Campus Mobility for the Future: The Electric Bicycle

Sustainable and practical personal mobility solutions for campus environments have traditionally revolved around the use of bicycles, or provision of pedestrian facilities. However many campus environments also experience traffic congestion, parking difficulties and pollution from fossil-fuelled vehicles. It appears that pedal power alone has not been sufficient to supplant the use of petrol and diesel vehicles to date, and therefore it is opportune to investigate both the reasons behind the continual use of environmentally unfriendly transport, and consider potential solutions. This paper presents the results from a year-long study into electric bicycle effectiveness for a large tropical campus, identifying barriers to bicycle use that can be overcome through the availability of public use electric bicycles

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

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

Reliability management of microgrids with renewal energy sources

Operational challenges of classical electric utilities, evolutionary changes in the regulatory and the emergence of smaller generating systems (e.g. micro turbines, wind turbines, hydroelectric generators etc.) have unlocked new prospects for distributed generation at site by electricity users. Distributed energy resources (DERs), i.e. small power generating systems which are typically located in the vicinity of end-users, have materialized as a favourable route in meeting the growing electric power needs of end-users and the increasing emphasis on power quality, reliability and energy resilience. DERs are integrated with load demand and energy storage systems (ESSs) providing an opportunity for an entirely new approach in establishing a microgrid. Such a microgrid can provide a lot of benefits to the end-users as it is designed and implemented to meet the immediate power and heat needs of the immediate site. The microgrid can provide uninterruptible power supply, decrease feeder cable losses, enhance local reliability and improve power quality. The energy resources connected to the microgrid include stable sources such as micro turbines, fuel cells and diesel generators; and intermittent energy resources such as photovoltaic system (PVS). A microgrid can be operated as a stand-alone electrical grid, or grid-tied when connected to the main utility grid. In remote villages, islands, emergency situations (e.g. refugee camps, tsunamis, earthquakes etc.), remote mining operations and military command posts, stand-alone microgrids would be a natural choice to provide electricity. This thesis serves to establish the probabilistic reliability of hierarchical level I (HL I) of stand-alone microgrid operational adequacy taking into considerations of uncertainties in ESSs, PVSs and conventional generators (CGs). The IEEE Reliability Test System (IEEE-RTS) constant intra-hour load model is modified and used to include a minutely random rapid-ramp microgrid demand model. Instead of using the classical constant intra-hour or intra-day time step, an operating period of one-minute time step to incorporate the effect of fast ramp up (or down) of system components is considered for microgrid operating adequacy due to load and resource variations. The ESS model is proposed with time dependent state-of-charge (SOC) in order to determine the output power from cell to system level. The PVS reliability is modelled using the basic solar photovoltaic cell to form the PV system in various different configurations and different penetration levels. The interaction between the variability of PVSs and random rapid-ramp demand; and between the variability of PVSs and ESSs are evaluated. The two reliability indices used for the stand-alone microgrid considered are the expected energy not supplied (EENS) and the expected energy not used (EENU).DOCTOR OF PHILOSOPHY (EEE

A 13.8-MHz RC Oscillator with Self-Calibration for ±0.4% Temperature Stability from -55 to 125◦C

This paper articulates a novel oscillator design that can provide stable frequency operation over a wide temperature range with self-calibration technique. A simple ring oscillator is implemented to sense the temperature variation, and some digital circuits tune the output frequency according to the sensing outcomes. Simulation results show that the circuit can generate a stable frequency of 13.8MHz. The power consumption of the whole system is only 52.8µW. The temperature coefficient is less than ±0.4% ranging from -55 to 125 ◦C in the worst process corner whereas the supply voltage is ultra-low at 0.6V.Accepted versio

Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm

New energy vehicles have become a global transportation development trend in order to achieve considerable fuel consumption and carbon emission reductions. However, as the number of new energy cars grows, new energy vehicle safety concerns are becoming more evident, posing a major threat to drivers' lives and property and limiting the industry's growth. This paper develops a charging safety early warning model for electric vehicles (EV) based on the Improved Grey Wolf Optimization (IGWO) algorithm in order to improve the timeliness and accuracy of charging safety early warning. The greatest voltage of a single battery was chosen as the study goal based on the polarization characteristics of lithium-ion batteries and the equalization features of a vehicle lithium-ion battery pack. The IGWO-BP algorithm is then used to fit the entire EV charging process and anticipate the vehicle's charging condition. At the same time, set the warning threshold and the warning error code. In real time, comparing the EV charging data with the fitted data, computing the residual, and building the EV charging safety warning model based on the residual change. Finally, case analysis is performed using daily charging data from both rapid and slow charging. The findings reveal that the proposed early warning model based on the IGWO-BP algorithm can reliably recognize the abnormal state of EV charging voltage and issue timely warnings.This research was supported in part by the International Science and Technology Cooperation Project of Jilin Province Science and Technology Department, grant number 20210402080GH, the author hereby expresses his gratitude to the above-mentioned institution for their support

Fuzzy control based virtual synchronous generator for self-adaptative control in hybrid microgrid

Maintaining the stability of low-inertia microgrid becomes a key challenge in the presence of high penetration of renewable energy sources. However, in such systems, the virtual inertia values are often fixed constants, and the choice of their values will significantly affect the frequency and voltage stability of the microgrid. Higher frequency and voltage oscillations may occur due to improper selection of fixed virtual inertia values. Therefore, virtual inertia-based control has attracted a lot of attention. In this paper, an adaptive virtual inertia control system using a fuzzy system is proposed by setting fuzzy logic rules and affiliation functions to provide adaptive inertia control for the system to ensure the frequency and voltage stability. In the proposed adaptive control strategy, the virtual inertia values are automatically adjusted according to the signal deviation and rate of change of the actual system, avoiding the selection of inappropriate inertia values and providing fast inertial response. Simulation and experimental results show that the proposed adaptive control algorithm, by combining the advantages of large inertia and small inertia, enables effective improvement of the dynamic response of the system voltage and frequency in both rectifier and inverter modes. The effectiveness of the proposed control strategy is verified.Published versionThis work was supported in part by the Jilin Provincial Science and Technology Department [grant numbers 20210402080GH, 20220203052SF]