Optimization Program Research Articles

Transmission expansion planning considering solar photovoltaic using novel binary particle swarm optimization

Due to the demand growth and an increase in uncertain generation from solar photovoltaics which are promoted instead of fossil fuel, the transmission expansion planner needs to take this uncertain generation into account. This paper proposes Transmission Expansion Planning (TEP) considering solar PhotoVoltaic (PV) using Novel Binary Particle Swarm Optimization (NBPSO) algorithm to define the location and number of the Transmission Lines Candidate (TLC) that will be installed into the existing power system. The goal is to minimize the total cost consisting of the investment cost of the newly installed TL and the operation cost of power generation. The case study consists of two cases. The TEP when solar PV is disregarded is solved and analyzed in the first case. For the second case, the TEP when considering solar PV generation is studied. The aforementioned cases were analyzed on the IEEE 24 bus test system. The optimization program is written by MATLAB software with the MATPOWER tool to solve optimal power flow. From the simulation result, the NBPSO can find the TEP solution with the lowest investment cost and operating cost while the obtained results do not violate the power system constraints.

PENENTUAN SKALA PRIORITAS DAN POLA PENANGANAN KERUSAKAN JALAN LINGKUNGAN KAWASAN PERMUKIMAN KUMUH DI KOTA MATARAM

Population growth, economy and urbanization are several factors causing the creation of slum areas in urban areas. One indicator of a slum area is the poor quality of the road surface. This condition becomes a negative paradigm towards the implementation of development by the Government. Weak databases lead to not optimal programming and planning processes that lead to not optimal implementation and supervision in the field. This study aims to compile a database for environmental road handling plans in slum settlement areas based on Geographic Information Systems (GIS), so as to provide information on priority scales and patterns of environmental road management in slum settlement areas. The method used is descriptive quantitative by weighting based on 3 parameters, namely: 1) road management program, 2) the number of dwellings/houses that have direct access to the road and, 3) other considerations including strategic location value, population, and condition socioeconomic. This research concludes that the programming and planning process which is carried out subjectively based on conditions and needs in the field can make efficient use of the budget so that it is right on target in the following years. So that it can be used as a recommendation for determining priorities on each environmental road segment and a program for handling patterns of slum area scale in the city of Mataram

Three-Dimensional Stability of Unsaturated Soil Slopes Reinforced by Frame Beam Anchor Plates

The frame beam anchor plate (FBAP) is a new supporting structure to reinforce backfill. The current studies of slope stability concentrate on homogeneous isotropic dry or saturated slopes according to two-dimensional assumptions. In engineering practice, however, the soils are heterogeneous and anisotropic and exhibit evident unsaturated characteristics, and the failure surface shows obvious three-dimensional (3D) characteristics universally. A new method for evaluating the 3D stability of unsaturated soil slopes reinforced with FBAPs is established in this paper. The upper-bound analytical solution of slope safety factor (Fs) is derived based on the energy balance equation incorporated with the 3D spiral failure mechanism and the gravity increase method (GIM). Comparisons verified the effectiveness of the methodology and the optimization program. The influence of unsaturated characteristics, support structure design parameters, and seismic force on the 3D slope stability is evaluated through parameter analysis. The results show that the shear strength prediction model is crucial in slope stability assessments. The stability of slopes can be significantly improved by about 29%–52% by using FBAPs. Seismic action has a significant negative impact on the slope stability. When kh increases from 0 to 0.3, Fs generally decreases by about 40%–55%.

ID: 209539 Effect of Automated Deep Brain Stimulation (DBS) on Essential Tremor (ET) Patient Experiences and Sleep

Essential tremor (ET) is a debilitating movement disorder causing tremor that can have a significant impact on daily activities. Deep brain stimulation (DBS) of the ventral intermediate (VIM) nucleus of the thalamus is an approved treatment for ET. Although most patients benefit from DBS, for optimal benefit it may be necessary to utilize different programming parameters throughout a 24-hour period. Manually switching between optimal programs can be cumbersome. Additionally, to extend battery longevity, patients often turn off stimulation while sleeping. In this study, we aim to determine the effects of an automated DBS paradigm on patient experiences and sleep outcomes.

