End-user Behavior Research Articles

A novel model to detect abnormal thermal energy consumption in existing buildings with limited metering infrastructure

In EU, excessive energy waste occurs in existing buildings due to multiple reasons, such as inadequate building design, frequent operation of the technical building systems far from the design and reference conditions, lack of proper maintenance, lack of building automation and control systems, unexpected or exceptional weather conditions and inappropriate end-user behaviours. In this context, the detection of “abnormal” energy consumption is crucial both to increase end-users’ awareness and to help energy managers and technicians in the energy diagnosis. In this paper, the authors present a novel model to detect abnormal thermal energy consumption in existing buildings equipped with limited metering infrastructure. Four heating seasons of daily thermal energy consumption and indoor environmental data of a case study building located in Central Italy were analysed to develop, train and test the model for data processing, benchmarking, abnormal energy consumption identification and diagnosis. The results of the test phase show the ability of the model to detect behavioural faults, such as the low consumption and indoor air temperature in two dwellings and the high ones in one other. Also, the poor thermal insulation properties of the building were clearly highlighted by the proposed model.

Insufficient duration of insecticidal efficacy of Yahe® insecticide-treated nets in Papua New Guinea

BackgroundInsecticide-treated nets (ITNs) are the backbone of anti-malarial vector control in Papua New Guinea (PNG). Over recent years the quality and performance of ITNs delivered to PNG decreased, which has likely contributed to the stagnation in the malaria control effort in the country. The present study reports results from the first 24 months of a durability study with the ITN product Yahe LN® in PNG.MethodsThe durability study was conducted in four villages on the northern coast of PNG, in an area with high malaria parasite transmission, following WHO-recommended methodology adapted to the local scenario. A cohort of n = 500 individually identifiable Yahe® ITNs was distributed by the PNG National Malaria Control Programme from October to December 2021. Insecticidal efficacy of the ITNs was tested using cone bioassays with fully pyrethroid susceptible Anopheles farauti colony mosquitoes at baseline and at 6 months intervals, alongside evaluation of physical integrity and the proportion of ITNs lost to follow-up. A questionnaire was used to collect information on ITN end user behaviour, such as the frequency of use and washing. The observations from the durability study were augmented with simulated laboratory wash assays.ResultsGradual uptake and replacement of previous campaign nets by the communities was observed, such that at 6 months 45% of all newly distributed nets were in use in their designated households. Insecticidal efficacy of the Yahe® nets, expressed as the percent 24 h mortality in cone bioassays decreased from 91 to 45% within the first 6 months of distribution, even though > 90% of study nets had never been washed. Insecticidal efficacy decreased further to < 20% after 24 months. ITNs accumulated physical damage (holes) at a rate similar to previous studies, and 35% were classified as ‘too torn’ by proportional hole index after 24 months. ITNs were lost to follow-up such that 61% of cohort nets were still present after 24 months. Laboratory wash assays indicated a rapid reduction in insecticidal performance with each consecutive wash such that average 24 h mortality was below 20% after 10 washes.ConclusionYahe® ITNs are not performing as per label claim in an area with fully pyrethroid susceptible vectors, and should be investigated more comprehensively and in other settings for compliance with currently recommended durability and efficacy thresholds. The mass distribution of low quality ITN products with variable performance is one of the major ongoing challenges for global malaria control in the last decade.

Load management design and techno-economic analysis for an islanded hybrid Pv-Teg microgrid

Integration of various renewable energy sources (RES) is one of the critical factors behind microgrid (MG) capacity development and MG expansion. Distributed energy resources with and without RES, electrical loads, controllers, and storage units constitute the essential components of MGs. On the other hand, with the addition of different power generation sources, load prediction on the user side of the MG becomes an important issue. Demand response solutions and changing end-user behavior are the other sources of uncertainty in the performance of MGs. Therefore, matching supply and demand is a very important issue that must be done immediately at every moment of the operation. A load management system (LMS) in a community MG is essential to ensure the system’s adaptability. Fuzzy logic (FL) controllers and energy management systems will ensure optimum use of energy resources and efficient operation of MG and reduce energy waste. In this study, an 85 kW photovoltaic (PV) and thermoelectric generator (TEG) hybrid MG system (PVTEG-MG), which can operate as an island grid in all regions of the World with hot water resources, has been designed for the first time and its techno-economic analysis has been made. The state of charge (SOC) for the proposed PVTEG-MG remained stable at 40% charge state by adding charge management. The average value of the Levelized Cost of Energy (LCOE) is $0.456/kWh for PV, $0.456/kWh for TEG, and 0.399/kWh over the total energy produced, 219,000 kWh for the PV and 493,650 kWh for the TEG. Overall, this PVTEG-MG system is a promising solution for hot water regions that can be used as an island grid to reduce electricity costs, increase efficiency and reliability, and provide a more sustainable energy source.

