Technology, Sustainability, and Marketing of Battery Electric and Hydrogen Fuel Cell Medium-Duty and Heavy-Duty Trucks and Buses in 2020-2040

The objective of this study is to project the introduction of battery-electric and fuel cell technologies into the medium-duty and heavy-duty vehicle markets and to identify which markets will be most suitable for each of technologies and the factors (technical, economic, operational) which will be most critical to their successful introduction. The use of renewable energy sources to generate electricity and produce hydrogen are key considerations of the analysis. The present status of the battery-electric and hydrogen/fuel cell technologies are reviewed in detail and the futures of these technologies are projected. The design and performance of various types of buses and trucks are described based on detailed simulations of the various electrified vehicles. The total cost of ownership (TCO) of each bus/truck type were calculated using EXCEL spreadsheets and their market prospects projected for 2020-2040. It was concluded that before any of the electrified vehicles can be cost competitive with the corresponding diesel powered vehicle, the unit cost of batteries must be $80-100/kWh and the unit cost of the fuel cell system must be$80-100/kW. The long term economics of battery-electric buses and trucks looks more favorable than that for the fuel cell/hydrogen option if the range requirement (miles) for the vehicle can be met using batteries. This is primarily due to the significantly lower energy operating cost ($/mi) using electricity than hydrogen.View the NCST Project Webpag

Groundwater Marketing in Nalanda District of Bihar State: A Socio-economic Appraisal

The cost and marketing of groundwater have been assessed in the Nalanda district, which is one of the most agriculturally advanced districts of the Bihar state. For the study, 60 farmers have been randomly selected from the district. It has been found that small and marginal farms use their tubewells mainly for hiring, whereas, large and medium farms use them mainly for their own purposes during the main crop seasons, i.e. kharif and rabi. The average installation cost on a tubewell has been found highest on large size of holdings (Rs 33,130), followed by medium (Rs 27,240), small (Rs 23,850), and marginal (Rs 19,610) holdings. The capital budgeting techniques, viz. net present value (NPV), benefit-cost ratio (B:C ratio) and internal rate of return (IRR) have been used for evaluating the investment on tubewells. The NPV has been found positive (Rs 1440) and B:C ratio more than one (1.05:1). The IRR has been estimated to be more than the capital cost (10.95%). But, the tubewells have failed to generate income flow equal to the investment by marginal farms. Farm size-wise analysis has revealed that the owner-seller farms category predominates in the water market in the study area. The participation in water market has been found to decline with increase in the size of farms. Financial analysis has revealed that the installation of tubewells is financially viable on large and medium farms but not on small and marginal farms. However, with the development of water market in the area, adoption of modern technologies in crop production and cultivation of cash crops would make the installation of tubewells on marginal and small size of farms financially viable.Resource /Energy Economics and Policy,

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks.Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustnes

Why and where?—Delay in Tuberculosis care cascade: A cross-sectional assessment in two Indian states, Jharkhand, Gujarat

Tuberculosis (TB) is the second leading cause of death due to infectious diseases globally, and delay in the TB care cascade is reported as one of the major challenges in achieving the goals of the TB control programs. The main aim of this study was to investigate the delay and responsible factors for the delay in the various phases of care cascade among TB patients in two Indian states, Jharkhand and Gujarat. This cross-sectional study was conducted among 990 TB patients from the selected tuberculosis units (TUs) of two states. This study adopted a mixed-method approach for the data collection. The study targeted a diverse profile of TB patients, such as drug-sensitive TB (DSTB), drug resistance TB (DRTB), pediatric TB, and extra-pulmonary TB. It included both public and private sector patients. The study findings suggested that about 41% of pulmonary and 51% of extra-pulmonary patients reported total delay. Delay in initial formal consultation is most common, followed by a delay in diagnosis and treatment initiation in pulmonary patients. While in extra-pulmonary patients, delay in treatment initiation is most common, followed by the diagnosis and first formal consultation. DR-TB patients are more prone to total delay and delay in the treatment initiation among pulmonary patients. Addiction, co-morbidity and awareness regarding monetary benefits available for TB patients contribute significantly to the total delay among pulmonary TB patients. There were system-side factors like inadequacy in active case findings, poor infrastructure, improper adverse drug reaction management and follow-up, resulting in delays in the TB care cascade in different phases. Thus, the multi-disciplinary strategies covering the gambit of both system and demand side attributes are recommended to minimize the delays in the TB care cascade

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Technology, Sustainability, and Marketing of Battery Electric and Hydrogen Fuel Cell Medium-Duty and Heavy-Duty Trucks and Buses in 2020-2040

The objective of this study is to project the introduction of battery-electric and fuel cell technologies into the medium-duty and heavy-duty vehicle markets and to identify which markets will be most suitable for each of technologies and the factors (technical, economic, operational) which will be most critical to their successful introduction. The use of renewable energy sources to generate electricity and produce hydrogen are key considerations of the analysis. The present status of the battery-electric and hydrogen/fuel cell technologies are reviewed in detail and the futures of these technologies are projected. The design and performance of various types of buses and trucks are described based on detailed simulations of the various electrified vehicles. The total cost of ownership (TCO) of each bus/truck type were calculated using EXCEL spreadsheets and their market prospects projected for 2020-2040. It was concluded that before any of the electrified vehicles can be cost competitive with the corresponding diesel powered vehicle, the unit cost of batteries must be $80-100/kWh and the unit cost of the fuel cell system must be$80-100/kW. The long term economics of battery-electric buses and trucks looks more favorable than that for the fuel cell/hydrogen option if the range requirement (miles) for the vehicle can be met using batteries. This is primarily due to the significantly lower energy operating cost ($/mi) using electricity than hydrogen.View the NCST Project Webpag