Planning Scheduling Research Articles

Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

Connected and automated vehicles (CAVs) have shown great potential in improving traffic efficiency in the area of intersection management. Numerous researches have been accomplished to solve the CAV scheduling problem at the intersection scenario. However, most of them focus on intersections where lane-changing behavior is prohibited, which is impractical for real-world implementation. To address this problem, this paper proposes a novel cooperation framework to solve the CAV arrival scheduling problem in unsignalized intersections where lane changing behavior is permitted. In the first stage, an iterative framework is proposed to solve target assignment and path planning problems, which prevents the deadlock of multiple CAVs with different target lanes. In the second stage, a graph-based minimum clique cover method is then established to obtain the optimal scheduling plan for the CAVs with a low calculation burden. Extensive numerical simulations for different numbers of vehicles and traffic volumes validate the effectiveness of the proposed algorithm.

Multistage Scheduling of Regional Power Grids Against Sequential Outage and Power Uncertainties

Aiming at the scheduling problem for regional power grids under both outage and power uncertainties, this paper puts forward a multistage robust optimization (RO) method that ensures the reliable power supply in system operations. Firstly, a multistage RO model is proposed according to the practical scheduling request of the regional power grid. The startup and shutdown of slow-acting generators are optimized in the first stage. The optimal topology is then determined in the second stage in preparation to the outage uncertainty of grid-connected lines. In the third to 2+ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> stages, the scheduling plans of generation units in each time period are further formulated regarding the sequential realizations of renewable-load power uncertainties, which ensures the nonanticipativity of the obtained robust schedules. Secondly, a tailored and efficient transformation-decomposition algorithm is exploited for the solution of multistage RO problem. Based on the affine control function, the nested max-min optimization of the third to 2+ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> stages is explicitly transformed to a single-stage max-min model via the multi-to-one strategy. A column and constraint generation (C&CG) algorithm with looped alternative optimization procedure (AOP) handles the equivalent three-stage RO problem with binary recourses, thus avoiding numerous forward and backward iterations as well as speeding up the modeling solution. Numerical experiments of real-world regional power grids verify the effectiveness, superiorities and scalability of the proposed multistage RO scheduling method, which indicates great guidance for engineering applications.

Potential utility of cone-beam CT-guided adaptive radiotherapy under end-exhalation breath-hold conditions for pancreatic cancer.

The purpose of this study was to demonstrate the potential utility of cone-beam computed tomography (CBCT)-guided online adaptive radiotherapy (ART) under end-exhalation breath-hold (EE-BH) conditions for pancreatic cancer (PC). Eleven PC patients who underwent 15-fraction volumetric-modulated arc therapy under EE-BH conditions were included. Planning CT images and daily 165 CBCT images were imported into a dedicated treatment planning system. The prescription dose was set to 48Gy in 15 fractions. The reference plan was automatically generated along with predefined clinical goals. After segmentation was completed on CBCT images, two different plans were generated: One was an adapted (ADP) plan in which re-optimization was performed on the anatomy of the day, and the other was a scheduled (SCH) plan, which was the same as the reference plan. The dose distributions calculated using the synthetic CT created from both planning CT and CBCT were compared between the two plans. Independent calculation-based quality assurance was also performed for the ADP plans, with a gamma passing rate of 3%/3mm. All clinical goals were successfully achieved during the reference plan generation. Of the 165 sessions, gross tumor volume D98% and clinical target volume D98% were higher in 100 (60.1%) and 122 (74.0%) ADP fractions. In each fraction, the V3Gy <1cm3 of the stomach and duodenum was violated in 47 (28.5%) and 48 (29.1%), respectively, of the SCH fractions, whereas no violations were observed in the ADP fractions. There were statistically significant differences in the dose-volume indices between the SCH and ADP fractions (p<0.05). The gamma passing rates were above 95% in all ADP fractions. The CBCT-guided online ART under EE-BH conditions successfully reduced the dose to the stomach and duodenum while maintaining target coverage.

Planning of Academic Supervision by Primary School Supervisors in West Lombok District, Indonesia

This study aims to describe the planning and implementation of Academic Supervision by elementary school supervisors in West Lombok Regency. This type of research is qualitative research. Data were taken from interviews, observations and documentation. Credibility, transferability, dependability and comparability tests were conducted to test the data's validity. The results of this study reveal that supervisors have an essential role in planning academic supervision, namely, from planning and implementation control's schedule to discussing with the teacher the contents of the instrument to be used during academic supervision so that teachers can carry out their duties without feeling supervised.

