DuPont Model and Product Profitability Analysis Based on Activity-based Costing and Economic Value Added

Although DuPont analysis is widely used it is not easy to provide accurate performance information based on DuPont profitability analysis, which is established on the basis of traditional accounting earnings. Since Activity-based Costing (ABC) and Economic Value Added (EVA) are advanced approaches to costing activities and estimating economic profit of a firm, DuPont analysis using ABC and EVA information can be more appropriate in understanding Return on Equity (ROE). In this paper we set up an improved EVA-ABC based DuPont analysis system as well as its relative indices. Then it is applied to traditional profitability analysis to get a better performance measurement. The results show that the improved system can reduce the negative impacts of accounting principles and objectively reflect the operating performance of the enterprise. It also provides more accurate information for decision makers. Keywords: DuPont Analysis; Activity-based Costing; Economic Value Added; Profitability Analysi

Regional nonintrusive load monitoring for low voltage substations and distributed energy resources

This paper presents a novel extension of the classic nonintrusive load monitoring (NILM) problem from household-appliance level to substation level. A new three-stage regional-NILM method is proposed to deduce the states of different types of loads in a region by disaggregating its substation demand. Three types of loads are considered in this study: (i) traditional loads; (ii) distributed generation such as photovoltaics (PVs); and (iii) flexible loads like electric vehicles (EVs). The proposed method firstly forecasts the traditional load using the long-term historical data and employing spectral analysis to boost the signal-to-noise ratio. Secondly, the PV capacity is deduced by performing peak coincidence analysis between negative residuals and local solar irradiance data. Finally, a novel limited activation matching pursuit method is proposed to estimate the states of the EVs, including the total EV load and number of EVs. The method is assessed on real data collected from 800 substations, 10 PVs and 50 EVs in the UK. Results show the proposed method for estimating the number of EVs outperforms the approaches based on sparse coding, orthogonal matching pursuit and non-negative matching pursuit by 16.5%, 10.2% and 10.0%, respectively. The proposed Regional-NILM solution provides a cost-effective way for distribution network operators to understand the network's state. It can therefore significantly increase the network visibility without requiring expensive monitoring and avoiding data privacy issues. As such, it can improve the efficiency of demand side management, which is required to accommodate the future large number of distributed energy resources connections.</p

Regional nonintrusive load monitoring for low voltage substations and distributed energy resources

This paper presents a novel extension of the classic nonintrusive load monitoring (NILM) problem from household-appliance level to substation level. A new three-stage regional-NILM method is proposed to deduce the states of different types of loads in a region by disaggregating its substation demand. Three types of loads are considered in this study: (i) traditional loads; (ii) distributed generation such as photovoltaics (PVs); and (iii) flexible loads like electric vehicles (EVs). The proposed method firstly forecasts the traditional load using the long-term historical data and employing spectral analysis to boost the signal-to-noise ratio. Secondly, the PV capacity is deduced by performing peak coincidence analysis between negative residuals and local solar irradiance data. Finally, a novel limited activation matching pursuit method is proposed to estimate the states of the EVs, including the total EV load and number of EVs. The method is assessed on real data collected from 800 substations, 10 PVs and 50 EVs in the UK. Results show the proposed method for estimating the number of EVs outperforms the approaches based on sparse coding, orthogonal matching pursuit and non-negative matching pursuit by 16.5%, 10.2% and 10.0%, respectively. The proposed Regional-NILM solution provides a cost-effective way for distribution network operators to understand the network's state. It can therefore significantly increase the network visibility without requiring expensive monitoring and avoiding data privacy issues. As such, it can improve the efficiency of demand side management, which is required to accommodate the future large number of distributed energy resources connections.</p

Regional non-intrusive electric vehicle monitoring based on graph signal processing

