Computer Processing Research Articles

Advancing Organizational Science With Computational Process Theories

Organizational scholars commonly refer to organizations as complex systems unfolding as a function of work processes. Consequently, the direct study of work processes necessitates our attention. However, organizational scholars tend not to study work processes directly. Instead, organizational scholars commonly develop theories about relationships among psychological construct phenomena that indirectly reference people’s affective, behavioral, cognitive, and/or social processes as underlying explanations. Specifically, construct-oriented theories summarize processes in operation across actors, time, and contexts, and thus, provide limited insights into how focal phenomena manifest directly as a function of process operations. Construct theories remain one-step removed from articulating sequences of actions and two-steps removed from describing generative mechanisms responsible for observed actions. By “missing the action,” construct theories offer incomplete explanatory accounts and imprecise interventions. We assert that researchers in organizational science can make progress towards addressing these concerns by directing greater attention to developing computational process theories. We begin by presenting a framework for differentiating theories based on their focus (constructs versus processes) and modality (narrative versus computational). We use the framework to contrast narrative construct theories to computational process theories. We then describe key design principles for developing computational process theories and explain those principles using a leadership example. We use simulated data, from the computational process model we develop, to explicitly demonstrate the differences between construct and process thinking. We then discuss how computational process theories advance theory development. We conclude with a discussion of the long-term benefits of computational process theories for organizational science.

ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves

BackgroundPlants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues.ResultsHere we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms.ConclusionTaken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.

A Novel Source Code Representation Approach Based on Multi-Head Attention

Code classification and code clone detection are crucial for understanding and maintaining large software systems. Although deep learning surpasses traditional techniques in capturing the features of source code, existing models suffer from low processing power and high complexity. We propose a novel source code representation method based on the multi-head attention mechanism (SCRMHA). SCRMHA captures the vector representation of entire code segments, enabling it to focus on different positions of the input sequence, capture richer semantic information, and simultaneously process different aspects and relationships of the sequence. Moreover, it can calculate multiple attention heads in parallel, speeding up the computational process. We evaluate SCRMHA on both the standard dataset and an actual industrial dataset, and analyze the differences between these two datasets. Experiment results in code classification and clone detection tasks show that SCRMHA consumes less time and reduces complexity by about one-third compared with traditional source code feature representation methods. The results demonstrate that SCRMHA reduces the computational complexity and time consumption of the model while maintaining accuracy.

TransConv: Transformer Meets Contextual Convolution for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) aims to reapply the classifier to be ever-trained on a labeled source domain to a related unlabeled target domain. Recent progress in this line has evolved with the advance of network architectures from convolutional neural networks (CNNs) to transformers or both hybrids. However, this advance has to pay the cost of high computational overheads or complex training processes. In this paper, we propose an efficient alternative hybrid architecture by marrying transformer to contextual convolution (TransConv) to solve UDA tasks. Different from previous transformer based UDA architectures, TransConv has two special aspects: (1) reviving the multilayer perception (MLP) of transformer encoders with Gaussian channel attention fusion for robustness, and (2) mixing contextual features to highly efficient dynamic convolutions for cross-domain interaction. As a result, TransConv enables to calibrate interdomain feature semantics from the global features and the local ones. Experimental results on five benchmarks show that TransConv attains remarkable results with high efficiency as compared to the existing UDA methods.

Numerical comparison of streamflow drought index (SDI) and standardized streamflow index (SSI) for evaluation of Isfahan drought status

ABSTRACT Hydrological droughts are the most serious and alarming variation of droughts due to their numerous long-term negative impacts on natural ecosystems, agricultural capacities, and the water regime of a specific region. Isfahan province, in Iran, is highly prone to this type of drought, and its arid climate has already made the efficient water management in this area a challenging task. Due to the lack of a uniform method for drought evaluation, various indices have been suggested by different researchers over the last few decades. The streamflow drought index (SDI) and standardized streamflow index (SSI) are the two primary hydrological drought indices that have been used in many regions with similar climatic conditions to Isfahan province. Their reliability in previous case studies and the easy computational process have made them suitable drought measurement tools for Isfahan. In this study, these two indices are calculated based on the obtained data from 10 hydrometric stations in Isfahan, for the timeframe (1988–2018). The analysis of SSI and SDI in this region shows consistent and station-specific drought patterns, with moderate drought conditions prevailing. These findings offer valuable insights for regional water resource management and climate adaptation, guiding policies to mitigate the impact of droughts.

