Random Algorithm Research Articles

TEA+ : A Novel Temporal Graph Random Walk Engine with Hybrid Storage Architecture

Many real-world networks are characterized by being temporal and dynamic, wherein the temporal information signifies the changes in connections, such as the addition or removal of links between nodes. Employing random walks on these temporal networks is a crucial technique for understanding the structural evolution of such graphs over time. However, existing state-of-the-art sampling methods are designed for traditional static graphs, and as such, they struggle to efficiently handle the dynamic aspects of temporal networks. This deficiency can be attributed to several challenges, including increased sampling complexity, extensive index space, limited programmability, and a lack of scalability. In this article, we introduce TEA+ , a robust, fast, and scalable engine for conducting random walks on temporal graphs. Central to TEA+ is an innovative hybrid sampling method that amalgamates two Monte Carlo sampling techniques. This fusion significantly diminishes space complexity while maintaining a fast sampling speed. Additionally, TEA+ integrates a range of optimizations that significantly enhance sampling efficiency. This is further supported by an effective graph updating strategy, skilled in managing dynamic graph modifications and adeptly handling the insertion and deletion of both edges and vertices. For ease of implementation, we propose a temporal-centric programming model, designed to simplify the development of various random walk algorithms on temporal graphs. To ensure optimal performance across storage constraints, TEA+ features a degree-aware hybrid storage architecture, capable of adeptly scaling in different memory environments. Experimental results showcase the prowess of TEA+ , as it attains up to three orders of magnitude speedups compared to current random walk engines on extensive temporal graphs.

Prediction of Cochlear Implant Fitting by Machine Learning Techniques.

This study aimed to evaluate the differences in electrically evoked compound action potential (ECAP) thresholds and postoperative mapping current (T) levels between electrode types after cochlear implantation, the correlation between ECAP thresholds and T levels, and the performance of machine learning techniques in predicting postoperative T levels. Retrospective case review. Tertiary hospital. We reviewed the charts of 124 ears of children with severe-to-profound hearing loss who had undergone cochlear implantation. We compared ECAP thresholds and T levels from different electrodes, calculated correlations between ECAP thresholds and T levels, and created five prediction models of T levels at switch-on and 6 months after surgery. The accuracy of prediction in postoperative mapping current (T) levels. The ECAP thresholds of the slim modiolar electrodes were significantly lower than those of the straight electrodes on the apical side. However, there was no significant difference in the neural response telemetry thresholds between the two electrodes on the basal side. Lasso regression achieved the most accurate prediction of T levels at switch-on, and the random forest algorithm achieved the most accurate prediction of T levels 6 months after surgery in this dataset. Machine learning techniques could be useful for accurately predicting postoperative T levels after cochlear implantation in children.

Identification of neurological complications in childhood influenza: a random forest model

BackgroundAmong the neurological complications of influenza in children, the most severe is acute necrotizing encephalopathy (ANE), with a high mortality rate and neurological sequelae. ANE is characterized by rapid progression to death within 1–2 days from onset. However, the knowledge about the early diagnosis of ANE is limited, which is often misdiagnosed as simple seizures/convulsions or mild acute influenza-associated encephalopathy (IAE).ObjectiveTo develop and validate an early prediction model to discriminate the ANE from two common neurological complications, seizures/convulsions and mild IAE in children with influenza.MethodsThis retrospective case-control study included patients with ANE (median age 3.8 (2.3,5.4) years), seizures/convulsions alone (median age 2.6 (1.7,4.3) years), or mild IAE (median age 2.8 (1.5,6.1) years) at a tertiary pediatric medical center in China between November 2012 to January 2020. The random forest algorithm was used to screen the characteristics and construct a prediction model.ResultsOf the 433 patients, 278 (64.2%) had seizures/convulsions alone, 106 (24.5%) had mild IAE, and 49 (11.3%) had ANE. The discrimination performance of the model was satisfactory, with an accuracy above 0.80 from both model development (84.2%) and internal validation (88.2%). Seizures/convulsions were less likely to be wrongly classified (3.7%, 2/54), but mild IAE (22.7%, 5/22) was prone to be misdiagnosed as seizures/convulsions, and a small proportion (4.5%, 1/22) of them was prone to be misdiagnosed as ANE. Of the children with ANE, 22.2% (2/9) were misdiagnosed as mild IAE, and none were misdiagnosed as seizures/convulsions.ConclusionThis model can distinguish the ANE from seizures/convulsions with high accuracy and from mild IAE close to 80% accuracy, providing valuable information for the early management of children with influenza.

