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The desire to maintain CO2 concentrations in the global atmosphere implies the need to introduce 'new' energy carriers for transport applications. Therefore, the operational consumption of each such potential medium in the 'natural' exploitation of vehicles must be assessed. A useful assessment method may be the vehicle's energy footprint resulting from the theory of cumulative fuel consumption, presented in the article. Using a (very modest) database of long-term use of hydrogen-powered cars, the usefulness of this method was demonstrated. Knowing the energy footprint of vehicles of a given brand and type and the statistical characteristics of the footprint elements, it is also possible to assess vehicle fleets in terms of energy demand. The database on the use of energy carriers, such as hydrogen, in the long-term operation of passenger vehicles is still relatively modest; however, as it has been shown, valuable data can be obtained to assess the energy demand of vehicles of a given brand and type. Access to a larger operational database will allow for wider use of the presented method. [ABSTRACT FROM AUTHOR]

ZrW2O8 (ZrO2•2WO3) and HfW2O8 (HfO2•2WO3) have been the focus of thermal expansion studies due to their isotropic negative thermal expansion (NTE) measured previously at temperatures below 775°C. This work presents measurements of these materials at their thermodynamically stable temperature ranges of 1105 and 1257°C for ZrW2O8 and 1105–1276°C for HfW2O8, where they were characterized with in situ, powder X‐ray diffraction. The linear coefficients of thermal expansion were measured to be −5.52 × 10−6 and −4.87 × 10−6°C−1 for ZrW2O8 and HfW2O8, respectively. The mechanism leading to this NTE is discussed. Powder samples were synthesized by a solution‐based process called the organic–inorganic steric entrapment method. In situ characterization in air was carried out at the National Synchrotron Light Source II using a hexapole lamp, optical furnace and the Advanced Photon Source using a quadrupole lamp, optical furnace to achieve elevated temperatures. [ABSTRACT FROM AUTHOR]

Large-scale disasters have exposed vulnerabilities in energy systems and interdependent infrastructure, underscoring the importance of proactively mitigating risks to critical infrastructure. This paper focuses on strengthening power system planning by incorporating the costs associated with prolonged and extensive power interruptions to bolster resilience. To achieve this, duration-dependent customer damage functions are developed, capturing the direct, indirect, and societal impacts of power interruptions, ranging from short, localized events to widespread and long-duration ones. The research methodology is applied to three islanded communities facing substantial resilience challenges with limited data availability. Three customer interruption cost surveys are conducted with local electricity customers, yielding valuable insights into duration-dependent customer damage functions for direct, indirect, and monetizable societal costs. Significantly, the power interruption cost estimates derived from these functions vary considerably from those in the contiguous U.S. and even between the different islanded communities, reflecting their distinct hazard profiles and geographical characteristics. In conclusion, this study demonstrates the potential of duration-dependent customer damage functions to enhance power system resilience. It also identifies several areas for further research, paving the way for a more robust and resilient power infrastructure. [ABSTRACT FROM AUTHOR]