Interlayer coupled dual-layer metagratings for broadband and high-efficiency anomalous reflection

Recent progress in metagratings highlights the promise of high-performance wavefront engineering devices, notably for their exterior capability to steer beams with near-unitary efficiency. However, the narrow operating bandwidth of conventional metagratings remains a significant limitation. Here, we propose and experimentally demonstrate a dual-layer metagrating, incorporating enhanced interlayer couplings to realize high-efficiency and broadband anomalous reflection within the microwave frequency band. The metagrating facilitated by both intralayer and interlayer couplings is designed through the combination of eigenmode expansion (EME) algorithm and particle swarm optimization (PSO) to significantly streamline the computational process. Our metagrating demonstrates the capacity to reroute a normally incident wave to +1 order diffraction direction across a broad spectrum, achieving an average efficiency approximately 90% within the 14.7 to 18 GHz range. This study may pave the way for future applications in sophisticated beam manipulations, including spatial dispersive devices, by harnessing the intricate dynamics of multi-layer metagratings with complex interlayer and intralayer interactions.

Improving YOLO Object Detection Performance on Single-Board Computer using Virtual Machine

Single-board computers have gained popularity in the recent decade, largely due to the immense advancements in deep learning. Deep learning involves complex computational processes that are beyond the capabilities of regular microcontrollers, thus necessitating the use of single-board computers. However, single-board computers are primarily designed to operate efficiently in low-power environments. Therefore, optimization is crucial for running deep learning algorithms effectively on single-board computers. In this work, we explore the impact of utilizing the DeepStream framework to run deep learning algorithms, specifically the YOLO algorithm, on NVIDIA Jetson single-board computers. The DeepStream framework can be executed in virtual machines, notably Docker, to improve the performance and portability of the model. Additionally, deploying the Docker virtual machine from removable disks can further enhance its portability and even increase the algorithm's speed. Our benchmarks indicate that real-time streaming of the YOLO algorithm can operate up to 8.5 times faster when deployed from a Docker virtual machine.

Alrecon: computed tomography reconstruction web application based on Solara

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Quantum block coherence with respect to projective measurements

Abstract Quantum coherence serves as a defining characteristic of quantum mechanics, finding extensive applications in quantum computing and quantum communication processing. This study explores quantum block coherence in the context of projective measurements, focusing on the quantification of such coherence. Firstly, we define the correlation function between the two general projective measurements P and Q, and analyze the connection between sets of block incoherent states related to two compatible projective measurements P and Q. Secondly, we discuss the measure of quantum block coherence with respect to projective measurements. Based on a given measure of quantum block coherence, we characterize the existence of maximal block coherent states through projective measurements. This research integrates the compatibility of projective measurements with the framework of quantum block coherence, contributing to the advancement of block coherence measurement theory.

Rural And Urban Land And Building Tax Calculation Application (PBB-P2)

The PBB-P2 application, which focuses on the case study of Cangkuang Wetan Village, is specifically created to streamline and enhance the inputting and managing of rural and urban land and building taxes. The data-gathering methods employed, including thorough observation, comprehensive interviews, and extensive literature review, ensure the accuracy and reliability of the system. The system design was created with descriptive techniques and the object-oriented analysis and design (OOAD) development methodology, employing, which involves a process of analysis, system design, and object-oriented implementation outlinas ed by the Unified Modeling Language (UML). This design aims to provide benefits to Cangkuang Wetan Village by automating the tax computation process, managing citizen data, and generating time-saving reports.

Reconfigurable image processing metasurfaces with phase-change materials.

Optical metasurfaces have enabled analog computing and image processing within sub-wavelength footprints, and with reduced power consumption and faster speeds. While various image processing metasurfaces have been demonstrated, most of the considered devices are static and lack reconfigurability. Yet, the ability to dynamically reconfigure processing operations is key for metasurfaces to be used within practical computing systems. Here, we demonstrate a passive edge-detection metasurface operating in the near-infrared regime whose response can be drastically modified by temperature variations smaller than 10 °C around a CMOS-compatible temperature of 65 °C. Such reconfigurability is achieved by leveraging the insulator-to-metal phase transition of a thin layer of vanadium dioxide, which strongly alters the metasurface nonlocal response. Importantly, this reconfigurability is accompanied by performance metrics-such as numerical aperture, efficiency, isotropy, and polarization-independence - close to optimal, and it is combined with a simple geometry compatible with large-scale manufacturing. Our work paves the way to a new generation of ultra-compact, tunableand passive devices for all-optical computation, with potential applications in augmented reality, remote sensing and bio-medical imaging.

