Time Cost Research Articles

A new approach in the development of a dental caries vaccine

The genomes of the bacteria Streptococcus mutans and Streptococcus sobrinus, which cause dental caries, currently have been fully sequenced. However, the secondary and tertiary structures of the full-size surface proteins of these microorganisms, by which they attach to the surface of teeth covered with saliva, have not been instrumentally determined at the moment. There are a number of experimental studies on the use of these proteins in the process of developing a dental caries vaccine. However, there is currently no commercially available dental caries vaccine.The aim of the study was to choose an antigen for subsequent molecular modeling of a unique peptide for the development of a dental caries vaccine.To develop an effective and safe dental caries vaccine, it is necessary to perform a number of experiments in silico, preceding experiments in vitro and in vivo. Today, this approach is not only generally recognized, but also allows to significantly reduce the cost of experiments and time at the preclinical and clinical studies. According to our hypothesis, as an antigen for the development of a dental caries vaccine, it is necessary to use a short fragment of the surface protein (a peptide) of Streptococcus mutans and/or Streptococcus sobrinus, whose homology in amino acid sequence is 84.8 %, the spatial structure of which should correspond to the spatial structure of the corresponding fragment in a full-sized protein. In addition, the selected protein fragment, which will be part of the vaccine peptide, must be available to antibodies, i. e. located on the surface of the protein and defined as a B-cell linear and spatial epitope. Also, according to our hypothesis, the vaccine peptide may consist of the most stable fragments of alanine and proline rich regions of the surface protein of Streptococcus mutans and/or Streptococcus sobrinus for mutual stabilization of the spatial structure.

Efficient acid hydrolysis for compound-specific δ15N analysis of amino acids for determining trophic positions

Compound-specific isotope analysis of nitrogen in amino acids (CSIA-AA, δ15NAA) has gained increasing popularity for elucidating energy flow within food chains and determining the trophic positions of various organisms. However, there is a lack of research on the impact of hydrolysis conditions, such as HCl concentration and hydrolysis time, on δ15NAA analysis in biota samples. In this study, we investigated two HCl concentrations (6 M and 12 M) and four hydrolysis times (2 h, 6 h, 12 h, and 24 h) for hydrolyzing and derivatizing AAs in reference materials (Tuna) and biological samples of little egret (n = 4), night heron (n = 4), sharpbelly (n = 4) and Algae (n = 1) using the n-pivaloyl-iso-propyl (NPIP) ester approach. A Dowex cation exchange resin was used to purify amino acids before derivatization. We then determined δ15NAA values using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). The results revealed no significant differences (p > 0.05) in δ15NAA values among samples treated with different HCl concentrations or hydrolysis times, particularly for δ15NGlx (range: 21.0–23.5‰) and δ15NPhe (range: 4.3–5.4‰) in Tuna (12 M). Trophic positions (TPs) calculated based on δ15NAA at 2 h (little egret: 2.9 ± 0.1, night heron: 2.8 ± 0.1, sharpbelly: 2.0 ± 0.1 and Algae: 1.3 ± 0.2) were consistent with those at 24 h (3.1 ± 0.1, 2.8 ± 0.1, 2.2 ± 0.1 and 1.1 ± 0.1, respectively), suggesting that a 2-h hydrolysis time and a 6 M HCl concentration are efficient pretreatment conditions for determining δ15NAA and estimating TP. Compared to the currently used hydrolysis conditions (24 h, 6 M), the proposed conditions (2 h, 6 M) accelerated the δ15NAA assay, making it faster, more convenient, and more efficient. Further research is needed to simplify the operational processes and reduce the time costs, enabling more efficient applications of CSIA-AA.

