Termination Criterion Research Articles

BPACE: A Bayesian, Patient-Centered Procedure for Matrix Speech Tests in Noise.

Matrix sentence tests in noise can be challenging to the listener and time-consuming. A trade-off should be found between testing time, listener's comfort and the precision of the results. Here, a novel test procedure based on an updated maximum likelihood method was developed and implemented in a German matrix sentence test. It determines the parameters of the psychometric function (threshold, slope, and lapse-rate) without constantly challenging the listener at the intelligibility threshold. A so-called "credible interval" was used as a mid-run estimate of reliability and can be used as a termination criterion for the test. The procedure was evaluated and compared to a STAIRCASE procedure in a study with 20 cochlear implant patients and 20 normal hearing participants. The proposed procedure offers comparable accuracy and reliability to the reference method, but with a lower listening effort, as rated by the listeners ( points on a 10-point scale). Test duration can be reduced by 1.3 min on average when a credible interval of 2 dB is used as the termination criterion instead of testing 30 sentences. Particularly, normal hearing listeners and well performing, cochlear implant users can benefit from shorter test duration. Although the novel procedure was developed for a German test, it can easily be applied to tests in any other language.

Deep Active Learning Intrusion Detection and Load Balancing in Software-Defined Vehicular Networks

Software-defined vehicular networks (SDVN) can help analyze and reconfigure networks. Massive data generation in autonomous vehicles can lead to issues in network configuration, routing, network characteristics, and system load factors. Load balancing in vehicle sensors helps reduce delays and improve resource utilization. In this paper, we propose a load balancing algorithm to map sensor data, vehicles and data centers performing tasks. A dynamic convergence method is proposed to help identify vehicle system load factors and compare their termination criteria. We also propose a packet-level intrusion detection model. After all load balancing, the model can track the attack on the network. The proposed model further combines the entropy-based active learning and the attention-based model to efficiently identify the attacks. Experiments are then conducted on the standard KDD data to validate the developed models with and without an attention-based active learning mechanism. Our experimental results show that the load balancing mechanism is able to achieve more performance gains than previous techniques. Moreover, the results show that the developed model can improve the decision boundary by using a pooling strategy and an entropy uncertainty measure.

Applicability and Efficiency of a Polytomous IRT-Based Computerized Adaptive Test for Measuring Psychological Traits

Currently, research on computerized adaptive testing (CAT) focuses mainly on dichotomous items and cognitive traits (achievement, aptitude, etc.). However, polytomous IRT-based CAT is a promising research area for measuring psychological traits that has attracted much attention. The main purpose of this study is to test the practicality of the polytomous IRT-based CAT and its equivalence with the paper-pencil version. Data were collected from 1449 high school students (45% female) via the paper-pencil version. The data were used for IRT parameter estimates and CAT simulation studies. For the equivalence study, the research group consisted of 81 students (47% female) who participated in both the paper-pencil and live CAT applications. The paper-pencil version of the vocational interest inventory consists of 17 factors and 164 items. When the EAP estimation method and setting SE < .50 as the termination criterion, better performance was obtained compared with other CAT designs. The Item selection did not help to reduce test duration or increase measurement accuracy. As a result, it was found that an area of interest can be assessed with four items. The results of the live CAT application showed that the estimates of CAT were strongly positively correlated with its paper-pencil version. In addition, the live CAT application increased applicability compared to the fixed-length test version by reducing test length by 50% and time by 77%. This study shows that the polytomous IRT-based CAT is applicable and efficient for measuring psychological traits.

An averaging in linear by control optimal control problem on discret time with variable delay

For discrete equations of controlled motion with a varying delay in the state of the system and with a linearly input control parameter, the possibility of applying the averaging method has been proved. For the optimal control problem on the trajectories of such a system with a terminal criterion, the theorem on the proximity of the values of the criterion of the non-averaged optimal control problem on the optimal control of the averaged problem with the optimal value of the criterion of the non-averaged (initial) problem has been proved. That is, the optimal control of the averaged problem is asymptotically optimal control for the original problem. A numerically asymptotic method for solving the optimal control problem of a system in discrete time, which contains a varying delay in the state and linearly depends on the control parameter, has been developed.

ATESA: An Automated Aimless Shooting Workflow.

