Standard Algorithm Research Articles

Enhanced grey wolf optimization for maximum power point tracking in photovoltaic systems with hybrid battery-supercapacitor storage

ABSTRACT This paper proposes an enhanced Grey Wolf Optimization algorithm integrated with a stochastic Cauchy-Gaussian mutation to improve maximum power point tracking in standalone photovoltaic systems. The system incorporates a hybrid energy storage solution combining a lithium-ion battery and a supercapacitor to address energy fluctuations and ensure stable power management. The proposed method was compared with conventional and metaheuristic optimisation techniques, including Particle Swarm Optimization, Ant Colony Optimization, and the standard Grey Wolf Optimization algorithm. Simulation results demonstrate faster convergence, higher tracking efficiency, and improved stability under varying solar and load conditions. The enhanced approach significantly improves energy extraction and reduces tracking time compared to traditional methods. Additionally, the hybrid storage system ensures a balanced and stable operation between the battery and supercapacitor, extending the lifespan of the energy storage components. These findings underscore the algorithm’s reliability and effectiveness in optimising and enhancing system performance, making it suitable for real-world applications.

Постквантовая подпись Меркла на основе модифицированного алгоритма Лампорта

The development of a Merkle post-quantum signature scheme based on a modified Lamport one-time signature algorithm is presented. The Merkle signature algorithm and the Lamport one-time signature algorithm are described. There is also a review of the current literature on the subject of the Merkle signature algorithm. A modified algorithm for Lamport's one-time electronic digital signature is described. The algorithms for key generation, signature generation, and verification of a previously generated signature are described in detail. The paper presents a software implementation of an electronic digital signature system with a graphical interface based on the developed algorithm, which allows key generation, signature generation and verification. A flowchart is provided for each of the main modules of the program, and the graphical interface of the developed software for each module is also demonstrated. The results of testing the modified algorithm are presented and its performance is compared with the standard algorithm. The test results confirm that using the modified algorithm allows faster verification of messages, while the speed of key generation and signature does not increase in comparison with the standard algorithm. The modified algorithm speeds up verification regardless of the message length. The results of the performed research are a modified Lamport one-time signature algorithm, which provides a higher signature verification rate compared to the classical algorithm, and a software tool with a graphical interface for generating and verifying a post-quantum electronic digital signature.

Evaluating AES Security: Correlation Power Analysis Attack Implementation using the Switching Distance Power Model

Cryptographic circuits play a critical role in safeguarding confidential information and ensuring secure communication, contributing to the resilience of digital infrastructure under SDG 9 (Industry, Innovation, and Infrastructure). These circuits store encryption keys for the Advanced Encryption Standard (AES) algorithm, including AES-128, AES-192, and AES-256, which are widely used in applications such as online banking and secure messaging platforms. This paper examines the effectiveness of Correlation Power Analysis (CPA), a side-channel attack technique that exploits power consumption patterns in cryptographic circuits, to highlight the challenges of implementing secure encryption systems. The study illustrates the CPA attack procedure against AES implemented on the SASEBO-GII FPGA platform. Experimental results reveal that while the CPA attack based on the Hamming Weight (HW) power consumption model fails to extract the encryption key, the Switching Distance (SD) power consumption model successfully recovers the entire key with a 100% success rate using approximately 4000 power traces. These findings underscore the vulnerability of cryptographic circuits to advanced side-channel attacks and emphasize the need for robust countermeasures to ensure secure data protection, thereby advancing secure and sustainable digital environments under SDG 11 (Sustainable Cities and Communities).