CHARACTERISTICS OF REHABILITATION OF PEOPLE WITH SPINAL CORD INJURY

The article shows the results of the study concerning the rehabilitation peculiarities of young people with cervical spinal cord injuries. The aim of the study is to develop the optimal rehabilitation program, motor development and motor compensation, physical characteristics, mental health, nerve and muscle function, and the formation of positive motivation during special training programs. It will help young people adapt to their new situation, and to integrate them into society. The results of the experiment have shown quite a low level of development of motor skills, manual motor skills, daily living skills, and little regulation of the visceral motor system. The aim of the present work is to present correctional rehabilitation during three phases (early, late, and residual). 150 students, including 90 boys and 60 girls aged 16 to 18 with incomplete cervical spinal cord injuries participated in the study. It has been proven that the main psychological and pedagogical conditions, which fully ensure the effectiveness of psychophysical and social rehabilitation of elder students with incomplete cervical spinal cord injuries are: staged approach to rehabilitation that takes into account the complexity of this disease and personal characteristics, creating comfortable conditions and situations that form optimistic expectations about students’ capabilities, and positive didactic rehabilitation motivation; a comprehensive approach to the correction using psychological and social factors, specific motor training, methods of control, motor skills formation, physical development, correction, and motor compensation; control of the level of fundamental motor skills; a comprehensive approach to social adaptation; and finally high physical activity level of each person with disabilities in official and independent types of physical therapy. Specially organized training according to all the criteria and indicators, positively influenced the students from the experimental group in contrast to the control group, where the indicators remained quite low.

Focus Issue Articles on Diagnostic Assay Development and Validation: The Science of Getting It Right

Focus Issue Articles on Diagnostic Assay Development and Validation: The Science of Getting It Right

Improving the Feasibility of Moment-Based Safety Analysis for Stochastic Dynamics

Given a dynamical system modelled via stochastic differential equations (SDEs), we evaluate the safety of the system through its exit time moments. Using appropriate semidefinite positive matrix constraints, an SDP moment-based approach can be used to compute moments of the exit time. However, the approach is impeded when analyzing higher dimensional physical systems as the dynamics are limited to polynomials. Computational scalability is also poor as the dimensionality of the state grows, largely due to the combinatorial growth of the optimization program. We propose methods to make feasible the safety analysis of higher dimensional physical systems. The restriction to polynomial dynamics is lifted by using state augmentation which allows one to generate the optimization for a broader class of nonlinear stochastic systems. We then reformulate the constraints to mitigate the computational limitations associated with an increase in state dimensionality. We employ our methods on two example processes to characterize their safety via exit times and show the ability to handle multi-dimensional systems that were previously unsupported by the existing SDP method of moments.

Electromagnetic design study of a superconducting longitudinal gradient bend magnet based on the HALF storage ring

The National Synchrotron Radiation Laboratory is planning to build a 2.2 GeV diffraction-limited storage ring, the Hefei Advanced Light Facility (HALF), with a 6BA lattice structure and a large number of longitudinal gradient bends (LGBs). In order to increase the radiated photon energy to the hard X-ray band and to reduce the natural emittance, a superconducting longitudinal gradient bend (SLGB) magnet is planned to be used on HALF in the future, which requires a magnetic field integral of 0.40 T·m over a length of 0.46 m and a peak field of about 5 T. A SLGB has many structural parameters, such as the geometric parameters of the superconducting coils and the thickness of the yoke, etc. Their influence on the magnetic field distribution has been studied, although the computation of the 3D magnet model is very slow. Considering the longitudinal field variation, the vertical gap limitation, the field homogeneity requirement and the critical current of the superconducting wire, a method is developed to simplify the 3D magnet simulation by a corresponding 2D model, so that the longitudinal magnetic field profile of a 3D model can be approximately calculated in a 2D model with faster speed. Due to the faster calculation speed of 2D models, a Mathematica-based optimization program is developed and relatively good structural parameters for the HALF SLGB are found by the program. The simulation results show that the physical design requirements can be well met. The electromagnetic design for the HALF SLGB magnet is based on the current JC level of NbTi superconductor due to its excellent mechanical performance.

Synergies and Trade-Offs in Water Resources Management in the Bafing Watershed under Climate Change

Hydropower is the world’s largest and most widely used renewable energy source. It is expected that climate and land use changes, as well as hydraulic engineering measures, will have profound impacts on future hydropower potential. In this study, the hydropower potential of the Bafing watershed was estimated for the near future (P1: 2035–2065) and the far future (P2: 2065–2095). For this purpose, the moderate scenario ssp 126 and the medium–high scenario ssp 370 were used to explore possible climate impacts. In three management scenarios, we tested the interaction of the existing Manantali Dam with two planned dams (Koukoutamba and Boureya) using an ecohydrological water management model. The results show that, under ssp 126, a 6% increase in annual river flow would result in a 3% increase in hydropower potential in the near future compared with the historical period of 1984–2014. In the far future, the annual river flow would decrease by 6%, resulting in an 8% decrease in hydropower potential. Under ssp 370, the hydropower potential would decrease by 0.7% and 14% in the near and far future, respectively. The investment in the planned dams has benefits, such as an increase in hydropower potential and improved flood protection. However, the dams will be negatively affected by climate change in the future (except in the near future (P1) under ssp 126), and their operation will result in hydropower potential losses of about 11% at the Manantali Dam. Therefore, to mitigate the effects of climate change and adjust the operation of the three dams, it is essential to develop new adaptation measures through an optimization program or an energy mix combining hydro, solar, and wind power.

A Bim-Based Automatic Design Optimization Method for Modular Steel Structures: Rectangular Modules as an Example

During the promotion of the modular steel structure in the architecture, engineering, and construction (AEC) industry, building information modeling (BIM) is leveraged to integrate the design process into the whole construction sequence. The absence of standards and interactive, tech-friendly tools for project participants limits the general implementation of the BIM-based design process. The present study proposes an automatic design optimization method based on the BIM platform for modular steel structures. The method consists of digital modeling sequences that contain data exchange between different software applications and the program of structural design optimization. A prototype workflow of the method is explained and assessed in a case study to indicate its reliability and practicability. The proposed design coheres with common design rules and enhances the utilization rate of column structure by 40–50% with minimal redundancy compared to initial designs. The proposed method is also discussed through interviews with and surveys of engineers working in the AEC industry in terms of its potential adoption in actual projects. The discussion shows that this method can reduce the time consumption of the model creation and optimization of modular steel structures effectively. Special knowledge of the relevant software is no longer a hindrance for engineers.

Optimization of a near-zero-emission energy system for the production of desalinated water and cooling using waste energy of fuel cells

In the present study, a biomass-based multi-purpose energy system that can generate power, desalinated water, hydrogen, and ammonia is presented. The gasification cycle, gas turbine, Rankine cycle, PEM electrolyzer, ammonia production cycle using the Haber-Bosch process, and MSF water desalination cycle are the primary subsystems of this power plant. On the suggested system, a thorough thermodynamic and thermoeconomic evaluation has been conducted. For the analysis, the system is first modeled and investigated from an energy point of view, after which it is similarly studied from an exergy point of view before the system is subjected to economic analysis (exergoeconomic analysis). The system is evaluated and modeled using artificial intelligence to aid in the system optimization process after energy, exergy, and economic modeling and analysis. The resulting model is then optimized using a genetic algorithm to maximize system efficiency and reduce system expenses. EES software does the first analysis. After that, it sends the data to MATLAB program for optimization and to see how operational factors affect thermodynamic performance and overall cost rate. To find the best solution with the maximum energy efficiency and lowest total cost, multi-objective optimization is used. In order to shorten computation time and speed up optimization, the artificial neural network acts as a middleman in the process. In order to identify the energy system's optimal point, the link between the objective function and the choice factors has been examined. The results show that increasing the flow of biomass enhances efficiency, output, and cost while raising the temperature of the gas turbine's input decreases cost while simultaneously boosting efficiency. Additionally, according to the system's optimization results, the power plant's cost and energy efficiency are 37% and 0.3950$/s, respectively, at the ideal point. The cycle's output is estimated at 18900 kW at this stage.