Dynamic Magnetic Field Analysis and Delivery Strategy Optimization for Scanning Magnet in Proton Therapy System

In pencil beam scanning proton therapy, the regulation and stabilization of the scanning magnetic field between two spots should be completed as quickly as possible in order to reduce treatment time. Because of the eddy current effect, the dynamic magnetic field lags behind the excitation current. It is significant to analyze the dynamic field and reduce the field stability time to minimize the delivery time and improve the therapy efficiency. In this paper, dynamic magnetic field simulation is carried out with a full lamination model of the scanning magnet in the Huazhong University of Science and Technology Proton Therapy Facility. In addition, a single lamination model instead of a full lamination model is explored to reduce time cost and memory for lamination of no more than 1-mm thickness. The eddy current diffusion trend and the influence of lamination on the eddy current are investigated. Moreover, the effect of lamination thickness (ranging from 5 to 0.1 mm) and current ramp rate (ranging from 20 to 100 A/ms) on the magnetic field stability time is studied. In addition, the characteristic of magnetic stability time for various spot steps is analyzed. Considering two spot patterns with discrete or clustered spots, an optimized delivery strategy with various scanning dead times according to the step is presented. When the lamination is 1 mm, the scanning time can be reduced by 39.2% for a clustered pattern and 38.4% for a discrete pattern using a genetic algorithm based on the different scanning dead-time strategy instead of the fixed dead-time strategy. With a thinner 0.1-mm lamination, the scanning time can be reduced by 49.8% for the clustered pattern and 43.3% for the discrete pattern, compared to that of the 1-mm lamination.

ABWOA: adaptive boundary whale optimization algorithm for large-scale digital twin network construction

Digital twin network (DTN) as an emerging network paradigm, have garnered growing attention. For large-scale networks, a crucial problem is how to effectively map physical networks onto the infrastructure platform of DTN. To address this issue, we propose a heuristic method of the adaptive boundary whale optimization algorithm (ABWOA) to solve the digital twin network construction problem, improving the efficiency and reducing operational costs of DTN. Extensive comparison experiments are conducted between ABWOA and various algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution algorithm, moth search algorithm and original whale optimization algorithm. The experimental results show that ABWOA is superior to other algorithms in terms of solution quality, convergence speed, and time cost. It can solve the digital twin network construction problem more effectively.

Simulation Model of Precast Concrete Element Delivery on Time and Operational Costs of the Precast Yard in the Board Mill Construction Project

Simulation Model of Precast Concrete Element Delivery on Time and Operational Costs of the Precast Yard in the Board Mill Construction Project

Cost optimization and reliability analysis of fault tolerant system with service interruption and reboot

Due to widespread usage in many real time systems, reliability modeling and cost optimization of fault tolerance system have drawn attention of the practitioners. The fault tolerance in these systems can be provided by the support of maintenance and redundant components that help in smooth operation of the system in spite of failure of some active components. This investigation deals with the performance modeling of a fault-tolerant system consisting of a finite number of active (online) and standby components. During the switching from active to standby, the recovery procedure is performed, which may be imperfect. In case of imperfect recovery, the system reboot takes place. The maintenance of all the components is managed by a repairman (server) which is subject to failure. When the server is interrupted for rendering the service, functioning does not get stopped due to the system switch-over from perfect working to working breakdown mode. The system works even when the server is on working vacation and performs repair jobs of the failed components. The machine repair model based on Markovian process is developed to derive the transient probabilities and other performance indices of the fault tolerant system using Laplace transforms and matrix analytical method. Using the direct search strategy and particle swarm optimization, the cost-benefit analysis is done. The optimal design of the control parameters for the fault-tolerant system are presented by framing a cost-effective ratio function. The model is examined computationally by performing the numerical simulation and cost optimization.

Formation of Repetitive Group Sampling Plan for Life Tests Based on Percentiles using Odd Frechet Inverse Rayleigh Distribution

Objectives: This paper presents the idea of Repetitive Group Sampling Plans using Odd Frechet Inverse Rayleigh Distribution, when the life test is truncated at a pre-specified time. The main objective of the proposed sampling plan is to minimize the sample size because the analogous inspection time and inspection cost will be reduced. Methods: A truncated life test may be conducted to evaluate the smallest sample size to insure certain percentile lifetime of products. The minimum sample size and its ratio for the specified lifetime percentile were derived for the producer’s risk. The operating characteristic values are also obtained for the proposed sampling plan. The corresponding table values are obtained through Monto Carlo Simulation technique. Findings: The results from the sampling plan developed in this research can easily be adaptable in practical situation with small sample sizes and fewer experimental times and hence the plan yields a better result for reliability sampling plan. Novelty: The work designed and developed with the aim that sampling plan in this paper may be helpful for the engineers and statistical plan developers in the field of statistical quality control especially in attribute reliability sampling plan. The tables are generated which is useful for both producer and consumer. This plan can highly recommendable since the products are randomly sampled and also the procedure for sampling involves less experiment time and with minimum sample size. Keywords: Odd Frechet Inverse Rayleigh Distribution, Percentiles, Repetitive Group Sampling Plan (RGS), Producer’s risk

