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One of the key strategies for decarbonization and green transportation is using electric vehicles (EVs). However, challenges like limited charging infrastructure, EV battery characteristics, and grid integration complexities persist. This study proposes a mixed-integer linear programming (MILP) approach to optimize a grid-connected solar PV-based commercial EV charging station (SPEVCS) with a battery energy storage system (BESS) for profit maximization. The MILP model efficiently manages SPEVCS operations, considering solar power fluctuations, EV charging patterns, and BESS usage. By coordinating charging schedules, grid stability is reinforced, and excess solar power can be lucratively managed. Comparing grid-connected and off-grid SPEVCS scenarios highlights grid integration benefits. Solar power mismatches with optimal charging periods pose a challenge, addressed here by BESS utilization and import/export of deficit/surplus power from/to the grid. The proposed framework incorporates solar power forecasts and probabilistic EV arrival predictions, enhancing decision accuracy. This approach fosters viable commercial EV charging, promotes green transportation, and reinforces grid resilience.

Solar photovoltaic (PV) systems will drive deep electrification of energy systems leading to clean energy 2050. However, connecting large amounts of solar PV systems on direct current (DC) networks, like solar farms and potential future DC distribution systems, would lead to over voltages and loss of solar PV power output due to voltage issues. Further, current PV integration within distribution networks operate exclusively to maximize output using maximum power point tracking algorithms, without network coordination, which may lead to reduced solar output due to voltage issues. Here, a coordinated optimization model for solar PV systems and distribution network voltage regulators is presented. The proposed model optimally controls the settings of voltage controllers (DC‐DC converters), placed at the outputs of solar PV units and selected distribution lines, while maximizing solar power output and minimizing substation power (i.e. system losses). The solar PV systems are modelled using a trained neural network. Testing various systems against uncoordinated situations revealed that the proposed model yielded an increase in solar power of up to 60.06%, in the 28‐bus case. The proposed method will be an excellent tool enabling deep electrification using solar PV system and it overcomes limitations of uncoordinated systems used in practice today. A new coordinated optimization model for solar PV systems and DC distribution systems optimally controls the settings of voltage controllers (DC‐DC converters), placed at the outputs of solar PV units and selected distribution lines, while maximizing solar power output and minimizing substation power (i.e. system losses).Testing various systems against uncoordinated situations revealed that the proposed model yielded an increase in solar power of up to 60.06% and overcomes limitations of uncoordinated systems used in practice today.

Background Despite increasing female enrolment into medical schools, persistent gender gaps exist in the physician workforce. There are limited published data on female representation in the critical care medicine workforce. Methods To obtain a global perspective, societies ( n  = 84; 79,834 members (40,363 physicians, 39,471 non-physicians)) registered with the World Federation of Societies of Intensive and Critical Care Medicine were surveyed. Longitudinal data on female trainee and specialist positions between 2006-2017 were obtained from Australia and New Zealand. Data regarding leadership and academic faculty representation were also collected from national training bodies and other organisations of critical care medicine. Results Of the 84 societies, 23 had a registered membership of greater than 500 members. Responses were received from 27 societies ( n  = 55,996), mainly high-income countries, covering 70.1% of the membership. Amongst the physician workforce, the gender distribution was available from six (22%) participating societies—mean proportion of females 37 ± 11% (range 26–50%). Longitudinal data from Australia and New Zealand between 2006 and 2017 demonstrate rising proportions of female trainees and specialists. Female trainee and specialist numbers increased from 26 to 37% and from 13 to 22% respectively. Globally, female representation in leadership positions was presidencies of critical care organisations (0–41%), representation on critical care medicine boards and councils (8–50%) and faculty representation at symposia (7–34%). Significant gaps in knowledge exist: data from low and middle-income countries, the age distribution and the time taken to enter and complete training. Conclusions Despite limited information globally, available data suggest that females are under-represented in training programmes, specialist positions, academic faculty and leadership roles in intensive care. There are significant gaps in data on female participation in the critical care workforce. Further data from intensive care organisations worldwide are required to understand the demographics, challenges and barriers to their professional progress.

