Search results for: innovation network

Authors: Van-Thanh Ho, Jaiyoung Ryu

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In soft robotics, the optimization of fluid dynamics through pneumatic methods plays a pivotal role in enhancing operational efficiency and reducing energy loss. This is particularly crucial when replacing conventional techniques such as cable-driven electromechanical systems. The pneumatic model employed in this study represents a sophisticated framework designed to efficiently channel pressure from a high-pressure reservoir to various muscle locations on the robot's body. This intricate network involves a branching system of tubes. The study introduces a comprehensive pneumatic model, encompassing the components of a reservoir, tubes, and Pneumatically Actuated Muscles (PAM). The development of this model is rooted in the principles of shock tube theory. Notably, the study leverages experimental data to enhance the understanding of the interplay between the PAM structure and the surrounding fluid. This improved interactive approach involves the use of morphing motion, guided by a contraction function. The study's findings demonstrate a high degree of accuracy in predicting pressure distribution within the PAM. The model's predictive capabilities ensure that the error in comparison to experimental data remains below a threshold of 10%. Additionally, the research employs a machine learning model, specifically a surrogate model based on the Kriging method, to assess and quantify uncertainty factors related to the initial reservoir pressure and tube diameter. This comprehensive approach enhances our understanding of pneumatic soft robotics and its potential for improved operational efficiency.

Keywords: pneumatic artificial muscles, pressure drop, morhing motion, branched network, surrogate model

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Dohwan Kim, Dongchoon Ryou, Pyungjong Yoo

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In public water facilities, drinking water distribution systems have played an important role along with water purification systems. The water distribution network is one of the most expensive components of water supply infrastructure systems. To improve the reliability for the drinking rate of tap water, advanced water treatment processes such as granular activated carbon and membrane filtration were used by water service providers in Korea. But, distrust of the people for tap water are still. Therefore, accurate diagnosis and condition assessment for water pipelines are required to supply the clean water. The internal corrosion of water pipe has increased as time passed. Also, the cross-sectional areas in pipe are reduced by the rust, deposits and tubercles. It is the water supply ability decreases as the increase of hydraulic pump capacity is required to supply an amount of water, such as the initial condition. If not, the poor area of water supply will be occurred by the decrease of water pressure. In order to solve these problems, water managers and engineers should be always checked for the current status of the water pipe, such as water leakage and damage of pipe. If problems occur, it should be able to respond rapidly and make an accurate estimate. In Korea, replacement and rehabilitation of aging drinking water pipes are carried out based on the circumstances of simply buried years. So, water distribution system management may not consider the entire water pipeline network. The long-term design and upgrading of a water distribution network should address economic, social, environmental, health, hydraulic, and other technical issues. This is a multi-objective problem with a high level of complexity. In this study, the thickness of the old water pipes, corrosion levels of the inner and outer surface for water pipes, basic data research (i.e. pipe types, buried years, accident record, embedded environment, etc.), specific resistance of soil, ultimate tensile strength and elongation of metal pipes, samples characteristics, and chemical composition analysis were performed about aging drinking water pipes. Samples of water pipes used in this study were cement mortar lining ductile cast iron pipe (CML-DCIP, diameter 100mm) and epoxy lining steel pipe (diameter 65 and 50mm). Buried years of CML-DCIP and epoxy lining steel pipe were respectively 32 and 23 years. The area of embedded environment was marine reclamation zone since 1940’s. The result of this study was that CML-DCIP needed replacement and epoxy lining steel pipe was still useful.

Keywords: drinking water distribution system, water supply, replacement, rehabilitation, water pipe

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Authors: Chris Nelson

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Quality and excellence are qualitative topics that are usually addressed based on knowledge and past experience from leadership and those in charge of the organization. The significance of this study is to highlight the differences and similarities between these two mindsets and how an operational staff can most appropriately use them in the workplace. Quality and excellence are two words that are talked about a lot in the manufacturing world. Buzzwords such as operational excellence, quality controls, and efficiencies are discussed in the boardroom as well on the shop floor. These terms are used quite frequently and with good reasons. When a person visits their favorite local restaurant, They go because 1) they like the food and 2) the people are some of the greatest individuals to be around. With that in mind, they know they always put out quality food. They do not always go because the quality of the food is far superior than other restaurants. But the quality of ingredients always meets their expectations. When they compare them to the term excellence, they are disappointed. The food never looks like the pictures on the menu. But when have you ever been to a restaurant where the food looks the same as on the menu? For them, when evaluating which buzzword to use as a guiding star, its simple: excellence. The corporation can accomplish these goals by operating at a standard that far exceeds customer’s wants and needs.

