Distribution Utilities Research Articles

A DRUG UTILIZATION STUDY OF ANTIMICROBIAL DRUGS AT TERTIARY CARE HOSPITAL SOUTHERN RAJASTHAN

Background: Irrational usage and higher utilization of antimicrobial agents has become a major problem in all health care facilities all over the world, further give rise to antimicrobial resistance. Inappropriate use of antibacterial drugs may result in increased adverse drug reaction and also increase cost of treatment. The aim of the study was to evaluate and study drug utilization or drug distribution pattern of antimicrobial drugs. The objective of the study was to determine the average number of antibiotics prescribed per prescription, to evaluate the prescribing pattern and frequency, and to determine the most commonly prescribed antibiotic. Method: The present study was a prospective study done in general medicine ward of tertiary care teaching hospital to evaluate the utilization pattern of antibiotics. A total number of 208 patients were included in the study. Results: The ratio of male population is more as compared to females and most of the patients were in the age group of (61-70) years. The study report shows that most common class of antibiotics prescribed were cephalosporin (45.01%) followed by penicillin (14.74%), quinolones(13.94%), macrolides (7.56%), tetracyclines (4.38%). Study reports shows that (40.23%) of the prescription were prescribed with ceftriaxone (monocef). Conclusion: In this study maximum number of prescription had prescribed only one antibiotic as per W.H.O. A strict protocol for prescriber is now at utmost requirement to promote safe and rational use of antibiotic. KEYWORDS: Antimicrobial resistance, drug utilization evaluation, rational use, prescribing pattern, drug utilization review

EE493 Equity in Healthcare Utilization and Reimbursement for Patients with Multiple Sclerosis in China: A Benefit Incidence Analysis Using Nationwide Patient-Reported Outcome Data

Multiple sclerosis (MS) is a chronic, autoimmune neurologic disorder resulting in substantial economic burden. With cost of illness research on MS being extensively studied worldwide, however, little is known about the equity concern of utilization and reimbursement. This study aimed to evaluate the distribution of utilization, cost, and reimbursement received by MS patients with different socio-economic status (SES) in China.

Managing Peak Load in the Past, Present, and Future: Challenges and Opportunities for a Flexible Energy Future

Taken at face value, peak load management involves controlling or influencing the time of day when electricity is used in homes, businesses, and public facilities. The desire to reduce peak electricity use has been based on the availability of generation resources and their associated costs. During periods of high electricity demand, retail energy providers, including distribution utilities, purchase power from generation sources that are less efficient than baseload generation resources such as hydro, nuclear, or coal. Operating the electrical grid has never been simple, but today the balance of supply and demand is getting more complex. On the supply side, the increasing penetration of renewable and distributed energy sources, such as solar and wind power, makes peak load management more complex. These sources are inherently intermittent, meaning that power generation cannot always be scheduled to meet demand. Additionally, electrification of the buildings and transportation sectors is changing the load profiles of customers and the regions of a distribution utility's service area. Yet, the rise of distributed energy resources (DERs), such as rooftop solar and battery storage, has created both opportunities and challenges for grid operators. Peak load management is rapidly evolving from past practices, with new use cases, economic drivers, hardware and software, and information technologies playing an increasingly important role. A cleaner energy future, while critical to our society and the environment, is not without increasing cost pressures, economic risks, and reliability challenges from extreme weather events. While we know where we are going, it is not exactly clear how we will get there. Nonetheless, the emphasis on peak load management will only increase in scale and sophistication. To better predict and prepare for the rapidly changing energy landscape, this editorial discusses the past and present state of peak load management and how it might be evolving into more flexible load management.

Supporting Grid Infrastructure Through Data‐Driven Analytics

In general, utilities are consistently investing in their electric systems to deliver power with increased reliability, safety, and an improved customer experience. A resilient and high‐performing electric system remains the backbone of our society and economic prosperity. While attempting to maintain reliability, improve safety, and also manage costs, distribution utilities find themselves working to meet decarbonation goals mandated by executive branches at both the state and federal level, congress and state legislatures, and regulators. In order to meet these goals efficiently and effectively, which can often be in conflict with one another, utilities are investing in technologies to digitize their enterprise and create a digital experience for employees and customers. Severe weather events are the most common cause of prolonged power outages and electric infrastructure disruptions. Between 2000 and 2001, approximately 83 percent of reported major outages in the US were attributed to weather‐related events. The average annual number of weather‐related power outages increased by roughly 78 percent during 2011–2021, compared to 2000–2010.

