User Cost Research Articles

Moneyminder: An Expense Tracking Application

Abstract: In today’s fast-paced world, the ability to track regular expenses efficiently is more vital than ever to maintaining a budget. Moneyminder is a web application. This application allows user to maintain and keep a track of expenses on a daily basis. It enables individuals to categorize their spending, such as groceries, utilizes, entertainment. This method is used to split the daily expenditures. This program manages a user's daily costs based on their income, allowing them to save money. You will receive a notice if your daily spending limit is exceeded, encouraging you to limit your spending on that particular day. The money that remains after spending is added to the user's savings if they spend less than the daily cost cap. Every month at the conclusion, the program creates a report of the costs. By tracking your expense, you can understand your financial habits on a deeper level. It helps in setting realistic financial goals and provide a clear picture of financial health. Overall, this is a smart automated solution for tracking expense

User Cost of Housing Analysis of the German Real Estate Market

Background: This study looks at the development of the German real estate landscape, which recorded a continuous rise in prices between 2009 and 2022. These were due to a genuine excess demand, a shortage of supply and favorable financing conditions. In the course of 2022, mortgage rates then rose after a historically long period of low interest rates. Aim: This study aims in particular to assess whether the financial advantage of home ownership remains robust. The aim is to determine the relative attractiveness of home ownership compared to renting in all 401 German districts and independent cities. Methods: The owner-occupier costs are determined using the user cost of housing approach and compared with the current rental prices in the respective cities and districts. Various factors are taken into account, including the purchase price, financing costs, maintenance and rental prices. Results: The results of the analysis show that home ownership still offers a considerable cost advantage on average across Germany: In mid-2023, it is around 57% cheaper per square meter to invest than to rent. However, it is important to point out that this result does not take into account differences in the population’s ability to afford home ownership. Furthermore, the analysis raises questions about the sustainability of past house price growth and whether this increase is due to fundamental factors or low mortgage rates. In this sense, the study confirms that home ownership on the German real estate market remains economically favorable compared to renting, even with increased interest rates. It underlines how important it is to consider the long-term benefits for wealth accumulation when deciding between owning and renting. Practical relevance/social implications: The study is of practical relevance for people who rent an apartment or house in Germany and are considering whether it is sensible and economical for them to invest in property. The findings from the analysis are helpful for current purchasing decisions. Originality/value: It also offers original results and provides the reader with added value, as it captures and evaluates current real estate developments on the German market in a well-founded manner.

An ADP framework for flexibility and cost aggregation: Guarantees and open problems

With the increasing amount of Distributed Energy Resources (DERs), coordination of Distribution Grid Operators (DSOs) and Transmission Grid Operators (TSOs) is of paramount importance. Managing a large number of DERs at the TSO level is, however, challenging. To address this problem, flexibility aggregation is a topic of frequent research activities. Aggregation means to describe the combined flexibility of the DERs at the vertical grid coupling between DSO and TSO. Existing works are often limited with respect to guaranteeing feasibility, with respect to efficient numerical implementation, and in terms of quantification of the cost of DER usage. In the present paper, we investigate aggregation based on Approximate Dynamic Programming (ADP). We propose efficient numerical aggregation schemes using tools from computational geometry thus avoiding the need to solve multiple OPF problems. We rely on different variants of the DistFlow model for radial grids, which are computationally efficient. This allows to model of current and voltage limits and enables the consideration of voltage dependencies in the aggregation. Furthermore, we propose a method for cost aggregation and identify open problems of flexibility aggregation.

Enhancing Electric Shuttle Bus Efficiency: A Case Study on Timetabling and Scheduling Optimization

As transit authorities increasingly adopt electric buses (EBs) to mitigate air quality concerns and greenhouse gas emissions, new challenges arise in bus scheduling and timetabling. Unlike traditional buses, EBs face operational obstacles due to their shorter range and extended charging times. Existing mathematical optimization models for operation planning of traditional buses must be revised to address these unique characteristics of EBs. This study introduces a new approach to integrate timetabling and bus scheduling to enhance the level of service and minimize operational costs, using a case study of a University shuttle bus service in Montreal, Canada. The level of service will be enhanced by reducing students waiting time and improving their in-vehicle comfort through seat availability. The scheduling aspect seeks to reduce the total operational costs, which include travel, electricity consumption, and usage costs of EBs. The proposed algorithm calculates the waiting time and seat availability for different headway values and addresses the scheduling problem using a mixed-integer linear programming (MILP) model with an arc-based approach, solved using the Cplex Optimization Studio software version 12.8. A normalized weighted sum technique is then applied to select the optimal headway, balancing waiting time, seat availability, and operational costs. The effectiveness of our approach was tested through a case study of Concordia University’s shuttle bus service. Comparative analysis of the current and proposed schedules shows that our approach significantly improves service quality by decreasing waiting times and increasing seat availability while optimizing cost-effectiveness compared to the existing timetable of the Concordia shuttle bus. The proposed approach ensures a smooth transition to a fully electric transit system for shuttle bus services.

