Search results for: user path prediction (UPP) and user pattern

Authors: Chia-Ying Lin

Abstract:

This paper explains how to enhance doctor-patient communication and nurse-patient communication through multiple intelligence signing methods and user-centered. It is hoped that through the implementation of the "electronic consent", the problems faced by the paper consent can be solved: storage methods, resource utilization, convenience, correctness of information, integrated management, statistical analysis and other related issues. Make better use and allocation of resources to provide better medical quality. First, invite the ｍedical records department to assist in the inventory of paper consent in the hospital: organising, classifying, merging, coding, and setting. Second, plan the electronic consent configuration file: set the form number, consent form group, fields and templates, and the corresponding doctor's order code. Next, Summarize four types of rapid methods of electronic consent: according to the doctor's order, according to the medical behavior, according to the schedule, and manually generate the consent form. Finally, system promotion and adjustment: form an "electronic consent promotion team" to improve, follow five major processes: planning, development, testing, release, and feedback, and invite clinical units to raise the difficulties faced in the promotion, and make improvements to the problems. The electronic signature rate of the whole hospital will increase from 4% in January 2022 to 79% in November 2022. Use the saved resources more effectively, including: reduce paper usage (reduce carbon footprint), reduce the cost of ink cartridges, re-plan and use the space for paper medical records, and save human resources to provide better services. Through the introduction of information technology and technology, the main spirit of "lean management" is implemented. Transforming and reengineering the process to eliminate unnecessary waste is also the highest purpose of this project.

Keywords: smart signature, electronic consent, electronic medical records, user-centered, doctor-patient communication, nurse-patient communication

Procedia PDF Downloads 114

Authors: A. Kebbeb, M. Mostefai, F. Benmerzoug, Y. Chahir

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The multimodal biometric identification is the combination of several biometric systems. The challenge of this combination is to reduce some limitations of systems based on a single modality while significantly improving performance. In this paper, we propose a new approach to the construction and the protection of a multimodal biometric database dedicated to an identification system. We use a topological watermarking to hide the relation between face image and the registered descriptors extracted from other modalities of the same person for more secure user identification.

Keywords: biometric databases, multimodal biometrics, security authentication, digital watermarking

Procedia PDF Downloads 376

Authors: Hady Hamidyan

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As the global population continues to grow, the demand for clean water and effective wastewater treatment becomes increasingly critical. By 2030, global water demand is projected to exceed supply by 40%, driven by population growth, increased water usage, and climate change. Currently, about 4.2 billion people lack access to safely managed sanitation services. The wastewater treatment sector faces numerous challenges, including the need for energy-efficient solutions, cost-effectiveness, ease of use, and low maintenance requirements. This abstract presents a groundbreaking wastewater treatment technology that addresses these challenges by offering an energy-saving approach, wastewater recovery capabilities, and a ready-made, affordable, and user-friendly package with minimal maintenance costs. The unique design of this ready-made package made it possible to eliminate the need for pumps, filters, airlift, and other common equipment. Consequently, it enables sustainable wastewater treatment management with exceptionally low energy and cost requirements, minimizing investment and maintenance expenses. The operation of these packages is based on continuous aeration, which involves injecting oxygen gas or air into the aeration chamber through a tubular diffuser with very small openings. This process supplies the necessary oxygen for aerobic bacteria. The recovered water, which amounts to almost 95% of the input, can be treated to meet specific quality standards, allowing safe reuse for irrigation, industrial processes, or even potable purposes. This not only reduces the strain on freshwater resources but also provides economic benefits by offsetting the costs associated with freshwater acquisition and wastewater discharge. The ready-made, affordable, and user-friendly nature of this technology makes it accessible to a wide range of users, including small communities, industries, and decentralized wastewater treatment systems. The system incorporates user-friendly interfaces, simplified operational procedures, and integrated automation, facilitating easy implementation and operation. Additionally, the use of durable materials, efficient equipment, and advanced monitoring systems significantly reduces maintenance requirements, resulting in low overall life-cycle costs and alleviating the burden on operators and maintenance personnel. In conclusion, the presented wastewater treatment technology offers a comprehensive solution to the challenges faced by the industry. Its energy-saving approach, combined with wastewater recovery capabilities, ensures sustainable resource management and enhances environmental stewardship. This affordable, ready-made, and low-maintenance package promotes broad adoption across various sectors and communities, contributing to a more sustainable future for water and wastewater management.

