliev, Farhad Yusifov

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Considering the international experience, conceptual and architectural principles of forming of electron government are researched and some suggestions were made. The assessment of monitoring of forming processes of electron government, intellectual analysis of web-resources, provision of information security, electron democracy problems were researched, conceptual approaches were suggested. By taking into consideration main principles of electron government theory, important research directions were specified.

Keywords: electron government, public administration, information security, web-analytics, social networks, data mining

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Illyas Isa, Siti Nur Akmar Mohd Yazid, Norhayati Hashim

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We report a simple, green and cost effective synthesis of graphene via chemical reduction of graphene oxide in alkaline aqueous solution. Extensive characterizations have been studied to confirm the formation of graphene in sodium carbonate solution. Cyclic voltammetry was used to study the electrochemical properties of the prepared graphene-modified glassy carbon electrode using potassium ferricyanide as a redox probe. Based on the result, with the addition of graphene to the glassy carbon electrode the current flow increases and the peak also broadens as compared to graphite and graphene oxide. This method is fast, cost effective, and green as nontoxic solvents are used which will not result in contamination of the products. Thus, this method can serve for the preparation of graphene which can be effectively used in sensors, electronic devices and supercapacitors.

Keywords: chemical reduction, electrochemical, graphene, green synthesis

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Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: G. Baraneedaran, Deepak Singh, Kollipara Tejesh

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The wireless sensors network has been an active research area over the y-ear passed. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively, an energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. A spontaneous reconstruction mechanism for Local topology is designed additionally. In this experiment, FAF-EBRM is compared with LEACH and EECU, experimental results show that FAF-EBRM outperforms LEACH and EECU, which balances the energy consumption, prolongs the function lifetime and guarantees high Qos of WSN.

Keywords: energy balance, forward-aware factor (FAF), forward energy density, link weight, network performance

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Authors: Nina Dasari, Satvik Dasari

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Smart city solutions are being developed across the globe to transform urban areas. However, the infrastructure enablement for alerting natural disasters such as floods and wildfires is deficient. This paper discusses an innovative device that could be used as part of the smart city initiative to detect and provide alerts in case of floods at road crossings and wildfires. An Internet of Things (IoT) smart city node was designed, tested, and deployed with collaboration from the City of Austin. The end to end solution includes a 3G enabled IoT device, flood and fire sensors, cloud, a mobile app, and IoT analytics. The real-time data was collected and analyzed using IoT analytics to refine the solution for the past year. The results demonstrate that the proposed solution is reliable and provides accurate results. This low-cost solution is viable, and it can replace the current solution which costs tens of thousands of dollars.

Keywords: analytics, internet of things, natural disasters, smart city

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Authors: Engy A. Mohamed, Y. G. Hegazy

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This paper presents a novel algorithm for modeling photovoltaic based distributed generators for the purpose of optimal planning of distribution networks. The proposed algorithm utilizes sequential Monte Carlo method in order to accurately consider the stochastic nature of photovoltaic based distributed generators. The proposed algorithm is implemented in MATLAB environment and the results obtained are presented and discussed.

Keywords: comulative distribution function, distributed generation, Monte Carlo

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Authors: Mora Kestelman

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This paper analyzes various family groups of vulnerable populations as regards their family, educational, labor trajectory and sociability from a relational and historical approach based on archive research and fieldwork. Therefrom, their position and life projects are reconsidered as regards the planning and design of the habitat in which they are immersed. It concludes that a critical review of objectivity and subjectivity emphasizes the nonrational, often unconscious, forces that drive human and non-human relationships to configure identities, which, thus, permanently become constituent to the subjects.