Robust dispatching of integrated energy system considering economic operation domain and low carbon emission

The traditional robust optimization algorithm can only provide the scheduling plan in the worst case, but due to the time coupling constraints such as climbing and energy storage SOC(State of Charge) constraints, it is difficult to provide an adjustment scheme for intra-day scheduling plan. In the first stage, the upper and lower bounds of equipment output are taken as decision variables, and the cumulative change of load is introduced to deal with the constraint of demand response, so as to form the economic operation domain of the park. The Column-and-Constraint generation method (CCG) is used to solve the three-layer optimization problem, and the scheduling plan and optimal operating domain in the worst case are obtained. The intra-day scheduling can be adjusted arbitrarily according to the new energy output within the interval. Carbon transaction cost is included in the goal function to account for reduced carbon emissions. Simultaneously, to take use of the benefits of flexible adjustment, FM income is incorporated into the goal function, and the charging and discharging behavior of energy storage is defined by optimizing calculations based on real-time capacity prices during daily scheduling. The calculation results show that the proposed robust optimization algorithm based on economic operation domain can be regarded as an extension of the traditional robust algorithm. Besides, the total cost can be reduced and the economy can be improved by adjusting the scheduling scheme according to the new energy output in real time under the premise of ensuring the time coupling constraint.

Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review

Transit agencies are increasingly embracing electric buses (EB) as an energy-efficient and emission-free alternative to the conventional bus fleets. They are rapidly replacing conventional buses with electric ones. As a result, emerging challenges of electrifying public transportation bus networks in cities should be addressed. Introducing electric buses to the bus transit system would affect the public transit operation planning steps. The steps are network design, timetabling, bus scheduling, and crew scheduling. Regarding the functional and operational differences between conventional buses and electric buses, such stages should be changed and optimized to enhance the level of service for the users while reducing operating costs for service providers. Many mathematical optimization models have been developed for conventional buses. However, such models would not fit the electric buses due to EBs’ limited traveling range and long charging time. Therefore, new mathematical models should be developed to consider the unique features of electric buses. We present a comprehensive literature review to critically review and classify the work done on these topics. This paper compares the studies that have been done in this field and highlight the missing links and gaps in the considered papers, and the potential future studies that could be done. The considered papers cover the integration of timetabling and vehicle scheduling, recharging scheduling planning, and fast charging infrastructure location planning and its impacts on the grid. The main goal of this research is to highlight the research gaps and potential directions for future studies in this domain to encourage more realistic and applicable models and solution approaches for fully electric bus transit systems.

Initial Clinical Experience of CBCT-Based Adaptive Online Radiotherapy for SAbR of Thoracic Malignancies

<h3>Purpose/Objective(s)</h3> There has been an increasing trend in the availability and utilization of cone beam computed tomography (CBCT)-based adaptive online radiotherapy (AORT) in radiation oncology clinics. In this study, we report our initial experience of utilizing AORT to deliver stereotactic ablative radiation therapy (SAbR) for either early-stage non-small cell lung cancers (NSCLCs) or metastases to the thorax. <h3>Materials/Methods</h3> We recruited 18 patients treated with daily AORT whose tumors were treated with a total prescribed dose ranging from 33-60Gy treated over 3-5 fractions. During the AORT fraction, the physician contoured an ITV and critical OARs on the CBCT and two plans were created for the physician to choose from, a scheduled plan and an adaptive plan. The scheduled plan was a re-calculation of the reference plan based on the anatomy of the patient on that day's fraction while the adaptive plan was a re-optimization of the reference plan. For each scheduled and adaptive plan, we recorded PTV and ITV coverage (V100%), dose conformality (R50 and R100), and the maximum dose (D_max) received by the esophagus, bronchus, trachea, heart, and spinal cord. Timing data for each fraction from CBCT acquisition to approved plan and patient ready for treatment were also recorded. Dosimetric data between the scheduled and adaptive plans were compared relative to the scheduled plan. Paired t-tests were used to evaluate differences between the scheduled and adaptive plans. <h3>Results</h3> Among the 68 fractions analyzed, there was a reduction in ITV volume over the course of the adaptive treatments in 54.4% of cases (range: 0.7%-82.4%, mean: 24.3%), with physicians selecting the adaptive over scheduled plan 88.2% of the time. Relative to scheduled plans, mean difference in PTV and ITV coverage was 2.99% (p=0.009) and 1.07% (p=0.005) higher, respectively. Dose conformality, R50 and R100, were improved in 62.5% (p=0.05) and 66.7% (p=0.098) of adaptive compared to the scheduled plans. Mean difference in D_max relative to the scheduled plan for critical OARs ranged from -5.62% to 4.03%, only heart and spinal cord showing significance (p=0.004, p=0.023). Timing data demonstrated an average of 9 min spent on contouring, 5 min on plan optimization, and 5 min for online quality assurance. <h3>Conclusion</h3> A significant proportion of the patients in our SAbR-treated CBCT-adaptive therapy cohort demonstrated ITV volume changes during overall course of treatment. AORT-based lung SAbR treatments significantly improved PTV/ITV prescription coverage (compared to scheduled plan deliveries), dose conformity, and OAR sparing, with <15 min added to the overall treatment time for online contour and reoptimization. Overall, CBCT based-AORT is a robust approach to offer high quality online reoptimized plans for SAbR treatment of thoracic malignancies.