Keywords: industrial engineering, innovation, management and technology, logistics and scheduling, six sigma

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Authors: Zulkifli Zainal Abidin, Ahmad Shahril Mohd Ghani

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Agent-based systems technology has been addressed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated systems that act autonomously across open and distributed environments in solving problems. Nevertheless, it is impractical to rely on a single agent to do all computing processes in solving complex problems. An increasing number of applications lately require multiple agents to work together. A multi-agent system (MAS) is a loosely coupled network of agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. However, the network of MAS still requires a main system to govern or oversees the operation of the agents in order to achieve a unified goal. We had developed a fleet management system (FMS) in order to manage the fleet of agents, plan route for the agents, perform real-time data processing and analysis, and issue sets of general and specific instructions to the agents. This FMS should be able to perform real-time data processing, communicate with the autonomous surface vehicle (ASV) agents and generate bathymetric map according to the data received from each ASV unit. The first algorithm is developed to communicate with the ASV via radio communication using standard National Marine Electronics Association (NMEA) protocol sentences. Next, the second algorithm will take care of the path planning, formation and pattern generation is tested using various sample data. Lastly, the bathymetry map generation algorithm will make use of data collected by the agents to create bathymetry map in real-time. The outcome of this research is expected can be applied on various other multi-agent systems.

Keywords: autonomous surface vehicle, fleet management system, multi agent system, bathymetry

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Authors: Konstantinos Kottikas, Vlasis Stathakopoulos, Ioannis G. Theodorakis, Efthymia Kottika

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Theorists have argued that Market Orientation is comprised of two facets, namely the Market Driven and the Market Driving components. The present theoretical paper centers on the latter, which to date has been notably under-investigated. The term Market Driving (MD) pertains to influencing the structure of the market, or the behavior of market players in a direction that enhances the competitive edge of the firm. Presently, the main objectives of the paper are the specification of key antecedents and outcomes of Market Driving behavior. Market Driving firms behave proactively, by leading their customers and changing the rules of the game rather than by responding passively to them. Leading scholars were the first to conceptually conceive the notion, followed by some qualitative studies and a limited number of quantitative publications. However, recently, academicians noted that research on the topic remains limited, expressing a strong necessity for further insights. Concerning the key antecedents, top management’s Transformational Leadership (i.e. the form of leadership which influences organizational members by aligning their values, goals and aspirations to facilitate value-consistent behaviors) is one of the key drivers of MD behavior. Moreover, scholars have linked the MD concept with Entrepreneurship. Finally, the role that Employee’s Creativity plays in the development of MD behavior has been theoretically exemplified by a stream of literature. With respect to the key outcomes, it has been demonstrated that MD Behavior positively triggers firm Performance, while theorists argue that it empowers the Competitive Advantage of the firm. Likewise, researchers explicate that MD Behavior produces Radical Innovation. In order to test the robustness of the proposed theoretical framework, a combination of qualitative and quantitative methods is proposed. In particular, the conduction of in-depth interviews with distinguished executives and academicians, accompanied with a large scale quantitative survey will be employed, in order to triangulate the empirical findings. Given that it triggers overall firm’s success, the MD concept is of high importance to managers. Managers can become aware that passively reacting to market conditions is no longer sufficient. On the contrary, behaving proactively, leading the market, and shaping its status quo are new innovative approaches that lead to a paramount competitive posture and Innovation outcomes. This study also exemplifies that managers can foster MD Behavior through Transformational Leadership, Entrepreneurship and recruitment of Creative Employees. To date, the majority of the publications on Market Orientation is unilaterally directed towards the responsive (i.e. the Market Driven) component. The present paper further builds on scholars’ exhortations, and investigates the Market Driving facet, ultimately aspiring to conceptually integrate the somehow fragmented scientific findings, in a holistic framework.