Are we relying on the wrong data? Analysis of e-journal usage data at seven R1 and R2 research universities

Using data from seven R1 and R2 universities in Ohio, this study seeks to determine the extent of usage of full-text e-journal articles stored across multiple platforms. Specifically, COUNTER 5 data is presented from five major academic journal publishers (Taylor & Francis, SAGE, Oxford, Wiley, and Springer), looking at usage within each of the publishers’ respective platforms, as well as in EBSCO, JSTOR, and OhioLINK’s Electronic Journal Center platform. The overarching goal of the analysis is to identify where exactly usage occurred for this e-journal content within the different platforms, especially in cases where an individual title has duplicate full-text access. Analysis of this data was conducted using Tableau and Excel. Findings raise significant questions about end-user behavior in terms of students’ and faculty’s ability to locate full text, regardless of whether it is discoverable from Google or other commercial search engines. The findings also suggest a strong connection between EBSCO usage and the academic calendar, with peak usage (as a percentage of overall usage) occurring in October, November, March, and April. Lastly, findings for Taylor & Francis e-journals within EBSCO call into question what these duplicate holdings mean for “big deal” subscription packages.

Deep hybrid model for attack detection in IoT-fog architecture with improved feature set and optimal training

IoT-Fog computing provides a wide range of services for end-based IoT systems. End IoT devices interface with cloud nodes and fog nodes to manage client tasks. Critical attacks like DDoS and other security risks are more likely to compromise IoT end devices while they are collecting data between the fog and the cloud layer. It’s important to find these network vulnerabilities early. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. However, deep learning cannot be carried out on Internet of Things devices because to their constrained calculation and storage capabilities. In this research, we suggest a three-stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog Architecture. Improved Z-score normalization-based data preparation will be carried out in the initial step. On the basis of preprocessed data, features like IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as input to hybrid classifiers dubbed optimized Deep Maxout and Deep Belief Network (DBN) in the third step of the process to classify the assaults based on the input dataset. A hybrid optimization model called the BMUJFO (Blue Monkey Updated Jellyfish Optimization) technique is presented for the best Deep Maxout training. Additionally, the suggested model produced higher accuracy, precision, sensitivity, and specificity results, with values of 95.26 percent, 94.84%, 96.28%, and 97.84%, respectively.

Sustainable Smart City Technologies and Their Impact on Users’ Energy Consumption Behaviour

Sustainable smart cities (SSCs) target decarbonisation by optimising energy consumption through the emerging capabilities of technology. Nevertheless, the energy consumption behaviour of end users has the potential to compromise the effectiveness of technological interventions, reflecting the importance of active social engagement in realising decarbonisation goals. Although extensive research exists on energy consumption behaviour, little is known about how technology engagement affects it, the nature of these technologies, and their role in SSC. The paper aims to identify, categorise, and investigate the smart technologies that impact household energy consumption behaviours and their integration into the larger SSC system. Following a systematic review of 60 articles from the Scopus database (2013–2023), the study found 45 smart technologies cited, with 49% affecting efficiency behaviour and 51% affecting curtailment behaviour. While these technologies inform the city administration level in the SSC framework, the role of end users remains unclear, suggesting a technocratic approach. The study proposes the Sustainable Smart City Network to facilitate a grassroots approach, identifying five key domains: government policies, smart technology adoption, smart technology engagement, smart city infrastructure, and urban sustainability. The study provides an original contribution to knowledge by unveiling the key technologies affecting energy consumption behaviour and outlining the pragmatic requirements for achieving decarbonisation through a grassroots approach.

РАЗВИТИЕ АРХИТЕКТУРЫ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

The paper analyzes the role of a complete architecture of intelligent transport systems (ITS) in ensuring their effectiveness. Many countries or groups of countries have ITS architecture with successful results of its application in practice. It is shown that the ITS architecture is constantly being updated and this is a characteristic trend. The paper proposes new approaches to the formation of the architecture of the integration platform within ITS. The scheme of organisation of data flows in different loops of software tools of the integration platform is developed. It is proposed to separate the control loops proper transport system data and identification data, which describe a partisipant of the ITS (digital object) with the expansion of capabilities in a cooperative ITS. It is possible to organise linked chains of identification of ITS digital objects, to participate in the formation of network structures for ITS work processes, describing the behaviour of digital objects during ITS processes and the behaviour of end users in accordance with the properties of network structures.

