Conventional Scheduling Research Articles

The fractionation dependence of tumor control in proton therapy for early-stage non-small cell lung cancer.

The relative biological effectiveness (RBE) of tumor control for proton beam therapy (PBT) compared to photon radiotherapy (RT) is typically assumed to be independent of fractionation. To test this, we modeled published PBT outcome results for early-stage non-small cell lung cancer (NSCLC) treatments across a range of fractionation schedules. All published and analyzable cohorts were included (399 patients, 413 treated lesions). Two models were used to fit the data: a previously published tumor simulation model that fits photon RT results of NSCLC across all fractionation regimes and the Fowler LQ model with a kick-off time term. The treatment effect of each cohort was referenced to the photon equivalent dose through mechanistic model simulations in a 2 Gy/weekday scenario, with radiobiological parameters determined to simultaneously best-fit all fractionation results. The tumor control RBE of each published treatment schedule, compared to the modeled photon RT effect of the same schedule, was then estimated. For cohorts whose treatments lasted less than three weeks (i.e., 12 fractions or less), the RBE of PBT was in the range of 1.08 to 1.11. However, for fractionated treatments stretching over four weeks or more (20-25 fractions), the relative effectiveness was much lower, with RBEs in the range of 0.82-0.89. This conclusion was unchanged using the simpler Fowler LQ + time model. The proton RBE for hypo-fractionated schedules was 20-30% higher than for conventional schedules. The derived radiobiological parameters of PBT differ significantly from those of photon RT, indicating that PBT is influenced differentially by radiobiological mechanisms which require further investigation.

P0783 High risk of adrenal insufficiency in patients with Inflammatory Bowel Disease treated with a conventional course of systemic corticosteroids: Results from a prospective study

Abstract Background Corticosteroids (CS) remain the first-line treatment for inflammatory bowel disease (IBD) although they can cause adrenal insufficiency (AI) due to the alteration of the hypothalamic-pituitary-adrenal (HPA) axis. Assessing this risk is essential, as these patients are often in stressful situations, and AI can mimic IBD symptoms, leading to misdiagnosis. However, there is a lack of studies adequately assessing adrenal function in these patients. The objectives of this study were to determine the proportion of IBD patients who present HPA axis suppression following CS treatment, assess the time required for axis recovery, and identify risk factors for AI. Methods A prospective observational study was conducted including adult patients with active IBD who started systemic CS treatment and with no exposure to CS within the last 6 months. They all followed a pre-defined regimen of 1 mg/kg/day of prednisone (maximum 60mg) for 2 weeks, tapering by 10 mg weekly until reaching 20 mg, then tapering by 5 mg weekly. The HPA axis was assessed 24 hours after CS discontinuation by the measurement of baseline cortisol and ACTH, and cortisol 30 and 60 minutes after stimulation with 250 mcg of ACTH. HPA axis recovery was evaluated 3 and 6 months after CS discontinuation. The association between clinical and demographic variables and the occurrence of AI was analysed. Results Ninety-two patients were included (58% Crohn's disease, 39% ulcerative colitis, mean age 39.8±16 years, 50% women), who received a total CS dose of 2000.7±543.1 mg for 63.5±14.1 days. Forty patients (43.5%) were diagnosed with AI (32 partial, 8 complete). The recovery test was not performed in 10/40 patients (5 due to loss of follow-up, 5 due to need of a new course of CS). 23/30 patients recovered the adrenal function 3 months after stopping CS, and 27/30 patients after 6 months. In the univariate analysis, only fecal calprotectin at the end of CS treatment was associated with AI (P=0.026). Conclusion A high proportion of IBD patients treated with a conventional CS treatment schedule develop HPA axis suppression, although most of them recover adrenal function within six months. The development of AI was not associated with any of the studied variables, except for faecal calprotectin levels. Our results highlight the potential risks associated with prolonged CS use and underscore the importance of considering AI in this context, particularly when the patient faces physical stress.