The interactions between the terrestrial biosphere, atmospheric chemistry, and climate involve complex feedbacks that have traditionally been modeled separately. We present a new framework that couples the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic plant-chemistry model, with the RegCM-Chem model. RegCM-Chem–YIBs integrates meteorological variables and atmospheric chemical composition from RegCM-Chem with land surface parameters from YIBs. The terrestrial carbon flux calculated by YIBs is fed back into RegCM-Chem interactively, thereby representing the interactions between fine particulate matter (PM 2.5), ozone (O 3), and carbon dioxide (CO 2). For testing purposes, we carry out a 1-year simulation (2016) at a 30 km horizontal resolution over East Asia with RegCM-Chem–YIBs. The model accurately captures the spatio-temporal distribution of climate, chemical composition, and ecological parameters. In particular, the estimated O 3 and PM 2.5 are consistent with ground observations, with correlation coefficients (R) of 0.74 and 0.65, respectively. The simulated CO 2 concentration is consistent with observations from six sites (R ranged from 0.89 to 0.97) and exhibits a similar spatial pattern when compared with carbon assimilation products. RegCM-Chem–YIBs produces reasonably good gross primary productivity (GPP) and net primary productivity (NPP), showing seasonal and spatial distributions consistent with satellite observations, and mean biases (MBs) of 0.13 and 0.05 kg C m -2 yr -1. This study illustrates that RegCM-Chem–YIBs is a valuable tool to investigate coupled interactions between the terrestrial carbon cycle, atmospheric chemistry, and climate change at a higher resolution on a regional scale. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive assessment of the offshore wind energy potential in Mexico across 40 years (1979–2018) of numerical simulations using the Weather Research and Forecasting (WRF) model and data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The WRF identifies three regions with moderate to good wind potential: off the north coast of Tamaulipas (Zone I), the northwest coast of Yucatan (Zone II), and the Gulf of Tehuantepec (Zone III). The analysis involves comparing 47 CMIP6 climate models with the WRF results and selecting the best performing models to obtain future projections for the short term (2040–2069) and the long term (2070–2099). Two ensemble-based strategies were implemented. The first one, which uses an intersection approach from which four CMIP6 models were considered, reveals positive percentage differences in Zone II for both future projections, especially for the long-term one. In Zones I and III, positive values are also observed near the coast, mainly for the long-term projection, but they are considerably lower compared to those in Zone II. The second ensemble strategy uses weight assignment through the Mean Absolute Percentage Error, so that a greater weight is given to the model that performed better in each particular zone, potentially providing more accurate results. The findings suggest the likelihood of increased offshore wind energy in these three zones of Mexico, for both short- and long-term future projections, with positive percentage differences of up to 10% in certain areas. [ABSTRACT FROM AUTHOR]

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics. Plain Language Summary: Estimating air temperature at street‐level is a difficult task because of the complex environment in cities and the limitations of the current urban numerical models. In recent years, with the rapid development of data collection and analysis techniques, it is possible to fully utilize the hyper‐local data harvested from urban areas by advanced machine learning algorithms. In this study, we present a modeling pipeline to estimate point‐scale street‐level air temperature from conventional urban weather model and a suite of hyper‐resolution urban data sets. These data sets were collected using state‐of‐art techniques, such as sub‐meter level Light Detection and Ranging technology and wireless weather observation network. We demonstrated the use of this modeling method over the City of Chicago and estimated the street‐level temperature. The modeling results have multiple real‐world applications, such as to provide navigation suggestions to reduce thermal discomfort of the pedestrians as an example. Moreover, it is possible to expand the use of our model to other areas given the current data availability. The results of this study can also help the development of the next‐generation urban climate and weather models and guide observation efforts in cities. These together can build the strength for the holistic understanding of urban microclimate dynamics. Key Points: The study presents a modeling framework to estimate street‐level air temperature using a suite of detailed urban climate informaticsModel results showed hyper‐local urban features have significant impacts on street‐level temperature but with a limited influence radiusThe investigations on model sensitivity implies the existence of the optimum scale in urban modeling and critical locations in observation [ABSTRACT FROM AUTHOR]

A method to optimize the thermal deformation of an indirectly cryo-cooled silicon crystal monochromator exposed to intense X-rays at a low-emittance diffraction-limited synchrotron radiation source is presented. The thermalinduced slope error of the monochromator crystal has been studied as a function of heat transfer efficiency, crystal temperature distribution and beam footprint size. A partial cooling method is proposed, which flattens the crystal surface profile within the beam footprint by modifying the cooling contact area to optimize the crystal peak temperature. The optimal temperature varies with different photon energies, which is investigated, and a proper cooling strategy is obtained to fulfil the thermal distortion requirements over the entire photon energy range. At an absorbed power up to 300 W with a maximum power density of 44.8 W mm-2 normal incidence beam from an in-vacuum undulator, the crystal thermal distortion does not exceed 0.3 mrad at 8.33 keV. This method will provide references for the monochromator design on diffraction-limited synchrotron radiation or free-electron laser light sources. [ABSTRACT FROM AUTHOR]

The hydrogen fuel cell vehicle is rapidly developing in China for carbon reduction and neutrality. This paper evaluated the life-cycle cost and carbon emission of hydrogen energy via lots of field surveys, including hydrogen production and packing in chlor-alkali plants, transport by tube trailers, storage and refueling in hydrogen refueling stations (HRSs), and application for use in two different cities. It also conducted a comparative study for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs). The result indicates that hydrogen fuel cell vehicle (FCV) has the best environmental performance but the highest energy cost. However, a sufficient hydrogen supply can significantly reduce the carbon intensity and FCV energy cost of the current system. The carbon emission for FCV application has the potential to decrease by 73.1% in City A and 43.8% in City B. It only takes 11.0%–20.1% of the BEV emission and 8.2%–9.8% of the ICEV emission. The cost of FCV driving can be reduced by 39.1% in City A. Further improvement can be obtained with an economical and "greener" hydrogen production pathway. [ABSTRACT FROM AUTHOR]