Research for Optimal Programs for Controlling the Relative Motion of a Spacecraft with Limited Thrust

Research for Optimal Programs for Controlling the Relative Motion of a Spacecraft with Limited Thrust

Does optimized daytime/nighttime programming reduce insertable cardiac monitor event burden?

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Boston Scientific. Background Insertable Cardiac Monitors (ICM) are widely used for long term cardiac arrhythmia monitoring and diagnosis. ICMs can generate episodes (and associated alerts) that are non-actionable from a clinical standpoint leading to alert fatigue. In particular, non-actionable bradycardia and pause events may occur at nighttime due to reduced sympathetic tone during sleep. We have used stored auto-detected events to better understand optimal programming with nighttime settings to eliminate potentially clinically non-actionable episodes and improve clinic workflow. Objective Evaluate the reduction in clinic episode review burden in bradycardia (brady) and pause episodes with a new daytime/nighttime programming optimization in LUX-Dx ICM. Methods We analyzed data from 2,555 patients implanted with LUX-Dx ICM that included 132,928 detected brady and pause episodes stored remotely. We evaluated optimized settings with separately programmed nocturnal (11p-7a) and nominal daytime (7a-11p) rates and durations as compared to full-time nominal settings (24-hour nominal). Optimized nocturnal programming was set to trigger event storage on a heart rate setting of ≤30 bpm as well as a duration setting of ≥5s for nocturnal brady and pause episodes. The nominal day time and 24-hour settings for brady and pause episodes were HR ≤40bpm and duration ≥3s. The episode burden was analyzed retrospectively using stored episodes comparing optimized versus full-time nominal programming. Results A simulation using the optimized settings showed that 98% and 90% of the nocturnal bradycardia (n = 1909) and pause episodes (n = 1198) had a HR >30bpm, and duration <5s respectively. As a result, the optimized settings for nocturnal episodes reduced 75% and 56% of bradycardia and pause episodes, respectively. (See Figure 1). At a patient level, 63% and 25% of patients had a substantial reduction (>=50%) in brady and pause episodes, respectively. Conclusion The optimized programming settings, which simply adjusts for nocturnal physiology, may substantially improve clinical workflow by reducing potentially clinically non-actionable episodes and associated alerts detected by the LUX-Dx ICM. More clinical correlative studies are needed to understand patient risk.

Expert opinion on optimal programming strategy to increase AV synchrony in different profiles of patients implanted with leadless ventricular pacemaker