Transition path sampling methods are powerful tools for studying the dynamics of rare events in molecular simulations. However, these methods are generally restricted to experts with the knowledge and resources to properly set up and analyze the often hundreds of thousands of simulations that constitute a complete study. Aimless Transition Ensemble Sampling and Analysis (ATESA) is a new open-source software program written in Python that automates a full transition path sampling workflow based on the aimless shooting algorithm, streamlining the process and reducing the barrier to use for researchers new to this approach. This introduction to ATESA includes a demonstration of a complete transition path sampling process flow for an example reaction, including finding an initial transition state, sampling with aimless shooting, building a reaction coordinate with inertial likelihood maximization, verifying that coordinate with committor analysis, and measuring the reaction energy profile with umbrella sampling. We also describe our implementation of a termination criterion for aimless shooting based on the Godambe information calculated during model building with likelihood maximization as well as a novel approach to constraining simulations to the desired rare event pathway during umbrella sampling.

Learning-augmented heuristics for scheduling parallel serial-batch processing machines

The addressed machine scheduling problem considers parallel machines with incompatible job families, sequence-dependent setup times, limited batch capacities, and arbitrary sizes combined with the serial-batch processing characteristic (i.e., the processing time of a batch is equal to the sum of processing times of all jobs grouped in a batch). The primary objective is the minimization of the total weighted tardiness, and a subordinate (secondary) objective is the minimization of the flow time. This scheduling problem arises in many production environments like cutting operations (metal-processing industry or garment industry) or in industrial 3D printing. For solving this problem, we propose a new multi-start construction heuristic with controlled batch urgencies. Furthermore, to improve solution efficiency, we use machine learning methods that are appropriate for multi-target regression with dependent outputs (i.e., Neural networks) to minimize the number of starts by predicting the most suitable heuristic parameters. Hereby, different learning aspects and pipeline parameters must be considered. Additionally, we apply a mixed-integer linear program and a local search mechanism with advanced termination criteria for solution improvement.To evaluate the performance of the new heuristic, we use an exhaustive set of small, large, and very large instances (with symmetric Euclidean, asymmetric Euclidean, and arbitrary sequence-dependent setup times) and heuristics from the literature. The results indicate the superiority of the new, learning-augmented heuristics in terms of solution quality and computation times.

A dimension-wise analysis driven active learning paired-Kriging (DWA-ALK) method for the hybrid reliability analysis

Hybrid reliability analysis (HRA) has been extensively explored and mainly performed based on two categories of models. Mathematical properties on these models are given and proven in a rigorous manner, providing insights into HRA problems. Potential obstacles of the most appealing Kriging-based method available in literature for HRA include: metamodeling in an augmented-dimensional space spanned by both random and interval parameters resulting in substantial inversion of a large correlation matrix, an absence of distance metric in learning functions causing sample clustering, repeated interval analysis mainly completed by either the sampling-based or optimization-based algorithm leading to excessive computational cost and memory requirements especially for high-dimensional interval inputs. In this sense, a dimension-wise analysis driven active learning paired-Kriging method is proposed, where a paired-Kriging metamodel is constructed solely in the random input space and, at each training point, the interval analysis is conducted by a dimension-wise analysis method. A new learning function and termination criterion are conceived for refining the paired-Kriging model. It is concluded that the proposed method outperforms the common-in-use ones for HRA with high-dimensional interval inputs while its accuracy and efficiency are comparable in the case of low-dimensional interval inputs after validating its numerical performance by typical issues.

CRIB: A Novel Method for Device-Based Physical Behavior Analysis

Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.

Characteristics of Spontaneously Regressed Retinopathy of Prematurity at Termination of Acute Retinal Screening Examinations Using a Novel Grading System.

To characterize and compare residual retinal changes of spontaneously regressing retinopathy of prematurity (ROP) at termination of acute-phase retinal screening examinations. A retrospective chart review of 521 infants screened for ROP from 2016 to 2019 was performed. Data were collected on patients with at least stage 1 ROP, but who were not treated. The primary outcome measure was presence of residual fibrotic retinal tissue at termination of acute ROP screening examinations. Secondary outcomes included: type of residual fibrotic retinal tissue, persistent avascular retina, vitreous hemorrhage, and retinal detachment. A novel grading system was used to categorize findings. Comparisons between groups were made using Fisher exact tests. A total of 255 eyes met inclusion criteria. A total of 206 of 255 eyes (81%) showed persistent avascular retina, whereas 57 of 255 eyes (22%) showed residual fibrotic retinal tissue at termination of acute screening. All eyes with previous stage 3 ROP showed residual fibrotic retinal tissue (8 of 8 eyes, 100%). Eyes whose highest stage of ROP was stage 2 showed more residual fibrotic retinal tissue at termination of acute screening (40 of 73 eyes, 55%) compared to eyes whose highest stage was stage 1 (9 of 174, 5%) (P < .0001). Stage 3 eyes were excluded from this comparison due to the small sample size. One eye had vitreous hemorrhage at post-menstrual age 44 1/7 weeks. No eyes had retinal detachment. Despite meeting at least one of the criteria for termination of acute ROP screening, residual fibrotic retinal tissue and persistent avascular retina are common after spontaneous regression of ROP. Further investigation into the long-term clinical significance of these residual changes is warranted. [J Pediatr Ophthalmol Strabismus. 2023;60(5):359-364.].