Photon deep penetration calculations were conducted to create a buildup factor database for a new point kernel code. The calculations included the effects of photonuclear reactions, which were not considered in previous buildup factor calculations, and the target energy was extended up to 30 MeV. For media with large photonuclear cross sections, the contribution of the neutron dose becomes significant. For example, in iron at 80 mean free paths, the total dose is 1024 times larger than when the photonuclear reactions are not considered. On the other hand, it was found that if photonuclear reactions are ignored, the calculation results are not necessarily conservative in media with a significant neutron shielding capacity, such as water. [ABSTRACT FROM AUTHOR]

Reducing environmental impacts in transport motivates many studies to offer more sustainable freight services. However, most methodologies focus on impacts from fuel consumption, and approaches trying to integrate other transport components have not facilitated its application to actual and specific transport services. In this study, we present a harmonized approach to address the transport services with a holistic way to increase the knowledge about hotspots of the transport sector based on the life cycle assessment methodology. In this framework, vehicle manufacturing, fuel production, and infrastructure construction are the key transport components around the traffic process. Besides fuel usage, the operation and maintenance of vehicles and infrastructures are also included. We developed a tool to create the life cycle inventories for each transport component to be applied to specific transport services in any location with a comprehensive view and low uncertainty in the results. This approach was applied to road-freight services in Colombia, Malaysia, and Spain. The main results showed the nature and origin of the environmental impacts, which are highly influenced by the emissions control technologies, road characteristics, and traffic volume. The contribution of atmospheric pollutants per tonne-km can decrease by a quarter when Euro VI trucks on highways instead of conventional trucks on single-lane roads are used. However, these contributions are highly affected by fuel production due to the origin of biofuels. The proposed methodology provides relevant information to estimate transport impacts in the life cycle assessment of products with superior precision and identify strategies for systemically improving sustainability. [ABSTRACT FROM AUTHOR]

Battery-based electric vehicles (BEVs) in the United States (U.S.) set a new sales record in 2022, driven by technology, policy, environmental, and economic objectives. However, the rapid deployment of BEVs and charging infrastructure without a careful review of their integration with the electric grid can have negative economic impacts on reliable and resilient electricity supply. Bi-directional power transfer (Bi-Di) vehicle-grid integration technologies and services such as vehicle-to-home or building (V2H/B) and vehicle-to-grid (V2G) can potentially lower local and system peak demand, improve economics for grid operators, and benefit BEV customers. Original equipment manufacturers (OEMs) in the automotive industry are exploring technologies and economics (techno-economics) for Bi-Di services. The study conducted a literature review of eleven case studies in the U.S. and Europe that featured Bi-Di demonstrations from 2005 to 2022 to highlight insights and techno-economic opportunities and challenges for OEMs. The findings should motivate the OEMs to prioritize technology innovation and business models to increase BEV sales and gain continuous revenue from Bi-Di services, which can potentially transition "car makers" to "technology solution" companies. [ABSTRACT FROM AUTHOR]

With the expansion of China's automobile market and the increase in the proportion of electric vehicles, the influence of the automobile industry on water resources has been increasingly, and as a result, water resources will become an important factor restricting the development of the electric vehicle industry in China. Until now, there are still no in-depth studies on the influences of the water footprint of electric vehicles. The paper establishes a life cycle assessment model by which to analyze the reduction potential of the water footprint of various types of passenger vehicles in their operation. The paper also compares the water footprint of passenger vehicles under different power structures, revealing the potential influence of developing electric vehicles on the demand of water resources. The results show that at the base year (2019), the plug-in hybrid electric vehicles and battery electric vehicles consume more water than the gasoline-based internal combustion engine vehicles do, while water consumption of the hybrid electric vehicles and fuel cell vehicles is lower than that of the gasoline-based internal combustion engine vehicles; as for the year 2035, even after the proportion of renewable energy generation increases, the water withdrawal and consumption of the battery electric vehicles and plug-in hybrid electric vehicles will still be larger than those of the gasoline-based internal combustion engine vehicles. [ABSTRACT FROM AUTHOR]

