Search results for: smart mater

Authors: Xuyang Song, Jun Yin

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The smart card has been an extremely universal tool in public transit. It collects a large amount of data on buses, urban railway transit, and ferries and provides possibilities for passenger profiling. This paper combines offline analysis of passenger profiling and real-time prediction to propose a method that can accurately predict the destination station in real-time when passengers tag on. Firstly, this article constructs a static database of user travel characteristics after identifying passenger travel patterns based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The dual travel passenger habits are identified: OD travel habits and D station travel habits. Then a rapid real-time prediction algorithm based on Transit Passenger Profiling is proposed, which can predict the destination of in-board passengers. This article combines offline learning with online prediction, providing a technical foundation for real-time passenger flow prediction, monitoring and simulation, and short-term passenger behavior and demand prediction. This technology facilitates the efficient and real-time acquisition of passengers' travel destinations and demand. The last, an actual case was simulated and demonstrated feasibility and efficiency.

Keywords: travel behavior, destination prediction, public transit, passenger profiling

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Authors: Sultan Aldaihani, Eiman Al-Fadhli

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Internet of Things (IoTs) expected to change the future of academic libraries operations. It enables academic libraries to be smart libraries through, for example, the connection of the physical objects with the Internet. The implementation of IoTs will improve library resources and services. Therefore, this research aims to investigate the applications of Internet of Things (IoTs) for information resources and services. Understanding perceptions of academic librarians toward IoTs before adopting of such applications will assist decision-makers in academic libraries in their strategic planning. An online questionnaire was administered to academic librarians at Kuwait University. The findings of this study showed that academic librarians have awareness for the IoTs. They have strongly believed that the IoTs contributes to the development of information resources, services, and understanding of the user's information behavior. Identifying new applications of the IoTs in libraries was the highest possible reason for future adoption. Academic librarians indicated that lack of privacy and data penetration were the greatest problem in their future adoption of IoTs. Academic libraries need to implement the IoTs for enhancing their information resources and services. One important step in the success of future adoption is to conduct awareness and training programs for academic librarians. They also need to maintain higher security and privacy measurements in their implementation for the IoTs. This study will assist academic libraries in accommodating this technology.

Keywords: academic libraries, internet of things, information resources, information services

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Authors: Aulon Shabani, Denis Panxhi, Orion Zavalani

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This paper presents the modelling and development of a simulator for residential electrical appliances. The simulator is developed on MATLAB providing the possibility to analyze and simulate energy consumption of frequently used home appliances in Albania. Modelling of devices considers the impact of different factors, mentioning occupant behavior and climacteric conditions. Most devices are modeled as an electric circuit, and the electric energy consumption is estimated by the solutions of the guiding differential equations. The provided models refer to devices like a dishwasher, oven, water heater, air conditioners, light bulbs, television, refrigerator water, and pump. The proposed model allows us to simulate beforehand the energetic behavior of the largest consumption home devices to estimate peak consumption and improving its reduction. Simulated home prototype results are compared to real measurement of a considered typical home. Obtained results from simulator framework compared to monitored typical household using EmonTxV3 show the effectiveness of the proposed simulation. This conclusion will help for future simulation of a large group of typical household for a better understanding of peak consumption.

Keywords: electrical appliances, energy management, modelling, peak estimation, simulation, smart home

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Authors: Kunihisa Kakumoto

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This technical paper is for the prototype of solar energy utilization for finding sustainable conditions. This model has been simulated under the climate conditions in Japan. At the beginning of the study, the solar model house was built up on site. And the concerned data was collected in this model house for several years. On the basis of these collected data, the concept on the solar community was built up. For the finding sustainable conditions, the amount of the solar energy generation and its reduction of carbon dioxide and the reduction of carbon dioxide by the green planting and the amount of carbon dioxide according to the normal daily life in the solar community and the amount of the necessary water for the daily life in the solar community and the amount of the water supply by the rainfall on-site were calculated. These all values were taken into consideration. The relations between each calculated result are shown in the expression of inequality. This solar community and its consideration for finding sustainable conditions can be one prototype to do the feasibility study for our life in the future

Keywords: carbon dioxide, green planting, smart city, solar community, sustainable condition, water activity