Bayesian Learning Based Elitist Nondominated Sorting Algorithm for a Kind of Multi-objective Integrated Production Scheduling and Transportation Problem

This paper focuses on a kind of multi-objective integrated production scheduling and transportation problem (MIPSTP), which is encountered in many real-life industrial processes. MIPSTP contains a production stage for processing products and a transportation stage for transporting products. In MIPSTP, we consider a dedicated integration of distributed scheduling and transportation regarding the two stages, arising from a practical project for a cooperative iron and steel company. The objective of MIPSTP is to minimize the total completion time of each factory and total transportation cost. To solve the problem, a Bayesian learning-based elitist nondominated sorting algorithm (BLENSA) is proposed. The primary highlights of this work are two-fold: 1) new solution structures of MIPSTP and 2) novel search model of BLENSA. For the solution structures, we propose for the first time the mathematical description of MIPSTP, accounting for several features in applications and so is different from conventional scheduling problems. For the search model of BLENSA, we propose a Bayesian learning-based probability model to learn valuable knowledge about nondominated solutions. Thereby, BLENSA combines the Bayesian learning-based probability model to produce a candidate population (CP) and the nondominated sorting method to generate a main population (MP). Next, we propose a greedy competition strategy to construct MP from CP and MP for the next generation. Results of experiments on 57 test instances and a real-life case study demonstrate the effectiveness and practical values of BLENSA.

Optimizing Terminal Water Management in Irrigation District Using Water Balance and Genetic Algorithm

Water delivery management in China’s irrigation districts has traditionally prioritized the main canal system, often overlooking the water-saving potential of the final canals and field irrigation, which offer substantial opportunities to enhance water use efficiency and conserve agricultural water resources. This study summarizes and defines the integrated water management of final canals and field irrigation as terminal water management. An optimization method was developed to improve terminal water management, which includes optimizing irrigation quotas based on water balance and scheduling final canal water delivery to minimize seepage losses. A genetic algorithm was employed to solve the problem. The method was applied to the Hongjin irrigation district in Jiangsu Province, China. In 2020, paddy water management was observed, revealing that the irrigation amount for organic and traditional rice was 1113 mm and 956 mm, respectively. Conventional irrigation and water delivery practices have led to extensive drainage, significant rainwater wastage, and inefficient water use. The optimized irrigation quotas for organic and traditional rice resulted in water savings of 302.5 mm and 325.9 mm, respectively, compared to the 2020 monitored data. An irrigation event in early August during a 75% hydrological frequency year was selected as an example. With conventional scheduling, optimized final canal water delivery scheduling reduced the seepage losses from 6.3% to 4.6%, shortened the irrigation time from 17 h to 14 h, and stabilized canal flow rates. The proposed optimization method is a valuable tool for enhancing terminal water management and supporting better irrigation decisions in irrigation districts.

Optimizing Scheduling Techniques for Enhanced Carrier Aggregation in LTE-Advanced Networks

In this study, we investigate resource scheduling strategies for the downlink in LTE-Advanced networks, focusing specifically on systems that employ Carrier Aggregation (CA) with multiple Component Carriers (CCs). Effective scheduling is crucial in CA-enabled LTE-Advanced to maximize efficiency and support diverse user needs. We examine three conventional scheduling schemes—Joint User Scheduling (JUS), Separated Random User Scheduling (SRUS), and Separated Burst-Level Scheduling (SBLS). Although JUS yields superior performance, it is complex and does not factor in Quality of Experience (QoE) metrics. In contrast, SRUS and SBLS, while less computationally intensive, underutilize CA and fall short in resource allocation fairness. To overcome these limitations, we propose a new scheduling scheme, Quality of Service and Channel Scheduling (QSCS), which dynamically adjusts CC allocations at the burst level based on user service priority and signal quality. Simulation results show that QSCS achieves throughput similar to JUS while selecting optimal CCs based on Quality of Service (QoS) and channel parameters, resulting in enhanced QoE compared to other methods. This proposed approach demonstrates significant potential for improving resource allocation and user satisfaction in LTE-Advanced networks.

