Peak Consumption Research Articles

Predictors of Exercise Capacity in Dilated Cardiomyopathy with Focus on Pulmonary Venous Flow Recorded with Transesophageal Eco-Doppler.

The aim of this study was to clarify the relative contribution of elevated left ventricle (LV) filling pressure (FP) estimated by pulmonary venous (PV) and mitral flow, transesophageal Doppler recording (TEE), and other extracardiac factors like obesity and renal insufficiency (KI) to exercise capacity (ExC) evaluated by cardiopulmonary exercise testing (CPX) in patients with dilated cardiomyopathy (DCM). During the CPX test, 119 patients (pts) with DCM underwent both peak VO2 consumption and then TEE with color-guided pulsed-wave Doppler recording of PVF and transmitral flow. In 78 patients (65%), peak VO2 was normal or mildly reduced (>14 mL/kg/min) (group 1) while it was markedly reduced (≤14 mL/kg/min) in 41 (group 2). In univariate analysis, systolic fraction (S Fract), a predictor of elevated pre-a LV diastolic FP, appeared to be the best diastolic parameter predicting a significantly reduced peak VO2. Logistic regression analysis identified five parameters yielding a unique, statistically significant contribution in predicting reduced ExC: creatinine clearance < 52 mL/min (odds ratio (OR) = 7.4, p = 0.007); female gender (OR = 7.1, p = 0.004); BMI > 28 (OR = 5.8, p = 0.029), age > 62 years (OR = 5.5, p = 0.03), S Fract < 59% (OR = 4.9, p = 0.02). Conclusion: KI was the strongest predictor of reduced ExC. The other modifiable factors were obesity and severe LV diastolic dysfunction expressed by blunted systolic venous flow. Contrarily, LV ejection fraction was not predictive, confirming other previous studies. This has important clinical implications.

Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response

There is a tendency to increase the use of demand response technology in the Russian Federation along with other developing countries, covering not only large industries, but also individual households and organizations. Reducing peak loads of electricity consumption and increasing energy efficient use of equipment in the power system is achieved by applying demand management technology based on modeling and predicting consumer behavior in an educational institution. The study proposes to consider the possibility of participating in the concept of demand management of educational institutions with a typical workload schedule of the work week. For the study, statistical data of open services and sources, Russian and foreign research on the use of digital and information technologies, analytical methods, methods of mathematical modeling, methods of analysis, and generalization of data and statistical methods of data processing are used. An algorithm for collecting and processing power consumption data and a load planning algorithm were developed, including all levels of interaction between devices. A comparison was made between the values of the maximum daily consumption before and after optimization, as well as the magnitude of the decrease in the maximum consumption after applying the genetic algorithm. The developed algorithm has the ability to scale, which will increase the effect of using the results of this study to more significant values. Load switching helps to reduce peak consumption charges, which often represent a significant portion of the electricity cost.

Increased Use of Antibiotics in the Intensive Care Unit During Coronavirus Disease (COVID-19) Pandemic in a Brazilian Hospital.

Background: Microbial drug resistance is one of the biggest public health problems. Antibiotic consumption is an essential factor for the emergence and spread of multiresistant bacteria. Therefore, we aimed to analyze the antibiotics consumption in the Intensive Care Unit (ICU), identifying trends in the antibiotics use profile and microbiological isolates throughout the COVID-19 pandemic. Methods: We performed this retrospective observational study in intensive care units of a Brazilian tertiary hospital from January 2019 to December 2020. The primary outcome was antimicrobial consumption in the ICU, measured by defined daily doses (DDDs) per 100 bed-days. As a secondary outcome, bacterial infections (microbiological isolates) were calculated in the same fashion. Outcomes trends were analyzed using Joinpoint regression models, considering constant variance (homoscedasticity) and first-order autocorrelation assumptions. A monthly percent change (MPC) was estimated for each analyzed segment. Results: Seven thousand and nine hundred fifty-three patients had data available on prescribed and received medications and were included in the analyses. Overall, the use of antibiotics increased over time in the ICU. The reserve group (World Health Organization Classification) had an increasing trend (MPC = 7.24) from February to April 2020. The azithromycin consumption (J01FA) increased rapidly, with a MPC of 5.21 from January to April 2020. Polymyxin B showed a relevant increase from March to June 2020 (MPC = 6.93). The peak of the antibiotic consumption of Reserve group did not overlap with the peak of the pathogenic agents they are intended to treat. Conclusion: Overall antimicrobial consumption in ICU has increased in the context of the COVID-19 pandemic. The peaks in the antimicrobial’s use were not associated with the rise of the pathogenic agents they intended to treat, indicating an empirical use, which is especially concerning in the context of treating multidrug-resistant (MDR) infections. This fact may contribute to the depletion of the therapeutic arsenal for MDR treatment.