Developing a CBCT-Guided Online Adaptive Radiotherapy Approach for Isotoxic Delivery of Ultra-High Dose to Oligometastatic Disease in Combination with Immunotherapy

<h3>Purpose/Objective(s)</h3> A Phase I trial combining stereotactic body radiotherapy (SBRT), 45Gy in 3 fractions(fx), with pembrolizumab was safe (9% grade 3 toxicity) with promising local control and overall survival. Whether SBRT dose escalation can improve outcomes is uncertain. A subsequent planning study showed feasibility of ultra-high SBRT dose, 90Gy in 3fx, while respecting organ at risk (OAR) tolerances. However, due to patient anatomy interfractional uncertainty, online adaptation may enhance safety/efficacy. This work develops a CBCT-guided adaptive radiotherapy approach for isotoxic treatment with ultra-high doses using retrospective data and simulated online adaptation. <h3>Materials/Methods</h3> Patient data from 15 subjects of the original Phase I trial were used (gross tumor volume (GTV) mean volume 177cc; range 67-390cc). Disease sites were liver(n=8), pelvis(n=4), abdomen(n=2), lung(n=1). Clinical prescriptions were to 65cc subvolumes of the GTV (SubGTV). In the adaptive setting, rather than define a specific sub volume, OAR expansions based on tolerance and achievable dose gradients were cropped from GTV so that the SubGTV receiving ultra-high dose adapts to changing anatomy. Initial 6 arc VMAT plans used a clinical goal based automated plan optimization platform. High priority was given to critical OAR Dmax goals, medium priority was given to GTV coverage (V90Gy) and OAR volumetric constraints. SubGTV core was defined with further contraction away from OARs with low priority goals to ensure a centralized GTV hot spot. For a subset of patients (8 total: 3 liver, 2 abdomen, 2 pelvis, 1 lung), an online adaptive session using clinical CBCTs was simulated with anatomy recontoured for plan reoptimization/evaluation and comparison of adapted (ADP) vs. scheduled (SCH) plans. <h3>Results</h3> For initial plans, all OAR goals were satisfied. The average GTV V90Gy was 80.8cc/57.3% (range: 53.8-144.5cc/24.4-99.1%). In online adaptive sessions, the ADP plan was preferred over the SCH plan in 7 of 8 cases. ADP plans generally had superior coverage (average GTV V90Gy of 86.3cc (ADP) vs. 77.9cc (SCH)). ADP plans nearly always met OAR tolerances (one minor exception: skin D0.03cc (tolerance of 11.0Gy/fx) was 11.5Gy (ADP) vs. 11.9Gy (SCH)). In contrast, SCH plans often did not meet OAR constraints, with critical violations to bowel (n=2), stomach(n=3), duodenum(n=1), inferior vena cava(n=1) as well as violations to skin(n=3) and bone(n=2). <h3>Conclusion</h3> This work develops a paradigm for true isotoxic radiotherapy treatment maximizing ablative target doses using CBCT-guided online adaptive SBRT. A forthcoming phase I trial will investigate potential clinical benefit from escalation to ultra-high doses. As adaptive technologies evolve, the OAR-guided isotoxic approach developed here may be an important future direction for radiation oncology. NCT02608385

Daily Artificial Intelligence-Assisted Adaptive Radiotherapy on Cone-Beam CT for Cancer of the Head and Neck

