Reference Scenario Research Articles

A Quantitative Risk Assessment Model for Listeria monocytogenes in Non-Ready-to-Eat Frozen Vegetables

A quantitative risk assessment (QRA) model was developed to evaluate the risk of invasive listeriosis from the consumption of non-ready-to-eat (non-RTE) frozen vegetables. On a lot basis, the QRA model simulates Listeria monocytogenes concentration and prevalence in a “Processing module” that comprises blanching, potential recontamination and packaging, any post-packaging inactivation treatment, and within-lot end-product testing and in a subsequent “Consumer’s handling module” that encompasses portioning of frozen vegetables, defrosting, and cooking. Based on available published data, the model was coded in nine sequential R functions designed to assess the effectiveness of blanching, the improvement in processing environment hygiene, the implementation of sampling schemes at the end of processing, and improved consumer instructions on the product’s package. In a reference scenario, the model estimated that 9.4% of 500 g packages of frozen vegetables would be contaminated, although at mean levels lower than 10 CFU/g, and assuming that 20% of the portions of frozen vegetables would be left to thaw at room temperature for 2 h, the lot-level mean risk of listeriosis in the susceptible population would be 2.935 × 10−14 (median 5.446 × 10−15) for uncooked 50 g servings and 2.765 × 10−17 (median 5.184 × 10−18) for cooked 50 g servings. Analysis of selected scenarios suggested that not cooking the non-RTE product contributes to the risk to a greater extent than the level of contamination in the incoming raw vegetables, the latter in turn being more influential than the level of contamination in the processing environment. The QRA model is freely available as an R package with full documentation and can be used as a tool to inform the consideration of strengthened risk management measures in view of the current changes in consumer behavior and new diet trends.

Unveiling the potential for decarbonization of the building sector: A comparative study of technological and non-technological low-carbon strategies

There is an urgent need to mitigate carbon emissions in the building sector, particularly from existing buildings. The existing literature focuses predominantly on technological strategies such as low-carbon materials. This prompts the question: Can technological strategies alone drive the decarbonization of buildings, or are non-technological strategies also essential? Although recent research considers the benefits of the latter, studies assessing the potential of non-technological strategies for decarbonization of buildings are lacking because of the challenges involved in evaluating the indirect impacts and potential trade-offs associated with these strategies such as their ripple effects on mobility. This study pioneers a comparative assessment to evaluate the environmental mitigation potential of non-technological strategies (adaptation, a subset of the sharing economy, and behavioral changes) against technological strategies (low-carbon materials, retrofitting, and recycled materials) to ascertain the effectiveness of non-technological approaches. Through life cycle assessment, this study extends beyond solely evaluating the GHG reduction potential to assess the overall environmental mitigation capacity. A single-family house in Montreal was used as a reference scenario. With significant mitigation potential observed from a non-technological perspective, the results robustly reveal that the adaptation scenario surpasses all scenarios, including retrofitting, which is the primary mitigation strategy for existing buildings, by up to 50 % and 41 % at the midpoint and damage levels, respectively. Furthermore, the adaptation scenario potentially provides sufficiency by saving considerable amounts of material and energy, thereby alleviating the environmental impact of the production and use stages by up to 27 % and 15 %, respectively. This study also evaluates the combined effects of adaptation and retrofitting for existing buildings, revealing by up to 8 % greater environmental benefits at the midpoint and damage levels than in the adaptation scenario individually. These results highlight the potential of non-technological strategies that are currently overlooked in the building sector. However, their implementation requires fewer resources and less energy than technological changes. Therefore, further investigation is warranted to explore how adopting these strategies, along with technological ones, is advantageous.