Keywords: entrepreneurial orientation, market driving behavior, market orientation

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Authors: Nadjib Drouiche, Abdenour Lami, Salaheddine Aoudj, Tarik Ouslimane

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Although solar cells manufacturing is a conservative industry, economics drivers continue to encourage innovation, feedstock savings and cost reduction. Kerf slurry waste is a complex product containing both valuable substances as well as contaminants. The valuable substances are: i) high purity silicon, ii) polyethylene glycol, and iii) silicon carbide. The contaminants mainly include metal fragments and organics. Therefore, recycling of the kerf slurry waste is an important subject not only from the treatment of waste but also from the recovery of valuable products. The present paper relates to processes for the recovery of valuable products from the kerf slurry waste in which they are contained, such products comprising nanoparticles, polyethylene glycol, high purity silicon, and silicon carbide.

Keywords: photovoltaic cell, Kerf slurry waste, recycling, silicon carbide

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Authors: Hyunsun Lee, Yi Zhu

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Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.

Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles

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Authors: Tesfaye Fenta Boka, Niu Zhendong

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Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms.

Keywords: recommender systems, cold start item recommendations, online grocery shopping platforms, graph neural networks

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Authors: Muhammad Mehran Arshad Khan, Chengliang Wang, Zou Minhui, Danyal Badar Soomro

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Security of the smart home in terms of behavior activity pattern recognition is a totally dissimilar and unique issue as compared to the security issues of other scenarios. Sensor devices (low capacity and high capacity) interact and negotiate each other by detecting the daily behavior activity of individuals to execute common tasks. Once a device (e.g., surveillance camera, smart phone and light detection sensor etc.) is compromised, an adversary can then get access to a specific device and can damage daily behavior activity by altering the data and commands. In this scenario, a group of common instruction processes may get involved to generate deadlock. Therefore, an effective suitable security solution is required for smart home architecture. This paper proposes seamless distributed Scheme which fortifies low computational wireless devices for secure communication. Proposed scheme is based on lightweight key-session process to upheld cryptic-link for trajectory by recognizing of individual’s behavior activities pattern. Every device and service provider unit (low capacity sensors (LCS) and high capacity sensors (HCS)) uses an authentication token and originates a secure trajectory connection in network. Analysis of experiments is revealed that proposed scheme strengthens the devices against device seizure attack by recognizing daily behavior activities, minimum utilization memory space of LCS and avoids network from deadlock. Additionally, the results of a comparison with other schemes indicate that scheme manages efficiency in term of computation and communication.

Keywords: authentication, key-session, security, wireless sensors

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: Fethi Bouksila, Nessrine Zemni, Fairouz Slama, Magnus Persson, Ronny Berndasson, Akissa Bahri

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Electromagnetic sensor EM 38 was used to predict and map soil salinity (ECe) in arid oasis. Despite the high spatial variation of soil moisture and shallow watertable, significant ECe-EM relationships were developed. The low drainage network efficiency is the main factor of soil salinization

Keywords: soil salinity map, electromagnetic induction, EM38, oasis, shallow watertable

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Richard Grant, Landolf Rhode-Barbarigos, Shouraseni Sen Roy, Lucas Brittan, Change Li, Aiden Rowe