Is There a Digital Rebound in the Process of Urban Green Development? New Empirical Evidence Using Ensemble Learning Methods

The convergence of digitization and greening is an unavoidable path of modern economic progress. Nonetheless, the digital economy does not consistently align with the principles of green development, potentially leading to a rebound effect in urban digitalization initiatives. To investigate the correlation between the digital rebound effect and urban green development, this study utilizes panel data from Chinese prefecture-level cities spanning from 2011 to 2019. By examining the dual impact of the digital economy on green development, the paper posits a theoretical hypothesis regarding the nonlinear marginal effect of the digital economy. This research demonstrates an inverted U-shaped correlation between the digital economy and urban green development via empirical analyses employing the random forest algorithm and partial dependency plots. It supports the existence of a moderate digital resiliency effect, which eventually reaches a state of stability rather than greatly diminishing the degree of green development in urban areas. In addition, the heterogeneity analysis reveals that the positive effects of the digital economy are more popular in cities located in the eastern and central regions, as well as in the National Comprehensive Pilot Zone for Big Data. However, these effects do not vary significantly among different ranks of cities. The mechanism test found that the information effect and the capital allocation effect are the mechanisms by which the digital economy affects green development, and there is a “U-shaped” relationship between the digital economy and information asymmetry and capital mismatch. According to the study’s results, improving the digital economy’s governance structure continues to make more sense than merely increasing the number of digital inputs.

Asymmetric influence-based superposed random walk link prediction algorithm in complex networks

Random walk-based link prediction algorithms have achieved desirable results for complex network mining, but in these algorithms, the transition probability of particles usually only considers node degrees, resulting in particles being able to randomly select adjacent nodes for random walks in an equal probability manner, to solve this problem, the asymmetric influence-based superposed random walk link prediction algorithm is proposed in this paper. This algorithm encourages particles to choose the next node at each step of the random walk process based on the asymmetric influence between nodes. To this end, we fully consider the topological information around each node and propose the asymmetric influence between nodes. Then, an adjustable parameter is applied to normalize the degree of nodes and the asymmetric influence between nodes into transition probability. Based on this, the proposed new transition probability is applied to superposed random walk process to measure the similarity between all nodes in the network. Empirical experiments are conducted on 16 real-world network datasets such as social network, ecology network, and animal network. The experimental results show that the proposed algorithm has high prediction accuracy in most network, compared with 10 benchmark indices.

VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

AbstractIn this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments.

Forecasting Dairy Sales Estimates: A Comparative Analysis of Logistic Regression and Random Forest Algorithm

This study examines the effectiveness of logistic regression and random forest algorithms in forecasting sales estimates for dairy products. Using a comprehensive data set that includes factors such as product features, pricing dynamics, promotional efforts and consumer demographics, the models are trained to accurately predict future sales figures. Logistic regression provides a transparent framework for estimating probabilities, while random forest uses ensemble learning to capture complex relationships between variables. Through careful evaluation and comparison, the research aims to identify the strengths and weaknesses of each algorithm in producing reliable sales forecasts for dairy products. The results show that while logistic regression offers interpretability and simplicity, random forest excels in handling non-linear relationships and achieving higher prediction accuracy. Insights gathered from this analysis can help dairy industry stakeholders make informed decisions, optimize resource allocation and improve sales forecasting strategies.

A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals

Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model’s ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management.

Toward the Reconciliation of Inconsistent Molecular Structures from Biochemical Databases.

Information on the structure of molecules, retrieved via biochemical databases, plays a pivotal role in various disciplines, including metabolomics, systems biology, and drug discovery. No such database can be complete and it is often necessary to incorporate data from several sources. However, the molecular structure for a given compound is not necessarily consistent between databases. This article presents StructRecon, a novel tool for resolving unique molecular structures from database identifiers. Currently, identifiers from BiGG, ChEBI, Escherichia coli Metabolome Database (ECMDB), MetaNetX, and PubChem are supported. StructRecon traverses the cross-links between entries in different databases to construct what we call identifier graphs. The goal of these graphs is to offer a more complete view of the total information available on a given compound across all the supported databases. To reconcile discrepancies met during the traversal of the databases, we develop an extensible model for molecular structure supporting multiple independent levels of detail, which allows standardization of the structure to be applied iteratively. In some cases, our standardization approach results in multiple candidate structures for a given compound, in which case a random walk-based algorithm is used to select the most likely structure among incompatible alternatives. As a case study, we applied StructRecon to the EColiCore2 model. We found at least one structure for 98.66% of its compounds, which is more than twice as many as possible when using the databases in more standard ways not considering the complex network of cross-database references captured by our identifier graphs. StructRecon is open-source and modular, which enables support for more databases in the future.

