Optimal Strategy Research Articles

A review of the current treatment methods for retroperitoneal fibrosis with obstructive uropathy

AbstractIntroduction and aimsRetroperitoneal fibrosis (RPF) is a fibroinflammatory disease in which patients may suffer obstructive uropathy (OU). The optimum treatment strategy for RPF with secondary OU is currently unclear, and the aim of this literature review is to assess the methods used to treat this patient cohort.MethodsMedline, Embase, Cinahl, the Cochrane Library and PubMed were systematically searched to find studies assessing treatment outcomes in this patient cohort. After reviewing the studies' titles, abstracts and full texts, 12 were found that matched our search aims. Data from these publications were analysed and reported.ResultsThe demographic and symptomatic features of patients across the 12 studies were representative of the general RPF population. No randomised control trials (RCTs) were found, and just one study formally compared outcomes between patients who underwent different treatment strategies. Many of the studies concluded that using medical and surgical methods in combination led to positive outcomes; whereas, others found positive outcomes following a variety of regimens. Many studies also highlighted, however, that significant minorities required further treatment after initial therapy. Conclusions regarding optimum treatment methods were limited as most publications did not formally compare outcomes following different strategies and had an observational study design.ConclusionAlthough positive outcomes were commonly seen following medical, surgical and a combination of treatments, the literature currently lacks research formally comparing outcomes after assigning specific treatment protocols to groups of RPF patients. More research is therefore required to determine how to best manage RPF leading to secondary OU.

Determining the Economically Optimum Metaphylactic Strategy for Cattle Cohorts of Varied Demographic Characteristics

Metaphylactic antibiotic use in feeder cattle is a common practice to control respiratory disease. Antimicrobial stewardship is important to ensure continued efficacy and to protect animal welfare. The objective of this study is to identify characteristics of cohorts of cattle that had not received metaphylaxis that would have benefited economically from the use of metaphylaxis. Cohorts (n = 12,785; 2,206,338 head) from 13 feedlots that did not receive metaphylaxis were modeled using an economic model to estimate net returns for three metaphylactic options. Logistic regression models with covariates for entry weight, sex, average daily weight gain, number of animals per cohort, and days on feed, with feedlot as a random effect, were used to determine the model-adjusted probability of cohorts benefiting economically from metaphylaxis. Most (72%) cohorts in this data set that had not received metaphylaxis at arrival would not economically benefit from metaphylaxis. Sex, entry weight category, number of cattle in the cohort, and average daily weight gain were associated with the likelihood of benefitting economically from metaphylaxis. The results illustrated that cattle cohort demographics influenced the probability that cohorts would benefit economically from metaphylaxis and the type of metaphylaxis utilized, and integrating this information has the potential to influence the metaphylaxis decision.

Substratifying the risk of covert malignancy in significant rectal polyps: Outcomes from a specialist multidisciplinary team (MDT).

A treatment strategy for patients with a significant polyp or early colon cancer (SPECC) of the rectum presents a challenge due to the significant rate of covert malignancy and lack of standardized assessment. For this reason, NICE recommends multidisciplinary meetings to improve outcomes. The primary aim of the present study was to report the performance of our specialist early rectal cancer (SERC) multidisciplinary team (MDT) in correctly substratifying the risk of cancer and to discuss the limitations of staging investigations in those patients with "poor outcomes". This was a retrospective review of patients referred to our SERC MDT from 2014 to 2019. Lesions were assigned by the MDT to three pre-resection categories (low, intermediate, high) according to the risk of covert malignancy. Resection method and final histology were compared to the pre-resection categories. Of 350 SPECC lesions, 174 were assessed as low-risk, 108 intermediate-risk and 68 high-risk. The cancer incidence was 4.8%, 8.3% and 53%, respectively (15.5% overall). Eight lesions were categorized as low-risk but following piecemeal resection were found to be malignant. Five lesions, three of which were categorized as high-risk, were ultimately benign following conventional surgery. One pT1sm1 cancer, removed by anterior resection, may have been treated by local excision. A total of 83% of malignant polyps were triaged to an en bloc resection technique and surgical resection avoided for nearly all benign lesions. However, 12 patients from this cohort were deemed to have a poor outcome because of miscategorization. Further comparative research is needed to establish the optimum strategy for rectal SPECC lesion assessment. There is currently no consensus for staging significant polyps of the rectum. This paper reports the effectiveness of a specialist early rectal cancer MDT to correctly risk-stratify significant rectal polyps. It underscores the importance of accurate categorization for treatment decision-making, while acknowledging the limitations of current staging modalities.