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Medtronic Background Recently published real-world data showed that Micra leadless pacemaker implanted in the right ventricle can provide atrioventricular (AV) synchronous pacing by accelerometer-based mechanically sensing atrial contractions in patients with a high pacing burden (1). Routinely performed programming optimization of several novel parameters (particularly concerning the A3 and A4 windows) is key to improving AV synchrony (AVS). Purpose This project aims to define optimal programming recommendations for defined patient profiles. Methods An Experts Panel (ExpP), made up of 11 electrophysiologists from high-volume centers (37±17 Micra AV procedures/year) contributing to the Medtronic Micra AV prospective Post Approval Registry (PAR), was assembled to share their opinion about the optimal programming strategy to improve AVS in patients with: 1) slow sinus rhythm and high degree AV block, 2) fast sinus rhythm and high degree AV block, 3) intermittent AV-block. The following steps were followed: - Data from Micra AV PAR database were retrospectively analyzed and shared with ExpP. - An online survey was completed by the ExpP to evaluate their current programming strategy, based on PAR data. - After a face-to-face meeting to discuss the survey results, a second survey round was completed to reach consensus on the optimal programming strategy. Agreement was considered reached when >75% of responders did not disagree. Results Specific detailed programming recommendations have been developed for the three different patient profiles (Figure 1). Most importantly, it was agreed that Auto A3 Threshold and tracking check should be turned off in all patients, AV conduction mode switch should be turned off in all patients with high degree AVB, and the lower rate should be programmed in general to 50bpm. In addition, the following general recommendations were agreed on by the ExpP: - A pre-implant echocardiogram with a particular focus on diastolic function should always be performed to identify patients who may benefit from Micra AV implantation. - Micra AV auto-setup should always be performed at implant. - Before discharge, basic adjustments based on results of the Manual Atrial. Mechanical (MAM) test, patient activity level, and age should be performed. - At each follow-up visit, fine-tuning programming based on patient feedback, device data (in particular %AM-VP) and performance of a MAM Test. Conclusions An ExpP of Micra AV implanters agreed on general recommendations for Micra AV patients’ management before implant and during follow-up. Specific programming recommendations for patients with slow sinus rhythm and high degree AV block, fast sinus rhythm and high degree AV block, and intermittent AV block have been developed. A validation phase will show if the application of these indications in clinical practice may improve AVS.

Optimal Preventive Maintenance, Repair, and Replacement Program for Catch Basins to Reduce Urban Flooding: Integrating Agent-Based Modeling and Monte Carlo Simulation

Urban sprawl has resulted in great losses of vegetation areas, an increase in impervious surfaces, and consequently the direct flow of stormwater into stream channels (i.e., the immediate flow of stormwater into stream channels, in comparison to the indirect flow that is represented by practices aiming to retain stormwater for a certain period of time and treat the polluted stormwater prior to flowing into the stream channels such as detention/retention basins, among others). Stormwater management systems such as catch basins (CBs) are needed to reduce the effect of stormwater runoff. Preventative maintenance, repair, and replacement of CBs are critical to achieve stormwater management best practices. Those practices prevent the blockage of the stormwater system, limit the pollutants in storm sewers, and reduce the risk of flooding. However, no preceding research studies have been conducted to model and simulate the serviceability of CBs and to determine optimal strategies for operating CBs. To that extent, this study establishes a framework to develop and validate an optimal and adaptive maintenance, repair, and overhaul (MRO) strategy for CBs. In relation to that, an agent-based model (ABM) integrated with Monte Carlo simulation was developed for all 560 CBs in New York City’s District 5 and was statistically validated using 99% confidence intervals. The MRO parameters were optimized to minimize the total cost of the system and attain the desired level of serviceability of CBs. Sensitivity analysis was conducted to guide the maintenance planning process of CBs and reveal the effect of the input parameters on the model’s behavior. In addition, ten thousand Monte Carlo iterations were simulated to derive the distributions of the defined parameters. The results proved that in order to minimize the overall cost of repair, maintenance, and replacement of CBs and attain a minimum serviceability threshold of 80%, the following optimal MRO policy needs to be implemented: having seven service crews (where service crews are human resources (i.e., MRO teams) needed to perform the required maintenance, repair, and replacement work), implementing a replacing policy, and replacing CBs after five maintenance periods. The findings revealed that the service crews represent the most critical parameter in affecting the total cost and serviceability of CBs. This research contributes to the existing literature by offering a better knowledge of the management process of CBs and devising optimal MRO strategies for properly operating them. Ultimately, this research helps decision-makers and engineers increase the lifespan of CBs and limit their risks of breakdown, increase their efficiency, and avoid unnecessary costs. The proposed model is flexible and can be implemented to any geographical area and with other model/system parameters, which makes it adaptive for any scenario and area presented by the user. Finally, maintaining stormwater management practices helps in protecting the environment by decreasing the demand on stormwater systems, reducing flooding, protecting people and properties, promoting healthier rivers, and consequently creating more sustainable communities.

Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs

Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO2 emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.

Impact of a cardio-oncology rehabilitation framework among high cardiovascular risk cancer survivors: results from the CORE trial

Abstract Funding Acknowledgements Type of funding sources: None. Background Cancer survivors are challenging patients, as they often present increased cardiovascular (CV) risk. In this background, cardio-oncology rehabilitation frameworks for specific cancer patients have been proposed. However, optimal program designs, as well as their overall safety and efficacy in different subsets of patients, are not fully ascertained. Purpose To assess the impact of a cardio-oncology rehabilitation based framework for cancer patients at increased CV risk, compared to a community-based exercise training (CBET), on cardiorespiratory fitness (CRF) and cardiovascular risk factor (CVRF) control. Methods The CORE study was a single-center, prospective, randomized controlled trial enrolling adult cancer survivors exposed to cardiotoxic cancer treatment (anthracycline, radiotherapy on thoracic wall, anti-human epidermal growth factor receptor-type 2 drugs) and/or with previous CV disease. Participants were randomized to an 8-week center-based CR program (CBCR) or CBET, twice a week. Primary endpoint was CRF (assessed by the VO2peak, derived from a symptom-limited cardiopulmonary exercise test, performed on a treadmill); secondary endpoints were physical activity [International Physical Activity Questionnaire (IPAQ)], psychosocial parameters [Hospital Anxiety and Depression Scale (HADS)], blood pressure, body composition, lipid profile and health literacy [Newest Vital Sign (NVS) questionnaire]. Results 80 patients were included (mean age 54.5±14,12, 61 women; 66% breast cancer, 27.5% lymphoma); 75 patients completed the intervention (CBCR N= 38; CBET N=37). There was a significant improvement in the CBCR compared to the CBET group in VO2peak (∆ +2.1±2.8 vs +0.8±2.5 mL.kg-1.min-1, p=0.033) and in the following outcomes: resting systolic blood pressure (p<0.0001); body mass index (p<0.0001), lean and fat body mass (p<0.0001), waist circumference (p<0.0001); total cholesterol (p =0.001), triglycerides (p =0.008) and LDL cholesterol (p =0.003); HADS Anxiety score (p =0.003) and Depression score (p =0.009), weekly physical activity levels (p<0.0001) and health literacy scores (p<0.0001). No major CV events were reported. Conclusion The CORE trial demonstrates superior results of a CBCR regarding CRF and CVRF control. Given the higher cardiovascular risk in several groups of cancer patients, our data provides novel insights into optimized preventive strategies in this complex patient population.