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Authors: Donatello Cardone

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Shape memory alloys (SMAs) have great potential for use in the field of civil engineering. The author of this manuscript has been involved, since 1996, in several experimental and theoretical studies on the application of SMAs in structural engineering, within national and international research projects. This paper provides an overview of the main results achieved, including the conceptual design, implementation, and testing of different SMA-based devices, namely: (i) energy-dissipating braces for RC buildings, (ii) seismic isolation devices for buildings and bridges, (iii) smart tie-rods for arches and vaults and (iv) seismic restrainers for bridges. The main advantages of using SMA-based devices in the seismic protection of structures derive from the double-flag shape of their hysteresis loops, which implies three favourable features, i.e., self-centering capability, good energy dissipation capability, and high stiffness for small displacements. The main advantages of SMA-based units for steel tie-rods are associated with the thermal behaviour of superelastic SMAs, which is antagonistic compared to that of steel. This implies a strong reduction of force changes due to air temperature variations. Finally, SMA-based seismic restrainers proved to be effective in preventing bridge deck unseating and pounding.

Keywords: seismic protection of structures, shape memory alloys, structural engineering, steel tie-rods, seismic restrainers for bridges

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Authors: Maha Rashid Al-Azib, Almuzn Qasem Alqathradi, Amal Munir Alshahrani, Bilqis Mohammed Assiri, Ali Almuflih

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The Occupational Safety and Health Management System in one of the largest Saudi companies has been experiencing in the last 10 years extensive direct and indirect expenses due to lack of proactive leading indicators and safety leadership effective procedures. And since there are no studies that are associated with this department of safety in the company, this research has been conducted. In this study we used a mixed method approach containing a literature review and experts input, then a qualitative questionnaire provided by Institute for Work and Health related to determining the company’s occupational safety and health management system level out from three levels (Compliance - Improvement - Continuous Learning) and the output regarding the company’s level was in Continuous Learning. After that Deming cycle was employed to create a set of proactive leading indicators and analyzed using the SMART method to make sure of its effectiveness and suitability to the company. The objective of this research is to provide a set of proactive indicators to contribute in making an efficient occupational safety and health management system that has less accidents which results in less expenses. Therefore, we provided the company with a prototype of an APP, designed and empowered with our final results to contribute in supporting decisions making processes.

Keywords: proactive leading indicators, OSH MS, safety leadership, accidents reduction

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Authors: Jarbas Lopes Cardoso Jr., Frederic Andres, Alexandre Guitton, Asanee Kawtrakul, Silvio E. Barbin

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The important objective of the CyberBrain Mass Agriculture Alarm Acquisition and Analysis (CBMa4) project is to minimize the impacts of diseases and disasters on rice cultivation. For example, early detection of insects will reduce the volume of insecticides that is applied to the rice fields through the use of CBMa4 platform. In order to reach this goal, two major factors need to be considered: (1) the social network of smart farmers; and (2) the warning data alarm acquisition and analysis component. This paper outlines the process for collecting the warning and improving the decision-making result to the warning. It involves two sub-processes: the warning collection and the understanding enrichment. Human sensors combine basic suitable data processing techniques in order to extract warning related semantic according to collective intelligence. We identify each warning by a semantic content called 'warncons' with multimedia metaphors and metadata related to these metaphors. It is important to describe the metric to measuring the relation among warncons. With this knowledge, a collective intelligence-based decision-making approach determines the action(s) to be launched regarding one or a set of warncons.

Keywords: agricultural engineering, warning systems, social network services, context awareness

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Authors: Handie Pramana Putra, Ani Dijah Rahajoe

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The proliferation of smart devices and advancements in mobile communication technologies have permeated various facets of life with the widespread influence of e-commerce. Detecting abnormal transactions holds paramount significance in this realm due to the potential for substantial financial losses. Moreover, the fusion of data flow and control flow assumes a critical role in the exploration of process modeling and data analysis, contributing significantly to the accuracy and security of business processes. This paper introduces an alternative approach to identify abnormal transactions through a model that integrates both data and control flows. Referred to as the Extended Data Petri net (DPNE), our model encapsulates the entire process, encompassing user login to the e-commerce platform and concluding with the payment stage, including the mobile transaction process. We scrutinize the model's structure, formulate an algorithm for detecting anomalies in pertinent data, and elucidate the rationale and efficacy of the comprehensive system model. A case study validates the responsive performance of each system component, demonstrating the system's adeptness in evaluating every activity within mobile transactions. Ultimately, the results of anomaly detection are derived through a thorough and comprehensive analysis.

