Cost Metrics Research Articles

A comparative study on deep‐learning methods for dense image matching of multi‐angle and multi‐date remote sensing stereo‐images

AbstractDeep‐learning (DL) stereomatching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo‐images lacking a systematic evaluation on how robust DL methods are on satellite stereo‐images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereomatching methods through hundreds of multi‐date multi‐site satellite stereopairs with varying geometric configurations, against the traditional well‐practiced Census‐semi‐global matching (SGM), to comprehensively understand their accuracy, robustness, generalisation capabilities, and their practical potential. The DL methods include a learning‐based cost metric through convolutional neural networks (MC‐CNN) followed by SGM, and three end‐to‐end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalise well for unseen data. The learning‐based cost metric and Census‐SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereopairs and are less sensitive in comparison to the Census‐SGM, while learning‐based cost metrics can generalise on satellite images when trained on different datasets (airborne or ground‐view).

Abstract 14878: Natural Language Processing of Unstructured Medical Record Can Predict Cardiac Arrhythmia by Quantifying Chronic Disease Burden

Introduction: Prediction of atrial fibrillation (AF) and sick sinus syndrome (SSS) is difficult. CMS Hierarchical Condition Categories scores (CMS-HCC) is a risk adjustment score used by CMS to account to risk adjust quality and cost metrics by quantifying the burden of chronic disease and patient complexity. Its calculation is based on patient’s demographics and ICD10 diagnostic codes. We have created an algorithm (MYTH) that can determine ICD10 diagnostic codes from unstructured medical chart using Natural Language Processing through syntax analysis, knowledge graph and regular expressions. The ICD-10 codes can then be used to calculate CMS-HCC. Because chronic disease burden may increase the probability of cardiac arrhythmia, we hypothesize that CMS-HCC can predict the onset of atrial fibrillation and sick sinus syndrome. Methods: We used MYTH to automate ICD10 coding and HCC score calculation based on textual outpatient cardiology records of 926 patients. The algorithm was additionally applied to calculate CHA2DS2-VASc scores. These were applied to predict the diagnosis of AF and SSS on clinical follow-up retrospectively. Results: CMS-HCC scores outperforms CHA2DS2-VASc scores in the prediction of onset of AF: (AUC: CHA2DS2-VASc 0.703, CMS-HCC 0.778, difference P=0.0006, individually P<0.0001). While HCC score is predictive of the onset of SSS (P<0.001), its improvement over CHA2DS2-VASc score does not reach statistical significance (AUC: CHA2DS2-VASc 0.702; CMS-HCC 0.744, difference: P=0.297, individually P<0.001). Conclusions: Natural Language Processing algorithms can understand unstructured medical text, automate ICD coding and automate CMS-HCC score calculation. The burden of chronic diseases, as reflected by the CMS-HCC score, is highly predictive of future atrial fibrillation and modestly predictive of sick sinus syndrome.

Additive manufacturing integration in E-commerce supply chain network to improve resilience and competitiveness

In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty.

A new offloading method in the green mobile cloud computing based on a hybrid meta-heuristic algorithm

Mobile Cloud Computing (MCC) results from the evolution of several Internet-based technologies that allow mobile consumers to gain cloud computing advantages and attain green computing by utilizing their cellphones. MCC's use may be restricted because of increasing energy utilization and restricted access to energy supplies. This challenge may be tackled using the green approach, which entails the development of several algorithms or approaches to attain the highest possible degree of service quality. Several techniques in this idea allow consumers to offload data processing and storage to cloud-based servers to reduce the energy utilization of mobile nodes. Offloading is an essential element that helps MCC reduce task load and optimize data storage utilizing a cloud resource pool. Regarding the advantages of the offloading procedure, issues such as reliability (node failure), energy efficiency (energy consumption, task processing), and time management (execution time and task deadline) must be handled. This work presents a unique offloading approach in green MCC depending on a hybrid meta-heuristic algorithm because of the NP-Hard nature of the issue. The African Wild Dog Algorithm (AWDA) and the cellular learning automata technique are merged to generate better responses. AWDA is based on the cooperative hunting behavior of African wild dogs. The quantity of cooperative hunters determines the efficacy of hunting. The optimization procedure is analogous to this collective hunting behavior. Furthermore, a learning automaton in the cellular learning automata technique describes each of these components. An element's learning automaton seeks to discover which action best complements its surrounding components. The simulation findings illustrated that the suggested hybrid technique outperforms the other algorithms regarding energy consumption, delay time, and cost. The proposed algorithm has improved the delay time, cost, and energy metric by 0.43%, 3.17%, and 8.37%, compared to other algorithms.