Integrated neuronetwork modeling of EEG for diagnostic disorders of brain activity

The article discusses the structured multistage modeling of EEG by means of applied mathematics to customize the input parameter space for neural network prediction. Also, the approaches and models are analyzed for their accuracy in determining the relevant signal features and adaptability to real data. The activity of potentials during brain activity is a biological process that depends on many factors and hides a space of parameters, the search for which and their definition can open us up to a new perspective on the nature and activity of the human brain. A rational way of obtaining data on brain activity is a non-invasive electroencephalogram, which registers the potential difference on the electrodes relative to the base. For further processing of the received data, it is necessary to remove noise and artifacts from them. In this work, a stan-dard algorithm of frequency filtering and filtering from noise caused by the power grid is used. After processing the data, an overview of mathematical models is offered, which with a certain degree of accuracy try to simulate the behavior of the signal or the peak moments of certain features. Added to this is the use of the LSTM model to predict the further behavior of the signal with the preliminary introduction of chaos into the model due to the modified activation function (Gaussian noise) and the input modeling of the weights.

Children with autoimmune hepatitis receiving standard-of-care therapy demonstrate long-term obesity and linear growth delay

Background: Standard-of-care therapy in children with autoimmune hepatitis (AIH) includes induction with prednisone 1–2 mg/kg daily with gradual weaning of the dose. We aimed to test the hypothesis that children with AIH receiving standard-of-care treatment have altered growth trajectories. Methods: Children diagnosed with AIH between 1997 and 2023 at SickKids had serial growth measurements. Mixed effect models assessed the impact of time and daily steroid exposure on z-scores. Kaplan-Meier survival methods were used to estimate the cumulative incidence of new-onset growth impairments. A time-dependent Cox proportional hazards model was constructed to determine predictors for growth impairments. Results: Sixty-one children (66% females, median age at diagnosis 11.5 y) were included. BMIz showed a sharp increase, and HAZ declined significantly without returning to baseline. Each 1 mg/kg/d prednisone exposure increased BMIz gain in the first 6 months by 0.27 ([95% CI: 0.11, 0.42], p = 0.001), and decreased HAZ by −0.02 ([95% CI: −0.03, −0.01], p = 0.005). Children diagnosed before puberty exhibited a higher occurrence of excessive weight gain (72.2% vs. 49.3%; log-rank p < 0.01) and obesity (63% vs. 31.5%; log-rank p < 0.01) compared to those diagnosed during puberty. In a Cox proportional-hazards model, young age at diagnosis and daily prednisone dose >10 mg 6 months after diagnosis were predictors for linear growth delay. Conclusions: This study demonstrates that children with AIH receiving standard-of-care therapy demonstrate altered growth trajectories, long-term excess weight gain, obesity, and linear growth delay. Young age at diagnosis and >10 mg of daily prednisone at 6 months are predictors for linear growth delay. These data indicate the need to re-evaluate standard treatment algorithms for pediatric AIH in terms of steroid dosing and potential nonsteroid alternatives.

Pioneering the Security of EHRs Using an Immersive Blockchain Conceptual Framework

This study develops a conceptual framework to enhance the security and functionality of Electronic Health Records (EHRs) in response to advancing healthcare needs. Objectives include strengthening data protection against both traditional and quantum cyber threats, increasing system resilience, and improving user experience and operational efficiency. Methods/Analysis involve a novel combination of Advanced Encryption Standard (AES) and quantum cryptographic algorithms CRYSTALS-Dilithium and CRYSTALS-Kyber within a hybrid blockchain architecture to secure EHRs. Decentralized Autonomous Organizations (DAOs) are incorporated to decentralize control and reinforce security, while artificial intelligence and metaverse integration facilitate user engagement and streamline operations. Findings indicate that this hybrid blockchain model, enhanced with quantum-resistant cryptography and decentralized governance, significantly improves EHR security. AI and the metaverse contribute to user interaction and operational flow. Novelty/Improvement lies in integrating hybrid blockchain, quantum cryptography, AI, and the metaverse into a unified framework, effectively addressing current and future healthcare data management challenges. This multi-layered approach represents a significant advancement over existing systems by bolstering EHR security, user engagement, and operational capabilities. Doi: 10.28991/ESJ-2025-09-01-010 Full Text: PDF

Inferring traits of hyperuniformity from local structures via persistent homology.