Toward granular search-based automatic unit test case generation

Unit testing verifies the presence of faults in individual software components. Previous research has been targeting the automatic generation of unit tests through the adoption of random or search-based algorithms. Despite their effectiveness, these approaches aim at creating tests by solely optimizing metrics like code coverage, without ensuring that the resulting tests have granularities that would allow them to verify both the behavior of individual production methods and the interaction between methods of the class under test. To address this limitation, we propose a two-step systematic approach to the generation of unit tests: we first force search-based algorithms to create tests that cover individual methods of the production code, hence implementing the so-called intra-method tests; then, we relax the constraints to enable the creation of intra-class tests that target the interactions among production code methods. The assessment of our approach is conducted through a mixed-method research design that combines statistical analyses with a user study. The key results report that our approach is able to keep the same level of code and mutation coverage while providing test suites that are more structured, more understandable and aligned to the design principles of unit testing.

Random forest predictive modeling of prolonged hospital length of stay in elderly hip fracture patients

BackgroundIn elderly individuals suffering from hip fractures, a prolonged hospital length of stay (PLOS) not only heightens the probability of patient complications but also amplifies mortality risks. Yet, most elderly hip fracture patients present compromised baseline health conditions. Additionally, PLOS leads to increased expenses for patient treatment and care, while also diminishing hospital turnover rates. This, in turn, jeopardizes the prompt allocation of beds for urgent cases.MethodsA retrospective study was carried out from October 2021 to November 2023 on 360 elderly hip fracture patients who underwent surgical treatment at West China Hospital. The 75th percentile of the total patient cohort’s hospital stay duration, which was 12 days, was used to define prolonged hospital length of stay (PLOS). The cohort was divided into training and testing datasets with a 70:30 split. A predictive model was developed using the random forest algorithm, and its performance was validated and compared with the Lasso regression model.ResultsOut of 360 patients, 103 (28.61%) experienced PLOS. A Random Forest classification model was developed using the training dataset, identifying 10 essential variables. The Random Forest model achieved perfect performance in the training set, with an area under the curve (AUC), balanced accuracy, Kappa value, and F1 score of 1.000. In the testing set, the model’s performance was assessed with an AUC of 0.846, balanced accuracy of 0.7294, Kappa value of 0.4325, and F1 score of 0.6061.ConclusionThis study aims to develop a prognostic model for predicting delayed discharge in elderly patients with hip fractures, thereby improving the accuracy of predicting PLOS in this population. By utilizing machine learning models, clinicians can optimize the allocation of medical resources and devise effective rehabilitation strategies for geriatric hip fracture patients. Additionally, this method can potentially improve hospital bed turnover rates, providing latent benefits for the healthcare system.

Do Acoustic Characteristics of Dysarthria in People With Parkinson's Disease Differ Across Languages?

Cross-language studies suggest more similarities than differences in how dysarthria affects the speech of people with Parkinson's disease (PwPD) who speak different languages. In this study, we aimed to identify the relative contribution of acoustic variables to distinguish PwPD from controls who spoke varieties of two Romance languages, French and Portuguese. This bi-national, cross-sectional, and case-controlled study included 129 PwPD and 124 healthy controls who spoke French or Portuguese. All participants underwent the same clinical examinations, voice/speech recordings, and self-assessment questionnaires. PwPD were evaluated off and on optimal medication. Inferential analyses included Disease (controls vs. PwPD) and Language (French vs. Portuguese) as factors, and random decision forest algorithms identified relevant acoustic variables able to distinguish participants: (a) by language (French vs. Portuguese) and (b) by clinical status (PwPD on and off medication vs. controls). French-speaking and Portuguese-speaking individuals were distinguished from each other with over 90% accuracy by five acoustic variables (the mean fundamental frequency and the shimmer of the sustained vowel /a/ production, the oral diadochokinesis performance index, the relative sound level pressure and the relative sound pressure level standard deviation of the text reading). A distinct set of parameters discriminated between controls and PwPD: for men, maximum phonation time and the oral diadochokinesis speech proportion were the most significant variables; for women, variables calculated from the oral diadochokinesis were the most discriminative. Acoustic variables related to phonation and voice quality distinguished between speakers of the two languages. Variables related to pneumophonic coordination and articulation rate were the more effective in distinguishing PwPD from controls. Thus, our research findings support that respiration and diadochokinesis tasks appear to be the most appropriate to pinpoint signs of dysarthria, which are largely homogeneous and language-universal. In contrast, identifying language-specific variables with the speech tasks and acoustic variables studied was less conclusive.