ВЫЯВЛЕНИЕ ПОТЕНЦИАЛЬНЫХ ГОРОДСКИХ ЦЕНТРОВ НА ОСНОВЕ МОДЕЛИРОВАНИЯ ПАССАЖИРОПОТОКОВ (НА ПРИМЕРЕ САНКТ-ПЕТЕРБУРГА)

The article considers the development of methodological foundations and suggests methodological approaches to identify urban areas with signs of subcenters in the implementation of a polycentric model of urban development. It is demonstrated that the application of mathematical modeling of passenger flows contributes to solving the problem of urban areas differentiation according to criteria of transport accessibility, time costs and travel speeds. As part of the study, calculations of passenger flows and passenger turnover were performed on the example of the St. Petersburg agglomeration – using network and pre-network models. Comparing the values of transport demand and transport supply in the nodes of the network made it possible to identify the location of potential urban centers.

P.131 Comparison of interhemispheric and transcortical approaches for resection of colloid cysts of the third ventricle

Background: Colloid cysts are rare benign lesions of the third ventricle that can cause hydrocephalus and intracranial hypertension. Their primary treatment is surgical removal. Although open surgery presents the best opportunity for total resection of the cyst, there is no consensus regarding the optimal choice between the interhemispheric and the transcortical approaches. We aim to compare these two approaches in regard of the radicality of excision, the recurrence rate, and the surgical imprint. Methods: Retrospective cohort study on all patients who underwent surgical resection of colloid cyst between 2003 and 2023 at CHU de Québec. Data on demographics, symptoms, complications, and imaging was gathered. Results: In a cohort of 28 patients (17 interhemispheric, 11 transcortical), the preliminary results demonstrate prolonging operative time (270min. vs. 187min.) and increasing blood loss (193cc vs 100cc) associated with the interhemispheric approach. Despite these results, the hospitalization duration remains similar (p=0.734). However, the interhemispheric approach results in significantly lower surgery-induced encephalomalacia (1.1cc vs. 4.4cc, p=0.006). Conclusions: The interhemispheric approach could lead to potentially lesser consequences due to the reduced volume of encephalomalacia left by the surgical intervention, at the cost of prolonged operating time and higher blood loss.

A soft resource optimization method based on autonomous coordination of unmanned swarms system driven by resilience

The resilience improvement of unmanned swarm systems endows them with enhanced survivability and mission execution capability in complex environments. However, the current methods for improving resilience in unmanned swarm systems face challenges such as high time costs, difficult implementation, and weak anti-interference ability. Therefore, a soft resource optimization method based on the autonomous collaboration of unmanned swarm systems is proposed in this paper. Firstly, a research framework illustrates the complete cycle of resilience improvement processes in distributed unmanned swarm systems. Secondly, a soft resource optimization model is developed based on autonomous collaboration among distributed unmanned swarm systems, providing detailed insights into autonomous collaboration in complex environments. Finally, simulation experiments are conducted to validate the advantages of the proposed soft resource optimization method. The experimental results show that compared with the centralized control, the soft resource optimization method through autonomous coordination not only improves the resilience, but also improves the running speed by 99.8%. In addition, a pre-analysis of resilience threats is also carried out to facilitate proactive implementation of resilience improvement measures for unmanned swarm systems by engineers in the simulation experiment.

#1381 Economic evaluation of an integrated care program versus conventional care for chronic kidney disease patients in rural communities of Thailand