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Ports are critical for the US economy, connecting farmers, manufacturers, retailers, consumers and an array of transport and storage operators. Port facilities vary widely in terms of their productivity, footprint, specializations, and governance. In this context, Port Miami is considered as one of the busiest ports providing both cargo and cruise services in connecting the wider region of the Caribbean and Mesoamerica to the global networks. It is considered as the “Cruise Capital of the World and Global Gateway of the Americas” and “leading container port in Florida.” Furthermore, it has also been ranked as one of the top container ports in the world and the second most efficient port in North America. In this regard, Port Miami has made significant investments in the strategic and capital infrastructure of about US$1 billion, including increasing the channel depth and other onshore infrastructural enhancements. Therefore, this study involves a detailed analysis of Port Miami’s network, using publicly available multiple years of data about marine vessel traffic, cargo, and connectivity and performance indices from 2015-2021. Through the analysis of cargo and cruise vessels to and from Port Miami and its relative performance at the global scale from 2015 to 2021, this study examines the port’s long-term resilience and future growth potential. The main results of the analyses indicate that the top category for both inbound and outbound cargo is manufactured products and textiles. In addition, there are a lot of fresh fruits, vegetables, and produce for inbound and processed food for outbound cargo. Furthermore, the top ten port connections for Port Miami are all located in the Caribbean region, the Gulf of Mexico, and the Southeast USA. About half of the inbound cargo comes from Savannah, Saint Thomas, and Puerto Plata, while outbound cargo is from Puerto Corte, Freeport, and Kingston. Additionally, for cruise vessels, a significantly large number of vessels originate from Nassau, followed by Freeport. The number of passenger's vessels pre-COVID was almost 1,000 per year, which dropped substantially in 2020 and 2021 to around 300 vessels. Finally, the resilience and competitiveness of Port Miami were also assessed in terms of its network connectivity by examining the inbound and outbound maritime vessel traffic. It is noteworthy that the most frequent port connections for Port Miami were Freeport and Savannah, followed by Kingston, Nassau, and New Orleans. However, several of these ports, Puerto Corte, Veracruz, Puerto Plata, and Santo Thomas, have low resilience and are highly vulnerable, which needs to be taken into consideration for the long-term resilience of Port Miami in the future.

Keywords: port, Miami, network, cargo, cruise

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Authors: Veronika Antoniou, Rene Carraz, Yiorgos Hadjichristou

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The Green Urban Lab took the form of public installations that were placed at various locations in four cities in Cyprus. These installations - through which a series of events, activities, workshops and research took place - were the main tools in regenerating a series of urban public spaces in Cyprus. The purpose of this project was to identify issues and opportunities related to public space and to offer guidelines on how design and participatory democracy improvements could strengthen civil society, while raising the quality of the urban public scene. Giant inflatable structures were injected in important urban fragments in order to accommodate series of events. The design and playful installation generated a wide community engagement. The fluid presence of the installations acted as a catalyst for social interaction. They were accessed and viewed effortlessly and surprisingly, creating opportunities to rediscover public spaces.

Keywords: bottom-up initiatives, creativity, public space, social innovation, urban environments

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Authors: Abdul Ghani Rajput

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Although monitoring and evaluation concept adopted by Technical Vocational Education and Training (TVET) organization to track the progress over the continuous interval of time based on planned interventions and subsequently, evaluating it for the impact, quality assurance and sustainability. In digital world, TVET providers are giving preference to have real time information to do monitoring of training activities. Identifying the benefits and challenges of digitized monitoring & evaluation real time information system has not been sufficiently tackled in this date. This research paper looks at the impact of digitized M&E in TVET sector by analyzing two case studies and describe the benefits and challenges of using digitized M&E system. Finally, digitized M&E have been identified as carriers for high potential of TVET sector.

Keywords: digitized M&E, innovation, quality assurance, TVET

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: M. Sundaram, H. R. Sanath Kumar, A. Ramprakash

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Energy saving is a key sustainability focus area for the Indian telecom industry today. This is especially true in rural India where energy consumption contributes to 70 % of the total network operating cost. In urban areas, the energy cost for network operation ranges between 15-30 %. This expenditure on energy as a result of the lack of grid power availability highlights a potential barrier to telecom industry growth. As a result of this, telecom tower companies switch to diesel generators, making them the second largest consumer of diesel in India, consuming over 2.5 billion litres per annum. The growing cost of energy due to increasing diesel prices and concerns over rising greenhouse emissions have caused these companies to look at other renewable energy options. Even the TRAI (Telecom Regulation Authority of India) has issued a number of guidelines to implement Renewable Energy Technologies (RETs) in the telecom towers as part of its ‘Implementation of Green Technologies in Telecom Sector’ initiative. Our proposal suggests the implementation of a Programmable Logic Controller (PLC) based ‘optimisation controller’ that can not only efficiently utilize the energy from RETs but also help to conserve the power used in the telecom towers. When there are multiple RETs available to supply energy, this controller will pick the optimum amount of energy from each RET based on the availability and feasibility at that point of time, reducing the dependence on diesel generators. For effective maintenance of the towers, we are planing to implement a SCADA based monitoring system along with the ‘optimization controller’.