Development of Random Forest Model for Stroke Prediction

Stroke is a significant cause of mortality and morbidity worldwide, and early detection and prevention of stroke are essential for improving patient outcomes. Machine learning algorithms have been used in recent years to predict the risk of stroke by leveraging large amounts of clinical and demographic data. The development of a stroke prediction system using Random Forest machine learning algorithm is the main objective of this thesis. The primary goal of the project is to increase the accuracy of stroke detection while addressing the shortcomings of the current system, which include real- time deployment and interpretability issues with logistic regression. The development and use of an ensemble machine learning-based stroke prediction system, performance optimization through the use of ensemble machine learning algorithms, performance assessment, and real-time model deployment through the use of Python Django are among the goals of the research. The study's potential to improve public health by lessening the severity and consequences of strokes through early diagnosis and treatment makes it significant. Data collection, preprocessing, model selection, evaluation, and real-time deployment using Python Django are all part of the research technique. Our dataset consists of 5110 rows of tuples and columns with total size of 69kg. The performance of our stroke prediction algorithm was evaluated using confusion metrics-consisting of accuracy, precision, recall and F1-score. At the end of the research, Random Forest model gave an accuracy of 98.5% compared to the existing model logistic regression which has 86% accuracy.

Remote sensing inversion on heavy metal content in salinized soil of Yellow River Delta based on Random Forest Regression—a case study of Gudao Town

To explore the potential of using the mineral alteration information extracted by remote sensing technology to indirectly estimate the heavy metal content of salinized soil, 23 sampling points were uniformly set up in the town of Gudao in the Yellow River Delta as the research area in 2022. The concentrations of seven heavy metals, Cr, Cu, Pb, Zn, As, Mn and Ni, at the sampling points were determined in laboratory tests. Spectral derivative indices, topographic factors, and mineral alteration information (iron staining, hydroxyl, and carbonate ions) were extracted and screened as modeling factors using Sentinel 2 imagery. An inverse model of heavy metal content was constructed using the random forest algorithm, and the model accuracy was evaluated using the cross-validation method. The results of the study show that: (1) Hydroxyl and carbonate ion alteration can be effectively used for the inversion of soil As and Ni content in this study area. Iron-stained alteration can be used as a modeling factor in the inversion of Cr, Cu, Pb, Zn, and Mn concentrations. (2) The inclusion of alteration information improves the accuracy of heavy metal content inversion. The Cu concentration was verified to be the best predictor, with an RMSE of 3.309, MAPE of 11.072%, and R2 of 0.904, followed by As, Ni, and Zn; the predictive value of Mn, Cr and Pb was average. (3) Based on the results of concentration inversion, the high concentration areas of As, Ni, and Mn are primarily distributed on both sides of the river and around lakes and ponds. The high-concentration areas of Zn were mainly distributed in the farmland areas on both sides of the river. Areas with high concentrations of Cu were mainly distributed in the eastern oil extraction area, both sides of the rivers, and around lakes.

Usage of Weka Software Based On Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis

The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour, C4.5 decision tree, K-star, random tree and reduced error pruning tree algorithms were used. Two distinct approaches were employed to evaluate the performances of machine learning algorithms. In the first approach, the entire features of dataset were utilized, while in the second approach, only the features selected through principal component analysis were used. Each approach was rigorously assessed using both 10-fold cross-validation and data splitting (70% train and 30% test) techniques. By conducting separate evaluations for each approach, a comprehensive understanding of the effectiveness of utilizing all features versus extracted features based principal component analysis was attained, providing valuable insights into the impact of feature dimensionality reduction on model performance. In this study, all analyses were implemented on WEKA data mining tool. In the first approach, the classification accuracies of random forest algorithm were 89.72% and 90.75% with the application of data splitting (70%-30%) and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis technique, the accuracy values obtained from data splitting and cross-validation techniques of random forest algorithm were 88.61% and 88.83%, respectively. The obtained results revealed out that random forest algorithm outperformed for both approaches. Besides, the application of principal component analysis technique negatively affected the classification performance of used machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosis/cirrhosis, potentially leading to death in its advanced stages.

Random forest regression on pullout resistance of a pile

AbstractThis research aims to study the pullout resistance of a helical pile using three methods of machine learning techniques, which are: random forest regression, support vector regression, and adaptive neuro-fuzzy inference system, based on experimental results of a helical pile. The performance of these three techniques has been d compared and the results show that random forest algorithm has best performance than neuro-fuzzy inference system and support vector technique. The results show that machine learning considered a good tool in terms of estimating the pullout resistance of helical piles in the soil.

Attrition Rates in HIV Viral Load Monitoring and Factors Associated With Overdue Testing Among Children Within South Africa's Antiretroviral Treatment Program: Retrospective Descriptive Analysis.