Поведінка споживача в умовах кризи та невизначеності: мікроекономічний аналіз

The aim of the article is to study the features of consumer behavior in conditions of crisis and uncertainty. The article analyzes the research on the rationality of consumer choice. The main approaches to assessing utility and consumer behavior have been allocated. Currently, half of ordinary citizens prioritize buying foods; the next category in terms of specific gravity is medicines; in third place – clothes and shoes; fourth place – hygiene and personal care products. It is found that the last positions are occupied by purchases related to things for the home, furniture and household appliances, because they are designed to satisfy secondary needs, therefore the demand for them at this time is very low. Particular attention is paid to the issue of consumer income and consumer choice in conditions of limited income. According to the results, the following trend can be observed: 40% of respondents are loyal to those labels and brands, same as the general set of products they would buy, and the reduction in income led to a reduction in quantitative indicators; in second place in terms of the number of responses is the category of people who switched to cheaper goods or substitute goods; only 7% said they did not change the level of their spending in any way (although in 2021 this figure was twice as much). Taking into account the microeconomic approach, the main factors of consumer choice have been identified. Among the most important factors, special attention is paid to: support for the Armed Forces of Ukraine; the brand’s activities and interaction with russia; volunteering; clear political position and brand language. If we study the results of the survey on the factors of brand choice by age categories, there is a clear tendency that the youngest group of respondents, namely people aged 18-24, are the most sensitive to the above factors. It is determined that young people are the most meticulous about brand policy and methods of conducting their activities, and it is especially important for them that the values of the brand and their behavior correspond to the current situation in which all Ukrainian citizens find themselves. A scheme of behavior of end users is presented. The influence of military aggression on the rational behavior of consumers is substantiated.

Energy Conservation at Home: A Critical Review on the Role of End-User Behavior

The recent literature emphasizes the significance of occupants’ behavior in shaping home energy demand. Several policies have been defined and tools and technologies have been developed to raise people’s awareness and encourage energy-saving practices at home, but households’ energy demand keeps rising. The thesis is that the fundamentals on this topic are still unclear and that available tools, strategies and measures should be approached in a more integrated way, as they are not now effective enough to encourage energy savings. How these could be successfully combined is still a major knowledge gap. Thus, this article proposes a critical review of the literature to discuss the potential role of end users in energy conservation at home, preparing the ground for truly effective engagement strategies and tools to encourage behavioral change. To that end, a systematic literature review is performed, including over 130 relevant articles. According to the critical interpretation of their content, after years of technologically driven strategies, the most promising approaches capable of overcoming the intention–action gap are those more user-centered. However, relying solely on the social aspect is not effective. Synergistic integration of the two main clusters of studies has been identified as a promising field of research for the future.

A systematic review of current cybersecurity training methods

Cybersecurity continues to be a growing issue, with cyberattacks causing financial losses and loss of productivity and reputation. Especially in an organisational setting, end-user behaviour plays an essential role in achieving a high level of cybersecurity. One way to improve end-user cybersecurity behaviour is through comprehensive training programmes.There are many contradictory statements and findings with regard to the optimal way to conduct a behavioural cybersecurity training. We conducted a systematic review to create a comprehensive overview of the methods used in cybersecurity training and their effectiveness in improving organisational cybersecurity behaviours. Web of Science, ACM Digital Library, ProQuest, PubMed and PsycINFO were searched and 16,771 papers were identified. After title, abstract and full text screenings were conducted, 142 relevant papers were included in our analysis.The analysis shows that the majority of studies report positive effects of training, regardless of the cybersecurity topic that was addressed or the training method that was employed. Game-based training methods were used most often. Most studies used a non-experimental design to test effectiveness, with pretest-posttest designs being the most frequent. Sample sizes were often small and many interventions were not tested on employees but other populations. Further findings with regard to intervention design, characteristics and evaluation are discussed.

Stackelberg–Nash game approach for price-based demand response in retail electricity trading

This paper studies the price-based demand response problem in a deregulated retail electricity trading, aiming to coordinate the energy consumption behavior of end-users under dynamic retail prices. The challenge here is that in addition to the hierarchical decision-making process between utility company and end-users considered in existing works, the non-cooperative and competitive interdependence among end-users cannot be ignored. To address this issue, we first construct a novel Stackelberg–Nash game, in which the Stackelberg game is used to capture the hierarchical decision-making process between utility company and end-users, while the Nash game is dedicated to describing the interdependence among end-users. Then the existence and uniqueness of the Stackelberg–Nash equilibrium is provided along with theoretical analysis. On the basis of the analysis of equilibrium, we propose a distributed iterative algorithm with an adaptive step size, which is benchmarked with a fixed step-size algorithm. The comparison results on a real-life residential retail electricity market show that our proposed algorithm has better performance in terms of effectiveness and scalability.