Research on optimal scheduling of minor and moderate floods for cascade reservoirs in the lower reaches of the jinsha river considering power generation

The comprehensive cascade reservoir system is extensively used for flood control and power generation. Traditional reservoir scheduling often treats these tasks as competing objectives, prioritizing flood control during the flood season and neglecting power generation benefits. This approach, primarily applied to large floods, leads to hydraulic resource wastage and power generation loss during minor and moderate floods. The lower Jinsha River cascade reservoirs have significant flood control storage capacity, allowing part of the capacity to be used for maintaining higher water levels without compromising safety under minor and moderate floods within a 20-year return period. This paper proposes an optimal scheduling model that considers both flood control and power generation tasks for the lower Jinsha River cascade reservoirs. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm was used to find non-inferior solution sets. An evaluation index system was developed to select optimal solutions under different flood frequencies, using the Minimum Discriminant Information Principle and VIKOR model. The results indicate that the optimal scheduling scheme under 20-year, 10-year, and 5-year return period floods can enhance power generation by 1.64, 1.71, and 1.35 billion kWh, respectively, compared to conventional scheduling. This approach supports coordinated flood control and power generation scheduling, contributing to the high-quality development of the Yangtze River Economic Belt.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Comparison of Conventional Palliative Radiotherapy Fractionation Schedule with Quad Shot Regimen in Locally Advanced Head and Neck Cancer Patients: A Randomised Clinical Trial

Introduction: Head and Neck Cancer (HNC) accounts for 14.3% of all cancers in India and 4.8% of all cancers worldwide. In India, the most common sites are the lip and oral cavity (>80%), which are more prevalent in men than in women. Histologically, most cases are Squamous Cell Carcinomas (SCC). The majority of patients present with locally advanced stages, where surgery and definitive chemoradiation therapy are not possible and palliative Radiotherapy (RT) is considered a treatment option for better symptomatic relief and improved Quality of Life (QoL). The Quad Shot (QS) palliative regimen has shortened treatment time, reduced toxicity and increased compliance. Aim: To compare the QS regimen administered over two consecutive days versus the Conventional Palliative Regimen in the treatment of locally advanced HNC, in terms of treatment response, acute toxicities and QoL. Materials and Methods: In this prospective randomised clinical trial conducted from May 2023 to July 2024 in the Department of Radiation Oncology at King George's Medical University (KGMU), Lucknow, Uttar Pradesh, India, patients with biopsyproven locally advanced carcinoma of the head and neck were recruited, with a sample size of 50 in each of the study Group B and control Group A arms. Patients in the control arm received 30 Gray (Gy) in 10 fractions, 5 fractions per week over two weeks (Conventional palliative arm). In the study arm, patients received 14 Gy in 4 fractions delivered in two daily sessions, six to eight hours apart, for two consecutive days over three cycles (QS arm). The Response Evaluation Criteria in Solid Tumours (RECIST) Criteria 1.1 was used to assess the tumour response objectively three months post-RT. Health-related QoL was assessed using questionnaires developed by the European Organisation for Research and Treatment of Cancer (EORTC), specifically the EORTC Quality of Life Questionnaire (QLQ). Continuous data were compared using the t-test for nominal data and the Mann-Whitney U test was used otherwise. Results: The mean age was 39.66±12.05 years in Group A and 42.76±11.65 years in Group B. The QS and conventional palliative arms each had 47 and 49 patients recruited, respectively. The QS arm exhibited fewer instances of skin toxicity, with 25 (53.2%) experiencing Grade I and 7 (14.9%) experiencing Grade II toxicity, compared to the conventional palliative arm, where 30 (61.2%) had Grade I and 12 (24.5%) had Grade II toxicity. Mucositis in the QS arm included 22 (46.9%) cases, with 14 (29.7%) classified as Grade I and Grade II, whereas the conventional arm had 12 (24.5%) Grade I cases, 29 (59.5%) Grade II cases and 3 (6.1%) Grade III cases. The treatment response, in terms of partial and stable disease combined, was observed in 61.2% of the conventional arm compared to 78.7% in the QS arm. QoL was reported to be better in the QS regimen post-treatment. Conclusion: Given the total number of patients recruited, the QS arm, with its shorter treatment time, demonstrated better benefits in terms of reduced toxicities and improved treatment response, as well as enhanced QoL compared to the conventional palliative arm.