Keywords: operation costs, consumption of fuel and carbon footprint, implementation of a programmable logic controller (PLC) based ‘optimisation controller’, efficient SCADA based monitoring system

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Authors: Eva Degraeuwe, Margo Vandenheede, Nicholas Rennie, Jolien Braem, Miryam Serry, Frederik Berrevoet, Piet Pattyn, Wouter Willaert, InciSioN Belgium Consortium

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Background: Worldwide, five billion people lack access to safe and affordable surgical care. An added 1.27 million surgeons, anesthesiologists, and obstetricians (SAO) are needed by 2030 to meet the target of 20 per 100,000 population and to reach the goal of the Lancet Commission on Global Surgery. A well-informed future generation exposed early on to the current challenges in global surgery (GS) is necessary to ensure a sustainable future. Methods: InciSioN, the International Student Surgical Network, is a non-profit organization by and for students, residents, and fellows in over 80 countries. InciSioN Belgium, one of the prominent national working groups, has made a vast progression and collaborated with other networks to fill the educational gap, stimulate advocacy efforts and increase interactions with the international network. This report describes a roadmap to achieve sustainable development and education within GS, with the example of InciSioN Belgium. Results: Since the establishment of the organization’s branch in 2019, it has hosted an educational workshop for first-year residents in surgery, engaging over 2500 participants, and established a recurring directing board of 15 members. In the year 2020-2021, InciSioN Ghent has organized three workshops combining educational and interactive sessions for future prime advocates and surgical candidates. InciSioN Belgium has set up a strong formal coalition with the Belgian Medical Students’ Association (BeMSA), with its own standing committee, reaching over 3000+ medical students annually. In 2021-2022, InciSioN Belgium broadened to a multidisciplinary approach, including dentistry and nursing students and graduates within workshops and research projects, leading to a member and exposure increase of 450%. This roadmap sets strategic goals and mechanisms for the GS community to achieve nationwide sustained improvements in the research and education of GS focused on future SAOs, in order to achieve the GS sustainable development goals. In the coming year, expansion is directed to a formal integration of GS into the medical curriculum and increased international advocacy whilst inspiring SAOs to integrate into GS in Belgium. Conclusion: The development and implementation of durable change for GS are necessary. The student organization InciSioN Belgium is growing and hopes to close the colossal gap in GS and inspire the growth of other branches while sharing the know-how of a student organization.

Keywords: advocacy, education, global surgery, InciSioN, student network

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Authors: Hasan Al Dabbas

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In this paper, it is addressed that the mechanical machinery captures a major share of innovation in drilling and milling chucks technology. Users demand higher speeds in milling because they are cutting more aluminum and are relying on higher speeds to eliminate secondary finishing operations. To meet that demand, milling-machine builders have enhanced their machine’s rigidity. Moreover, faster cutting has caught up with boring mills. Cooling these machine’s internal components is a challenge at high speeds. Another trend predicted that it is more use of controlled axes to let the machines do many more operations on 5 sides without having to move or re-fix the work. Advances of technology in mechanical engineering have helped to make high-speed machining equipment. To accompany these changes in milling and drilling machines chucks, the demand of easiest software is increased. An open architecture controller is being sought that would allow flexibility and information exchange.

Keywords: drilling, milling, chucks, cutting edges, tools, machines

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Khalil Ahmad Kakar

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In developing countries increasing the automobile and low capacity public transport (para-transit) which are creating congestion, pollution, noise, and traffic accident are the most critical quandary. These issues are under the analysis of assessors to break down the puzzle and propose sustainable urban public transport system. Kabul city is one of those urban areas that the inhabitants are suffering from lack of tolerable and friendly public transport system. The city is the most-populous and overcrowded with around 4.5 million population. The para-transit is the only dominant public transit system with a very poor level of services and low capacity vehicles (6-20 passengers). Therefore, this study after detailed investigations suggests bus rapid transit (BRT) system in Kabul City. It is aimed to mitigate the role of informal transport and decreases congestion. The research covers three parts. In the first part, aggregated travel demand modelling (four-step) is applied to determine the number of users for para-transit and assesses BRT network based on higher passenger demand for public transport mode. In the second part, state preference (SP) survey and binary logit model are exerted to figure out the utility of existing para-transit mode and planned BRT system. Finally, the impact of predicted BRT system on para-transit is evaluated. The extracted outcome based on high travel demand suggests 10 km network for the proposed BRT system, which is originated from the district tenth and it is ended at Kabul International Airport. As well as, the result from the disaggregate travel mode-choice model, based on SP and logit model indicates that the predicted mass rapid transit system has higher utility with the significant impact regarding the reduction of para-transit.

