End-use Customers Research Articles

A highly-configurable session designer for VR nursing training

Virtual Reality (VR) has proven to be a valuable tool for medical and nursing education. VR simulators are available at any time and from anywhere, and can be used with or without faculty supervision, which results in a significant optimization of time, space, and resources. In this paper we present a highly-configurable session designer for VR-based nursing education following the Standards for QUality Improvement Reporting Excellence: SQUIRE 2.0 and SQUIRE-EDU. Unlike existing platforms, we focus on letting educators quickly customize the training sessions in multiple aspects. This is achieved through the use of a visual editor, where educators can choose the environment and the elements in the session, and a node-based visual system for programming the behavior of the sessions. Thanks to end-user customization, educators can develop different variants of a session, adapt the sessions to new nursing protocols, incorporate new equipment or instruments, or accommodate custom environments. We have implemented the designer and tested it on various nursing training scenarios. The designer can be integrated into VR simulators to help educators save time in delivering highly customizable training.

Control algorithm for an island microgrid under DSTATCOM using a Third Order Sinusoidal Integrator (TOSSI) to improve power quality for a local nonlinear load

Energy delivery to end-user customers is critically dependent on the distribution system. The system must synchronize correctly during island operation while connected as a microgrid to provide better power quality. The consumer experiences distortion and uneven power quality during these activities, raising severe research concerns. In order to mitigate the power quality issues at the end-user side, the modified Third Order Sinusoidal Integrator (TOSSI)-based DSTATCOM is the main topic of this research work. In order to instruct the DSTATCOM, the TOSSI receives feedback on phase, voltage, and frequency via the phase-locked loop (PLL) system. Through the use of the harmonic component of the load current, the enhanced TOSSI-based feedback enhances the dynamics of the distribution side. Enhancing DSTATCOM’s performance in distorted and unbalanced system circumstances is the aim of the suggested research. This work explores power factor correction and harmonic reduction in unbalanced and non-linear load settings utilizing modified TOSSI feedback, achieving an accuracy of 92%. The planned microgrid is modelled and simulated in MATLAB’s Simulink environment to demonstrate the importance of the suggested mitigation technique.

Exploring the Potentials of Chitin and Chitosan-Based Bioinks for 3D-Printing of Flexible Electronics: The Future of Sustainable Bioelectronics.

Chitin and chitosan-based bioink for 3D-printed flexible electronics have tremendous potential for innovation in healthcare, agriculture, the environment, and industry. This biomaterial is suitable for 3D printing because it is highly stretchable, super-flexible, affordable, ultrathin, and lightweight. Owing to its ease of use, on-demand manufacturing, accurate and regulated deposition, and versatility with flexible and soft functional materials, 3D printing has revolutionized free-form construction and end-user customization. This study examined the potential of employing chitin and chitosan-based bioinks to build 3D-printed flexible electronic devices and optimize bioink formulation, printing parameters, and postprocessing processes to improve mechanical and electrical properties. The exploration of 3D-printed chitin and chitosan-based flexible bioelectronics will open new avenues for new flexible materials for numerous industrial applications.

Dynamic Programming of Resilient Smart Distribution Network Expansion Planning against Cyber-Attacks on end-user customers

Dynamic Programming of Resilient Smart Distribution Network Expansion Planning against Cyber-Attacks on end-user customers

Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting

The future of energy flexibility in microgrids (MGs) is steering towards a highly granular control of the end-user customers. This calls for more highly accurate uncertainty forecasting and optimal management of risk and flexibility options. This paper presents a novel data-driven model to optimize the operation of MGs based on a risk-averse flexi-intelligent energy management system (RFEMS), considering the rising challenge of global climate change. It considers the presence of renewables, a diesel generator, and flexibility resources (FRs) containing a demand response program (DRP), distributed electric vehicles (EVs), and electric springs (ESs). In the first phase, the proposed model, by means of a novel hybrid deep-learning (DL) model, forecasts uncertain parameters associated with wind and solar generations, load demand, and day-ahead energy market price. The architecture of the proposed hybrid forecasting model is composed of several stacked restricted Boltzmann machines and a cascade neural network. In the second phase, the MG operator (MGO), based on the obtained uncertainty forecasting results, in the context of a hybrid risk-controlling model, optimizes the MG operation using the provided demand-side flexibility. The proposed optimization problem is linearized stochastic programming with robust concepts, subject to AC optimal power flow constraints, MG flexibility restrictions, and operating limits of local resources. Finally, the efficiency of the proposed RFEMS by using real German datasets on a 33-bus test MG is analyzed. Numerical results demonstrate the superior performance of the proposed forecasting model over several hybrid DL models. In particular, the root mean square error (RMSE) index for wind, solar, load, and price forecasting is improved by 53.35%, 73.24%, 80.24%, and 58.1%, respectively. Further analysis of the proposed RFEMS reveals that operating indices in two 33-bus and 69-bus test networks are significantly improved. It paves pathways to risk-averse, flexible, and economic operation of smart active distribution networks.

Test Automation of Motor Over Temperature Protection Extension Module of Drive

In industries, for releasing any product into the market, various stages of testing procedure must be done on the product to test its functionality and reliability. The testing procedure has to be frequent and accurate enough to enable early defect detection. Manual testing procedures are time-consuming, need more human resources at the place of testing, have no concept of documentation, and have more chances of human errors. To overcome the variously described limitations of manual testing, test automation is a key tool for providing prompt feedback, tracking of developed firmware versions time-to-time and reducing the tester’s efforts to perform tests task repetitively. The main goal is to speed up test execution cycles and improve the accuracy as well as the performance of the module for end-user customer satisfaction. The proposed system works based on replacing the potentiometer with a relay configuration controlled by the digital output signal of the Programmable Logical Controller and Automated Test Framework. The study describes the transition from manual testing of modules with complex setup environments to automatic testing of modules with implemented hardware changes.

Strategic Bidding of Retailers in Wholesale Markets: Continuous Intraday Markets and Hybrid Forecast Methods.

The deregulation process of the electricity sector has led to competition in wholesale and retail markets. In particular, retailers submit bids to wholesale markets to satisfy the energy needs associated with portfolios of end-use customers. This paper describes a strategic process for retailers bidding in a wholesale market composed of a day-ahead market, an intraday market, and a balancing market. It considers a market design that involves a hybrid model for the intraday market, based on daily auctions and a continuous procedure. The paper also presents a computational study to illustrate and test both the market design and the strategic bidding process of retailers. The results confirm the advantages of considering a continuous intraday market, show that bidding in short-term markets is more beneficial than bidding in medium-term markets, and indicate important aspects to consider when selecting customers to add to the portfolios of retailers.

Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers. It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities. The volume and volatility of the business makes it one of the prospective fields for analytical study and data modeling. This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting, customer targeting, customized offers, value proposition etc. The segmentation could be on various aspects such as demographics, historic behavior or preferences based on the use cases. In this paper, historic retail transactional data is used to segment the customers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm. This helps in calculating the futuristic value a segment or a customer brings to the business. Strategic marketing designs and budgeting can be implemented using these results. The study is specifically useful for large scale marketing in domains such as e-commerce, insurance or retailers to segment, profile and measure the customer lifecycle value over a short period of time.

Electrically conductive and 3D-printable copolymer/MWCNT nanocomposites for strain sensing

