Minimum Operating Research Articles

Improving service qualities of cyclic line plans considering heterogeneous passenger origin-destination (OD) flow groups

ABSTRACT Cyclic train services provide huge conveniences to passengers. This work aims to fix one vital promise in major line planning studies that passengers were homogeneous. In fact, passengers’ rail travel expectations are heterogeneous. But those heterogeneities show common needs correlating with travel distances and defined station levels. Therefore, we cluster passenger heterogeneities into divided OD groups, extend service qualities, and add more train line factors to our line planning model. Cyclic line plan from this joint optimisation not only receives service quality improvements but also provides multiple train service types to match clustered OD groups. Moreover, we allow passengers to be served by different train service types, deliver a high level of direct services, and maintain the minimum operation cost. We compare concluded line plans with the results of the generic line planning model and the real-world line plan to show our solution advantages.

Minimum downtime operation and maintenance scheduling for resource-constrained system

Traditional reliability models typically assume that a system can operate until it is failed or not needed due to the mission completion. In practice, the system may have limited resource (e.g., battery power, fuel) and thus have certain maximum operation time due to the resource depletion. This paper makes contributions by modeling and optimizing the expected mission downtime (EMD) for a resource-constrained system that requires replenishing the resource according to a pre-specified operation and maintenance schedule (OMS) for accomplishing a mission of certain duration. Different types of maintenance options characterized by different maintenance durations, efficiencies in reducing the system's cumulative exposure time, and resource replenishing amounts are considered. Based on a new numerical method suggested for the EMD evaluation, the optimal OMS problem is formulated and solved minimizing the EMD. A detailed case study on an unmanned aerial vehicle performing a surveillance mission is conducted to investigate the influence of several system parameters on the EMD and optimization solutions, including the maximum amount of resource, system reliability, resource consumption during system activation and operation, resource replenishment speed, as well as maintenance duration, efficiency, and failure probability.

A review of sewage sludge dewatering and stabilisation in reed bed system: towards the process-based modelling

AbstractThe sludge treatment reed bed is a sustainable sewage sludge management technology that offers promising sludge dewatering and stabilisation, due to its low energy and labour requirements, and minimal operating and maintenance costs. Despite numerous existing studies and increasing applications of sludge treatment reed bed in both developed and developing countries, the lack of standard system configurations and operating regimes led to several operational problems, including bed clogging and poor dewatering and mineralisation efficiency. Sludge dewatering and stabilisation in STBRs are complex processes governed by system- and operation-related factors such as the substrate media, macrophytes, loading regime, sludge characteristics, and climate conditions. Rather than the complex experimental study, process-based modelling has become a promising approach to investigating the influence of specific factors on the efficiency of sludge dewatering and stabilisation. This paper presents an overview of the critical system and operating parameters in sludge treatment reed beds and a summary of the interactions between the factors. Then, a framework of process-based modelling is proposed, which provides a useful platform to study the interactions of operating parameters and other factors in the complex processes of sludge dewatering and stabilisation, which supports the system design and optimisation of sludge treatment reed beds.

Practical Markov Boundary Learning without Strong Assumptions

Theoretically, the Markov boundary (MB) is the optimal solution for feature selection. However, existing MB learning algorithms often fail to identify some critical features in real-world feature selection tasks, mainly because the strict assumptions of existing algorithms, on either data distribution, variable types, or correctness of criteria, cannot be satisfied in application scenarios. This paper takes further steps toward opening the door to real-world applications for MB. We contribute in particular to a practical MB learning strategy, which can maintain feasibility and effectiveness in real-world data where variables can be numerical or categorical with linear or nonlinear, pairwise or multivariate relationships. Specifically, the equivalence between MB and the minimal conditional covariance operator (CCO) is investigated, which inspires us to design the objective function based on the predictability evaluation of the mapping variables in a reproducing kernel Hilbert space. Based on this, a kernel MB learning algorithm is proposed, where nonlinear multivariate dependence could be considered without extra requirements on data distribution and variable types. Extensive experiments demonstrate the efficacy of these contributions.

A nomogram to predict ventricular thrombus in dilated cardiomyopathy patients.