Hyperuniformity refers to the suppression of density fluctuations at large scales. Typical for ordered systems, this property also emerges in several disordered physical and biological systems, where it is particularly relevant to understand mechanisms of pattern formation and to exploit peculiar attributes, e.g., interaction with light and transport phenomena. While hyperuniformity is a global property, ideally defined for infinitely extended systems, several disordered correlated systems have finite size. It has been shown in [Phys. Rev. Research 6, 023107 (2024)] that global hyperuniform characteristics systematically correlate with distributions of topological properties representative of local arrangements. In this work, building on this information, we explore and assess the inverse relationship between hyperuniformity and local structures in point patterns as described by persistent homology. Standard machine learning algorithms trained on persistence diagrams are shown to detect hyperuniformity of periodic point patterns with high accuracy. Therefore, we demonstrate that the information on patterns' local structures allows for characterizing whether finite size arrangements are analogous to those realized in hyperuniform patterns. Then, addressing more quantitative aspects, we show that parameters defining hyperuniformity globally can be reconstructed by comparing persistence diagrams of targeted patterns with reference ones. We also explore the generation of patterns entailing given topological properties. The results of this study pave the way for advanced analysis of hyperuniform patterns including local information, and introduce basic concepts for their inverse design.

Association between standardized management algorithm and outcomes of patients undergoing high-risk percutaneous coronary interventions included in the IMPELLA-PL registry.

Impella CP is a percutaneous left ventricular assist device used in selected patients undergoing high‑risk percutaneous coronary interventions (HR‑PCIs). To improve outcomes of Impella‑supported HR‑PCI, institutional Impella programs have been developed. We evaluated the association between the use of a standardized periprocedural management algorithm and outcomes of patients undergoing HR‑PCI included in the national IMPELLA‑PL registry. Consecutive patients undergoing HR‑PCI supported with Impella CP (n = 253), enrolled in the IMPELLA‑PL registry between January 2014 and December 2021, were retrospectively divided into those fulfilling (n = 77) and not fulfilling (n = 176) the criteria of a standardized management algorithm, as proposed in the Roadmap Towards an Institutional Impella Program for HR‑PCI (ROAD TIP). Implementation of the standardized management algorithm allowed for selection of patients at a higher baseline risk, manifested by higher prevalence of acute coronary syndrome (P = 0.001), higher EuroScore (P = 0.02), and greater coronary artery disease complexity (P = 0.003). It also allowed for performing more complex PCI procedures, including a higher proportion of left main PCIs (P = 0.005), bifurcation PCIs (P <0.001), and use of calcium modification techniques (P = 0.02), more frequent Impella implantation before PCI (P = 0.002), and a higher proportion of ultrasound‑guided punctures (P <0.001). Despite higher baseline risk and greater procedural complexity, 12‑month outcomes of the patients treated according to the ROAD TIP algorithm were comparable to those of the individuals not fulfilling the algorithm criteria, who had a more favorable risk profile and underwent less complex procedures. In low‑volume centers, 12‑month mortality was lower in the standardized management group (P = 0.047), whereas in high‑volume centers, it was comparable between the groups. Implementation of a dedicated management algorithm might improve outcomes of Impella‑assisted HR‑PCI, especially in low‑volume centers.

Community-based model for management and follow-up by non-physician healthcare workers to improve awareness, treatment, and control of hypertension: The COTRACO study protocol.