Gas adsorption on activated AlGaN photocathode: A first-principle study on charge redistribution, work function variation and emission performance attenuation

AlGaN photocathode has important applications in photoelectronic imaging and vacuum electronic devices. However, The short lifetime of the photocathode results in higher costs of usage. This can be attributed to the decrease in vacuum degree and the adsorption of residual gases. In this study, first-principle study and transmission matrix are utilized to investigate five gases (H2O, CO, CO2, H2, CH4) adsorbed on Cs-activated AlGaN (100) surface. Results suggest that all these five gases are prior to deposit on the surface at low coverage of 0.25 ML with adsorption energy of -2 eV. CO has the highest adsorption possibility with its adsorption energy lower than 0 eV. Significant charge transfer occurs at Cs-gas interface, particularly near the oxygen atoms of oxygen-containing molecules. CO2 and CO exhibit a noticeable increase in work function. The increase in the work function of the H2O adsorption is primarily due to its longer dipole length rather than dipole charge by analysing the surface dipole moment. CH4 and H2 have negligible impact on surface properties. Upon gas adsorption, the electrons on the AlGaN surface flow back to the adatoms, causing the CBM shift towards higher energy and upwards band bending. Combined with the surface electron affinity, the iteration of band structures further explains the increase of work function. The open energy of free electron escaping into the vacuum after CO2 adsorption increases to the level of inactivated surface of 5.55 eV from PBE-D2. This study reveals the mechanism of performance decay of the photocathode and is expected to guide the optimization of packaging technology.

Virtual entertainment robot based on artificial intelligence image capture system for sports and fitness posture recognition

Currently, artificial intelligence and virtual technology are widely used in motion monitoring. Virtual entertainment robots can provide adaptive and personalized fitness services for different user groups, allowing users to experience the exercise process in games. This article studies the virtual entertainment robot based on artificial intelligence image capture system for sports and fitness posture recognition. Firstly, this article explores deep learning methods for motion recognition. By constructing a generative adversarial network to obtain discrimination results, and calculating its anti-loss function. This approach can improve the efficiency of human body movement style conversion through adversarial training. Finally, through the evaluation of movement posture and action recognition, quantitative evaluation results and targeted guidance strategies can be provided to help correct incorrect movements and master correct techniques. Using motion capture systems to identify user movements during exercise not only accurately extracts user fitness movement information, but also makes it easier to use and reduces user costs. The system uses motion evaluation techniques for evaluation to help users correct incorrect fitness habits, prevent exercise losses, and exercise more safely and effectively.

Wireless sensor network security management based on virtual resource scheduling model

ABSTRACT For lofting the performance of wireless sensor networks, a lightweight gradient elevator algorithm model is improved using a virtual resource scheduling model and sparrow search algorithm particle swarm optimization algorithm. A new network intrusion detection method (SSAPSO-LightGBM) has been proposed to improve detection accuracy and efficiency. Then, the data security transmission level and user historical priority resource scheduling model (SDT-HRU-VRSM) are used to schedule resources to ensure network resource revenue. The results show that the SSAPSO LightGBM algorithm has a detection accuracy of 99.61% for Normal, 98.42% for R2L, 97.03% for U2R, 96.01% for Probe, and 98.41% for DoS. The SDT-HRU-VRSM model not only meets the needs of secure data transmission, but also takes into account the stickiness of increasing high-quality users. And according to security requirements, the number of virtual resource scheduling is increased, reducing the number of service network slicing instances and improving security. This model can effectively increase the attack cost of malicious users and increase network revenue, which is crucial for ensuring the security and efficient operation of wireless sensor networks.

Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles.

Optimized the Microgrid Scheduling with Ice-Storage Air-Conditioning for New Energy Consumption

In the face of the stochastic, fluctuating, and intermittent nature of the new energy output, which brings significant challenges to the safe and stable operation of the power system, it is proposed to use the ice-storage air-conditioning to participate in the microgrid optimal scheduling to improve wind and light dissipation. This paper constructs an optimal scheduling model for the ice-storage air-conditioning to participate in the microgrid, analyzes the regulation advantages of the ice-storage air-conditioning’s cold storage and cold release process and its participation in the scheduling process; secondly, based on the scenario method, the scenario modeling of the microgrid distributed wind and light output uncertainty and cold load uncertainty is carried out; lastly, an optimal scheduling model is constructed to minimize the operation and maintenance cost of each unit and the storage cost of the ice-storage air-conditioning, the highest rate of clean energy generation and the lowest cost of electricity consumption of the air-conditioning users as the objective function. The example simulations show that the proposed optimal scheduling model can promote the new energy consumption rate of the microgrid, proving that the ice-storage air-conditioning is more economical compared with ordinary air-conditioning and that the operating cost within the optimized microgrid is reduced by about 10.5%. The cost of the air-conditioning users’ electricity consumption has been reduced by about 11.7% after responding to the regulation.