Keywords: social psychology, urban planning, self concept, social networks, identity theory

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Patrik Scajev, Pavels Onufrijevs, Algirdas Mekys, Tadas Malinauskas, Dominykas Augulis, Liudvikas Subacius, Kuo-Chih Lee, Jevgenijs Kaupuzs, Arturs Medvids, Hung Hsiang Cheng

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In this study, we focused on the optoelectronic properties of the photodiodes prepared by using 200 nm thick Ge₀.₉₅Sn₀.₀₅ epitaxial layers on Ge/n-Si substrate with aluminum contacts. Photodiodes were formed on non-irradiated and Nd: YAG laser irradiated Ge₀.₉₅Sn₀.₀₅ layers. The samples were irradiated by pulsed Nd: YAG laser with 136.7-462.6 MW/cm² intensity. The photodiodes were characterized by using short laser pulses with the wavelength in the 2.0-2.6 μm range. The laser-irradiated diode was found more sensitive in the long-wavelength range due to laser-induced Sn atoms redistribution providing formation of graded bandgap structure. Sub-millisecond photocurrent relaxation in the diodes revealed their suitability for image sensors. Our findings open the perspective for improving the photo-sensitivity of GeSn alloys in the mid-infrared by pulsed laser processing.

Keywords: GeSn, laser processing, photodetector, infrared

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Authors: F. Tang, S. Boskovich, A. Raheja, Z. Aliyazicioglu, S. Bhandari, N. Tsuchiya

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Calpoly Autonomous Transportation Experience (CATE) is a driverless vehicle that we are developing to provide safe, accessible, and efficient transportation of passengers throughout the Cal Poly Pomona campus for events such as orientation tours. Unlike the other self-driving vehicles that are usually developed to operate with other vehicles and reside only on the road networks, CATE will operate exclusively on walk-paths of the campus (potentially narrow passages) with pedestrians traveling from multiple locations. Safety becomes paramount as CATE operates within the same environment as pedestrians. As driverless vehicles assume greater roles in today’s transportation, this project will contribute to autonomous driving with pedestrian traffic in a highly dynamic environment. The CATE project requires significant interdisciplinary work. Researchers from mechanical engineering, electrical engineering and computer science are working together to attack the problem from different perspectives (hardware, software and system). In this abstract, we describe the software aspects of the project, with a focus on the requirements and the major components. CATE shall provide a GUI interface for the average user to interact with the car and access its available functionalities, such as selecting a destination from any origin on campus. We have developed an interface that provides an aerial view of the campus map, the current car location, routes, and the goal location. Users can interact with CATE through audio or manual inputs. CATE shall plan routes from the origin to the selected destination for the vehicle to travel. We will use an existing aerial map for the campus and convert it to a spatial graph configuration where the vertices represent the landmarks and edges represent paths that the car should follow with some designated behaviors (such as stay on the right side of the lane or follow an edge). Graph search algorithms such as A* will be implemented as the default path planning algorithm. D* Lite will be explored to efficiently recompute the path when there are any changes to the map. CATE shall avoid any static obstacles and walking pedestrians within some safe distance. Unlike traveling along traditional roadways, CATE’s route directly coexists with pedestrians. To ensure the safety of the pedestrians, we will use sensor fusion techniques that combine data from both lidar and stereo vision for obstacle avoidance while also allowing CATE to operate along its intended route. We will also build prediction models for pedestrian traffic patterns. CATE shall improve its location and work under a GPS-denied situation. CATE relies on its GPS to give its current location, which has a precision of a few meters. We have implemented an Unscented Kalman Filter (UKF) that allows the fusion of data from multiple sensors (such as GPS, IMU, odometry) in order to increase the confidence of localization. We also noticed that GPS signals can easily get degraded or blocked on campus due to high-rise buildings or trees. UKF can also help here to generate a better state estimate. In summary, CATE will provide on-campus transportation experience that coexists with dynamic pedestrian traffic. In future work, we will extend it to multi-vehicle scenarios.

Keywords: driverless vehicle, path planning, sensor fusion, state estimate

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Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh

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A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.

Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety

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Authors: Manvelidze irakli, Iashvili Genadi

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Research has shown that public institutions in Autonomous Republic of Ajara try their best to make their official electronic data (web-pages, social websites) more informative and improve them. Part of public institutions offer interesting electronic services and initiatives to the public although they are seldom used in communication process. The statistical analysis of the use of web-pages and social websites of public institutions for example their facebook page show lack of activity. The reason could be the fact that public institutions give people less possibility of interaction in official web-pages. Second reason could be the fact that these web-pages are less known to the public and the third reason could be the fact that heads of these institutions lack awareness about the necessity of strengthening citizens’ involvement. In order to increase people’s involvement in this process it is necessary to have at least 23 e-services in one web-page. The research has shown that 11 of the 16 public institutions have only 5 services which are contact, social networks and hotline. Besides introducing innovative services government institutions should evaluate them and make them popular and easily accessible for the public. It would be easy to solve this problem if public institutions had concrete strategic plan of public relations which involved matters connected with maximum usage of electronic services while interaction with citizens. For this moment only one governmental body has a functioning action plan of public relations. As a result of the research organizational, social, methodological and technical problems have been revealed. It should be considered that there are many feedback possibilities like forum, RSS, blogs, wiki, twitter, social networks, etc. usage of only one or three of such instruments indicate that there is no strategy of regional electronic governance. It is necessary to develop more mechanisms of feedback which will increase electronic interaction, discussions and it is necessary to introduce the service of online petitions. It is important to reduce the so-called “digital inequality” and increase internet access for the public. State actions should decrease such problems. In the end if such shortcomings will be improved the role of electronic interactions in democratic processes will increase.

Keywords: e-Government, electronic services, information technology, regional government, regional government

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Authors: Andrew Youssef, David Matthews, Jie Pan

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Identifying the magnitude and direction of a force on an elastic solid is highly desirable, as this allows for investigation and continual monitoring of the dynamic loading. This was traditionally conducted by connecting the solid to the supporting structure by multi-axial force transducer, providing that the transducer will not change the mounting conditions. Polyvinylidene fluoride (PVDF) film is a versatile force transducer that can be easily embedded in structures. Here a PVDF sensor array is embedded inside a simple structure in an effort to determine the force vector applied to the structure is an inverse problem. In this paper, forces of different magnitudes and directions where applied to the structure with an impact hammer, and the output of the PVDF was captured and processed to gain an estimate of the forces applied by the hammer. The outcome extends the scope of application of PVDF sensors for measuring the external or contact force vectors.

Keywords: embedded sensor, monitoring, PVDF, vibration

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Authors: Somar Boubou, Tatsuo Narikiyo, Michihiro Kawanishi

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In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object key points, we propose a global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each frame using a 2D spatial histogram capturing the normalized distribution of differential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier.

Keywords: vision in control, robotics, histogram, differential histogram of normal vectors

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Authors: Sumedha Weerasekara, Morgan Miles, Mark Morrison, Branka Krivokapic-Skoko