In this paper, we propose an environment perception framework for autonomous driving using state representation learning (SRL). Unlike existing Q-learning based methods for efficient environment perception and object detection, our proposed method takes the learning loss into account under deterministic as well as stochastic policy gradient. Through a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC), we focus on uninterrupted and reasonably safe autonomous driving without steering off the track for a considerable driving distance. Our proposed technique exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. To ensure the effectiveness of the scheme over a sustained period of time, we employ a reward-penalty based system where a negative reward is associated with an unfavourable action and a positive reward is awarded for favourable actions. The results obtained through simulations on DonKey simulator show the effectiveness of our proposed method by examining the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.

We present an approach to network formation based on the notion that social networks are formed by individual decisions that trade off the costs of forming and maintaining links against the potential rewards from doing so. We suppose that a link with another agent allows access, in part and in due course, to the benefits available to the latter via his own links. Thus individual links generate externalities whose value depends on the level of decay/delay associated with indirect links. A distinctive aspect of our approach is that the costs of link formation are incurred only by the person who initiates the link. This allows us to formulate the network formation process as a noncooperative game. We first provide a characterization of the architecture of equilibrium networks. We then study the dynamics of network formation. We find that individual efforts to access benefits offered by others lead, rapidly, to the emergence of an equilibrium social network, under a variety of circumstances. The limiting networks have simple architectures, e.g., the wheel, the star, or generalizations of these networks. In many cases, such networks are also socially efficient.

Objective To report trends in Australian hospitalisations coded for sepsis and their associated costs. Design Retrospective analysis of Australian national hospitalisation data from 2002 to 2021. Methods Sepsis-coded hospitalisations were identified using the Global Burden of Disease study sepsis-specific ICD-10 codes modified for Australia. Costs were calculated using Australian-Refined Diagnosis Related Group codes and National Hospital Cost Data Collection. Results Sepsis-coded hospitalisations increased from 36,628 in 2002-03 to 131,826 in 2020-21, an annual rate of 7.8%. Principal admission diagnosis codes contributed 13,843 (37.8%) in 2002-03 and 44,186 (33.5%) in 2020-21; secondary diagnosis codes contributed 22,785 (62.2%) in 2002-03 and 87,640 (66.5%) in 2020-21. Unspecified sepsis was the most common sepsis code, increasing from 15,178 hospitalisations in 2002-03 to 68,910 in 2020-21. The population-based incidence of sepsis-coded hospitalisations increased from 18.6 to 10,000 population (2002-03) to 51.3 per 10,000 (2021-21); representing an increase from 55.1 to 10,000 hospitalisations in 2002-03 to 111.4 in 2020-21. Sepsis-coded hospitalisations occurred more commonly in the elderly; those aged 65 years or above accounting for 20,573 (55.6%) sepsis-coded hospitalisations in 2002-03 and 86,135 (65.3%) in 2020-21. The cost of sepsis-coded hospitalisations increased at an annual rate of 20.6%, from AUD199M (€127 M) in financial year 2012 to AUD711M (€455 M) in 2019. Conclusion Hospitalisations coded for sepsis and associated costs increased significantly from 2002 to 2021 and from 2012 to 2019, respectively.

In the distribution system, customers have increasingly use renewable energy sources and battery energy storage systems (BESS), transforming traditional loads into active prosumers. Therefore, methodologies are needed to provide prosumers with tools to optimize their investments and increase business opportunities. In this paper, a stochastic mixed integer linear programming (MILP) formulation is proposed to solve for optimal sizes of prosumer assets, considering the use of a BESS and photovoltaic (PV) units. The objective is to minimize the total cost of the system, which is defined as the combination of a solar PV system investment, BESS investment, maintenance costs of assets, and the cost of electricity supplied by the grid. The developed method defines the optimal size of PV units, the power/energy capacities of the BESS, and the optimal value for initial energy stored in the BESS. Both deterministic and stochastic approaches were explored. For each approach, the proposed model was tested for three cases, providing a varying combination of the use of grid power, PV units, and BESS. The optimal values from each case were compared, showing that there is potential to achieve more economic plans for prosumers when PV and BESS technologies are taken into account.