Load adaptive multi-threshold scheduling to concurrently improvise transmission time and wait time performance of C-RAN

Low-latency requirements of 5G networks are one of the fundamental needs to support delay-sensitive applications. Uplink scheduling mechanism at wired midhaul of Centralized Radio Access Network (C-RAN) architecture significantly affects the transmission speed and delay performance of the network. Inadequate performance of conventional scheduling algorithms necessitates improvements to attain these requirements. In this paper, load adaptive scheduling with single threshold (LAS-ST) and load adaptive scheduling with multi-threshold (LAS-MT) algorithms on C-RAN architecture are proposed to reduce transmission time and wait time parameters concurrently. The proposed algorithms have been compared with two conventional scheduling algorithms, multi-scheduling domain (MSD) & single scheduling domain (SSD) and applied at fronthaul and midhaul of C-RAN for its comprehensive analysis. MSD and SSD are widely used in Ethernet Passive Optical Network (EPON) architectures. The results confirm that the proposed multi-threshold scheduling algorithm performs best in meeting the objectives of the studycompared to the existing scheduling algorithms in terms of transmission time and wait time by adapting to conditions. Further, resource utilization and resource availability are observed for several transmission cycles (TCs). In addition, data load variation and variation in number of available resources is analysed and its effect on transmission time and wait time is studied.

UNISON framework with fuzzy decision tree for water conservation in the dynamic scheduling of the textile dyeing process

PurposeThis study aimed to optimize the dyeing scheduling process with uncertain job completion time to reduce resource consumption and wastewater generation, and while reconciling the conflicting objectives of minimizing the makespan and the need to limit the production on specific machines to minimize rework.Design/methodology/approachWe employed a UNISON framework that integrates fuzzy decision tree (FDT) to optimize dyeing machine scheduling by minimizing the makespan and water consumption, in which the critical attributes such as machine capacity and processing time can be incorporated into the scheduling model for smart production.FindingsAn empirical study of a high-tech textile company has shown the validity and effectiveness of the proposed approach in reducing the makespan and water consumption by over 8% while high product quality and efficiency being maintained.Originality/valueHigh-tech textile industry is facing the challenges in reducing the environmental impact of the dyeing process while maintaining product quality and efficiency for smart production. Conventional scheduling approaches have not addressed the relationship between machine groups and reworking, resulting in difficulty in controlling the makespan and water consumption and increasing costs and environmental issues. The proposed approach has addressed uncertain job completion via integrating FDT into the scheduling process to effectively reduce makespan and wastewater. The results have shown practical viability of the developed solution in real settings.

Deep Reinforcement Learning for Dynamic Task Scheduling in Edge-Cloud Environments

With The advent of the Internet of Things (IoT) and its use cases there is a necessity for improved latency which has led to edgecomputing technologies. IoT applications need a cloud environment and appropriate scheduling based on the underlying requirements of a given workload. Due to the mobility nature of IoT devices and resource constraints and resource heterogeneity, IoT application tasks need more efficient scheduling which is a challenging problem. The existing conventional and deep learning scheduling techniques have limitations such as lack of adaptability, issues with synchronous nature and inability to deal with temporal patterns in the workloads. To address these issues, we proposed a learning-based framework known as the Deep Reinforcement Learning Framework (DRLF). This is designed in such a way that it exploits Deep Reinforcement Learning (DRL) with underlying mechanisms and enhanced deep network architecture based on Recurrent Neural Network (RNN). We also proposed an algorithm named Reinforcement Learning Dynamic Scheduling (RLbDS) which exploits different hyperparameters and DRL-based decision-making for efficient scheduling. Real-time traces of edge-cloud infrastructure are used for empirical study. We implemented our framework by defining new classes for CloudSim and iFogSim simulation frameworks. Our empirical study has revealed that RLbDS out performs many existing scheduling methods.

Normal tissue complication probability modeling for late rectal bleeding after conventional or hypofractionated radiotherapy for prostate cancer