VT (Ventricular Thrombus) is a serious complication of dilated cardiomyopathy (DCM). Our goal is to develop a nomogram for personalized prediction of incident VT in DCM patients. 1267 patients (52.87 ± 11.75 years old, 73.8% male) were analyzed retrospectively from January 01, 2015, to December 31, 2020. A nomogram model for VT risk assessment was established using minimum absolute contraction and selection operator (LASSO) and multivariate logistic regression analysis, and its effectiveness was validated by internal guidance. The model was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). We compared the performance in predicting VT between nomogram and CHA2DS2, CHA2DS2- VASc or ATRIA by AUC, akaike information criterion (AIC), bayesian information criterion (BIC), net reclassification index (NRI), and integrated discrimination index (IDI). 89 patients (7.02%) experienced VT. Multivariate logistic regression analysis revealed that age, left ventricular ejection fraction (LVEF), uric acid (UA), N-terminal precursor B-type diuretic peptide (NT-proBNP), and D-dimer (DD) were important independent predictors of VT. The nomogram model correctly separates patients with and without VT, with an optimistic C score of 0.92 (95%CI: 0.90-0.94) and good calibration (Hosmer-Lemeshow χ2 = 11.51, P = 0.12). Our model showed improved prediction of VT compared to CHA2DS2, CHA2DS2-VASc or ATRIA (all P < 0.05). The novel nomogram demonstrated better than presenting scores and showed an improvement in predicting VT in DCM patients.

Pengembangan Media Pembelajaran Fisika Berbasis Aplikasi Apper Dengan Model Discovery Learning Pada Materi Hukum Newton

&lt;!--StartFragment--&gt;&lt;p class="MsoNormal" style="margin-top: 6.0pt; margin-right: -1.2pt; margin-bottom: .0001pt; margin-left: 0cm;"&gt;&lt;em&gt;&lt;span&gt;This study aims to: (1) Describe the characteristics of the Apper application-based Physics learning media with the discovery learning model on Newton's Law material, (2) To find out how experts evaluate Apper application-based Physics learning media with discovery learning models on Newton's Law material based on material aspects , media design and language. The research method used is research and development with the ADDIE research model only in three stages, namely analysis, design and development. Sources of research data were obtained through the results of assessments by 2 supervisors as expert validators, 3 students as peer reviewers, 6 teachers as reviewers, and 72 students. This research was conducted in 2 senior high schools (SMA), namely SMA Negeri 1 Karanganyar and SMA Negeri Kebakkramat. Data types are divided into Qualitative Data and Quantitative Data. Qualitative data obtained from the results of the assessment of suggestions, input and criticism. Quantitative data were obtained from the validation stage, one-on-one trials, small group trials, and large group trials. Data collection techniques using questionnaires. Data analysis was carried out qualitatively and quantitatively based on Azwar's research. The conclusions of this study are: (1) The characteristics of Apper application-based Physics learning media with discovery learning models on Newton's Law material use features such as Guidelines, Competence, Newton I, Newton II, Newton III, Friction, Application, Evaluation , and Information. The application is 167 kB in size and can only be accessed online via a smartphone with a minimum operating system Android 5.0 Lollipop and iOS 8.1, (2) Expert assessment of the Apper application-based Physics learning media with the discovery learning model on Newton's Law material meets the very good category based on material aspects, media design and language.&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;&lt;!--EndFragment--&gt;

M6A-related lncRNAs predict prognosis and indicate cell cycle in gastric cancer.