Structural health monitoring (SHM) of safety-critical composite components is essential to ensure mechanical stasis and detect local damage before it can produce global failure. SHM technologies must also adapt to ever-evolving materials and geometries, but traditional piezoresistive strain sensors lack the ability for end-user customization and modifications throughout service-life. Additive manufacturing via fused filament fabrication (FFF) provides a practical pathway to overcome such sensor shortcomings. However, the electrical conductivity of conventional polymer feedstock is not sufficient for accurate strain measurements without compromise to melt viscosity and thus printability. Here we report the development of a new 3D-printable and electrically responsive nanocomposite by melt-mixing poly(ethylene-co-methacrylic acid) (EMAA) with multi-walled carbon nanotubes (MWCNT). Bulk electrical conductivity of 43.9 S m−1 is achieved at only 5 wt% loading – higher than comparable materials – where the nano-dispersion heterogeneity of MWCNT in EMAA is linked to the favorable conductivity while retaining molten flowability. FFF is employed to print thin (150μm) serpentine strain sensors onto the surface of a glass fiber-reinforced polymer composite, which exhibit strong adhesion and accurate piezoresistive sensing under cyclic flexural loading. Twenty consecutive cycles with converged sensor readings (i.e., < 1% variation in measured resistance) demonstrates reliable performance across a relevant service strain range (0.4 – 0.8 %) for such fiber-composites. This rapid fabrication and transferable sensing strategy, suitable for new and existing structures, thus provides a crosscutting SHM solution.

PowerMorph: QoS-Aware Server Power Reshaping for Data Center Regulation Service

Adoption of renewable energy in power grids introduces stability challenges in regulating the operation frequency of the electricity grid. Thus, electrical grid operators call for provisioning of frequency regulation services from end-user customers, such as data centers, to help balance the power grid’s stability by dynamically adjusting their energy consumption based on the power grid’s need. As renewable energy adoption grows, the average reward price of frequency regulation services has become much higher than that of the electricity cost. Therefore, there is a great cost incentive for data centers to provide frequency regulation service. Many existing techniques modulating data center power result in significant performance slowdown or provide a low amount of frequency regulation provision. We present PowerMorph , a tight QoS-aware data center power-reshaping framework, which enables commodity servers to provide practical frequency regulation service. The key behind PowerMorph is using “complementary workload” as an additional knob to modulate server power, which provides high provision capacity while satisfying tight QoS constraints of latency-critical workloads. We achieve up to 58% improvement to TCO under common conditions, and in certain cases can even completely eliminate the data center electricity bill and provide a net profit.

Smart Logistic System for Enhancing the Farmer-Customer Corridor in Smart Agriculture Sector Using Artificial Intelligence

In an agriculture sector, the quality of several raw crops depends upon the time factor. After harvesting, it is necessary to bring the crops either to cold storage or directly to customer though wholesale dealers. Keeping crops in cold storage decrease the nutrition value and also increase the overall cost of the crop leading to price hike. So, it will be best if the crops are put in the market as soon as possible. This can only be possible if the logistic system is updated and can handle the real-time requirement of agriculture product transport. This study investigates and highlights the possible IoT-based logistic support using artificial intelligence for the farmers such that a fast corridor is created between farmers’ lands to end-user customers. This will benefit the farmers in two-fold; first, it will increase the revenue of farmers by decreasing the time span avoiding cold storage fees, and second, it will maintain the quality of crops.

A game theory based demand-side management in a smart microgrid considering price-responsive loads via a twofold sustainable energy justice portfolio

This paper introduces a demand-side integration (DSI) framework that upholds the efficacious electrical energy consumption to attain the aims of smart grid as well as the customers’ requirement. The proposed DSI framework is based on a pre-paid orderly energy consumption strategy for a smart microgrid. The interaction between aggregators and end-use customers is captured as a Nash Bargaining Game. A dynamic electricity pricing scheme is implemented to derive the profitable daily electricity tariffs considering an elasticity-based model of price-responsive load. The resources caused by proper response of small-scale consumers are integrated into day-ahead energy scheduling problem. In order to cope the real-time deviations, the DSI program is accompanied by a supplementary pay-off module. The DSI framework is formulated as a stochastic optimization problem in the form of mixed integer nonlinear programming involving probabilistic representation of uncertainty in generation pattern of renewable energy resources. According to the simulation results, in contrast with the normal consumption paradigm, the load factor improves at least 1.36%, the net profit of unified entity enhances 0.27%, and the total gas emissions are mitigated about 10.9%. The outcomes demonstrate the accuracy and merit of the proposed method.