PurposeTo develop a single NTCP model for grade ≥ 2 late rectal bleeding (G2 LRB) after conventional or hypofractionated radiotherapy for prostate cancer. Methods and MaterialsThe development dataset consisted of prostate cancer patients (n = 656) previously randomized to conventional (39 x 2 Gy) or hypofractionated (19 x 3.4 Gy) external beam radiotherapy with N = 89 G2 LRB cases. Candidate predictors were obtained from literature. We fitted five separate logistic regression models to the data, each with one of the following dose parameters as candidate predictors in biological effective dose (BED), assuming α/β = 3 Gy: Equivalent uniform dose (EUD) with n = 0.1, EUD with n = 0.2, the relative volume receiving ≥ 111.9 Gy in BED (V111.9, the equivalent of physical V70 for a conventional schedule), minimum BED to the hottest 0.1 cm3 (D0.1cm3) or 2 cm3 (D2cm3). Previous abdominal surgery was included in every model and fractionation schedule was tested as predictor in each model. A sensitivity analysis was performed by varying the α/β-ratio, n and dose-volume cutoff. ResultsThe pre-selected candidate dosimetric predictor and previous abdominal surgery were significantly associated with the outcome in all five models. Fractionation schedule was eliminated by the backward scheme in only the EUD (n = 0.1), D0.1cm3 and D2cm3-based models. In internal validation these models showed AUC’s of 0.64, 0.60 & 0.62, respectively. The sensitivity analyses showed that EUD models with n ≥ 0.15 and / or α/β ≥ 4 Gy failed, and EUD models based on α/β = 2 Gy with n = 0.05–0.2 showed good fits as well. ConclusionsOur trial data set with different fractionation schedules offered the unique possibility to generate unbiased BED-based models. EUD (n = 0.1), D0.1cm3 and D2cm3 performed overall best in predicting G2 LRB; with α/β = 2 Gy equally good models were obtained. External validation is required to confirm our results.

Optimized Architecture for Efficient OFDMA Network Design

This study presents a novel approach to enhancing the design and performance of OFDMA (Orthogonal Frequency Division Multiple Access) networks, with a particular focus on WiMAX (Worldwide Interoperability for Microwave Access) for Best Effort (BE) services. The proposed method integrates a robust Markovian analytical model with four advanced scheduling algorithms: throughput fairness, resource fairness, opportunistic scheduling, and throttling. A sophisticated simulator was developed, incorporating an ON/OFF traffic generator, user-specific wireless channels, and a dynamic central scheduler to validate the model’s accuracy and evaluate its robustness by dynamically allocating radio resources per frame. The validation study showed that the proposed model reduced simulation time by over 90%, completing analytical calculations in just 15 min, compared to nearly 2 days for simulations using conventional scheduling algorithms. Performance metrics such as the average number of active users and resource utilization closely matched those from the validation study, confirming the model’s accuracy. In the robustness study, the model consistently performed well across diverse traffic distributions (exponential and Pareto) and channel conditions. The proposed architecture increased network throughput by up to 25% and reduced latency under dynamic conditions, demonstrating its scalability, adaptability, and efficiency as a crucial solution for next-generation wireless communication systems.

Breast Cancer Adjuvant Radiotherapy and Chemotherapy Sequencing: Sequential, Concomitant, or What Else? A Comprehensive Review of the Adjuvant Combinations Journey.

Background: To date, in breast cancer (BC) treatment, adjuvant chemotherapy (A-CT) has preceded adjuvant radiotherapy (A-RT). In the last twenty years, the adjuvant treatment of BC has quickly evolved due to better knowledge of its molecular biology, genetic profile, and α/β ratio of 3/4 Gy for tumor and normal tissue radiosensitivity. Thus, new schedules with hypofractionated radiotherapy have been tested, and a third generation of A-CT has been introduced, raising the question of whether it is time to rethink the sequencing between these two approaches. Methods: In the last 20 years, many attempts have been made worldwide to optimize the best sequencing strategy between these two approaches in terms of sequential CT-RT and RT-CT and concomitant and sandwich modalities using drugs and schedules. This paper presents a comprehensive review of the state of the art, analyzing all the available studies to assess the sequencing between A-CT and A-RT with different generations of chemotherapy schedules. Results: More than 8000 patients from 30 studies treated with adjuvant chemotherapy and whole breast radiotherapy who were enrolled in randomized, retrospective, and prospective studies were analyzed. Sequential, concomitant, and sandwich modalities of chemotherapy with conventional or hypofractionated RT schedules from the most important studies were included. The most used sequence was adjuvant chemotherapy followed by conventional or hypofractionated radiotherapy. In the concomitant approach, i.v. CMF has been the most important adopted schedule, while the concomitant use of anthracyclines and taxanes with conventional or hypofractionated radiotherapy has been found to be more toxic. One study analyzed the benefit in terms of reducing adjuvant treatment time with upfront hypofractionated radiotherapy and third-generation chemotherapy. Conclusions: At present, the best sequencing strategy has not yet been defined. This comprehensive review is a journey among the most important randomized, retrospective, and prospective studies that highlights the past, current, and novel time sequencing proposals between A-CT and A-RT to assess the state of the art and provide useful information for future adjuvant approaches in breast cancer treatment.