<h3>Purpose/Objective(s)</h3> Adaptive radiation therapy (ART) provides a method to modify the radiation treatment plan secondary to structural and spatial changes that occur during a treatment course. Specifically, ART is relevant in head and neck cancers as many patients will experience changes of the target volumes and organs-at-risk (OARs) during treatment due to combination of treatment response, weight loss, inflammation and effects on normal tissues. Our institution recently began performing daily ART for select cases of definitive Head and Neck cancers using a commercially available, online adaptive platform with AI-assisted workflows on daily cone-beam computed tomography (CBCT). Herein we report our initial dosimetric experience using this novel technique. <h3>Materials/Methods</h3> Twelve consecutive head and neck cancer patients (nasopharynx, oropharynx, larynx) underwent CT-simulation and planned for adaptive treatments. A reference IMRT plan was created and approved using institutional dose/fractionation schema, target goals and OAR constraints. For treatment, the patient was aligned and CBCT acquired. The simulation CT scan was subsequently registered to the CBCT using deformable registration and a synthetic CT scan was generated. AI-based auto-contour and structure deformation of OARs and Targets on CBCT were reviewed and edited by the treating physician. Two plans were generated including, a CT sim-based plan with deformed structures (scheduled) and a re-optimized plan (adaptive) of which both plans evaluated and the best one approved and delivered. Statistical analyses were performed comparing adaptive versus scheduled plans using a paired student's t-test. <h3>Results</h3> 419 adaptive and scheduled radiotherapy plans were generated of which 82.2% adaptive plans were chosen with an average time per session of 19.4 minutes. Adaptive plans demonstrated superior V95 PTV70, PTV60, PTV56 mean coverage by 0.8 (95% CI 0.5%-1.1%, p < 0.001), 2.4% (95% CI 2%-3%, p < 0.001), and 5.9% (95% CI 5.4%-6.5%, p < 0.001). Compared to scheduled plans, high priority OARs demonstrated a reduction in: mean parotid gland right and left dose by 3.2 Gy (95% CI 1.9-4.4, p < 0.001) and 2.7 Gy (95% CI 1-4.4, p < 0.001), respectively and brainstem max dose was reduced by 5.2% (95% CI 3.7%-6.7%, p < 0.001). <h3>Conclusion</h3> Daily AI-assisted adaptive radiation therapy for head and neck cancer may potentially improve the therapeutic index for different tumor types by improving the target coverage and reducing OAR dose. Prospective clinical evaluation is indicated to further this novel approach.

Real-Time Online Adaptation for Accelerated Partial Breast Irradiation Significantly Improves Target Coverage without Compromising Organs at Risk

<h3>Purpose/Objective(s)</h3> Adaptive online radiotherapy (AORT) is an emerging technology that re-optimizes treatment plans based on cone-beam CT (CBCT) for accurate delivery of prescription and avoidance of critical organs at risk (OARs). Women with breast cancer who qualify for accelerated partial breast irradiation (APBI) are ideal patients for AORT as plans can adapt to daily positional and shape changes. Here we report the dosimetry benefit and the efficiency of patients receiving AORT APBI within the last year in our department. <h3>Materials/Methods</h3> We retrospectively reviewed 18 patients who received AORT APBI, with a total of 84 evaluable fractions. Two plans are made after OARs and targets are contoured on CBCT: a "scheduled" plan, the simulated plan overlaid on CBCT, and an "adapted" plan, where dose delivery is re-optimized to new contours. PTV and CTV volumes, PTV V30 Gy, CTV V30 Gy, ipsilateral breast V50% and V100%, skin Dmax, ipsilateral lung V9Gy, and heart V150cGy (if left sided disease) were collected for both the scheduled and adapted plans per fraction. CTV volume was created from GTV, defined as cavity delineated by surgical clips, plus a 1 cm radial margin limited from musculature of chest wall and 5 mm from skin. PTV was a 0-5 mm radial expansion of CTV volume. Time for adaptive planning was collected prospectively by radiation therapists. Three fractions were excluded from analysis due to setup error or pre-planned lack of adaptation. Paired t-test was used for parametric data and Wilcoxon rank sum test for nonparametric data; analyses were done in R v4.1.2 (www.r-project.org). <h3>Results</h3> Mean patient age was 66.4 years (range 42-80). All patients met ASTRO APBI "suitable" or "cautionary" category. 15 patients had pathologic T1-2N0M0 invasive ductal carcinoma and 3 patients had ductal carcinoma in situ (1/3 grade 2 and 2/3 grade 3). 82% (69/84) of fractions were delivered adapted. CTV and PTV volumes ranged from 15.7-197 cm³ (mean 80.7 cm³) and 33.4-227 cm³ (mean 119.8 cm³). Scheduled PTV V30 was <95% in 60 fractions (71%) and <90% in 30 fractions (36%). On adapted fractions, PTV V30Gy and CTV V30Gy coverage were significantly improved comparing scheduled versus adaptive plans, median (IQR) 91.4 (85.9-94.4%) v 95% (95-95%) and 98 (93.4-99%) v 99.7% (98.8-99.8%), respectively, p < 0.0001. There were no differences in PTV/CTV coverage when scheduled plans were chosen over adapted plans. Only skin Dmax was statistically decreased among the OARs (635.3 vs 629.1 cGy per fraction, p = 0.018); all other OAR coverages were not statistically different. Contouring and plan generation took on average (IQR) 9.4 minutes (6-12 min) and 5 min (4-6 min) respectively. <h3>Conclusion</h3> Adaptation with AORT significantly improved PTV/CTV prescription coverage and skin dose in APBI with <15 min added treatment time. AORT may improve outcomes for ABPI due to improved PTV/CTV prescription delivery.