Keywords: database, data analysis, DPNE, extended data flow, e-commerce

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Chiung-Hui Chen

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With advancements in science and technology, the concept of the Internet of Things (IoT) has gradually developed. The development of the intelligent environment adds intelligence to objects in the living space by using the IoT. In the smart environment, when multiple users share the living space, if different service requirements from different users arise, then the context-aware system will have conflicting situations for making decisions about providing services. Therefore, the purpose of establishing a communication and negotiation mechanism among objects in the intelligent environment is to resolve those service conflicts among users. This study proposes developing a decision-making methodology that uses “Event Agents” as its core. When the sensor system receives information, it evaluates a user’s current events and conditions; analyses object, location, time, and environmental information; calculates the priority of the object; and provides the user services based on the event. Moreover, when the event is not single but overlaps with another, conflicts arise. This study adopts the “Multiple Events Correlation Matrix” in order to calculate the degree values of incidents and support values for each object. The matrix uses these values as the basis for making inferences for system service, and to further determine appropriate services when there is a conflict.

Keywords: internet of things, intelligent object, event agents, negotiation mechanism, degree of similarity

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Authors: Nayden Takev, Remy van Leeuwen, Shiva Chotoe, Hani Alers, Xiao Peng

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Citizens, businesses, and public authorities all around the world are becoming aware of the impact that they have on the environment. Currently, climate change is an apparent cause to urge everyone to act and move to sustainable energy solutions. After the Paris Climate Agreement, every country has thought of a way to cut down carbon emissions. The Netherlands formulated the National Climate Agreement. “The government’s central goal with the National Climate Agreement is to reduce greenhouse gas emissions in the Netherlands by 49% compared to 1990 levels. At a European level, the government is advocating a 55% reduction of greenhouse gas emissions by 2030.” [5]. From a survey of the CBS, it is apparent that citizens are not putting in as much effort into the transition to sustainable energy as the government would like them to. After analysing the data, it became clear that the citizens miss the motivation to switch to sustainable energy because they do not believe it is urgent at this point and it is too expensive for them [2]. This needs to be changed. The citizens need to be aware of their impact on the climate and the advantages that this process will bring them. For example, the implementation of smart home displays 4 for real time energy measuring will give the citizens an overview of their energy usage so they are aware of the impact they have. Researchers have also found that the citizens must be included in the decision-making aimed at changing their behaviour [4, 3, 1]. In the future, the government will need to include the citizens when they create campaigns, strategies or introduce new policies [7, 6]. By including and informing the citizens about the policies it will be more attractive for them to choose sustainable energy. However, is all of this enough to motivate the citizens towards energy transition? Or are there other and better ways to do it?

Keywords: Awereness, Energy Transition, Netherlands, citizens

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Authors: Khushboo Bhavsar, Nisha K. Shah, Neha Parekh

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The status of wastewater treatment needs a novel and quick method for treating the wastewater containing ammoniacal nitrogen. Adsorption behavior of ammoniacal nitrogen from wastewater using the nanoparticles loaded coconut coir was investigated in the present work. Manganese Oxide (MnO2) and Zinc Oxide (ZnO) nanoparticles were prepared and used for the further adsorption study. Manganese nanoparticles loaded coconut coir (MNLCC) and Zinc nanoparticles loaded coconut coir (ZNLCC) were prepared via a simple method and was fully characterized. The properties of both MNLCC and ZNLCC were characterized by Scanning electron microscopy, Fourier Transform Infrared Spectroscopy and X-ray diffraction. Adsorption characteristics were studied using batch technique considering various parameters like pH, adsorbent dosage, time, temperature and agitation time. The NH3-N adsorption process for MNLCC and ZNLCC was thoroughly studied from both kinetic and equilibrium isotherm view-points. The results indicated that the adsorption efficiency of ZNLCC was better when compared to MNLCC. The adsorption kinetics at different experimental conditions showed that second order kinetic model best fits ensuring the monovalent binding sites existing in the present experimental system. The outcome of the entire study suggests that the ZNLCC can be a smart option for the treatment of the ammoniacal nitrogen containing wastewater.