Distributed Optimal Solutions for Multiagent Pursuit-Evasion Games for Capture and Formation Control

In this paper, distributed optimal solutions are designed for networked multiagent pursuit-evasion (MPE) games for capture and formation control. In the games, the pursuers aim to minimize the distance from their target evaders while the evaders attempt to maximize it, and at the same time, all players desire to maintain cohesion with their teammates. The goals of agents are obviously reflected in the obtained optimal control strategies which consist of an attracting term and/or a repelling term. Nash equilibrium is obtained by means of optimal strategies using the solutions of the HJI equations. Furthermore, three scenarios are considered in the MPE game: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader, where sufficient conditions are given for pursuers in achieving exponential capture or formation control with ultimate zero or bounded errors. It is shown that the conditions depend on the structure of the communication graph, the parameters in the controllers, and the expected formation configurations. Finally, both simulations and real flight experiments successfully demonstrate the effectiveness of the proposed strategies.

Simultaneously Calibration of Multi Hand–Eye Robot System Based on Graph

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multi-eyes (cameras) and multi-hands (robots) are particularly sensitive to calibration errors. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the pose between each unit, but the serialized incremental calibration strategy cannot avoid the error accumulation problem in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization with different sensor's accuracy considered. As a general calibration method, it can be applied to different multi-robot systems. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.

Effects of Early Versus Delayed Feeding in Patients With Acute Pancreatitis: A Systematic Review and Meta-analysis.

The aim of this study was to summarize the optimal strategy for early feeding in patients with acute pancreatitis. The search was undertaken in electronic databases, which compared early with delayed feeding in acute pancreatitis. The primary outcome was the length of hospital stay (LOHS). The second outcomes were intolerance of refeeding, mortality, and total cost of each patient. This meta-analysis followed the "Preferred Reporting Items for Systematic Reviews and Meta-analyses" guideline. Research is registered by PROSPERO, CRD42020192133. A total of 20 trials involving 2168 patients were included, randomly assigned to the early feeding group (N = 1033) and delayed feeding group (N = 1135). The LOHS was significantly lower in the early feeding group than the delayed feeding group (mean difference: -2.35, 95% CI: -2.89 to -1.80; P < 0.0001), no matter the mild or severe subgroup ( Pint = 0.69). The secondary outcome of feeding intolerance and mortality were no significant difference (risk ratio: 0.96, 0.40 to 2.16, P = 0.87 and 0.91, 0.57 to 1.46, P = 0.69; respectively). Moreover, the hospitalization cost was significantly less in the early feeding group, resulting in an average savings of 50%. In patients with severe pancreatitis, early feeding after 24 hours may be beneficial ( Pint = 0.001). Early oral feeding can significantly reduce the LOHS and hospitalization costs in patients with acute pancreatitis without increasing feeding intolerance or mortality. In patients with severe pancreatitis, early feeding after 24 hours may be beneficial.

Abstract LB444: Identification of a novel and selective Transglutaminase 2 (TGM2) inhibitors modulate tumor microenvironment in Glioblastoma