Fish Skin–Derived Wound Adjunct as an Alternative to Vacuum-Assisted Wound Closure for Contaminated and Infected Wounds

INTRODUCTION: Vacuum-assisted wound closure (VAWC) is standard therapy for contaminated wounds. Fish skin–derived wound adjunct (FDWA) is used in wound care for contaminated wounds with great success. We postulated that FDWA would be an effective alternative, reducing overall cost and pain. METHODS: Our FDWA database of all patients where FDWA (Kerecis, Reykjavik Iceland) was placed intraoperatively was retrospectively reviewed to identify patients who met the criteria for the use of VAWC. Surgical wound infection (SSI) was defined as described by the CDC. Cost metrics were calculated based on actual cost for VAWC compared with the cost of FDWA. Before the use of FDWA, the average length of use for VAWC was 11.5 days. RESULTS: Fifteen patients (7 men: 8 women) with a mean age of 60 years (range 41to 84 years) and a mean follow-up of 29 days (range 6 to 59 days) were identified who met criteria. Level 1 criteria for VAWC were identified in 7 patients. Two or more level 2 criteria were identified in 8 patients. No SSI was identified in the FDWA patients (Table 1). The daily cost of VAWC is $111.18/day for a total of $1,278.57. The cost for a single application of FDWA is $890. Patients do not require any dressing changes in the FDWA group. Table 1. - Criteria for Vacuum-Assisted Wound Closure Level 1 Level 2 Risk factors No. of patients Risk factors No. of patients Extended duration of surgery 4 ASA physical status ≥ 2 14 Previous surgery at the site/multiple incisions 2 Age >65 y 5 BMI ≥40 kg 1 Steroids for a chronic condition 5 Regularly scheduled dialysis 1 Smoking 4 BMI (30–39.9) kg 3 Peripheral vascular disease 1 CONCLUSION: This pilot study suggests that FDWA is an effective alternative to VAWC with lower cost and less patient discomfort. Further study is needed to fully define the role of FDWA in the management of contaminated surgical wounds.

Impact of proposed 2022 changes to the Merit-Based Incentive Payment System (MIPS) on oncologist performance and reimbursement.

