Parallel Processing Research Articles

Leader, Lead Thyself: The Benefit of Contract Grading in Social Work Leadership Education

ABSTRACT Contract grading provides graduate-level students with greater control and autonomy within the learning process. The contract grading approach requires students to take greater responsibility for achieving course learning outcomes as well as allowing learners to adjust the style and focus of assignments to meet their own unique needs and desires. Within this article, contract grading is applied to social work leadership education and explored as a parallel learning process designed to reflect some of the fundamentals of leadership in action while also teaching about leadership. A review of the scholarship of contract grading, an example of the application of the method, and a reflection on potential challenges that may result are also included in the article.

Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions.

Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.

Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware

Recent advancements in neuromorphic computing have led to the development of hardware architectures inspired by Spiking Neural Networks (SNNs) to emulate the efficiency and parallel processing capabilities of the human brain. This work focuses on testing the HEENS architecture, specifically designed for high parallel processing and biological realism in SNN emulation, implemented on a ZYNQ family FPGA. The study applies this architecture to the classification of digits using the well-known MNIST database. The image resolutions were adjusted to match HEENS' processing capacity. Results were compared with existing work, demonstrating HEENS' performance comparable to other solutions. This study highlights the importance of balancing accuracy and efficiency in the execution of applications. HEENS offers a flexible solution for SNN emulation, allowing for the implementation of programmable neural and synaptic models. It encourages the exploration of novel algorithms and network architectures, providing an alternative for real-time processing with efficient energy consumption.

Bessel beams generation with biphase transition of vanadium dioxide metasurface

Bessel beams are highly attractive due to their non-diffraction properties, parallel processing capabilities, and large capacity. However, conventional methods for generating Bessel beams, such as using spatial light modulators, axicons, and diffraction optical elements, face limitations in terms of system complexity, bulkiness, low uniformity, and limited numerical aperture (NA). In this work, we exploited the phase change material vanadium dioxide (VO2) to generate both transmitted and reflected Bessel beams. Moreover, the self-healing property of Bessel beams was verified. Our results reveal that VO2 in the insulating state achieves a transmittance of 85% in the transmitting mode, while VO2 in the metallic state exhibits a reflection efficiency of 77% in the reflecting mode. This performance indicates the potential applications in efficient switchable metasurfaces.

Working Individually and Collectively: Assisting Young People with Cognitive Disability Transitioning from Statutory Systems

AbstractYoung people with cognitive disability exiting statutory child protection and/or youth justice systems are a particularly vulnerable group. They have varied, complex and dynamic needs. These are often poorly responded to, due in part to a lack of formal and informal supports. There remains a lack of specialised supports and a dearth of evidence regarding best practice in supporting this group. This paper explores social work practices which respond to the social isolation of this cohort and the gap in support as young people transition to adulthood. The paper focuses on a group program in Queensland (Australia) which was specifically designed to support young people with a cognitive disability leaving child protection and/or youth justice systems. It reports on a subset of data from a mixed-methods study. It presents the perspectives of the young people and their practitioners regarding the practices that supported the engagement of young people and created opportunities for connection and skill development within the group program. The findings highlight the importance of relationship-driven practices that are supported and resourced by the organisation, and the need for parallel individual and group work processes for achieving positive outcomes with young people with cognitive disability transitioning from statutory systems.

The Ohio Adventure Therapy Coalition: A Statewide Trauma Informed Group Work Initiative for Youth Involved in CPS

ABSTRACT Youth involved in the child welfare system need creative ways to meet their needs. One type of active group intervention, adventure therapy, is currently gaining momentum as an additional support for youth with trauma histories. The Ohio Adventure Therapy Coalition (OATC) developed an implementation model to support youth workers in facilitating adventure therapy groups across the state with a focus on trauma-informed care and involving providers in a parallel process, so they can personally understand the process of adventure therapy. A pilot study collected data from youth, parents and providers looking at overall mental health functioning and trauma symptoms. Overall, youth reported significant improvements in overall mental health functioning, while parents reported significant overall improvements as well as improvements on five subscales as measured by the Youth Outcomes Questionnaire. Providers reported significant decreases in youth trauma symptoms, behavioral and emotional needs and risk behaviors as captured by the CANS.