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This paper is aimed at exploring the interrelationships among entrepreneurial ecosystem factors and entrepreneurial cognition in regional and metropolitan ecosystems. Entrepreneurial ecosystem factors examined include: culture, infrastructure, access to finance, informal networks, support services, access to universities, and the depth and breadth of the talent pool. Using a multivariate approach we explore the impact of these ecosystem factors or elements on entrepreneurial cognition. In doing so, the existing body of knowledge from the literature on entrepreneurial ecosystem and cognition have been blended to explore the relationship between entrepreneurial ecosystem factors and cognition in a way not hitherto investigated. The concept of the entrepreneurial ecosystem has received increased attention as governments, universities and communities have started to recognize the potential of integrated policies, structures, programs and processes that foster entrepreneurship activities by supporting innovation, productivity and employment growth. The notion of entrepreneurial ecosystems has evolved and grown with the advancement of theoretical research and empirical studies. Importance of incorporating external factors like culture, political environment, and the economic environment within a single framework will enhance the capacity of examining the whole systems functionality to better understand the interaction of the entrepreneurial actors and factors within a single framework. The literature on clusters underplays the role of entrepreneurs and entrepreneurial management in creating and co-creating organizations, markets, and supporting ecosystems. Entrepreneurs are only one actor following a limited set of roles and dependent upon many other factors to thrive. As a consequence, entrepreneurs and relevant authorities should be aware of the other actors and factors with which they engage and rely, and make strategic choices to achieve both self and also collective objectives. The study uses stratified random sampling method to collect survey data from 12 different regions in regional and metropolitan regions of NSW, Australia. A questionnaire was administered online among 512 Small and medium enterprise owners operating their business in selected 12 regions in NSW, Australia. Data were analyzed using descriptive analyzing techniques and partial least squares - structural equation modeling. The findings show that even though there is a significant relationship between each and every entrepreneurial ecosystem factors, there is a weak relationship between most entrepreneurial ecosystem factors and entrepreneurial cognition. In the metropolitan context, the availability of finance and informal networks have the largest impact on entrepreneurial cognition while culture, infrastructure, and support services having the smallest impact and the talent pool and universities having a moderate impact on entrepreneurial cognition. Interestingly, in a regional context, culture, availability of finance, and the talent pool have the highest impact on entrepreneurial cognition, while informal networks having the smallest impact and the remaining factors – infrastructure, universities, and support services have a moderate impact on entrepreneurial cognition. These findings suggest the need for a location-specific strategy for supporting the development of entrepreneurial cognition.

Keywords: academic achievement, colour response card, feedback

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Authors: Tahir Majeed, Michael Handschuh, René Meier

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Rheumatoid arthritis patients have swelling and pain at the joints of the hand. The regions where the patient feels pain also show increased body temperature. Thermal cameras can be used to detect the rise in temperature of the affected regions. To monitor the disease progression of rheumatoid arthritis patients, they must visit the clinic regularly for scanning and examination. After scanning and evaluation, the dosage of the medicine is regulated accordingly. To monitor the disease progression over time, the correlation between the images between different visits must be established. It has been observed that by using low-cost thermal cameras, the thermal measurements do not remain the same over time, even within a single scanning. In some situations, temperatures can vary as much as 2°C within the same scanning sequence. In this paper, it has been shown that although the absolute temperature varies over time, the relative difference between the different regions remains similar. Results have been computed over four scanning sequences and are presented.

Keywords: relative thermal difference, rheumatoid arthritis, thermal imaging, thermal sensors

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Authors: Alireza Mirrashid, Mohammad Khoshbin, Ali Atghaei, Hassan Shahbazi

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In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: attention, fire detection, smoke detection, spatio-temporal

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Authors: Mohammad Tahmasebipour, Hosein Salarpour

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Micro cantilevers are one of the components used in the manufacture of micro-electromechanical systems. Epoxy microcantilevers have a variety of applications in the manufacture of micro-sensors and micro-actuators. In this paper, the Timoshenko Micro cantilever was statically and dynamically analyzed using the finite element method. First, all boundary conditions and initial conditions governing micro cantilevers were considered. The effect of size on the deflection, angle of rotation, natural frequencies, and mode shapes were then analyzed and evaluated under different frequencies. It was observed that an increased micro cantilever thickness reduces the deflection, rotation, and resonant frequency. A good agreement was observed between our results and those obtained by the couple stress theory, the classical theory, and the strain gradient elasticity theory.

