Computational Steps Research Articles

A 2D cell segmentation protocol for monitoring multiple STAT signaling pathways by fluorescence microscopy.

Microscopic cell segmentation typically requires complex imaging, staining, and computational steps to achieve acceptable consistency. Here, we describe a protocol for the high-fidelity segmentation of the nucleus and cytoplasm in cell culture and apply it to monitor interferon-induced signal transducer and activator of transcription (STAT) signaling. We provide guidelines for sample preparation, image acquisition, and segmentation. The approach performs indistinguishably from neural-network-based segmentation while requiring only conventional and cost-effective techniques. The protocol can be adapted to other signaling molecules undergoing nucleo-cytoplasmic shuttling and to high-throughput applications. This protocol enables simultaneous monitoring of two STAT isoforms using only conventional techniques and equipment and improves uponthe assay published in Szanda etal.1.

Design and Performance Evaluation of Brent Kung Adder based 8-Bit Vedic Multiplier

Multiplier is an essential functional block of a microprocessor because multiplication is needed to be performed repeatedly in almost all scientific calculations. Therefore, design of fast and low power binary multiplier is very important particularly for Digital Signal Processors. Vedic mathematics has improved the performance of multiplier. Vedic mathematics, a system of ancient Indian mathematics, which has a unique technique of solutions based on only 16 sutras. The novel point is the efficient use of Vedic algorithm (sutras) that reduces the number of computational steps considerably compared with any conventional method. This paper presents design and Performance Evaluation of Brent Kung Adder based 8-Bit Vedic Multiplier. Urdhva Tiryagbhyam sutra has been used for multiplication purpose. The partial product addition in Vedic multiplier is realized using Brent Kung Adder. Simulation results shows that described Brent Kung Adder based 8-Bit Vedic Multiplier is efficiently decreases the Delay, power consumption and Area than other multipliers.

Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.

Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.

Explanation of the principles of the Booth algorithm, Verilog implementation and simulation

In an era marked by heightened constraints on computer processing speed and storage capacity, optimizing computational efficiency and space utilization assumes paramount importance. Consequently, the deployment of proficient algorithmic tools holds significant significance within the realm of computing. The principal categories of multipliers encompass conventional multipliers, shift-and-add multipliers, Look-Up Table (LUT) multipliers, and Booth algorithm multipliers. Among these, the Booth algorithm substantially influences multiplier performance enhancement. This paper investigation commences with a comprehensive elucidation of the mathematical underpinnings of the Booth algorithm. Subsequently, it involves the development of code corresponding to the mathematical expressions, which is executed within the VIVADO environment. Given the inherent simplicity of the experiment, virtual simulation is directly conducted in VIVADO. The analysis is grounded in the examination of data waveforms. Ultimately, the output waveform is correlated with the BOOTH mathematical formula, culminating in the successful implementation of the BOOTH algorithm. This paper aims to effectively curtail the presence of '1's in computer data, thereby reducing the computational steps and conserving operational space. This, in turn, leads to a notable acceleration in the computational speed, accomplishing the objective of enhancing computational efficiency.

Service quality management under risk prioritization and imprecise information: a hybrid fuzzy approach

PurposeConventional risk prioritization methods rely on crisp inputs but struggle with imprecise data and hesitancy, resulting in inaccurate assessments that affect service and information quality and performance monitoring. This study proposes a fuzzy data-driven risk prioritization model for service quality under imprecise information.Design/methodology/approachEnterprise risk management is crucial for service quality management, ensuring effective identification, assessment and mitigation of risks impacting service delivery and customer satisfaction. This paper proposes a fuzzy data-driven multi-criteria model for risk prioritization involving multiple decision-makers. It introduces a hybrid method combining intuitionistic and hesitant fuzzy group decision-making to assess better and prioritize risks based on decision-maker preferences.FindingsThe proposed hybrid fuzzy model improves service quality in business operations by efficiently representing uncertain information in traditional frameworks. It helps identify potential risks in advance and enhances control over business operations, enabling organizations to benchmark service quality and identify best practices. Accordingly, organizations acquire information and background knowledge to benchmark their service quality. This, in turn, improves service quality under performance management.Research limitations/implicationsDespite the advantages of fuzzy models in risk prioritization, such as mimicking human reasoning more accurately, their complexity can hinder adoption. The intricate computational steps may deter shop-floor managers who prefer the more straightforward conventional crisp RPN approach, which is easier to understand and implement. However, while developing a hybrid fuzzy risk prioritization model may require more effort, its benefits become apparent over time. Once developed, the model can be integrated into software applications, allowing decision-makers to use it easily. This integration simplifies fuzzy computations and enhances risk prioritization, leading to more informed decision-making and improved risk management in the long term.Practical implicationsThe proposed robust fuzzy framework improves risk management by integrating uncertain information and multiple decision-makers expertise, leading to more reliable outputs that enhance strategic decisions and operational efficiency.Originality/valueWe validate the proposed approach at an integrated steel plant’s risk management process, covering broad areas of the service quality domain. To the best of our knowledge, no study exists in existing literature attempting to explore the efficacy of the proposed hybrid fuzzy approach in risk management practices at prime sectors like steel. The study’s novelty is backed by this validation experiment, which indicates that the effectiveness of the results obtained from the proposed multi-attribute hybrid fuzzy methodology is more practical. The model’s outcome substantially adds value to the current risk assessment and prioritization literature that significantly affects service quality.

