Total Delivery Time Research Articles

Abstract 4119658: Catheter Ablation for Ventricular Arrhythmias in Transthyretin Cardiac Amyloidosis: A Single Center Experience

Background: Transthyretin cardiac amyloidosis (ATTR-CA) carries an elevated risk of developing ventricular arrhythmias (VA) but there remains limited evidence supporting the role of catheter ablation in this population. Purpose: To assess the safety and efficacy of catheter ablation for VA in ATTR-CA. Methods: An electronic database was used to identify all patients suspected of having ATTR-CA who underwent catheter ablation for VA at our institution between January 2010 and December 2023. Patients who did not meet the diagnosis of ATTR-CA and had a negative workup who underwent catheter ablation for ventricular tachycardia (VT) or premature ventricular contractions (PVCs) were used to form a control group matched for age, sex and left ventricular ejection fraction at a ratio of 2:1. Results: 20 controls and 10 cases of ATTR-CA were identified. Examining the two groups at baseline (Table 1), the mean age at index ablation was 67.7±7.0 and 68.6±8.1 years (p=0.359), mean follow-up time was 4.1±3.1 and 4.2±2.6 years (p=0.494), and the proportion of males was 100% and 90% respectively. The ATTR-CA group had a significantly higher proportion of chronic kidney disease (90% vs. 50%, p=0.032) and prior stroke or thrombotic events compared to the control group (50% vs. 10%, p=0.026) with similar CHA 2 DS 2 VASc scores (4.2±1.6 vs. 3.8±1.3, p=0.233). Both groups had comparable left ventricular ejection fraction (41.4%±14% vs. 46.9%±15.2%, p=0.173), VT/PVCs at presentation (40%/60% vs. 25%/75%, p=0.77), use of antiarrhythmics (70% vs. 60%, p=0.592), and high proportion of atrial fibrillation/flutter (70% vs. 90%, p=0.168). Procedural (261.2±101.2 vs. 265.7±125.6 mins, p=0.449) and total energy delivery time (1442.9±1509.1 vs. 1744.6±956.2 secs, p=0.296) was not significantly different in the two groups. Both groups had a significant decrease in PVC burden postablation with a reduction from 26.8% to 3.8% (p=0.024) and from 27.1% to 13.6% (p=0.028) in the ATTR-CA and control group respectively. A downward trend was seen in antitachycardia pacing events for VT postablation in the ATTR-CA (14.7±9.8 vs. 5.3±6.7, p =0.184) and control (193±387.6 vs. 0.67±1.2. p=0.139) groups. No major complications occurred after ablation (Table 2). Mean time to recurrence post-index ablation was similar between both groups (1620.4±483.8 vs. 1498.8±291.4 days, p=0.987, Figure 1). Conclusions: Catheter ablation appears to be safe and effective for VT and PVCs in patients with ATTR-CA.

Designing a resilient supply chain network: A multi-objective data-driven distributionally robust optimization method

Uncertainty of supply chain disruption poses a significant challenge to decision-making in many real-life problems. In this paper, a new multi-objective data-driven distributionally robust optimization (MDDRO) model for the resilient supply chain network (RSCN) design problem under disruption scenario is developed, which includes minimizing the total cost, carbon emissions, and delivery time. In the context of supply chain disruption, it is important self-evidently that products can be supplied normally and the practical demand can be met. In handling the partial probability distribution information of the considered uncertain demand, this paper uses a data-driven Wasserstein-Moment ambiguity set (WMAS), which incorporate the Wasserstein metric and Moment information, and a robust counterpart to transform the developed model into a tractable approximation form. The finite-sample performance guarantee of the optimal solution of MDDRO model with Wasserstein-Moment ambiguous chance constraint is given. An accelerated Branch and cut algorithm (BCA) is constructed to solve the MDDRO model. Finally, through the investigation of a real-life case and sensitivity analysis of key parameters, some managerial insights are put forward.

