Reduction In Runtime Research Articles

Design and simulation of hybrid renewable energy system integration of micro grids using Simulink

This research explores the novel design and simulation of a hybrid renewable energy system integrating photovoltaic (PV) panels, wind turbines, and a diesel generator backup to address the energy demands of an independent hostel in Nottingham, United Kingdom. The system prioritizes minimizing long-term costs and emissions while maintaining energy reliability. A detailed load profile was developed to optimize battery performance, reduce reliance on diesel, and extend battery lifespan, thereby lowering operational costs. Voltage source control and load profile modeling were implemented and evaluated using MATLAB R2023b. Simulation outcomes demonstrated a significant reduction in the diesel generator's runtime, leading to lower emissions. Additionally, the integrated energy management system ensures safe battery operations, enhancing system efficiency, sustainability, and overall performance. This research underscores the potential of optimizing renewable energy integration within microgrids using Simulink.

A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance

To address the limitations of the original algorithm, several optimization techniques are proposed. This article presents an original RRT*-Connect algorithm for the planning of obstacle avoidance paths on robotic arms. These strategies include implementing a target biasing algorithm, using elliptic space sampling to enhance the sampling process, the revision of the cost function to better guide path planning, and implementing an artificial potential field and gradient descent strategy to design adaptive step sizes. Furthermore, the use of segmented Bézier curves facilitates the generation of a more fluid trajectory when constructing the final path. The effectiveness of these augmentation strategies is corroborated by both simulations and experimental verification on a robotic arm. The simulations showed a 19.39% reduction in average run time and a 5% reduction in average path length compared to the existing RRT*-Connect algorithm. Therefore, The enhanced algorithm meets the requirement for optimal obstacle avoidance path planning by consistently finding the shortest path while avoiding obstacles.

Predicting electronic screening for fast Koopmans spectral functional calculations

Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that—with minimal training—can predict these screening parameters directly from orbital densities calculated at the DFT level. We show in two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run times with minimal loss of accuracy, it will enable the application of Koopmans spectral functionals to classes of problems that previously would have been prohibitively expensive, such as the prediction of temperature-dependent spectral properties. More broadly, this work demonstrates that measuring violations of piecewise linearity (i.e., curvature in total energies with respect to occupancies) can be done efficiently by combining frozen-orbital approximations and machine learning.

Clinical Implementation of a High-Throughput Automated Comprehensive Genomic Profiling Test: TruSight Oncology 500 HT.

The adoption of comprehensive genomic profiling in oncology has rapidly increased the demand for standardized tumor sample processing in diagnostic laboratories. Automation of DNA and RNA library preparation workflows offers the possibility to scale-up and standardize sample processing. We report on the clinical implementation of the automated TruSight Oncology 500 High-Throughput library preparation workflow from formalin-fixed, paraffin-embedded tumor samples using the Biomek i7 hybrid Workstation. Using the same input amount, the automated workflow was validated against manual library preparation. Quality control metrics (total and mapped reads, median insert size, and median exon coverage) and the detection of tumor mutational burden, a complex biomarker, were concordant between the manual and automated workflows. The automated workflow was implemented on a total of 2997 pan-cancer clinical samples to detect genomic variants and complex biomarkers. Workflow automation resulted in a 4-fold reduction in hands-on time and a 1.7-fold reduction in total runtime compared with manual library preparation (6 hours vs. 23 hours; 24 hours vs. 42.5 hours, respectively) for a 48 DNA+48 RNA sample batch. The automated workflow required one technician versus three technicians to manually prepare the same number of libraries. This study shows that implementation of the automated TruSight Oncology 500 High-Throughput workflow significantly reduced hands-on time and processing time per sample compared with manual library preparation.