Development and Research of Chinese-English Slang Translation Retrieval System under Internet Environment

Slang plays an important role in cross-cultural communication. It is frequently used in spoken language and constantly updated. Under the drive of new needs, intelligent integrated retrieval system came into being. Based on information platforms such as the Internet, big data network and cloud computing, intelligent retrieval technology classifies and integrates English slang and develops an "English slang translation retrieval system", which greatly reduces the search cost of users and provides convenience for English learners and cross-cultural communicators.

Formulate an Adaptive Technique to Validate Near Field Communication Technology using Attributed Graph Grammar-AGG tool

Recently, developments in such Near field Communication Technology (NFC) and associated integrated devices have pace out an acceleration the demand of transactions in electronic devices system such wearable readers which have short-power consumption and proficiency in high transferring data. NFC has showed the alternative active key solution to existing smart electronic devices that depend on huge batteries to supply the energy in paying bills though such devices. NFC widely utilized via wireless power transfer, where devices share and connect with each other, to increase the efficiency of transferring data, with high superb properties such as permeability. NFC has emerged as a powerful technology and its apparatus devices. The ongoing of NFC growth could be related to its extensibility and more sustainable. NFC can deliver different shapes at providing software application capabilities to connect with each other. NFCs demonstrates an adaptable method to enhance and offer current elements accessible to applications across the Internet. NFCs are a new fad and modern technology that shares embedded and ubiquitous devices with others, various corporations are finding the advantage by the NFC. This research will propose a formulation technique to explore and adapt an approach of developing and validating the NFC which permits to exchange data via technology wireless (NFC) between the parties. This technique will facilitate the feasibility of having e.g., a system to reduce of losing duration time and cost for users, as well as, an agile technique for validating the suggested approach via Attributed Graph Grammar (AGG) tools.

Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks

This paper introduces a novel hierarchical architecture aimed at enhancing coordination between distribution system operators and electric vehicle aggregators in order to minimize Electric Vehicle (EV) charging costs for users while optimizing EV hosting capacity to alleviate network congestion. Real-world distribution networks are employed to evaluate EV charging strategies and their impact on medium and low-voltage networks. Two distinct EV charging optimization strategies are proposed to ensure fair power allocation among EV Aggregators (EVAs), alleviating congestion while managing EV charging power efficiently. Results demonstrate that the proposed collaborative EV charging effectively flattens the load curve, reducing peak power and avoiding grid congestion. The main findings underscore the importance of incentivizing EV flexibility to support Distribution System Operator (DSO) objectives beyond static tariffs. Furthermore, a battery degradation model is introduced into the optimization problem, reducing high currents and capacity decay. Despite capturing a higher mean electricity price, the total cost of EV charging is reduced.

Optimizing resource allocation for cluster D2D-assisted fog computing networks: A three-layer Stackelberg game approach

Resource management in fog computing is a highly challenging issue. Resource constraints of fog devices, the dynamic and diverse nature of workloads such as data-intensive, computationally intensive, and latency-sensitive applications, and the unpredictability of fog computing environments make it more challenging. How to collaboratively schedule edge, and end multi-layer ubiquitous computing resources to break through single node scale and resource bottlenecks to further improve computing performance also needs to be addressed. In this paper, we leverage idle resources on the user side, and study the resource allocation problems in the D2D-assisted fog computing networks. Firstly, we use an improved Louvain algorithm to cluster users and select the D2D device with the highest priority in each cluster as the cluster head to assist fog computing, where cluster heads can act as both servers and relays. Secondly, considering that fog nodes and D2D devices have limited resources and are not obligated to share their resources for free, we propose a price incentive method to construct a three-layer Stackelberg game model that maximizes the benefits of fog nodes and cluster heads while minimizing user costs. Then, we use the Lagrange multiplier method and the Karush–Kuhn–Tucker (KKT) conditions to solve the equilibrium point of the model to achieve our goal. Finally, the simulation results show that our proposed scheme markedly decreases the overall system cost in comparison to the four benchmark schemes. Moreover, in various scenarios the total cost of our scheme consistently remains lower than the other four schemes, affirming the efficiency of the scheme proposed in this paper.

Study on charge and discharge control strategy of improved PSO for EV

In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs.