Interaction between atrial-ventricular and ventricular-ventricular delay in CRT

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): United Hospital Medical Education and Research Committee, United Hospital Foundation. Background Appropriate programming of atrial-ventricular delay (AVD) and ventricular-ventricular delay (VVD) is critical for delivery of optimal cardiac resynchronization therapy (CRT). However, there is debate over how to determine optimal programming for a given patient. This is complicated by the use of 2 distinct methods of defining the activation timing at a given AVD and VVD used by different device manufacturers. Purpose To describe how electrical dyssynchrony is affected by changes in AVD, VVD and the method used to program AVD and VVD. Methods We measured electrical synchrony at multiple combinations of AVD/VVD in 41 patients with underlying left bundle branch block (LBBB) using a novel methodology called electrical dyssynchrony mapping (EDM). Electrical dyssynchrony was quantified by cardiac resynchronization index (CRI), calculated as % change in area under the curve between combinations of 9 anterior and 9 posterior surface electrograms. Results EDM (Figure 1) shows CRI at multiple combinations of atrial-to-RV-paced, (A-RVp, y-axis) and atrial-to-LV-paced (A-LVp, x-axis) intervals. VVD changes to produce LV preactivation are shown at short and long AVDs using the methods of 2 different CRT device manufacturers: Method 1: fixing A-RVp and shortening A-LVp; and Method 2: fixing A-LVp and lengthening A-RVp. During simultaneous BiV pacing, CRI at short AVD (61.7±20.9) was significantly (p<0.001) greater than at long AVD (46.9±14.0). On average, CRI was significantly greater at short AVD than at long AVD during LV preactivation of 20 or 40 ms (Figure 2). At short AVD, Method 2 resulted in slightly but significantly better CRI at VVD 20 and 40 ms. At long AVD, Method 1 resulted in large and significantly better CRI at VVD 20 and 40 ms. Conclusions The interaction between AVD and VVD is complex, and the electrical resynchronization achieved when programming depends on both the AVD at which VVD is programmed, and the programming algorithm used by the device manufacturer. At longer AVDs, Method 1 yields significantly better resynchronization than Method 2 when programming VVD. Studies assessing effects of AVD and VVD programming that involve patients with CRT devices from different manufacturers need to modify programming parameters appropriately in order to accurately interpret results. Methods For programming VVD and EDM

Writing the Programs of Programmable Catalysis

It has long been known that non-steady state and periodic catalytic reactor operation in terms of temperature, pressure, and composition can lead to higher overall productivity and/or product selectivity than the best steady operation. Recently, the emergence of catalysts whose intrinsic properties can be made to oscillate with time, introduces advanced forcing capabilities that can be “programmed” into the catalysts to broaden the scope and applicability of periodic operation to surface chemistry. In this work, an algorithmic approach is implemented to significantly accelerate the discovery and optimization of periodic steady states of catalytic reactors. Decomposition of complex dynamics into fundamental mechanistic fast–slow steps is seen to improve conceptual understanding of the relationship between binding energy oscillation protocols and overall catalytic rates. Finding structured forcing protocols, optimally tailored to the multiple time scales of a given individual mechanism, requires an efficient search of high-dimensional parameter spaces. This is enabled here through active learning (Bayesian optimization, enhanced by our proposed Bayesian continuation). Implementation of these methods is shown to accelerate the evaluation of catalyst programs by up to several orders of magnitude. Faster screening of programmable catalysts to discover periodic steady states enables the optimization of catalytic operating protocols and thus opens the possibility for catalyst engineering based on optimal forcing programs to control rate and product selectivity, even for complex multistep catalytic mechanisms.

A novel data-driven energy management strategy for fuel cell hybrid electric bus based on improved twin delayed deep deterministic policy gradient algorithm

With the prosperity of artificial intelligence in recent years, energy management strategies (EMSs) based on deep reinforcement learning (DRL) have become the mainstream approach to ensure efficient energy distribution of hybrid electric vehicles (HEVs). Moreover, in terms of developing sustainable urban public transportation systems, zero-emission fuel cell hybrid electric buses (FCHEBs) are more promising than hybrid electric buses (HEBs). Given that, this article proposes a novel data-driven EMS based on DRL to enhance the training efficiency of the proposed EMS while improving the fuel economy of an FCHEB. In this article, to optimize the power allocation of the FCHEB, an improved twin delayed deep deterministic policy gradient (TD3) algorithm combined with prioritized experience replay is innovatively formulated, and then a promising EMS based on it is proposed. Furthermore, to enhance the adaptability and improve the training efficiency of the proposed EMS, a stochastic training environment is established with massive real-world velocity data, and a pre-training method using the global optimal experience is designed. Simulation results show that the proposed EMS improves fuel economy by 5.87% compared with the EMS based on original TD3, reaching 97.15% of the global optimal dynamic programming method.