The authors propose bagged and boosted convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks, and compare their performance with the bagged and boosted traditional shallow artificial neural networks (ANNs) for short‐term electricity load forecasting. Unlike existing references that mainly compare the performance of ensemble deep learning with single deep learning and machine learning techniques, three further performance comparisons are carried out: (1) bagged CNNs and bagged LSTMs, (2) boosted CNNs and LSTMs, and (3) bagged CNNs and bagged LSTMs, and boosted CNNs and LSTMs. This allows an insight into the individual effects of ensemble learning on CNNs and LSTMs. The proposed models' inputs consist of weather and time‐related features in addition to the past load. The use of these features allows CNNs and LSTMs to estimate further complex relationship between them and the load. We implement all these methods and compare their performance on the same New England electricity load forecasting data set via statistical analysis. Effects on the forecasting performance with reduced training data are further shown. The LSTM models have the largest performance variation and are also more sensitive to a reduction in training data. In these models, boosting can improve both prediction accuracy and consistency.

An incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure future generation capacity that reduces GHG emissions. In this paper, we propose two ICA models incorporating energy‐limited generation (renewables and storage) and a GHG emission constraint. All offers are converted into unforced capacity, negating any effect of energy limitations of generation offers. The first ICA model uses classical optimisation and considers GHG emission limits and maximises social welfare (SW). The second ICA model uses a fuzzy optimisation technique to simultaneously optimise the objectives of SW maximisation and GHG emission minimisation. Both ICA models are tested on two datasets with 10 and 338 capacity supply offers constructed using Ontario data. While both models control GHG emissions as desired, the ICA model with fuzzy optimisation is shown to find a better balance between maximising net SW and minimising GHG emissions, with superior reductions in GHG for minor decreases in SW. Results demonstrate how GHG emission reduction results in increased selection of low carbon generation.

Background During the COVID-19 pandemic, many intensive care units (ICUs) halted research to focus on COVID-19-specific studies. Objective To describe the conduct of an international randomized trial of stress ulcer prophylaxis ( R e- Ev aluating the I nhibition of S tress E rosions in the ICU [REVISE]) during the pandemic, addressing enrolment patterns, center engagement, informed consent processes, data collection, a COVID-specific substudy, patient transfers, and data monitoring. Methods REVISE is a randomized trial among mechanically ventilated patients, comparing pantoprazole 40 mg IV to placebo on the primary efficacy outcome of clinically important upper gastrointestinal bleeding and the primary safety outcome of 90-day mortality. We documented protocol implementation status from March 11th 2020-August 30th 2022. Results The Steering Committee did not change the scientific protocol. From the first enrolment on July 9th 2019 to March 10th 2020 (8 months preceding the pandemic), 267 patients were enrolled in 18 centers. From March 11th 2020-August 30th 2022 (30 months thereafter), 41 new centers joined; 59 were participating by August 30th 2022 which enrolled 2961 patients. During a total of 1235 enrolment-months in the pandemic phase, enrolment paused for 106 (8.6%) months in aggregate (median 3 months, interquartile range 2;6). Protocol implementation involved a shift from the a priori consent model pre-pandemic (188, 58.8%) to the consent to continue model (1615, 54.1%, p  < 0.01). In one new center, an opt-out model was approved. The informed consent rate increased slightly (80.7% to 85.0%, p  = 0.05). Telephone consent encounters increased (16.6% to 68.2%, p  < 0.001). Surge capacity necessitated intra-institutional transfers; receiving centers continued protocol implementation whenever possible. We developed a nested COVID-19 substudy. The Methods Centers continued central statistical monitoring of trial metrics. Site monitoring was initially remote, then in-person when restrictions lifted. Conclusion Protocol implementation adaptations during the pandemic included a shift in the consent model, a sustained high consent rate, and launch of a COVID-19 substudy. Recruitment increased as new centers joined, patient transfers were optimized, and monitoring methods were adapted.