Are We Approaching Peak Meat Consumption? Analysis of Meat Consumption from 2000 to 2019 in 35 Countries and Its Relationship to Gross Domestic Product.

Simple SummaryMeat production has been associated with environmental degradation, yet at the same time many countries are increasing their consumption. We analyzed which countries are increasing and which decreasing their consumption of all the major meats consumed over a period from 2000 to 2019. There is evidence of peak meat consumption having been reached in several countries, but also evidence of continued consumption increases in many of the emerging-economy countries, probably due to greater affordability. We also found evidence of this when we attempted to link Gross Domestic Product (GDP) per capita to meat consumption per capita. In many emerging-economy countries, there was a direct link over the years studied, but in the higher income countries there was no relationship. We discuss how this observed dichotomous relationship will shape future trends in meat consumption.Growing prosperity, but also disease outbreaks, natural disasters, and consumer preferences are changing global meat consumption. We investigated the 2000–2019 trends in 35 countries monitored by the Food and Agriculture Organization and the Organisation for Economic Co-operation and Development. We also tested relationships with Gross Domestic Product (GDP). Several countries appeared to be reaching peak consumption of some meats, and three (New Zealand, Canada, and Switzerland) have reached this. Poultry consumption increased over time in most countries, and beef and mutton/lamb consumption decreased in many. Using cluster analysis, we divided countries into two clusters: one in which increases in GDP per capita matched increases in meat consumption; and a second one of nine countries, for which there was no association between per capita change in GDP and meat consumption. There was evidence of a tipping point around USD 40,000 of GDP per capita, after which increases in economic well-being do not lead to increased meat consumption.

Energy production and storage from a polygeneration system based on parabolic trough solar collector, proton exchange membrane fuel cell, organic Rankine cycle, and alkaline electrolyzer

Due to the limitations with fossil fuels consumption, it is necessary to pay more attention to clean energy sources such as solar energy. Using solar energy systems as a driven of hybrid systems and integrating with an energy storage technology can alleviate some of the problems. In the present paper, a novel solar driven-polygeneration energy system with electrical energy storage is introduced and investigated. The cycle power generation section is composed of parabolic trough collector field, proton-exchange membrane fuel cell, organic Rankine cycle, alkaline electrolyzer, and thermoelectric generator. The light energy of the sun is converted into thermal energy by the solar collector. This thermal energy prepares the necessary thermal duty of the organic Rankine cycle's evaporator. Then, the electricity generated by the organic Rankine cycle is used to electrify an electrolyzer. The oxygen and hydrogen obtained from electrolyzer are used to generate power and heat by the fuel cell. Then, the waste heat of the fuel cell goes to the hot end of the thermoelectric generator and electricity is generated. The electricity generated from the fuel cell, thermoelectric generator, and surplus of the organic Rankine cycle is stored via a hybrid storage system for times of need (at night, cloudy days and/ or peak consumption hours). Findings revealed that the proposed polygeneration cycle is capable of generating 22.5 kW of power. Furthermore, 140.8 kW of thermal power and 97.3 g/h of hydrogen fuel are generated by the solar collector field and electrolyzer, respectively. Moreover, sum exergy destruction, system efficiency, and net capacity of the storage system are 18.83 kW, 60.3%, and 89 m3, respectively. Three different scenarios are considered for the solar field design based on the determined collector numbers.