Background: N6-methyladenosine (m6A) modification is a common epigenetic methylation modification of RNA, which plays an important role in gastric carcinogenesis and progression by regulating long non-coding RNA (lncRNA). This study is aimed to investigate the potential prognostic signatures of m6A -related lncRNAs in STAD. Methods: The m6A-related lncRNAs with the most significant impact on gastric cancer prognosis in the TCGA database were identified by bioinformatics and machine learning methods. The m6A-related lncRNA prognostic model (m6A-LPS) and nomogram was constructed by Cox regression analysis with the minimum absolute contraction and selection operator (LASSO) algorithm. The functional enrichment analysis of m6A-related lncRNAs was also investigated. The miRTarBase, miRDB and TargetScan databases were utilized to establish a prognosis-related network of competing endogenous RNA (ceRNA) by bioinformatics methods. The correlation of AL391152.1 expressions and cell cycle were experimentally testified by qRT-PCR and flow cytometry. Results: In total, 697 lncRNAs that were identified as m6A-related lncRNAs in GC samples. The survival analysis showed that 18 lncRNAs demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of GC patients. Cox regression analysis and ROC curve indicated that this lncRNA prediction model was an independent risk factor for survival rates. Functional enrichment analysis and ceRNA network revealed that the nomogram was notably associated with cell cycle. qRT-PCR and flow cytometry revealed that downregulation of GC m6A-related lncRNA AL391152.1 could decrease cyclins expression in SGC7901 cells. Conclusion: A m6A-related lncRNAs prognostic model was established in this study, which can be applied to predict prognosis and cell cycle in gastric cancer.

Impulsive Dirac system on time scales

UDC 517.9 We consider an impulsive Dirac system on Sturmian time scales. An existence theorem is given for this system. А maximal, minimal and self-adjoint operators generated by the impulsive dynamic Dirac system are constructed. We also construct the Green function for this problem. Finally, an eigenfunction expansion is obtained.

Transforming collections of Pauli operators into equivalent collections of Pauli operators over minimal registers

Transformations which convert between fermionic modes and qubit operations have become a ubiquitous tool in quantum algorithms for simulating systems. Similarly, collections of Pauli operators might be obtained from solutions of nonlocal games and satisfiability problems. Drawing on ideas from entanglement-assisted quantum error-correcting codes and quantum convolutional codes, we prove the obtainable lower bound for the number of qubits needed to represent such Pauli operations which are equivalent to the initial ones and provide a procedure for determining such a set of minimal register Pauli operations.

Research on the optimal planning method of hydrogen-storage units in wind–hydrogen energy system considering hydrogen energy source

Utilizing wind power (WP) for hydrogen production can alleviate wind curtailment and improve wind energy utilization. The optimal planning of hydrogen-storage units(HSUs) in wind–hydrogen energy system(W-HES) will affect its economic operation. This paper proposes a W-HES model comprising WP, HSUs, and loads. We then utilize clustering algorithm-based WP and power load scenario division method. And taking the minimum investment, operation, and maintenance cost of W-HES as the objective function, use the particle swarm optimization(PSO) algorithm to optimize the planning of HSUs of W-HES. Finally, we investigate the impact of diverse hydrogen energy sources and daily hydrogen load variations on the optimal planning outcomes of HSUs. It will provide a valuable insights for the planning and configuration of HSUs in W-HES.

Signature construction and molecular subtype identification based on immune-related genes for better prediction of prognosis in hepatocellular carcinoma

ObjectiveHepatocellular carcinoma (HCC) immunotherapy is a focus of current research. We established a model that can effectively predict the prognosis and efficacy of HCC immunotherapy by analyzing the immune genes of HCC.MethodsThrough the data mining of hepatocellular carcinoma in The Cancer Genome Atlas (TCGA), the immune genes with differences in tumor and normal tissues are screened, and then the univariate regression analysis is carried out to screen the immune genes with differences related to prognosis. The prognosis model of immune related genes is constructed by using the minimum absolute contraction and selection operator (lasso) Cox regression model in the TCGA training set data, The risk score of each sample was calculated, and the survival was compared with the Kaplan Meier curve and the receiver operating characteristic (ROC) curve to evaluate the predictive ability. Data sets from ICGC and TCGA were used to verify the reliability of signatures. The correlation between clinicopathological features, immune infiltration, immune escape and risk score was analyzed.ResultsSeven immune genes were finally determined as the prognostic model of liver cancer. According to these 7 genes, the samples were divided into the high and low risk groups, and the results suggested that the high-risk group had a poorer prognosis, lower risk of immune escape, and better immunotherapy effect. In addition, the expression of TP53 and MSI was positively correlated in the high-risk group. Consensus clustering was performed to identify two main molecular subtypes (named clusters 1 and 2) based on the signature. It was found that compared with cluster 1, better survival outcome was observed in cluster 2.ConclusionSignature construction and molecular subtype identification of immune-related genes could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC immunotherapy.

