User Cost Research Articles

Systematic review: e-cigarettes vs. conventional cigarettes

Introduction Electronic cigarettes have gained widespread popularity by being marketed as a safer alternative to traditional smoking. Possibility of inhaling vapor created by heating nicotine liquid instead of combusting tobacco leaves revolutionized tobacco market, bringing a lot of questions about harmfulness or advantages of e-cigarettes. Their popularity is increasing and many aspects about their influence on health are still unclear. At the same time, better social acceptance than tobacco cigarettes brings out questions if they are a step to smoking cessation or if they encourage more people to start using nicotine. Aim of the review This systematic review provide an analysis of the existing literature on the influence of e-cigarettes on the human body with their economic and behavioral aspects. By examining a wide range of studies, we aim to elucidate the potential risks and benefits associated with e-cigarette use and provide a comparison of both electronic and conventional forms of nicotine consumption. Methods A systematic search was conducted using electronic databases such as PubMed, Scopus, and Web of Science. Keywords included "e-cigarettes", "vaping", "health effects", "conventional cigarettes", "tobacco", "respiratory system", "cardiovascular system", "immune system", "oral health", and "cellular effects". The search was limited to studies published up to December 2023. Both observational and experimental studies were included. Conclusion While e-cigarettes are often promoted as a safer alternative to conventional cigarettes, the evidence suggests that they are not without significant health risks. It is important to remember that they are a new form of substance use, and many aspects related to them are still unknown. E-cigarettes and conventional cigarettes present distinct usage patterns and cost profiles. Years of research on larger sample sizes are needed to determine their impact on humans.

Optimization and Implementation Framework for Connected Demand Responsive Transit (DRT) Considering Punctuality

Demand Responsive Transit (DRT) is gaining attention as a flexible and efficient solution for connecting urban transit hubs, but challenges such as travel time variability and punctuality remain significant barriers. This study develops a robust optimization framework with variable travel speed to address these issues, minimizing user and operator costs while reducing transfer waiting times. The framework incorporates variable travel speeds and employs a genetic algorithm to optimize routes and operations compared to many studies using constant commercial speed. Experiments conducted in Hwaseong, South Korea, analyzed scenarios with varying service rates, vehicle capacities, and detour ratios. Results show that implementing punctuality-constrained DRT reduces total travel times by 14% compared to subways and 36% compared to buses, highlighting its potential to significantly improve user convenience and operational efficiency. The findings suggest that carefully designed DRT systems with highly reliable punctuality can enhance urban mobility by integrating seamlessly with existing transit networks, providing a cost-effective and reliable alternative to traditional public transport.

Design Strategy of PepNzymes-SH for an Emerging Catalyst with Serine Hydrolase-Like Functionality.

Serine hydrolases, as a class of green catalysts with hydrolytic and dehydrating activities, hold significant application value in the fields of biosynthesis and organic synthesis. However, practical applications face numerous challenges, including maintaining enzyme stability and managing usage costs. PepNzymes-SH, an emerging green catalytic material with enzyme-like activity, overcomes the operational limitations of natural enzymes and holds great promise as a substitute for hydrolases. Unfortunately, a systematic review of the design strategies for PepNzymes-SH is currently lacking. The core significance of this report lies in providing researchers with a comprehensive understanding and theoretical guidance through the summarization and performance evaluation of different design strategies of PepNzymes-SH. This review summarizes strategies for simulating and enhancing the stability of serine hydrolase active sites, oxyanion holes, and hydrophobic environmental structures. By comparing their catalytic activities, we assess the performance changes brought about by different strategies. Furthermore, the applications of PepNzymes-SH in the chemical, biomedicine, and environmental fields are also discussed.

Modelling and Evaluation of Work Zone Queues: Istanbul Case Study

Road/lane closures due to maintenance works adversely affect the traffic conditions, causing delays in the various locations of the whole network. In Istanbul, Metropolitan Municipality charge the companies for the planned closure of the highways for a given time interval using simple spreadsheet calculations. These calculations include assumed capacity and speed values for each type of highway. In this paper, it is aimed to establish a methodology for the work zone evaluation that considers seasonality in demand and spatiotemporal variability in the flow and capacity. This goal will be achieved by observing and assessing the characteristics of İstanbul roadways and work zone areas. After the assessment, a traffic microsimulation software, namely PARAMICS, was used to simulate various road and work zone combinations to produce traffic data, including queue length, waiting times, vehicle speeds, flow and density of work zone section. Various machine-learning models were developed and their performance on test data will be analyzed. The selected model was used as a sub-model to estimate the expected delays, speeds and accelerations on different road types, different demands and lane closure combinations. A second sub-model used the delay, speed and acceleration values to estimate the total excess user cost due to road closure with determined delay-cost parameters.