Biomechanics of physical exercise: A data-driven approach to enhancing mental health in college students

With growing awareness of the importance of mental health, the biomechanical mechanisms of physical exercise have gained attention as an effective intervention for improving mental well-being, particularly among college students. Physical activity not only enhances physical fitness and disease resistance but also contributes to cognitive and emotional health through specific biomechanical pathways. This study explores the interplay between exercise biomechanics and mental health by investigating the psychological challenges faced by college students. Utilizing advanced data analysis and correlation techniques, we refine the Apriori algorithm through a novel database partitioning strategy, achieving a 23.68% improvement in accuracy and a 10.17% reduction in runtime compared to the baseline. Additionally, this study examines how biomechanical factors, such as joint movement and muscle activity, influence brain function and mental health outcomes. The findings offer innovative perspectives for integrating biomechanical insights into mental health education and exercise-based interventions for college students.

A uniformed adaptive early termination model through probabilistic feature to speed up quantization-based search

The quantization-based approaches are the effective techniques for addressing the problem of approximate nearest neighbor search. However, most of these methods typically employ a fixed number of nProbes as the termination condition for each query. This practice can lead to unnecessary query processing time because many queries can be terminated early, before completing the greedy search in the nearest nProbes subspace. To address this issue, we propose an early termination method to speed up the search processing of quantization-based methods. At the heart of our proposal is to exploit an effective probabilistic feature generated by locality sensitive hashing to learn a uniformed prediction model, allowing the termination condition for each query to be predicted adaptively. Experimental results over various real-world high dimensional datasets show that our proposal outperforms competitors in both query accuracy and efficiency, achieving an average running time reduction of up to around 7x.

Enhancing DF-GAN for Text-to-Image Synthesis: Improved Text-Encoding and Network Structure

Abstract. Text-to-image synthesis is one of the most challenging and popular tasks in machine learning, with many models developed to improve performance in this area. Deep Fusion Generative Adversarial Networks (DF-GAN) is a straightforward but efficient model for image generation, but it has three key limitations. First, it only supports sentence-level textual descriptions, restricting its ability to extract fine-grained features from word-level inputs. Second, the structure of the residual layers and blocks, along with key parameters, could be optimized for better performance. Third, existing evaluation metrics, such as Frchet Inception Distance (FID), tend to place undue emphasis on irrelevant features like background, which is problematic when the focus is on generating specific objects. To address these issues, we introduced a new text encoder that enhances the model having capacity to process word-level descriptions, leading to more precise and text-consistent image generation. Additionally, we optimized key parameters and redesigned the convolutional and residual network structures, resulting in higher-quality images and reduced running time. Lastly, we proposed a new evaluation theory tailored to assess the quality of specific objects within the generated images. These improvements make the enhanced DF-GAN more effective in generating high-quality, text-aligned images efficiently.

Unsupervised contaminated user profile identification against shilling attack in recommender system

A recommender system is susceptible to manipulation through the injection of carefully crafted profiles. Some recent profile identification methods only perform well in specific attack scenarios. A general attack detection method is usually complicated or requires label samples. Such methods are prone to overtraining easily, and the process of annotation incurs high expenses. This study proposes an unsupervised divide-and-conquer method aiming to identify attack profiles, utilizing a specifically designed model for each kind of shilling attack. Initially, our method categorizes the profile set into two attack types, namely Standard and Obfuscated Behavior Attacks. Subsequently, profiles are separated into clusters within the extracted feature space based on the identified attack type. The selection of attack profiles is then determined through target item analysis within the suspected cluster. Notably, our method offers the advantage of requiring no prior knowledge or annotation. Furthermore, the precision is heightened as the identification method is designed to a specific attack type, employing a less complicated model. The outstanding performance of our model, validated through experimental results on MovieLens-100K and Netflix under various attack settings, demonstrates superior accuracy and reduced running time compared to current detection methods in identifying Standard and Obfuscated Behavior Attacks.