Keywords: wastewater treatment, energy saving, wastewater recovery, affordable package, low maintenance costs, sustainable resource management, environmental stewardship

Procedia PDF Downloads 72

Authors: Fatemeh Sadat Sharifi, A. A. Bidokhti, M. Ezam, F. Ahmadi Givi

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This study focuses on the tidal oscillation and its speed to create a general pattern in seas. The purpose of the analysis is to find out the amplitude and phase for several important tidal components. Therefore, Regional Ocean Models (ROMS) was rendered to consider the correlation and accuracy of this pattern. Finding tidal harmonic components allows us to predict tide at this region. Better prediction of these tides, making standard platform, making suitable wave breakers, helping coastal building, navigation, fisheries, port management and tsunami research. Result shows a fair accuracy in the SSH. It reveals tidal currents are highest in Hormuz Strait and the narrow and shallow region between Kish Island. To investigate flow patterns of the region, the results of limited size model of FVCOM were utilized. Many features of the present day view of ocean circulation have some precedent in tidal and long- wave studies. Tidal waves are categorized to be among the long waves. So that tidal currents studies have indeed effects in subsequent studies of sea and ocean circulations.

Keywords: barotropic tide, FVCOM, numerical model, OTPS, ROMS

Procedia PDF Downloads 217

Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji

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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.

Keywords: easy accessibility, recommends, accuracy, privacy

Procedia PDF Downloads 56

Authors: Samar Swaid

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Today, corporates are moving toward the adoption of Design-Thinking techniques to develop products and services, putting their consumer as the heart of the development process. One of the leading companies in Design-Thinking, IDEO (Innovation, Design, Engineering Organization), defines Design-Thinking as an approach to problem-solving that relies on a set of multi-layered skills, processes, and mindsets that help people generate novel solutions to problems. Design thinking may result in new ideas, narratives, objects or systems. It is about redesigning systems, organizations, infrastructures, processes, and solutions in an innovative fashion based on the users' feedback. Tim Brown, president and CEO of IDEO, sees design thinking as a human-centered approach that draws from the designer's toolkit to integrate people's needs, innovative technologies, and business requirements. The application of design thinking has been witnessed to be the road to developing innovative applications, interactive systems, scientific software, healthcare application, and even to utilizing Design-Thinking to re-think business operations, as in the case of Airbnb. Recently, there has been a movement to apply design thinking to machine learning and artificial intelligence to ensure creating the "wow" effect on consumers. The Association of Computing Machinery task force on Data Science program states that" Data scientists should be able to implement and understand algorithms for data collection and analysis. They should understand the time and space considerations of algorithms. They should follow good design principles developing software, understanding the importance of those principles for testability and maintainability" However, this definition hides the user behind the machine who works on data preparation, algorithm selection and model interpretation. Thus, the Data Science program includes design thinking to ensure meeting the user demands, generating more usable machine learning tools, and developing ways of framing computational thinking. Here, describe the fundamentals of Design-Thinking and teaching modules for data science programs.