Health and air pollutant emission impacts of net zero CO2 by 2050 scenarios from the energy modeling forum 37 study

Carbon dioxide and non-greenhouse gas air pollutants are emitted from many of the same sources. Decarbonization actions thus typically yield air pollutant emission reductions, resulting in significant air quality benefits. Although several studies have highlighted this connection, including in the context of net zero carbon emission targets, substantial uncertainty remains regarding how alternative technological pathways to this goal will affect the spatial distribution and magnitude of air pollutants. Comprehensive multi-model and multi-scenario analyzes are needed to explore the relative impacts of alternative pathways. Our study begins to address this gap by leveraging the results from the recent Energy Modeling Forum 37 inter-model comparison exercise on U.S. decarbonization pathways. Comparing the results of the six teams who submitted air pollutant emissions suggests that strategies that target net zero U.S. carbon emissions would yield significant reductions in many air pollutants, and that this finding is generally robust across pathways. However, some energy sources, such as biomass and fossil fuels with carbon capture, will emit air pollutants and can potentially influence the magnitude, spatial distribution, and even sign of localized air pollutant emission changes. In the second part of this analysis, a simplified air quality and health impacts screening model is used to evaluate the air quality impacts in 2035 of sectoral emission changes from the three models that provided sectoral detail. Relative to a reference scenario, a net zero pathway is estimated to reduce fine particulate matter concentrations across the contiguous U.S., with health benefits from reduced mortality ranging from $65 billion to $250 billion in 2035 alone (2023$s). These benefits would be expected to grow over time as the net zero trajectory becomes more stringent. Both the magnitude of potential benefits and the substantial variation of the projections across models underscore the need for an EMF-like inter-model comparison exercise focused on air quality.

Exploring the demand for inter-annual storage for balancing wind energy variability in 100% renewable energy systems

As research in 100% renewable energy systems progresses, closing remaining research gaps, such as quantifying inter-annual balancing requirements, is necessary. For this study, inter-annual variations of wind power yield and the resulting balancing requirements are analysed for the energy transition towards 100% renewable energy of the United Kingdom and the Republic of Ireland. The energy system components are determined and cost implications are quantified comparing two options for inter-annual storage: e-hydrogen and e-methane for a low, medium, and high security case, which are expanded over ten years. The results indicate that more than 300 TWhth,LHV and 500 TWhth,LHV of inter-annual gas storage capacity in 2050 for the low and the high security case, respectively are required. For both investigated cases, the annual system costs increase notably compared to the reference scenario without inter-annual storage. For the case of e-hydrogen, the annualised system costs increase by 19% compared to an increase of 7% for e-methane for medium security cases. The difference in cost growth is due to significantly higher hydrogen underground storage costs that overcompensate additional system costs for e-methane production. This study provides an expansion from seasonal to inter-annual storage and a first estimate of inter-annual balancing requirements and costs.

Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review

Abstract Recent studies have focused on different aspects of green management, practices, and green consumption in the hotel industry. However, there is a need to explore and better understand the association between indoor environmental quality (IEQ) and green hotel guest’s comfort. Therefore, it is essential to explore the effects of IEQ on the comfort and satisfaction of green hotel guests. This study conducts a comprehensive review of the effects of various IEQ parameters, including indoor air quality, thermal comfort, lighting, visual/view, acoustic comfort, building characteristics, decoration, and indoor greenery, on guest’s comfort and satisfaction in green hotels. Based on previous literature, it was also revealed that most current green building schemes lack of comprehensive evaluation of the performance of IEQ dimensions in green hotels. It was also observed that these IEQ parameters show a significant influence on the hotel guest’s comfort and satisfaction. Based on the findings of the literature review, a conceptual model was developed to represent the relationship between the IEQ parameters and guest’s comfort and satisfaction. The proposed conceptual model can be implemented by the hotel management for a comprehensive assessment of guests’ perceptions toward the IEQ in green hotels. The novelty of this study is based on its findings that establish a more effective IEQ evaluation method and serve as the reference scenario of IEQ, which can be a useful tool for both academician and practitioners and contribute to improving the indoor environmental performance of green hotels through highlighting the key IEQ parameters, which affect the comfort and satisfaction of hotel guests.