A New Heuristic Hybrid Model for Cancer-Based P-Glycoprotein Drug Classification: Automated Prediction

As the population improves in real-time applications, drug identification becomes an increasingly difficult task. Various biotechnological applications based on in vitro fertilization can benefit from better drug identification. For many nations, agriculture is the backbone of cancer medication development; increasing crop yields is the key to better drug identification in agricultural contexts. Computer vision applications suffer as a result of the efficient degradation of production quality and quantity caused by p-glycoprotein drugs used in cancer treatment. The identification of cancer-based p-glycoprotein drugs is an aggressive concept in the present day, due to the various symptoms present in cancer. Various authors use soft computing techniques and machine learning approaches to predict these drugs. For the purpose of automatically identifying based p-glycoprotein drugs in cancers, this paper proposes a Hybrid Novel Heuristic Model (HNHM) that combines Self-Organizing Maps (SOMs) with Convolution neural networks (CNNs). The primary challenge in identifying cancer drugs based on p-glycoprotein is feature extraction; hence, dimensionality reduction is crucial in filtering out noise from the cancer sub-cancer drug dataset and separating the subset of the cancer drug dataset affected by the p-glycoprotein drug using support vector machines (SOM). It is a promising approach to automatically identify based p-glycoprotein drugs in cancers by exploring protein features in cancer and predicting which patches are affected by virus-bacteria. CNN is used to match cancer sub-cancer drug data sets. In this study, we use sub-Cancer drug data sets to develop a hybrid heuristic approach to identify based p-glycoprotein drugs found in cancers. Clinical trials of the suggested hybrid model were conducted using sub-cancer drug data sets derived from peach cancers and the publicly available Cancer's drug datasets housed in the UCI repository. With less training required to explore based on p-glycoprotein drugs from sub-Cancer drug datasets, the suggested hybrid model achieves an accuracy of nearly 99.93% when tested on different testing datasets for sub cancer drugs. When compared to other state-of-the-art methods, this one significantly reduces running time while providing efficient performance in a number of p-glycoprotein drug prediction metrics, including recall, sensitivity, and specificity

QuAC: Quick Attribute-Centric Type Inference for Python

Python’s dynamic typing facilitates rapid prototyping and underlies its popularity in many domains. However, dynamic typing reduces the power of many static checking and bug-finding tools. Python type annotations can make these tools more useful. Type inference tools aim to reduce developers’ burden of adding them. However, existing type inference tools struggle to support dynamic features, infer correct types (especially container type parameters and non-builtin types), and run in reasonable time. Inspired by Python’s duck typing, where the attributes accessed on Python expressions characterize their implicit interfaces, we propose QuAC (Quick Attribute-Centric Type Inference for Python). At its core, QuAC collects attribute sets for Python expressions and leverages information retrieval techniques to predict classes from these attribute sets. It also recursively predicts container type parameters. We evaluate QuAC’s performance on popular Python projects. Compared to state-of-the-art non-LLM baselines, QuAC predicts types with high accuracy complementary to those predicted by the baselines while not sacrificing coverage. It also demonstrates clear advantages in predicting container type parameters and non-builtin types and reduces run times. Furthermore, QuAC is nearly two orders of magnitude faster than an LLM-based method while covering nearly half of its errorless non-trivial type predictions. It is also significantly more consistent at predicting container type parameters and non-builtin types than the LLM-based method, regardless of whether the project has ground-truth type annotations.

Dynamic performance analysis and optimization research on solar dual-stage evaporation multigeneration system

A novel solar dual-stage evaporation multigeneration system is proposed, with analysis on the impact of the parabolic trough collector area (APTC) and the volume of the heat storage tank (VHST) on performance under dynamic conditions. The particle swarm optimization- genetic algorithm and the minimum path method optimized the system’s overall performance. Results show that reducing APTC and increasing VHST can enhance energy performance within a certain range, but beyond specific thresholds, changes in APTC and VHST no longer affect it. As APTC and VHST increase, economic performance initially improves but then declines. The particle swarm optimization- genetic algorithm increased optimization efficiency and avoided local optima. When using the minimum path method, the dimensionless cumulative error was 0.63, balancing thermal and economic performance. Using toluene as the working fluid, the optimized system achieved thermal and exergy efficiencies of 56.06 % and 9.15 %, respectively, with a net present value of 58.12 M$, a payback period of 5.23 years, and an equivalent CO2 reduction of 24.8 kt, outperforming the solar organic Rankine cycle system in energy efficiency, economic performance, and carbon reduction potential. The solar dual-stage evaporation multigeneration system achieved the best performance with toluene as the working fluid, while heptane provided longer running time and greater carbon reduction potential. At low rated power generation, performance differences between toluene and heptane were minimal, while as the rated power generation increased, toluene showed significant advantages in thermal and economic performance.