An Improved Dual Asymmetric Penalized Least Squares Baseline Correction Method for High-Noise Spectral Data Analysis

Baseline drift and noise can blur or even drown out a signal and affect analysis results, especially in multivariate analysis. To address the problem of spectrum denoising and baseline correction, this paper proposes an improved dual asymmetric penalized least squares (IDAPLS) baseline correction method. The proposed method first changes the single parameter λ used for balancing fidelity and roughness in the traditional penalty least squares (PLS) method into a new diagonal matrix Λ and uses the fast convergent inverse tangent S-type penalty function to iteratively estimate the noise level. Then, the diagonal matrix Ψ is introduced into the fidelity of the updated energy spectrum, and the element ψi is updated iteratively by using the inverse tangent S-type penalty function. Finally, the baseline of the original signal is obtained when a preset number of iterations or termination criteria are reached. Compared with other methods, IDAPLS solves the problem of underfitted curves when dealing with additive noise that the asymmetric least squares method and adaptive iterative reweighted penalized least squares method would get. The proposed method also retains the advantage of fast PLS and realizes the further approximation of the fitting baseline to the real baseline. Especially, in the case of high noise, this method reduces the error of the traditional PLS method from 30% to less than 5%, which gives a useful reference for nuclear data analysis.

Weight gain is an early indicator of injury in ex vivo normothermic limb perfusion (EVNLP).

There are no established criteria for discontinuing ex vivo normothermic limb perfusion (EVNLP) before irreversible damage occurs. This study evaluates weight gain as an indicator of injury during EVNLP. Sixteen Yorkshire pig forelimbs were procured and preserved using EVNLP with a hemoglobin-based oxygen carrier (HBOC-201) or static cold storage. EVNLP continued until termination criteria were met: arterial pressure ≥ 115 mm Hg, compartment pressure > 30 mm Hg, or 20% reduction of oxygen saturation. Limb weight, contractility, hemodynamics, perfusate electrolytes, metabolites and gases were recorded. Muscles were biopsied 6-h, and muscle injury scores (MIS) calculated. Forearm compartment pressures and indocyanine green (ICG) angiography were recorded at endpoint. Outcomes were compared at 2%, 5%, 10%, and 20% limb weight gain. EVNLP lasted 20 ± 3 h. Weight gain was observed after 13 ± 5h (2%), 15 ± 6h (5%), 16 ± 6h (10%), and 19 ± 4h (20%). Weight correlated positively with MIS (ρ=0.92, p < 0.0001), potassium (ρ=-1.00, p < 0.0001), pressure (ρ=0.78, p < 0.0001), and negatively with contractility (ρ=-0.96, p=0.011). At 5% weight gain, MIS (p < 0.0001), potassium (p=0.03), and lactate (p < 0.0001) were significantly higher than baseline. Median muscle contractility was 5 [3-5] at 2% weight gain, 4 [1-5] at 5%, 3 [0-4] and 2 [0-2] at 10% and 20%, respectively. At 20% weight gain, contractility was significantly lower than baseline (p=0.003). Percent weight gain correlated negatively with endpoint ICG hoof fluorescence (r= -0.712, p= 0.047). Weight gain correlated with microscopic muscle injury and was the earliest evidence of limb dysfunction. Weight gain may serve as a criterion for discontinuation of EVNLP.