The tropical Pacific decadal variability (TPDV) is an important component of the global interdecadal variability. Previous studies have shown that the TPDV in the sea surface temperature (SST) has two clear patterns, the El Niño–Southern Oscillation (ENSO)‐like and ENSO‐induced patterns, but the pattern of TPDV in the ocean subsurface is still a matter of debate in observations and models. The present study analyses the subsurface TPDV in the simulations of 26 CMIP6 models. The ENSO‐like and ENSO‐induced TPDVs in the subsurface are defined by the regression of the interdecadal anomalies of the oceanic subsurface temperature (Tsub) onto the PCs of two leading EOF modes of the interdecadal SST anomalies in the tropical Pacific. The pattern of the ENSO‐like TPDV in the subsurface shows high model consistency, whereas the ENSO‐induced TPDV in the subsurface has two distinct modes among the models, one with a centre in the central Pacific and the other showing a zonal dipole in the equatorial Pacific. The zonal pattern of the ENSO‐induced TPDV in the subsurface is mainly induced by the SST skewness in the equatorial eastern Pacific, which is further related to the surface heat flux feedback during La Niña. [ABSTRACT FROM AUTHOR]

Environmental impact assessment (EIA) procedures required in the United States and many other countries are often highlighted as a major hindrance to timely and efficient deployment of critical infrastructure projects. Under the U.S. National Environmental Policy Act, a more extensive environmental impact statement (EIS) review can take several more years and cost much more than a succinct environmental assessment (EA). This not only affects the project in question, but also likely informs how—or whether—additional projects are pursued. Thus, understanding key predictors of the EA versus EIS choice sheds light on supply‐side considerations affecting infrastructure deficits. Using the case of NEPA reviews conducted for 244 transmission line projects between 2005 and 2018 by two U.S. federal agencies in the western United States, the Bureau of Land Management (BLM) and Department of Energy (DOE), this addresses the following question: What project features most predict whether EA or an EIS is used to assess a transmission line project? Drawing upon NEPA assessment guidance and agency NEPA records, we use a regression classification tree to analyze how protocols and project attributes relate to assessment choice. The result is essentially a null finding: transmission line length is by far the most important predictor of whether a project receives an extensive EIS or a shorter EA, with little predictive value provided by other attributes. While absolute project size undoubtedly influences impacts, the lack of further differentiation in what predicts use of EISs versus EAs suggests assessment does not simply respond to project details but also shapes proposal and design choices beforehand. [ABSTRACT FROM AUTHOR]

Hookah smoking has become very popular in Iraq among women and men. Hookah tobacco contains natural radioactive elements, such as radon, radium, and uranium, as well as toxic elements, such as polonium, which are released during the combustion of tobacco and are inhaled by smoking. Most reviews focus on hookah tobacco, and only a few have investigated the blood of hookah smokers. In this study, a CR-39 detector was used to measure radon, radium, and polonium concentrations and conduct risk assessments in female hookah smokers of different ages. The results show that the concentrations of radon-222, polonium-218, and polonium-214 varied between 61.62 and 384.80, 5.45–33.64 on the wall of the can, and 2.43–15.00 Bq/m3 on the surface of the detector, respectively. The effective radium-226 concentration varied between 4.52 and 56.31 Bq/kg. The absorbed effective dose varied between 1.55 and 9.71 mSv/y, which is within the recommended limit (3–10 mSv/y) by International Commission on Radiological Protection (ICRP). The average case of lung cancer 107.91 cppp, which exceeds the European Union (EU) limit (96.9–104.8 cppp). The rates of radon activity and radon exhalation from the intake of a natural radionuclide due to hookah smoking in a female's blood were calculated and discussed. This study aimed to establish preliminary results on the risks of radioactivity concentrations and assess the dose in the blood of women who smoke hookah and assess the possibility of developing cancer. [ABSTRACT FROM AUTHOR]