Cardiovascular diseases are the leading cause of death and morbidity worldwide, with a significantly higher burden in low- and middle-income countries. Hypertension, a major risk factor for cardiovascular morbidity and mortality, remains under-diagnosed and poorly controlled, especially in regions such as Latin America. The HOPE-4 study demonstrated that the involvement of non-physician health workers (NPHWs), the use of standardized treatment algorithms, the provision of free antihypertensive drugs and home follow-up can significantly improve hypertension control and reduce cardiovascular risk, as demonstrated in Colombia and Malaysia. On this basis, the COTRACO study aims to address the barriers to hypertension treatment in low- and middle-income countries by implementing a similar standardized treatment approach delivered by non-specialist health workers. The COTRACO study is a quasi-experimental, parallel-group, non-randomized, before-and-after study. A community-based model will be implemented in 600 patients in Colombia and the Dominican Republic, involving NPHWs to: 1) apply standardized treatment algorithms, 2) promote adherence to healthy lifestyles, and 3) provide standardized pharmacological treatment. Propensity Score Matching will be used to select 300 patients in Chile and 1200 in Spain for comparison with standard care in these populations. The primary outcome at 12 months of follow-up is the percentage of patients achieving controlled hypertension (defined as systolic BP<140mmHg and diastolic BP<90mmHg, or<130mmHg, and diastolic BP<80mmHg for diabetic patients), ensuring it is not inferior to that achieved in the control countries. Secondary outcomes include changes in BP levels, cholesterol levels, BMI, handgrip strength, waist-to-hip ratio, smoking status, Interheart risk score, diet, and physical activity at 6 and 12 months. If this model demonstrates superior outcomes compared to usual care, it is recommended that health authorities in low- and middle-income regions adopt and implement this approach. Using non-medical health professionals, standardized treatment algorithms and free access to antihypertensive medications, these regions can significantly improve awareness, diagnosis and management of hypertension. This strategy has the potential to reduce cardiovascular morbidity and mortality, thereby improving overall public health outcomes.

Pulse engineering via projection of response functions

We present an iterative optimal control method of quantum systems, aimed at an implementation of a desired operation with optimal fidelity. The update step of the method is based on the linear response of the fidelity to the control operators, and its projection onto the mode functions of the corresponding operator. Our method extends methods such as gradient-ascent pulse engineering (GRAPE) and variational quantum algorithms, by determining the fidelity gradient in a hyperparameter-free manner, and using it for a multiparameter update, capitalizing on the multimode overlap of the perturbation and the mode functions. This directly reduces the number of dynamical trajectories that need to be evaluated in order to update a set of parameters. We demonstrate this approach, and compare it to the standard GRAPE algorithm, for the example of a quantum gate on two qubits, demonstrating a clear improvement in convergence and optimal fidelity of the generated protocol. Published by the American Physical Society 2025

Statistics of Smoke Sphericity and Optical Properties Using Spaceborne Lidar Measurements

Smoke particles from biomass burning events are typically assumed to be spherical despite previous observations of non-spherical smoke. As such, large uncertainties exist in some physical and optical parameters used in lidar aerosol retrievals, including depolarization and lidar ratio of non-spherical smoke aerosols. In this analysis, using NASA’s Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data during the biomass burning season over Africa from 2015 to 2017, we studied the frequency and distribution of non-spherical smoke particles to compare with findings of smoke particle non-sphericity from the Cloud-Aerosol Transport System (CATS) lidar. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non-spherical smoke particles, which might otherwise be misclassified as desert dust, polluted dust, or dusty marine by the CALIOP standard aerosol typing algorithm. Then, the relationships between smoke particle sphericity, lidar ratio, and relative humidity are analyzed for CATS and CALIOP observations over Africa. Approximately 18% of smoke layers observed by CALIOP over Africa are non-spherical (depolarization ratio &gt; 0.075) and agree with spatial distributions of non-spherical smoke found in CATS observations. A dependance of lidar ratio on relative humidity was found for layers of spherical smoke over Africa in both CATS and CALIOP data; however, no such dependance was evident for the depolarization ratio and layer relative humidity. While the supplemental smoke aerosol typing algorithm presented in this analysis was targeted only for specific biomass burning regions during biomass burning seasons and is not meant for global operational use, it presents one potential method for improved backscatter lidar aerosol typing. These results suggest that a dynamic lidar ratio, based on layer-relative humidity for spherical smoke, could be used to reduce uncertainties in smoke aerosol extinction retrievals for future backscatter lidars.

Application of an Externally Developed Algorithm to Identify Research Cases and Controls from Electronic Health Record Data: Failures and Successes.