6 Background: In 2017, the Centers for Medicare & Medicaid Services (CMS) executed a landmark bipartisan legislation aimed at gradually shifting American healthcare from a fee-for-service model to a value-based system. The resulting payment model, MIPS, uses cost and quality metrics to assign clinicians an overall MIPS score, where higher scores correspond to greater payments for practices. In 2018, cost metrics contributed only 10% of this composite score, while quality metrics contributed 50%. Beginning with 2022, the cost and quality categories will be equally weighted (30% each) to encourage clinicians to reduce healthcare costs. As the cost of cancer care is disproportionately high, we investigated the implications of the updated scoring on reimbursements for oncologists and other providers participating in MIPS. Methods: We included physicians participating in the 2018 MIPS Performance Year files. We categorized hematological-, medical, radiation, gynecological, and surgical oncologists as oncologists and delineated those with National Cancer Institute-designated Cancer Center (NCICC)-affiliation. We excluded individuals who were exempt from reporting cost scores due to their MIPS-filing method. We calculated the projected 2022 MIPS composite scores (range, 0-100) by multiplying the 2022 weights by the 2018 component scores. We compared the change in composite MIPS score and the corresponding negative payment adjustments due to reweighing across specialties. Clopper–Pearson 95% confidence intervals were reported. Results: The cohort included 168,659 physicians, of which 163,150 (97%) were non-oncologists and 5,509 (3%) were oncologists. Of oncologists, 1,757 (32%) were at a NCICC. Cost scores (higher is better) for each group were as follows: non-oncologists, 75.2; all oncologists, 65.5; NCICC oncologists, 57.1. Reweighing the 2018 MIPS composite scores with the updated 2022 cost/quality weights resulted in a mean 1.7-point decrease (88.7 to 87) for non-oncologists, a 3.8-point decrease (89.7 to 85.9) for all oncologists, and a 5.2-point decrease (89.2 to 84) for NCICC oncologists. Following the reweighing, the proportion of non-oncologists incurring penalties will increase by 12.8% (95% CI, 12.7%–12.9%); all oncologists by 14.3% (95% CI, 13.2%–15.4%); and NCICC oncologists by 20% (95% CI, 18.1%–21.9%). Conclusions: Beginning with 2022 performance year, federally mandated reweighing of MIPS cost metrics will result in a disproportionate increase in oncologists receiving negative payment adjustments. With a majority Medicare population with new cancer diagnoses, higher patient complexity, and declining operating margins at many oncologic practices, we highlight striking upcoming changes that may inform further optimization of MIPS cost metrics by CMS to ensure oncologists providing high quality care are not penalized.

Relieving Pressure on the Emergency Department with a New Treatment Pathway for Hand Trauma Patients - A Three-Year Experience with 15,539 Patients.

Background: Hand injuries are a significant and rising burden on the Emergency Department (ED), often leading to protracted waiting times for patients awaiting specialist input. To combat this, a new treatment pathway for hand trauma was introduced at our institution to reduce waiting times and pressure on the ED. Methods: The treatment pathway performance using waiting times, length of stay and cost metrics was measured prior to and following the introduction of a new treatment pathway. Results: There were 15,539 patients reviewed in total. After the new pathway had been introduced, the number of assessments in ED significantly reduced (Year 1: 907 [19.9%] vs. Year 2: 422 [7.9%]; p < 0.001), and the proportion of patients who had an operation on the same day that they were assessed significantly increased (69 [1.5%] vs. 403 [7.5%] patients; p < 0.001). The median waiting time from assessment to operation and length of stay also significantly reduced following the introduction of the treatment pathway (Year 1: 53 hours and Year 2: 45 hours; p < 0.001). Conclusions: Our data over 3 years shows that these changes have been maintained and, in some cases, have continued to improve since the introduction of the new treatment pathway. We advocate the use of such an approach for all hand trauma centres worldwide to replicate these improvements in patient care. Level of Evidence: Level III.

Evaluating the time toxicity of cancer treatment in the CCTG CO.17 randomized clinical trial (RCT).

248 Background: Most individual treatments for advanced cancer are associated with modest survival benefits. The time spent in pursuing these treatments can be substantial. We have previously developed a pragmatic and patient-centered metric of these time costs, which we term ‘’time toxicity’’, as any day with physical healthcare system contact. This includes outpatient visits (e.g., for bloodwork, scans, infusions, urgent care), emergency room visits, and overnight stays in a healthcare facility. Herein we sought to assess time toxicity in a completed RCT. Methods: We conducted a secondary analysis of the Canadian Cancer Trials Group CO.17 RCT that evaluated weekly cetuximab infusions vs supportive care alone in 572 patients with advanced colorectal cancer. CO.17 recruited participants in Canada, Australia and New Zealand. Initial results reported a 6-week improvement in median overall survival (OS) with cetuximab (6.1 vs 4.6 months, p = 0.005). Subsequent analyses reported that OS benefit was restricted to and significantly more in patients with K-ras wild-type tumors. We calculated patient-level time toxicity by analyzing treatment, follow-up, and resource utilization forms. We considered a day without physical healthcare contact as a ‘’home day’’. Thus, for a patient, OS was time toxic days + home days. We compared medians of time measures across arms, and stratified results by K-ras status. Results: In the overall population, median time toxic days were higher in the cetuximab arm (28, vs 10, p &lt; 0.001), although median home days were not statistically different (140, vs 121, p = 0.09). The proportion of time toxic days (time toxic days/OS) were significantly more with cetuximab (18%, vs 6%, p &lt; 0.001). Of the 28 time toxic days in the overall cetuximab arm, 14 (50%) were protocol-related (e.g., scheduled infusions etc). Stratified results are in the table. Conclusions: Time toxicity can be extracted through secondary analyses of RCTs. In CO.17, despite an overall OS-benefit with cetuximab, home days were statistically similar across arms. In the K-ras-mutated group, cetuximab was associated with numerically similar OS but more time toxic days. In the K-ras-wild type group, cetuximab was associated with higher home days. Thus, time toxicity data need not always be sobering. Time toxicity measures can supplement traditional survival endpoints in RCTs to guide patient-oncologist decision-making. Cooperative group RCTs are well positioned for such analyses. Clinical trial information: NCT00079066. [Table: see text]