This paper offers a solution to overcome the complexities of production system I/O performance monitoring. We present Beacon, an end-to-end I/O resource monitoring and diagnosis system for the 40960-node Sunway TaihuLight supercomputer, currently the fourth-ranked supercomputer in the world. Beacon simultaneously collects and correlates I/O tracing/profiling data from all the compute nodes, forwarding nodes, storage nodes, and metadata servers. With mechanisms such as aggressive online and offline trace compression and distributed caching/storage, it delivers scalable, low-overhead, and sustainable I/O diagnosis under production use. With Beacon's deployment on TaihuLight for more than three years, we demonstrate Beacon's effectiveness with real-world use cases for I/O performance issue identification and diagnosis. It has already successfully helped center administrators identify obscure design or configuration flaws, system anomaly occurrences, I/O performance interference, and resource under- or over-provisioning problems. Several of the exposed problems have already been fixed, with others being currently addressed. Encouraged by Beacon's success in I/O monitoring, we extend it to monitor interconnection networks, which is another contention point on supercomputers. In addition, we demonstrate Beacon's generality by extending it to other supercomputers. Both Beacon codes and part of collected monitoring data are released. [ABSTRACT FROM AUTHOR]

The model uncertainty, one of the major sources of projection uncertainty, is still a challenge in the climate simulation. In this study, we investigated the inter‐model spread of the simulated winter surface air temperature (SAT) over the Eurasian continent and the physical link to the upper‐level jet streams from the Coupled Model Inter‐comparison Project Phase 6 models. Extracted by the inter‐model empirical orthogonal function analysis, the leading inter‐model spread of SAT over the Eurasian continent is characterized by a cold pattern, particularly over high latitudes, which is closely associated with the out‐of‐phase variation in the intensity of the East Asian polar front jet (EAPJ) and East Asian subtropical jet (EASJ). The weakened‐EAPJ‐enhanced‐EASJ pattern is linked to the intensified Siberian high, the strengthened Aleutian low, and the deepened East Asian trough. All the variations would benefit the cold air invasion, resulting in the leading inter‐model spread of the SAT. The possible mechanism for the inter‐model uncertainty in the out‐of‐phase variation in the intensity of the two jets would be traced to the cooling over the Northwest Pacific oceans, which has reduced (enhanced) the meridional temperature gradient over high (low) latitudes and thereby contributed to the out of phase variation in the two jets. Our findings would help provide a potential metric for understanding the winter SAT change over the Eurasian continent. Plain Language Summary: The winter surface air temperature (SAT) over the Eurasian continent has profound economic and social impacts, however, there are large uncertainties in the climate model simulations. In this study, the inter‐model spread is utilized to derive the inter‐model uncertainty in the winter SAT over the Eurasian continent. The leading mode is a cold pattern over the Eurasian continent, which is closely related to the out‐of‐phase concurrent variation in the intensity of the upper‐level jet streams, resulting in the strengthened East Asian Winter monsoon. With an emphasis on the physical link between the East Asian jet streams and the winter SAT over the Eurasian continent, our findings provide a perspective of physical linkage to understand the traces of model uncertainty. Key Points: The leading inter‐model spread in winter surface air temperature over the Eurasian continent is a cold pattern from the CMIP6 modelsThe weakened‐East Asian polar front jet‐enhanced‐East Asian subtropical jet variation would result in this inter‐model spread due to the strengthened East Asian winter monsoonThe cooling spreads in the Northwest Pacific have physically linked this out‐of‐phase variation in the intensity of the two jets [ABSTRACT FROM AUTHOR]

Climate change and its impact on agriculture productivity vary among crops and regions. The southeastern United States (SE-US) is agro-ecologically diversified, economically dependent on agriculture, and mostly overlooked by agroclimatic researchers. The objective of this study was to compute the effect of climatic variables; daily maximum temperature (Tmax), daily minimum temperature (Tmin), and rainfall on the yield of major cereal crops i.e., corn (Zea mays L.), rice (Oryza sativa L.), and wheat (Triticum aestivum L.) in SE-US. A fixed-effect model (panel data approach) was used by applying the production function on panel data from 1980 to 2020 from 11 SE-US states. An asymmetrical warming pattern was observed, where nocturnal warming was 105.90%, 106.30%, and 32.14%, higher than the diurnal warming during corn, rice, and wheat growing seasons, respectively. Additionally, a shift in rainfall was noticed ranging from 19.2 to 37.2 mm over different growing seasons. Rainfall significantly reduced wheat yield, while, it had no effect on corn and rice yields. The Tmax and Tmin had no significant effect on wheat yield. A 1 °C rise in Tmax significantly decreased corn (− 34%) and rice (− 8.30%) yield which was offset by a 1 °C increase in Tmin increasing corn (47%) and rice (22.40%) yield. Conclusively, overall temperature change of 1 °C in the SE-US significantly improved corn yield by 13%, rice yield by 14.10%, and had no effect on wheat yield. [ABSTRACT FROM AUTHOR]