Sap Flow and Water Consumption of Captain Cook Tree [Cascabela thevetia (l.) Lippold

A two-year field study documented the diurnal and nocturnal sap flow rates and water consumption of young (YCC), adult (ACC) and mature (MCC) Captain Cook trees [Cascabela thevetia (L.) Lippold] that were invading a riparian habitat in northern Queensland. For comparison, two native trees [black tea tree (Melaleuca bracteata F. Muell.) and Moreton Bay ash (Corymbia tessellaris (F.Muell.) K.D.Hill &amp; L.A.S.Johnson)] growing in association with Captain Cook tree were also monitored. Sap flow measurements were grouped into eight timeframes per day (early morning, late morning, early afternoon, late afternoon, early night, late night, early dawn and late dawn). Significant interactions in sap flow rate occurred between plant types, timeframes, and months. The magnitude of sap flow rate was Moreton Bay ash&gt;YCC&gt;ACC&gt;black tea tree&gt;MCC. Maximum sap flow rates tended to occur during early (1-3 pm) to mid-afternoon (4-6 pm) for all age groups of Captain Cook tree and the two native trees. Diurnal sap flow rates were significantly greater than nocturnal, and on a monthly basis sap flow rates were highest over the spring to autumn period (September-May) and lowest during winter (June–August). Significant differences in water consumption also occurred between species and months. Water consumption peak time varied between plant types with most plants peaking in January except for MCC and Moreton Bay ash trees for which peak water consumption occurred in June and July respectively. Water consumption was high across all seasons except winter. The magnitude of water consumption was Moreton Bay ash&gt;black tea tree&gt;YCC&gt;ACC&gt;MCC trees. Moreton Bay ash registered maximal monthly water consumption (4700 L) compared with minimal consumption by MCC trees (55 L). On average, Captain Cook trees used 99% and 72% less water than Moreton Bay ash and black tea trees respectively. The significantly lower water consumption by Captain Cook trees compared with Moreton Bay ash and black tea trees may be offset by high population densities. Results also suggest that knowledge of optimal sap flow timeframes may be advantageous in exploring optimal timing for application of control operations related to management of Captain Cook trees.

Demand response potential: An economic analysis for MIBEL and EEX

Day-ahead electricity markets were established based on the premise that short run price elasticity of demand is small, due to electricity importance, convenience and low cost. If demand respond to price signals through curtailing or shifting loads, spot prices are expected to decrease due to a more efficient market operation.This research aims to identify if there is a market design structure more suitable for Demand Response, specifically, regarding electricity markets either with or without bid caps or floor mechanisms. Results indicate that load flexibility in MIBEL would result in a larger number of hours in which is economically viable to perform demand response when comparing to EEX. Increasing flexible loads do not necessarily correspond to spot prices decrease and lower flexibility levels would produce a similar price variation. Moreover, years dominated by non-fossil technologies, negative prices, peak hours, week days and months with lower temperatures were found to decrease the economic potential. Results indicate that in during peak consumption hours the price volatility is lower, which may indicate a lower concurrency level among the generation companies. The research results contribute to improve the debate on policies, providing indications for the development of future energy market design structures.

Analysis of Building Archetypes for Optimising New Photovoltaic Energy Facilities: A Case Study

Fuel poverty rates are high in southern Europe, particularly in social housing, despite the enormous potential for capturing solar power inherent in the roofs of apartment buildings. The in situ generation, distribution and consumption of photovoltaic energy carry obvious advantages including vastly improved efficiency attendant upon the reduction in distribution-related losses and costs, and the energy empowerment afforded lower income communities. The primary drawback is the imbalance between photovoltaic production patterns and users’ actual needs (peak consumption vs. peak generation). That mismatch is difficult to reconcile without resorting to energy storage or net metering, both of which entail grid involvement and greater management complexity. The present study introduces a methodology for analysing residential archetypes to determine the values of the parameters essential to optimising photovoltaic energy production and use. The aim is to determine where excess generation can be shared with other users in the vicinity and optimally pool residential rooftop facilities to meet community-scale energy demand, ultimately enhancing such disadvantaged neighbourhoods’ self-sufficiency. The case study discussed defines archetypes for just such a neighbourhood in Madrid, Spain. The solar energy production potential of the example is promising for its application in large southern European cities, with self-sufficiency rates obtained ranging from 15% to 25% and self-consumption rates from 61% to 80%.