Abstract Background: TGM (Transglutaminase) family enzymes are Ca2+ dependent and engaged in specific posttranslational modifications by cross-linking extracellular matrix (ECM) proteins. ECM protein complex is more stable and resistant to enzymatic degradation. These enzymes modify proteins by cross-linking epsilon-(gamma-glutamyl), lysine, or (gamma-glutamyl) polyamine bonds. TGM2 (Transglutaminase 2) is a ubiquitous enzyme, and its expression is identified in the cytoplasm, plasma membrane, and the nucleus of various cells. The membrane-bound form of TGM2 binds GTP and functions as a G protein. TGM2 regulates cell survival, proliferation, migration, and modulation through ECM organization. We explored the role of TGM2 in Glioblastoma cells and its role in modulating the tumor microenvironment, cell proliferation, and survival. In addition to TGM2's role in GBM tumors, its expression has been linked to resistance to certain therapies in glioblastoma was investigated. Targeted modulation of macrophages with TGM2 inhibitors impacts the immune response against GBM, potentially enhancing the efficacy. Understanding the intricate interplay between TGM2, and GBM-associated macrophages is a key component of the tumor microenvironment (TME) and crucial for the development of targeted TGM2 inhibitors. Materials and Methods: Human Transglutaminase 2 (hTGM2) activity of inhibitors was assayed using the fluorescent transamination assay incorporating dansylcadaverine into glutamine-donor substrate N, N-dimethyl casein, and DCC. Cellular efficacies of selected TGM2 inhibitors evaluated in U138 and U87 glioblastoma cells in CellTiter-Glo Luminescent cell viability assay. Results: We have identified a series of novel small molecule TGM2 inhibitors based on our initial lead fragment hits with an IC50s 6-29 uM from our in-house MolecuLern fragment library. Fragments2Lead (F2L) optimizations strategies, synthesis, and SAR studies provided a lead SLX-9029 and SLX-9031 inhibitors with IC50s of 1.2, 1.5 uM in inhibiting TGM2 activity in our established cell-free fluorescent transamination assay. The initial TMG2 inhibitor leads further screening for TGM2-expressed U138 and U87 glioblastoma cells demonstrating promising cellular efficacies as singles agents. Additional GBM cell profiling, combination studies along with safety secondary pharmacology, cellular toxicities, and PK results will be discussed. Conclusion: In summary, we identified promising lead TGM2 inhibitors that show activity in both cell-free and cell-based TGM2-expressed GMB cells. The TGM family members, selectivity, additional GBM cellular profiling, ADME, cellular toxicity, and in vivo PK studies are underway, and these results will be presented. Citation Format: Hariprasad Vankayalapati, Chenyu Lin, Zhaoang li, Kyle Medley, David J. Bearss. Identification of a novel and selective Transglutaminase 2 (TGM2) inhibitors modulate tumor microenvironment in Glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB444.

Surgical management of lower limb radiculopathy following acute singe-level osteoporotic vertebral fracture of lower lumbar spine in geriatric patient: a retrospective study

BackgroundRadiculopathy of the lower limb after acute osteoporotic vertebral fractures (OVFs) in the lower lumbar spine is uncommon in geriatric patients. Moreover, surgical intervention is generally recommended in patients who are irresponsive to conservative treatment. Determining an optimum surgical strategy is challenging considering the poor general condition of this population. Thus, herein, we established an algorithm for surgically managing this clinical scenario, hoping to provide a reference for making a surgical decision.MethodsWe retrospectively studied patients who suffered from new-onset radiculopathy of the lower limb after acute single-level OVFs in the lower lumbar spine and eventually underwent surgical intervention at our department. Information on the demographics, bone quality, AO spine classification of the vertebral fracture, pre-existing degenerative changes, including foraminal stenosis and lumbar disc herniation, and surgical intervention type was collected. Additionally, clinical outcomes, including preoperative and postoperative visual analog scale (VAS) scores for back and leg pain, Oswestry disability index (ODI), and MacNab criterion for response to surgery, were evaluated.ResultsFrom September 2019 to December 2021, a total of 22 patients with a mean age of 68.59 ± 9.74 years were analyzed. The most involved vertebra was L5 (54.5%), followed by L4 (27.3%) and L3 (18.2%). Among the 22 patients, 15 (68.2%) were diagnosed with the A1 type fracture of AO classification, and among them, 11 (73.3%) were characterized by the collapse of the inferior end plate (IEP). Three patients (13.6%) suffered from A2-type fractures, whereas four patients (18.2%) suffered from A3-type fractures. Pre-existing degenerative changes were observed in 12 patients (54.5%) of the patients. A total of 16 patients (72.7%) were treated by percutaneous kyphoplasty (PKP). Additionally, three patients underwent posterior instrumentation and fusion, two patients underwent a secondary endoscopic foraminoplasty, and one patient underwent a secondary radiofrequency ablation. The mean follow-up period was 17.42 ± 9.62 months. The mean VAS scores for leg and back pain and ODI decreased significantly after the surgery (P < 0.05). The total satisfaction rate at the last follow-up was 90.9% per the Macnab criterion.ConclusionPatients with OVFs in the IEP are predisposed to suffer from radiculopathy of the lower limb. PKP alone or in combination with other minimally invasive surgical strategies is safe and effective in treating stable fractures. Additionally, aggressive surgical intervention should be considered in patients with unstable fractures or severe foraminal encroachment.