Abstract Background and Aims This study evaluates an integrated care program for chronic kidney disease (CKD), which has demonstrated effectiveness in delaying kidney function decline in the rural population of Thailand. Efficacy of the program was initially established through the ESCORT-1 study, a cluster-randomized controlled trial (doi: 10.1186/s12882-016-0414-4), and further demonstrated the effectiveness by the larger-scale prospective cohort study, ESCORT-2 (doi: 10.1111/nep.13849). The aim of this study is to assess the cost-effectiveness of incorporating this program into Thailand's public health benefit package, with the goal of establishing it as a model for similar rural areas in resource-limited settings. Method Following the Thailand's guideline for health technology assessment, a cost-utility analysis utilizing data from the ESCORT studies, local databases, and literatures. The integrated care program was tailored to primary healthcare system by using preexisting workforces. It involved a collaboration between Multi-Disciplinary Care Teams (MDCT) at district hospitals and Community Care Network (CCN) teams at Sub-District Health Centers (SDHC). The MDCT, comprising general practitioners, CKD nurse managers, pharmacists, a nutritionist, and a physical therapist, provided key patient education on diet, medication adherence, exercise, and avoiding certain medications and herbal remedies. The CCN teams, including community nurses and village health volunteers, conducted home visits for health monitoring and lifestyle guidance. Both MDCT and CCN personnel received training in CKD care based on the NKF-K/DOQI and KDIGO Clinical Practice Guidelines. A decision-analytic Markov model and Monte-Carlo simulation were applied to quantify and compare the lifetime costs and quality-adjusted life years (QALY) outcomes of the intervention and conventional-care from a societal perspective considering both direct and indirect costs. The analysis included extensive sensitivity analyses, incorporating a probabilistic model and scenario-analyses based on patient ages, CKD stages, and varying program efficacy. Additionally, a budget impact analysis from the payer's perspective assessed the financial impact and affordability over a five-year period. Results The integrated care program was found to dominate conventional care. The results indicated an incremental cost-effectiveness of (-)278,000 Thai Baht (∼7,267 EUR) per QALY for the base case analysis. Probabilistic analysis demonstrated consistent outcomes, with the integrated care program being cost-effective at almost 100% of the time during analysis at the 160,000 Thai Baht (∼4,182 EUR) willingness-to-pay threshold (Fig. 1). These results are driven by significant cost savings due to reduced time and overall costs in dialysis before death. The program is most cost-effective when provided early to patients, in terms of both age and CKD stages (Fig. 2). The budget impact analysis showed that introducing the intervention could save the payer 184 billion Thai Baht (4.76 billion EUR) over five years. (The exchange rate; 1 Thai Baht = 38.26 EUR) Conclusion The integrated care program for patients with stage 3-4 CKD is cost-effective and suitable for inclusion in Thailand's health benefit package, mainly due to its significant cost savings and enhanced health outcomes. The program's design, focusing on sustainability and feasibility by utilizing existing workforces, positions it as a viable model for similar economic and healthcare contexts, especially in resource-limited countries.

Extraction of Corn Plant Phenotypic Parameters with Keypoint Detection and Stereo Images

Corn is a global crop that requires the breeding of superior varieties. A crucial aspect of the breeding process is the accurate extraction of phenotypic parameters from corn plants. The existing challenges in phenotypic parameter extraction include low precision, excessive manual involvement, prolonged processing time, and equipment complexity. This study addresses these challenges by opting for binocular cameras as the data acquisition equipment. The proposed stereo corn phenotype extraction algorithm (SCPE) leverages binocular images for phenotypic parameter extraction. The SCPE consists of two modules: the YOLOv7-SlimPose model and the phenotypic parameter extraction module. The YOLOv7-SlimPose model was developed by optimizing the neck component, refining the loss function, and pruning the model based on YOLOv7-Pose. This model can better detect bounding boxes and keypoints with fewer parameters. The phenotypic parameter extraction module can construct the skeleton of the corn plant and extract phenotypic parameters based on the coordinates of the keypoints detected. The results showed the effectiveness of the approach, with the YOLOv7-SlimPose model achieving a keypoint mean average precision (mAP) of 96.8% with 65.1 million parameters and a speed of 0.09 s/item. The phenotypic parameter extraction module processed one corn plant in approximately 0.2 s, resulting in a total time cost of 0.38 s for the entire SCPE algorithm to construct the skeleton and extract the phenotypic parameters. The SCPE algorithm is economical and effective for extracting phenotypic parameters from corn plants, and the skeleton of corn plants can be constructed to evaluate the growth of corn as a reference. This proposal can also serve as a valuable reference for similar functions in other crops such as sorghum, rice, and wheat.

Determination of chlorothalonil in vegetables/fruits using liquid chromatography-tandem mass spectrometry with a discharge adapter interface.