Systematic Screening of Autosomal Dominant Tubulointerstitial Kidney Disease- MUC1 27dupC Pathogenic Variant through Exome Sequencing.

The MUC1 gene is associated with autosomal dominant tubulointerstitial kidney disease (ADTKD), leading to CKD. Current methods of sequencing, like exome sequencing, rarely detect MUC1 pathogenic variants because of the variable number of tandem repeats (VNTR) in MUC1 exon2. We demonstrated that combining fast read filtering with a sensitive VNTR genotyping strategy enables systematic screening of 27dupC pathogenic MUC1 variant from exome data. We initially validated our bioinformatics pipeline in a proof-of-concept cohort incorporating exome data from 33 participants with a known MUC1 pathogenic variant identified by Snapshot PCR and confirmed by 54 MUC1-negative individuals for negative control. We then retrospectively analyzed exome sequencing data from January 2019 to October 2023 from 3512 adult participants with nephropathy of unknown origin. Finally, we prospectively validated our pipeline in 825 additional participants enrolled from November 2023. SharkVNTyper accurately identified MUC1 variants in 32 out of 33 participants and excluded its presence in all the 54 negative controls in the proof-of-concept cohort (sensitivity of 97%, specificity of 100%). Integration of Shark tool with VNTyper significantly reduced running time from 6-12 hours to 5-10 minutes per sample, allowing both retrospective and prospective analysis. In the retrospective cohort, SharkVNTyper identified 23 additional positive cases that were not suspected clinically and had been missed in the initial exome analysis; 18 of these cases were confirmed as carrying the MUC1 27dupC mutation by low-throughput Snapshot PCR. In the prospective cohort of 825 CKD cases, systematic screening discovered 13 positive cases, with 12 confirmed by PCR. Overall, of 63 participants (1.4% of 4653) with molecularly confirmed ADTKD-MUC1, comprehensive diagnoses and descriptions of the disease were available for 24 participants. Median age of kidney failure was 50 years, 38% exhibited bilateral multiple kidney cysts, 8% had early-onset gout, and 58% had arterial hypertension. SharkVNTyper enables the analysis of highly repeated regions, such as the MUC1 VNTR, and facilitates the systematic screening of ADTKD-MUC1 from exome data, fostering 27dupC variation identification.

A design of computational fuzzy set-based semantics for extracting linguistic summaries

Linguistic summarization is to extract a set of summary sentences in the form of natural language, so-called linguistic summaries, from numeric data. The extracted linguistic summaries should be compact and diverse, and have a validity measure greater than a given threshold, so genetic algorithms are applied to extract such linguistic summaries. Besides, the interpretability of linguistic summary content is considered in recent studies in such a way that enlarged hedge algebras are applied to generate multi-semantic structures for linguistic words of linguistic variables ensuring the interpretability of the content of the linguistic summaries. However, the membership function of computational fuzzy-set-based semantics of linguistic words is usually in the shape of trapezoid. In this paper, a membership function of the form S function is applied to improve the quality of extracted linguistic summaries. Besides, the applied algorithm is parallelized to reduce running time. The experimental results with the creep dataset have demonstrated the effectiveness of the proposed method

Quadratic horizontally elastic not-first/not-last filtering algorithm for cumulative constraint