Keywords: microcantilever, microsensor; epoxy, dynamic behavior, static behavior, finite element method

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Authors: Innocent Uzougbo Onwuegbuzie

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Healthcare delivery in low-income markets is fraught with numerous challenges, including limited access to essential medical resources, inadequate healthcare infrastructure, and a significant shortage of trained healthcare professionals. These constraints lead to suboptimal health outcomes and a higher incidence of preventable diseases. This paper explores the application of Wireless Sensor Networks (WSNs) as a transformative solution to enhance healthcare delivery in these underserved regions. WSNs, comprising spatially distributed sensor nodes that collect and transmit health-related data, present opportunities to address critical healthcare needs. Leveraging WSN technology facilitates real-time health monitoring and remote diagnostics, enabling continuous patient observation and early detection of medical issues, especially in areas with limited healthcare facilities and professionals. The implementation of WSNs can enhance the overall efficiency of healthcare systems by enabling timely interventions, reducing the strain on healthcare facilities, and optimizing resource allocation. This paper highlights the potential benefits of WSNs in low-income markets, such as cost-effectiveness, increased accessibility, and data-driven decision-making. However, deploying WSNs involves significant challenges, including technical barriers like limited internet connectivity and power supply, alongside concerns about data privacy and security. Moreover, robust infrastructure and adequate training for local healthcare providers are essential for successful implementation. It further examines future directions for WSNs, emphasizing innovation, scalable solutions, and public-private partnerships. By addressing these challenges and harnessing the potential of WSNs, it is possible to revolutionize healthcare delivery and improve health outcomes in low-income markets.

Keywords: wireless sensor networks (WSNs), healthcare delivery, low-Income markets, remote patient monitoring, health data security

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Authors: Yanqun Li, Hong Geng

Abstract:

The occurrence of Wenchuan earthquake in 2008 has led to severe damage to the rural areas of Chengdu city, such as the rupture of the social network, the stagnation of economic production and the rupture of living space. The post-disaster reconstruction has become a sustainable issue. As an important link to maintain the order of rural social development, social network should be an important content of post-disaster reconstruction. Therefore, this paper takes rural reconstruction communities in earthquake-stricken areas of Chengdu as the research object and adopts sociological research methods such as field survey, observation and interview to try to understand the transformation of rural social relations network under the influence of earthquake and its impact on rural space. It has found that rural societies under the earthquake generally experienced three phases: the break of stable social relations, the transition of temporary non-normal state, and the reorganization of social networks. The connotation of phased rural social relations also changed accordingly: turn to a new division of labor on the social orientation, turn to a capital flow and redistribution in new production mode on the capital orientation, and turn to relative decentralization after concentration on the spatial dimension. Along with such changes, rural areas have emerged some social issues such as the alienation of competition in the new industry division, the low social connection, the significant redistribution of capital, and the lack of public space. Based on a comprehensive review of these issues, this paper proposes the corresponding response mechanism. First of all, a reasonable division of labor should be established within the villages to realize diversified commodity supply. Secondly, the villages should adjust the industrial type to promote the equitable participation of capital allocation groups. Finally, external public spaces should be added to strengthen the field of social interaction within the communities.

Keywords: social relations, social support networks, industrial division, capital allocation, public space

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Authors: Sallama Resen, Celal Öztürk

Abstract:

Distance estimation between tow nodes has wide scope of surveillance and tracking applications. This paper suggests a Bluetooth Low Energy (BLE) technology as a media for transceiver and receiver signal in small indoor areas. As an example, BLE communication technologies used in child safety domains. Local network is designed to detect child position in indoor school area consisting Mobile Beacons (MB), Access Points (AP) and Smart Phones (SP) where MBs stuck in children’s shoes as wearable sensors. This paper presents a technique that can detect mobile beacons’ position and help finding children’s location within dynamic environment. By means of bluetooth beacons that are attached to child’s shoes, the distance between the MB and teachers SP is estimated with an accuracy of less than one meter. From the simulation results, it is shown that high accuracy of position coordinates are achieved for multi-mobile beacons in different environments.