Pulse program for improving learning accuracy and reducing programming energy consumption of ferroelectric synaptic transistor

Neuromorphic computing is a next‐generation computing technology featured by parallel data processing and adaptive learning. Two significant factors that improve learning accuracy are the ‘dynamic range’ and ‘linearity’ of the weight update. In a ferroelectric synaptic transistor, the weight update can be modulated by adjusting the applied voltage. The voltage pulse train should be carefully optimized to improve the learning accuracy and reduce programming energy consumption. In this study, we investigated the learning accuracy of neuromorphic computing based on the characteristics of synaptic devices and the program energy consumption according to pulse programs. We demonstrated changes in the analog conductance characteristics of ferroelectric thin‐film transistors by varying the pulse program for synaptic plasticity, discussed the characteristics for improving learning accuracy, and compared the programming energy consumption according to the pulse programs. We proposed a logarithmic‐incremental‐step pulse program that reduces programming energy consumption and improves learning accuracy.

Multi-stage planning and calculation method of power system considering energy storage selection

Abstract Under new power system flexibility, this text proposes a multi-stage random generation-transmission-energy storage, integrated programming method that considers the short-period flexibility demand. The model can accurately reflect the relationship between short-period operation and long-period uncertainty within an extraordinary method and can also correctly evaluate the value of investment flexibility. Based on this, this text proposes a parallel processing solution, which can be separated into subproblems through column generation and shared decomposition and enables simulation calculation using a distributed measuring platform in long-period programming. The calculation proves the optimal solution speed of the proposed method and can effectively solve problems that cannot be dealt with by the known methods.

Robust Control of a Wind Energy Conversion System: FPGA Real-Time Implementation

This study employs an FPGA board to implement a robust control technique for wind energy conversion systems (WECS). This approach facilitates extensive testing and validation of the control system across diverse wind conditions, utilizing the FPGA's parallel processing capabilities and advanced control algorithms. This method ensures robustness against nonlinearities and system uncertainties. FPGA-in-the-loop (FIL) testing provides precise and effective simulation results, enabling rapid prototyping and iterative modifications of control algorithms. The effectiveness of the robust control strategy is confirmed by FIL findings, demonstrating significant improvements in WECS stability and efficiency. Furthermore, the study highlights the strategy's potential to enhance WECS reliability and efficiency in real-world applications.

A Novel Chaos-Based Encryption Technique with Parallel Processing Using CUDA for Mobile Powerful GPU Control Center

Chaotic systems possess unique properties that can be leveraged for cybersecurity due to their complex and unpredictable nature, making it challenging for systems to interpret them. When combined with CUDA, chaotic systems benefit from high-efficiency parallel processing capabilities, allowing for the rapid and secure handling of large data sets. In this study, a CUDA-supported chaos-based parallel processing encryption mechanism for mobile control centers is developed. The powerful GPU of the control center enables fast encryption and decryption of image data from multiple connected devices. The Logistic Map is used for random number generation, and XOR operations are applied to encrypt the R, G, B, and Gray Scale channels of images. Initially, an analysis of the numbers generated from the Logistic Map is conducted, followed by a detailed explanation of the encryption technique. The technique is then applied to image data, and image analyses are performed. Finally, the performance of the encryption technique is compared with other studies, and encryption speeds are examined. Results indicate that the new encryption technique provides significantly fast encryption and security levels comparable to other studies. The key discovery is that the mechanism is well-suited for parallel processing, allowing for rapid image encryption using the proposed method. For encrypting large IoT files, random number generation is performed first, followed by statistical tests, and then encryption using the developed algorithm. Security analyses are conducted, and the performance of the mechanism is compared with other studies. Results from image analysis and encryption performance demonstrate that the developed mechanism can be effectively used with high security for IoT applications.