Traceability analysis for low-voltage distribution network abnormal line loss using a data-driven power flow model

The abnormal behavior of end-users is one of the main causes of abnormal line loss in distribution networks. The integration of a large amount of distributed renewable energy into a low-voltage distribution network (LVDN) complicates line loss analysis. Traceability analysis for abnormal line loss aims to identify the specific end-user responsible for the anomaly in line loss. This paper proposes, for LVDNs with incomplete topology and line parameters, a practical traceability analysis approach using a data-driven power flow model. A data-driven power flow model based on a neural network is first established to capture the power flow mapping relationship without topology and line parameter information. A backpropagation algorithm is then presented to correct the actual power consumption data according to the measured voltage data. By comparing actual power consumption data with measured power data, users with abnormal behavior can be accurately identified and tracked. Finally, the effectiveness of the proposed approach is verified by actual data.

SMiShing Attack Vector: Surveying End-User Behavior, Experience, and Knowledge

Phishing attacks steal sensitive and valuable information from end-users. Human Factors researchers have extensively studied Phishing over decades, revealing numerous psychological vulnerabilities. A greater understanding of the interaction between attackers and end-users has produced more effective: policies, models, training, and defensive mechanisms. The Human Factors literature lacks information about non-technical users’ knowledge, experience, and behaviors in response to SMiShing. The current survey collected pilot data from college students with average cyber hygiene scores. Our results suggest that while users do not clearly understand the term SMiShing, they report knowing the danger. While all participants reported that using links within text messages from unknown senders can lead to security vulnerabilities, most reported using the links anyway. Users vet messages based on the sender’s phone number and typos, rather than delivery time and other message recipients. These descriptive findings can be used to support future work attempting to defend against SMiShing attacks.

Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health (SOH) is a critical factor in evaluating the performance of the lithium-ion batteries (LIBs). Due to various end-user behaviors, the LIBs exhibit different degradation modes, which makes it challenging to estimate the SOHs in a personalized way. In this article, we present a novel particle swarm optimization-assisted deep domain adaptation (PSO-DDA) method to estimate the SOH of LIBs in a personalized manner, where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy. The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method. The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials, ambient temperatures and charge-discharge configurations. Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method. The PyTorch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

A First “Stab” at Defining Temporal Flexibility-to-Cost Mapping

Despite the myriad of techniques for calculating flexibility envelopes in buildings and power grids, their analytical monetization has received practically zero attention. This obscure issue reaches even more unknown depths in the multi-period (MP) setting, where intertemporality hinders the design of a clear pricing framework. This letter builds on the author's previous work, which proposed a novel methodology for the one-to-one flexibility-to-cost mapping in grid-friendly smart sustainable buildings (GF-SSBs), and discusses alternatives for mapping MP flexibility envelope to costs in practice. Their comparison provides key insights into how different cost structures may affect end-user behavior and the grid's ability to procure flexibility. This letter hopes to kick-start the discussion around “fair” or intrinsic flexibility value, an issue that has largely been sidelined.

Investigating pedestrian behaviour in urban environments: A Wi-Fi tracking and machine learning approach

Urban geometry plays a critical role in determining paths for pedestrian flow in urban areas. To improve the urban planning processes and to enhance quality of life for end-users in urban spaces, a better understanding of the factors influencing pedestrian movement is required by decision-makers within the urban design and planning industry. The aim of this study is to present a novel means to assess pedestrian routing in urban environments. As a unique contribution to knowledge and practice, this study: (a) enhances the body of knowledge by developing a conceptual model to assess and classify pedestrian movement behaviours, utilising machine learning algorithms and location data in conjunction with spatial attributes, and (b) extends previous research by revealing spatial visibility as a driver for pedestrian movement in urban environments. The importance of the findings lies in the perspective of revealing novel insights concerning individual preferences and behaviours of end-users and the utilisation of urban spaces. The approaches developed can be utilised for observations in large-scale contexts, as an addition to traditional methods. Application of the model in a high pedestrian traffic-dense retail urban area in London reveals clear and consistent relationships amongst spatial visibility, individuals’ motivation, and knowledge of the area. Key behaviours established in the study area are grouped into two activity categories: (i) Utilitarian walking (with motivation - expert and novice striders) and (ii) Leisure walking (no motivation - expert and novice strollers). The approach offers an insightful and automated means to understand pedestrian flow in urban contexts and informs wider wayfinding, walkability, and transportation knowledge.