Impact of vehicle charging on Portugal's national electricity load profile in 2030

This study aims to explore the impact of light-duty passenger (LdP) electric vehicle (EV) charging on the Portuguese national load profile (LP) in 2030. The goal is to identify EV charging strategies that enable a sustainable configuration of the Portuguese LP under different potential levels of LdP EV penetration in 2030. The research offers Portuguese utilities and policymakers information regarding potential threats and solutions of different EV charging strategies on the national grid infrastructure in 2030. Furthermore, it proposes a methodology that can also be adopted in other countries to analyse similar problems. Low, medium, and high penetration scenarios were designated based on the number of LdP EVs. The low and medium penetration scenarios indicate that an intelligent grid is not necessary to perform charging activities. However, coordinating EV charging in the evening via a smart grid (SG) is imperative in the high penetration scenario, as unsustainable levels of power demand will otherwise be reached, compared with the current production and distribution capacity. Moreover, morning charging sessions must also be addressed, as they may induce new peaks of daily consumption given the significant amount of charging activity taking place within that period.

Design and operation optimization of city-level off-grid hydro–photovoltaic complementary system

Increasing attentions have been paid on the design and operation optimization of hydro-photovoltaic complementary systems due to the inherent uncertainty associated withthe solar energy. However, studies mainly focused on the household or community integrated power system while the city-level off-grid systems remained less explored. That was because both the large scale of the studied area and the complex factors associated with the city attached difficulties to the problems. This paper proposed an off-grid PV-hydro hybrid optimization model for the cities which were located nearby sufficient water resources based on Geographic Information System. The proposed model can guarantee the electricity demand with less economic cost and less storage facilities by taking full advantage of water resources. Firstly, the optimization-based economic analysis considering PV capital cost and hydroelectricity price was proposed to determine whether to install PV panels and the optimal size if install. Then, we set six scenarios to identify the relationship between the electricity consumption structure and the optimal PV scale due to the structure transformation according to the current energy policies. Finally, the detailed optimal power generation plan in typically weather conditions during operation period was analyzed. Results indicated that: (1) The city level off-grid PV-hydro complementary system was feasible according to the studied case. (2) The critical PV panel capital cost was 920 $/kW when the hydroelectricity price was 5¢/kW·h, and there was a linear relationship between these two parameters. (3) Optimal PV size was influenced by electricity consumption structure and peak month type, and the current structure transformation policy was beneficial for the PV-hydro systems. (4) The power generation plan varied a lot in different weather conditions and the detailed operation plan could be obtained in this paper.

Effect of β-Blocker Withdrawal on Functional Capacity in Heart Failure and Preserved Ejection Fraction