Radiographer-Led, CBCT-Guided Online Adaptive Radiotherapy for Muscle Invasive Bladder Cancer Using Artificial Intelligence

<h3>Purpose/Objective(s)</h3> The advent of commercially-available CBCT-guided online adaptive radiotherapy (oART) has made daily replanning possible in our center. We investigated the dosimetric impact and clinical feasibility of a treatment radiographer (RTT)-led oART with daily plan re-optimization service for muscle invasive bladder cancer (MIBC) at our center. <h3>Materials/Methods</h3> Between January 2021 and February 2022 ten patients with MIBC were treated with oART. The patients underwent a radiotherapy (RT) planning CT scan with an empty bladder. RT was prescribed to a dose of 55 Gy in 20 daily fractions to the planning target volume (PTV) and delivered using either 7- or 9-field IMRT. CTV to PTV margins were 1.5cm superiorly/anteriorly, 1cm posteriorly and 0.8cm inferiorly/laterally. In the oART workflow, artificial intelligence (AI) was utilized during daily plan generation for: organs at risk (OAR) contour delineation; structure-guided target contour propagation; and automated RT plan generation. RTTs were trained to perform the tasks of the oART workflow and a local competency program for RTTs was applied. For the first five patients a radiation oncologist (RO) was present at each treatment, for the second five patients, the RTTs performed the process independently with a RO present if needed. A RO reviewed the images weekly. A comparison of dose delivered using the daily adaptive plan (ADP) versus the original scheduled plan (SP) was performed using Wilcoxon signed rank test, for both PTV coverage and OAR doses. Timing measurements were recorded for the workflow. <h3>Results</h3> Seven male and three female patients with a median age of 79 years (range 66-82) were treated with oART for MIBC. Four patients received concomitant chemotherapy. A total of 186 oART treatments were planned. Of these, the SP was delivered on 10 occasions as a result of technical issues with the online ADP quality assurance. Using the ADP, PTV D98% was increased by a mean of 3.3% (p<0.05), and bowel and rectal V52 Gy were reduced by a mean of 9.1cc (p<0.05) and 2.6% (p<0.05), respectively, compared to the SP. For both the RO and RTT-led workflows, the median adaptive process duration from CBCT acquisition to beam-on was 19 minutes (range 9-35 minutes). <h3>Conclusion</h3> The use of AI has enabled routine clinical implementation of daily CBCT-guided oART which is RTT led. The use of oART increased PTV coverage and reduced OAR doses for MIBC. The development of appropriately trained RTTs to run this service increases the likelihood that this technological advancement can be maintained. Steps to reduce PTV margins are to be explored which should further enhance the dosimetric benefits of oART.

Early Experience of Online Adaptive Radiation Therapy of Definitive Head and Neck Cancer Patients

<h3>Purpose/Objective(s)</h3> Advent of cone beam computed tomography (CBCT) based online adaptive radiotherapy (OnART) systems has reduced the barriers of adaptation by allowing rapid generation of new plans. However, due to the complexity of head and neck cancer (HNC) treatment planning, there is a paucity of OnART data on HNC patients. We present our early OnART experience in definitive radiation of HNC with the hypothesis that OnART would improve target coverage and reduce dose to organs at risk (OAR). <h3>Materials/Methods</h3> Patients with HNC receiving definitive (chemo)radiation who underwent at least one OnART were enrolled on a prospective registry study between 7/2021 and 1/2022. The same OnART software was used for on-couch adaptation for all patients. The frequency of adaptation was at the discretion of the treating physician. Physicians were given the option of delivering one of two plans during adaptation: the original radiation plan transposed onto the CBCT with adapted contours (scheduled) and a new adapted plan generated from the updated contours (adapted). Paired t-test was used to compare the mean doses between scheduled and adapted plans. <h3>Results</h3> Sixteen patients (12 oropharynx, 2 larynx/hypopharynx, 1 sinus, and 1 salivary gland primary) underwent 38 adaptation sessions (median 2; range 1 – 8). Most patients (14/16) were treated in 33 or 35 standard fractions, with SABR delivered to the other two patients. The mean new plan generation and approval time was 22 minutes (median 21; range 9 – 39), the mean physician time at the console was 26 minutes (median 23; range 14 – 45), and the mean patient time in the vault was 44 minutes (median 42; range 28 – 96). The adapted plan was chosen 89.5% (34/38) of the time. The scheduled plan vs adapted plan mean V100% for high-risk PTVs was 87.8% vs 92.4% (p = 0.07), intermediate-risk PTVs was 86.6% vs 98.2% (p = 0.02), and low-risk PTVs was 96.3% vs 98.1% (p < 0.01). The mean hotspot was also lower with adaptation, 108.7% vs 106.7% (p < 0.01). Doses to OARs saw heterogeneous changes from the scheduled to the adapted plan (Table). Most OARs (9/12) showed a decrease in the mean relative difference in dose with the adapted plans, with the larynx (p = 0.045), spinal cord (p < 0.01), mandible (p < 0.01), and brainstem (p = 0.03) reaching significance. <h3>Conclusion</h3> OnART is feasible for HNC patients. Early results show significant improvement in target coverage and homogeneity. On average, OARs saw a modest decrease in dose.