Overland and Oversea: Domestic Trade Frictions in the Philippines

Domestic trade costs are anecdotally high in the Philippines, and yet a systematic and spatially comparable metric of trade costs is unavailable. This paper fills this gap by estimating province border effects, or a province’s tendency to trade with itself rather than with other provinces. Subsequently, the effects of a maritime transport program based on roll-on roll-off (RORO) ships on province border effects are assessed. Using two novel datasets—the starting dates of RORO shipping services by route, and the intraprovince trade of agricultural products—this study finds that province border effects are substantial, with a typical province trading 51 times more with itself than with other provinces. The RORO transport program is associated with an average reduction of 35% of this home province bias. However, the border effect reductions are unevenly distributed, with the largest reductions concentrated in provinces near the biggest demand centers.

Workload management and server speed setting for cost‐performance ratio optimization

AbstractThe cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

Assessment of energy consumption, carbon emissions and cost metrics under hybrid MQL-Dry ice blasting system: A novel cleaner production technology for manufacturing sectors

Modern industries are seeking innovative and clean cooling techniques in the machining domain to reduce the cost and environmental impact of manufacturing. To diminish the environmental impact of machining, cryogenic and minimum quantity lubrication (MQL) are recent known cooling/lubrication techniques to determine a perfect alternative to mineral-based emulsion cooling. The cryogenic has superior cooling capabilities and poor lubrication and MQL is vice versa. In this endeavor, a new hybrid MQL-dry ice blasting lubri-cooling technology is proposed in machining as eco-benign technology to investigate sustainability as well as machinability metrics in the milling of AISI-52100. The findings have shown that cutting speed and cooling/lubrication showed the most substantial effect on machinability and sustainability metrics. The experimental findings showed that the cutting speed has more significant effect on specific energy consumption, carbon emission, and machining cost. At highest level of cutting speed (300 m/min), processing time, specific energy consumption, carbon emission and surface roughness was 2.6%, 2.3%, 2.35%, and 22.6% less under hybrid dry ice blasting compared to MQL medium. The shares of the standby time in total energy consumption, carbon emission, and total cost are noticed to be dominant. The hybrid dry ice blasting shared a small fraction of the total cost compared to MQL, and dry ice blasting.

U.S. State approaches to cannabis licensing.

U.S. states have taken varied approaches to licensing cannabis businesses under federal prohibition, but up to now there is limited research on cross-state licensing approaches. This paper provides a systematic analysis of the current licensing strategies taken by all states that have passed medical cannabis laws (MCLs)/recreational cannabis laws (RCLs). We construct comprehensive data on cannabis business licenses offered in each state, as well as metrics for license categories, cost, and issuance volume. We then analyze patterns between these metrics, also considering how long ago states implemented MCLs/RCLs, qualitative licensing aspects, state ideology and voting preference, and state cannabis taxation data. We observe that states tend to license medical cannabis more restrictively than adult-use cannabis: i.e., by offering licenses in fewer categories, at higher cost, in lower issuance volume, and more often mandating vertical integration. Additionally, states that implemented MCLs/RCLs earlier tend to offer licenses in more categories, at lower cost, and in greater volumes. Further, though states that implemented MCLs recently lean conservative and Republican, we do not observe clear relationships between ideology or voting preference and licensing policy. In our supporting results, we observe that a greater share of states with complex licensing structures impose non-retail price cannabis taxes than states overall, and we discuss how states have changed their licensing policies over time.