Systems modelling and simulation to guide targeted investments to reduce youth suicide and mental health problems in a low–middle-income country

BackgroundDespite suicide’s public health significance and global mental health awareness, current suicide prevention efforts show limited impact, posing a challenge for low- and middle-income countries. This study aimed to develop a dynamic simulation model that could be used to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior in Bogotá, Colombia.MethodsA system dynamics model was designed using a participatory approach involving three workshops conducted in 2021 and 2022. These workshops engaged 78 stakeholders from various health and social sectors to map key mental health outcomes and influential factors affecting them. A model was subsequently developed, tested, and presented to the participants for interactive feedback, guided by a moderator. Simulation analyses were conducted to compare projected mental health outcomes for a range of intervention scenarios with projections for a reference scenario corresponding to business-as-usual.ResultsA total of 6670 suicide attempts and 347 suicides are projected among 7 − 17-year-olds from January 1, 2023, to early 2031 under the business-as-usual scenario. Mental health issues among 12 − 17-year-olds are projected to increase from 18.9% (2023) to 27.8% (2031), and substance use issues from 2.29 to 2.49% over the same period. School-based suicide prevention and gatekeeper training are the most effective strategies, reducing total numbers of suicide attempts and suicides by more than 20% (i.e., compared to business-as-usual). However, discontinuous funding significantly hinders these effective suicide prevention efforts.ConclusionsSystems modelling is an important tool for understanding where the best strategic financial and political investments lie for improving youth mental health in resource-constrained settings.

Multi-objective simulation–optimization for water resources management and uses in multi-dam systems in low-water regions

One of the challenging issues in surface water resources management is the optimal operation of multi-dam systems. In addition to the applicability in determining the optimal pattern of operation of existing dams, this approach can be implemented in specifying the optimal capacity of under design dams. In this research, the simulation–optimization technique is used along with the reservoir zoning method in order to specify the optimal role curve of each of the dams in a multi-dam system (i.e., the Marun and Jarreh dams) in monthly intervals. The objective of this study is to develop a multi-objective algorithm on the basis of the zoning of reservoirs for the optimal operation of multi-dam systems. In this technique, instead of increasing the reliability of supplying demands in the whole period regardless the dry months, some of the inflow of rainy periods is stored in dam reservoirs to be consumed in dry months in order to mitigate the severity of deficiency. To examine the efficiency of the proposed model, the results obtained from the system operation in the current conditions are compared under two scenarios including optimal and reference. In the reference scenario, during 30-years, in some dry and low-water years, especially in the last six years of planning, the percentage of the demand supply is zero or less than five percent in several consecutive months. After optimizing the system, the minimum supply percentage in critical and dry months reaches 30 to 60 percent. Also, in the optimization scenario, the percentage of downstream ecological demands is improved. This research indicates that using the solution of this research leads to the better management of reservoirs in multi-dam systems and reduces the severity of deficiency in supplying various uses in low-water months.

An Entanglement-Aware Middleware for Digital Twins

The development of the Digital Twin (DT) approach is tilting research from initial approaches that aim to promote early adoption to sophisticated attempts to develop, deploy, and maintain applications based on DTs. In this context, we propose a highly dynamic and distributed ecosystem where containerized DTs co-evolve with an orchestration middleware. DTs provide digitalized representations of the targeted physical systems, while the orchestration middleware monitors and re-configures the deployed DTs in light of application constraints, available resources, and the quality of cyber-physical entanglement. First, we lay out the reference scenario. Then, we discuss the limitations of current approaches and identify a set of requirements that shape both DTs and the orchestration middleware. Subsequently, we describe a blueprint architecture that meets those requirements. Finally, we report empirical evidence on both the feasibility and the effectiveness of a proof-of-concept implementation of the proposed ecosystem.

Pilot-scale nanofiltration and techno-economic evaluation of lignin recovery from kraft black liquor

This pilot-scale study investigates the possibility of using nanofiltration after ultrafiltration to improve lignin recovery from kraft black liquor in a pulp mill. The nanofiltration step can increase overall lignin recovery and provide a clean permeate that could be used in the smelt dissolving tank to produce green liquor. Nanofiltration of kraft black liquor ultrafiltration permeate was performed under two operating conditions: (A) 50 °C at 25 bar, and (B) 70 °C at 15 bar, which led to a lignin retention of 82% and 75%, respectively. Experimental data from condition A were used in a techno-economic analysis where four scenarios were evaluated: (1) a reference scenario, without lignin separation; (2) a scenario in which the ultrafiltration permeate was returned to the evaporators; (3) a scenario including ultrafiltration and nanofiltration, where the nanofiltration permeate was sent to the smelt dissolving tank; and (4) a scenario including ultrafiltration and nanofiltration, where the nanofiltration retentate was sent to the evaporators and the nanofiltration permeate to the smelt dissolving tank. The reduced energy consumption of the evaporators in scenario 4 resulted in an increase in sold electrical power of 19.4 GWh/y, giving a pay-back time on the investment in the nanofiltration plant of 2.2 years.