Tracking OAR Volume and DVH Variability in the Initial Cohort of Pelvic Patients Treated with CBCT-Guided, Online Adaptive Therapy

<h3>Purpose/Objective(s)</h3> We have recently commissioned and began treatment using a novel, CBCT-guided, online adaptive radiotherapy (ART) platform. We have evaluated the volumetric and dosimetric variation in our initial cohort of patients. <h3>Materials/Methods</h3> We have tracked our 6 initial online adaptive pelvis patients for 208 delivered fractions. A custom script was developed to extract volume and dosimetry data from our second check software. Target and organ-at-risk (OAR) volumes were gathered for every fraction. Structure volume variations were computed relative to the reference plan for each fraction and averaged per patient. Dose-volume histogram (DVH) data was collected for the primary treatment plan for our four prostate patients, all treated to 45 Gy in 25 fractions. The range of OAR DVH values at 20, 30, and 40 Gy and PTV V45 Gy over the primary course of each patient's treatment was computed for both scheduled and daily adapted plans. <h3>Results</h3> OAR variability was very patient dependent. Bladder volumes showed the largest variation, with most patients unable to replicate their bladder filling from simulation. Mean deviations in bladder volume ranged from -47% to +12%, with standard deviations >20% for two-thirds of patients. Rectal volume was also variable, but to a lesser extent, with mean variations ranging from -31% to +16%. DVH data demonstrates that ART produces a much tighter distribution of DVH curves compared to the scheduled plan for all structures while maintaining PTV coverage to within 1% of the reference value. The variability of V40Gy, V30Gy, and V20Gy showed reductions of 13.9%, 29.8%, 30.9% for bladder and 52.6%, 48.9%, and 24.6% for rectum relative to the scheduled plan, respectively. Using only the scheduled plan would result in reductions of up to 8% of target coverage compared to the reference plan. <h3>Conclusion</h3> We have observed considerable OAR interfraction volume variations during the course of pelvic radiation that can be highly patient dependent. Use of ART maintains adequate target coverage while reducing the variability in dose delivery to these nearby OARs. Combined with the large variability in OAR filling, we have demonstrated that ART enables clinicians to have a higher likelihood of delivering their intended doses to targets and OARs compared to standard-of-care radiotherapy.

Initial Experience Using Daily Artificial Intelligence-Assisted Adaptive Radiotherapy on Cone-Beam CT for Bladder Cancer