Traveling salesman problem with time windows and a drone-utilizing intermediate points (TSPTWD-IP)

The fast growing of e-commerce has raised challenges to the last-mile logistics. Our study focuses on an emerging mode of transportation, i.e., a collaboration of a truck and an autonomous drone to deliver parcels. We develop a novel model of traveling salesman problem with time windows that jointly utilizes a truck and a drone in a coordinated manner. This model allows operating a drone not only on customers’ sites but also on other discrete intermediate points on arcs, and incorporates characteristics not considered previously, such as drone inoperable areas. We formulate this problem as a mixed integer linear program. We also develop a metaheuristic method that can solve the problems with up to 100 customers. Numerical experiments showed that our metaheuristic can find good solutions for many instances. Our model can cut the total delivery time by as much as 26% when compared to the traditional delivery model with only a single truck, and by 4% when compared to the existing model of a truck-and-drone operations that allows a drone to be launched or received at customers’ sites only.

A time-driven simulation–optimization framework for the dynamic heterogeneous order-courier assignment problem for instant deliveries

In recent years, instant delivery services have become very popular for transporting meals to urban areas, with an extensive range of products now available to order. The platforms that offer these services rely on crowdsourced couriers who utilize their personal vehicles, resulting in heterogeneous fleets. Furthermore, the competition among companies to retain both customers and couriers is very intense, which underscores the importance of developing superior decision support systems. These systems must generate real-time assignments that meet the expectations of service providers, customers, and couriers. In this study, we designed a time-driven simulation–optimization framework that addresses the dynamic heterogeneous order-courier assignment problem and incorporates order-vehicle restrictions. The framework efficiently manages real-time order arrivals, courier movements, and positional updates while considering dynamic factors such as traffic congestion and regional speed limits for various vehicle types. Extensive testing using literature instances demonstrated the framework’s ability to satisfactorily address the defined problem. Additionally, the time-driven simulation–optimization framework was applied to a realistic case study, resulting in an approximately 4.5% reduction in the total delivery times (from the submission of the order until the delivery to the client) for all orders when compared to the original assignment.

Efficacy and safety of a novel temperature-controlled catheter for cavotricuspid isthmus ablation.

Maintaining an adequate temperature at the target site is essential for effective ablation. We hypothesized that a tissue temperature-controlled (T-Con) catheter for cavotricuspid isthmus (CTI) ablation could improve the procedural ablation parameters. To evaluate the efficacy and safety of the T-Con (DiamondTemp™) catheter for CTI ablation compared with non-irrigation (Non-Irri) and irrigation (Irri) catheters. We analyzed 150 patients who underwent prophylactic CTI ablation combined with pulmonary vein isolation. The Non-Irri, Irri, and T-Con catheter groups comprised 50 patients each, and the ablation procedural parameters and complications were compared between these groups. There were no significant differences in clinical background characteristics among the three groups. The Kruskal-Wallis and post hoc tests demonstrated that the T-Con group showed the lowest total radiofrequency energy delivery time among the three groups (median [25 and 75 percentiles]: 340 [209, 357], 147 [100, 199], and 83 [61, 109] s, respectively in the Non-Irri, Irri, and T-Con groups; T-Con versus Non-Irri, p < .01; T-Con versus Irri, p < .01). The total procedural time and acute reconnection rate in the T-Con group (264 s and 4%, respectively) were lower than those in the Non-Irri group (438 s and 24%) but were similar to those in the Irri group (268 s and 6%). No significant complications were observed in any group. The T-Con catheter achieved a short energy delivery time and a low acute reconnection rate, indicating its potential as an alternative catheter for CTI ablation.

Multi-Objective Optimization of Short-Inverted Transport Scheduling Strategy Based on Road–Railway Intermodal Transport

This study focuses on the ‘short-inverted transportation’ scenario of intermodal transport. It proposes a vehicle unloading reservation mechanism to optimize the point-of-demand scheduling system for the inefficiency of transport due to the complexity and uncertainty of the scheduling strategy. This paper establishes a scheduling strategy optimization model to minimize the cost of short backhaul and obtain the shortest delivery time window and designs a hybrid NSGWO algorithm suitable for multi-objective optimization to solve the problem. The algorithm incorporates the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm based on the Grey Wolf Optimizer (GWO) algorithm, compensating for a single algorithm’s premature convergence. The experiment selects a logistics carrier’s actual road–rail intermodal short-inverted data and compares and verifies the above data. The results show that the scheduling scheme obtained by this algorithm can save 41.01% of the transport cost and shorten the total delivery time by 46.94% compared with the original scheme, which can effectively protect the enterprise’s economic benefits while achieving timely delivery. At the same time, the optimized scheduling plan resulted in a lower number of transport vehicles, which positively impacted the sustainability of green logistics.