Edge-device collaborative computing for multi-view classification

Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore collaborative inference at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce selective schemes that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.

Prediction of land use/land cover and environmental estimation of carbon stocks in the Atlantic forest: A study in the state of Sergipe, Brazil

The forest ecosystems of the Atlantic Forest biome in Brazil are experiencing intense deforestation, contributing to climate change. Valuing the ecosystem service of CO2 sequestration can help in environmental policies to reduce deforestation and promote ecological restoration in this region. In this study, we sought to understand the dynamics of land use and land cover in the Atlantic Forest region of the state of Sergipe between 1985 and 2021 and to simulate land use and land cover for 2031 and 2051, assuming the current business as usual (BAU) trend was used as a reference scenario. The results showed that the agriculture-pasture mosaic class obtained the greatest percentage (5.27 %) and net (around 514.00 km²) increase among the land use and cover classes evaluated in the study area between 1985 and 2021. In the BAU scenario, total emissions reach 1,137,942.07 Mg CO2 in 2031 and 692,815.16 Mg CO2 in 2051, imposing an opportunity cost for society of US$ 7.94 million and US$ 4.35 million, respectively.

Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Smoking is the leading behavioural risk factor for mortality globally, accounting for more than 175 million deaths and nearly 4·30 billion years of life lost (YLLs) from 1990 to 2021. The pace of decline in smoking prevalence has slowed in recent years for many countries, and although strategies have recently been proposed to achieve tobacco-free generations, none have been implemented to date. Assessing what could happen if current trends in smoking prevalence persist, and what could happen if additional smoking prevalence reductions occur, is important for communicating the effect of potential smoking policies. In this analysis, we use the Institute for Health Metrics and Evaluation's Future Health Scenarios platform to forecast the effects of three smoking prevalence scenarios on all-cause and cause-specific YLLs and life expectancy at birth until 2050. YLLs were computed for each scenario using the Global Burden of Disease Study 2021 reference life table and forecasts of cause-specific mortality under each scenario. The reference scenario forecasts what could occur if past smoking prevalence and other risk factor trends continue, the Tobacco Smoking Elimination as of 2023 (Elimination-2023) scenario quantifies the maximum potential future health benefits from assuming zero percent smoking prevalence from 2023 onwards, whereas the Tobacco Smoking Elimination by 2050 (Elimination-2050) scenario provides estimates for countries considering policies to steadily reduce smoking prevalence to 5%. Together, these scenarios underscore the magnitude of health benefits that could be reached by 2050 if countries take decisive action to eliminate smoking. The 95% uncertainty interval (UI) of estimates is based on the 2·5th and 97·5th percentile of draws that were carried through the multistage computational framework. Global age-standardised smoking prevalence was estimated to be 28·5% (95% UI 27·9-29·1) among males and 5·96% (5·76-6·21) among females in 2022. In the reference scenario, smoking prevalence declined by 25·9% (25·2-26·6) among males, and 30·0% (26·1-32·1) among females from 2022 to 2050. Under this scenario, we forecast a cumulative 29·3 billion (95% UI 26·8-32·4) overall YLLs among males and 22·2 billion (20·1-24·6) YLLs among females over this period. Life expectancy at birth under this scenario would increase from 73·6 years (95% UI 72·8-74·4) in 2022 to 78·3 years (75·9-80·3) in 2050. Under our Elimination-2023 scenario, we forecast 2·04 billion (95% UI 1·90-2·21) fewer cumulative YLLs by 2050 compared with the reference scenario, and life expectancy at birth would increase to 77·6 years (95% UI 75·1-79·6) among males and 81·0 years (78·5-83·1) among females. Under our Elimination-2050 scenario, we forecast 735 million (675-808) and 141 million (131-154) cumulative YLLs would be avoided among males and females, respectively. Life expectancy in 2050 would increase to 77·1 years (95% UI 74·6-79·0) among males and 80·8 years (78·3-82·9) among females. Existing tobacco policies must be maintained if smoking prevalence is to continue to decline as forecast by the reference scenario. In addition, substantial smoking-attributable burden can be avoided by accelerating the pace of smoking elimination. Implementation of new tobacco control policies are crucial in avoiding additional smoking-attributable burden in the coming decades and to ensure that the gains won over the past three decades are not lost. Bloomberg Philanthropies and the Bill & Melinda Gates Foundation.