Gaussian Process Latent Variable Model-Based Multi-Output Modeling of Incomplete Data

The rapid development of sensor technologies allows the acquisition of high dimensional sensing data. Multi-output modeling techniques have been developed to leverage the data for decision making. However, the data often contain segments of missing values, which cause great information loss and thus affect the modeling performance. This study explores the missing pattern and the correlation structure of missing segments and maximally exploits useful information in the data to improve multi-output modeling accuracy. Specifically, a new multi-output modeling method is developed based on Gaussian Process Latent Variable Model (GPLVM). A decision score is developed to seek an optimal modeling strategy and then a tailored Expectation-Maximization (EM) algorithm based on GPLVM is designed to estimate the missing segments while optimizing model parameters. The proposed method demonstrates superior performance in both a simulation study and a case study, which makes it a powerful tool to enable process automation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In real-life applications, missing values are constantly present in multi-output sensing data, which greatly affects data-driven decision-making. Modeling of such data becomes more difficult when consecutive observations within or across different outputs are missing. Existing methods often discard the missing values and extract information only from the available observations. However, the pattern of missing values may contain important messages that can potentially boost the modeling performance. This research develops a new framework based on GPLVM to model multi-output data with segmented missing patterns. A tailored EM algorithm is developed to iteratively impute the missing values and optimize model parameters. In addition, a decision score that quantifies both the missing pattern and correlation is designed to determine an optimal modeling strategy. The proposed method can benefit many applications across different industries that require modeling of multi-output incomplete data, especially when the data have many segments of missing observations.

Adaptive Virotherapy Strategy for Organism With Constrained Input Using Medicine Dosage Regulation Mechanism.

In this article, the constrained adaptive control strategy based on virotherapy is investigated for organism using the medicine dosage regulation mechanism (MDRM). First, the tumor-virus-immune interaction dynamics is established to model the relations among the tumor cells (TCs), virus particles, and the immune response. The adaptive dynamic programming (ADP) method is extended to approximately obtain the optimal strategy for the interaction system to reduce the populations of TCs. Due to the consideration of asymmetric control constraints, the nonquadratic functions are proposed to formulate the value function such that the corresponding Hamilton-Jacobi-Bellman equation (HJBE) is derived which can be deemed as the cornerstone of ADP algorithms. Then, the ADP method of a single-critic network architecture which integrates MDRM is proposed to obtain the approximate solutions of HJBE and eventually derive the optimal strategy. The design of MDRM makes it possible for the dosage of the agentia containing oncolytic virus particles to be regulated timely and necessarily. Furthermore, the uniform ultimate boundedness of the system states and critic weight estimation errors is validated by Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness of the derived therapeutic strategy.

705 Urgent or Emergent Endovascular Intervention for Neurological Deficits Post-carotid Endarterectomy: A Single-institutional Case Series and Systematic Review of the Literature

INTRODUCTION: Historically the management of acute neurological deficit following carotid endarterectomy (CEA) has been operative re-exploration; however, given the proliferation of endovascular techniques, and utilization of CT angiography, endovascular intervention is an alternative to redo-CEA. METHODS: All patients undergoing CEA in the Department of Neurosurgery at Rhode Island Hospital (Providence, RI) were reviewed. A systematic literature review was conducted in PubMed according to PRISMA guidelines to identify papers relevant to endovascular procedures addressing acute neurological deficits following CEA, which yielded 2499 unique articles. Review by 2 investigators revealed 12 relevant articles. RESULTS: Four patients from our institutional cohort met inclusion criteria. Among these, stenting was technically successful in 3 patients, 2 of which achieved near-full recovery. One patient underwent successful carotid stenting and ipsilateral M2 thrombectomy for tandem occlusion. Stent placement was unsuccessful in the remaining patient due to mechanical constriction. Articles obtained through the literature review revealed 57 further relevant cases. Of those with sufficiently complete data, 30/39 (76.9%) were male with an age range of 43 - 78 years old. Twenty-eight of 32 (87.5%) patients underwent CEA for symptomatic carotid stenosis. Post-CEA angiographic evaluation was most commonly conducted for routine completion imaging (21/57, 36.8%), non-specific stroke symptoms (15/57, 26.3%), and extremity weakness (7/57, 12.3%). Thrombotic stenosis (23/43, 53.4%) and arterial dissection (15/43, 34.9%) were the most common angiography findings. Two (3.5%) stent procedures were unsuccessful, and in the immediate postoperative period 8 patients (14.0%) had significant neurological deficits and only 3 deaths (5.3%) were reported. CONCLUSIONS: Endovascular intervention may be considered in situations where patients have neurological deficit after CEA. Direct comparison of outcomes in patients who have endovascular intervention vs. redo CEA may help identify the optimal management strategy.