Keywords: ammoniacal nitrogen, MnO2, Nanoparticles, ZnO

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Authors: Mahdi Salarian, Xi Xu, Rashid Ansari

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Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database. This paper proposes an efficient method for limiting the search space for image retrieval engine by extracting and leveraging additional media information about Estimated Positional Error (EP E) to address complexity and accuracy issues in the search, especially to be used for compensating GPS location inaccuracy in dense urban areas. The improved performance is achieved by up to a hundred-fold reduction in the search area used in available reference methods while providing improved accuracy. To test our procedure we created a database by acquiring Google Street View (GSV) images for down town of Chicago. Other available databases are not suitable for our approach due to lack of EP E for the query images. We tested the procedure using more than 200 query images along with EP E acquired mostly in the densest areas of Chicago with different phones and in different conditions such as low illumination and from under rail tracks. The effectiveness of our approach and the effect of size and sector angle of the search area are discussed and experimental results demonstrate how our proposed method can improve performance just by utilizing a data that is available for mobile systems such as smart phones.

Keywords: localization, retrieval, GPS uncertainty, bag of word

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Authors: Panpan Feng

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The concept of green infrastructure originated in Europe and the United States. It aims to ensure smart growth of urban and rural ecosystems and achieve sustainable urban and rural ecological, social, and economic development by combining it with gray infrastructure in traditional planning. Based on the literature review of the theoretical origin, value connotation, and measurement methods of green infrastructure, this study summarizes the research content of green infrastructure at different scales from the three spatial levels of region, city, and block and divides it into functional dimensions, spatial dimension, and strategic dimension. The results show that in the functional dimension, from region-city-block, the research on green infrastructure gradually shifts from ecological function to social function. In the spatial dimension, from region-city-block, the research on the spatial form of green infrastructure has shifted from two-dimensional to three-dimensional, and the spatial structure of green infrastructure has shifted from single ecological elements to multiple composite elements. From a strategic perspective, green infrastructure research is more of a spatial planning tool based on land management, environmental livability and ecological psychology, providing certain decision-making support.

Keywords: green infrastructure, multi-scale, social and ecological functions, spatial strategic decision-making tools

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Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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Authors: Jacob Wahl, Jane Zhang

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This paper describes the development and characterization of a prototype IoT network for use with agricultural imaging and monitoring applications. The sensor and gateway nodes are designed using the ESP32 SoC with integrated Bluetooth Low Energy 4.2 and Wi-Fi. A development board, the Arducam IoTai ESP32, is used for prototyping, testing, and power measurements. Google’s Firebase is used as the cloud storage site for image data collected by the sensor. The sensor node captures images using the OV2640 2MP camera module and transmits the image data to the gateway via Bluetooth Low Energy. The gateway then uploads the collected images to Firebase via a known nearby Wi-Fi network connection. This image data can then be processed and analyzed by computer vision and machine learning pipelines to assess crop growth or other needs. The sensor node achieves a wireless transmission data throughput of 220kbps while consuming 150mA of current; the sensor sleeps at 162µA. The sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day based on the development board power measurements. This network can be utilized by any application that requires high data rates, low power consumption, short-range communication, and large amounts of data to be transmitted at low-frequency intervals.

Keywords: Bluetooth low energy, ESP32, firebase cloud, IoT, smart farming

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Authors: A. Chandra, M. Arthur, L. Mize, A. Pomeroy-Stevens