Keywords: BRT, para-transit, travel demand modelling, Kabul City, logit model

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Authors: Qing Hui Wang, You Lin Fang, Min Chen

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In 2003 the percentage of students in Shanghai taking part in tertiary education reached 53% of the age cohort, which is at the universal level according to Trow's typology. This achievement led to a conscientious effort by the government to improve internal efficiency of local universities and colleges through a series of programs, the most important of which is the 085 Project. This paper considers the effects of the 085 Project- the 5 initiatives launched in 2008 on increasing the investment to improve institutional overall visibilities, teaching excellence, knowledge innovation, faculty development as well as internationalization. Using the approach of ERC (a theory of equality, reciprocity, and competition) model, it was found that the initiatives helped the lower tier universities and colleges make full play in the aspects of strategic planning and identifying themselves. It was also found that the rate of growth of social resources for universities as a whole increased more quickly after the implementation of the 085 Project.

Keywords: 085 Project, impact, internal efficiency, local universities and colleges

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Authors: Frodouard Minani

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Since last decade, wireless sensor networks (WSNs) have been used in many areas like health care, agriculture, defense, military, disaster hit areas and so on. Wireless Sensor Networks consist of a Base Station (BS) and more number of wireless sensors in order to monitor temperature, pressure, motion in different environment conditions. The key parameter that plays a major role in designing a protocol for Wireless Sensor Networks is energy efficiency which is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. Maximizing sensor node’s lifetime is an important issue in the design of applications and protocols for Wireless Sensor Networks. Clustering sensor nodes mechanism is an effective topology control approach for helping to achieve the goal of this research. In this paper, the researcher presents an energy efficiency protocol to prolong the network lifetime based on Energy efficient clustering algorithm. The Low Energy Adaptive Clustering Hierarchy (LEACH) is a routing protocol for clusters which is used to lower the energy consumption and also to improve the lifetime of the Wireless Sensor Networks. Maximizing energy dissipation and network lifetime are important matters in the design of applications and protocols for wireless sensor networks. Proposed system is to maximize the lifetime of the Wireless Sensor Networks by choosing the farthest cluster head (CH) instead of the closest CH and forming the cluster by considering the following parameter metrics such as Node’s density, residual-energy and distance between clusters (inter-cluster distance). In this paper, comparisons between the proposed protocol and comparative protocols in different scenarios have been done and the simulation results showed that the proposed protocol performs well over other comparative protocols in various scenarios.

Keywords: base station, clustering algorithm, energy efficient, sensors, wireless sensor networks

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Authors: Maria V. Mishatkina

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The modelling of dialogical environment is an essential feature of modern education. The dialogue of cultures is a foundation and an important prerequisite for a formation of a human’s main moral qualities such as an ability to understand another person, which is manifested in such values as tolerance, respect, mutual assistance and mercy. A formation of a modern expert occurs in an educational environment that is significantly different from what we had several years ago. Nowadays university education has qualitatively new characteristics. They may be observed in Peoples’ Friendship University of Russia (RUDN University), a top Russian higher education institution which unites representatives of more than 150 countries. The content of its educational strategies is not an adapted cultural experience but material between science and innovation. Besides, RUDN University’s profiles and specialization are not equal to the professional structures. People study not a profession in a strict sense but a basic scientific foundation of an activity in different socio-cultural areas (science, business and education). RUDN University also provides a considerable unit of professional education components. They are foreign languages skills, economic, political, ethnic, communication and computer culture, theory of information and basic management skills. Moreover, there is a rich social life (festive multicultural events, theme parties, journeys) and prospects concerning the inclusive approach to education (for example, a special course ‘Social Pedagogy: Issues of Tolerance’). In our research, we use such methods as analysis of modern and contemporary scientific literature, opinion poll (involving students, teachers and research workers) and comparative data analysis. We came to the conclusion that knowledge transfer of RUDN student in the activity happens through making goals, problems, issues, tasks and situations which simulate future innovative ambiguous environment that potentially prepares him/her to dialogical way of life. However, all these factors may not take effect if there is no ‘personal inspiration’ of students by communicative and dialogic values, their participation in a system of meanings and tools of learning activity that is represented by cooperation within the framework of scientific and pedagogical schools dialogue. We also found out that dominating strategies of ensuring the quality of education are those that put students in the position of the subject of their own education. Today these strategies and approaches should involve such approaches and methods as task, contextual, modelling, specialized, game-imitating and dialogical approaches, the method of practical situations, etc. Therefore, University in the modern sense is not only an educational institution, but also a generator of innovation, cooperation among nations and cultural progress. RUDN University has been performing exactly this mission for many decades.