A dual-critic DQN architecture for lifter assignment in multi-floor semiconductor FAB

This paper proposes a deep reinforcement learning-based lifter assignment algorithm utilising Deep Q-network (DQN) to minimise the total inter-floor delivery time in semiconductor manufacturing. Given the complexities and randomness inherent in manufacturing environments, predicting delivery times poses a significant challenge for companies operating in such domains. To address this challenge and improve the accuracy of delay prediction, we partition the end-to-end delivery process of individual lot systematically, focusing on predicting segment delays rather than the overall end-to-end delay. We introduce a unique dual critic architecture designed to handle these segmented steps. This innovative approach enhances accuracy by capturing nuanced information at each step, which is stored as trajectories. Simulation results substantiate the effectiveness of the proposed architecture, comparing favorably against existing algorithms. We conduct comparative analyses with benchmark algorithms, revealing that the proposed algorithm outperforms other algorithms.

Single institution experience of MRI-guided radiotherapy for thoracic tumors and clinical characteristics impacting treatment duty cycle.

MRI-guided radiotherapy (MRgRT) allows for direct motion management and real-time radiation treatment plan adaptation. We report our institutional experience using low strength 0.35T MRgRT for thoracic malignancies, and evaluate changes in treatment duty cycle between first and final MRgRT fractions. All patients with intrathoracic tumors treated with MRgRT were included. The primary reason for MRgRT (adjacent organ at risk [OAR] vs. motion management [MM] vs. other) was recorded. Tumor location was classified as central (within 2cm of tracheobronchial tree) vs. non-central, and further classified by the Expanded HILUS grouping. Gross tumor volume (GTV) motion, planning target volume expansions, dose/fractionation, treatment plan time, and total delivery time were extracted from the treatment planning system. Treatment plan time was defined as the time for beam delivery, including multileaf collimator (MLC) motion, and gantry rotation. Treatment delivery time was defined as the time from beam on to completion of treatment, including treatment plan time and patient respiratory breath holds. Duty cycle was calculated as treatment plan time/treatment delivery time. Duty cycles were compared between first and final fraction using a two-sample t-test. Twenty-seven patients with thoracic tumors (16 non-small cell lung cancer and 11 thoracic metastases) were treated with MRgRT between 12/2021 and 06/2023. Fifteen patients received MRgRT due to OAR and 11 patients received MRgRT for motion management. 11 patients had central tumors and all were treated with MRgRT due to OAR risk. The median dose/fractionation was 50 Gy/5 fractions. For patients treated due to OAR (n=15), 80% had at least 1 adapted fraction during their course of radiotherapy. There was no plan adaptation for patients treated due to motion management (n=11). Mean GTV motion was significantly higher for patients treated due to motion management compared to OAR (16.1mm vs. 6.5mm, p=0.011). Mean duty cycle for fraction 1 was 54.2% compared to 62.1% with final fraction (p=0.004). Mean fraction 1 duty cycle was higher for patients treated due to OAR compared to patients treated for MM (61% vs. 45.0%, p=0.012). Duty cycle improved from first fraction to final fraction possibly due to patient familiarity with treatment. Duty cycle was improved for patients treated due to OAR risk, likely due to more central location and thus decreased target motion.