Electricity network is leading to a low carbon future with high penetration of plug-in electric vehicles (EVs). However, it is extraordinarily difficult to acquire detailed information on regional EV electrification with an incomplete monitoring system for network operators. In this study, a flexible graph signal processing (GSP)-based non-intrusive monitoring on aggregated EVs is proposed to enhance the EVs visibility for operating power system safely and cost-efficiently. It can deduce the individual EV charging status with the highest possibility iteratively from the limited dataset using a GSP-based possibility calculation after processing a daytime EV characteristic charging patterns. The experiment is developed with realistic EV charging datasets collected in London, and the results show the daily EVs number in a specific region of 500 EVs daily aggregation can be estimated efficiently with an around 4.77% value of relative mean absolute deviation applying the proposed method.</p

A Hybrid Demon Algorithm for the Two-Dimensional Orthogonal Strip Packing Problem

This paper develops a hybrid demon algorithm for a two-dimensional orthogonal strip packing problem. This algorithm combines a placement procedure based on an improved heuristic, local search, and demon algorithm involved in setting one parameter. The hybrid algorithm is tested on a wide set of benchmark instances taken from the literature and compared with other well-known algorithms. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm

Prediction for the surface settlement of double-track subway tunnels for shallow buried loess based on peck formula

In the process of constructing double-track subway tunnels in shallow buried loess areas, the interaction of double-track tunnels is significantly influenced by the net distance and the cross-section size, which is challenging to control the surface settlement. Therefore, the surface settlement prediction is essential while constructing double-track subway tunnels in shallow buried loess areas. The paper analyzed the surface settlement law of shallow buried double-track tunnels in loess areas through theoretical research and numerical simulation. The research results show that with the decrease of the net distance, the surface settlement superimposed curve was double V shape -W shape - single V shape. When the superimposed curve is double V shape and W shape, the Peck formula was used to calculate the surface settlement curve of the single-track tunnel, then superimposed to obtain the final surface settlement curve. When the superimposed surface settlement curve was V shape, based on the Peck formula, the formula for predicting the surface settlement suitable for symmetry and asymmetry was established. The net distance ratio and the area ratio were defined, and considering the tunnel’s interaction, the value and position of the maximum were corrected. Then numerical tests were carried out 16 times with different net distance ratios and area ratios, to determine the parameters of increments and position offsets of the maximum regarding the net distance ratio and the area ratio. Finally, two engineering were conducted for verifying the rationality and applicability exhibited by the prediction formula. The prediction formula served for predicting the surface settlement of double-track subway tunnels in shallow buried loess areas. Which can reduce construction risks and assure the safety of buildings above the ground

Accuracy of steps measured by smartphones-based WeRun compared with ActiGraph-GT3X accelerometer in free-living conditions

ObjectivesThe purpose of this study was to evaluate the accuracy and reliability of steps tracked by smartphone-based WeChat app compared with Actigraph-GT3X accelerometer in free-living conditions.DesignA cross-sectional study and repeated measures.MethodsA total of 103 employees in the Pudong New Area of Shanghai, China, participated in this study. The participants wore an ActiGraph-GT3X accelerometer during the period of August to September 2019 (Time 1), December 2019 (Time 2) and September 2020 (Time 3). Each time, they wore the ActiGraph-GT3X accelerometer continuously for 7 days to assess their 7-day step counts. The smartphone-based WeRun step counts were collected in the corresponding period when subjects wore accelerometers. The subjects were invited to complete basic demographic characteristics questionnaires and to perform physical examination to obtain health-related results such as height, body weight, body fat percentage, waist circumference, hip circumference, and blood pressure.ResultsBased on 103 participants' 21 days of data, we found that the Spearman correlation coefficient between them was 0.733 (P &lt; 0.01). The average number of WeRun steps measured by smartphones was 8,975 (4,059) per day, which was higher than those measured by accelerometers (8,462 ± 3,486 per day, P &lt; 0.01). Demographic characteristics and different conditions can affect the consistency of measurements. The consistency was higher in those who were male, older, master's degree and above educated, and traveled by walking. Steps measured by smartphone and accelerometer in working days and August showed stronger correlation than other working conditions and time. Mean absolute percent error (MAPE) for step counts ranged from 0.5 to 15.9%. The test-retest reliability coefficients of WeRun steps ranged from 0.392 to 0.646. A multiple regression analysis adjusted for age, gender, and MVPA/step counts measured during Time 1 showed that body composition (body weight, BMI, body fat percentage, waist circumference, and hip circumference) was correlated with moderate-to-vigorous intensity physical activity, but it was not correlated with WeRun step counts.ConclusionsThe smartphone-based WeChat app can be used to assess physical activity step counts and is a reliable tool for measuring steps in free-living conditions. However, WeRun step counts' utilization is potentially limited in predicting body composition