Joint control of manufacturing and onsite microgrid system via novel neural-network integrated reinforcement learning algorithms

Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method.

Dyslexia and Accessibility Guidelines – How to Avoid Barriers to Access in Public Services

Abstract Interaction becomes increasingly digital, including interactions with public authorities, requiring websites to be accessible for all. The strong focus on written words in digital interactions allows for assistive technology to improve access for many users. However, it might impede usability for users with reading and writing difficulties. The present paper examines whether guidelines such as the Web Content Accessibility Guidelines (WCAG) sufficiently cover users with dyslexia and how usability can be improved for this user group. This paper expands a previously published version at the Mensch und Computer 2021 conference [1]. Using literature research and interviews with users with dyslexia and focusing on an application of the WCAG on the country level (a German law regulating accessibility for e-government websites), we confirmed and identified gaps in the WCAG for this group. We focus on within-site search, as this function is frequently used to find relevant information, esp. on infrequently visited sites such as e-government websites. Modifications to improve search were developed based on literature and the results of the interviews. They were empirically evaluated in an online study with 31 users with dyslexia and 71 without. Results indicate that an auto-complete function, a search that compensates for spelling errors, an indicator that the search was corrected, search term summary information, and avoidance of capital letters were useful for both groups, while wider line spacing should only be used in end-user customization.

3D Printing Technologies in Biosensors Production: Recent Developments

Recent advances in 3D printing technologies and materials have enabled rapid development of innovative sensors for applications in different aspects of human life. Various 3D printing technologies have been adopted to fabricate biosensors or some of their components thanks to the advantages of these methodologies over the traditional ones, such as end-user customization and rapid prototyping. In this review, the works published in the last two years on 3D-printed biosensors are considered and grouped on the basis of the 3D printing technologies applied in different fields of application, highlighting the main analytical parameters. In the first part, 3D methods are discussed, after which the principal achievements and promising aspects obtained with the 3D-printed sensors are reported. An overview of the recent developments on this current topic is provided, as established by the considered works in this multidisciplinary field. Finally, future challenges on the improvement and innovation of the 3D printing technologies utilized for biosensors production are discussed.

Institutional pioneers and articulation work in digital platform infrastructure-building

Digital platforms are an important organising form in business-to-business markets and have mirrored increasing research in end-user customers' interactions with digital platforms. Much less studied are the digital platform infrastructures underpinning this customer interfacing activity which must be built and maintained for digital platforms to exist and operate. We explore how institutional pioneers attempted to build a new digital platform with a vision of the cashless society beyond the traditional payment methods. Our findings demonstrate the insightful role of institutional pioneers in digital infrastructure-building through energizing the direction, network goals, positioning with other market-actors in the backstage. We show how the tensions produced by the organizing and ordering activities in the digital infrastructure field are resolved through brokering, alignment and workarounds. We unravel the way institutional pioneers use articulation work to define a legitimate course of actions for all actors in their organizing of standards, structure and behavioural focus.

Economic analysis on the potential of palm oil empty fruit bunch pellets as alternative fuel to reduce the cost of the government’s 3 kg LPG subsidy and its role in achieving sustainable development goals

Government subsidies related to 3 kg LPG per year are significant in the last three years if averaged up to IDR 50 trillion per year. Even though the number of users continue to increase, unfortunately, there are still many shortages of 3 kg LPG in areas outside Java; even in Java, there is still a scarcity. The Minister of Energy and Mineral Resources has issued Ministerial Decree No. 128 K / 70 / MEM / 2020 concerning the Task Force on Energy Security and Energy Utilization. In the second statement point, d stipulates the LPG Import Reduction Working Group. The purpose of this study is to calculate the economic value of the empty fruit bunch (EFB) pellet business as a recommendation to the government and the private sector. The method used is the calculation of NPV, MIRR, PBP, and ROI. In addition, ethical business and distribution models are used to deliver products to the end-user (customer) and guaranteed availability. Therefore, a breakthrough is needed to process empty fruit bunches (EFB) into industrial-scale solid fuel (pellets) that can replace 3 kg LPG, especially for poor people.