Frost

"Bad" data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it. Duplicates - multiple but different representations of the same real-world entities - are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential. Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results. To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform. It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results. Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results, as shown in Figure 1.

Levelized Full System Costs of Electricity

Different electricity generating technologies are often compared using the Levelized Costs of Electricity (LCOE), which summarize different ratios of fixed to variable costs into a single cost metric. They have been criticized for ignoring the effects of intermittency and non-dispatchability. This paper introduces the Levelized Full System Costs of Electricity (LFSCOE), a novel cost evaluation metric that compares the costs of serving the entire market using just one source plus storage. Like LCOE, and in contrast to alternatives such as System LCOE, LFSCOE condense the cost for each technology into one number per market. The paper calculates LFSCOE for several technologies using data from two different markets. It then discusses some refinements, including the LFSCOE-95 metric that require each technology to supply only 95% of total demand.

Design and FEM Analysis of High-Torque Power Density Permanent Magnet Synchronous Motor (PMSM) for Two-Wheeler E-Vehicle Applications

Launch of electric vehicles have seen a substantial rise for the past few years in emerging economies like India. In countries like India, the growth and penetration of the electric vehicles in the Indian automotive industry specifically for the two-wheeler segments are driven by the demand surge where cost and motor metrics have a substantial deciding factor. The in-wheel hub-motor, which is the prime mover for the two wheelers, decides the comfort zone of the customer in various metrics such as efficiency, torque, speed range, charging, and hence the distance covered. This paper addresses the design formulation of achieving a high torque Permanent Magnet Synchronous Motor (PMSM) conventionally known as the hub-motor, explicitly for electric two-wheeler application. The hub-motor is aimed for the defined D and L (280 × 30 mm) of volumetric constraints to deliver the rated torque of 50 Nm at the spinning speed of 400 rpm. The hub-motor design is aimed for distance range of 108 km/charge, at the vehicle speed of 54 km/hr for the designed diametric and volumetric constraints. This will lead to a typical cost-effective e-vehicle system since the required distance range of 108 km is achievable at the defined rim size and geometry with an enhanced efficiency greater than 90%. The design is carried out by Finite Element Analysis (FEA) using the electromagnetic software MotorSolve. The results computed are analyzed and validated for the optimal loading conditions for the ambient temperature of 50°C. The results confirm the effectiveness of the proposed design formulation and methodology for achieving the high power density hub-motors for satisfying the customer’s comfort zone in establishing the performance metrics of the electromagnetic system.

The Impact of the COVID-19 Pandemic on Health Care Utilization Among Insured Individuals With Common Chronic Conditions.

Objective:The COVID pandemic has had a significant impact on the US health care system. Our primary objective was to understand the impact of the COVID pandemic on non–COVID-related health care utilization among insured individuals with chronic conditions. Our secondary objective was to examine the differential impact by individual characteristics.Main Data Source:Medical and pharmacy claims data for individuals enrolled in a large insurer across the United States.Research Design:A retrospective and repeated cross-sectional study. Overall and condition-specific health care utilization and cost metrics in (1) March 1 to June 15 and (2) June 16 to September 30, 2020 were compared with the same months during 2016–2019.Subjects:Members of all ages with a diagnosis of diabetes, cardiovascular disease, or chronic kidney disease with commercial or Medicare Advantage insurance.Results:Most non–COVID-related health care utilization decreased drastically on March 1 to June 15, 2020 [odds ratio (OR) range across condition-specific tests: 0.55–0.69; incidence rate ratio (IRR) range for hospitalization/emergency department (ED) visit/outpatient visit: 0.65–0.77] but returned to closer to pre-COVID levels by June 16 to September 30, 2020 [OR range across condition-specific tests: 0.93–1.08; IRR range for hospitalization/ED visit/outpatient visit: 0.77–0.97]. Our study found an enormous increase in telehealth use on March 1 to June 15, 2020 (90–170 times prepandemic levels). A differential impact was observed by age, sex, region of residence, and insurance type.Implications:Further investigation is needed to assess the impact of these changes in health care utilization on long-term health outcomes.