The not-first/not-last rule is a pendant of the edge finding rule, generally embedded in the cumulative constraint during constraint-based scheduling. It is combined with other filtering rules for more pruning of the tree search. In this paper, the Profile data structure in which tasks are scheduled in a horizontally elastic way is used to strengthen the classic not-first/not-last rule. Potential not-first task intervals are selected using criteria (specified later in the paper), and the Profile data structure is applied to selected task intervals. We prove that this new rule subsumes the classic not-first rule. A quadratic filtering algorithm is proposed for the new rule, thus improving the complexity of the horizontally elastic not-first/not-last algorithm from O(n3) to O(n2). The fixed part of external tasks that overlap with the selected task intervals is considered during the computation of the earliest completion time of task intervals. This improvement increases the filtering power of the algorithm while remaining quadratic. Experimental results, on a well-known suite of benchmark instances of Resource-Constrained Project Scheduling Problems (RCPSPs), show that the propounded algorithms are competitive with the state-of-the-art not-first algorithms in terms of tree search and running time reduction.

Applied static analysis and specialization of cross-core syscalls for multi-core AUTOSAR OS

The development of static real-time control systems often follows a closed-world assumption, allowing extensive RTOS-aware whole-program optimization. For single-core systems, previous work could show the high potential of control-flow-aware static system-call tailoring. However, due to an exponential state explosion in the analysis phase, it cannot simply be extended to a multi-core setting, since the core’s relative timing to each other is undetermined. In this work, we present MultiSSE, a multi-core capable and RTOS-aware static whole-system optimization. First, MultiSSE analyzes the system by determining the relative positions of multiple cores only when necessary. For that, it exploits structural control flow and optionally timing information to handle each core separately as much as possible. Based on the analysis result, a synthesis applies lock elision and system-call optimization to generate specialized multi-core real-time systems for AUTOSAR OS. To enable a static prediction of the run-time reduction, we additionally provide cost models for the optimized cross-core system calls and evaluate the approach with synthetic benchmarks and a real-world quadrotor application. MultiSSE was able to optimize or even completely elide costly cross-core system calls and system objects leading to a reduction of up to 14% of a task’s execution time. In this extended version of a conference publication (Entrup et al. 2023), we provide an advanced description, new cost models, and an end-to-end measurement by developing a synthesis complementing the analysis.

An end-to-end framework for private DGA detection as a service.

Domain Generation Algorithms (DGAs) are used by malware to generate pseudorandom domain names to establish communication between infected bots and command and control servers. While DGAs can be detected by machine learning (ML) models with great accuracy, offering DGA detection as a service raises privacy concerns when requiring network administrators to disclose their DNS traffic to the service provider. The main scientific contribution of this paper is to propose the first end-to-end framework for privacy-preserving classification as a service of domain names into DGA (malicious) or non-DGA (benign) domains. Our framework achieves these goals by carefully designed protocols that combine two privacy-enhancing technologies (PETs), namely secure multi-party computation (MPC) and differential privacy (DP). Through MPC, our framework enables an enterprise network administrator to outsource the problem of classifying a DNS (Domain Name System) domain as DGA or non-DGA to an external organization without revealing any information about the domain name. Moreover, the service provider's ML model used for DGA detection is never revealed to the network administrator. Furthermore, by using DP, we also ensure that the classification result cannot be used to learn information about individual entries of the training data. Finally, we leverage post-training float16 quantization of deep learning models in MPC to achieve efficient, secure DGA detection. We demonstrate that by using quantization achieves a significant speed-up, resulting in a 23% to 42% reduction in inference runtime without reducing accuracy using a three party secure computation protocol tolerating one corruption. Previous solutions are not end-to-end private, do not provide differential privacy guarantees for the model's outputs, and assume that model embeddings are publicly known. Our best protocol in terms of accuracy runs in about 0.22s.