A highlight this year was the construction of elemental maps of microalgae cells at the organelle level using XRF-CT with a beam size of just 15 nm. These probably represent the highest ever spatial resolution achieved for XRF-CT images. An innovative approach for reconstruction algorithms used a deep convolutional neural network to correct for the self-absorption effects in the XRF-CT sinogram domain. Such corrections will enable the use of conventional tomographic reconstruction algorithms for XRF-CT analysis. A remarkable advantage of the proposed method was that it could correct for self-absorption effects without any prior knowledge of the scanning setups. A particularly innovative dual-energy X-ray-beam ptycho-fluorescence imaging method combined XRF spectrometry and X-ray ptychography scanning with simultaneous data-collection. To overcome the incompatibility of the different excitation and scanning conditions required for fast ptychography and high spatial resolution XRF spectrometry, two coaxial beams of different sizes were used on the sample simultaneously. In comparison to sequential scans, this combined approach has the potential to reduce the data collection time by a factor of 25. The very good performance of laboratory 2D-XRF spectrometry for the analysis of minute amounts of radioactive material has made it a potential alternative to TXRF spectrometry. Reconstruction algorithms are now capable of significantly enhancing imaging resolution in μXRF spectrometry. The forensic classification of incredibly small amounts of samples such as single fibres was accomplished using elemental profiles determined by TXRF spectrometry. Although characterisation of the low-Z matrix in milk and polymers by conventional TXRF spectrometry is challenging, it was successfully achieved by studying the Compton and Rayleigh scatter with both univariate and multivariate approaches. Portable XRF spectrometry is a well-established technique for a wide range of applications. Emphasis continues to be placed on improving calibration and reducing matrix and inter-element effects. The sample type remains a challenge for portable XRF analysis. Computer vision and statistical methods such as spectral angle mapper are being increasingly applied to maximise the information that can be extracted from the data. For example, an innovative approach exploited the elemental features from XRF spectra to improve the interpretation of molecular information provided by reflectance spectroscopy, and vice versa. [ABSTRACT FROM AUTHOR]

In this article we deal with the main critical issues of using electric vehicles in urban transport given the battery related limitations on driving range, and the network layout related limitations on the number of recharging stations. In this context, we seek the optimal routing plans together with the optimal locations of recharging stations for electric vehicle fleets through the Electric Vehicle Location Routing Problem with Intermediate Nodes (ELRP-IN) we propose. The ELRP-IN we formulate as a mathematical program considers the actual characteristics of battery discharging and recovering the braking energy. Energy consumption and recovery are determined through vehicle motion dynamics in conjunction with the 3-dimensional feature of the road geometry, passengers’/customers’ demands on getting on and off, and the pre-defined speed profiles, where the graph corresponding to the road network is extended with the explicit consideration of intersections. By alternating the objectives adopted in the ELRP-IN formulation, we discuss through a number of numerical experiments involving real case instances the effects of both the objective functions and the parameters, including the consumption and the gain of energy, cost, traveled distance, and travel time, on the routing plans. Highlighting the finding that energy is not recovered in all the sections with descending grade, we reveal the direct effect of the elevation on the energy consumption, and hence on the location of a recharging station, where we discuss as well the limitation on energy recovery. [ABSTRACT FROM AUTHOR]

In light of the elevating pressures toward adopting intensifying technologies, centrifugal extractors are increasingly drawing attention as a promising alternative to traditional extraction equipment. Recently, many fields have applied them in research and that resulted in a considerable amount of information that may be hard to follow. To keep interested researchers updated, this work presents the state‐of‐the‐art status of those devices. That is, the different types of centrifugal extractors are first surveyed with emphasis on those available on the market nowadays. Then, their recent applications are covered with exclusion of two fields: oil‐water separations and nuclear industry, as the purpose is to accent centrifugal extractors' potential and compatibility with the widest possible range of applications beyond traditional ones. A further section addresses the hydrodynamics of annular centrifugal contactors that form a major subfamily of centrifugal extractors. Those annular variants have received special focus throughout this work, since not only are they the most encountered devices in literature but also still to date under development with latest versions being 3D‐printed. [ABSTRACT FROM AUTHOR]