The cost of CO2 emissions abatement in a micro energy community in a Belgian context

This paper investigates and quantifies the benefits and the cost of CO2 emissions abatement of a Micro Energy Community (MEC) in a tiny residential cluster of three houses in a Belgian context. A simulation-based comparative analysis is performed of two individual and two MEC concepts, i.e. a reference scenario, an individual electrification scenario, an electricity sharing scenario, and an energy community scenario. A deterministic dynamic white-box modelling approach, including optimal control, is used, considering heat for space heating and domestic hot water, and electricity for electrical appliances. The energy system that achieves the greatest emission reduction at the lowest cost, has a collective heat pump and a photovoltaic installation sized based on the plug load demand. This system reduces the annual CO2 emissions by 11.48 tons at a cost of 179 EUR/ton CO2 considering 2021 dynamic retail electricity prices with a median of 251 EUR/MWh and a fixed retail gas price of 64 EUR/MWh. However, the results are strongly dependent on the gas-electricity price ratio. The highest value of the retail gas price in 2021 leads to a negative CO2 emissions abatement cost of 154 EUR/ton CO2, putting energy community concepts on the radar for a cost-effective energy transition.

Representative CO2 emissions pathways for China's provinces toward carbon neutrality under the Paris Agreement's 2 °C target

China has committed to achieving carbon neutrality by 2060. It is essential to develop representative CO2 emissions pathways at the provincial level that align with the national target to facilitate effective policy implementation and scientific research. To address inconsistencies between provincial aggregate emissions and national estimates, this study compares the 2021 CO2 emissions estimates of China's provinces from the bottom‒up emissions factor method and the top‒down atmospheric CO2 concentration inversion method. We find that these methods yield comparable results for the emissions from energy combustion and industrial processes at the provincial level. Based on a review of existing research on CO2 pathways for China's provinces, we propose a set of representative pathways for China's provinces. These pathways align with past practices of allocating national emissions intensity reduction targets to provinces and are consistent with the national Tsinghua‒CMA pathway. The proposed pathways require provinces to sequentially peak their emissions by 2030, followed by rapid emissions reduction. Compared to a reference scenario without the carbon neutrality target, these pathways would incur an estimated cumulative GDP loss of about 1% between 2020 and 2060. However, there are notable regional variations, with some regions in the northwest potentially experiencing higher economic growth due to the availability of high-quality low-carbon resources. We recommend that China enhance provincial-level CO2 emissions accounting by cross-validating bottom‒up and top‒down methods. Additionally, careful consideration should be given to aligning provincial targets with national and global commitments when updating pathways toward carbon neutrality.

Assessment on Water Resource Management for Sedawgyi Dam: A WEAP Analysis Approach

This study employs the Water Evaluation and Planning (WEAP) system to analyze water resource management for the Sedawgyi Dam in Myanmar. The research integrates hydrological data, demand projections, and infrastructure information to simulate current and future water scenarios. Key steps include model setup, calibration, scenario development, and analysis. The results indicate that Mandalay City's highest domestic water demands are in Chanmyatharzi and Aungmyaetharzan townships, with agricultural water demands peaking in Mattaya township. The reference scenario highlights a significant domestic water supply shortfall, meeting only 10% of the demand, while agricultural demands are nearly fully met. The study underscores the urgent need for improved water management strategies to address rising unmet water demands, particularly for domestic use, to ensure sustainable water resource allocation in the region.

