l1-norm Minimization Based Algorithm for Non-Intrusive Load Monitoring

International audienceNon-Intrusive Load Monitoring (NILM) plays an important role in energy management and energy reduction in buildings and homes. An NILM system does not need a large amount of deployed power meters to monitor the power usage of home devices. Instead, only one meter on the main power line is necessary to detect and identify the operating devices. There are many approaches to solve the problem of device determination in NILM. The features applied in low-frequency based approach essentially include the step-change (or edge) and the steady state. This paper introduces three algorithms to solve the l1-norm minimization problem in NILM and results on power measurements obtained from a real appliance deployment. With a small number of devices, the obtained precision varies from 75% to 99%, depending on the tolerance criterion to determine the steady state of a given device

Interaction between triphenylphosphine or 1,2-bis(diphenylphosphino)ethane with some complexes K[PtCl3(olefin)] (olefin: methyleugenol, safrole, isopropyl eugenoxyacetate)

Novel study on the interaction between K[PtCl3(olefin)] (olefin: methyleugenol, safrole and isopropyl eugenoxyacetate) with TPP and DPPE shows that TPP and DPPE readily replace the olefins to form complexes [PtCl2(TPP)2] (P4), [PtCl2(DPPE)] (P5) and [Pt(DPPE)2]Cl2 (P6). P4 possesses trans configuration when the molar ratio of the mono olefin and TPP of 1:1. When the ratio is 1:2, P4 is a mixture of trans and cis isomers of which trans one is prevailing. The cis isomer trends to convert to trans one in chloroform solvent. P5 and P6 were formed when the molar ratio of mono isopropyl eugenoxyacetate and DPPE of 1:1 and 1:2, respectively. The structures of P4÷P6 were elucidated by Pt analysis, ESI-MS, IR and 1H NMR spectra studies. Keywords. Pt(II) complexes, olefins, phosphine derivatives

In vitro antioxidant activity and bioactive compounds from Calocybe indica

Nowadays, the use of mushrooms in medicine is ubiquitous and has achieved particular success. The antioxidants in mushrooms can deactivate free radicals. This study assesses the antioxidant potential of mushroom Calocybe indica with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) radical scavenging methods and the total antioxidant capacity. The mushroom’s ethanol extract exhibits acceptable activity with a low IC50 value (240.11 μg/mL), approximately 2.9 times lower than that of the mushroom Ophiocordyceps sobolifera extract. The ABTS scavenging rate of the extract is around 60% at 500 µg/mL, and the total antioxidant capacity is equivalent to 64.94 ± 1.03 mg of GA/g or 77.42 ± 0.42 μmol of AS/g.  The total phenolics, flavonoids, polysaccharides, and triterpenoids are equivalent to 29.33 ± 0.16 mg of GAE/g, 17.84 ± 0.11 mg of QUE/g (5.04 ± 0.04%), and 4.96 ± 0.04 mg of oleanolic acid/g, respectively. Specifically, the total triterpenoid content has been reported for the first time. The mushroom can have potential biomedical applications

Investigation of Sodium Manganese Oxide Nanowires Synthesized by Hydrothermal Method for Alkaline Ion Battery

Sodium Manganese Oxide (NaxMnO2) has attracted much attention as cathode materials for alkaline ion battery due to the ability of fast charge and discharge ion Na+, in particular in nanoscale. We report on the synthesis of NaxMnO2 nanowires via hydrothermal synthesis route from Mn2O3 and NaOH solution. The morphological observation indicates that the obtained Na0.44MnO2 nanowires with diameters of about 20-30 nm, length up to several micrometers were formed by this process. The electrochemical properties of fabricated materials were investigated by means of cyclic voltammetry technique and show that Sodium Manganese Oxide (NaxMnO2) is a promising material in the field of research and fabrication alkaline ion battery

LIGNANS FROM LEAVES OF AMESIODENDRON CHINENSE AND THEIR CYTOTOXIC ACTIVITY

ABSTRACTFrom leaves of Amesiodendron chinense (Mer.) Hu four lignans (+)-aptosimon (1), (+)-isolariciresinol (2), (-)-cleomiscosin A (3), and (-)-cleomiscosin C (3) were isolated. Their structures were determined by spectroscopic analysis including MS, 1D and 2D NMR as well as by comparison with reported data in literature. All compounds were evaluated for cytotoxic activities against five human cancer cell lines, KB, SK-LU-1, MCF-7, HepG-2, and SW-480. They showed weak cytotoxic activity on five tested human cancer cell lines with IC50 values ranging from 32.61 to 95.18 µg/ml

Supercapacitors (electrochemical capacitors)