Keywords: data science, design thinking, AI, currculum, transformation

Procedia PDF Downloads 64

Authors: Jessica Achebe, Susan Tighe

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The recent Canadian infrastructure report card reveals the unhealthy state of municipal infrastructure intensified challenged faced by municipalities to maintain adequate infrastructure performance thresholds and meet user’s required service levels. For a road agency, huge funding gap issue is inflated by growing concerns of the environmental repercussion of road construction, operation and maintenance activities. As the reduction of material consumption and greenhouse gas emission when maintain and rehabilitating road networks can achieve added benefits including improved life cycle performance of pavements, reduced climate change impacts and human health effect due to less air pollution, improved productivity due to optimal allocation of resources and reduced road user cost. Incorporating environmental sustainability measure into pavement management is solution widely cited and studied. However measuring the environmental performance of road network is still a far-fetched practice in road network management, more so an ostensive agency-wide environmental sustainability or sustainable maintenance specifications is missing. To address this challenge, this present research focuses on the environmental sustainability performance of network-level pavement management. The ultimate goal is to develop a framework to incorporate environmental sustainability in pavement management systems for network-level maintenance programming. In order to achieve this goal, this study reviewed previous studies that employed environmental performance measures, as well as the suitability of environmental performance indicators for the evaluation of the sustainability of network-level pavement maintenance strategies. Through an industry practice survey, this paper provides a brief forward regarding the pavement manager motivations and barriers to making more sustainable decisions, and data needed to support the network-level environmental sustainability. The trends in network-level sustainable pavement management are also presented, existing gaps are highlighted, and ideas are proposed for sustainable network-level pavement management.

Keywords: pavement management, sustainability, network-level evaluation, environment measures

Procedia PDF Downloads 199

Authors: Abu Zafor Sadek

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Pharmaceuticals were one of the least impacted business sectors during the corona pandemic as they are the center point of Covid-19 fight. Emergency use authorization, unproven indication of some commonly used drugs, self-medication, research and production capacity of an individual country, capacity of producing vaccine by many countries, Active Pharmaceutical Ingredients (APIs) related uncertainty, information gap among manufacturer, practitioners and user, export restriction, duration of lock-down, lack of harmony in transportation, disruption in the regulatory approval process, sudden increased demand of hospital items and protective equipment, panic buying, difficulties in in-person product promotion, e-prescription, geo-politics and associated issues added a new dimension to this industry. Although the industry maintains a reasonable growth throughout Covid-19 days; however, it has been characterized by both long- and short-term effects. Short-term effects have already been visible to so many countries, especially those who are import-dependent and have limited research capacity. On the other hand, it will take a few more time to see the long-term effects. Nevertheless, supply chain disruption, changes in strategic planning, new communication model, squeezing of job opportunity, rapid digitalization are the major short-term effects, whereas long-term effects include a shift towards self-sufficiency, growth pattern changes of certain products, special attention towards clinical studies, automation in operations, the increased arena of ethical issues etc. Therefore, this qualitative and exploratory study identifies the post-covid-19 landscape of the global pharmaceutical industry.

Keywords: covid-19, pharmaceutical, businees, landscape

Procedia PDF Downloads 85

Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

Procedia PDF Downloads 132

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

Procedia PDF Downloads 60

Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

Procedia PDF Downloads 298

Authors: Alaa El-Din Rezk

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In Egypt, management of crops still away from what is being used today by utilizing the advances of mechanical design capabilities, sensing and electronics technology. These technologies have been introduced in many places and recorm, for Straight Path, Curved Path, Sine Wave ded high accuracy in different field operations. So, an autonomous robotic platform based on machine vision has been developed and constructed to be implemented in Egyptian conditions as self-propelled mobile vehicle for carrying tools for inter/intra-row crop management based on different control modules. The experiments were carried out at plant protection research institute (PPRI) during 2014-2015 to optimize the accuracy of agricultural robotic platform control using machine vision in term of the autonomous navigation and performance of the robot’s guidance system. Results showed that the robotic platform' guidance system with machine vision was able to adequately distinguish the path and resisted image noise and did better than human operators for getting less lateral offset error. The average error of autonomous was 2.75, 19.33, 21.22, 34.18, and 16.69 mm. while the human operator was 32.70, 4.85, 7.85, 38.35 and 14.75 mm Path, Offset Discontinuity and Angle Discontinuity respectively.