Unpleasant words can affect the detection of morphosyntactic errors: An ERP study on individual differences.

In recent years, several ERP studies have investigated whether the early computation of agreement is permeable to the emotional content of words. Some studies have reported interactive effects of grammaticality and emotionality in the left anterior negativity (LAN) component, while others have failed to replicate these results. Furthermore, novel findings suggest that grammatical processing can elicit different neural patterns across individuals. In this study, we aim to investigate whether the interaction between grammaticality and emotionality is restricted to participants with a specific neural profile. Sixty-one female native speakers of Spanish performed an agreement judgment task in noun phrases composed of a determiner, a noun, and an unpleasant or neutral adjective that could agree or disagree in gender with the preceding noun. Our results support the existence of two different brain profiles: negative and positive dominance (individuals showing either larger LAN or larger P600 amplitudes in ungrammatical stimuli than in grammatical ones, respectively). Interestingly, the neural pattern of these two groups diverged at different points along the time course. Thus, the negative dominance group showed grammaticality effects as early as 200 ms, along with parallel and autonomous processing of grammaticality and emotionality at the LAN/N400 time window. Instead, for the positive dominance group an early interaction was found at around 200 ms, evidencing a grammaticality effect that emerged only for unpleasant words. Our findings confirm the role of individual differences in the interplay between grammar and emotion at the neural level and call for the inclusion of this perspective in studies on syntactic processing.

Управление процессом обслуживания высокотехнологичного оборудования с использованием сетей Петри

The paper considers the management of the maintenance process of high-tech equipment. The mechanism of operation, maintenance and repair of equipment elements and the availability of highly qualified personnel capable of performing certain types of work are described. The functioning of the Petri net reflects the process of using and maintaining high-tech equipment. The Petri net explores events that correlate certain transitions. The conditions for triggering transitions are considered, and the connections between positions and transitions are described. Each position of the selected set corresponds to the types of high-tech equipment being analyzed, the routine maintenance work performed and the teams carrying out these works. To simulate the processes of functioning of the Petri net, a transition function is formed, which allows determining new network states obtained when a transition is triggered. The rules of operation of the Petri net are described. Allowed transitions are implemented, which have non-zero values in each position at the label input. The transition is triggered not necessarily at the nearest allowed time. The change in the state of the Petri net and its labeling during the implementation of various transitions and changes in the order of their implementation is investigated. When performing any transition, the labeling of the Petri net in question changes. The labels in each position at the input of the implemented transition are reduced by one, and in each output position they increase by one, and the addition of chips at the output positions and decrease at the input positions occur simultaneously. A matrix approach is used to describe the process of functioning of a Petri net. The equipment in question can be aircraft, vehicles, machine tools, etc. As a result, the mechanism of operation, maintenance and repair of equipment elements is considered in the form of parallel processes that occur in the system under consideration independently of each other.

Multi-Feature Cross Attention-Induced Transformer Network for Hyperspectral and LiDAR Data Classification

Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed on how to more deeply integrate the features of two modalities in attention mechanisms. In this paper, we propose a novel Multi-Feature Cross Attention-Induced Transformer Network (MCAITN) designed to enhance the classification accuracy of hyperspectral and LiDAR data. The MCAITN integrates the strengths of both data modalities by leveraging a cross-attention mechanism that effectively captures the complementary information between hyperspectral and LiDAR features. By utilizing a transformer-based architecture, the network is capable of learning complex spatial-spectral relationships and long-range dependencies. The cross-attention module facilitates the fusion of multi-source data, improving the network’s ability to discriminate between different land cover types. Extensive experiments conducted on benchmark datasets demonstrate that the MCAITN outperforms state-of-the-art methods in terms of classification accuracy and robustness.

