Departure Time Research Articles

The impact of spinal versus general anesthesia on the variability of surgical times: a systematic review and meta-analysis.

With spinal anesthesia, when cases are taking longer than usual, there may be behavioural tendencies for surgical teams to work more quickly. We conducted a systematic review with meta-analysis to examine standard deviations of surgical times for single-dose spinal anesthetics versus general anesthesia. We compared ratios of mean surgical times as a secondary endpoint. We included randomized trials of humans where general or spinal anesthesia was used for one category of surgical procedure (e.g., hip arthroplasty) and the article reported the means and standard deviations of operative durations. We used statistical methods suitable for surgical times following log-normal distributions. We used generalized confidence intervals to calculate point estimates of ratios and standard errors for each study, followed by pooling among studies using DerSimonian and Laird random-effects meta-analysis with Knapp-Hartung adjustment. Among the 77 included studies, 96% were of high quality for our endpoint (i.e., had a low risk of bias), as no (0%) study focused on comparing variability of surgical times and none had surgical time as the primary endpoint. Spinal anesthesia was associated with 6.6% smaller standard deviations than general anesthesia (95% confidence interval, 15.8% smaller to 1.9% larger, P = 0.13). By meta-regression, there was no significant association of the ratios of standard deviations with study quality (P = 0.39), year of publication (P = 0.76), or categories of procedures (all five P ≥ 0.28). Spinal anesthesia was associated with 1.1% smaller means than general anesthesia (95% confidence interval, 3.7% smaller to 1.5% larger, P = 0.42). There were no significant associations between the ratios of means and study quality (P = 0.47), year of publication (P = 0.95), or categories of procedures (all five, P ≥ 0.63). The results of this systematic review and meta-analysis show with high confidence that the effect of choosing spinal anesthesia on variability in surgical time, if present, is sufficiently small to have no substantive direct economic effect. The same conclusion applies to mean surgical time. Therefore, although anesthetic choice has a clinical (biological) impact and affects anesthesia times, the direct effects on surgical times and workflow are minimal at most. Anesthetic choice does not influence operating theatre productivity via changes to surgical times. The impact of spinal anesthetic effects is limited to nonoperative times (e.g., reducing anesthesia-controlled times by using a block room before the patient enters the operating room). PROSPERO ( CRD42023461952 ); first submitted 8 September 2023.

Employer-provided parking: Departure time choice, investment decision, and welfare effects

Employer-provided parking (EP) has become a prevalent way to reduce employees' parking delays and late arrivals through offering them free or low-price parking spaces at the workplace. This paper explores the EP effects on employees' trip scheduling, employer's EP investment decision, and commercial parking operator's pricing decision. An analytical trip scheduling equilibrium model is first presented to model the interaction between EP provision and employees' departure time choices during morning commute. A profit maximization model incorporating the employee productivity is then developed to determine the employer's optimal EP investment decision. A competitive game between employer's investment decision and commercial parking operator's parking pricing decision is analytically investigated, together with the effects of EP investment on social welfare. The results show that the EP investment can lead to a win-win situation with decreased employee commuting cost and increased firm production output; and the employer would like to provide only part of the employees with EP services. The competitive game solutions depend very much on the marginal costs of EP and commercial parking spaces. The EP investment with an excessively high commercial parking fee may hurt the society due to decreased social welfare.

Error-robust hybrid atom-photon entangling gate via Gaussian soft control

Hybrid atom-photon gates play an important role for the realization of a quantum interface capable of mapping atomic states to photons for communication across quantum networks. Here, we propose a feasible theoretical scheme for implementing a hybrid atom-photon controlled-Z gate between an atom and a microwave photon in a superconducting coplanar waveguide resonator based on the Gaussian soft control technique. The gate protocol employs a classical auxiliary field that induces an atomic transition between one state of the atomic qubit and Rydberg states for obtaining strong coupling of the atom and microwave resonator. By tailoring the amplitude of this field with Gaussian temporal modulation, the gate performances are improved in various aspects. Numerical simulations demonstrate that the controlled-Z gate based on Gaussian soft control is resilient to the variation of the atom-photon coupling strength and deviation in the gate time, and it is less sensitive to the Rydberg level shifts caused by stray electric fields.

EXPRESS: Distributionally Robust Appointment Scheduling That Can Deal with Independent Service Times

Consider a single server that should serve a given number of customers during a fixed period. The Appointment Scheduling Problem (ASP) determines the schedule of planned appointments that minimizes some cost function that accounts for both the cost of idle times and the cost of waiting. When service time distributions are fully specified, the ASP presents a much investigated computationally challenging stochastic program. When service time distributions are only partially specified, one can apply distributionally robust optimization (DRO) to find the schedule that minimizes costs in worst-case circumstances. We assume that only the mean, mean absolute deviation and range of the service times are known, and develop a DRO method that finds the optimal (minimax) schedule. For independent service times, the minmax problem becomes nonlinear and difficult, if not impossible, to solve exactly. Existing DRO methods for ASP with partial information (such as mean and variance) therefore consider relaxations that allow correlations between service times. Such relaxations have major repercussions, as the worst-case scenario will then be highly correlated. Our method thus deals with independent service times and finds the robust schedule as the solution to a linear program. We identify several new structural features of optimal robust schedules. We also apply the method to model extensions including sequencing and alternative objective functions.

On computing optimal temporal branchings and spanning subgraphs

We extend the concept of out/in-branchings spanning the vertices of a digraph to temporal graphs, which are digraphs where arcs are available only at prescribed times. While the literature has focused on minimum weight/earliest arrival time Temporal Out-Branchings (tob), we solve the problem for other optimization criteria (travel duration, departure time, number of transfers, total waiting time, traveling time). For some criteria we provide a log linear algorithm for computing such branchings, while for others we prove that deciding the existence of a spanning tob is NP-complete. The same results hold for optimal temporal in-branchings. We also investigate the related problem of computing a spanning temporal subgraph with the minimum number of arcs and optimizing a chosen criterion; this problem turns out to be always NP-hard. The hardness results are quite surprising, as computing optimal paths between nodes is always polynomial-time.

Passive sensing of smartphone use, physical activity and sedentary behavior among adolescents and young adults during the COVID-19 pandemic.

The research that links excessive screen time to adverse health outcomes is based on self-reported screen use. Few studies have documented how passively-sensed smartphone and app use relate to health behaviors like activity and sitting. Furthermore, they have not considered that daily fluctuations in smartphone/app use may have different relationships to these behaviors than a person's usual smartphone/app use. This study evaluated whether physical activity or sedentary (sitting) behavior are associated with either smartphone screen time or specific smartphone app use by adolescents and young adults during the COVID-19 pandemic. Adolescents and young adults aged 13-29 years wore activPAL4 micro activity monitors while their smartphones logged daily screen time and app use durations for nine days. Data were collected in 2020-2021 and analyzed in 2022-2023. Participants (N = 125) had a mean (SD) age of 19.7 (4.3) years. Participants' usual smartphone screen time was negatively associated with daily step counts. Daily deviations in smartphone screen time were negatively associated with daily step counts and moderate-vigorous physical activity durations. Time spent on Instagram, YouTube and, to a lesser extent, TikTok were linked with reduced activity levels. Daily sedentary behavior was not associated with usual or daily screen time. Interventions to promote physical activity during the transition into adulthood may benefit from limiting excessive smartphone screen time. Specific intervention targets could include limiting use of apps with infinite scrolling feeds algorithmically tuned to maintain user engagement, such as Instagram, YouTube and TikTok.

Comparison of piperine content, antimicrobial and antioxidant activity of Piper chaba root and stem

Piper chaba (locally known as “Choi Jhal”) is used traditionally as spices and folk medicine in different parts of Bangladesh. One of the most important bioactive compounds in this plant is piperine. In this study, the amount of piperine in P. chaba root and stem was investigated and the optimal solvent for piperine extraction at room temperature was also studied. High performance liquid chromatography (HPLC) was operated using a reverse phase column where methanol and water (70:30) were used as mobile phase. The detection was performed using photo diode array (PID) detector at a wavelength of 345 nm. The standard piperine showed linearity between 0.005 % and 0.04 % and the correlation co-efficient found for the linearity was 0.9933. The percentage of relative standard deviation (RSD) for both retention time and peak area were less than 2.0 %. The theoretical plate number (N) > 3000 and a tailing factor (T) < 1.5 were found in the acceptable range. The recovery percentage (%) of standard piperine was 99.16 %. Low value of co-efficient of variation and standard deviation are recognized for high precision of the method. The highest amount of piperine was found in root extracted with methanol (MR) amounting to 1.75 % in the root powder. The maximum amount of piperine in the stem was 1.59 % when extracted with methanol (MS). The piperine quantification in other extract like n-hexane root (HR), ethyl acetate root (ER), n-hexane stem (HS), ethyl acetate stem (ES) were 0.76 %, 1.69 %, 0.33 % and 1.46 % respectively. Methanol has given the highest yield of piperine compared to ethyl acetate and n-hexane for both root and stem. The developed method was simple, rapid, economic and validated in terms precision, accuracy and recovery. This selective method is found to be repeatable, accurate and successfully utilized for the Piper extract in marketed and pharmaceutical samples with well chromatographic conditions. The ethyl acetate extract of root and stem showed promising DPPH (1, 1-diphenyl-2-picrylhydrazyl) free radical scavenging activity with an IC50 value of 39.62 ± 0.95 μg/mL and 43.85 ± 1.50 μg/mL respectively. The study reports potential antibacterial activity and antifungal activity of P. chaba root and stem extracts. These outcomes revealed that different extracts of P. chaba may be used as natural preservatives.

Robust convoy movement problem under travel time uncertainty

A convoy represents a collection of vehicles traveling with a spacing of 50–100 m between them for tactical purposes. The convoy movement problem is a variant of the vehicle routing problem, an NP-hard problem aimed at determining the paths and schedules of convoys. Given the uncertainties in travel times during wartime, attributable to various factors such as road conditions and enemy threats, it is essential to consider uncertain travel times when determining convoy paths and schedules. Therefore, this study introduces a robust convoy movement problem under travel time uncertainty. A polyhedral set for uncertain travel times is used to derive a robust counterpart for the problem. To solve the proposed problem, we establish an exact algorithm that determines optimal solutions by iteratively generating and integrating multiple paths of convoys. This algorithm involves four steps: generation of k-th robust shortest paths, construction of path combinations, adjustment of departure times, and conduction of optimality check. These steps are iterated sequentially until the optimal solution is obtained. In computational experiments, the exact algorithm demonstrates superior performance and reduced computation time compared with the commercial solver CPLEX on both real instances and randomly generated instances. In addition, we conduct a sensitivity analysis for several parameters related to the problem, providing valuable managerial insights for decision-makers.

Comparison of Air Fare Prediction Using Various Machine Learning Techniques

Due to a rise in air service links globally, air travel is now a popular, significant, and faster form of transportation. Airline rates are thought to be impacted by several factors and are subject to frequent fluctuation, making estimation of these rates a challenging but essential task. The airline uses a pricing structure for the plane ticket. The cost of airline tickets varies based on the time of day, according to the survey. Even the weekends, the tourist season, and the pageant season have an impact on this. An aircraft ticket's price is influenced by several different elements. The buyer only has access to a limited amount of information, which is truly insufficient to estimate flight pricing, but the seller is aware of all the requirements. In this study, we use machine learning regression approaches such as K Nearest Neighbourhood, Decision Tree, and Random Forest to estimate flight fares based on basic data including source, destination, departure time and date, arrival timings, halts, and airline type. The results of the investigation show that the Random Forest Regression Model yields extremely optimal results.

Feasibility of 10-Minute Arrival Time to Departure Time Metric for STEMI Patients

Objectives Delays in reperfusion treatment in ST-elevation myocardial infarction (STEMI) patients leads to higher morbidity and mortality. Previous reports for Helicopter Emergency Medical Services (HEMS) suggests a 10-minute skid-to-skid (arrival to departure) time to achieve appropriate reperfusion times. However, there is no published data on whether this goal is achievable for a HEMS service. This study aims to see if a midwestern critical care service can consistently achieve a 10-minute helicopter skid-to-skid time or ground critical care service arrival to departure time. Further, comparing this metric between ground and helicopter transportations will help evaluate the ideal transportation method to optimize time to percutaneous intervention (PCI). Methods This was a retrospective chart review utilizing 10 years of data from our ground and HEMS program to assess whether a 10-minute arrival to departure time for STEMI patients could be achieved. Patients included were at least 18 years of age and were transported from the referring facility for further STEMI management. Wilcoxon rank sum test and Chi-square tests were used to evaluate data between helicopter and ground services. Results Included in the study were 686 patients, 608 by helicopter transport and 78 by ground transport. The median arrival to departure time was 14 min (IQR = 5) for helicopter patients and 13 min (IQR = 6) for ground patients. There was not a statistically significant difference in this metric for STEMI patients transported by helicopter versus ground. A statistically significant difference, though, existed between helicopter and ground transports among percent with times less than or equal to 10 min versus percent times greater than 10 min, X2 = 5.46, df = 1, p = 0.02. Two referring facilities had statistically significant differences in median arrival to departure times. Conclusions Our study found that a median EMS arrival to departure time of 10 min to transport STEMI patients was not consistently achieved via either helicopter or ground transportation.

Bus arrival and departure time updates in the Greater Sydney Area

Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

Information model for assessing the impact of tactical material procurement risks on order fulfillment in make-to-order manufacturing

Manufacturing businesses are showing increased interest in the issue of supply risks for materials and components. In recent decades, numerous studies and reviews have been published on the subject of supply chain risks. However, most research examines the global impact of risks on business as a whole and proposes a multi-level procedure for identifying, assessing, and developing risk mitigation measures, which should be carried out in advance with the involvement of specialists and experts. Nevertheless in maketo-order manufacturing, it is important to assess the risks of material supply for individual production orders, at the same time taking into account constant changes in production state and supply chains. The problem of assessing the risks of material supply gets even more complicated at enterprises with a high mix of manufactured products. To solve the above-mentioned problems, the authors propose an automated model for risks evaluation. The model is implemented as a component of the enterprise's information system (ERP) and uses data from the technological, production, inventory, and logistics modules to calculate the probability of deviation in order fulfillment time from the planned schedule due to potential disruptions in material supply chains. When executing the model, it analyzes the production's material requirements in both volumetric and calendar terms, inventory levels, and the condition of supply channels. The risks of delayed delivery for each material are expressed as the standard deviation of the delivery date from the planned date and are calculated by composing the risks for segments (elements) of the supply chain, the risks for which are, in turn, calculated based on performance data accumulated in the logistics module, with the possibility of introducing correction coefficients and expert evaluations. The overall risk of order material supply is determined by summing up the delivery risks of individual materials, expressed as the corresponding standard deviations. The model's results can be used for managerial decision-making in production and procurement or for communicating expected order fulfillment times to customers. The model has been tested at an enterprise in the electrical engineering industry.

Analysis of rogue wave in the mid-infrared supercontinuum under femtosecond weak seed pulse conditions based on deep learning

The generation process of rogue wave (RW) is affected by noise, which is an unstable state, and the existence of RW will reduce the stability of mid-infrared supercontinuum. However, the process of studying RW requires a large amount of data simulation and statistics, and traditional methods are time-consuming and inefficient. Therefore, this paper adopts long short-term memory (LSTM) neural network to obtain the spectrum information after transmission for a certain distance according to the waveform information of the incident pulse. The results show that the LSTM neural network structure can train and predict the peak power, time deviation information, time intensity evolution and spectrum evolution of RW after 10 cm propagation with only changing the number of internal units. And it performs well on both large and small data sets.

Factors affecting the accuracy of prehospital triage application and prehospital scene time in simulated mass casualty incidents

BackgroundThe contemporary management of mass casualty incidents (MCIs) relies on the effective application of predetermined, dedicated response plans based on current best evidence. Currently, there is limited evidence regarding the factors influencing the accuracy of first responders (FRs) in applying the START protocol and the associated prehospital times during the response to MCIs. The objective of this study was to investigate factors affecting FRs’ accuracy in performing prehospital triage in a series of simulated mass casualty exercises. Secondly, we assessed factors affecting triage-to-scene exit time in the same series of exercises.MethodsThis retrospective study focused on simulated casualties in a series of simulated MCIs Full Scale Exercises. START triage was the triage method of choice. For each Full-Scale Exercise (FSEx), collected data included exercise and casualty-related information, simulated casualty vital parameters, simulated casualty anatomic lesions, scenario management times, and responder experience.ResultsAmong the 1090 casualties included in the primary analysis, 912 (83.6%) were correctly triaged, 137 (12.6%) were overtriaged, and 41 (3.7%) were undertriaged. The multinomial regression model indicated that increasing heart rate (RRR = 1.012, p = 0.008), H-AIS (RRR = 1.532, p < 0.001), and thorax AIS (T-AIS) (RRR = 1.344, p = 0.007), and lower ISS (RRR = 0.957, p = 0.042) were independently associated with overtriage. Undertriage was significantly associated with increasing systolic blood pressure (RRR = 1.013, p = 0.005), AVPU class (RRR = 3.104 per class increase), and A-AIS (RRR = 1.290, p = 0.035). The model investigating the factors associated with triage-to-scene departure time showed that the assigned prehospital triage code red (TR = 0.841, p = 0.002), expert providers (TR = 0.909, p = 0.015), and higher peripheral oxygen saturation (TR = 0.998, p < 0.001) were associated with a reduction in triage-to-scene departure time. Conversely, increasing ISS was associated with a longer triage-to-scene departure time (TR = 1.004, 0.017).ConclusionsUnderstanding the predictors influencing triage and scene management decision-making by healthcare professionals responding to a mass casualty may facilitate the development of tailored training pathways regarding mass casualty triage and scene management.

The impact of fenugreek, black cumin, and garlic on dough rheology, bread quality, antimicrobial activity, and microstructural analysis using a scanning electron microscope

Fenugreek, black cumin, and garlic are herbs that are rich sources of antimicrobial and nutritional bioactive compounds. This study investigated the influence of herbs on the rheological characteristics of dough and the quality parameters of bread. The antimicrobial activity of the herbs was determined against the pathogens at 50 and 100 mg/mL concentrations. Maximum zone inhibition was measured for fenugreek against Salmonella typhimurium (16.5 mm) at 100 mg/mL concentration. Dough development time and departure time increased with the increase in concentration of the herbs. Farinograph quality number (FQN), mixing tolerance index, and stability varied significantly with the treatments such as physio-chemical, structural, textural, and sensory parameters at herbs con-centration 0.25, 0.5, and 0.75%, respectively. Ash, fiber, fat, protein content, firmness, and total phenolic content were increased with treatments. However, a decrease in moisture content and total plate count was observed due to the addition of herbs. The microstructure revealed that the addition of fenugreek significantly improves the bread structure. The sensory profile of bread was not affected with the addition of herbs at a concentration of 0.50%.

An online auction-based mechanism for pricing and allocation of instant delivery services

Recently, instant delivery has been growing rapidly, with numerous platforms emerging to offer such services. Requestors dynamically arrive at the platform to place delivery service requests that detail their pickup locations, recipient locations, package weights, departure times, and willingness-to-pay (WTP). The platform then uses its dedicated riders, scattered in different places, to fulfill these requests. Given the dynamic and fluctuating characteristics of the demand, coupled with limited rider resources and heterogenous pickup costs, the platform faces the critical problem of dynamically pricing the requests and assigning the riders to maximize social welfare. To address this problem, we propose an online auction-based transaction mechanism. Specifically, we first propose a scoring function to evaluate the values of the requests over multi-period operations taking into account the requests’ attributes, riders’ delivery costs, and resource availability. Based on the scoring function, we design a time-varying Vickrey–Clarke–Groves (VCG)-like payment rule that can reflect the impacts of fluctuating supply-demand imbalances. Under this rule, a requestor will pay more during undersupply periods than during oversupply periods. To carve out the different impact degrees of the supply-demand imbalances, we further consider the linear, quadratic, and exponential time-varying resource parameters to construct the payment rule. In addition, we develop an online instant delivery resource allocation model to efficiently assign the riders to fulfill the accepted requests. We show that the proposed mechanism has desirable properties (individual rationality, budget balance, and incentive compatibility) and is computationally efficient. Furthermore, we give a lower bound for the mechanism efficiency. To validate the practicality of our mechanism and get some managerial insights into the operations of the instant delivery platform, we conduct numerical studies to compare the performance of our mechanism to the First-in, first-out (FIFO) allocation mechanism and to investigate the impacts of pricing functions, rolling horizon configurations, and rider numbers on the mechanism's performance.

Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices

The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.

Remote control of the technical condition of metro trains using seismological tools

As urban road congestion increases and people shift to using the subway, the safety of under-ground transportation becomes a priority. Therefore, it is necessary to establish a monitoring sys-tem for subway trains to detect malfunctions that occur during operation. The article proposes an engineering seismology method for remote monitoring of the technical condition of metro trains based on the analysis of micro seismic vibrations. This method allows remote control of the dynamic parameters of each train and their deviations from the norm. The authors demonstrate how a seismic recorder placed in a building adjacent to a metro station can be used to monitor changes in the amplitude-frequency spectrum of vibro seismic recordings. It is used to determine the arrival and departure times of trains, and it is shown that observations are best made for departures due to their more pronounced signal amplitude. The recorded signal of a metro train should fall within a certain frequency range, which is determined based on its average values, and any deviation of the observed signal spectrum from this norm indicates changes in the train's condition. The proposed approach not only helps improve passenger safety, but also reduces the risk of emergencies associated with technical malfunctions of rolling stock. In the future, it is possible to develop software equipped with a database of statistical data, which will allow even more efficient control of metro operations and prompt response to any deviations, thus ensuring a high level of safety and comfort for passengers.

Assessment of ventricular electrical heterogeneity in left bundle branch pacing and left ventricular septal pacing by using various electrophysiological methods.

Left bundle branch area pacing (LBBAP) comprises pacing at the left ventricular septum (LVSP) or left bundle branch (LBBP). The aim of the present study was to investigate the differences in ventricular electrical heterogeneity between LVSP, LBBP, right ventricular pacing (RVP) and intrinsic conduction with different dyssynchrony measures using the ECG, vectorcardiograpy, ECG belt, and Ultrahigh frequency (UHF-)ECG. Thirty-seven patients with a pacemaker indication for bradycardia or cardiac resynchronization therapy underwent LBBAP implantation. ECG, vectorcardiogram, ECG belt and UHF-ECG signals were recorded during RVP, LVSP and LBBP, and intrinsic activation. QRS duration (QRSd) was measured from the ECG, QRS area was calculated from the vectorcardiogram, LV activation time (LVAT) and standard deviation of activation time (SDAT) from ECG belt and electrical dyssynchrony (e-DYS16) from UHF-ECG. Both LVSP and LBBP significantly reduced ventricular electrical heterogeneity as compared to underlying LBBB and RV pacing in terms of QRS area (p < .001), SDAT (p < .001), LVAT (p < .001) and e-DYS16 (p < .001). QRSd was only reduced as compared to RV pacing(p < .001). QRS area was similar during LBBP and normal intrinsic conduction, e-DYS16 was similar during LVSP and normal intrinsic conduction, whereas SDAT was similar for LVSP, LBBP and normal intrinsic conduction. For all these variables there was no significant difference between LVSP and LBBP. Both LVSP and LBBP resulted in a more synchronous LV activation than LBBB and RVP. Especially LBBP resulted in levels of LV synchrony comparable to normal intrinsic conduction.

Energy-saving path planning navigation for solar-powered vehicles considering shadows

Globally, the adverse climate effects caused by greenhouse gas emissions are becoming increasingly apparent, and solutions to increase the use of eco-friendly transportation methods are urgently needed. Introducing solar-powered vehicles (SPVs), which are cars integrated with solar panels capable of generating power, presents a promising solution to reduce urban carbon footprints. However, the low adoption rate of SPVs implies that the benefits—such as environmental friendliness and ability to charge while driving—need to be more palpably experienced by consumers. To address this aspect, in this study, we aimed to develop a navigation system algorithm that guides users along routes that optimize energy consumption and solar energy production from the starting point to the destination. This was done with the objective of providing more tangible benefits from using SPVs. The study focused on the high-traffic urban center of Seoul, where determining solar power availability for a moving SPV is challenging, given the presence of shadows cast by roadside features such as buildings and trees. To achieve this, panoramic images from Google Street View were collected at 10 m intervals from all roads within the research area. From these images, sky and non-sky elements were separated. Subsequently, a hemispherical map was constructed and superimposed with the sun's path. The presence of shadows was determined by assessing whether the sun's path was obstructed by non-sky elements; if the path was unimpeded in the sky, no shadow was recorded. The shadow data obtained at each spot were efficiently stored in a database for quick retrieval and application based on specific locations and departure times. Using this shadow information, the navigation algorithm calculates power generation along a given route and considers the energy consumption of the SPV. Analysis led to the identification of an energy-saving route, which enabled the achievement of energy conservation and CO2 reduction benefits. Furthermore, a comprehensive sensitivity analysis was conducted to examine the impact of four critical parameters—module efficiency, solar panel area, vehicle speed, and departure time—on route selection and net energy consumption. The energy-saving path planning algorithm enhances the economic feasibility of solar charging for SPVs during travel; thus, this study can contribute significantly to the widespread adoption of SPVs, which play a definitive role in reducing transportation's carbon footprint.