Modified two-dimensional differential transform method for solving proportional delay partial differential equations

In this study, we develop a modified version of the two-dimensional differential transform (TDDT) method for solving proportional delay partial differential equations (PDPDEs) that frequently arise in engineering and scientific models. This modification is achieved by integrating the TDDT method with the Laplace transform and the Padé approximant, thereby leveraging the strengths of each technique to improve overall performance. Theorems are provided in a general manner to cover various types of PDEs, with constant or variable coefficients. To validate the approach, we apply it to three test problems, demonstrating its effectiveness in extending the convergence domain of the traditional TDDT approach, reducing computational complexity, and yielding analytic solutions with fewer computational steps. Results indicate that the method is a viable alternative for addressing PDPDEs, especially in scenarios where traditional analytic solutions are challenging to obtain. This combination opens new avenues for efficiently solving complex delayed systems in engineering and science, potentially outperforming existing numerical and analytical techniques in both speed and reliability.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75 %–34.63 % operation cost reductions on average, while reducing computation steps by 87.75 %–92.60 % compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

Short Communication: Numerically simulated time to steady state is not a reliable measure of landscape response time

Abstract. Quantifying the timescales over which landscapes evolve is critical for understanding past and future environmental change. Computational landscape evolution models are one tool among many that have been used in this pursuit. We compare numerically modeled times to reach steady state for a landscape adjusting to an increase in rock uplift rate. We use three different numerical modeling libraries and explore the impact of time step, grid type, numerical method for solving the erosion equation, and metric for quantifying the time to steady state. We find that modeled time to steady state is impacted by all of these variables. Time to steady state varies inconsistently with time step length, both within a single model and among different models. In some cases, drainage rearrangement extends the time to reach steady state, but this is not consistent in all models or grid types. The two sets of experiments operating on Voronoi grids have the most consistent times to steady state when comparing across time step and metrics. On a raster grid, if we force the drainage network to remain stable, time to steady state varies much less with computational time step. In all cases we find that many measures of modeled time to steady state are longer than that predicted by an analytical equation for bedrock river response time. Our results show that the predicted time to steady state from a numerical model is, in many cases, more reflective of drainage rearrangement and numerical artifacts than the time for an uplift wave to propagate through a fixed drainage network.

Fast CU Partition Decision Algorithm Based on Bayesian and Texture Features

As internet speeds increase and user demands for video quality grow, video coding standards continue to evolve. H.266/Versatile Video Coding (VVC), as the new generation of video coding standards, further improves compression efficiency but also brings higher computational complexity. Despite the significant advancements VVC has made in compression ratio and video quality, the introduction of new coding techniques and complex coding unit (CU) partitioning methods have also led to increased encoding complexity. This complexity not only extends encoding time but also increases hardware resource consumption, limiting the application of VVC in real-time video processing and low-power devices.To alleviate the encoding complexity of VVC, this paper puts forward a Bayesian and texture-feature-based fast splitting algorithm for coding intraframe bloc of VVC, which aims to reduce unnecessary computational steps, enhance encoding efficiency, and maintain video quality as much as possible. In the stage of rapid coding, the video frames are coded by the original VVC test model (VTM), and Joint Rough Mode Decision (JRMD) evaluation cost is used to update the parameter in the Bayesian algorithm to come and set the two thresholds to judge whether the current coding block continues to be split or not. Then, for coding blocks larger than those satisfying the above threshold conditions, the predominant direction of the texture within the coding block is ascertained by calculating the standard deviations along both the horizontal and vertical axes so as to skip some unnecessary splits in the current coding block patterns. The findings from our experiments demonstrate that our proposed approach improves the encoding rate by 1.40% on average, and the execution time of the encoder has been reduced by 49.50%. The overall algorithm has optimized the VVC intraframe coding technology and reduced the coding complexity of VVC.

Introducing variables in the evolution rules of P systems

In membrane systems evolution rules are constructed using only objects from a finite alphabet. In this paper we investigate rules in which variables are used. Namely, we define Variable P systems in which the rules containing variables need to be instantiated at the start of each computational step with values from some predefined sets of sets of objects.The modelling power of variable P systems is described by simulating some basic arithmetic operations over a (multi)set of positive numbers (addition, multiplication, or a combination of them). The main advantage of using variable P systems consists in the small number of used rules regardless how many numbers are involved in the operation: e.g., the addition requires only 3 rules, while the multiplication only 27 rules.