Conceptual process design and process optimization of triple-column pressure-swing distillation for the separation of ternary mixtures embedding two azeotropes with different feed composition via thermodynamic insights and genetic algorithm

In this article, we take the tetrahydrofuran (THF)/ethanol (ETOH)/methanol (MeOH) as an example to investigate the process design of the triple-column pressure-swing distillation (TCPSD) for the separation of Serafimov’s class 2.0-2b mixtures with different feed composition. Two basic topology structures (TSI, TSII) and two separation sequences (SSI, SSII) are investigated, then, process optimization with total annual cost (TAC) as objective function is conducted via genetic algorithms. Heat integration (HI) is also applied to TCPSD. From a new perspective, the separation of THF/ETOH/MeOH can be regarded as the azeotropic distillation for THF/ETOH with MeOH as azeotropic agents based on thermodynamic features of Serafimov’s class 2.0-2b mixtures. The feasible topology structure and separation sequence of TCPSD for Serafimov’s class 2.0-2b mixtures depend on the feed composition and operating pressure. The feed composition is divided into three regions according to the concentration of methanol in azeotropic distillation column. The two critical points are azeotropic points at allowed minimum and maximum operating pressure. Especially, when feed composition is below azeotropic point at allowed minimum operating pressure, TSI is not feasible due to less MeOH as azeotropic agents. Furthermore, TCPSD combining pure azeotropic agents (ATPSD) can be developed based on this new insight. Then, two feed compositions are used to illustrate the detailed conceptual design of three topology structures as well as corresponding two separation sequences and their economic performance. The results show that when feed composition is 0.4ETOH/0.25THF/0.35MeOH, TSI and TSII are feasible, the best economical configurations without and with heat integration is TSII-SSII and TSII-SSII-HI. When feed composition is 0.4ETOH/0.4THF/0.2MeOH, TSII and ATPSD are feasible, the best economical configurations without and with heat integration is ATPSD-SSI and ATPSD-SSI-HI. Furthermore, ATPSD-SSI and ATPSD-SSI-HI have 18.28% and 19.38% TAC reduction than TSII-SSI and TSII-SSI-HI, respectively. In summary, feasible and the best economic topology structure and separation sequence of TCPSD for Serafimov’s class 2.0-2b mixtures depend on the feed composition and operating pressure. In addition, the proposed ATPSD shows good economic performance for Serafimov’s class 2.0-2b mixtures.

Robust optimal dispatching model and a benefit allocation strategy for rural novel virtual power plants incorporating biomass waste energy conversion and carbon cycle utilization

To optimize the utilization of rural biomass waste resources (e.g., straw and solid waste), biomass waste energy conversion (BWEC) and carbon cycle utilization (CCU) are integrated into a traditional virtual power plant, i.e., a rural BWEC-CCU-based virtual power plant. Furthermore, a fuzzy robust two-stage dispatching optimal model for the BWEC-CCU-based virtual power plant is established considering the non-determinacy from a wind power plant (WPP) and photovoltaic (PV) power. The scheduling model includes the day-ahead deterministic dispatching model and real-time uncertainty dispatching model. Among them, in the day-ahead dispatching phase, the dispatching plan is formulated with minimum operating cost and carbon emission targets. In the real-time dispatching phase, the optimal dispatching strategy is formulated aiming at minimum deviation adjustment cost by applying the Latin hypercube sampling method. The robust stochastic theory is used to describe the uncertainty. Third, in order to achieve optimal distribution of multi-agent cooperation benefits, a benefit distribution strategy based on Nash negotiation is designed considering the three-dimensional interfering factor of the marginal benefit contribution, carbon emission contribution, and deviation risk. Finally, a rural distribution network in Jiangsu province, China, is selected for case analysis, and the results show that 1) the synergistic optimal effect of BWEC and CCU is obvious, and the operation cost and deviation adjustment cost could decrease by 26.21% and 39.78%, respectively. While the capacity ratio of WPP + PV, BWEC, and CCU is 5:3:2, the dispatching scheme is optimum. 2) This scheduling model can be used to formulate the optimal scheduling scheme. Compared with the robust coefficient Γ = 0, when Γ = 1, the WPP and PV output decreased by 15.72% and 15.12%, and the output of BWEC and CCU increased by 30.7% and 188.19%, respectively. When Γ∈ (0.3, 0.9), the growth of Γ has the most direct impact on the dispatching scheme. 3) The proposed benefit equilibrium allocation strategy can formulate the most reasonable benefit allocation plan. Compared with the traditional benefit allocation strategy, when the proposed method is used, the benefit share of the WPP and PV reduces by 5.2%, and the benefit share of a small hydropower station, BWEC, and CCU increases by 1.7%, 9.7%, and 3.8%, respectively. Overall, the proposed optimal dispatching and benefit allocation strategy could improve the aggregated utilization of rural biomass waste resources and distributed energy resources while balancing the benefit appeal of different agents.

RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing, and agriculture problems. However, the DBTL cycle's Learn (L)step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, "RespectM," based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4,321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a "heterogeneity-powered learning (HPL)" based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.

Fuel Cell Fed Electrical Vehicle Performance Analysis with Enriched Switched Parameter Cuk Converter

Background: This study aims to evaluate the performance of a 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC) fed Electric Vehicle (EV) using an Enriched Switched Parameter Cuk (ESPC) converter and an Elman Back Propagation (EBP) maximum power point tracking algorithm (MPPT). The acceptance of fuel cell-fed EVs in modern society is critical to the development of a pollution-free environment. One of the significant contributors to excessive pollution is transportation on public roads using internal combustion engines powered by crude oil as their primary energy source. Objective: This study identifies suitable high voltage gain DC-DC converters with minimum duty cycle operation for fuel cell-fed electric vehicle systems and develops an intelligent MPPT controller for hybrid electric vehicle applications. Methods: In this study, MATLAB/Simulink environment is used to design a 1.26 kW PEMFC powered electric vehicle. To integrate PEMFC to BLDC motor, an Enriched Switched Parameter Cuk converter is built with a high static converter voltage gain. Results: The effectiveness and performance of the fuel cell-fed EV system are investigated using perturb and observe method and Elman Back Propagation MPPT approaches for various fuel cell input temperature conditions and intervals. Conclusion: This study discusses the use of low-voltage fuel cell sources with power electronic converters that are available for various high gains in the literature. The proposed ESPC is designed to reduce stress on power converter components and is intended for low-voltage FC-fed electric vehicle applications.

An optimal transport theory based approach for efficient dispatch of transactions in energy markets

AbstractNowadays, transactive energy markets (TEMs) are emerging as interesting frameworks in deregulated power markets to control the balance of supply and demand in the entire electrical network. Due to wide deployment of renewable energy resources, grid connected micro‐grids, and open access transmission and distribution networks, the planning and operation of TEMs become complex. So, an efficient optimal dispatch model for TEMs should be developed to achieve the objectives of TEMs, such as feasible sizes of transactions and optimal dispatch of these transactions with minimal operating costs. The transactive dispatch problem is similar to the resource allocation/matching problem. Recently, optimal transport (OT) has received significant attention in various fields including optimization theory and resource matching problems due to its potency and relevance in modeling and optimization. An OT‐based approach is proposed here for optimal dispatch of transactions in energy markets while minimizing the cost of transactions considering the operating constraints of the system. The proposed approach can efficiently determine the feasible sizes of transactions without any security issues. The optimal solutions of the OT‐based approach are obtained using a Sinkhorn iterative technique. Also, the load uncertainties are considered in this work to analyse the impacts of load uncertainties on the optimal dispatch of transactions. The numerical simulation results on the modified IEEE 9‐bus system, modified IEEE 57‐bus system, modified IEEE 118‐bus system, and Indian Northern Regional Power Grid (NRPG) system illustrate the efficacy of the proposed OT‐based framework.

Simulasi Pola Operasi Waduk Batutegi, Provinsi Lampung

Batutegi Reservoir is a multi-purpose reservoir that is used to increase the water supply to the Argoguruh Weir to expand the irrigation area in the Sekampung Irrigation System. Batutegi Reservoir supplies the water demand not only for irrigation, covering an area of 66.573 Ha, but also for hydroelectric power generation (hydropower) of 2 x 14 MW and the raw water supply of 2.250lt/s. The operational curves of the Batutegi Reservoir were prepared in 2017, aiming to control the water release to ensure the planned demand is fulfilled. However, the reservoir operation curves must be reviewed at least once every 5 (five) years to comply with the changes in reservoir conditions and the development of its benefits/functions. The study aimed to determine the reservoir reliability by updating the reservoir operation curves in 3 conditions; wet year, normal year, and dry year using the SOR (Standart Operating Release) simulation method according to the inflow and outflow during the mid-month period. Results showed that in the wet year, normal year, and dry year, the reservoir reliability in supplying water for irrigation, raw water, and hydropower is satisfactory in each period. In conclusion, although some simulation periods showed the final RSL (Reservoir Water Levels) of the reservoir are below the upper limit of the operation curves, they are still above MOL (Minimum Operation Level) hydropower level meaning all supply for irrigation, raw water, and hydropower can be 100% fulfilled.&#x0D; &#x0D;

Improved droop control strategy for an MMC-MTDC connected to offshore wind farms with dynamic correction of the actual operating point

To solve the problems of overload of converter stations and to reduce operating loss of multi-terminal High-Voltage DC (MTDC) grids based on modular multilevel converters (MMC), an improved droop control strategy is proposed in this paper. The novelty of the suggested strategy is that two droop coefficients aimed at reducing system loss and saving power margin are non-linear weighted combined to realize multi-objective optimized operation in the full operating voltage range. First, a power allocation mechanism model of an MTDC system is built to show the influence of the droop coefficient on the DC voltage under the droop control strategy. Then, the droop coefficient under minimum operating loss is calculated and the adaptive droop coefficient is introduced to save enough power margin when the system meets large unbalanced power. In addition, a self-coordinating weight factor with a low-voltage acceleration factor is employed so that MMC can correct the operating point dynamically. Finally, a derived five-terminal MMC-MTDC is built in PSCAD/EMTDC and the effectiveness and feasibility of the proposed control method is validated by simulation.

Collaborative Planning of Community Charging Facilities and Distribution Networks

The construction of community charging facilities and supporting distribution networks based on the predicted results of electric vehicle (EV) charging power in saturation year has resulted in a large initial idleness of the distribution network and a serious waste of assets. To solve this problem, this paper proposes a collaborative planning method for urban community charging facilities and distribution networks. First, based on the load density method and occupancy rate to predict the base electricity load in the community, the Bass model and charging probability are used to predict the community’s electric vehicle charging load. Taking the minimum annual construction and operation costs of the community distribution network as the objective function, the power supply topology of the distribution network for a new community is optimized by using Prim and single-parent genetic algorithms. Finally, the proposed scheme is verified by using the actual community data of a certain city in China as an analysis example, and the scheme of one-time planning of the distribution network and yearly construction of charging facilities is given.

On the SRAM with comb-shaped nano FETs advancing to 3 nm node and beyond

In this paper, we describe several scaling challenges of SRAM consisting of FinFETs and horizontal Gate-All-Around (GAA) Nano-sheet Field-Effect-Transistors (NshFETs), especially investigations related to Design-Technology Co-Optimization (DTCO). Comb-shaped channel FETs (CombFETs), which integrates the advantages of FinFETs and NshFETs were introduced to a six-transistor (6 T) SRAM cell and the corresponding simulations were established. The results show that compared with both FinFETs and NshFETs, CombFETs have larger the effective channel width or higher current at the same footprint and larger room for improving the mobility mismatch between N/P transistors. Moreover, CombFET SRAM showed ∼55% increase in effective channel width, 15% improvement of read static noise margin, ∼25% write speed gain, 88% read speed gain or 20% decrease in the minimum operating voltage (Vmin).