Statistical Analysis of Train Operation and Passenger Distribution Based on Real Records: A Case Study of Wuhan-Guangzhou HSR

This paper summarizes the results of an effort aimed at improving train operation schedules on Wuhan-Guangzhou high-speed railway (WG-HSR). The real-record train operation and passenger tickets-booking records of WG-HSR are used for statistical analysis on the train service quality and passenger distribution. More specifically, the train service frequency and interval time at each station are analyzed. Based on this, the temporal and spatial distribution of capacity utilization in each section are investigated. In order to get a holistic view of passenger flow characteristics, the passenger volume during different time periods and between several origin and destination (OD) pairs are investigated to characterize travellers’ spatial-temporal preferences. The passenger distributions on some long-distance trains are shown to get the number and proportion of cross-line passengers travelling on the WG-HSR. Moreover, for a better understanding of the seat capacity utilization of trains, the load rates of trains in various sections and time periods are investigated. Specifically, the relationship between the average load rate of trains and trains’ running distance is explored, finding that the longer the non-cross-line train travels is, the higher the average load rate is. This study provides insightful findings that help understanding HSR operation and conducting further research.

Efficient resource utilization of the electricity distribution sector using nonparametric data envelopment analysis and influential factors

This study aims to investigate the efficient resource utilization of the electricity distribution sector in Pakistan and identifies the influential factors affecting it. In Pakistan, there is excess production capacity and under-utilized power plants, which pose a challenge to the economy due to their burden on the exchequer account and the high power costs. The present study employs the most recent 11 distribution firms panel data from Pakistan for the period 2016–2020 to evaluate the performance ranking of distribution utilities. The study employs standard frontiers, composite indexes, and a novel DEA-windows analysis to measure the efficiency of the distribution utilities. The results of the analysis show that electricity sales, consumer density, and industrial consumers have a positive impact, while distribution losses have a negative association with the efficiency of the distribution utilities. These findings provide valuable insights for policymakers and industry stakeholders in improving the resource utilization and overall performance of the electricity distribution sector. The study highlights the implications of the results in terms of reducing greenhouse gas emissions, which are one of the main contributors to global warming. In conclusion, this study provides an essential contribution to the literature on efficient resource utilization in the electricity distribution sector in Pakistan.

Load forecasting of district heating system based on improved FB-Prophet model

Accurate load forecasting of the district heating network (DHN) is essential to guarantee effective energy production, distribution, and rational utilization. An improved Facebook-Prophet (FB-Prophet) model with additional positional encoding layers has been developed to forecast the DHN heat consumption. The accuracy of univariate and multivariate FB-Prophet models is evaluated; this paper also evaluates the optimum training dataset length. To explore the performance of the improved FB-Prophet model in heating load forecasting tasks, another seven machine learning models, namely FB-Prophet, DeepVAR, long-short term memory, extreme gradient boosting, multilayer perceptron, recurrent neural network, and support vector regression are used for comparison. The historical heating load, outside temperature, relative humidity, speed of wind, direction of wind, and weather type of a DHN in Serbia are utilized to extensively investigate the effectiveness of the improved FB-Prophet model. The prediction outcomes of all the models are thoroughly analyzed. The results indicate that the improved FB-Prophet model can generate the most precise and consistent predictions and it showed better results for sparse DHN data. The prediction curve is fitted to the trend of hourly DHN consumption change, which can play an effective guiding function in the distribution of heat.

Behavior of prosumers in Smart Grid: A comparison of Net Energy Metering and Billing Schemes, and Game Theory-based Local Electricity Market

Net energy metering (NEM) and net energy billing (NEB) are the most commonly used self-consumption schemes for prosumers in countries around the world. The concept of the local electricity market (LEM) is emerging as the potential successor of the self-consumption schemes, with several already implemented pilot projects. The comparative study of prosumers’ behavior in terms of optimal economic self-dispatching under NEM, NEB, and game-theory-based LEM energy policies is performed in this paper. The non-cooperative game-theoretic methodology is used for the calculation of Nash–Cournot equilibrium which is adopted as an LEM clearing mechanism. The prosumers’ daily energy patterns (generation, consumption, and storage) and payoffs are analyzed. Diversities of prosumers’ assets (distributed energy resources (DERs) and storage devices) and load profiles are considered. The distribution utility’s role is considered in terms of its own payoff for supplying the necessary energy to ensure the power balance equation. The numerical analysis is performed using the model of the benchmark test distribution network IEEE 33-node. The obtained results are discussed in terms of the correlation between prosumers’ behavior and the adopted energy policy. The strengths and weaknesses of the most common energy policies are highlighted from the prosumers’ perspective. The obtained results confirm that the transition from NEM to NEB self-consumption scheme could provide a better payoff to prosumers with dispatchable DERs under variable electricity price for excess energy produced. The residential prosumers equipped with SDs or dispatchable DERs were able to achieve the best payoffs in the case of Game Theory-based LEM.

Biochar–supported sulfurized nanoscale zero–valent iron facilitates extensive dechlorination and rapid removal of 2,4,6–trichlorophenol in aqueous solution

Nanoscale zero–valent iron (NZVI) has been widely used in rapid remediation of contaminants. However, several obstacles such as aggregation and surface passivation hampered NZVI from further application. In this study, sulfurized nanoscale–zero valent iron supported by biochar (BC–SNZVI) was successfully synthesized and utilized for highly efficient 2,4,6–trichlorophenol (2,4,6–TCP) dechlorination in aqueous solution. SEM-EDS analysis revealed the even distribution of SNZVI on the surface of BC. FTIR, XRD, XPS and N2 Brunauer–Emmett–Teller (BET) adsorption analyses were carried out to characterize the materials. Results showed that BC–SNZVI with S/Fe molar ratio of 0.088, Na2S2O3 as sulfurization agent, and pre–sulfurization as the sulfurization strategy exhibited the superior performance for 2,4,6–TCP removal. The overall removal of 2,4,6–TCP was well described with the pseudo–first–order kinetics (R2 > 0.9), and the observed kinetics constant Kobs was 0.083 min−1 with BC–SNZVI, which was one order of magnitude higher than that of BC–NZVI (0.0092 min−1) and SNZVI (0.0042 min−1), and two orders of magnitude higher than that of NZVI (0.00092 min−1). Moreover, the removal efficiency of 2,4,6–TCP reached 99.5% by BC–SNZVI with dosage of 0.5 g L−1, initial 2,4,6–TCP concentration of 30 mg L−1 and initial solution pH of 3.0 within 180 min. The removal of 2,4,6–TCP by BC–SNZVI was acid–promoted and the removal efficiencies of 2,4,6–TCP decreased with the increase of initial 2,4,6–TCP concentrations. Furthermore, more extensive dechlorination of 2,4,6–TCP was achieved with BC–SNZVI and complete dechlorination product phenol became predominant. The facilitation of sulfur for Fe0 utilization and electron distribution in the presence of biochar remarkably enhanced the dechlorination performance of BC–SNZVI for 2,4,6–TCP. These findings provide insights into BC–SNZVI as an alternative engineering carbon based NZVI material for treating chlorinated phenols.

Research Priorities to Support Women Veterans' Reproductive Health and Health Care Within a Learning Health Care System.

Research Priorities to Support Women Veterans' Reproductive Health and Health Care Within a Learning Health Care System.

Biased Beliefs in Search Markets

We study the implications of biased consumer beliefs for search market outcomes in the seminal framework due to Diamond (1971). Biased consumers base their search strategy on a belief function that specifies for any (true) distribution of utility offers in the market a possibly incorrect distribution of utility offers. If biased consumers overestimate the best offer in the market, a novel type of equilibrium may emerge in which firms make exceptionally favorable offers in order to meet biased consumers’ unreasonably high expectations, which then become partially self-fulfilling. Consequently, the presence of biased consumers may improve the welfare of all consumers. (JEL D11, D21, D83, D84)

Literature Review of Unmanned Aerial Systems and LIDAR with Application to Distribution Utility Vegetation Management

Standardized tree risk assessment protocols are beneficial to utility vegetation management (UVM) in that they provide the most consistent qualitative assessment of a tree’s likelihood of failure, likelihood of impact, and overall risk. Yet, utility foresters do not often inspect off-right-of-way (ROW) vegetation due to constraints such as accessibility and time, which leaves many off-ROW trees unmonitored or with limited monitoring. This review focuses on the key studies addressing the application of unmanned aerial systems (UAS)-based LIDAR systems, especially in terms of UVM along electrical distribution systems. We reviewed the scientific literature in terms of the acquisition of UAS-based LIDAR data and the processing of the data. The incorporation of UAS-based LIDAR will allow utility foresters to better manage both on- and off-ROW trees to better identify tree risks and thus reduce outages and increase resiliency of distribution power lines.

Coordinated Use of Smart Inverters With Legacy Voltage Regulating Devices in Distribution Systems With High Distributed PV Penetration—Increase CVR Energy Savings

This paper studies the coordinated use of smart inverters with legacy voltage regulating devices to help increase the energy savings from conservation voltage reduction (CVR) in distribution systems with high photovoltaic penetration. Two voltage optimization algorithms are developed to evaluate the CVR benefit by considering two types of smart inverter functions: aggregated reactive power control and autonomous volt/VAR control. In each algorithm, smart inverters are coordinately controlled with the operation of legacy voltage regulating devices including load tap changers and capacitor banks. Both algorithms are tested on representative utility distribution system models. Simulation results demonstrate that the proposed algorithms can achieve around 1.8-3.6% energy savings when only using legacy voltage regulating devices and an additional 0.3-0.9% when adding smart inverters.

Synthesizing the market clearing mechanism based on the national power grid using hybrid of deep learning and econometric models: Evidence from the Japan Electric Power Exchange (JEPX) market

In liberalized electricity markets, formulating the techno-economic functions of each market's participant restricts the resulting market clearing mechanism (MCM) into the market equilibrium. This study aims to synthesize the MCM based on a new perspective that correlates the nationwide energy supply and demand equilibrium with the wholesale market's prices. The nationwide energy capacities are collected from all the Japanese transmission and distribution utilities from 2016 to 2022. The demand curve is structured based on estimating the price elasticity of demand (PED) considering regional and hourly cross-sections using two-stage econometrics. The supply curve is structured based on the merit order effect of all existing energy production technologies. A long short-term-memory forecasting model is employed to capture the non-stationarity and -linearity of the prices. The estimated PED varies spatiotemporally from −0.017 to −0.25. From the synthesized MCMs, Kyushu region's price drop to nearly zero is justified by the 93.5% clean supply and the use of regional interconnections. Nationally, nuclear, hydroelectric, and hydro-storage contributions to the supply mix and the balancing market launching have mitigated the price spike of 2022. In addition, the planned share increase of baseload resources in 2030 drives the prices lower with higher stability versus the current electricity mix.

Solar based EV Charging Station using RFID

A new and emerging technology in the electricity and transportation industries, electric vehicles offer numerous advantages from both an economic and environmental standpoint. This study provides a thorough analysis of different types of electric vehicles, as well as the accessories that go with them, including battery chargers and charging stations. Comparing the electric range, battery size, charger power, and charging time of commercial and prototype electric vehicles is done. The different kinds of electric car charging stations and industry standards have been described, and the effects of electric vehicle charging on utility distribution networks have also been covered.

On-campus mental health services for Australian university students: A retrospective analysis of service utilisation.

To investigate on-campus mental health service utilisation by Australian university students. Retrospective analysis of clinical data from two on-campus health services (general practice and psychology and counselling service). Descriptive statistics include total consults, demographic factors, diagnoses, presenting concerns and rates of suicidal ideation. Mental health conditions account for the largest proportion of ongoing illness in on-campus health service users, representing 46% of all ongoing health conditions. Depression and anxiety were the most common diagnoses, and stress, anxiety and low mood were the most common presenting concerns. Females utilise mental health services more frequently than males, accounting for 65.3% and 60.1% of patients for the respective services. International students present for specific mental health consults less frequently than domestic students. Rates of suicidal ideation at presentation were high (37%). This retrospective analysis provides important information regarding the proportion and distribution of mental health conditions and service utilisation amongst Australian university students. There is clear scope for increased access to specialist care, renewed efforts to decrease stigma and increase rates of presentation (particularly amongst international students and males), greater support for general practitioners and more rigorous routine data collection and reporting, both within and across universities nationally.

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

Self-powered electrocatalytic nitrate to ammonia driven by lightweight triboelectric nanogenerators for wind energy harvesting

Ammonia (NH3) is an essential feedstock for modern industry and agriculture, as well as a promising energy carrier owing to its high energy density (4.3 kWh kg–1) and storage security. The electrocatalytic nitrate reduction reaction to ammonia (NRA) is a promising alternative route for convenient and distributed NH3 synthesis coupled with clean energy, and is attracting widespread attention. Building a coupled system for NH3 production between NRA and clean energy capture is an exciting direction for future distributed energy utilization and conversion. Herein, a lightweight triboelectric nanogenerators (TENGs) with ultra-low start-up wind speed (3.0 m s–1) is coupled to NRA system loaded with polycrystalline copper, enabling energy harvesting in a wide wind speed band for the efficient NH3 production. An adaptation strategy for capacitor embedded in the energy conversion process is proposed to further enhance the efficiency of coupled system, achieving a high NH3 yield of 11.48 μg cm−2 h–1 with the high catalytic activity of polycrystalline copper on cathode. This work opens up a coupled electrocatalysis and natural energy capture system for the conversion of NO3– to NH3, and provides an idea for the renewable energy conversion and nitrogen cycle remediation.

Spatiotemporal evolution of urban populations and housing: A dynamic utility-driven market-mediated model.

A model of the spatiotemporal evolution of urban areas is developed that simultaneously includes the effects on household utility of geography, population density, income distribution, and household preference for characteristics of dwellings and neighbors. The result is a utility function whose structure is similar to that of the energy of interacting spin systems in external fields. Spatiotemporal housing market evolution then results via transactions driven by increases in utility and changes in numbers of households and dwellings. It is shown that the model successfully predicts formation of monocentric and polycentric urban areas, stratification by wealth, segregation due to preferences for housing or neighbors, and the balance of supply and demand. These results go well beyond those of prior models that each dealt with subsets of these phenomena, and do so within a single, unified framework. Potential generalizations are discussed and further applications are suggested.

Geospatial Distribution of Ambulatory Surgery Center Utilization for Otorhinolaryngologic Surgeries Among Medicare Patients From 2015 to 2019.

To investigate the geographic clustering of ambulatory surgical center (ASC) utilization in otolaryngology to determine hot spot areas of high utilization and cold spot areas of low utilization and socioeconomic factors that correlate with these hot spots and cold spots. To develop a national epidemiologic study of ASC utilization in otolaryngology in the United States. United States of America. Multiple county-level national databases were reviewed including Center for Medicare Services (CMS) physician billing data, CMS Medicare demographic data, and US Census socioeconomic data. The analysis was conducted using the average of all Medicare billing information from 2015 to 2019. Whether a procedure was performed in an ASC was extracted from CMS data using the CMS definition of an ASC. The percentage ASC billing was calculated as the fraction of CMS payments that were performed in ASCs for the top ENT procedures. A Python-based script for database building and GeoDa, Moran's I clustering coefficient, and a 1-way analysis of variance was utilized to chart and analyze demographic, geographic, and socioeconomic trends. Hot spots of utilization, with an average ASC billing of 80.13%, were seen in Southern California, Florida, Mid-Atlantic, and clusters throughout the Deep South. Cold spot clusters, with an average ASC billing of 2.21%, were located in large swaths of New England, Ohio, and the Deep South with clusters bisecting the Midwest. Cold spots had a higher percentage of poverty and percent eligible for Medicaid. ASC utilization is best used to improve cost-effectiveness and accessibility of care but what is seen is that ASC use is currently highest in cities in coastal areas which already have high levels of care access and are making the most proportional money compared to their rural counterparts.