Improving the accuracy of an integrated watershed-reservoir water quality modeling approach (SWAT and CE-QUAL-W2) in data scarce watersheds

Watershed-reservoir modeling is an essential tool in lake and reservoir management. Despite its importance, accurately modeling these systems remains challenging due to their inherent complexity and the scarcity of reliable forcing data. This study focuses on a watershed and reservoir located in northern Portugal, where data scarcity presents significant challenges for accurate modeling. Therefore, the primary goal of this study was to improve the prediction capability of an integrated watershed-reservoir water quality modeling solution and to establish a robust methodology that can be effectively applied to other cases. The modelling solution proposed in this study computes a daily ratio (per water quality variable) between the watershed reference and load reduction modeling scenarios, obtained with the SWAT model, which is then applied to the observed forcing water quality data of a reservoir water quality model (CE-QUAL-W2). The load reduction scenario increases the watershed municipal wastewater treatment plant’s efficiency from secondary to tertiary and applies a haul-off scenario to the livestock manure, reducing the reservoir input load of Total P, Total N, and BOD5 in the reservoir by 87%,86%, and 83%, respectively. The results obtained with the classical model solution provided an inaccurate conclusion regarding the load reduction scenario output that was corrected with the proposed modeling approach. Overall, after the correction, the load reduction scenario served to considerably improve the reservoir water quality by reducing the Total P and the phytoplankton biomass by 39% and 72%, respectively, while increasing the DO concentration by 27%. The results of this study show that the proposed modeling approach improves the performance of integrated watershed-reservoir modeling systems in data scarce settings and can serve as a useful tool to support decision-making with regard to water resource management and planning at regional and national level.

The role of the pulp and paper industry in achieving net zero U.S. CO2 emissions in 2050

The pulp and paper industry is energy-intensive, making up about 9 % of total United States industrial energy consumption and 2.5 % of U.S. industrial greenhouse gas emissions. The pulp and paper industry is unique among industrial sectors due to its existing reliance on waste biomass to generate energy for mill operations. Pulp and paper mills could theoretically offer opportunities for negative emissions through carbon capture and storage (CCS) technologies along with use of biomass. In addition, the paper sector's use of low-temperature industrial heat creates opportunities for CO2 reductions through electrification technologies.We employ the Global Change Analysis Model (GCAM) to evaluate decarbonization pathways for the pulp and paper sector in the United States, as well as the sector's role in a net zero scenario and impacts on the energy, land, and water sectors. The version of GCAM used in this study includes detailed representation of major industrial sectors, including the pulp and paper industry. Representation of the linkage between forest products and paper production allow us to account for upstream carbon emissions, sequestration, and land-use impacts.Preliminary results under a pathway to net zero U.S. CO2 emissions in 2050 show that the pulp and paper industry can reach net zero CO2 emissions before 2050, earlier than the overall energy system, and contribute negative emissions thereafter. Use of fossil fuels is significantly reduced by 2050, shifting to increased electricity use in process heat generation. Consumption of biomass energy in process heat also increases compared to the reference scenario. Though paper production decreases in the policy scenario in response to higher prices of wood products, a high carbon price can incentivize increased use of biomass with CCS and thus paper production. Negative emissions opportunities in the paper industry have impacts on the land sector. Increasing use of biomass accelerates the shift from unmanaged to managed forests, with associated tradeoffs between technological carbon sequestration and natural ecosystem services.

Loss-of-heat-sink transient simulation with RELAP5/Mod3.3 code for the ATHENA facility