Reinforcing personalized persuasion in task-oriented virtual sales assistant.

Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable. Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. Furthermore, we also curate a personalized persuasive dialogue (PPD) corpus, which contains utterance-level intent, slot, sentiment, and persuasion strategy annotation. The obtained results and detailed analysis firmly establish the effectiveness of the proposed persuasive virtual assistant over traditional task-oriented virtual assistants. The proposed framework considerably increases the quality of dialogue generation in terms of consistency and repetitiveness. Additionally, our experiment with a few shot and zero-shot settings proves that our meta-learned model learns to quickly adopt new domains with a few or even zero no. of training epochs. It outperforms the non-meta-learning-based approaches keeping the base model constant. To the best of our knowledge, this is the first effort to improve a task-oriented virtual agent's persuasiveness and domain adaptation.

Selling by clicks or leasing by bricks? A dynamic game for pricing durable products in a dual-channel supply chain

&lt;p style='text-indent:20px;'&gt;In this paper, we discuss if and which pricing policies by a manufacturer who sells its products online motivate a retailer as an independent part to enter the market to provide selling and leasing options through a brick store. Moreover, the impact of online shopping preferences and brand image on end-user behavior is examined, and different consumption patterns are considered. For this purpose, a dynamic game is applied to model a supply chain consisting of one manufacturer and one retailer. The model aims to specify the optimal pricing policies in the second-hand market and according to physical utility associated with depreciation, brand image, and online shopping preferences for different end-users in an infinite time horizon. Markov perfect equilibria are considered as the solution concept to predict the behavior of end-users in the long term. The results revealed that enriching brand image always benefits the manufacturer and the retailer, while it does not mean there is the same optimal brand image level for both manufacturer and retailer. Besides, the improvement of physical utility makes more demand for leasing products and motivates the retailer to be active in the market. Notably, online shopping preferences play a prominent role in market segmentation and retailer decision as a result. Also, growing production costs have a significant reverse effect on the profitability of both manufacturer and retailer. Therefore, the manufacturer must focus on economic production.&lt;/p&gt;

Modeling how different user groups perceive webpage aesthetics

Aesthetics is a central consideration in user interface design. It is known to affect end-user behavior and perception, in particular the first impression of graphical user interfaces. However, what users perceive as pleasant or good design is highly subjective. We contribute a computational model that estimates the visual appeal of a given webpage for several common cohorts, or user groups, including gender, age, and education level. Our model, a convolutional neural network trained on 418 webpage screenshots having 771k aesthetic scores (in a 1–9 Likert scale) from 32k users, achieves high accuracy and is always less than 1 point off from ground-truth ratings. Designers can use our model to anticipate how people would rate their webpage, offer personalized designs according to the visual preferences of their users, and support rapid evaluations of webpage design prototypes for specific cohorts.

DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications

Nowadays, fog computing plays a very vital role in providing many services to end-based IoT (Internet of Things) systems. The end IoT devices communicate with the middle layer fog nodes and to the above cloud layer to process the user tasks. However, this large data communication experiences many security challenges as IoT devices are being compromised and thus the fog nodes at the fog layer are more prone to a very critical attack known as Distributed Denial of Service (DDoS) attack. The attackers or the compromised IoT devices need to be detected well in the network. Deep Learning (DL) plays a prominent role in predicting the end-user behavior by extracting features and classifying the adversary in the network. But, due to IoT device’s constrained nature in computation and storage facilities, DL cannot be administered on those. In this paper, a deep intelligent DDoS attack detection scheme (DI-ADS) is proposed for fog-based IoT applications. The framework mainly uses a deep learning model (DLM) to detect DDoS attacks in the network. The DLM is installed on the computation module of the fog node that predicts the end IoT device behavior. For the selection of the best DLM model at the fog layer, the performance comparison is made on Deep Neural Multilayer Perceptron (DNMLP) and Long Short-Term Memory (LSTM) models along with the conventional machine learning (ML) models such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LR), and Random Forest (RF). The simulation is performed using the Python Anaconda platform by considering a new DDoS-SDN (Mendeley Dataset) dataset that consists of three DDoS attacks such as TCP Syn, UDP Flood, and ICMP attacks. From the results, DNMLP showed the best accuracy of 99.44% as compared to other DL and ML models. By outperforming nature in the detection of DDoS attacks, DNMLP is considered in the proposed framework for being implemented at the fog layer.