International audienceRapid development of the technologies based on electric energy in the last decades have stimulated intensive research on efficient power sources. Electrochemical energy conversion and storage systems are based on Faradaic reactions (charge transfer) and electrostatic attraction of ions at the electrode/electrolyte interface. The latter might be an interesting solution for applications requiring moderate energy density, high power rates, and long cycle life. Electrochemical capacitors (ECs) store the charge in a physical manner, hence, their energy density is moderate. At the same time, the lack of electrochemical reactions ensures very high power and long cycle life compared to batteries. Activated carbons with their versatile properties (like specific surface area, well-developed and suitable porosity, heteroatoms in the graphene matrix) are the most popular materials in EC application. This chapter provides a comprehensive overview of the carbon-based materials recently developed, with special attention devoted to those obtained by biomass carbonization and activation. Electrochemical properties demonstrated by such carbons are discussed in respect to their physicochemical characteristic

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

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Amélioration des performances de supervision de charges non intrusive à l'aide de capteurs sans fil à faible coût

In smart homes, human intervention in the energy system needs to be eliminated as much as possible and an energy management system is required to automatically fluctuate the power consumption of the electrical devices. To design such system, a load monitoring system is necessary to be deployed in two ways: intrusive or non-intrusive. The intrusive approach requires a high deployment cost and too much technical intervention in the power supply. Therefore, the Non-Intrusive Load Monitoring (NILM) approach, in which the operation of a device can be detected based on the features extracted from the aggregate power consumption, is more promising. The difficulty of any NILM algorithm is the ambiguity among the devices with the same power characteristics. To overcome this challenge, in this thesis, we propose to use an external information to improve the performance of the existing NILM algorithms. The first proposed additional features relate to the previous state of each device such as state transition probability or the Hamming distance between the current state and the previous state. They are used to select the most suitable set of operating devices among all possible combinations when solving the l1-norm minimization problem of NILM by a brute force algorithm. Besides, we also propose to use another external feature that is the operating probability of each device provided by an additional Wireless Sensor Network (WSN). Different from the intrusive load monitoring, in this so-called SmartSense system, only a subset of all devices is monitored by the sensors, which makes the system quite less intrusive. Two approaches are applied in the SmartSense system. The first approach applies an edge detector to detect the step-changes on the power signal and then compare with the existing library to identify the corresponding devices. Meanwhile, the second approach tries to solve the l1-norm minimization problem in NILM with a compositional Pareto-algebraic heuristic and dynamic programming algorithms. The simulation results show that the performance of the proposed algorithms is significantly improved with the operating probability of the monitored devices provided by the WSN. Because only part of the devices are monitored, the selected ones must satisfy some criteria including high using rate and more confusions on the selected patterns with the others.Dans les maisons et bâtiments intelligents, il devient nécessaire de limiter l'intervention humaine sur le système énergétique, afin de fluctuer automatiquement l'énergie consommée par les appareils consommateurs. Pour cela, un système de mesure de la consommation électrique d'équipements est aussi nécessaire et peut être déployé de deux façons : intrusive ou non-intrusive. La première solution consiste à relever la consommation de chaque appareil, ce qui est inenvisageable à une grande échelle pour des raisons pratiques liées à l'entretien et aux coûts. Donc, la solution non-intrusive (NILM pour Non-Intrusive Load Monitoring), qui est capable d'identifier les différents appareils en se basant sur les signatures extraites d'une consommation globale, est plus prometteuse. Le problème le plus difficile des algorithmes NILM est comment discriminer les appareils qui ont la même caractéristique énergétique. Pour surmonter ce problème, dans cette thèse, nous proposons d'utiliser une information externe pour améliorer la performance des algorithmes existants. Les premières informations additionnelles proposées considèrent l'état précédent de chaque appareil comme la probabilité de transition d'état ou la distance de Hamming entre l'état courant et l'état précédent. Ces informations sont utilisées pour sélectionner l'ensemble le plus approprié des dispositifs actifs parmi toutes les combinaisons possibles. Nous résolvons ce problème de minimisation en norme l1 par un algorithme d'exploration exhaustive. Nous proposons également d'utiliser une autre information externe qui est la probabilité de fonctionnement de chaque appareil fournie par un réseau de capteurs sans fil (WSN pour Wireless Sensor Network) déployé dans le bâtiment. Ce système baptisé SmartSense, est différent de la solution intrusive car seul un sous-ensemble de tous les dispositifs est surveillé par les capteurs, ce qui rend le système moins intrusif. Trois approches sont appliquées dans le système SmartSense. La première approche applique une détection de changements de niveau sur le signal global de puissance consommé et les compare avec ceux existants pour identifier les dispositifs correspondants. La deuxième approche vise à résoudre le problème de minimisation en norme l1 avec les algorithmes heuristiques de composition Paréto-algébrique et de programmation dynamique. Les résultats de simulation montrent que la performance des algorithmes proposés augmente significativement avec la probabilité d'opération des dispositifs surveillés par le WSN. Comme il n'y a qu'un sous-ensemble de tous les appareils qui sont surveillés par les capteurs, ceux qui sont sélectionnés doivent satisfaire quelques critères tels qu'un taux d'utilisation élevé ou des confusions dans les signatures sélectionnées avec celles des autres