Strategic trading equilibria in local community considering co-existence of pool-based and peer-to-peer energy markets with free-rider problem in network usage cost

The peer-to-peer (P2P) energy trading market is considered a promising method for allocating prosumers’ locally produced power in smart buildings. However, the existing literature overlooks the coexistence of P2P and pool-based energy markets. Therefore, this paper establishes a bi-level energy market model that incorporates both P2P and pool-based markets. The prosumers’ individual profit maximization model considering their network usage costs in the P2P energy trading network at the upper level is first developed. A hybrid market-clearing framework determining the optimal trading amounts with the prosumers’ strategic offers at the lower level is then exploited. The distribution network power flow model in the market clearing framework is then convexified. The lower-level problem is replaced with its strong duality conditions. This bi-level problem can be reformulated as an equilibrium problem with equilibrium constraints (EPEC). This EPEC is then solved after being transformed into an equivalent mixed integer linear programming (MILP) model. Finally, a fifteen-bus system is employed to illustrate the characteristics of the hybrid market equilibrium depicted by this EPEC model. The influences of the prosumers’ strategic offers on the distribution locational marginal prices (DLMPs) and network usage costs are highlighted. Since the prosumers can choose to transmit their energy in the pool-based or P2P markets, the emission and pollution costs caused by power transmission can be minimized. A cleaner energy production is thus guaranteed. The free-rider problem is verified by comparing the network usage charges in the strategic and perfect competition scenarios.

Developing a fuzzy bi-objective programming and MCDM model for bridge maintenance strategy optimization

Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge’s performance is more substantial.

Concurrent service auto-scaling for Knative resource quota-based serverless system

Serverless computing platforms currently provide application developers with two ways to control their services’ resource usage cost: resource usage-based and resource quota-based. In usage-based systems, resources are assigned to services based on the amount they consume. Meanwhile, a fixed resource amount is reserved and shared between services for quota-based systems. Several studies have been proposed to enhancing the default serverless auto- scaling algorithms to optimize resource usage and service quality. However, almost all previous works targeted usage-based systems and optimized auto-scaling performance for each separate service. In contrast, this work targets auto-scaling of concurrent services in serverless quota-based systems. In serverless quota-based systems, over-quota resource usage and latency Service Level Objective (SLO) violation may occur when auto-scaling new instances during burst traffic moments. Hence, we aim to find an optimal hybrid auto-scaling decision to minimize over-quota resource usage and latency SLO violation. To solve this problem, We applied deep reinforcement learning and traffic prediction techniques. Our solution is developed and implemented based on the characteristics of the most popular open-source serverless platform Knative. For evaluation, we compared our solution with the default Knative auto-scaler and our previously proposed usage-based hybrid Knative auto-scaler. We configured the default Knative auto-scaler with two different settings that can optimize the total resource usage of all services or minimize service latency. Compared to the optimized resource usage version, our solution traded a slightly higher resource usage for an 11.3% latency SLO violation duration reduction. Meanwhile, compared with the optimized latency version, our solution reduced the over-quota resource usage rate by 18.25% while achieving a similar latency SLO preservation performance. Compared to the usage-based hybrid auto-scaler, the latency SLO violation rate and over-quota resource usage were approximately reduced by half.

365‐day sectional work zone schedule optimization for road networks considering economies of scale and user cost

AbstractThis study proposes a methodology for deriving the optimal work zone schedule for the annual routine maintenance planning in an infrastructure asset management system considering the (i) economies of scale in work zone costs due to work zone synchronization and (ii) user costs across the road network with traffic assignments. A key aspect of the proposed methodology is the ability to derive in detail optimal work zone schedules of realistic‐scale road networks in 100 m sections for 365 days, which is beneficial in practice. To this end, an optimization model of the work zone schedule is newly formulated as a mixed‐integer programming (MIP) problem, and a novel bilevel solution method for the model utilizing the conventional solver Gurobi Optimizer with MIP algorithms such as root relaxation, dual simplex, barrier methods, and cutting planes is proposed. In application examples, the proposed methodology is applied to two real‐world road networks, which confirms that the optimal work zone schedule can be obtained in 313.7 s for a network with 2640, 100 m, sections and in 168,180 s (46 h and 43 min) for a network with 5038, 100 m, sections.

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

With the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. In this context, this paper proposes an optimization dispatching strategy for HEMS to reduce total cost with full consideration of uncertainties, while ensuring the users’ comfort. Firstly, a HEMS dispatching model is constructed to reasonably schedule the start/stop time of the dispatchable appliances and energy storage system to minimize the total cost for home users. Besides, this dispatching strategy also controls the switching time of temperature-controlled load such as air conditioning to reduce the energy consumption while maintaining the indoor temperature in a comfortable level. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called deep deterministic policy gradient. The example results verify the effectiveness and superiority of the proposed method. The energy cost can be effectively reduced by 21.9% at least compared with other benchmarks and the indoor temperature can be well maintained.

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

AbstractGroundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater.