An Introduction to “Alternative Fuel Grades” for Electric Vehicle Fast Charging

For large electricity users, such as smelting plants, their electric loads cannot exceed a concerted limit in production. Traditional single-furnace optimization methods aim to satisfy the electric demand of a furnace to improve its production, and hence cannot consider the maximum demand constraint in a smelting plant. Maximum demand (MD) control is often utilized to keep the total electric demand within the limit via shedding the electric loads of some furnaces once the demand approaches the limit. However, the control method will enlarge the fluctuation of electric loads, which does harm to the production and causes a decline in energy-efficiency. In this paper, we propose a multifurnace optimization strategy to improve the production targets of a whole plant instead of a single furnace. In the strategy, an offline multiobjective load scheduling is first performed to assign electric loads for furnaces in each sampling period, taking into account of the MD constraint and production constraints. A multiobjective particle swarm optimization algorithm, combined with population initialization and constraint-handing strategies, is proposed to search for the Pareto optimal set of the scheduling problem, from which decision-makers can select one solution as the load scheduling program. A double closed-loop control mechanism is used to change the scheduled load into detailed load setpoints of furnaces and keep the actual loads up with the load setpoints. In the outer loop, the detailed load setpoints of furnaces are dynamically adjusted based on the deviation of actual loads from the scheduled loads. Thereafter, the desired setpoints are sent to the automatic control mechanism of each furnace, which is in the inner loop and responsible to keep the actual load up with the setpoint via a proportional-integral-derivative (PID) controller. The case study on a typical magnesia-smelting plant shows that the proposed multifurnace optimization strategy can achieve an increase of about 12.29% in the production output, an improvement of about 0.46% of the magnesia in the product, and a slight reduction of 2.35% in electricity cost over the results of MD control. Note to Practitioners - For large electricity users, such as smelting plants, they are subjected to the maximum electric demand constraint. The maximum demand control device is widely adopted to solve the problem, but it will cause a decline in production output and energy-efficiency. This paper was motivated by improving the multiple production targets (i.e, the total production output, the product quality, and the total electricity cost) of a plant via load scheduling and control. In contrast to the maximum demand control, the load scheduling and control approach is a beforehand strategy that can optimize the operation. A case study on a magnesia-smelting plant shows that the proposed approach performs better than the maximum demand control technique.

INDUSTRIAL electricity tariffs usually contain two types of consumption-related charges-the energy charge, assessed on total kilowatthours, and the demand (or capacity) charge, assessed on maximum kilowatts. Both charges can be differentiated by time of day, applying a premium to kilowatts and kilowatthours consumed during specified peak hours. The costs of both these dimensions-energy and maximum demand-vary by time of day and by season. Plant capacity must be constructed to meet the maximum, or peak, demand. Also, capacity is comprised of plants of varying fuel efficiencies. 1 Figure i depicts a typical firm's pattern of demand, or load curve, over 24 hours. Under traditional electricity prices, the firm pays, usually on a monthly basis, an energy price P for total energy (f'o E dt) and demand price P* for maximum demand (E*). If these charges are used in a time-of-day tariff, they will be at a higher level during potential utility system peak hours. Defining the interval (to, t1) as peak, intrapeak maximum demand equals Ep and peak energy use equals it' E dt. If both prices are differentiated by time-of-day, then Pp > Po and Pp > P*, where the subscripts denote the peak and off-peak periods.2 Time-of-day demand and energy charges can have differing effects. The peak energy charge encourages, within the peak hours, a reduction in the area beneath the load curve, that is, a reduction in peak energy (kilowatthour) use. It does not explicitly encourage a rearrangement of the load pattern within this interval, and so may not reduce the maximum intrapeak demand. The peak demand charge encourages a reduction in the maximum demand; this can be accomplished directly by flattening peak period use, or less directly by reducing use at each instant within the peak. Hence, the peak demand charge has its primary effect on the pattern of use, while the energy charge primarily nffprte th 1,p1 nf llq

Problem/ConditionDrug overdoses are a leading cause of injury death in the United States, resulting in approximately 52,000 deaths in 2015. Understanding differences in illicit drug use, illicit drug use disorders, and overall drug overdose deaths in metropolitan and nonmetropolitan areas is important for informing public health programs, interventions, and policies.Reporting PeriodIllicit drug use and drug use disorders during 2003–2014, and drug overdose deaths during 1999–2015.Description of DataThe National Survey of Drug Use and Health (NSDUH) collects information through face-to-face household interviews about the use of illicit drugs, alcohol, and tobacco among the U.S. noninstitutionalized civilian population aged ≥12 years. Respondents include residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories, migratory workers’ camps, and halfway houses) and civilians living on military bases. NSDUH variables include sex, age, race/ethnicity, residence (metropolitan/nonmetropolitan), annual household income, self-reported drug use, and drug use disorders.National Vital Statistics System Mortality (NVSS-M) data for U.S. residents include information from death certificates filed in the 50 states and the District of Columbia. Cases were selected with an underlying cause of death based on the ICD-10 codes for drug overdoses (X40–X44, X60–X64, X85, and Y10–Y14). NVSS-M variables include decedent characteristics (sex, age, and race/ethnicity) and information on intent (unintentional, suicide, homicide, or undetermined), location of death (medical facility, in a home, or other [including nursing homes, hospices, unknown, and other locations]) and county of residence (metropolitan/nonmetropolitan).Metropolitan/nonmetropolitan status is assigned independently in each data system. NSDUH uses a three-category system: Core Based Statistical Area (CBSA) of ≥1 million persons; CBSA of <1 million persons; and not a CBSA, which for simplicity were labeled large metropolitan, small metropolitan, and nonmetropolitan. Deaths from NVSS-M are categorized by the county of residence of the decedent using CDC’s National Center for Health Statistics 2013 Urban-Rural Classification Scheme, collapsed into two categories (metropolitan and nonmetropolitan).ResultsAlthough both metropolitan and nonmetropolitan areas experienced significant increases from 2003–2005 to 2012–2014 in self-reported past-month use of illicit drugs, the prevalence was highest for the large metropolitan areas compared with small metropolitan or nonmetropolitan areas throughout the study period. Notably, past-month use of illicit drugs declined over the study period for the youngest respondents (aged 12–17 years). The prevalence of past-year illicit drug use disorders among persons using illicit drugs in the past year varied by metropolitan/nonmetropolitan status and changed over time. Across both metropolitan and nonmetropolitan areas, the prevalence of past-year illicit drug use disorders declined during 2003–2014.In 2015, approximately six times as many drug overdose deaths occurred in metropolitan areas than occurred in nonmetropolitan areas (metropolitan: 45,059; nonmetropolitan: 7,345). Drug overdose death rates (per 100,000 population) for metropolitan areas were higher than in nonmetropolitan areas in 1999 (6.4 versus 4.0), however, the rates converged in 2004, and by 2015, the nonmetropolitan rate (17.0) was slightly higher than the metropolitan rate (16.2).InterpretationDrug use and subsequent overdoses continue to be a critical and complicated public health challenge across metropolitan/nonmetropolitan areas. The decline in illicit drug use by youth and the lower prevalence of illicit drug use disorders in rural areas during 2012–2014 are encouraging signs. However, the increasing rate of drug overdose deaths in rural areas, which surpassed rates in urban areas, is cause for concern.Public Health ActionsUnderstanding the differences between metropolitan and nonmetropolitan areas in drug use, drug use disorders, and drug overdose deaths can help public health professionals to identify, monitor, and prioritize responses. Consideration of where persons live and where they die from overdose could enhance specific overdose prevention interventions, such as training on naloxone administration or rescue breathing. Educating prescribers on CDC’s guideline for prescribing opioids for chronic pain (Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. MMWR Recomm Rep 2016;66[No. RR-1]) and facilitating better access to medication-assisted treatment with methadone, buprenorphine, or naltrexone could benefit communities with high opioid use disorder rates.

Problem/ConditionGenetic testing for breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) gene mutations can identify women at increased risk for breast and ovarian cancer. These testing results can be used to select preventive interventions and guide treatment. Differences between nonmetropolitan and metropolitan populations in rates of BRCA testing and receipt of preventive interventions after testing have not previously been examined. Period Covered2009–2014.Description of SystemMedical claims data from Truven Health Analytics MarketScan Commercial Claims and Encounters databases were used to estimate rates of BRCA testing and receipt of preventive interventions after BRCA testing among women aged 18–64 years with employer-sponsored health insurance in metropolitan and nonmetropolitan areas of the United States, both nationally and regionally.ResultsFrom 2009 to 2014, BRCA testing rates per 100,000 women aged 18–64 years with employer-sponsored health insurance increased 2.3 times (102.7 to 237.8) in metropolitan areas and 3.0 times (64.8 to 191.3) in nonmetropolitan areas. The relative difference in BRCA testing rates between metropolitan and nonmetropolitan areas decreased from 37% in 2009 (102.7 versus 64.8) to 20% in 2014 (237.8 versus 191.3). The relative difference in BRCA testing rates between metropolitan and nonmetropolitan areas decreased more over time in younger women than in older women and decreased in all regions except the West. Receipt of preventive services 90 days after BRCA testing in metropolitan versus nonmetropolitan areas throughout the period varied by service: the percentage of women who received a mastectomy was similar, the percentage of women who received magnetic resonance imaging of the breast was lower in nonmetropolitan areas (as low as 5.8% in 2014 to as high as 8.2% in 2011) than metropolitan areas (as low as 7.3% in 2014 to as high as 10.3% in 2011), and the percentage of women who received mammography was lower in nonmetropolitan areas in earlier years but was similar in later years.InterpretationPossible explanations for the 47% decrease in the relative difference in BRCA testing rates over the study period include increased access to genetic services in nonmetropolitan areas and increased demand nationally as a result of publicity. The relative differences in metropolitan and nonmetropolitan BRCA testing rates were smaller among women at younger ages compared with older ages.Public Health ActionImproved data sources and surveillance tools are needed to gather comprehensive data on BRCA testing in the United States, monitor adherence to evidence-based guidelines for BRCA testing, and assess receipt of preventive interventions for women with BRCA mutations. Programs can build on the recent decrease in geographic disparities in receipt of BRCA testing while simultaneously educating the public and health care providers about U.S. Preventive Services Task Force recommendations and other clinical guidelines for BRCA testing and counseling.

Uncertainties in renewable energy resources and electricity demand have introduced new challenges to energy and reserve scheduling of microgrids, particularly in autonomous mode. In this study, a risk‐constrained stochastic framework is presented to maximise the expected profit of a microgrid operator under uncertainties of renewable resources, demand load and electricity price. In the proposed model, the trade‐off between maximising the operator's expected profit and the risk of getting low profits in undesired scenarios is modelled by using the conditional value‐at‐risk (CVaR) method. The influence of consumers’ participation in demand response (DR) programs and their emergency load shedding for different values of lost load (VOLL) are then investigated on the expected profit of the operator, CVaR, expected energy not served and scheduled reserves of the microgrid. Moreover, the impacts of different VOLL and risk aversion parameters are illustrated on the system reliability. Extensive simulation results are also presented to illustrate the impact of risk aversion on system security issues with and without DR. Numerical results demonstrate the advantages of customers’ participation in the DR program on the expected profit of the microgrid operator and the reliability indices.