Phase II Study of a JAK1/2 Inhibitor Ruxolitinib for Systemic Chronic Active Epstein-Barr Virus Disease: An Investigator-Initiated Trial

Background: Systemic chronic active Epstein-Barr virus disease (sCAEBV) is a rare EBV-positive T- or NK-lymphoproliferative disease. Although the only curative treatment for sCAEBV available now is allogeneic hematopoietic stem cell transplantation (allo-HSCT), the presence of disease activity, persistent inflammation, at the time of allo-HSCT is associated with poor outcomes (Am J Hem.2022, 97, p780). STAT3 is constitutively activated contributing to cytokine production and cell survival of EBV-infected cells in sCAEBV (Oncotarget. 2019, 9, p31077). Thus, STAT3 inhibition by ruxolitinib, a JAK1/2 inhibitor, may be effective against disease activity of sCAEBV. Methods: We conducted an investigator-initiated trial of open-label, phase 2, prospective and multicenter to assess the efficacy and safety of ruxolitinib in sCAEBV patients with disease activity. The diagnostic criteria were as follows: elevated EBV-DNA load in the peripheral blood (PB) (>102.5 copies/µg DNA); EBV infected T or NK cells detected in affected tissues or PB; systemic inflammatory symptoms persisting for more than three months; exclusion of other possible diagnoses, such as immune deficiencies, autoimmune diseases, or lymphoma (Blood adv. 2021, 4, p2918). sCAEBV patients ≥13 y.o. with fever (≥ 37.5 ºC), and/or liver dysfunction (ALT ≥ 2.0 × ULN) were enrolled. Anti-VCA-IgM-positive patients were excluded. The initiation dose of ruxolitinib was 5-20 mg twice a day determined by the platelet count in PB. The treatment effects were classified as complete response (CR), partial response (PR), progressive disease (PD), and stable disease (SD). CR was defined as the disappearance of all the elements of disease activity such as fever, liver dysfunction, uveitis, progressive skin lesions, and vasculitis. PR was defined as the disappearance of at least one of the elements listed above. PD was defined as the conversion to active disease, exacerbation of active disease, or the development of overt T- or NK-cell lymphoma. SD was defined as presenting no progression. Ruxolitinib was administered for eight weeks. If CR was observed and allo-HSCT was scheduled, ruxolitinib was terminated early even if these events had occurred within less than eight weeks after initiating investigational drug administration. The primary endpoint of efficacy assessments was CR rate at eight weeks after the initiation of drug administration or at the time of early termination. Safety was assessed based on the frequency of adverse events defined by Common Terminology Criteria for Adverse Events, version 4.0. Results: Nine patients were enrolled between January 2019 and August 2021; five males and four females aged 13-64 y.o.; median age of 40 y.o. The types of EBV-infected cells were as follows: four were CD4, one was CD8, and four were CD56. Seven patients completed the study. Five among them received ruxolitinib for eight weeks and were treated as outpatients. Two patients fulfilled the criteria of early termination and each was treated for 35 and 12 days. The responses are shown in the figure. CR rate and OR rate after eight weeks or at early termination, the primary and the secondary endpoints were 28.6% (2/7) and 42.9% (3/7), respectively. No one showed progressive disease. One patient developed a grade 4 adverse event of oral bleeding during the treatment and resulted in discontinuing the administration of ruxolitinib on day 13. Seven patients received allo-HSCT. Five patients achieved undetectable level of EBV-DNA PB after allo-HSCT. Conclusion: The results indicate that ruxolitinib has demonstrated encouraging effects and is a potent therapeutic reagent against sCAEBV with active disease. Further analysis shall complete its assessment. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Preliminary Biomarker Data from a Phase 1/2 Study of Golidocitinib Demonstrates Targeting JAK/STAT Pathway to Treat Peripheral T-Cell Lymphoma