Keywords: autonomous robotic, Hough transform, image processing, machine vision

Procedia PDF Downloads 306

Authors: Meng-Cong Zheng

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When the public space is in an emergency, how to quickly establish spatial cognition and emergency shelter in the closed underground space is the urgent task. This study takes Taipei Station as the research base and aims to apply the use of Internet of things (IoT) application for underground evacuation mobility design. The first experiment identified passengers' evacuation behaviors and spatial cognition in underground spaces by wayfinding tasks and thinking aloud, then defined the design conditions of User Interface (UI) and proposed the UI design.  The second experiment evaluated the UI design based on passengers' evacuation behaviors by wayfinding tasks and think aloud again as same as the first experiment. The first experiment found that the design conditions that the subjects were most concerned about were "map" and hoping to learn the relative position of themselves with other landmarks by the map and watch the overall route. "Position" needs to be accurately labeled to determine the location in underground space. Each step of the escape instructions should be presented clearly in "navigation bar." The "message bar" should be informed of the next or final target exit. In the second experiment with the UI design, we found that the "spatial map" distinguishing between walking and non-walking areas with shades of color is useful. The addition of 2.5D maps of the UI design increased the user's perception of space. Amending the color of the corner diagram in the "escape route" also reduces the confusion between the symbol and other diagrams. The larger volume of toilets and elevators can be a judgment of users' relative location in "Hardware facilities." Fire extinguisher icon should be highlighted. "Fire point tips" of the UI design indicated fire with a graphical fireball can convey precise information to the escaped person. "Fire point tips" of the UI design indicated fire with a graphical fireball can convey precise information to the escaped person. However, "Compass and return to present location" are less used in underground space.

Keywords: evacuation behaviors, IoT application, map UI design, underground station

Procedia PDF Downloads 188

Authors: Hong Lu, Yongquan Zhang, Wei Fan, Xiangang Su

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Bent mandrel has been widely used as precise mould in automobile industry, shipping industry and aviation industry. To improve the versatility and efficiency of turning method of bent mandrel with fixed rotational center, an instantaneous machining model based on cutting parameters and machine dimension is prospered in this paper. The spiral-like tool path generation approach in non-axisymmetric turning process of bent mandrel is developed as well to deal with the error of part-to-part repeatability in existed turning model. The actual cutter-location points are calculated by cutter-contact points, which are obtained from the approach of spiral sweep process using equal-arc-length segment principle in polar coordinate system. The tool offset is set to avoid the interference between tool and work piece is also considered in the machining model. Depend on the spindle rotational angle, synchronization control of X-axis, Z-axis and C-axis is adopted to generate the tool-path of the turning process. The simulation method is developed to generate NC program according to the presented model, which includes calculation of cutter-location points and generation of tool-path of cutting process. With the approach of a bent mandrel taken as an example, the maximum offset of center axis is 4mm in the 3D space. Experiment results verify that the machining model and turning method are appropriate for the characteristics of bent mandrel.

Keywords: bent mandrel, instantaneous machining model, simulation method, tool-path generation