BackgroundThe COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence.ObjectiveThe goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level.MethodsUsing a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsMinneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week.ConclusionsThree of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

Antibacterial products come on the market

Laboratory dishwasher

Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study

Evaluation of time series techniques to characterise domestic electricity demand

Investigation of fumigant efficacy in flour mills under real-world fumigation conditions

e22544 Background: Gallbladder cancer (GBC) is a rare but aggressive malignancy, accounting for over 50% of biliary tract cancers. The disparities in GBC-related mortality remain underexplored, particularly across rural and urban populations. This study examines trends in GBC-related mortality over two decades, focusing on differences by urbanization to identify at-risk populations and guide equitable care strategies. Methods: We obtained de-identified data from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) multiple causes of death database (years: 1999–2020) for the U.S. population with “malignant neoplasm of the gallbladder” (ICD-10 C23) listed as either “underlying” or “contributing” cause of death. Age-adjusted mortality rates (AAMRs) per million population were evaluated, and temporal trends in average annual percent change (AAPC) were assessed using Joinpoint regression. Pairwise comparisons examined differences in AAMR trends using average annual percent change difference (AAPCD) across large metropolitan, medium-to-small metropolitan, and rural areas based on the 2016 NCHS Urban-Rural Classification Scheme. Results: A total of 48,426 GBC-related deaths were recorded between 1999 and 2020. Overall, the AAMR significantly decreased during this period, from 8.1 to 5.8 per million individuals (AAPC: -1.39; 95% CI: -1.58 to -1.16; p &lt; 0.01). The average AAMR was highest in large metropolitan areas (7.0), followed by rural areas (6.2) and small to medium metropolitan areas (6.0). Significant declines in AAMR were observed across all areas (table). However, the decline in AAMR was significantly greater in medium to small metropolitan areas compared to large metropolitan areas (AAPCD: 0.42; 95% CI: 0.10 to 0.74; p &lt; 0.01). In contrast, no significant differences in AAMR trends were noted between large metropolitan and rural areas (AAPCD: 0.23; 95% CI: -0.29 to 0.74; p = 0.39) or between medium to small metropolitan and rural areas (AAPCD: -0.20; 95% CI: -0.72 to 0.32; p = 0.46). Conclusions: GBC-related mortality declined significantly from 1999 to 2020, with the steepest decrease observed in medium to small metropolitan areas. While large metropolitan areas had the highest average AAMR, the rate of decline in AAMR did not significantly differ between large metropolitan and rural areas or between medium to small metropolitan and rural areas. Efforts should focus on addressing persistent disparities and improving access to care in high-risk populations. The table below depicts trends in AAMRs per million using average AAPC from 1999 to 2020, stratified by urbanization status. Cohort Duration AAPC Lower CI Upper CI p-value Large Metropolitan 1999-2020 -1.27 -1.50 -1.03 &lt;0.01 Medium to Small Metropolitan 1999-2020 -1.69 -1.93 -1.44 &lt;0.01 Rural 1999-2020 -1.49 -1.99 -1.00 &lt;0.01 Overall 1999-2020 -1.39 -1.58 -1.16 &lt;0.01

The smart city integrating the smart grid as an integral part of it to guarantee the ever-increasing electricity demand. After the recent outbreak of the COVID-19 pandemic, the socioeconomic severances affecting total levels of electricity demand, price, and usage trends. These unanticipated changes introducing new uncertainties in short-term demand forecasting since its result depends on the recent usage as an input variable. Addressing this challenging situation, this paper proposes an electricity demand and price forecast model based on the LSTM Deep Learning method considering the recent demand trends. Real electricity market data from the Australian Energy Market Operator (AEMO) is used to validate the effectiveness of the proposed model and elaborated with two scenarios to get a wider context of the pandemic impact. Exploratory data analyses results show hourly electricity demand and price reductions throughout the pandemic weeks, especially during peak hours of 8 am- 12 noon and 6 pm–10 pm. Electricity demand and price has been dropped by 3% and 42% respectively on average. However, overall usage patterns have not changed significantly compared to the same period last year. The predictive accuracy of the proposed model is quite effective with an acceptably smaller error despite trend change phenomena triggered by the pandemic. The model performance is comprehensively compared with a few conventional forecast methods, Support Vector Machine (SVM) and Regression Tree (RT), and as a result, the performance indices RMSE and MAE have been improved using the proposed LSTM model.

Illicit Drug Use, Illicit Drug Use Disorders, and Drug Overdose Deaths in Metropolitan and Nonmetropolitan Areas-United States.

Strategies for microgrid operation under real-world conditions