The use of Electronic Health Records (EHRs) in research demands robust, interoperable systems. By linking biorepositories to EHR algorithms, researchers can efficiently identify cases and controls for large observational studies (e.g., Genome-Wide Association Studies (GWAS)). This is critical for ensuring efficient and cost-effective research. However, the lack of standardized metadata and algorithms across different EHRs complicates their sharing and application. Our study presents an example of a successful implementation and validation process. To implement and validate a rule-based algorithm from a tertiary medical center in Tennessee to classify cases and controls from a research study on rotator cuff tear nested within a tertiary medical center in North Texas and to assess the algorithm's performance. We applied a phenotypic algorithm (designed and validated in a tertiary medical center in Tennessee) using EHR data from 492 patients enrolled in case-control study recruited from a tertiary medical center in North Texas. The algorithm leveraged ICD (International Classification of Diseases) and CPT (Current Procedural Terminology) codes to identify case and control status for degenerative rotator cuff tears. A manual review was conducted to compare the algorithm's classification with a previously recorded gold standard documented by clinical researchers. Initially the algorithm identified 398 (80.9%) patients correctly as cases or controls. After fine-tunning and corrections of errors in our gold standard dataset, we calculated a sensitivity of 0.94 and specificity of 0.76. The implementation of the algorithm presented challenges due to the variability in coding practices between medical centers. To enhance performance, we refined the algorithm's data dictionary by incorporating additional codes. The process highlighted the need for meticulous code verification and standardization in multi-center studies. Sharing case-control algorithms boosts EHR research. Our rule-based algorithm improved multi-site patient identification and revealed 12 data entry errors, helping validate our results.

A Single Institution's Surgical Care Model for Pediatric Burns With ≤10% Body Surface Area Involvement.

Small burn injuries are extremely prevalent in the pediatric population and continue to pose a challenge for clinicians. Despite their high incidence, a standardized algorithm for treating small burns does not currently exist, and care is often guided by clinical judgement and resource availability. The aim of this study was to explore the utility of a two-stage grafting technique, involving allograft and autograft, for treating small burns (≤10% total body surface area) in pediatric patients. A retrospective review of patients aged 0-18 years who had a small burn and underwent a two-stage grafting procedure between September 1, 2018 and September 1, 2022 was conducted. One hundred and seventy-five patients with 220 wounds met the inclusion criteria for this study. The mean time from presentation to allograft surgery was 11.4 days (SD 5.2) followed by autograft surgery approximately one week later. Most patients were discharged within 24 hours following allograft surgery (87.4%) and autograft surgery (81.1%). Mean autograft take was 97.7% (SD 11.8) with only four patients experiencing significant graft loss requiring subsequent re-grafting. These positive outcomes demonstrate that the two-stage technique can be successfully utilized for treating smaller pediatric burns. Moreover, these findings help to address the significant knowledge gap regarding the optimal approach to treating small burn wounds. Further research is warranted to learn more about aesthetic outcomes following two-stage grafting and determine how it compares to other techniques for treating small burns.

A Reservoir Dam Monitoring Technology Integrating Improved ABC Algorithm and SVM Algorithm

A reservoir dam is a water conservancy project with large investment and high social and economic benefits, which plays an irreplaceable role in flood control, power generation, water storage, and urban water supply. There is a risk of accidents in the process of reservoir dams, so dam monitoring is an important means to achieve the safe operation of reservoirs. In this paper, taking advantage of the high-dimensional and nonlinear characteristics of dam monitoring data samples, the fusion-improved ABC (artificial bee colony) algorithm is introduced, and the SVM (support vector machine) algorithm is used to optimize the penalty factor and kernel function parameters. The test results of the ABC and SVM algorithm are relatively stable, with small fluctuation amplitude, which can continuously monitor water level, pore water pressure, dam deformation, temperature, humidity, vibration, and other indicators is less than 10%, which is significantly lower than the standard ABC algorithm, the standard ANN algorithm, and the standard SVM algorithm. The independence and characteristics of the ABC–SVM algorithm are significantly higher, and the correlation is 0.03, the RMS (root mean square) is 0.2334, which is lower than that of the standard ABC algorithm of 0.09, and the standard ANN algorithm of 0.8. The stability of the results and performance stability are analyzed, which is greater than 90%. The ABC and SVM is used to predict the displacement and deformation law of the reservoir dam.