A multi-period optimal distribution model of emergency resources for responding to COVID-19 under uncertain conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

How quickly does FLASH need to be delivered? A theoretical study of radiolytic oxygen depletion kinetics in tissues

Purpose. Radiation delivered over ultra-short timescales (‘FLASH’ radiotherapy) leads to a reduction in normal tissue toxicities for a range of tissues in the preclinical setting. Experiments have shown this reduction occurs for total delivery times less than a ‘critical’ time that varies by two orders of magnitude between brain (∼0.3 s) and skin (⪆10 s), and three orders of magnitude across different bowel experiments, from ∼0.01 to ⪆(1–10) s. Understanding the factors responsible for this broad variation may be important for translation of FLASH into the clinic and understanding the mechanisms behind FLASH. Methods. Assuming radiolytic oxygen depletion (ROD) to be the primary driver of FLASH effects, oxygen diffusion, consumption, and ROD were evaluated numerically for simulated tissues with pseudorandom vasculatures for a range of radiation delivery times, capillary densities, and oxygen consumption rates (OCR’s). The resulting time-dependent oxygen partial pressure distribution histograms were used to estimate cell survival in these tissues using the linear quadratic model, modified to incorporate oxygen-enhancement ratio effects. Results. Independent of the capillary density, there was a substantial increase in predicted cell survival when the total delivery time was less than the capillary oxygen tension (mmHg) divided by the OCR (expressed in units of mmHg/s), setting the critical delivery time for FLASH in simulated tissues. Using literature OCR values for different normal tissues, the predicted range of critical delivery times agreed well with experimental values for skin and brain and, modifying our model to allow for fluctuating perfusion, bowel. Conclusions. The broad three-orders-of-magnitude variation in critical irradiation delivery times observed in in vivo preclinical experiments can be accounted for by the ROD hypothesis and differences in the OCR amongst simulated normal tissues. Characterization of these may help guide future experiments and open the door to optimized tissue-specific clinical protocols.

Logistics Efficiency in Product Distribution with Genetic Algorithms for Optimal Routes

This research aims to optimize product distribution routes in logistics using computer simulation approaches and genetic algorithms. This research produces more efficient distribution routes by utilizing mathematical models that reflect actual distribution processes, including variables such as warehouse locations, distribution points, product types, customer demand, and vehicle availability. Genetic algorithms are used to design optimal solutions with implementation stages, which include solution representation, population initialization, fitness evaluation, selection, crossover, mutation, and stopping criteria. The visualization results show that the genetic algorithm can produce more structured and efficient distribution routes, reducing total travel distance, distribution costs, and delivery time. Statistical analysis supports significant improvements in distribution performance after implementing the genetic algorithm, with substantial reductions in total mileage, distribution costs, and delivery times and substantial improvements in customer satisfaction. Financial analysis shows significant cost savings and positive ROI from investing in genetic algorithms, while sensitivity analysis reveals the impact of critical factors on distribution costs. This research confirms the financial and operational benefits of applying genetic algorithms in product distribution optimization, with significant efficiency, cost savings, and customer satisfaction results.

Developing an integrated blood supply chain network in disaster conditions considering multi-purpose capabilities

PurposeThis study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.Design/methodology/approachThis paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.FindingsThe findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.Originality/valueThe researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

Evaluation of the Minimum Segment Width and Fluence Smoothing Tools for Intensity-modulated Techniques in Monaco Treatment Planning System.

This study aims to minimize monitor units (MUs) of intensity-modulated treatments in the Monaco treatment planning system while preserving plan quality by optimizing the "Minimum Segment Width" (MSW) and "Fluence Smoothing" parameters. We retrospectively analyzed 30 prostate, 30 gynecological, 15 breast cancer, 10 head and neck tumor, 11 radiosurgery, and 10 hypo-fractionated plans. Original prostate plans employed "Fluence Smoothing" = Off and were reoptimized with Low, Medium, and High settings. The remaining pathologies initially used MSW = 0.5 cm and were reoptimized with MSW = 1.0 cm. Plan quality, including total MU, delivery time, and dosimetric constraints, was statistically analyzed with a paired t-test. Prostate plans exhibited the highest MU variation when changing "Fluence Smoothing" from Off to High (average ΔMU = -5.1%; P < 0.001). However, a High setting may increase overall MU when MSW = 0.5 cm. Gynecological plans changed substantially when MSW increased from 0.5 cm to 1.0 cm (average ΔMU = -29%; P < 0.001). Organs at risk sparing and planning target volumes remained within 1.2% differences. Replanning other pathologies with MSW = 1.0 cm affected breast and head and neck tumor plans (average ΔMU = -168.38, average Δt = -11.74 s, and average ΔMU = -256.56, average Δt = -15.05 s, respectively; all with P < 0.004). Radiosurgery and hypofractioned highly modulated plans did not yield statistically significant results. In breast, pelvis, head and neck, and prostate plans, starting with MSW = 1.0 cm optimally reduces MU and treatment time without compromising plan quality. MSW has a greater impact on MU than the "Fluence Smoothing" parameter. Plans with high modulation might present divergent behavior, requiring a case-specific analysis with MSW values higher than 0.5 cm.

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

A hybrid ANN-MILP model for agile recovery production planning for PPE products under sharp demands

Today, supply chains (SCs) have been struggling with a new type of disruption known as outbreaks such as epidemics or pandemics. This type of disruption has features such as long-term and uncertain lifespan and leads to severe fluctuations in product demand. This paper elaborates on a hybrid approach based on artificial neural networks (ANN) and mathematical programming techniques to efficiently deal with this type of disruption in the production planning of SCs. In the first phase of the hybrid approach, a multi-layer perceptron ANN model with an optimised structure is developed to efficiently predict the demand and its peak points. In the second phase, the predicted demands are considered as input in a new multiobjective agile recovery production planning model. The proposed model minimises total costs and delivery times and maximises responsiveness. A real case study in Iran is conducted to verify and validate the proposed hybrid approach. The prediction error of the ANN method is about 1 percent. According to the predicted demand, optimal decisions are determined by the proposed model. The impact of under-estimation and over-estimation of demand is evaluated in terms of total costs, delivery time, responsiveness and shortage costs in the SCs.

Designing a sustainable, resilient, and responsive wheat supply chain under mixed uncertainty: A multi-objective approach

The stability and long-term viability of agri-food supply chains are at risk due to factors such as rapid population growth and global disruptions like the COVID-19 pandemic. This study develops a multi-objective robust fuzzy stochastic programming model to integrate sustainability, resilience, and responsiveness in designing agri-food supply chains operating under uncertain conditions. The sustainability-related objectives of the proposed model aim to reduce the overall cost and negative environmental and social effects. The resilience-related objectives in this model focus on decreasing the node criticality, complexity, and customer dissatisfaction. Additionally, minimizing the total delivery time serves as a measure of responsiveness. In this study, the Torabi and Hassini (TH) interactive fuzzy programming method and the Best-Worst Method (BWM) are applied as a solution approach to handle the multi-objective model. The proposed multi-objective model is applied to a Canadian wheat supply chain, considering its distinct features like blending, wheat quality, weather, and intermodal transport. Moreover, comprehensive sensitivity analyses are carried out to examine the effects of changing the model's parameters on the objective functions. Finally, the performance of the presented model is evaluated by conducting a comparative analysis. The results demonstrate the model's ability to handle hybrid uncertainty and provide tradeoffs between various objectives, which can benefit decision-makers.

Research on multi-rider collaborative dynamic scheduling model and algorithm of takeaway delivery

Aiming at the field of high-demand takeaway delivery, a dynamic scheduling strategy based on multi-rider collaborative delivery status is proposed, and the model implementation is obtained by constructing a collaborative dynamic scheduling optimization model and using the optimization TS algorithm to obtain a multi-rider scheduling scheme in a dynamic environment, so that the total delivery time is minimized. Finally, the experimental results show the effectiveness and feasibility of the model and algorithm implementation.

An investigation into the impact of volumetric rescanning and fractionation treatment on dose homogeneity in liver cancer proton therapy.