Optimal Dual-Side Asymmetric Modulation Strategy for Dual Active Bridge Converter to Improve Efficiency Over a Wide Voltage Range

Optimal Dual-Side Asymmetric Modulation Strategy for Dual Active Bridge Converter to Improve Efficiency Over a Wide Voltage Range

A New Manipulating Method of Metal Micro-Components Based on Electrochemistry

This article presents a new manipulating method of metal micro-components based on electrochemistry aiming at the problems of inaccurate release and easy damage to manipulated objects by conventional micro gripping tools and vacuum adsorption tools. This method uses a small-sized micropipette to manipulate large-sized metal micro-components. The reliable picking up is achieved by forming a strong chemical bond between the small-sized pipette and the manipulation object. The accurate and non-destructive release is achieved by converting the chemical bonding force into a more easily overcome capillary force through electrolysis. The electrochemical and the force models of the metal microcomponents based on electrochemistry are simulated by COMSOL and Abaqus, to obtain optimal electrochemical parameters and manipulation strategies. The experiments of picking up and releasing were performed to demonstrate the performance of the proposed method. The results show that this method can enable reliable and accurate manipulation of large-size objects with small-size tools and have a high success rate and efficiency.

Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems

Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.

Energy-Aware Positioning Service Provisioning for Cloud-Edge-Vehicle Collaborative Network Based on DRL and Service Function Chain

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

Performance evaluation and optimization of the cascade refrigeration system based on the digital twin model

Cascade refrigeration systems (CRSs) have become the priority choice in low-temperature applications, causing severe energy waste due to the lack of practical control strategies. Currently, refrigeration simulations are mainly based on the simplified thermodynamic or Artificial Intelligence (AI) model. However, the former is insufficiently accurate, while the latter is of unclear thermodynamic significance. By connecting the physical and digital space, the digital twin technology provides exciting opportunities for combining thermodynamic and AI methods. In this paper, an integrated digital twin model for the CRS is innovatively proposed based on the modified semi-empirical model of the twin-screw refrigeration compressor. As a major update, the identification and update method of characteristic parameters is developed based on real-time operating data to ensure performance consistency between digital and physical twins. By comparing with the simplified model, results show that the digital twin model can improve prediction accuracy by 7.42%–23.62%. Founded on the proposed model, the thermodynamic performance of the CRS is evaluated. To facilitate on-site applications, the definition of flat intermediate temperature is given, which is fitted to a control correlation with several easily observed variables as the inputs. Taking a food factory as a case study, simulation results reveal that the optimum intermediate temperature should be significantly lower than the original, and the coefficient of performance (COP) has an average improvement potential of 9.1%. By deploying the optimum strategy under the on-site condition, a superior energy-saving rate of up to 13.1% is attained according to test reports.

A biomimetic spore nanoplatform for boosting chemodynamic therapy and bacteria-mediated antitumor immunity for synergistic cancer treatment

Bacterial-based antitumor immunity has become a promising strategy to activate the immune system for fighting cancer. However, the potential application of bacterial therapy is hindered by the presence of instability and susceptibility to infections within bacterial populations. Furthermore, monotherapy is ineffective in completely eliminating complex cancer with multiple contributing factors. In this study, based on our discovery that spore shell (SS) of Bacillus coagulans exhibits excellent tumor-targeting ability and adjuvant activity, we develop a biomimetic spore nanoplatform to boost bacteria-mediated antitumor therapy, chemodynamic therapy and antitumor immunity for synergistic cancer treatment. In detail, SS is separated from probiotic spores and then attached to the surface of liposome (Lipo) that was loaded with hemoglobin (Hb), glucose oxidase (GOx) and JQ1 to construct SS@Lipo/Hb/GOx/JQ1. In tumor tissue, highly toxic hydroxyl radicals (•OH) are generated via sequential catalytic reactions: GOx catalyzing glucose into H2O2 and Fe2+ in Hb decomposing H2O2 into •OH. The combination of •OH and SS adjuvant can improve tumor immunogenicity and activate immune system. Meanwhile, JQ1-mediated down-regulation of PD-L1 and Hb-induced hypoxia alleviation synergistically reshape immunosuppressive tumor microenvironment and potentiate immune responses. In this manner, SS@Lipo/Hb/GOx/JQ1 significantly suppresses tumor growth and metastasis. To summarize, the nanoplatform represents an optimum strategy to potentiate bacteria-based cancer immunotherapy.