MaTableGPT: GPT-Based Table Data Extractor from Materials Science Literature.

Efficiently extracting data from tables in the scientific literature is pivotal for building large-scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule-based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT-based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow-up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero-shot, few-shot, and fine-tuning, the study presents a Pareto-front mapping where the few-shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score>95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.

GAN augmented phase noise modeling of PLL for use in automated testing

ABSTRACT Phase noise measurement of Phase Locked Loop (PLL) in automated test equipment (ATE) is expensive and time-consuming. Indirect phase noise measurement relies on a few correlated low-cost measurements called signatures obtained from specific nodes of the PLL. A regression model using these signatures is learned during the design phase for phase noise estimation. Extraction of these signatures and phase noise performance in PLL requires significant simulation time, raising non-recurring design costs and computer aided design (CAD) tool usage costs. In this work, a new methodology for indirect phase noise measurement is developed where Generative Adversarial Network (GAN), a deep learning framework, has been used for generating a substantial amount of model-driven synthetic signatures that are strongly correlated to simulation-driven real signatures. These model-driven signatures are then used to construct a regression model for phase noise prediction. The GAN model efficacy is verified by comparing the prediction accuracy of the simulation-driven signature-based model with the GAN-augmented signature-based regression model.

Life Cycle Cost Assessment of Engineered Cementitious Composite (ECC) Precast Pavement in Singapore

Heavier trafficked road network in Singapore leads to some concerns about extended road closure during rigid pavement maintenance/rehabilitation process. A quicker pavement repair method using precast panels is potentially good alternative. This paper focuses on the life cycle cost analysis (LCCA) of a proposed precast concrete pavement (PCP) made of Engineered Cementitious Composite (ECC). A precast slab 5.0m x 3.6m x 0.22m is proposed as an alternative for maintenance and rehabilitation of Singapore rigid pavement. With the help of finite element method, this proposed slab is proved to withstand well construction load and has a lifetime of at least 40 years in Singapore heavy traffic condition. User delay cost due to lane closure, one of the key components of LCCA, is estimated with the help of the state of the art microscopic simulation tool for a selected case study area in Singapore. The LCC results show that initial cost contributes mainly on the total cost of ECC PCP whereas user cost is the most significant component in LCC of cast-in-place concrete pavement. ECC PCP can be a very economical solution in term of LCC for concrete pavement rehabilitation and maintenance depends on construction site locations and traffic volume.

Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids

Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids

Costs of outpatient services at selected primary healthcare centers in Bangladesh: A cross-sectional study.

Upazila Health Complexes (UzHC) serve as the backbone of primary healthcare (PHC) at the sub-district level in Bangladesh, delivering comprehensive healthcare services including both inpatient and outpatient services to the grassroots levels. However, not all the prescribed medicines and diagnostics services are always available at these facilities for outpatient care. This results in out-of-pocket expenditure (OOPE) to the patients for getting prescribed medicines and diagnostics services which has not been properly explored. Thus, we aimed to estimate the overall provider and user costs for outpatient care services at selected UzHCs in Bangladesh. An ingredient-based costing approach was applied to estimate the costs for the most commonly reported illnesses at outpatient of UzHCs from a societal perspective. We conducted a health facility survey at four purposively selected UzHCs to estimate provider costs and a patient exit survey among 452 patients of selected illnesses to estimate the user costs. Commonly reported illnesses were identified in consultation with healthcare providers of these facilities. The difference between costs of prescribed and provided medicines at UzHCs was estimated using the market prices. Data was collected between February to March 2021. The societal costs of the common outpatient illness or symptoms varied significantly, ranging from BDT 642 to BDT 1,384 per episode. Antenatal care had the highest cost burden at BDT 1,384, followed by respiratory illness at BDT 783 and urinary tract infection at BDT 670. On average, the provider spent BDT 289 for treating an outpatient, while a patient incurred BDT 446 as OOPE. Further, a patient was expected to spend an average of BDT 341 for purchasing medicines not provided from UzHCs. Our study found significant gaps between prescribed and provided medicines at UzHCs, leading to higher OOPE for patients. The current healthcare resource allocation strategy does not consider the outpatient load and healthcare demand at PHC facilities, which further exacerbates this gap. Addressing this gap requires a fundamental shift towards a demand-driven resource allocation model within the healthcare financing strategy to improve healthcare access and achieve health for all.