<h3>Purpose/Objective(s)</h3> Adaptive radiation therapy (ART) provides a method to observe daily changes in the patient's internal anatomy and revise the radiation plan. In patients undergoing radiation for bladder cancer, changes in the bladder contour due to bladder filling result in significant changes in the radiation dose distribution potentially reducing the target coverage. Large margins are required in order to account for these anatomic changes, increasing the dose to the nearby organs at risk (OARs). Daily ART is an attractive method for bladder cancer in an attempt to reduce treatment margins. Herein we report our initial experience using commercially available, online adaptive platform with Artificial Intelligence (AI)-assisted workflows on daily cone-beam computed tomography (CBCT) for bladder cancer. <h3>Materials/Methods</h3> 9 consecutive patients eligible for bladder conserving radiotherapy underwent CT-simulation (CT-sim) and planned for ART. Clinical Target Volume included the bladder and proximal prostatic urethra +/- pelvic lymph nodes with an additional 0.5-1cm margin for the planning target volume (PTV). Fractionation schemas utilized included 64Gy/32 fractions, 55Gy/20 fractions, and 36Gy/6 fractions. A reference IMRT plan was created and approved using institutional target goals and OAR constraints. For treatment, the patient was aligned and CBCT acquired. The CT-sim was registered to the CBCT using deformable registration and a synthetic CT scan was generated. AI-based auto-contour and structure deformation of OARs and Targets on CBCT were reviewed and edited by the treating physician. 2 plans were generated including, a CT sim-based plan with deformed structures (scheduled) and a re-optimized plan (adaptive) of which both plans evaluated and the best one approved and delivered. Statistical analyses were performed comparing PTV coverage and OAR constraints between adaptive versus scheduled plans and a dosimetric comparison of ART compared to non-ART large-margin (1.5cm) treatment. <h3>Results</h3> 174 comparative adaptive and scheduled plans were generated of which the adaptive plan was chosen in 91.0% of sessions. V95 PTV mean coverage was improved with adaptation 97.0% versus 86.9% (p<.001). Average PTV volume change during ART was 16.4% (Range:0.4%-98.0%). All completed ART courses met institutional OAR constraints. With adaptation, there was a mean decrease in rectal V45%, V75%, V100% by 6% (95% CI 4.2%-7.6%, p<.003), 2.3% (95% CI 1.6%-3%, p<.003), 0.7% (95% CI 0.5%-0.8%, p<.001) respectively, and an increase in bowel V85%, V90% by 0.5% (95% CI 0.3%-0.8%, p<.04), 0.6% (95% CI 0.4%-0.8%, p<.01), respectively. Compared to a plan with large margins, there was a significant reduction in all bowel and rectal parameters with daily ART and smaller PTV margins. <h3>Conclusion</h3> Daily AI-assisted ART using CBCT for bladder cancer appears feasible and may potentially improve the therapeutic index by reducing the need for large PTV margins while ensuring acceptable target coverage.

Effectiveness of the Construction Projects: A Comparison between Users Committees and Contractor Approach

Purpose: The policymakers are found to be undecided which assures the need for a refined policy on local governance in the formation of a user committee. It is expected to be beneficial for the project management team to point out the factors that directly or indirectly affect project performance. To assess the effectiveness of the construction projects implemented through Users Committees in comparison to the Contract Approach. Design/Methodology/Approach: The number of Informal Consultations was conducted at each sector of the project location involving UCs and beneficiaries. Furthermore, the key Informant's Interview was taken with the Planning officials, Implementing officials, and elected representatives followed by policy maker and department representative. A comparison is also made with contractor-based work. Using Content Analysis as a data analysis technique, the raw data was presented in the form of explanation, understanding or interpretation. Findings/Result: Need of empowerment &amp; awareness to the beneficiaries too for their commitment by contributing to the construction projects and also providing orientation training to develop technical and managerial skills to UCs. A mandatory system of feasibility study of the projects for the financing of beneficiaries' contributions is essential. The work schedule or work plan should be attached to the agreement. Proper combination of both approaches would be better to be followed in a single project based on the nature, content, and complexity of rural road construction work. Originality/Value: Both relevant and practical aspects for projects implemented through users in comparison to contractors have been significant for policymakers to decide the correct approach for correct work. Paper Type: Research paper

Integrated optimization of train formation plan and rolling stock scheduling under Time-dependent demand

Both train formation plan (TFP) and rolling stock scheduling (RSS) have significant impacts on train operation. However, traditional sequential optimization could lead to infeasible RSS for the predetermined TFP, especially when the number of available rolling stocks is limited. This paper focuses on integrally optimizing TFP and RSS, where each train service can utilize different train formations and multiple turnaround operations occur in the depot. The key to solving the problem is to determine how many formations are needed for each train service and how train services are connected by rolling stocks. The multiple turnaround operations include immediate turnaround, turnaround with entering/exiting depot operations, and turnaround with coupling/decoupling operations. Considering the fixed costs and operating costs, we propose a multi-objective mixed-integer nonlinear programming (MINLP) model to minimize the number of rolling stocks (NR), the number of formations (NF), and the number of coupling/decoupling operations (NC) based on the time–space network. The model is further reformulated into a single-objective mixed-integer linear programming (MILP) model by the linearization method and fuzzy programming, and the reformulated model can be effectively solved by the MILP solver CPLEX. We test the model on realistic instances of the Batong Line in Beijing Subway Network to verify its effectiveness. The results demonstrate that the integrated model effectively solves the shortage of rolling stocks when the number of available rolling stocks is limited. Furthermore, in the integrated model, the decrease in the number of the rolling stocks and coupling/decoupling operations are at the cost of more formations used to balance the conflicts among objectives.