Chlorothalonil (CHT), a broad-spectrum fungicide, has been employed widely to control foliar diseases, whereas with a major metabolite of polar 4-hydroxychlorothalonil (CHT-4-OH), only an acceptable nonpolar CHT residue is allowed by most countries. This study involves the method development for CHT residue in vegetables/fruits using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a novel modified discharge-adaptor (DA) interface. CHT residue was analyzed using LC-MS/MS with DA interface (LC-DA-MS/MS), developed in our previous works. A DA was placed on the electrospray tip to switch the ionization modes. A modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) method was applied to extract CHT residue of vegetables/fruits efficiently with less sample preparation time and analysis cost. CHT and CHT-4-OH spiked in four different vegetables/fruits were extracted using the modified QuEChERS method. After LC with isocratic elution, CHT and CHT-4-OH were separated within 3min. Using LC-DA-MS/MS, the ion signals of CHT were improved two to three times, and the limit of quantification of 5ng/g and linearity (r2 > 0.99) in the range of 5-200 ng/g were achieved using 10g of vegetables/fruits. The precision and accuracy were within 15% each. The modified QuEChERS and LC-DA-MS/MS were applied to examine eight field-grown vegetables/fruits; 9.5 and 2588.9ng/g of CHT were detected in two vegetables/fruits. LC-DA-MS/MS combined with modified QuEChERS was successfully applied to determine CHT residue <10ng/g in vegetables/fruits and with satisfied validation results. The developed method could reduce both analysis cost and time, attributing to simplifications in modified QuEChERS, isocratic elution, and DA interface in LC-DA-MS/MS.

Research on Task Scheduling for Internet of Things Cloud Computing Based on Improved Chicken Swarm Optimization Algorithm

Aiming at the shortcomings of long completion time and high consumption cost of cloud computing batch task scheduling in IoT, an Improved Chicken Swarm Optimization Algorithm (ICSO) for task scheduling in cloud computing scenarios is proposed. Specifically, in order to solve the problems of slow convergence and falling into local optimum of the chicken swarm optimization algorithm, we adopt the nonlinear decreasing technique of the rooster and the weighting technique of the hen, optimize the following coefficients of the chicks, and apply ICSO to cloud computing task scheduling. In simulation experiments, we conducted a large number of experiments using four standard benchmark functions with different number of tasks and the results show that ICSO algorithm reduces 25.8%, 9.3%, 8.8%, 7.5% in small task time compared to CSO, DCSO, GCSO, ABCSO in large task time by 30.8%, 8.3%, 7.8%, 6.3%, 11.8%, 10.3%, 8.8%, 7.5% savings in small task cost and 25.8%, 11.2%, 10.8%, 9.3% savings in large task cost. This method effectively reduces task scheduling time and cost consumption. Meanwhile, we tested it in combination with an IoT-based cloud platform and achieved very satisfying Results.

The Impact of Climate Change Transition Innovations on the Default Risk

In the context of climate change mitigation and adaptation, climate risks stemming from climate transition innovations have garnered significant attention due to concerns about the inadequate climate finance. To shed light on the climate transition risk posed by innovations, this study constructed low-carbon innovation indicators of listed firms in China spanning 2015 to 2021. This study investigated the impact of climate transition innovations on the default risk, considering the mediation effect of investor attention, total factor productivity, and technology spillovers. The findings suggest that low-carbon innovations can mitigate the default risk of listed firms, as evidenced by three low-carbon innovation indicators. Additionally, the research reveals that the climate innovation effect on default risks was moderated when considering climate policy through heterogenous analysis. Furthermore, instrumental variable regressions using the time costs of innovation support the findings. Lastly, default risk was mitigated through lower levels of investor attention, greater total factor productivity, and technology spillovers.

Improving Operating Room Efficiency in Otolaryngology-Head and Neck Surgery: A Scoping Review.