Procedia PDF Downloads 326

Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

Procedia PDF Downloads 148

Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions

Procedia PDF Downloads 468

Authors: Bander Alzahrani, Mohammed Alreshoodi

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Information-centric networking (ICN) using architectures such as the Publish-Subscribe Internet Technology (PURSUIT) has been proposed as a new networking model that aims at replacing the current used end-centric networking model of the Internet. This emerged model focuses on what is being exchanged rather than which network entities are exchanging information, which gives the control plane functions such as routing and host location the ability to be specified according to the content items. The forwarding plane of the PURSUIT ICN architecture uses a simple and light mechanism based on Bloom filter technologies to forward the packets. Although this forwarding scheme solve many problems of the today’s Internet such as the growth of the routing table and the scalability issues, it is vulnerable to brute force attacks which are starting point to distributed- denial-of-service (DDoS) attacks. In this work, we design and analyze a novel source-routing and information delivery technique that keeps the simplicity of using Bloom filter-based forwarding while being able to deter different attacks such as denial of service attacks at the ingress of the network. To achieve this, special forwarding nodes called Edge-FW are directly attached to end user nodes and used to perform a security test for malicious injected random packets at the ingress of the path to prevent any possible attack brute force attacks at early stage. In this technique, a core entity of the PURSUIT ICN architecture called topology manager, that is responsible for finding shortest path and creating a forwarding identifiers (FId), uses a cryptographically secure hash function to create a 64-bit hash, h, over the formed FId for authentication purpose to be included in the packet. Our proposal restricts the attacker from injecting packets carrying random FIds with a high amount of filling factor ρ, by optimizing and reducing the maximum allowed filling factor ρm in the network. We optimize the FId to the minimum possible filling factor where ρ ≤ ρm, while it supports longer delivery trees, so the network scalability is not affected by the chosen ρm. With this scheme, the filling factor of any legitimate FId never exceeds the ρm while the filling factor of illegitimate FIds cannot exceed the chosen small value of ρm. Therefore, injecting a packet containing an FId with a large value of filling factor, to achieve higher attack probability, is not possible anymore. The preliminary analysis of this proposal indicates that with the designed scheme, the forwarding function can detect and prevent malicious activities such DDoS attacks at early stage and with very high probability.

Keywords: forwarding identifier, filling factor, information centric network, topology manager

Procedia PDF Downloads 139

Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

Procedia PDF Downloads 299

Authors: Habtamu Tkubet Ebuy

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Purpose Conventional dynamic energy simulation tools go beyond the static dimension of simplified methods by providing better and more accurate prediction of building performance. However, their ability to forecast actual performance is undermined by a low representation of human interactions. The purpose of this study is to examine the potential benefits of incorporating information on occupant diversity into occupant behavior models used to simulate building performance. The co-simulation of the stochastic behavior of the occupants substantially increases the accuracy of the simulation. Design/methodology/approach In this article, probabilistic models of the "opening and closing" behavior of the window of inhabitants have been developed in a separate multi-agent platform, SimOcc, and implemented in the building simulation, TRNSYS, in such a way that the behavior of the window with the interconnectivity can be reflected in the simulation analysis of the building. Findings The results of the study prove that the application of complex behaviors is important to research in predicting actual building performance. The results aid in the identification of the gap between reality and existing simulation methods. We hope this study and its results will serve as a guide for researchers interested in investigating occupant behavior in the future. Research limitations/implications Further case studies involving multi-user behavior for complex commercial buildings need to more understand the impact of the occupant behavior on building performance. Originality/value This study is considered as a good opportunity to achieve the national strategy by showing a suitable tool to help stakeholders in the design phase of new or retrofitted buildings to improve the performance of office buildings.

Keywords: occupant behavior, co-simulation, energy consumption, thermal comfort

Procedia PDF Downloads 87

Authors: Ahmad Saleem

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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

Procedia PDF Downloads 86

Authors: J. A. López, J. E. Olguín, C. W. Camargo, G. A. Quijano, R. Martínez

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This paper presents an anthropometric study conducted to 300 employees in a maquiladora industry that belongs to the cluster of medical products as part of a research project to pretend simulate workplace conditions under which operators conduct their activities. This project is relevant because traditionally performed a study to design ergonomic workspaces according to anthropometric profile of users, however, this paper demonstrates the importance of making decisions when the infrastructure cannot be adapted for economic whichever put emphasis on user activity.

Keywords: anthropometry, biomechanics, design, ergonomics, productivity

Procedia PDF Downloads 440

Authors: Larbi Guezouli, Imane Azzouz

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This work is located in the domain of distributed information retrieval ‘DIR’. A simplified view of the DIR requires a multi-search in a set of collections, which forces the system to analyze results found in these collections, and merge results back before sending them to the user in a single list. Our work is to find a fusion method based on the relevance score of each result received from collections and the relevance of the local search engine of each collection.