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Background: Lower and middle-income countries (LMICs) in Asia face rapid urbanization resulting in both economic opportunities (the urban advantage) and emerging health challenges. Urban health risks are magnified in informal settlements and include infectious disease outbreaks, inadequate access to health services, and poor air quality. Over the coming years, urban spaces in Asia will face accelerating public health risks related to migration, climate change, and environmental health. These challenges are complex and require multi-sectoral and multi-stakeholder solutions. The Building Health Cities (BHC) program is funded by the United States Agency for International Development (USAID) to work with smart city initiatives in the Asia region. BHC approaches urban health challenges by addressing policies, planning, and services through a health equity lens, with a particular focus on informal settlements and migrant communities. The program works to develop data-driven decision-making, build inclusivity through stakeholder engagement, and facilitate the uptake of appropriate technology. Methodology: The BHC program has partnered with the smart city initiatives of Indore in India, Makassar in Indonesia, and Da Nang in Vietnam. Implementing partners support municipalities to improve health delivery and equity using two key approaches: political economy analysis and participatory systems mapping. Political economy analyses evaluate barriers to collective action, including corruption, security, accountability, and incentives. Systems mapping evaluates community health challenges using a cross-sectoral approach, analyzing the impact of economic, environmental, transport, security, health system, and built environment factors. The mapping exercise draws on the experience and expertise of a diverse cohort of stakeholders, including government officials, municipal service providers, and civil society organizations. Results: Systems mapping and political economy analyses identified significant barriers for health care in migrant populations. In Makassar, migrants are unable to obtain the necessary card that entitles them to subsidized health services. This finding is being used to engage with municipal governments to mitigate the barriers that limit migrant enrollment in the public social health insurance scheme. In Indore, the project identified poor drainage of storm and wastewater in migrant settlements as a cause of poor health. Unsafe and inadequate infrastructure placed residents of these settlements at risk for both waterborne diseases and injuries. The program also evaluated the capacity of urban primary health centers serving migrant communities, identifying challenges related to their hours of service and shortages of health workers. In Da Nang, the systems mapping process has only recently begun, with the formal partnership launched in December 2019. Conclusion: This paper explores lessons learned from BHC’s systems mapping, political economy analyses, and stakeholder engagement approaches. The paper shares progress related to the health of migrants in informal settlements. Case studies feature barriers identified and mitigating steps, including governance actions, taken by local stakeholders in partner cities. The paper includes an update on ongoing progress from Indore and Makassar and experience from the first six months of program implementation from Da Nang.

Keywords: informal settlements, migration, stakeholder engagement mapping, urban health

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Authors: Savvas Rogotis, Nikolaos Kalatzis, Stergios Dimou-Sakellariou, Nikolaos Marianos

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As precision holds the key for the introduction of distinct benefits in agriculture (e.g., energy savings, reduced labor costs, optimal application of inputs, improved products, and yields), it steadily becomes evident that new initiatives should focus on rendering Precision Agriculture (PA) more accessible to the average farmer. PA leverages on technologies such as the Internet of Things (IoT), earth observation, robotics and positioning systems (e.g., the Global Navigation Satellite System – GNSS - as well as individual positioning systems like GPS, Glonass, Galileo) that allow: from simple data georeferencing to optimal navigation of agricultural machinery to even more complex tasks like Variable Rate Applications. An identified customer pain point is that, from one hand, typical triangulation-based positioning systems are not accurate enough (with errors up to several meters), while on the other hand, high precision positioning systems reaching centimeter-level accuracy, are very costly (up to thousands of euros). Within this paper, a Ground-Based Augmentation System (GBAS) is introduced, that can be adapted to any existing IoT field-sensing station infrastructure. The latter should cover a minimum set of requirements, and in particular, each station should operate as a fixed, obstruction-free towards the sky, energy supplying unit. Station augmentation will allow them to function in pairs with GNSS rovers following the differential GNSS base-rover paradigm. This constitutes a key innovation element for the proposed solution that encompasses differential GNSS capabilities into an IoT field-sensing infrastructure. Integrating this kind of information supports the provision of several additional PA beneficial services such as spatial mapping, route planning, and automatic field navigation of unmanned vehicles (UVs). Right at the heart of the designed system, there is a high-end GNSS toolkit with base-rover variants and Real-Time Kinematic (RTK) capabilities. The GNSS toolkit had to tackle all availability, performance, interfacing, and energy-related challenges that are faced for a real-time, low-power, and reliable in the field operation. Specifically, in terms of performance, preliminary findings exhibit a high rover positioning precision that can even reach less than 10-centimeters. As this precision is propagated to the full dataset collection, it enables tractors, UVs, Android-powered devices, and measuring units to deal with challenging real-world scenarios. The system is validated with the help of Gaiatrons, a mature network of agro-climatic telemetry stations with presence all over Greece and beyond ( > 60.000ha of agricultural land covered) that constitutes part of “gaiasense” (www.gaiasense.gr) smart farming (SF) solution. Gaiatrons constantly monitor atmospheric and soil parameters, thus, providing exact fit to operational requirements asked from modern SF infrastructures. Gaiatrons are ultra-low-cost, compact, and energy-autonomous stations with a modular design that enables the integration of advanced GNSS base station capabilities on top of them. A set of demanding pilot demonstrations has been initiated in Stimagka, Greece, an area with a diverse geomorphological landscape where grape cultivation is particularly popular. Pilot demonstrations are in the course of validating the preliminary system findings in its intended environment, tackle all technical challenges, and effectively highlight the added-value offered by the system in action.