BackgroundChronotropic incompetence has shown to be associated with a decrease in exercise capacity in heart failure with preserved ejection fraction (HFpEF), yet β-blockers are commonly used in HFpEF despite the lack of robust evidence. ObjectivesThis study aimed to evaluate the effect of β-blocker withdrawal on peak oxygen consumption (peak Vo2) in patients with HFpEF and chronotropic incompetence. MethodsThis is a multicenter, randomized, investigator-blinded, crossover clinical trial consisting of 2 treatment periods of 2 weeks separated by a washout period of 2 weeks. Patients with stable HFpEF, New York Heart Association functional classes II and III, previous treatment with β-blockers, and chronotropic incompetence were first randomized to withdrawing from (arm A: n = 26) versus continuing (arm B: n = 26) β-blocker treatment and were then crossed over to receive the opposite intervention. Changes in peak Vo2 and percentage of predicted peak Vo2 (peak Vo2%) measured at the end of the trial were the primary outcome measures. To account for the paired-data nature of this crossover trial, linear mixed regression analysis was used. ResultsThe mean age was 72.6 ± 13.1 years, and most of the patients were women (59.6%) in New York Heart Association functional class II (66.7%). The mean peakVo2 and peak Vo2% were 12.4 ± 2.9 mL/kg/min, and 72.4 ± 17.8%, respectively. No significant baseline differences were found across treatment arms. Peak Vo2 and peak Vo2% increased significantly after β-blocker withdrawal (14.3 vs 12.2 mL/kg/min [Δ +2.1 mL/kg/min]; P < 0.001 and 81.1 vs 69.4% [Δ +11.7%]; P < 0.001, respectively). Conclusionsβ-blocker withdrawal improved maximal functional capacity in patients with HFpEF and chronotropic incompetence. β-blocker use in HFpEF deserves profound re-evaluation. (β-blockers Withdrawal in Patients With HFpEF and Chronotropic Incompetence: Effect on Functional Capacity [PRESERVE-HR]; NCT03871803; 2017-005077-39)

Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption

The unevenness of the electricity consumption schedule at enterprises leads to a peak power increase, which leads to an increase in the cost of electricity supply. Energy storage devices can optimize the energy schedule by compensating the planned schedule deviations, as well as reducing consumption from the external network when participating in a demand response. However, during the day, there may be several peaks in consumption, which lead to a complete discharge of the battery to one of the peaks; as a result, total peak power consumption does not decrease. To optimize the operation of storage devices, a day-ahead forecast is often used, which allows to determine the total number of peaks. However, the power of the storage system may not be sufficient for optimal peak compensation. In this study, a long-term forecast of power consumption based on the use of exogenous parameters in the decision tree model is used. Based on the forecast, a novel algorithm for determining the optimal storage capacity for a specific consumer is developed, which optimizes the costs of leveling the load schedule.

ANTIHISTAMINES: RESEARCH AND ANALYSIS OF THE REGIONAL RETAIL MARKET

the aim. Focused on optimizing the assortment portfolio of pharmacy organizations and improving the process of drug supply to end-consumers, the aim of the study was to analyze the regional pharmaceutical market for antihistamines.Materials and methods. In the study, the following methods were used: a content analysis of regulatory documents; a documentary observation method of the volume of antihistamines sales; a sociological survey method. The objects of the study were accounting registers in 32 pharmacy organizations for 2020, as well as the sociological survey results of 174 respondents – consumers of antihistamines.Results. The classification analysis of 38 international non-proprietary trade names of antihistamines, represented by 187 names of drugs, revealed the prevalence of the first-generation drugs (63%). On the Russian pharmaceutical market, there were also 55% of foreign production drugs. At the regional level, there are 66 types of drugs in circulation, 50% of which are second-generation ones. The cost analysis showed rather a wide rage of the pricing proposal and the economic availability of antihistamines for patients. The sociological survey revealed the fact that 46% of the consumers were ready to pay for the necessary drugs in the price range “over 100 and up to 500 rubles” (over $ 1.38 and up to $ 6.88) for one conventional package. A medical-demographic profile of the antihistamines consumer at the regional level has been made up, and guidelines for pharmaceutical specialists on managing the assortment portfolio of pharmaceutical organizations have been developed.Conclusion. As a result of the study, the following facts have been established: the seasonal peaks in the antihistamines consumption; a gradual renewal of the pharmacies assortment portfolio due to the increased consumption of the second and third generation antihistamines. The medical and demographic profile of the consumer should be taken into consideration when planning a drug provision for the patients with allergic pathologies, and it is connected with the growth in pharmacies profits due to the sale of drugs in the range from 100 to 500 rubles (from $ 1.38 to $ 6.88). The methodical recommendations have been brought to the attention of the management of regional pharmacy organizations.

HOW DOES WATER DEFICIT AFFECT GLADIOLUS GROWTH AND DEVELOPMENT?