Jacobian sparsity detection using Bloom filters

Determining Jacobian sparsity structure is an important step in the efficient computation of sparse Jacobians. We introduce a new method for determining Jacobian sparsity patterns by combining bit vector probing with Bloom filters. We further refine Bloom filter probing by combining it with hierarchical probing to yield a highly effective strategy for Jacobian sparsity pattern determination.

Comparative analysis between continuous and discontinuous methods for the assessment of a cultural heritage structure

AbstractIn an era marked by the urgent need to ensure the safety of existing buildings according to current standards, evaluating the stability of masonry structures against hazard events has become a significant challenge. Despite the versatility and durability of masonry, structural assessments are hampered by factors such as limited information on material properties, irregular geometries, and ageing. To address this issue, numerous modelling techniques have been developed, supported by extensive scientific literature. However, significant factors related to the case study replication, such as the geometric complexity, the mechanical behaviour of masonry, the loading applications, contribute to the challenges associated with modelling procedures, including computational time, discretization procedures, and step incrementation. This paper critically discusses the most innovative modelling approaches. Specifically, it aims to compare the efficiency of the Distinct Element (discontinuous) Methods and the Finite Element (continuous) Method, both applied to the numerical simulation of a case study structure severely damaged by the 2016 Central Italy earthquake under lateral loading conditions. The continuous method is analysed using Midas FEA NX©, while the discontinuous methods are studied using 3DEC© and LMGC90© software, each with different contact conditions. Finally, the investigation highlights the main advantages and disadvantages of each method. In particular, the discontinuous method demonstrates reliability in accurately replicating failure patterns, whereas the continuous method allows for a faster model setup, making it suitable for preliminary studies on structural dynamics.

Real-Time Object Detection and Tracking for Occupational Health and Safety (OHS) in Mining Operations Using Streamed CCTV Data

Abstract Occupational Safety and Health (OHS) is very important in the mining industry which is prone to accident risks. Automatic detection of violations through CCTV streaming with the concept of a perimeter area is needed to prevent accidents. The purpose of this research is to create a violation detection system without re-modeling training that can adapt to different objects and regions. The proposed violation detection model includes four computational steps, namely (i) data reading, (ii) data pre-processing, (iii) object detection, and (iv) object tracking and violation detection. The computer vision technique uses the SIFT (Scale Invariant Feature Transform) algorithm and heuristics implemented with OpenCV. We used five CCTV video recordings of mining operations as examples. The results showed an average precision of 61.14%, a precision of 80.41%, and a recall of 69.38%. The average computation time of the model is 0.3 seconds per frame with heuristics that allow the model to be used in real-time. The model can be widely used in various fields and locations without having to retrain the model to improve OHS in the mining industry and other sectors with similar risks. This study will contribute to the development of a flexible, fast, and real-time automatic detection system.

An efficient flux-reconstructed lattice boltzmann flux solver for flow interaction of multi-structure with curved boundary

Recently, the escalating computational capability provided by advanced GPU technology for numerical simulations is well-suited for tackling large-scale engineering challenges. The lattice Boltzmann flux solver (LBFS), as a relatively new fluid-solving method, combines the parallelization characteristics of the lattice Boltzmann method (LBM) with the ability to handle non-uniform grids. Building upon these advantages, this study aligns its flux computational steps with the demands of the GPU parallel computing environment, thus developing an efficient flux-reconstructed lattice Boltzmann flux solver (FRLBFS), the improved algorithm not only ensures precision but also achieves a significant enhancement in efficiency, reaching up to a remarkable 500-fold improvement. Additionally, this work combines the immersed boundary method, significantly improving the efficiency of addressing hydrodynamic problems with multiple structures. Through numerical validation, under identical GPU hardware conditions, the proposed method in this paper outperform LBM in terms of computational efficiency. Lastly, simulations of flow past an array of eight cylinders at different Reynolds numbers are conducted, instantaneous contour plots at various dimensionless time points and time-dependent curves of drag and lift coefficients for different cylinders are provided, to demonstrate the complex vortex shedding surrounding multiple cylinders and its extraordinary computational efficiency.