Numerous studies in South Africa have reported low HIV viral load (VL) suppression and high attrition rates within the pediatric HIV treatment program. Using routine laboratory data, we evaluated HIV VL monitoring, including mobility and overdue VL (OVL) testing, within 5 priority districts in South Africa. We performed a retrospective descriptive analysis of National Health Laboratory Service (NHLS) data for children and adolescents aged 1-15 years having undergone HIV VL testing between May 1, 2019, and April 30, 2020, from 152 facilities within the City of Johannesburg, City of Tshwane, eThekwini, uMgungundlovu, and Zululand. HIV VL test-level data were deduplicated to patient-level data using the NHLS CDW (Corporate Data Warehouse) probabilistic record-linking algorithm and then further manually deduplicated. An OVL was defined as no subsequent VL determined within 18 months of the last test. Variables associated with the last VL test, including age, sex, VL findings, district type, and facility type, are described. A multivariate logistic regression analysis was performed to identify variables associated with an OVL test. Among 21,338 children and adolescents aged 1-15 years who had an HIV VL test, 72.70% (n=15,512) had a follow-up VL test within 18 months. Furthermore, 13.33% (n=2194) of them were followed up at a different facility, of whom 3.79% (n=624) were in a different district and 1.71% (n=281) were in a different province. Among patients with a VL of ≥1000 RNA copies/mL of plasma, the median time to subsequent testing was 6 (IQR 4-10) months. The younger the age of the patient, the greater the proportion with an OVL, ranging from a peak of 52% among 1-year-olds to a trough of 21% among 14-year-olds. On multivariate analysis, 2 consecutive HIV VL findings of ≥1000 RNA copies/mL of plasma were associated with an increased adjusted odds ratio (AOR) of having an OVL (AOR 2.07, 95% CI 1.71-2.51). Conversely, patients examined at a hospital (AOR 0.86, 95% CI 0.77-0.96), those with ≥2 previous tests (AOR 0.78, 95% CI 0.70-0.86), those examined in a rural district (AOR 0.63, 95% CI 0.54-0.73), and older age groups of 5-9 years (AOR 0.56, 95% CI 0.47-0.65) and 10-14 years (AOR 0.51, 95% CI 0.44-0.59) compared to 1-4 years were associated with a significantly decreased odds of having an OVL test. Considerable attrition occurs within South Africa's pediatric HIV treatment program, with over one-fourth of children having an OVL test 18 months subsequent to their previous test. In particular, younger children and those with virological failure were found to be at increased risk of having an OVL test. Improved HIV VL monitoring is essential for improving outcomes within South Africa's pediatric antiretroviral treatment program.

Fabrication of FeP2/C/CNTs@3D interconnected graphene aerogel composite for lithium-ion battery anodes and the electrochemical performance evaluation using machine learning

Featuring low cost and high theoretical capacity, phosphorus-rich iron diphosphides (FeP2) have emerged as excellent anode candidates for lithium-ion batteries. However, the typical chemical synthses involving high temperatures and toxic gases have restricted the mass production of FeP2. We now report a FeP2/C/CNTs@3D interconnected graphene aerogel composite synthesized by ball milling and low temperature heat treatment. Because of the unique microstructure and synergistic effect between well-dispersed FeP2 particles and graphene aerogel, the as-prepared FeP2/C/CNTs@GA-2.5% displays excellent lithium storage performance in terms of reversible capacity (886 mA h g−1 at 0.1 A g−1), cycling stability (476 mA h g−1 at 2 A g−1 even after 1000 cycles), and rate capability (685 mA h g−1 at 5 A g−1). Machine learning methods (decision tree and random forest algorithms) prove effective in evaluating and predicting electrochemical performance of the electrode materials.

Quadratic acceleration of multistep probabilistic algorithms for state preparation

Quantum state preparation is a fundamental building block for various problems on a quantum computer. A nonunitary operator is typically designed to decay unwanted states contained in an initial state using ancilla qubits and a probabilistically action. In this Letter, we clarify that this probabilistic nature is a drag for quantum advantages: The probabilistic algorithms do not accelerate the computational process compared to classical ones. Combining quantum amplitude amplification (QAA) with multistep probabilistic algorithms is proposed to address this drawback, achieving quadratic acceleration and quantum advantages. We also find that the multistep probabilistic method with QAA exhibits advantages over quantum phase estimation in terms of infidelity. We also demonstrate it to confirm the quadratic acceleration, using a probabilistic imaginary-time evolution method as an example. Published by the American Physical Society 2024

Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting doselocations.