The growing demand for gladiolus, especially at peaks of consumption, requires efficient scheduled production and good quality of floral stems. The objective of this study was to understand how water deficit affects the growth and development of gladiolus and, consequently, the quality of the floral stems produced. Plant development, quantitative parameters of the floral stems, and dry mass partitioning were evaluated in two field experiments with four gladiolus cultivars, with and without irrigation. The development cycle was longer in the non-irrigated treatment, whereas its duration under no water deficit was similar to that predicted by PhenoGlad, which is a gladiolus phenology model. As PhenoGlad predicts gladiolus development only for scenarios without water deficit, the inclusion of a water-stress submodel in the model would allow its application to any water supply scenario and help decision makers and farmers to plan gladiolus production more accurately. Water deficit also reduced gladiolus growth and flower stem size, but did not affect dry mass partitioning. As the development and quality of the floral stems of gladiolus were negatively affected by water stress, irrigation is essential for producing high-quality gladiolus floral stems and enabling production planning for peaks of consumption.

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy data analytic techniques have provided the convenience to collect the data from the end devices on a large scale and to manipulate all the recorded data. Forecasting an electric load is fairly challenging due to the high uncertainty and dynamic nature involved due to spatiotemporal pattern consumption. Existing conventional forecasting models lack the ability to deal with the spatio-temporally varying data. To overcome the above-mentioned challenges, this work proposes an encoder–decoder model based on convolutional long short-term memory networks (ConvLSTM) for energy load forecasting. The proposed architecture uses encode consisting of multiple ConvLSTM layers to extract the salient features in the data and to learn the sequential dependency and then passes the output to the decoder, having LSTM layers to make forecasting. The forecasting results produced by the proposed approach are favorably comparable to the existing state-of-the-art and better than the conventional methods with the least error rate. Quantitative analyses show that a mean absolute percentage error (MAPE) of 6.966% for household energy consumption and 16.81% for city-wide energy consumption is obtained for the proposed forecasting model in comparison with existing encoder–decoder-based deep learning models for two real-world datasets.

Alcohol-Based Hand Rub Consumption and World Health Organization Hand Hygiene Self-Assessment Framework: A Comparison Between the 2 Surveillances in a 4-Year Region-Wide Experience.

Hand hygiene is essential for infection prevention. In Piedmont (Northwest Italy), hand hygiene is routinely monitored through 2 surveillance systems, the World Health Organization Hand Hygiene Self-Assessment Framework (HHSAF) and alcohol-based hand rub (ABHR) consumption. This study was aimed at (1) comparing the results of these surveillances, investigating the consistence of the 2 systems and (2) inquiring whether organizational differences among healthcare facilities significantly affected hand hygiene performances. A retrospective analysis was performed on data collected annually from 2015 to 2018 for both surveillances from 40 hospitals of the region: HHSAF score (500 points maximum) and ABHR consumption in milliliters per patient-day (mL/PD) were considered. Logistic regression models were built to evaluate possible correlations between these variables, and observations were clustered considering both variables to identify whether groups with significant differences could be discerned. A tree-structured partitioning model was used to confirm the obtained results. A positive correlation was observed between HHSAF score and odds of belonging to the high ABHR consumption group, particularly for cutoffs set to 19 to 23 mL/PD (P = 0.033 for 23 mL/PD). Two ABHR consumption peaks were identified at approximately 10 and 22 mL/PD, corresponding to median HHSAF scores of 353.75 and 375 points, respectively. The group with better performances was mainly composed of hub hospitals (with single-hospital management). The 2 surveillance systems are consistent, and the HHSAF score could work as a reasonable predictor of hand hygiene compliance in healthcare settings. Different management characteristics are crucial in creating a more/less favorable environment for hand hygiene compliance and infection prevention.