AXIAL DEFORMATION OF SPINEBIO-TENSEGRITY MODELAT DIFFERENT DEPLOYABLE SCHEMES

The study presents the axial deformation of a spine bio-tensegrity model through a shape change strategy with different deployment schemes. The spine bio-tensegrity model mimics the total height, tapered form and natural curvature of a human spine. Three deployable schemes S1-S3 with a different combination of cables at alterable and fixed lengths (at the lower, middle and upper part of the model, respectively) were investigated to achieve a series of axial displacements at 200 mm, 400 mm and 600 mm in the z-direction. The shape change algorithm was developed to optimise the forced elongation of cables, incorporating an objective function designed for monitored nodes in the system to reach their prescribed targets during the optimisation process. Sequential quadratic programming was employed to solve a nonlinear optimisation problem in the shape change analysis involving inequality constraints. The efficiency of the deployable schemes was evaluated based on the deformed shapes, convergence curve and axial forces of the spine bio-tensegrity model. The finding shows in addition to axial deformation, the model in deployment scheme S1 preserves the slenderness characteristic of the spine. In contrast, the model exhibits excessive expansion in the thoracic region in schemes S2 and S3. Greater total computational steps in deployment scheme S3, followed by S2 and S1, reveal that the active cables set near the monitored nodes allow the model to sense and act faster to reach their targets. The study contributes to understanding the structural behaviour and deployment strategy for a structure mimicking a biological system.

A user's guide for the International Classification of Cognitive Disorders in Epilepsy.

To present the background, rationale, details pertaining to use and essential computational steps, synopsis of findings to date, and future directions for the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE)-an initiative of the ILAE Neuropsychology Task Force. Examined are: (a) the 6 steps leading to the derivation of a cognitive phenotype from neuropsychological test data with an accompanying case example, (b) concise review of all IC-CoDE research to date, (c) summary of identified correlates of IC-CoDE outcomes, and (d) future research and clinical directions for the initiative. The IC-CoDE is computationally uncomplicated with individual or group data and represents a novel approach leading to new insights in the neuropsychology of epilepsy, with applications to diverse datasets internationally informing the reliability and validity of the approach. The IC-CoDE represents a novel approach to the analysis and interpretation of neuropsychological data in epilepsy that offers to advance a global taxonomy of cognitive disorders in epilepsy facilitating international collaboration and big data science.

Robust predictive control strategy for grid‐connected inverters with ultra‐local model based on linear matrix inequality

AbstractTo address the issue of poor robustness in the model predictive control of grid‐connected inverters due to disturbances in load model parameters, this article developed an ultra‐local model robust predictive controller based on Linear Matrix Inequality (LMI). First, to avoid dependence on model parameters, an ultra‐local model of the system was constructed. Second, in the process of setting the state variable compensation gain, to simplify the complex computational steps of the traditional Lyapunov algorithm, the task of finding the gain that satisfies the Lyapunov stability condition was ingeniously transformed into an optimization problem of solving constrained LMI. Third, the optimized state variables are utilized to design a new cost function predictive model, thereby enhancing the precision of system control. Finally, the effectiveness of the proposed approach has been validated through simulations and experiments.

Decision Analysis and Practical Pathways for Innovative Teaching Methods in Information Technology Education

With the rapid advancement of information technology, its integration into modern education has become essential, with a focus on enhancing teaching outcomes and the student learning experiences through innovative teaching methods. However, existing studies predominantly suffer from a lack of systematic evaluation indicators and scientific decision models. The selection of evaluation indicators is often subjective, incomplete, and primarily qualitative, lacking quantitative data support. A systematic decision analysis method was proposed in this study to address these issues. This study comprises three components: the construction of an evaluation indicator system for innovative teaching methods in information technology education, a scale analysis based on gray relational degree, and the computational steps for the decision model concerning innovative teaching methods. This study provides scientific bases and practical pathways for the selection of innovative teaching methods in information technology education, aiming to enhance teaching effectiveness and educational quality.

Dynamic grey relational analysis-based supplier selection in a health-care unit

Purpose In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem. Design/methodology/approach In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores. Findings Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A2 and A11 occupy the top two positions having almost the same performance with respect to the considered financial metrics. Originality/value Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

Variational data encoding and correlations in quantum-enhanced machine learning

Abstract Leveraging the extraordinary phenomena of quantum superposition and quantum correlation, quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers. This paper tackles two pivotal challenges in the realm of quantum computing: firstly, the development of an effective encoding protocol for translating classical data into quantum states, a critical step for any quantum computation. Different encoding strategies can significantly influence quantum computer performance. Secondly, we address the need to counteract the inevitable noise that can hinder quantum acceleration. Our primary contribution is the introduction of a novel variational data encoding method, grounded in quantum regression algorithm models. By adapting the learning concept from machine learning, we render data encoding a learnable process. This allowed us to study the role of quantum correlation in data encoding. Through numerical simulations of various regression tasks, we demonstrate the efficacy of our variational data encoding, particularly post-learning from instructional data. Moreover, we delve into the role of quantum correlation in enhancing task performance, especially in noisy environments. Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference, thus advancing the frontier of quantum computing.