Characteristics of Abs and Pla Material in 3D Printing for Car Backseat Headrest Hanger/Hook Model

Additive manufacturing (AM) is known as the technology which enable using a layer wise in fabrication of a complex part directly from CAD files without using any specific tooling. This manufacturing techniques offers many strategic advantages which include design freedom for the build of complex part geometries which cannot be made in other way, the ability to build functional part in a small size for the end user customization and its ability to do improvement for the expensive part in aerospace and other industries. The aim of this research is to study the effect of process parameter such as layer thickness, infill density and object orientation to the accuracy of printed part measurement with CAD model, surface roughness and mechanical strength of PLA and ABS material. Therefore, it is important to find the optimum value of dimensional accuracy, surface roughness and mechanical strength for both materials. To achieve the optimum value of dimensional accuracy, surface roughness and mechanical strength for both materials, Taguchi method L4 orthogonal array is used to conduct this experiment and Minitab 18 software will analyze the result and shows the best optimum value. The result from ANOVA analysis shows that object orientation gives highest contribution to the dimensional accuracy and surface roughness for both materials. Meanwhile, for mechanical strength layer thickness highly contributed to the ABS material and object orientation for the PLA material. A Car Backseat Headrest Hanger/Hook model is fabricated by the best optimal combination and level of process parameter of mechanical strength.

Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA 2 B approach

Demand response acts as an effectual tool to better balance electricity demand and supply in the smart grid. The response to prices and incentives is described as “changes on electricity usage from conventional consumption patterns to end-use customers.” In this paper, a hybrid approach is proposed for Electric Vehicle Based Grid connected to the distribution generation (DG). The proposed system is joined performance of student psychology optimization algorithm (SPOA) and AdaBoost algorithm, thus it known as SPOA2B approach. The major aspire of this study is “to minimize the peak power cutoff, voltage regulation, spin reserve for making the optimization mode ideally convex, and accurate second-order conic relaxations.” Additionally, the SPOA2B method is a proficient deal by standard business solvers with allocation models, and then acquires least annualized costs. In this study, the electric vehicle charging station (EVCS) is linked to DG, and then it gives charging service to electric vehicles (EVs). When the EV appears at the charging station, it informs the EVCS operator of their own energy requirement with anticipated departure time. Every EVCS can capture the information of all EVs using an SPOA2B technique. By then, the proposed system is performed on MATLAB/Simulink site and the efficiency is compared with other processes. The number of iteration, like, 100, 250, 500, and 1000, the simulation time of the proposed technique is 14.8, 29.2, 69.9, and 77.1 s, respectively.

Agent-based retail competition and portfolio optimization in liberalized electricity markets: A study involving real-world consumers

The liberalization of energy markets brought full competition to the electric power industry. In the wholesale sector, producers and retailers submit bids to day-ahead markets, where prices are uncertain, or alternatively, they sign bilateral contracts to hedge against pool price volatility. In the retail sector, retailers compete to sign bilateral contracts with end-use customers. Typically, such contracts are subject to a high-risk premium—that is, retailers request a high premium to consumers to cover their potential risk of trading energy in wholesale markets. Accordingly, consumers pay a price for energy typically higher than the wholesale market price. This article addresses the optimization of the portfolios of retailers, which are composed of end-use customers. To this end, it makes use of a risk-return optimization model based on the Markowitz theory. The article presents a simulation-based study conducted with the help of the MATREM system, involving 6 retailer agents, with different risk preferences, and 312 real-world consumers. The retailers select a pricing strategy and compute a tariff to offer to target consumers, optimize their portfolio of consumers using data from the Iberian market, sign bilateral contracts with consumers, and compute their target return during contract duration. The results support the conclusion that retail markets are more favourable to risk-seeking retailers, since substantial variations in return lead to small variations in risk. However, for a given target return, risk-averse retailers consider lower risk portfolios, meaning that they may obtain higher returns in both favourable and unfavourable situations.