Planetary radio interferometry and Doppler experiment as an operational component of the Jupiter Icy Moons Explorer mission

AbstractWe present an overview of the operations and engineering interface for Planetary Radio Interferometry and Doppler Experiment (PRIDE) radio astronomy observations as a scientific component of the ESA’s Jupiter Icy Moons Explorer (JUICE) mission, as well as other prospective planetary and space science missions. The article discusses advanced scheduling and planning methods that make it possible to create observing schedules for observations of specific spacecraft in concurrence with observations of natural radio sources. In order to put this into practice and find suitable natural background calibrator sources for PRIDE of JUICE mission, we developed planning and scheduling software. The conventional scheduling software for natural celestial radio sources is not set up to include spacecraft as observation targets in the necessary control files. Therefore, difficulties already arise during observation planning. We report on the development of new and the adaptation of existing routines used in astrophysical and geodetic VLBI for satellite scheduling and planning. The analysis of the PRIDE science observations led to improved observational planning, and the mission’s scheduling methodologies were studied using a systems engineering approach. In addition, we highlighted the new procedures, like finding charts for selecting calibrator radio sources over a range of frequency bands and the outcomes of those strategies for science operation planning. A simulation of the flyby of Venus during the cruise phase of the JUICE spacecraft, based on the Tudat software, is also presented, resulting in a promising opportunity to test PRIDE techniques and evaluate the improvements that PRIDE observables can make to natural bodies’ ephemerides. The first $$\hbox {K}_{a}$$ K a -band (32 GHz) observations of the ESA’s BepiColombo by a radio telescope in the VLBI network, which employs a similar radio communications system as JUICE, were also demonstrated as a test case. The primary objective of these activities is to serve as a practice run for the upcoming operational PRIDE JUICE operations. We showcase the capabilities of the planning and scheduling software for other space missions.

Hydrogel-based Drug Delivery System in Diabetes Management.

It is estimated that there are over 200 million people living with diabetes mellitus (DM) all over the world. It is a metabolic condition caused by decreased insulin action or secretion. Diabetes Mellitus is also known as Type 2 Diabetes Mellitus. Type 1 diabetes mellitus and type 2 diabetes mellitus are the two most common types of DM. Treatment for type 1 diabetes often consists of insulin replacement therapy, while treatment for type 2 diabetes typically consists of oral hypoglycemics. Conventional dosing schedules for the vast majority of these medications come with a number of drawbacks, the most common of which are frequent dosing, a short half-life, and low bioavailability. Thus, innovative and regulated oral hypoglycemic medication delivery methods have been developed to reduce the limitations of standard dose forms. The studies and reviews published under the title were looked up in several databases (including PubMed, Elsevier, and Google Scholar). Hydrogels made from biopolymers are three-dimensional polymeric networks that can be physically or chemically crosslinked. These networks are based on natural polymers and have an inherent hydrophilic quality because of the functional groups they contain. They have a very high affinity for biological fluids in addition to a high water content, softness, flexibility, permeability, and biocompatibility. The fact that these features are similar to those of a wide variety of soft living tissues paves the way for several potentials in the field of biomedicine. In this sense, hydrogels offer excellent platforms for the transport of medications and the controlled release of those drugs. Additionally, biopolymer-based hydrogels can be put as coatings on medical implants in order to improve the biocompatibility of the implants and to prevent medical diseases. The current review focuses on the most recent advancements made in the field of using biopolymeric hydrogels that are physically and chemically crosslinked, in addition to hydrogel coatings, for the purpose of providing sustained drug release of oral hypoglycemics and avoiding problems that are associated with the traditional dosage forms of oral hypoglycemics.