Keywords: information retrieval, distributed IR systems, merging results, datamining

Procedia PDF Downloads 321

Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

Procedia PDF Downloads 211

Authors: Soroosh Torabi, Brad Berron, Ren Xu, Christine Trinkle

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Microfluidics integrated with 3D cell culture is a powerful technology to mimic cellular environment, and can be used to study cell activities such as proliferation, migration and response to drugs. This technology has gained more attention in cancer studies over the past years, and many organ-on-a-chip systems have been developed to study cancer cell behaviors in an ex-vivo tumor microenvironment. However, there are still some barriers to adoption which include low throughput, complexity in 3D cell culture integration and limitations on non-optical analysis of cells. In this study, a user-friendly microfluidic multi-well plate was developed to mimic the in vivo tumor microenvironment. The microfluidic platform feeds multiple 3D cell culture sites at the same time which enhances the throughput of the system. The platform uses hydrophobic Cassie-Baxter surfaces created by microchannels to enable convenient loading of hydrogel/cell suspensions into the device, while providing barrier free placement of the hydrogel and cells adjacent to the fluidic path. The microchannels support convective flow and diffusion of nutrients to the cells and a removable lid is used to enable further chemical and physiological analysis on the cells. Different breast cancer cell lines were cultured in the device and then monitored to characterize nutrient delivery to the cells as well as cell invasion and proliferation. In addition, the drug response of breast cancer cell lines cultured in the device was compared to the response in xenograft models to the same drugs to analyze relevance of this platform for use in future drug-response studies.

Keywords: microfluidics, multi-well 3d cell culture, tumor microenvironment, tumor-on-a-chip

Procedia PDF Downloads 255

Authors: Mohsen Rezaee

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Although robust security softwares, including anti-viruses, anti-spywares, anti-spam and firewalls are amalgamated with new technologies such as safe zone, hybrid cloud, sand box and etc., and although it can be said that they have managed to prepare highest level of security against viruses, spywares and other malwares in 2012, in fact, hacker attacks to websites are increasingly becoming more and more complicated. Because of security matters developments it can be said it was expected to happen so. Here in this work we try to point out some functional and vital notes to enhance security on the web, enabling the user to browse safely in unlimited web world and to use virtual space securely.

Keywords: firewalls, security, web services, computer science

Procedia PDF Downloads 387

Authors: Prajakta M. Wazat, D. N. Raut

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The study consists of a fast moving consumer goods (FMCG) beverage company wherein a portion of the supply chain which deals with outbound logistics is taken for improvement in order to reduce its logistics cost by using critical path method (CPM) method. Logistics is a major portion of the supply chain where customers are not willing to pay as it adds cost to product without adding value. In this study, it is necessary to ensure that products are delivered to clients at the right time while preserving high-quality standards from the beginning to the end of the supply chain. CPM is a logical sequencing method where in the most efficient route is achieved by arranging the series of events. CPM enables to identify a critical factor in order to minimize the delays and interruption by providing a feasible solution.

Keywords: FMCG, supply chain, outbound logistics, vehicle turnaround time, critical path method, cost reduction

Procedia PDF Downloads 153

Authors: Hyun-Woo Cho

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Unexpected events may occur with serious impacts on industrial process. This work utilizes a data representation technique to model and to analyze process data pattern for the purpose of diagnosis. In this work, the use of triangular representation of process data is evaluated using simulation process. Furthermore, the effect of using different pre-treatment techniques based on such as linear or nonlinear reduced spaces was compared. This work extracted the fault pattern in the reduced space, not in the original data space. The results have shown that the non-linear technique based diagnosis method produced more reliable results and outperforms linear method.