Keywords: GNSS, GBAS, precision agriculture, RTK, smart farming

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Authors: Hassan Manouchehri

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The present study aimed to identify and evaluate the factors affecting violence against women. The statistical population included all couples referring to family counseling centers in Tehran due to domestic violence during the past year. A number of 305 people were selected as a statistical sample using simple random sampling and Cochran's formula in unlimited conditions. A researcher-made questionnaire including 110 items was used for data collection. The face validity and content validity of the questionnaire were confirmed by 30 experts and its reliability was obtained above 0.7 for all studied variables in a preliminary test with 30 subjects and it was acceptable. In order to analyze the data, descriptive statistical methods were used with SPSS software version 22 and inferential statistics were used for modeling structural equations in Smart PLS software version 2. Evaluating the theoretical framework and domestic and foreign studies indicated that, in general, four main factors, including cultural and social factors, economic factors, legal factors, as well as medical factors, underlie violence against women. In addition, structural equation modeling findings indicated that cultural and social factors, economic factors, legal factors, and medical factors affect violence against women.

Keywords: violence against women, cultural and social factors, economic factors, legal factors, medical factors

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Authors: Muhammad Kamran, Xue Pu, Naveed Ahmed

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Sandwich foam structures are efficient in impact energy absorption and making components lightweight; however their efficient use require a detailed understanding of its mechanical response. In this study, the foam core, laminated facings’ sandwich panel with internal triangular rib configuration is impacted by a spherical steel projectile at different angles using ABAQUS finite element package and damage mechanics is studied. Laminated ribs’ structure is sub-divided into three formations; all zeros, all 45 and optimized combination of zeros and 45 degrees. Impact velocity is varied from 250 m/s to 500 m/s with an increment of 50 m/s. The impact damage can significantly demolish the structural integrity and energy absorption due to fiber breakage, matrix cracking, and de-bonding. Macroscopic fracture study of the panel and core along with load-displacement responses and failure modes are the key parameters in the design of smart ballistic resistant structures. Ballistic impact characteristics of panels are studied on different speed, different inclination angles and its dependency on the base, and core materials, ribs formation, and cross-sectional spaces among them are determined. Impact momentum, penetration and kinetic energy absorption data and curves are compiled to predict the first and proximity impact in an effort to enhance the dynamic energy absorption.

Keywords: dynamic energy absorption, proximity impact, sandwich panels, impact momentum

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Authors: Chaithanya Pothuganti

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Open and semi-open spaces in educational buildings like corridors, mid landings, seating spaces, lobby, courtyards are traditionally have been the places of social communion and interaction which helps in promoting the knowledge, performance, activeness, and motivation in students. Factors like availability of land, commercialization, of educational facilities, especially in e-techno and smart schools, led to closed classrooms to accommodate students thereby lack quality open and semi-open spaces. This insufficient attention towards open space design which is a means of informal learning misses an opportunity to encourage the student’s skill development, behavior and learning skills. The core objective of this paper is to find the level of impact on student learning behavior and to identify the suitable proportions and configuration of spaces that shape the schools. In order to achieve this, different types of open spaces in schools and their impact on student’s performance in various existing models are analysed using case studies to draw some design principles. The study is limited to indoor open spaces like corridors, break out spaces and courtyards. The expected outcome of the paper is to suggest better design considerations for the development of semi-open and open spaces which functions as an element for informal learnings. Its focus is to provide further thinking on designing and development of open spaces in educational buildings.