This review focuses on consolidating solvent extraction performed in the process intensification equipment known as Centrifugal Contactors (CCs), implemented in Spent Nuclear Fuel (SNF) reprocessing and radioactive waste processing. Recovery of valuable actinides is important from sustainability perspectives as it is a source of metals of technological interest from SNF, specifically the recovery of fissile and fertile material, and can also be employed in the processing of Waste Electrical and Electronic Equipment (WEEE). Solvent extraction (also referred to as liquid–liquid extraction, or aqueous separation), is employed in the separation of f-block elements and fission products in SNF. The sequential isolation using different flowsheets has been performed on a range of scales using CCs. However, solids, either present in the feed solution or formed in situ, are always cited as a concern for the operability of CCs, and their extraction efficiencies. This review quantifies the unexpected solid arisings and accumulation during operation in the presence and absence of highly radioactive isotopes from bench to plant scale. The review concludes with techniques implemented for the removal of solids from CCs. [ABSTRACT FROM AUTHOR]

Environmental impact assessment (EIA) procedures required in the United States and many other countries are often highlighted as a major hindrance to timely and efficient deployment of critical infrastructure projects. Under the U.S. National Environmental Policy Act, a more extensive environmental impact statement (EIS) review can take several more years and cost much more than a succinct environmental assessment (EA). This not only affects the project in question, but also likely informs how-or whether-additional projects are pursued. Thus, understanding key predictors of the EA versus EIS choice sheds light on supply-side considerations affecting infrastructure deficits. Using the case of NEPA reviews conducted for 244 transmission line projects between 2005 and 2018 by two U.S. federal agencies in the western United States, the Bureau of Land Management (BLM) and Department of Energy (DOE), this addresses the following question: What project features most predict whether EA or an EIS is used to assess a transmission line project? Drawing upon NEPA assessment guidance and agency NEPA records, we use a regression classification tree to analyze how protocols and project attributes relate to assessment choice. The result is essentially a null finding: transmission line length is by far the most important predictor of whether a project receives an extensive EIS or a shorter EA, with little predictive value provided by other attributes. While absolute project size undoubtedly influences impacts, the lack of further differentiation in what predicts use of EISs versus EAs suggests assessment does not simply respond to project details but also shapes proposal and design choices beforehand., (© 2022 Society for Risk Analysis.)

Dihydroneopterin aldolase (DHNA) is essential for folate biosynthesis in microorganisms. Without a counterpart in mammals, DHNA is an attractive target for antimicrobial agents. Helicobacter pylori infection occurs in human stomach of over 50% of the world population, but first-line therapies for the infection are facing rapidly increasing resistance. Novel antibiotics are urgently needed, toward which structural information on potential targets is critical. We have determined the crystal structure of H. pylori DHNA (HpDHNA) in complex with a pterin molecule (HpDHNA:Pterin) at 1.49-Å resolution. The HpDHNA:Pterin complex forms a tetramer in crystal. The tetramer is also observed in solution by dynamic light scattering and confirmed by small-angle X-ray scattering. To date, all but one reported DHNA structures are octameric complexes. As the only exception, ligand-free Mycobacterium tuberculosis DHNA (apo-MtDHNA) forms a tetramer in crystal, but its active sites are only partially formed. In contrast, the tetrameric HpDHNA:Pterin complex has well-formed active sites. Each active site accommodates one pterin molecule, but the exit of active site is blocked by two amino acid residues exhibiting a contact distance of 5.2 ​Å. In contrast, the corresponding contact distance in Staphylococcus aureus DHNA (SaDHNA) is twice the size, ranging from 9.8 to 10.5 ​Å, for ligand-free enzyme, the substrate complex, the product complex, and an inhibitor complex. This large contact distance indicates that the active site of SaDHNA is wide open. We propose that this isozyme-specific contact distance (ISCD) is a characteristic feature of DHNA active site. Comparative analysis of HpDHNA and SaDHNA structures suggests a fragment-based strategy for the development of isozyme-specific inhibitors., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.