Coordination of medium-voltage distribution networks and microgrids based on an aggregate flexibility region approach

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Targeting the Tumor Vascular Supply to Enhance Radiation Therapy Administered in Single or Clinically Relevant Fractionated Schedules.

This pre-clinical study was designed to demonstrate how vascular disrupting agents (VDAs) should be administered, either alone or when combined with radiation in clinically relevant fractionated radiation schedules, for the optimal anti-tumor effect. CDF1 mice, implanted in the right rear foot with a 200 mm3 murine C3H mammary carcinoma, were injected with various doses of the most potent VDA drug, combretastatin A-1 phosphate (CA1P), under different schedules. Tumors were also locally irradiated with single-dose, or stereotactic (3 × 5-20 Gy) or conventional (30 × 2 Gy) fractionation schedules. Tumor growth and control were the endpoints used. Untreated tumors had a tumor growth time (TGT5; time to grow to 5 times the original treatment volume) of around 6 days. This increased with increasing drug doses (5-100 mg/kg). However, with single-drug treatments, the maximum TGT5 was only 10 days, yet this increased to 19 days when injecting the drug on a weekly basis or as three treatments in one week. CA1P enhanced radiation response regardless of the schedule or interval between the VDA and radiation. There was a dose-dependent increase in radiation response when the combined with a single, stereotactic, or conventional fractionated irradiation, but these enhancements plateaued at around a drug dose of 25 mg/kg. This pre-clinical study demonstrated how VDAs should be combined with clinically applicable fractionated radiation schedules for the optimal anti-tumor effect, thus suggesting the necessary pre-clinical testing required to ultimately establish VDAs in clinical practice.

Parallel dual adaptive genetic algorithm: A method for satellite constellation task assignment in time-sensitive target tracking

The evolution of satellite surveillance technology, bolstered by advanced onboard intelligent systems and enhanced attitude maneuver capabilities, has thrust mission scheduling and execution into the spotlight as a prominent and dynamic research field in recent years. As the demand intensifies for mission scheduling and execution to transition from static ground targets to time-sensitive moving targets, conventional scheduling methods often fall short of delivering satisfactory results for continuously tracking these dynamic targets with constellation. The paper introduces a rapid yet efficacious satellite constellation task assignment method, termed the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), for the task assignment of multiple moving target tracking. Specifically, the dual adaptive mechanism isolates the genetic algorithm’s sensitivity to parameters, while the parallel mechanism increases the evolutionary process’s computation speed by deploying complex computations to the GPU. Based on the meticulous analysis of the relevant factors that need to be considered in real tracking scenarios, the proposed PDA-GA can improve the search quality and efficiency of the task assignment solution. We conduct an extensive array of contrast and ablation experiments to showcase the performance and efficiency of PDA-GA in conjunction with autonomous attitude control algorithms across five simulated tracking scenarios. Furthermore, to enable high-fidelity simulation of tracking scenarios, we introduce the Constellation Target Tracking Environment (CTTE), which is equipped with a physics engine and algorithms for multi-satellite task assignment and single-satellite attitude control. This endeavor lays a foundation for future research endeavors focused on autonomous tracking of multiple time-sensitive moving targets within large-scale constellation.

Research on Multimodal College English Teaching Model Based on Genetic Algorithm

Analyzing College English texts is essential for quantitatively evaluating their grammar, phrases, and words to enhance their use in writing, conversation, and other contexts. The precise and clear use of College English words, phrases, and sentences is essential to knowledge-based and foundational learning systems. Text data analytics run into problems with data amount, data diversity, data integration and interoperability. It is challenging to accomplish human-computer interaction in spoken College English communication and to assist students with corrections using the conventional methodology of teaching College English. Therefore, this paper proposed the Genetic Algorithm-based intelligent English course optimization system (GA-IECOS) to handle the scheduling above issue of college English classes and optimize college English teaching courses. The results demonstrate that the conventional BP neural network's local scheduling optimization issue may be resolved using the multidirectional mutation genetic BP neural network method. Subsequently, a mix of formative and summative assessments will be used to establish a couple of groups to evaluate the effectiveness using a control population and a trial group of a GA-IECOS for English language classes using a multidirectional mutation genetic algorithm and an optimization neural network. The results demonstrate that the GA-IECOS algorithm is more effective in the classroom and may greatly improve students' English performance