Stochastic programming approaches for an energy-aware lot-sizing and sequencing problem with incentive

Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentive-based programs, when electric utilities face power consumption peaks, they request electricity-consuming firms to curtail their electric load, rewarding the industrial firms with incentives if they comply with the curtailment requests. Otherwise, industrial firms must pay financial penalties for an excessive electricity consumption. A two-stage stochastic formulation is presented to cover the case where a manufacturer wants to satisfy any curtailment request. A chance-constrained formulation is also proposed, and its relevance in practice is discussed. Finally, computational studies are conducted to compare mathematical models and highlight critical parameters and show potential savings when subscribing incentive-based programs. We show that the setup cost ratio, the capacity utilisation rate, the number of products and the timing of curtailment requests are critical parameters for manufacturers.

Rib fractures in blunt chest trauma: factors that influence daily patient controlled opiate use during acute care

BackgroundOpiates are frequently used in the inpatient management of chest wall injury following blunt trauma. However, the daily sum of opiates used during acute care, and the impact that additional injuries or rib fracture displacement may have on daily opiate requirement is unknown. MethodsA retrospective sample of 85 adult patients admitted to a tertiary trauma centre between April 2018 and October 2019 after a major chest wall injury (Abbreviated Injury Scale >2) and referral to Acute Pain Management Service was used in this study. Daily opiate usage was calculated each day for the first seven days following initial admission and converted to morphine milliequivalents (MME). Additional adjunct analgesia therapy was also recorded each day. The presence of rib fracture displacement and concurrent clavicle/scapular fractures was also noted. A comparison of the average daily MME for the various subgroups of interest was performed. ResultsThe maximum average MME in patients with rib fractures typically occurs at day 2 post injury and admission, with the highest day 2 average MME being in the Patient Controlled Analgesia (PCA) and ketamine subgroup. Presence of rib displacement delayed the onset of maximal MME to day 3 and resulted in higher average MME over the total seven days. Patients with concurrent clavicle or scapular fractures also had higher average MME each day, regardless of the addition of a regional block. ConclusionsThis study has demonstrated the daily opioid requirement is maximal on day 2 post-admission following isolated major chest wall injury. The addition of a regional block resulted in a reduction of the average MME used each day over the first seven days post-admission, compared to ketamine when added to PCA. The presence of displaced rib fractures or clavicle/scapular fractures increased the MME used each day, changed the day of peak consumption and increased the average daily opioid requirement during acute hospitalisation.

Improving the Load Estimation Process in the Design of Rural Electrification Systems

The design of reliable and sustainable rural electrification systems relies on accurate prediction of the electrical load. This paper evaluates the current methods for load estimation and proposes an improved approach for load estimation for off-grid unelectrified rural communities that yields more accurate estimates. Improved accuracy is mainly due to better modelling of the influence of customer habits and gender on the estimated current and future load using the Markov chain process. A program was developed using MATLAB software to generate load profiles. The results show that gender considerations have a significant impact on load profiles and that the Markov chain process can suitably be used to determine year-to-year load profiles by incorporating the effect of changes in customer habits on the estimated load. The results from the case study on energy consumption in rural community households showed an increase in average daily consumption when gender was considered during load estimation. The peak consumption when gender was considered was about 50% higher than the value for when gender was not considered.

Towards Resilient Critical Infrastructures – Motivating Users to Contribute to Smart Grid Resilience

AbstractSmart cities aim at improving efficiency while providing safety and security by merging conventional infrastructures with information and communication technology. One strategy for mitigating hazardous situations and improving the overall resilience of the system is to involve citizens. For instance, smart grids involveprosumers—capable of producing and consuming electricity—who can adjust their electricity profile dynamically (i. e., decrease or increase electricity consumption), or use their local production to supply electricity to the grid. This mitigates the impact of peak consumption periods on the grid and makes it easier for operators to control the grid. This involvement of prosumers is accompanied by numerous socio-technical challenges, including motivating citizens to contribute by adjusting their electricity consumption to the requirements of the energy grid. Towards this end, this work investigates motivational strategies and tools, including nudging, persuasive technologies, and incentives, that can be leveraged to increase the motivation of citizens. We discuss long-term and side effects and ethical and privacy considerations, before portraying bug bounty programs, gamification and apps as technologies and strategies to communicate the motivational strategies to citizens.