Dissecting the causal effect between gut microbiota, DHA, and urate metabolism: A large-scale bidirectional Mendelian randomization

ObjectivesOur aim was to investigate the interactive causal effects between gut microbiota and host urate metabolism and explore the underlying mechanism using genetic methods.MethodsWe extracted summary statistics from the abundance of 211 microbiota taxa from the MiBioGen (N =18,340), 205 microbiota metabolism pathways from the Dutch Microbiome Project (N =7738), gout from the Global Biobank Meta-analysis Initiative (N =1,448,128), urate from CKDGen (N =288,649), and replication datasets from the Global Urate Genetics Consortium (N gout =69,374; N urate =110,347). We used linkage disequilibrium score regression and bidirectional Mendelian randomization (MR) to detect genetic causality between microbiota and gout/urate. Mediation MR and colocalization were performed to investigate potential mediators in the association between microbiota and urate metabolism.ResultsTwo taxa had a common causal effect on both gout and urate, whereas the Victivallaceae family was replicable. Six taxa were commonly affected by both gout and urate, whereas the Ruminococcus gnavus group genus was replicable. Genetic correlation supported significant results in MR. Two microbiota metabolic pathways were commonly affected by gout and urate. Mediation analysis indicated that the Bifidobacteriales order and Bifidobacteriaceae family had protective effects on urate mediated by increasing docosahexaenoic acid. These two bacteria shared a common causal variant rs182549 with both docosahexaenoic acid and urate, which was located within MCM6/LCT locus.ConclusionsGut microbiota and host urate metabolism had a bidirectional causal association, implicating the critical role of host-microbiota crosstalk in hyperuricemic patients. Changes in gut microbiota can not only ameliorate host urate metabolism but also become a foreboding indicator of urate metabolic diseases

Modification effect of changes in cardiometabolic traits in association between kidney stones and cardiovascular events

BackgroundsWhether longitudinal changes in metabolic status influence the effect of kidney stones on cardiovascular disease (CVD) remains unclarified. We investigated the modification effect of status changes in metabolic syndrome (MetS) in the association of kidney stones with risk of incident CVD events.MethodsWe performed a prospective association and interaction study in a nationwide cohort including 129,172 participants aged ≥ 40 years without CVDs at baseline and followed up for an average of 3.8 years. Kidney stones information was collected by using a questionnaire and validated by medical records. The repeated biochemical measurements were performed to ascertain the metabolic status at both baseline and follow-up.Results4,017 incident total CVDs, 1,413 coronary heart diseases (CHDs) and 2,682 strokes were documented and ascertained during follow-up. Kidney stones presence was significantly associated with 44%, 70% and 31% higher risk of CVDs, CHDs and stroke, respectively. The stratified analysis showed significant associations were found in the incident and sustained MetS patients, while no significant associations were found in the non-MetS at both baseline and follow-up subjects or the MetS remission ones, especially in women. For the change status of each single component of the MetS, though the trends were not always the same, the associations with CVD were consistently significant in those with sustained metabolic disorders, except for the sustained high blood glucose group, while the associations were consistently significant in those with incident metabolic disorders except for the incident blood pressure group. We also found a significant association of kidney stone and CVD or CHD risk in the remain normal glucose or triglycerides groups; while the associations were consistently significant in those with incident metabolic disorders except for the incident blood pressure group. We also found a significant association of kidney stone and CVD or CHD risk in the remain normal glucose or triglycerides groups.ConclusionsA history of kidney stones in women with newly developed MetS or long-standing MetS associated with increased risk of CVD. The mechanisms link kidney stones and CVD risk in the metabolic and non-metabolic pathways were warranted for further studies