The Pencil Beam Scanning (PBS) technique in modern particle therapy offers a highly conformal dose distribution but poses challenges due to the interplay effect, an interaction between respiration-induced organ movement and PBS. This study evaluates the effectiveness of different volumetric rescanning strategies in mitigating this effect in liver cancer proton therapy. We used a Geant4-based Monte Carlo simulation toolkit, 'TOPAS,' and an image registration toolbox, 'Elastix,' to calculate 4D dose distributions from 5 patients' four-dimensional computed tomography (4DCT). We analyzed the homogeneity index (HI) value of the Clinical Tumor Volume (CTV) at different rescan numbers and treatment times. Our results indicate that dose homogeneity stabilizes at a low point after a week of treatment, implying that both rescanning and fractionation treatments help mitigate the interplay effect. Notably, an increase in the number of rescans doesn't significantly reduce the mean dose to normal tissue but effectively prevents high localized doses to tissue adjacent to the CTV. Rescanning techniques, based on statistical averaging, require no extra equipment or patient cooperation, making them widely accessible. However, the number of rescans, tumor location, diaphragm movement, and treatment fractionation significantly influence their effectiveness. Therefore, deciding the number of rescans should involve considering the number of beams, treatment fraction size, and total delivery time to avoid unnecessary treatment extension without significant clinical benefits. The results showed that 2-3 rescans are more clinically suitable for liver cancer patients undergoing proton therapy.

Effect of Resistance Training, Aerobic Exercise Before Doula-Assisted Delivery on the Psychological Well-Being and Labor Duration of Parturients

Objective: To investigate the impact of resistance training and aerobic exercise before doula-assisted delivery on the psychological well-being and labor duration of parturients. Methods: 105 cases of parturients who gave birth in our hospital from February 2021 to April 2023 were included in this study. The patients were divided into a control group of 52 cases and an observation group of 53 cases. The patients in the control group received no particular intervention before delivery and only received doula assistance. The patients in the observation group received resistance training, aerobic exercise, and doula assistance prior to giving birth. After delivery, the mothers were observed for 2 hours in the delivery room and then returned to the maternal and infant ward. The observation indicators of the two groups were compared. Results: In comparison to the control group, the observation group exhibited shorter durations in the first, second, and third stages of labor, as well as the total delivery time. Additionally, the amount of postpartum blood loss at 2 hours was smaller in the observation group. The rate of cesarean section was lower, and the rate of vaginal natural delivery was higher in the observation group. Following the intervention, both groups of postpartum women showed decreased SAS and SDS scores compared to before the intervention. Moreover, the observation group had lower scores than the control group. These differences were all statistically significant (P &lt; 0.05). Conclusion: Resistance training and aerobic exercise before doula-assisted delivery can effectively improve the psychological state of parturients, shorten labor duration, increase the rate of natural vaginal delivery, and reduce pain and postpartum hemorrhage.

The first investigation of spot-scanning proton arc (SPArc) delivery time and accuracy with different delivery tolerance window settings

Objective. To investigate the impact of various delivery tolerance window settings on the treatment delivery time and dosimetric accuracy of spot-scanning proton arc (SPArc) therapy. Approach. SPArc plans were generated for three representative disease sites (brain, lung, and liver cancer) with an angle sampling frequency of 2.5°. An in-house dynamic arc controller was used to simulate the arc treatment delivery with various tolerance windows (±0.25, ±0.5, ±1, and ±1.25°). The controller generates virtual logfiles during the arc delivery simulation, such as gantry speed, acceleration and deceleration, spot position, and delivery sequence, similar to machine logfiles. The virtual logfile was then imported to the treatment planning system to reconstruct the delivered dose distribution and compare it to the initial SPArc nominal plan. A three-dimensional gamma index was used to quantitatively assess delivery accuracy. Total treatment delivery time and relative lost time (dynamic arc delivery time—fix beam delivery time)/fix beam delivery time) were reported. Main Results. The 3D gamma passing rate (GPR) was greater than 99% for all cases when using 3%/3 mm and 2%/2 mm criteria and the GPR (1%/1 mm criteria) degraded as the tolerance window opens. The total delivery time for dynamic arc delivery increased with the decreasing delivery tolerance window length. The average delivery time and the relative lost time (%) were 630 ± 212 s (253% ± 68%), 322 ± 101 s (81% ± 31%), 225 ± 60 s (27% ± 16%), 196 ± 41 s (11% ± 6%), 187 ± 29 s (6% ± 1%) for tolerance windows ±0.25, ±0.5, ±1, and ±1.25° respectively. Significance. The study quantitatively analyzed the dynamic SPArc delivery time and accuracy with different delivery tolerance window settings, which offer a critical reference in the future SPArc plan optimization and delivery controller design.