Embodied energy and carbon from the manufacture of cadmium telluride and silicon photovoltaics

<h2>Summary</h2> Looking beyond the traditional cost and efficiency metrics of photovoltaics (PV), this work evaluates the impact of embodied energy, embodied carbon, and energy payback time of two dominant technologies (CdTe and Si) on global decarbonization goals. The relative effects of PV technology type, technological advances, energy grid mix, and recycling are evaluated in terms of fostering decarbonization goals. If the highest carbon-intensity scenarios are realized, 2%–14% of the remaining estimated global carbon budget might be consumed to manufacture modules without including their balance of systems. Applying a carbon cost indicates that CdTe might have an additional value of $0.02–$0.04/W relative to Si PV manufactured with the same energy mix. Due to the scale of the challenge, any actions leading to an increased deployment of thin-film PV and/or a significant decrease in the deployed PV's embodied carbon through changing the manufacturing grid mix have demonstrable value in helping the world stay within its remaining estimated carbon budget.

Modeling System-Wide Urban Rail Transit Energy Consumption: A Case Study of Boston

Rapid transit systems are critical components of urban public transportation networks in their impact, not only on personal mobility but also on the energy and environmental costs associated with network operations. To facilitate effective planning for current and future needs, a framework is required that provides important consumption metrics and also explains the various contributors to energy consumption, along with their interactions. To address this gap, we estimated models that utilized operational and ridership data for the Massachusetts Bay Transportation Authority’s rapid transit system, as well as ambient temperature, to accurately predict system-wide electricity consumption. The models were trained with data from 2019 and tested with data from 2020. The estimated multiple linear regression (MLR) and random forest (RF) models explained 93% and 95% of the variance in the data set, respectively. The MLR model provided predictions with a root mean squared error (RMSE) of 2.7 MWh and mean absolute percentage error (MAPE) of 4.68%, while the RF model resulted in an RMSE of 2.94 MWh and MAPE of 5.01%. We also investigated the impacts of COVID-19 on the transit system by exploring the effects on ridership, energy consumption, cost, and train movement metrics before and during the pandemic. We find that the models are robust and perform well, even with the significant disruptions associated with the COVID-19 pandemic.

Energy enhancement and efficient route selection mechanism using H-SWIPT for multi-hop IoT networks

Simultaneous wireless information and power transfer (SWIPT) is recently emerging as one of the vital solutions to prolong the lifetime of energy constrained wireless sensor nodes. However, current works on SWIPT considered only the immediate past-hop node's RF signal as a source of energy harvesting in multi-hop Internet of things (IoT) networks. In case of weak radio frequency (RF) signal, the amount of harvested energy does not support for continuous communication. Hence, in this paper a new energy harvesting mechanism is proposed which considers multiple sources (MS) such as (1) sink broadcasting energy, (2) co-channel interference, (3) neighbor nodes' RF signal, and (4) immediate past-hop node's RF signal for energy harvesting. Towards such prospect, a new SWIPT architecture is proposed called hybrid SWIPT (H-SWIPT) by integrating time switching (TS) and power splitting (PS) architectures. Furthermore, an efficient route selection mechanism is introduced to minimize the total energy consumption of the path based on an energy cost metric. To validate the proposed mechanism, simulation experiments are conducted and obtained the superiority of H-SWIPT compared with existing methods in terms of average harvested energy. Further, the effectiveness of proposed method performance is investigated through energy cost at different node density and barrier rates.

Economic dispatch by optimization techniques

The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.