ATHENA (Advanced Thermal-Hydraulic Experiment for Nuclear Applications) is a large multipurpose pool-type lead-cooled facility under construction at the Mioveni site in Romania. It has been identified by the FALCON (Fostering ALfred CONstruction) Consortium to characterize large to full-scale ALFRED components, to conduct integral tests, and to investigate the main thermal–hydraulic phenomena inherent in pool-type systems. ATHENA is representative of ALFRED in terms of the difference in height of the thermal barycenters of the heat source and heat sink, i.e., 3.3 m, in order to reproduce the buoyancy forces in the system. Similar to ALFRED’s design, ATHENA minimizes thermal stratification within the main vessel even under natural circulation conditions, through an internal structure referred to as “barrel”. This structure directs the fluid flow towards the main vessel, preventing fluid stagnation near the vessel itself. The paper initially provides a steady-state thermal–hydraulic characterization of the facility, including details of the numerical model developed using the RELAP5/Mod3.3 thermal–hydraulic code. Then, focus is given to the transient analysis considering as a reference scenario a Loss-of-Heat-Sink (LOHS) accidental transient. In this scenario, the Main Circulation Pump (MCP) is assumed to remain operational while the Core Simulator (CS) is deactivated once the lead temperature at the Main Heat Exchanger (MHX) outlet reaches a predefined threshold. A sensitivity analysis is conducted with set points of 430 °C, 450 °C, 470 °C, and 490 °C, assessing the system’s response following MHX isolation from the secondary loop. The study evaluates the impact of different CS deactivation set points on reactor SCRAM delay (reducing CS power to a level representative of decay heat) as well as on system maximum and minimum temperatures.

Demand-side management scenario analysis for the energy-efficient future of Pakistan: Bridging the gap between market interests and national priorities

Pakistan is facing the worst level of energy and economic crisis of its history. The underlying reason of the economic crisis is basically due to the energy crisis. Various approaches have been adopted to tackle the energy crises which the country is facing for over 2 decades. Demand-side management (DSM) is the most potent and cost-effective option to redress the energy crisis, which, unfortunately, has been the most neglected strategy in Pakistan. DSM has the potential to save up to 10.0%–15.0% of primary energy to ensure the country’s future energy security. Laws and codes do exist, but ironically, the most vital element of DSM’s policy framework and implementation mechanism is missing. Hence, the main objective of this research is to develop a DSM model for Pakistan and analyze the reference and DSM scenarios. Low-emission analysis platform (LEAP) is used to develop Pakistan’s DSM model for the study period 2021–2050. The three alternative DSM scenarios developed include energy efficiency (EEF), energy conservation (EC), and load management (LOM), and they are all analyzed. The model results estimate the electricity demand forecast of 1009.8 TWh under the reference scenario in 2050, whereas DSM tends to result in a 26.38% decreased electricity demand compared to the reference scenario. The alternative DSM scenarios also outperform the reference scenario. In 2050, the EC scenario reduces consumption by 178.0 TWh and GHG emissions by 19.20 million metric tons, the EEF scenario reduces consumption by 110.30 TWh and GHG emissions by 10.04 million metric tons, and the LOM scenario suggests reduced consumption by 101.0 TWh and GHG emissions by 6.20 million metric tons. This study concluded that the DSM must be institutionalized in Pakistan by building a robust regulatory framework and execution mechanism at the government and utility levels.

Assessing head injury risks in electric scooter accidents: A multi-body simulation study with insights into sex differences

E-scooters have become increasingly popular for short-distance travel in urban areas, but this rise in usage also brings about an increased risk of accidents. Studies have shown that approximately 40% of electric scooter accident victims admitted to hospitals suffer head injuries. Therefore, it is crucial to implement safety measures and improve safety systems and equipment to mitigate these risks. One approach to gaining insights into the injuries users face is through simulations using the multi-body method. This method allows for the reconstruction of accidents by modeling and analyzing the dynamic behavior of interconnected bodies. This study aims to assess the impacts on the user’s head and the injuries they may sustain in electric scooter accidents using numerical methods. Initially, a reference scenario was established based on a YouTube video, with the assumption that the user was an average-height man. Simulations were conducted for various percentiles, including both males and females. Different velocities were simulated to determine the threshold velocity at which survival becomes practically impossible. Two scenarios were considered: one where the car braked for 0.333 s and another where the distance between the start the braking task and the collision was kept constant. The location of the first head impact on the vehicle was also examined. Injury assessment was conducted using two criteria: Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC). The study found that smaller individuals are more vulnerable to severe injuries, and higher car velocities correlate with more severe user injuries. Furthermore, the location of the first impact varies between genders, with women more likely to experience impacts in the lower part of the windshield, while men tend to experience impacts in the central zone. This study highlights the importance of considering user characteristics and accident dynamics in assessing injury risks associated with e-scooters.