Chemical engineering and the circular water economy: Simulations for sustainable water management in environmental systems

In the face of escalating water scarcity and environmental degradation, the imperative for sustainable water management has never been more urgent. Chemical engineering emerges as a pivotal discipline in the pursuit of solutions to these pressing challenges. This review explores the role of chemical engineering in advancing the circular water economy paradigm through simulations aimed at fostering sustainable water management within environmental systems. The circular water economy concept advocates for the efficient utilization, recycling, and reclamation of water resources to minimize waste and maximize resource efficiency. Chemical engineering techniques play a fundamental role in realizing this vision through the design and optimization of water treatment processes, resource recovery systems, and advanced simulation methodologies. This review delves into the application of computational simulations within the realm of chemical engineering to model and analyze various aspects of the water cycle. Such simulations enable the assessment of complex environmental systems, aiding in the identification of optimal strategies for water resource allocation, pollution control, and ecosystem preservation. Key areas of focus include the simulation of wastewater treatment processes, such as biological, physical, and chemical treatment methods, to enhance pollutant removal efficiency and promote water reuse. Furthermore, advanced modeling techniques facilitate the evaluation of innovative technologies like membrane filtration, adsorption, and electrochemical processes for the purification and desalination of water resources. Moreover, chemical engineering simulations enable the assessment of integrated water management strategies, encompassing aspects of urban water systems, industrial processes, and agricultural practices. By considering the interconnectedness of various sectors, holistic approaches to water resource management can be formulated, promoting resilience and sustainability in the face of changing environmental conditions. The integration of chemical engineering simulations into the framework of the circular water economy offers a promising avenue for advancing sustainable water management practices. Through comprehensive modeling and analysis, informed decision-making can pave the way towards a more resilient and equitable water future, ensuring the long-term viability of our environmental systems.

Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) techniques, enables the development of personalized treatment strategies for severe illnesses by using a learning agent to recommend optimal policies. In this study, we propose a Deep Reinforcement Learning (DRL) model with a tailored reward function and an LSTM-GRU-derived state representation to formulate optimal treatment policies for vasopressor administration in stabilizing patient physiological states in critical care settings. Using an ICU dataset and the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we focus on patients with Acute Respiratory Distress Syndrome (ARDS) that has led to Sepsis, to derive optimal policies that can prioritize patient recovery over patient survival. Both the DDQN ( RepDRL-DDQN ) and Dueling DDQN ( RepDRL-DDDQN ) versions of the DRL model surpass the baseline performance, with the proposed model’s learning agent achieving an optimal learning process across our performance measuring schemes. The robust state representation served as the foundation for enhancing the model’s performance, ultimately providing an optimal treatment policy focused on rapid patient recovery.

Preparation of Multi‐Grain Flour with High Content of Resistant Starch and the Mechanism Underlying the Improved Digestion Resistance

AbstractTo acquire the optimum treatment strategy that leads to the highest yield of resistant starch (RS) in multigrain flour (MF), wheat flour, whole oat flour, and whole buckwheat flour are used to prepare antidigestive MF by using pullulanase debranching combining heat–moisture treatment (P‐HMT). Based on the single factor tests and response surface analysis, the optimum conditions for the treatment of MF are determined as follows: pullulanase 62.6 U g−1, autoclaving 21.3 min, HMT at 100 °C, and 30.4% water content for 6.0 h. Under these conditions, the yield of RS in the flour reaches 77.42%. P‐HMT causes protein denaturation, starch gelatinization, and flour clumps in MF, facilitating starch recrystallization and interactions among short‐chain starch, monomeric proteins, and lipids. All these findings are further confirmed by the conversion of starch crystal from A‐type to B+V‐type, the improvement of starch short‐range molecular order, and the presence of more binary/ternary complexes in treated flour. In brief, the starch digestion resistance improvement in the treated flour is attributed to the recrystallization of short‐chain starch and interactions between starch and protein/lipid.