Fusion MRI/Ultrasound-Guided Transperineal Biopsy: A Game Changer in Prostate Cancer Diagnosis.

Background/Objectives: Multiparametric-Magnetic Resonance Imaging(mp-MRI) presents the ability to detect clinically significant cancer, aiming to avoid biopsy if the results are negative or target an abnormal lesion if a suspected lesion of the prostate is found. Recent guidelines recommend the performance of 12 standard biopsies along with 3 to 5 targeted biopsies in suspected prostate lesions, depending on the size of the prostate lesion. In addition, prostate biopsy can be performed by either the transperineal or the transrectal approach. The aim of this comprehensive review is to highlight the role of both standard and targeted MRI/Ultrasound (US) fusion transperineal biopsy (TPB) in the diagnostic approach of prostate cancer cases, to report its diagnostic efficacy and complication rates and to suggest the promising usage of MRI/US fusion TPB in the future. Methods: A comprehensive review of the existing literature, including systematic reviews, meta-analyses, and clinical guidelines, was conducted to compare the efficacy and safety of transperineal and transrectal approaches in prostate cancer detection. Special emphasis was placed on mp-MRI-guided targeted biopsy and its combination with systematic sampling. Results: Prostate biopsy via the transperineal approach is related to increased detection rates, especially for anterior lesions, and decreased infection risk compared to the transrectal approach, while complication rates (hematuria, hemospermia, etc.) remain similar. Due to lower infection rates via the transperineal route, the performance of prostate biopsy using the transperineal approach is strongly recommended. Finally, transperineal fusion MRI/US biopsy can be valuable for repeat biopsies in patients who had an initial negative biopsy or for the follow-up of patients that undergo active surveillance. Conclusions: MRI/US fusion-guided TPB represents a significant advancement in prostate cancer diagnostics, combining improved precision with reduced infection risks. Although TPB presents higher detection rates for anterior prostatic lesions and lower post-biopsy infection rates, there is no significant difference in cancer detection rates compared to TRB. Targeted training and investment may reduce long-term expenses of TPB by lowering hospitalizations, antibiotic usage, and related costs. Future research should further refine this approach and explore its integration with emerging technologies like artificial intelligence for enhanced lesion targeting and diagnostic accuracy.

PEG 400-Catalyzed C3 & O-Alkylation Reactions of 4-Hydroxycoumarin-A Study

PEG-400 has been found to be an efficient recyclable catalyst/solvent system for C3- and O-alkylation of 4-hydroxycoumarin with various electronically and structurally divergent alcohols with moderate to good yield of products. For the C3-alkylation using styrenes required Zn(OAc)2.2H2O (5 mol%) in PEG-400 at 70 °C. For the O-alkylation of 4-hydroxycoumarin with acetates required PEG-400 at 60 °C. This process offered a gentle and uncomplicated way to obtain multi-substituted pyranocoumarins, as well. This protocol's benefits include its wide use, moderate environment, low cost, reusable catalyst (PEG-400), and ease of usage due to the only byproducts being acetic acid and water, respectively.

The impact of variable grid fee tariffs on the electricity costs of EV users in Germany

The impact of variable grid fee tariffs on the electricity costs of EV users in Germany

A two‐level optimal scheduling control strategy for air source heat pump loads with phase change energy storage