Keywords: process monitoring, data analysis, pattern modeling, fault, nonlinear techniques

Procedia PDF Downloads 377

Authors: Antonia Egli, Theo Lynn, Pierangelo Rosati, Gary Sinclair

Abstract:

The World Health Organization (WHO) defines vaccine hesitancy as the postponement or complete denial of vaccines and estimates a direct linkage to approximately 1.5 million avoidable deaths annually. This figure is not immune to public health developments, as has become evident since the global spread of COVID-19 from Wuhan, China in early 2020. Since then, the proliferation of influential, but oftentimes inaccurate, outdated, incomplete, or false vaccine-related information on social media has impacted hesitancy levels to a degree described by the WHO as an infodemic. The COVID-19 pandemic and related vaccine hesitancy levels have in 2022 resulted in the largest drop in childhood vaccinations of the 21st century, while the prevalence of online stigma towards vaccine hesitant consumers continues to grow. Simultaneously, a second disease has risen to global importance: Monkeypox is an infection originating from west and central Africa and, due to racially motivated online hate, was in August 2022 set to be renamed by the WHO. To better understand public reactions towards two viral infections that became global threats to public health no two years apart, this research examines user replies to threads published by the WHO on Twitter. Replies to two Tweets from the @WHO account declaring COVID-19 and Monkeypox as ‘public health emergencies of international concern’ on January 30, 2020, and July 23, 2022, are gathered using the Twitter application programming interface and user mention timeline endpoint. Research methodology is unique in its analysis of stigmatizing, racist, and hateful content shared on social media within the vaccine discourse over the course of two disease outbreaks. Three distinct analyses are conducted to provide insight into (i) the most prevalent topics and sub-topics among user reactions, (ii) changes in sentiment towards the spread of the two diseases, and (iii) the presence of stigma, racism, and online hate. Findings indicate an increase in hesitancy to accept further vaccines and social distancing measures, the presence of stigmatizing content aimed primarily at anti-vaccine cohorts and racially motivated abusive messages, and a prevalent fatigue towards disease-related news overall. This research provides value to non-profit organizations or government agencies associated with vaccines and vaccination programs in emphasizing the need for public health communication fitted to consumers' vaccine sentiments, levels of health information literacy, and degrees of trust towards public health institutions. Considering the importance of addressing fears among the vaccine hesitant, findings also illustrate the risk of alienation through stigmatization, lead future research in probing the relatively underexamined field of online, vaccine-related stigma, and discuss the potential effects of stigma towards vaccine hesitant Twitter users in their decisions to vaccinate.

Keywords: social marketing, social media, public health communication, vaccines

Procedia PDF Downloads 88

Authors: Luis A. Lizama-Perez, Manuel J. Linares, Mauricio Lopez

Abstract:

We introduce a method to perform remote user authentication on what we call non-invertible cryptography. It exploits the fact that the multiplication of an invertible integer and a non-invertible integer in a ring Zn produces a non-invertible integer making infeasible to compute factorization. The protocol requires the smallest key size when is compared with the main public key algorithms as Diffie-Hellman, Rivest-Shamir-Adleman or Elliptic Curve Cryptography. Since we found that the unique opportunity for the eavesdropper is to mount an exhaustive search on the keys, the protocol seems to be post-quantum.

Keywords: invertible, non-invertible, ring, key transfer

Procedia PDF Downloads 166

Authors: Jing Ren, Guifeng Han

Abstract:

The mental health of most college students in China is negative due to the multiple pressures of academics, life, and employment. The problem of psychological stress has been widely discussed and needs to be resolved immediately. Therefore, six typical green spaces in Chongqing University, China, were selected to explore the relationship between eight environmental characteristics and students' stress relief. A path analysis model is established using Amos26.0 to explain the paths for environmental characteristics influencing psychological stress relief. The results show that (1) tree species diversity (TSD) has a positive effect on stress relief, thus green coverage ratio (GCR), the proportion of water area (WAP), visual green index (VGI), and color richness (CR) have both positive and negative effects; (2) CR could reduce stress directly and indirectly, while GCR, TSD, WAP, and VGI could only reduce stress indirectly, and the most effective path is TSD→extent→stress relief; (3) CR can reduce stress more greatly for males than females, CR and VGI have better effects for art students than science students. The study can provide a theoretical reference for planning and designing campus living environments to improve students' mental health.

Keywords: public health, residential environment, space planning and management, mental health, path analysis

Procedia PDF Downloads 55