OPTIMIZING LONG TEXT CLASSIFICATION PERFORMANCE THROUGH KEYWORD-BASED SENTENCE SELECTION: A CASE STUDY ON ONLINE NEWS CLASSIFICATION FOR INDONESIAN GDP GROWTH-RATE DETECTION

Efficiently managing lengthy textual data, particularly in online news, is crucial for enhancing the performance of long text classification. This study delves into innovative approaches to streamline the Gross Domestic Product (GDP) computation process by harnessing modern data analytics, Natural Language Processing (NLP), and online news sources. Leveraging online news data introduces real-time information, promising to improve the accuracy and timeliness of economic indicators like GDP. However, handling the complexity of extensive textual data poses a challenge, demanding advanced NLP techniques. This research shifts from traditional word-weight-based methods to keyword-based extractive summarization techniques to address this. These tailored approaches ensure that selected sentences align precisely with specific keywords relevant to the research case, such as GDP growth rate detection. The study emphasizes the necessity of adapting summarization methods to capture information in unique research contexts effectively. According to classification results, the implementation of sentence selection successfully demonstrated improved performance in terms of classification accuracy. Specifically, there was an average accuracy increase of 0.0226 for machine learning and 0.0164 for transfer learning models. Additionally, in terms of computational efficiency, sentence selection also accelerates processing time during hyperparameter tuning and fine-tuning, as observed using the same computational resources.

MODELING STOCHASTIC ADVERSE EFFECTS OF CBN 2023 REDESIGNED NAIRA NOTES POLICY ON RURAL FARMERS IN NIGERIA

The recent Central Bank of Nigeria (CBN) 2023 redesigned naira notes is of good benefits to strengthen the economy of the country by checking counterfeiting and hoarding of large volume of banknotes by the public. Despite all the efforts made by the CBN for citizens to enjoy the benefits of this implementation, most rural farmers were faced with adverse effects of uncertainties in the production and marketing of their agricultural produce due to lack of redesigned new naira notes in circulation. The adverse effects of these uncertainties are modeled as Advanced Stochastic Time-Delay Differential Equation (ASTDDE). The modeled equation is solved using Extended Second Derivative Block Backward Differentiation Formulae Method (ESDBBDFM) without the use of interpolation techniques in the evaluations of the delay term and noise term. In comparing the numerical results of this method with other existing methods in literature, the newly developed mathematical expressions for the evaluations of the delay term and the noise term in solving ASDDEs with the discrete schemes of ESDBBDFM gives better results for step number than step numbers and 3 by producing Least Minimum Absolute Random Error (LMARE) in a Lower Computational Processing Unit Time (LCPUT) faster than other existing methods that applied interpolation techniques in evaluations of the delay term and the noise term.

Identification of bridge influence line and multiple-vehicle loads based on physics-informed neural networks

Influence lines (ILs) and vehicle loads identification are critical in the design, health monitoring, and damage detection of bridges. Traditionally, the approach used in most existing literature has been to solve the system of equations directly. However, these approaches require complex calculations such as matrix decomposition and regularization coefficient optimization, making them difficult to implement. In addition, there are difficulties in obtaining accurate axle information and effectively separating the bridge response due to each vehicle. Thus, the improvement of identification algorithms for ILs and multi-vehicle loads remains of significant importance. To address these issues, this paper presents a novel approach that integrates prior physical equations and neural networks. This is achieved by integrating the equation that reflects the relationship between axle loads and bridge response into the neural network, utilizing existing methods for acquiring axle information of vehicles. To validate the effectiveness of the proposed method, it was first applied to theoretical and simulation data. The study then investigated the impact of noise and dynamic effects on the accuracy of the results, as well as the range of the neural network layers and sampling intervals. Finally, the method was implemented for identifying multiple-vehicle loads. The findings of the study confirm the feasibility and numerical stability of the proposed approach. The proposed method eliminates the need for complex computational processes, including matrix decomposition, diagonalization, regularization coefficient optimization, and solution vector smoothing fitting. As a result, the implementation of the algorithm is significantly less challenging, and identification accuracy is improved. It is important to note, however, that the proposed method is relatively more time-consuming due to the iterative learning and training required by the neural network.

Development of controller for position sensor on the base of sine-cosine rotary transformer

Background: Sine-cosine rotary transformers, being electromechanical position sensors, have a relatively high accuracy and resistance to harsh operating conditions, which makes them attractive for use in automotive technique and equipment installed on its base. To generate excitation voltages, process measuring signals and calculate, depending on them, the angular position of the axis on which such a sensor is installed, special controllers with software and hardware implementation are designed. Aims: The development of software and hardware for a controller of position sensor which is based on a sine-cosine rotary transformer in the amplitude mode. Methods and materials: Taking into account electromagnetic processes on which the operation of a sine-cosine rotary transformer in the amplitude mode is based, methods of system analysis, methods of developing and debugging software, methods of experimental researches and processing their results were applied. Results: A detailed description of the controller of a sine-cosine rotary transformer in amplitude mode is given, as well as its implementation using microprocessor technology and specialized tools for generating, capturing and processing signals. The structure of the controller software is given, as well as technical solutions are described aimed at improving the accuracy of measuring the angular position and the rational implementation of computational processes. The results of the experimental researches illustrate the achievement of the given purpose. Conclusion: The development of a controller for a sine-cosine rotary transformer in the amplitude mode required the detailed consideration and coordination of electromagnetic, electrical and computational processes, as well as the use of various technical solutions aimed at improving the accuracy of measuring the angular position. The results obtained can be applied in the development of controllers for various electromechanical position sensors.

Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review.

Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.

A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

PurposeThis study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.Design/methodology/approachThe proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.FindingsThe proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.Originality/valueThis paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

Synthesis and sorption properties of hybrid composite materials based on saponite and copolymer 5-(4′-nitro)-phenylazo-8-methacryloxyquinoline: MMA

A new polymer–mineral composite was synthesized by adsorption of the copolymer of 5-(4′-nitro)-phenylazo-8-methacryloxyquinoline with methyl methacrylate on the surface of natural saponite clay. Using computer processing of isotherms of low-temperature adsorption–desorption of nitrogen, a decrease in the specific surface area of saponite after fixing this copolymer was recorded. Using the BJH method, it was calculated that after the adsorption of the copolymer, the average pore volume of the surface of the original mineral decreases and the average pore diameter increases. The sorption capacity of the synthesized composite concerning metal ions compared to the original mineral increases.

A holistic life cycle assessment of steel bridge deck pavement

Transportation serves as a cornerstone of economic development and is a significant contributor to carbon emissions. This study established a life cycle assessment model that incorporates refined carbon emission calculation parameters, streamlining the computation process while maintaining precision. A case study was conducted to quantify the life-cycle CO2 emissions of steel bridge deck pavements, a pivotal component within the transport network. The study suggests using the average annual CO2 emissions as a metric to gauge the carbon emission potential of steel bridge deck pavements with different service lives. This study addresses a void in the existing work by standardizing the grading of typical pavement diseases and quantifying the carbon emissions of the corresponding maintenance measures. It reveals that the pivotal determinant of life-cycle CO2 emissions for steel bridge deck pavements is the service performance of the paving material. Comparative analysis indicates that epoxy asphalt concrete could be deemed a low-carbon material, achieving a 77.6% reduction in life-cycle carbon emissions compared to gussasphalt concrete. Emissions during the maintenance phase constitute 66.7%–71.4% of the total life-cycle emissions, predominantly due to end-of-life and traffic delay units. The methodology and calculated data presented herein can inform subsequent carbon reduction strategies in transportation and promote the development of resilient infrastructure, thus making a substantial contribution towards carbon neutrality and climate change mitigation.

Laterally coupled photonic crystal surface emitting laser arrays

We propose and investigate a novel coherent laser array design based on laterally coupled photonic crystal surface-emitting lasers (PCSELs). As a new type of semiconductor laser technology, PCSELs have field confinement in a planar cavity and laser beam emission in the surface normal direction. By engineering lateral couplings between PCSELs with heterostructure photonic crystal designs, we can achieve coherent operations from an array of PCSELs. In this paper, we demonstrate coherent operation from a passively coupled PCSEL array design. We fabricated PCSEL array devices on a GaAs-based quantum well heterostructure at a target wavelength of 1040 nm. Experimental results show that the 2-by-2 PCSEL arrays have spectral linewidth of 0.14–0.22 nm. Beam combining performance was characterized by self-interference experiments. Similar coherency between the PCSEL array and single PCSEL device was observed. Our compact PCSEL array designs by passive lateral coupling have potential applications in fields of on-chip photonic computing, quantum, and information processing.