AbstractReasonable scheduling and control of air‐source heat pumps (ASHPs) contribute to reducing operational costs for users while encouraging their participation in grid demand response. This article proposes a two‐layer optimal scheduling and control strategy for ASHP loads incorporating phase change energy storage (PCES). First, an electricity‐heat coupling model for ASHP loads is proposed. This model integrates PCES technology and considers the influence of outlet water temperature, ambient temperature, and the cold island effect on the coefficient of performance. Subsequently, a PCES capacity configuration model is established with the objective of maximizing the overall benefits of the PCES device. Finally, a two‐layer optimal scheduling and control strategy for ASHPs is introduced: The upper‐layer model employs model predictive control to minimize heating costs based on user comfort, leveraging PCES and building thermal storage characteristics, in conjunction with time‐of‐use electricity pricing. The lower‐layer model schedules periodic start–stop cycles of the ASHP units to respond to the upper‐layer power demand and mitigates the impact of the cold island effect on ASHP performance through unit interval sequencing. The simulation results show that the proposed strategy reduces the daily operation cost and power consumption by 38.9% and 25.0%, respectively, significantly improving the building's regulation capability.

A generalized Nash-in-Nash bargaining solution to allocating energy loss and network usage cost of buildings in peer-to-peer trading market

A generalized Nash-in-Nash bargaining solution to allocating energy loss and network usage cost of buildings in peer-to-peer trading market

Towards practical and privacy-preserving CNN inference service for cloud-based medical imaging analysis: A homomorphic encryption-based approach.

Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions. These limitations make current solutions impractical for real-world applications. In this paper, we address the challenges in mobile cloud-based medical imaging analysis, where users aim to classify private body-related radiological images using a Convolutional Neural Network (CNN) model hosted on a cloud server while ensuring data privacy for both parties. We propose PPCNN, a practical and privacy-preserving framework for CNN Inference. It introduces a novel mixed protocol that combines a low-expansion homomorphic encryption scheme with the noise-based masking method. Our framework is designed based on three key ideas: (1) optimizing computation costs by shifting unnecessary and expensive homomorphic multiplication operations to the offline phase, (2) introducing a coefficient-aware packing method to enable efficient homomorphic operations during the linear layer of the CNN, and (3) employing data masking techniques for nonlinear operations of the CNN to reduce communication costs. We implemented PPCNN and evaluated its performance on three real-world radiological image datasets. Experimental results show that PPCNN outperforms state-of-the-art methods in mobile cloud scenarios, achieving superior response times and lower usage costs. This study introduces an efficient and privacy-preserving framework for cloud-based medical imaging analysis, marking a significant step towards practical, secure, and trustworthy AI-driven healthcare solutions.

Evaluating the impact of 2011 tort reform limiting noneconomic damages in North Carolina and Tennessee on testing, imaging, and procedure utilization.

To evaluate the impact of tort reform laws passed in 2011 capping noneconomic damages in North Carolina and Tennessee on rates and adjusted per user costs of tests, imaging, and procedures in the Medicare fee-for-service population. State-level synthetic difference-in-differences, adjusting for the percent of FFS Medicare beneficiaries in the state who were female, had ever been on Medicare Advantage, were eligible for Medicaid for at least 1 month of the year, and total state risk-adjusted, standardized per-capita costs. Analyses of North Carolina and Tennessee were performed separately. We measured the average treatment effect on the treated. Centers for Medicare and Medicaid Services Geographic Variation Public Use File, 2007-2019. Our analysis showed no economically significant impact of these laws in either state, though we found a small but statistically significant increase (average treatment effect on the treated: $46, 95% confidence interval: $6-$87) in adjusted per user cost of procedures in Tennessee. Our findings suggest that caps on noneconomic damages alone may be insufficient to modify physician practice habits and impact utilization. Future work should attempt to better understand the economic and noneconomic incentives that shape physician ordering decisions.

Power Usage Monitoring Simulator for Estimating Usage Tariffs and Carbon Emission Levels at STO Bogor Based on IoT

In this modern era, the efficiency of electricity usage has become one of the main concerns in the efforts toward sustainable energy management. The limitations in monitoring electricity usage have become a challenge for technicians, resulting in inefficiencies in data collection and discrepancies between the actual power usage and manual recordings. This research aims to design and develop a prototype of an Internet of Things (IoT)-based electricity usage monitoring tool to monitor electricity consumption in real-time, provide usage cost estimates, and measure the emission levels generated by the devices being used. It also facilitates data recording, accelerates decision-making, and improves electricity management with high accuracy, with a margin of error of 0.8% for power calculations and 0.5% for total power calculations. This allows for accurate measurement of voltage, load, power, and total power. The data obtained is used to calculate electricity tariffs based on actual consumption, such as a subscription fee of IDR 127,134, a rate per kWh of IDR 528.76, and a tax of 4%, resulting in a total cost of IDR 132,768 for 0.366 kWh usage over 60 minutes. The use of electronic devices also increases carbon emissions, with a 69% increase in the first 10 minutes and 28% in 60 minutes, highlighting the importance of environmental awareness and wise electricity usage.