Keywords: configuration of spaces and proportions, informal learning, open spaces, schools, student’s behavior

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

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Fahad Alam

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Work stress directly affects job performance, specifically in a worse environment. Consequently, a social provision plays a crucial part for enhancement. Therefore, the current research investigates the direct and moderating effect between religious activities, sustainability and peer support on job performance at hospitals in Khyber PakhtunKhwa (KPK), Pakistan. Both primary and secondary data are collected through 261 questionnaires of medical employees from four district hospitals in Khyber PakhtunKhwa, Pakistan, in 2018. The analysis was carried out by SPSS16 and SMART PLS3, to test the direct effect of religious activities, sustainability and social support on job performance and the effect of moderating variable 'work environment' on job performance. The finding confirmed that direct and moderating variables play a significant positive effect among religious activities, sustainability and peer support on job performance, the variables help to diminish the strain level or the stress level, consequently helps in the job completed. Affirmative social approaches produce desirable effects on job performance. The research revealed that social provisions are significant triggers for superior practices. Moreover, the results are stimulating because some of the past literature revealed an insignificant correlation between social provision and performance. This study found that there is a significant relationship which persuades health care organizations.

Keywords: job performance, peer’s support, religious activities, sustainability, work environment

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Authors: Paul Barasa Wanyama, Tom Wanyama

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Governments and development partners in low-income countries often prioritize building Water Sanitation and Hygiene (WaSH) infrastructure of healthcare facilities to improve maternal healthcare outcomes. However, the operation, maintenance, and utilization of this infrastructure are almost never considered. Many healthcare facilities in these countries use untreated water that is not monitored for quality or quantity. Consequently, it is common to run out of water while a patient is on their way to or in the operating theater. Further, the handwashing stations in healthcare facilities regularly run out of water or soap for months, and the latrines are typically not clean, in part due to the lack of water. In this paper, we present a system that uses Internet of Things (IoT), big data, cloud computing, and AI to initiate WaSH security in healthcare facilities, with a specific focus on maternal health. We have implemented smart sensors and actuators to monitor and control WaSH systems from afar to ensure their objectives are achieved. We have also developed a cloud-based system to analyze WaSH data in real time and communicate relevant information back to the healthcare facilities and their stakeholders (e.g., medical personnel, NGOs, ministry of health officials, facilities managers, community leaders, pregnant women, and new mothers and their families) to avert or mitigate problems before they occur.

Keywords: WaSH, internet of things, artificial intelligence, maternal health, rural communities, healthcare facilities

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Authors: Lorena Caires Moreira, Marcos Kauffman

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This paper outlines the results of experimental research on deploying an emerging augmented reality (AR) system for real-time task assistance of highly customized and high-risk manual operations. The focus is on operators’ training capabilities and the aim is to test if such technologies can support achieving higher levels of knowledge retention and accuracy of task execution to improve health and safety (H and S) levels. The proposed solution is tested and validated using a real-world case study of electric vehicles’ battery module assembly. The experimental results revealed that the proposed AR method improved the training practices by increasing the knowledge retention levels from 40% to 84% and improved the accuracy of task execution from 20% to 71%, compared to the traditional paper-based method. The results of this research can be used as a demonstration of how emerging technologies are advancing the choice of manual, hybrid, or fully automated processes by promoting the connected worker (Industry 5.0) and supporting manufacturing in becoming more resilient in times of constant market changes.

Keywords: augmented reality, extended reality, connected worker, XR-assisted operator, manual assembly, industry 5.0, smart training, battery assembly

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Authors: Syeda Shawana Mahasan, Muhammad Farooq Akhtar

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The objective of this study is to analyze the influence of the green marketing mix strategy and corporate social responsibility (CSR) on the performance of an organization, taking into account the mediating effect of corporate image. The impact of frugal innovation and corporate activism is being examined. The data was gathered from executives at various levels of management, including top, middle, and lower-level managers, from a total of 550 manufacturing enterprises of different sizes, ranging from small to medium to large. The collected replies are processed and analyzed using SMART PLS version 4.0.0.0. The application of PLS-SEM demonstrates that the green marketing mix strategy and corporate social responsibility have a significant impact on organizational performance. Therefore, it is imperative for organizations to effectively adopt environmentally sustainable and socially conscious methods within their operations. The results indicate that the corporate image has a key role in mediating the relationship between the green marketing mix strategy, corporate social responsibility, and organizational performance. This demonstrates the imperative for organizations to actively enhance their favorable reputation among stakeholders. The combination of frugal innovation and corporate activism enhances the connection between corporate image and organizational performance. The current study assists managers in recognizing the significance of these particular constructs in maintaining the long-term performance of the organization.