Background Peripheral T-cell lymphoma (PTCL) is a group of heterogenous non-Hodgkin lymphomas originating from mature T lymphocytes. There is no consensus on treatment for patients with PTCL who failed 1st line treatment (r/r PTCL). It has been well documented that many PTCL patients have elevated JAK/STAT pathway activities. Golidocitinib (AZD4205) is an orally available, JAK1-specific inhibitor currently in late-stage clinical development. We previously reported encouraging efficacy data from an ongoing phase 1/2 multicenter global study (#NCT04105010, JACKPOT8). Here we presented safety and efficacy data and the biomarker analysis of the samples collected from this study. Method JACKPOT8 is a single arm, multicenter, multi-cohort global study. Patients with r/r PTCL were enrolled to receive golidocitinib at either 150 mg or 250 mg, once daily until disease progression or meeting pre-defined termination criteria. Antitumor efficacy was assessed according to Lugano criteria. All patients were requested to provide archived or freshly obtained tumor and plasma samples during the screening. Genome level mutation detection was carried out using next generation sequencing (NGS). JAK/STAT pathway activation was assessed according to pSTAT level. Result At the cut-off date on May 31, 2021, 51 patients with r/r PTCL (21 PTCL-NOS, 20 AITL, 4 ALCL ALK-, 4 NKTCL, 2 MEITL) had been enrolled into the study and treated with golidocitinib at 150 mg (n = 35) or 250 mg (n = 16). Median number of prior treatment regimens was 2; 10 patients had a prior stem cell transplant. Overall, golidocitinib showed an acceptable safety profile. Grade 3 or above adverse events occurring in 10% or more of patients included neutropenia (29.4%), thrombocytopenia (15.7%), and pneumonia (11.8%). The majority of AEs were reversible or clinically manageable with dose modifications. Of all 49 patients who completed at least one investigator-based tumor response assessment, 21 achieved tumor response (ORR 42.9%), including 11 complete responses (CRs, 22.4%). Tumor samples from 20 patients were determined suitable for pSTAT3 IHC analysis. A positive trend between clinical activity and high level of pSTAT3 was observed (Table 1). Targeted genomic sequencing was carried out in samples from 19 patients. No genetic mutations associated with JAK/STAT pathway (including JAK1/2/3, STAT3/5A/5B/6) were identified in the tested samples. Conclusion Golidocitinib demonstrated its promising potential as a novel targeted therapy for the treatment of PTCL. Clinical activity was observed across different subtypes. No apparent correlation between mutations in JAK/STAT pathway and clinical response was observed while baseline high pSTAT level seems to predict clinical benefit. Larger sample size is needed to confirm the findings. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Metalearning

An adaptive guidance system suitable for the terminal phase trajectory of a hypersonic strike weapon is optimized using reinforcement meta learning. The guidance system maps observations directly to commanded bank angle, angle of attack, and sideslip angle rates. Importantly, the observations are directly measurable from radar seeker outputs with minimal processing. The optimization framework implements a shaping reward that minimizes the line-of-sight rotation rate, with a terminal reward given if the agent satisfies path constraints and meets terminal accuracy and speed criteria. This paper shows that the guidance system can adapt to off-nominal flight conditions, including perturbation of aerodynamic coefficient parameters, actuator failure scenarios, sensor scale factor errors, and actuator lag, while satisfying heating rate, dynamic pressure, and load path constraints, as well as a minimum impact speed constraint. This paper demonstrates precision strike capability against a maneuvering ground target and the ability to divert to a new target, the latter being important to maximize strike effectiveness for a group of hypersonic strike weapons. Moreover, this paper demonstrates a threat evasion strategy against interceptors with limited midcourse correction capability, where the hypersonic strike weapon implements multiple diverts to alternate targets, with the last divert to the actual target.

First Massive Transfusion Protocol (MTP) Implementation Proposal in Egypt

Abstract Introduction/Objective Massive transfusion is often defined as transfusion of adults with 10 or more of packed red blood cells units in 24 hours period or transfusion of 4 or more units of packed red blood cells (PRBCs) in one hour when on-going need for more transfusion is foreseeable. Massive transfusion protocol in children is defined as transfusion of &amp;gt;40 mL/kg. Massive transfusion protocol (MTP) is a vital element of damage control resuscitation and provides a coordinated clinical response to hemorrhage in patients requiring massive blood transfusions after hospital admission. Hemorrhage is the most common cause of death within the first hour of arrival to the trauma center. Methods/Case Report This study aims to develop and establish the first massive protocol in Egypt to provide a consistent treatment for blood transfusion for trauma patients that will Reduce delay in ordering and administering blood products and Deliver a reasonable ratio of plasma to red blood cells. The protocol will be first implemented in a military hospital in Egypt. Developing the massive transfusion protocol will allow the practitioners to follow algorithm for rapid replacement of blood products using uncrossmatched packed red blood cells, Plasma, platelets and cryoprecipitate if necessary. Adopting the American Association of Blood Banks (AABB) and the American College of Surgeons (ACS) guidelines are the best practice around the world. The Protocol will be developed by multidisciplinary committee engagement, Blood bank, Diagnostic laboratory and Clinical care areas (OR, ER, ICU, OBS). Results (if a Case Study enter NA) The massive transfusion protocol ratio will be 3:2 (RBC: FFP) and one units of platelets for every 6 units of RBC. according to American College of Surgeons (ACS) guidelines the ratio should be 1:1 to 1:2 (RBC: FFP). The committee established defined criteria for MTP activation and termination. The delivery time of the blood products should be between 5-10 minutes. Administration of tranexamic acid to inhibit plasminogen activation and stabilize the clot. Conclusion The Developing and Implementing MTP in Egypt will provide reduction in mortality for trauma and uncontrolled hemorrhage patients. Once implemented, we will regularly evaluate the use and efficacy of the MTP to ensure positive patient outcomes., the MTP will be evaluated to ensure best practice.