Clock mesh synthesis through dynamic programming with physical parameters consideration

In response to the evolving technological landscape, the traditional clock network architecture faces challenges in meeting the complexities of modern System-on-Chip (SoC) designs. While the clock mesh topology offers resilience against On-Chip Variation (OCV) fluctuations, its manual implementation leaves room for advancements in methodology and swift analytical techniques. This paper introduces an innovative clock mesh synthesis approach, leveraging dynamic programming algorithms and emphasizing compliance with critical physical implementation parameters. Our experimental results demonstrate a significant 26.6% reduction in power consumption compared to baseline methodologies. Moreover, it achieves an impressive average runtime reduction of 78.0% when contrasted with traditional simulation methods. These findings underscore the potential of our methodology to enhance the efficiency and power management of clock mesh designs.

Proactive blockchain deployment mechanism in resource-constrained rate-splitting multiple access IoT networks

In this paper, we propose an innovative blockchain deployment mechanism tailored for resource-constrained rate-splitting multiple access (RSMA) Internet of Things (IoT) networks. To address the storage limitations and security concerns inherent in IoT environments, our approach includes several advanced techniques. First, we utilize a Block Allocation Strategy Contract (BASC) to manage storage efficiently. Second, we employ a deep reinforcement learning (DRL) model to dynamically perform block assignment, ensuring optimal storage utilization. To accommodate the resource constraints of IoT devices, we adopt the Smart Byzantine Fault Tolerance (SBFT) consensus mechanism, which offers low latency and energy efficiency. Our framework demonstrates superior performance in storage optimization and reduced running time compared to existing methods, making it well-suited for large-scale IoT networks. Through extensive simulations, we validate the effectiveness of our proposed solution in enhancing security and operational efficiency in RSMA IoT networks.

Application of symmetric uncertainty and emperor penguin–grey wolf optimisation for feature selection in motor fault classification

AbstractThe authors present a model for diagnosing motor faults based on machine learning, demonstrating advantages over other algorithms in terms of both improved fitness values and reduced running time. The structure of the model involves three primary phases: feature extraction, feature selection and classification. During the feature extraction phase, crucial features are identified using empirical mode decomposition, fast Fourier transform and multiresolution analysis, resulting in a total of 144 features. The feature selection stage employs a new strategy that combines symmetrical uncertainty in the filter approach with the binary grey wolf optimiser and emperor penguin optimiser in the wrapper approach. Finally, a support vector machine is used for classification to generate fitness values. To validate the model's effectiveness and accuracy, motor fault current signal datasets, case Western Reserve University (CWRU) benchmark datasets and mechanical failure prevention technology benchmark datasets are utilised. In the motor fault current signal dataset, the highest average accuracy achieved is 99.95%, with a minimum average running time of 88.02 s obtained under ∞dB conditions. Regarding benchmark datasets and mechanical failures at CWRU, using the prevention technology benchmark dataset resulted in classification accuracies of 99.54% and 99.52%, respectively. Comparative analysis with traditional algorithms reveals that symmetric uncertainty and emperor penguin–grey wolf optimisation model outperforms traditional models in terms of performance.

An insight into the effect of different parameters for optimizing parallel reservoir simulation performance

Parallel simulation significantly reduces runtime in oil reservoir simulations, enabling higher-resolution analysis and a better understanding of reservoir behavior. This study comprehensively evaluates how simulation time, hardware configuration, and various physical parameters impact the efficiency, scalability, and performance metrics such as run time reduction (RTR) and number of optimal cores (NOC) in parallel simulations. This study introduced the reduced simulation time (RST) concept, an innovative approach that enhances parallel simulation efficiency, and dynamic load balance. In this approach, simulations were performed using only 5% of the total simulation time. The findings reveal that in 80% of the models, switching to RST mode has no effect on NOC but significantly reduces overall execution time in most cases. In complex models, this time reduction can be as high as 60% compared to serial mode. In the remaining 20%, the number of cores in the total simulation time (TST) mode may be higher than in the RST mode, but this difference can be disregarded due to limited hardware resources and no change in RTR.