The opportunities and effectiveness of the use of autologous platelet-rich plasma in plastic surgery, anti-age medicine and dermatology

Today, the use of autologous platelet-rich plasma expands the horizons of its use in medicine. However, to uncover the key factors of plasma effectiveness, more data is required, describing in detail the method of plasma preparation, the amount of material introduced, as well as data on a comparative analysis of various methods of its administration. The purpose of this review was to search for data on the use of autologous plasma in the fields of Anti-Age medicine, plastic surgery and dermatology with a description of the methodology, as well as local and general effects. Publications of foreign and domestic authors in the Scopus and Pubmed databases were analyzed. When searching using the keywords: platelet rich plasma, safety, effects, dermatology, cosmetology, surgery, melasma, acne, 570 publications were found. Taking into account the depth of the search (the last 5 years), the language of publications (Russian, English), 32 full-text articles were included in the analysis. The presence of a large number of studies demonstrates the high interest of scientists from all over the world in studying the properties and effectiveness of plasma. Positive results of using plasma were presented in the field of plastic surgery, cosmetology, and dermatology. However, the lack of a detailed description of the methodology of the procedure, and moreover, a unified standard algorithm for the procedure and criteria for admission to receiving plasma, creates difficulty in comparability of results, both positive and negative. Therefore, additional larger studies are required to help answer these unresolved questions.

Hybrid Probabilistic Road Map Path Planning for Maritime Autonomous Surface Ships Based on Historical AIS Information and Improved DP Compression

A hybrid probabilistic road map (PRM) path planning algorithm based on historical automatic identification system (AIS) information and Douglas–Peucker (DP) compression is proposed to address the issues of low path quality and the need for extensive sampling in the traditional PRM algorithm. This innovative approach significantly reduces the number of required samples and decreases path planning time. The process begins with the collection of historical AIS data from the autonomous vessel’s navigation area, followed by comprehensive data-cleaning procedures to eliminate invalid and incomplete records. Subsequently, an enhanced DP compression algorithm is employed to streamline the cleaned AIS data, minimizing waypoint data while retaining essential trajectory characteristics. Intersection points among various vessel trajectories are then calculated, and these points, along with the compressed AIS data, form the foundational dataset for path searching. Building upon the traditional PRM framework, the proposed hybrid PRM algorithm integrates the B-spline algorithm to smooth and optimize the generated paths. Comparative experiments conducted against the standard PRM algorithm, A*, and Dijkstra algorithms demonstrate that the hybrid PRM approach not only reduces planning time but also achieves superior path smoothness. These improvements enhance both the efficiency and accuracy of path planning for maritime autonomous surface ships (MASS), marking a significant advancement in autonomous maritime navigation.

Application of Advanced Encryption Standard (AES) Algorithm in E-Commerce Login System for User Data Security

E-commerce becomes an electronic media that uses a login system used by users. User data in the form of usernames and passwords is vulnerable to hacking. One technique to improve user security is the implementation of AES algorithms on login systems in E-Commerce applications. The purpose of this study is to apply the AES algorithm in the login system of e-commerce websites and analyze the improvement of information security for users after the implementation is carried out. The research method used is an experiment with the application of the use of the AES algorithm before and after. Therefore, the application of the AES algorithm on the login system of e-commerce websites can be used as a solution to improve user data security. Testing using Wireshark and Burpsuite tools. The results obtained are that AES successfully secures the username and password on the e-commerce login system .

Real-time control of radiofrequency ablation using three-dimensional ultrasound echo decorrelation imaging in normal and diseasedex vivohuman liver.