Firefighter pre-frontal cortex oxygenation and hemodynamics during rapid heat stress

This study evaluated the impact of rapid heat stress on prefrontal cortex (PFC) oxygenation and hemodynamics. Previous work has demonstrated that heat stress affects cerebral oxygenation and hemodynamics. Fourteen male subjects performed a graded exercise test to a termination criterion (volitional maximum, core temperature = 39.5 °C, or a 2-hour time cap) with (GEAR) and without (NOGEAR) firefighting gear in a laboratory with an ambient temperature of 25–26 °C. Changes in oxyhemoglobin (O2Hb), total hemoglobin (tHb), and tissue oxygen saturation index (TSI %) were monitored in the left and right PFC using near-infrared spectroscopy (NIRS). Significant NIRS results were a plateau in the left-side O2Hb and tHb at 80 % of the time to termination (TTT) in NOGEAR, and 60 % of TTT in GEAR. These TTT points were when the subject's core temperature (Tc) was equal to 38 °C. Additionally, there was higher left-side PFC activation during GEAR, as indicated by a significant decrease in TSI % from start to end of exercise and double the reduction in TSI % per minute in PPE compared to NOGEAR. There were no significant differences during the NOGEAR session. These data suggest that a rapid heat stress scenario (GEAR) causes altered cerebral oxygenation and hemodynamic response in the left-side PFC. The left PFC could be working harder to prevent fatigue in GEAR. This could affect cognitive processes during or following exercise in the heat while wearing personal protective equipment. Our results also support previous research demonstrating that NIRS is a sensitive metric of fatigue.

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.

Thermal Electrical Tests for Battery Safety Standardization

Battery safety tests are defined by several international standards in different ways and with heterogenous termination and failure criteria. In this work, lithium-ion cells were examined regarding their behavior in the event of overcharging and also in the event of an external short circuit with varied parameters specified by standards. The voltage, current, and temperature curves were evaluated. In addition, the changes in the cells were analyzed using electrochemical impedance spectroscopy (EIS). It is shown that the cells exhibit reproducible behavior in a clamped state. Further, it could be determined that the position of the cell opening during an overcharge has an influence on the further behavior of the cell. EIS data showed that the cells have a significantly higher internal resistance after an overcharge. The short-circuit tests at different ambient temperatures indicated that the internal resistance of the cell decreases with increasing temperature. However, no reproducible effects in impedance spectra were present after the short-circuit test. This work illustrates that the choice of test parameters and termination criteria undoubtedly influence the test results and thus may change the classification of cells as either safe or unsafe. Thus, cells may be classified as safe regarding a certain standard but unsafe regarding another.

Area and power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy bacterial foraging algorithm

Area and power optimization have become the primary constraints in chip design. Existing area and power optimization approaches have poor optimization performance and high CPU time in logic circuits with multiple inputs. To solve this problems, we propose a multi-strategy bacterial foraging algorithm (MBFA) for multi-objective optimization, which includes nondominated add procedure, dual-population cooperative procedure, high-quality replication procedure, distributed migration procedure and the termination criterion. In addition, considering the characteristics of mixed polarity Reed–Muller (MPRM) logic circuits such as high dimension and large solution space domain, we propose an area and power optimization approach (APOA) based on XNOR/OR, which uses MBFA to search MPRM circuits with Pareto optimal solutions. Experimental results show that the APOA has better performance in optimizing MPRM circuits area and power.

Optimal consensus of a class of discrete-time linear multi-agent systems via value iteration with guaranteed admissibility

This paper investigates the optimal consensus problem for heterogeneous discrete-time(DT) linear multi-agent systems. The optimal consensus problem is formulated as finding a global Nash equilibrium solution subjected to the defined local performance index. A reinforcement learning(RL) value iteration(VI) algorithm is introduced to obtain the optimal policies in the sense of Nash equilibrium. To ensure the effectiveness of the VI algorithm, the admissibility of the iterative control policies for multi-agent systems is considered. With theoretical analysis, a new termination criterion is established to guarantee the admissibility of the iterative control policies. Furthermore, an online learning framework is designed with an actor-critic neural network(NN) to implement the VI algorithm. Finally, two simulation examples are presented respectively for leader–follower and leaderless multi-agent systems to verify the effectiveness of the proposed method.