One minute of operating room (OR) time costs $36 to 37. However, ORs are notoriously inefficient. There is growing literature on improving OR efficiency, but no formal review of this topic within otolaryngology has been performed. This study reviews and synthesizes the current literature on improving OR efficiency within otolaryngology. MEDLINE, EMBASE, Web of Science, CINAHL, Cochrane Library, preprints.org, and medRxiv were searched on November 4, 2022. Published English studies were included if they reported on metrics for improving OR efficiency within otolaryngology. There were no publication date restrictions. Articles were screened by 2 reviewers. Preferred Reporting Items for Systematic Reviews and Meta-analysis reporting for scoping reviews was followed. The search yielded 9316 no-duplicate articles; 129 articles were included. Most of the studies reported on head and neck procedures (n = 52/129). The main tactics included surgical considerations: hemostatic devices, techniques, and team/simultaneous approaches; anesthetic considerations: local anesthetic and laryngeal mask airways; procedure location considerations: procedures outside of the OR and remote technologies; standardization: equipment, checklists, and personnel; scheduling considerations: use of machine learning for booking, considering patient/surgeon factors, and utilizing dedicated OR time/multidisciplinary teams for on-call cases. The current literature brings to attention numerous strategies for improving OR efficiency within otolaryngology. Applying these strategies and implementing novel techniques to manage surgical cases may assist in offloading overloaded health care systems and improving access to care while facilitating patient safety and outcomes. Anticipated barriers to implementation include resistance to change, funding, and the current strain on health care systems and providers.

Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification

Domain-specific knowledge can significantly contribute to addressing a wide variety of vision tasks. However, the generation of such knowledge entails considerable human labor and time costs. This study investigates the potential of Large Language Models (LLMs) in generating and providing domain-specific information through semantic embeddings. To achieve this, an LLM is integrated into a pipeline that utilizes Knowledge Graphs and pre-trained semantic vectors in the context of the Vision-based Zero-shot Object State Classification task. We thoroughly examine the behavior of the LLM through an extensive ablation study. Our findings reveal that the integration of LLM-based embeddings, in combination with general-purpose pre-trained embeddings, leads to substantial performance improvements. Drawing insights from this ablation study, we conduct a comparative analysis against competing models, thereby highlighting the state-of-the-art performance achieved by the proposed approach.

RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization.

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

Task-driven Risk-bounded Hierarchical Reinforcement Learning Based on Iterative Refinement

Deep Reinforcement Learning (DRL) has garnered substantial acclaim for its versatility and widespread applications across diverse domains. Aligned with human-like learning, DRL is grounded in the fundamental principle of learning from interaction, wherein agents dynamically adjust behavior based on environmental feedback in the form of rewards. This iterative trial-and-error process, mirroring human learning, underscores the importance of observation, experimentation, and feedback in shaping understanding and behavior. DRL agents, trained to navigate complex surroundings, refine their knowledge through hierarchical and abstract representations, empowered by deep neural networks. These representations enable efficient handling of long-horizon tasks and flexible adaptation to novel situations, akin to the human ability to construct mental models for comprehending complex concepts and predicting outcomes. Hence, abstract representation building emerges as a critical aspect in the learning processes of both artificial agents and human learners, particularly in long-horizon tasks. Furthermore, human decision-making, deeply rooted in evolutionary history, exhibits a remarkable capacity to balance the tradeoff between risk and cost across various domains. This cognitive process involves assessing potential negative consequences, evaluating factors such as the likelihood of adverse outcomes, severity of potential harm, and overall uncertainty. Humans intuitively gauge inherent risks and adeptly weigh associated costs, extending beyond monetary expenses to include time, effort, and opportunity costs. The nuanced ability of humans to consider the tradeoff between risk and cost highlights the complexity and adaptability of human decision-making, a skill lacking in typical DRL agents. Principles like these derived from human-like learning present an avenue for inspiring advancements in DRL, fostering the development of more adaptive and intelligent artificial agents. Motivated by these observations and focusing on practical challenges in robotics, our efforts target risk-aware stochastic sequential decision-making problem which is crucial for tasks with extended time frames and varied strategies. A novel integration of model-based conditional planning with DRL is proposed, inspired by hierarchical techniques. This approach breaks down complex tasks into manageable subtasks(motion primitives), ensuring safety constraints and informed decision-making. Unlike existing methods, our approach addresses motion primitive improvement iteratively, employing diverse prioritization functions to guide the search process effectively. This risk-bounded planning algorithm seamlessly integrates conditional planning and motion primitive learning, prioritizing computational efforts for enhanced efficiency within specified time limits.