Keywords: bluetooth low energy, child safety, mobile beacons, received signal strength

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Authors: Anita Chhatwal

Abstract:

Purpose: The purpose of this paper is to evaluate and investigate the scope of usage of Academic Social Networking Websites (ASNs) by the Science faculty members across universities of North India, viz. Panjab University, Punjabi University and University of Delhi, Delhi. Design/Methodology/Approach: The present study is based upon the primary data collected from 81 science faculty participants from three universities of North India. Questionnaire method was used as an instrument for survey. The study is descriptive and research-based to investigate the popular ASNs amongst the participants from three sample universities and the purpose for which they use them along with the problems they encounter while using ASNs. Findings: The findings of the study revealed that majority of the participants were using ASNs for their academic needs. It was observed that majority of the participants (78%) used ASNs to access scientific papers, while 73.8% of the participants used them to share their research publications. ResearchGate (60.5%) and Google Scholar (59.7%) were the top two most preferred and widely used ASNs by the participants. The critical analysis of the data shows that laptops (86.3%) emerged as major tools for accessing ASNs. Shortage of computers was found to be the chief obstacle in accessing ASNs by the participants. Results of the study demonstrate that 56.3% of participants suggested conduct of seminars and training as the most effective method to increase the awareness of ASNs. Research Limitations/Implications: The study in hand absorbed the 81 faculty (Assistant Professors) members from 15 Science teaching departments across three sample universities of North India. The findings of this study will help the Government of India to regulate and simultaneously make effort to develop and enhance ASNs usage among faculty, researchers, and students. The present study will add to the existing library and information science literature and will be advantageous for all the information professionals as well. Originality/Value: This study is original survey based on primary data investigate the usage of ASNs by the academia. This study will be useful for research scholars, academicians and students all over the world.

Keywords: academic social networks, awareness and usage, North India, scholarly communication, web 2.0

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Authors: Moumita Chanda, Md. Fazlul Karim Patwary

Abstract:

Smoking is one of the most popular recreational drug use behaviors, and it contributes to birth defects, COPD, heart attacks, and erectile dysfunction. To completely eradicate this disease, it is imperative that it be identified and treated. Numerous smoking cessation programs have been created, and they demonstrate how beneficial it may be to help someone stop smoking at the ideal time. A tomography meter is an effective smoking detector. Other wearables, such as RF-based proximity sensors worn on the collar and wrist to detect when the hand is close to the mouth, have been proposed in the past, but they are not impervious to deceptive variables. In this study, we create a machine that can discriminate between smokers and non-smokers in real-time with high sensitivity and specificity by watching and collecting the human lung and analyzing the X-ray data using machine learning. If it has the highest accuracy, this machine could be utilized in a hospital, in the selection of candidates for the army or police, or in university entrance.

Keywords: CNN, smoker detection, non-smoker detection, OpenCV, artificial Intelligence, X-ray Image detection

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Authors: Ece Kök Yetimoğlu, Soner Çubuk, Neşe Taşci, M. Vezir Kahraman

Abstract:

Lead(II) is one of the most toxic environmental pollutants in the world, due to its high toxicity and non-biodegradability. Lead exposure causes severe risks to human health such as central brain damages, convulsions, kidney damages, and even death. To determine lead(II) in environmental or biological samples, scientists use atomic absorption spectrometry (AAS), inductively coupled plasma mass spectrometry (ICPMS), fluorescence spectrometry and electrochemical techniques. Among these systems the fluorescence spectrometry and fluorescent chemical sensors have attracted considerable attention because of their good selectivity and high sensitivity. The fluorescent polymers usually contain covalently bonded fluorophores. In this study imidazole based UV cured polymeric film was prepared and designed to act as a fluorescence chemo sensor for lead (II) analysis. The optimum conditions such as influence of pH value and time on the fluorescence intensity of the sensor have also been investigated. The sensor was highly sensitive with a detection limit as low as 1.87 × 10−8 mol L-1 and it was successful in the determination of Pb(II) in water samples.

Keywords: fluorescence, lead(II), photopolymerization, polymeric sensor

Procedia PDF Downloads 658