Ultrasound echo decorrelation imaging can successfully monitor and control thermal ablation of animal liver and tumor tissue ex vivo and in vivo. However, normal and diseased human liver has substantially different physical properties that affect echo decorrelation. Here, effects of human liver tissue condition on ablation guidance by three-dimensional echo decorrelation imaging are elucidated in experiments testing closed-loop control of radiofrequency ablation (RFA) in normal and diseased human liver tissue ex vivo. &#xD;&#xD;Approach: Samples of normal, steatotic, and cirrhotic human liver tissue underwent radiofrequency ablation (RFA), targeting a 20 mm-diameter spherical ablation zone. For each tissue condition, RFA was controlled by echo decorrelation in N > 14 trials, automatically ceasing if average cumulative decorrelation within the targeted ablation zone surpassed a predetermined threshold (successfully controlled trials), or otherwise completing a standard ablation cycle of the RFA generator (unsuccessfully controlled). For comparison, N = 14 RFA trials for each tissue condition followed the RFA generator's standard algorithm without echo decorrelation feedback (uncontrolled). Receiver operating characteristic (ROC) and precision-recall curve analyses compared 3D echo decorrelation maps to segmented ablation zones. To assess effects of closed-loop control and liver condition on treatment reliability, ablation volumes, rates, and Dice coefficients for measured vs. targeted ablation zones were statistically compared among control conditions and liver types. &#xD;&#xD;Results: ROC curves showed effective prediction of local ablation by echo decorrelation across all liver types and control conditions (0.876 ≤ AUROC ≤ 0.953). Successful control was significantly more frequent, ablated volumes were generally larger, and optimal echo decorrelation thresholds were smaller for normal compared to diseased liver. &#xD;&#xD;Significance: This study validates three-dimensional echo decorrelation imaging for monitoring and control of RFA in healthy and diseased human liver while elucidating the dependence of radiofrequency ablation and echo decorrelation outcomes on liver condition and resulting implications for clinical applications.

Predicting conversion to psychosis using machine learning: response to Cannon.

We previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) for psychosis with up to 90% accuracy using the North American Prodrome Longitudinal Study-3 (NAPLS-3) dataset. A definitive test of our predictive model that was trained on the NAPLS-3 data, however, requires further support through implementation in an independent dataset. In this report we tested for model generalization using the previous iteration of NAPLS-3, the NAPLS-2, using the identical machine learning algorithms employed in our previous study. Standard machine learning algorithms were trained to predict conversion to psychosis in clinical high risk individuals on the NAPLS-3 dataset and tested on the NAPLS-2 dataset. NAPLS-2 and -3 individuals significantly differed on most features used in machine learning models. All models performed above chance, with Naive Bayes and random forest methods showing the best overall performance. Importantly, however, overall performance did not match those previously observed when using only NAPLS-3 data. The results of this study suggest that a machine learning model trained to predict conversion to psychosis on one dataset can be used to train an independent dataset. Performance on the test set was not in the range necessary for clinical application, however. Possible reasons that limited performance are discussed.

An Energy-efficient Parallel ASIC Implementation of Advanced Encryption Standard (AES) Algorithm Robust against Side-channel Attacks

Encryption becomes more crucial than ever in an increasingly interconnected world. Advanced Encryption Standard (AES) is still considered secure after more than 20 years thanks to its mathematical properties. However, side-channel attacks (SCA) threaten improper AES implementations. In this paper, different AES implementations are introduced, and their resistances against power SCA, namely Correlation Power Analysis (CPA) attack, are shown. For energy efficiency, the increase in power consumption due to the extras added for countering SCA was minimized by register-level organizations and process-related optimizations. Different AES implementations were constructed and processed through Cadence ASIC flow (TSMC 65 nm LP technology). SCA resistance was evaluated using the ChipWhisperer platform operating on realistic power consumption values obtained after RTL-to-GDSII flow. The results demonstrate that pipelining and unrolling the AES rounds increase the SCA resistance at the expense of a minimal reduction in energy efficiency. The proposed implementations are suitable for use with different side-channel attack countermeasures.