Keywords: green marketing mix strategy, CSR, corporate image, organizational performance, frugal innovation, corporate activism

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Authors: Anders S. Kristensen, Dewan Ahsan, Saqib Mehmood, Shakeel Ahmed

Abstract:

Traffic accidents are a result of the convergence of hazards, malfunctioning of vehicles and human negligence that have adverse economic and health impacts and effects. Unfortunately, avoiding them completely is very difficult, but with quick response to rescue and first aid, the mortality rate of inflicted persons can be reduced significantly. Smart and innovative technologies can play a pivotal role to respond faster to traffic crash emergencies comparing conventional means of transportation. For instance, Rescue Emergency Drone (RED) can provide faster and real-time crash site risk assessment to emergency medical services, thereby helping them to quickly and accurately assess a situation, dispatch the right equipment and assist bystanders to treat inflicted person properly. To conduct a research in this regard, the case of a traffic roundabout that is prone to frequent traffic accidents on the outskirts of Esbjerg, a town located on western coast of Denmark is hypothetically considered. Along with manual calculations, Emergency Disaster Management Simulation (EDMSIM) has been used to verify the response time of RED from a fire station of the town to the presumed crash site. The results of the study demonstrate the robustness of RED into emergency services to help save lives. 

Keywords: automated external defibrillator, medical emergency, response time, unmanned aerial system

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Authors: Sarayu Agarwal

Abstract:

This paper aims to teach English (secondary language) by bridging the understanding between the Regional language (primary language) and the English Language (secondary language). Here primary language is the one a person has learned from birth or within the critical period, while secondary language would be any other language one learns or speaks. The paper also focuses on evolving old teaching methods to a contemporary participatory model of learning and teaching. Pilot studies were conducted to gauge an understanding of student’s knowledge of the English language. Teachers and students were interviewed and their academic curriculum was assessed as a part of the initial study. Extensive literature study and design thinking principles were used to devise a solution to the problem. The objective is met using a holistic learning kit/card game to teach children word recognition, word pronunciation, word spelling and writing words. Implication of the paper is a noticeable improvement in the understanding and grasping of English language. With increasing usage and applicability of English as a second language (ESL) world over, the paper becomes relevant due to its easy replicability to any other primary or secondary language. Future scope of this paper would be transforming the idea of participatory learning into self-regulated learning methods. With the upcoming govt. learning centres in rural areas and provision of smart devices such as tablets, the development of the card games into digital applications seems very feasible.

Keywords: English as a second language, vocabulary-building card games, learning through gamification, rural education

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Authors: Arth Bohra, Marwa Mahmoud

Abstract:

In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

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Authors: Byoungjoon Yu, Jihwan Park, Sujung Sin, Junghyun Im, Minsoo Park, Sehwan Park, Seunghee Park

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In this paper, we propose a technology to monitor the soundness of an Internet-based bridge using a non-conductive inspection function. There has been a collapse accident due to the aging of the bridge structure, and it is necessary to prepare for the deterioration of the bridge. The NDT/SHM system for maintenance of existing bridge structures requires a large number of inspection personnel and expensive inspection costs, and access of expensive and large equipment to measurement points is required. Because current drone inspection equipment can only be inspected through camera, it is difficult to inspect inside damage accurately, and the results of an internal damage evaluation are subjective, and it is difficult for non-specialists to recognize the evaluation results. Therefore, it is necessary to develop NDT/SHM techniques for maintenance of new-concept bridge structures that allow for free movement and real-time evaluation of measurement results. This work is financially supported by Korea Ministry of Land, Infrastructure, and Transport (MOLIT) as 'Smart City Master and Doctor Course Grant Program' and a grant (14SCIP-B088624-01) from Construction Technology Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Keywords: Structural Health Monitoring, SHM, non-contact sensing, nondestructive testing, NDT, Internet of Things, autonomous self-driving drone

Procedia PDF Downloads 268