Keywords: dialogical developing situation, dialogue of cultures, readiness for dialogue, university graduate

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Authors: Anukriti Verma, Bhawna Rathi, Shivani Sharda

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Reactive Arthritis (ReA) is a disorder that causes inflammation in joints due to certain infections at distant sites in the body. ReA begins with stiffness, pain, and inflammation in these areas especially the ankles, knees, and hips. It gradually causes several complications such as conjunctivitis in the eyes, skin lesions in hand, feet and nails and ulcers in the mouth. Nowadays the diagnosis of ReA is based upon a differential diagnosis pattern. The parameters for differentiating ReA from other similar disorders include physical examination, history of the patient and a high index of suspicion. There are no standard lab tests or markers available for ReA hence the early diagnosis of ReA becomes difficult and the chronicity of disease increases with time. It is reported that enteric disorders such as Inflammatory Bowel Disease (IBD) that is inflammation in gastrointestinal tract namely Crohn’s Disease (CD) and Ulcerative Colitis (UC) are reported to be linked with ReA. Several microorganisms are found such as Campylobacter, Salmonella, Shigella and Yersinia causing IBD leading to ReA. The aim of our study was to perform the in-silico analysis in order to find interactions between microorganisms and human host causing IBD leading to ReA. A systems biology approach for metabolic network reconstruction and simulation was used to find the essential genes of the reported microorganisms. Interactomics study was used to find the interactions between the pathogen genes and human host. Genes such as nhaA (pathogen), dpyD (human), nagK (human) and kynU (human) were obtained that were analysed further using the functional, pathway and network analysis. These genes can be used as putative drug targets and biomarkers in future for early diagnosis, prevention, and treatment of IBD leading to ReA.

Keywords: drug targets, inflammatory bowel disease, reactive arthritis, systems biology

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Authors: Mariam Wagih Nagib Eskandar

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The engineering design process is the activities formulation, to help an engineer raising a plan with a specified goal and performance. The engineering design process is a multi-stage course of action including the conceptualization, research, feasibility studies, establishment of design parameters, preliminary and finally the detailed design. It is a progression from the abstract to the concrete; starting with probably abstract ideas about need, and thereafter elaborating detailed specifications of the object that would satisfy the needs, identified. Engineering design issues, problems, and solutions are discussed in this paper using qualitative approach from an information structure perspective. The objective is to identify the problems, to analyze them and propose solutions by integrating; innovation, practical experience, time and resource management, communications skills, isolating the problem in coordination with all stakeholders. Consequently, this would be beneficial for the engineering community to improve the Engineering design practices.

Keywords: education, engineering, math, performanceengineering design, architectural engineering, team-based learning, construction safetyrequirement engineering, models, practices, organizations

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Fahad Suleiman

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Queuing, in medical system is the process of moving patients in a specific sequence to a specific service according to the patients’ nature of illness. The term scheduling stands for the process of computing a schedule. This may be done by a queuing based scheduler. This paper focuses on the medical consultancy system, the different queuing algorithms that are used in healthcare system to serve the patients, and the average waiting time. The aim of this paper is to build automatic queuing system for organizing the medical queuing system that can analyses the queue status and take decision which patient to serve. The new queuing architecture model can switch between different scheduling algorithms according to the testing results and the factor of the average waiting time. The main innovation of this work concerns the modeling of the average waiting time is taken into processing, in addition with the process of switching to the scheduling algorithm that gives the best average waiting time.

Keywords: queuing systems, queuing system models, scheduling algorithms, patients

Procedia PDF Downloads 354