An efficient Energy Management System for long term planning and real time scheduling of flexible polygeneration systems

The ever-increasing participation of new energy sources, storage systems and controllable loads to electricity markets has introduced more complexity in the operation of power plants, microgrids (MGs) and networks. To face these issues, it is often necessary to design Distributed Energy Systems to optimize the power exchange. Usually, MG operation is managed by an Energy Management System (EMS) dealing with large amounts of parameters to be taken into account and that may drive to problems difficult to be faced and requiring long computational timings. In this direction, this paper describes the Mixed Integer Linear Programming MATLAB Based Energy Management System (MB-EMS) developed at the University of Genoa, aimed at optimizing the operating costs of a generic polygeneration system, considering different electrical and thermal production units, energy storage systems, non-programmable loads and flexible loads. Many important technical details are accounted: the duration and costs related to maintenance interventions and CO2 emissions costs are considered for all production units as well as constraints and incentives related to the High-Efficiency Cogeneration systems. Constraints related to the minimum and maximum State-Of-Charge of energy storage systems, plus their deterioration, with the consequent optimization of their utilization, are also modelled. Specific test cases for all functionalities are presented. Moreover, a test related to a real MG performed over a time horizon of one year with a time step of 1 h is performed and solved in a short computational time. The linearization of constraints reduces the computational effort, avoiding the need of clustering or averaging parameters for similar production units. Consequently, calculations are rapidly performed, also when dealing with long term planning problems (a realistic test case with time horizon of one year and time step of 15 min can be simulated in some seconds). In this sense, the developed tool can be used both in real time operation and in long term planning problems.

Trends in Mobile Transit Information Utilization: An Exploratory Analysis of Transit App in New York City

Smartphone applications that provide transit information are now very popular. However, there is limited research that examines when and where passengers use mobile transit information. The objective of this research was to perform an exploratory analysis of the use of a smartphone application known as Transit App, which provides real-time transit information and trip planning (schedule) functionality. Backend data from Transit App were examined by time of day and day of week in the New York City metropolitan area. The results show that the pattern of both the trip planning feature and overall real-time information usage follow the typical pattern of transit ridership, which has morning and evening peaks. Additionally, self-reported household locations of Transit App users in the New York City area were compared with household socioeconomic characteristics (specifically, income, ethnicity, and age) from census data using GIS visualizations and the Pearson correlation coefficient, but they do not appear to be correlated. This implies that passengers use Transit App regardless of household income, race, or age trends in their neighborhood. This exploratory study examined a rich new data source—backend data from a transit information smartphone application—that could be used in many future analyses to help transit agencies better understand how transit riders use information and plan their trips.

Integrated modeling and optimization of production planning and scheduling in hybrid flow shop for order production mode

This paper studies the integration of production planning and scheduling in the forging process of small batch orders. Firstly, the demand for orders and the processing constraints of forging equipment are comprehensively considered. Then by integrating periodic requirements for orders, the cycle of production is assigned based on production status and inventory, and each cycle of production is independently scheduled, a two-level decision-making model for production planning and adjustment of hybrid flow shop for forging is established. Secondly, in order to realize the closed-loop integration of high-level planning and low-level scheduling, a particle swarm-genetic hybrid algorithm is proposed to obtain the feasible integrated solution of production planning and scheduling. Thirdly, through a large number of numerical experiments, the genetic algorithm parameters of scheduling layer are optimized and its optimization ability is verified, and the optimal results also show that the parameters improve the overall algorithm performance. Lastly, a case of hybrid flow workshop for forging is presented to demonstrate the practicability of the proposed model and algorithm.

Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model

Integrated Energy Microgrid (IEM) has emerged as a critical energy utilization mechanism for alleviating environmental and economic pressures. As a part of demand-side energy prediction, multi-energy load forecasting is a vital precondition for the planning and operation scheduling of IEM. In order to increase data diversity and improve model generalization while protecting data privacy, this paper proposes a method that uses the CNN-Attention-LSTM model based on federated learning to forecast the multi-energy load of IEMs. CNN-Attention-LSTM is the global model for extracting features. Federated learning (FL) helps IEMs to train a forecasting model in a distributed manner without sharing local data. This paper examines the individual, central, and federated models with four federated learning strategies (FedAvg, FedAdagrad, FedYogi, and FedAdam). Moreover, considering that FL uses communication technology, the impact of false data injection attacks (FDIA) is also investigated. The results show that federated models can achieve an accuracy comparable to the central model while having a higher precision than individual models, and FedAdagrad has the best prediction performance. Furthermore, FedAdagrad can maintain stability when attacked by false data injection.