Optimal configuration of shared energy storage for industrial users considering lifetime and charge-discharge strategy coupling

With the development of renewable energy, energy storage has become one of the key technologies to solve the uncertainty of power generation and the disorder of power consumption and shared energy storage has become the focus of attention for its cost-effective characteristics. However, it is always difficult to quantify the coupling relationship between charge and discharge strategy and life expectancy in energy storage configuration. Based on this, this paper proposes an industrial user-side shared energy storage optimal configuration model, which takes into account the coupling characteristics of life and charge and discharge strategy. Firstly, the life loss model of lithium iron phosphate battery is constructed by using the rain-flow counting method. In order to further optimize the user-side shared energy storage configuration in the multi-user scenario, a two-layer model of energy storage configuration is built, and the Big M method and the Karush-Kuhn-Tucker (KKT) conditions are used to equivalently transform the constraints. Based on the predicted life of energy storage and the dichotomy method, the optimal energy storage configuration results are obtained. Comparing the energy cost of users under the three scenarios of no storage configuration, storage configuration according to fixed storage life, and storage configuration according to the model proposed in this paper, the results show that the proposed method can help accurately describe the energy storage model, increase the utilization rate of the power station, and improve the electricity economy of users.

TimbreSense: Timbre Abnormality Detection for Bel Canto with Smart Devices

With the rise of mobile devices, bel canto practitioners increasingly utilize smart devices as auxiliary tools for improving their singing skills. However, they frequently encounter timbre abnormalities during practice, which, if left unaddressed, can potentially harm their vocal organs. Existing singing assessment systems primarily focus on pitch and melody and lack real-time detection of bel canto timbre abnormalities. Moreover, the diverse vocal habits and timbre compositions among individuals present significant challenges in cross-user recognition of such abnormalities. To address these limitations, we propose TimbreSense, a novel bel canto timbre abnormality detection system. TimbreSense enables real-time detection of the five major timbre abnormalities commonly observed in bel canto singing. We introduce an effective feature extraction pipeline that captures the acoustic characteristics of bel canto singing. By applying temporal average pooling to the Short-Time Fourier Transform (STFT) spectrogram, we reduce redundancy while preserving essential frequency-domain information. Our system leverages a transformer model with self-attention mechanisms to extract correlation and semantic features of overtones in the frequency domain. Additionally, we employ a few-shot learning approach involving pre-training, meta-learning, and fine-tuning to enhance the system’s cross-domain recognition performance while minimizing user usage costs. Experimental results demonstrate the system’s strong cross-user domain recognition performance and real-time capabilities.

Biodiversity monitoring with intelligent sensors: An integrated pipeline for mitigating animal-vehicle collisions

Transports of people and goods contribute to the ongoing 6th mass extinction of species. They impact species viability by reducing the availability of suitable habitat, by limiting connectivity between suitable patches, and by increasing direct mortality due to collisions with vehicles. Not only does it represent a threat for some species conservation capabilities, but animal vehicle collisions (AVC) is also a threat for human safety and security in transport and has a massive cost for transport infrastructure (TI) managers and users. Using the opportunities offered by the increasing number of sensors embedded into TI and the development of their digital twins, we developed a framework aiming at managing AVC by mapping the collision risk between trains and ungulates (roe deer and wild boar) thanks to the deployment of a camera trap network. The proposed framework uses population dynamic simulations to identify collision hotspots and assist with the design of sensors deployment. Once sensors are deployed, the data collected, here photos, are processed through deep learning to detect and identify species at the TI vicinity. Then, the processed data are fed to an abundance model able to map species relative abundance around the TI as a proxy of the collision risk. We implement the framework on an actual section of railway in south-western France benefiting from a mitigation and monitoring strategy. The implementation thus highlighted the technical and fundamental requirements to effectively mainstream biodiversity concerns in the TI digital twins. This would contribute to the AVC management in autonomous vehicles thanks to connected TI.