Big Data Analytics Model with Deep Learning Architecture to Evaluate Live Dance Ecology Through the Internet

Dance ecology, a burgeoning field at the intersection of dance, technology, and environmental studies, relies on real-time data analysis for understanding and optimizing dance performances. This paper proposed a novel Parallel Edge Big Data Analytics (PEBDA) framework, designed to efficiently process and analyze dance movement data in real time. The proposed PEBDA model uses parallel processing in the edge computing model for the analysis of the live dance ecology. Through the parallel processing of the edge model in the network big data analytics is implemented for the estimation of the multiple nodes in the network. The PEBDA model estimates the nodes across multiple environments for the examination of the ecology in the live dance. Finally, through parallel processing classification is performed with the deep learning model for the classification of live dance ecology in the computing platform. The proposed PEBDA framework, assesses classification accuracy, precision, recall, and F1-score. The simulation analysis expressed that Node 8 consistently outperforms others, achieving exceptional accuracy and precision levels above 0.97. These findings highlight the potential of edge computing in revolutionizing dance ecology analysis, enabling enhanced real-time monitoring, decision-making, and optimization of dance performances.

The Divergent Fates of Wartime Israeli and Gazan (Palestinian) Cultural Heritage: A Preliminary Inventory and Conceptual Analysis of Heritagization Processes in GLAMs on Separate Sides of the Israel-Hamas War

ABSTRACT Divergent fates are befalling Israeli and Gazan cultural heritage during the continuing Israel-Hamas war. This article conducts a case study of the war’s implications for both Israeli and Gazan (Palestinian) cultural heritage sectors, represented by galleries, libraries, archives, and museums (GLAMs), to help establish a preliminary account and overview of the state of cultural heritage over the war’s first half-year. A tripartite typology of heritagization processes is further furnished in which to situate, examine, and contrast the case study. Specifically, this tripartite typology involves: heritagization that creates or designates phenomena as cultural heritage; re-heritagization that reaffirms, revalidates, or reinforces existing cultural heritage; and de-heritagization that damages, demolishes, or destroys cultural heritage. Foregrounding the case study of wartime Israeli and Gazan cultural heritage within this typology serves two purposes. First, it spotlights the parallel processes of heritagization, re-heritagization and de-heritagization, to help describe, interpret, and understand them within this volatile setting. Second, it enables a conceptual and practical mapping of the convolutions regarding the war’s productive (heritagization/re-heritagization) and destructive (de-heritagization) effects on cultural heritage on separate sides of the conflict. Ultimately, parallel heritagization processes related to the war are simultaneously unfolding within and between Israel and Gaza, resulting in contrasting consequences on their respective cultural heritage sectors and contexts.

Tunable anti-ambipolar vertical bilayer organic electrochemical transistor enable neuromorphic retinal pathway

Increasing demand for bio-interfaced human-machine interfaces propels the development of organic neuromorphic electronics with small form factors leveraging both ionic and electronic processes. Ion-based organic electrochemical transistors (OECTs) showing anti-ambipolarity (OFF-ON-OFF states) reduce the complexity and size of bio-realistic Hodgkin-Huxley(HH) spiking circuits and logic circuits. However, limited stable anti-ambipolar organic materials prevent the design of integrated, tunable, and multifunctional neuromorphic and logic-based systems. In this work, a general approach for tuning anti-ambipolar characteristics is presented through assembly of a p-n bilayer in a vertical OECT (vOECT) architecture. The vertical OECT design reduces device footprint, while the bilayer material tuning controls the anti-ambipolarity characteristics, allowing control of the device’s on and off threshold voltages, and peak position, while reducing size thereby enabling tunable threshold spiking neurons and logic gates. Combining these components, a mimic of the retinal pathway reproducing the wavelength and light intensity encoding of horizontal cells to spiking retinal ganglion cells is demonstrated. This work enables further incorporation of conformable and adaptive OECT electronics into biointegrated devices featuring sensory coding through parallel processing for diverse artificial intelligence and computing applications.

Centralised visual processing center for remote sensing target detection

Target detection in satellite images is an essential topic in the field of remote sensing and computer vision. Despite extensive research efforts, accurate and efficient target detection in remote sensing images remains unsolved due to the large target scale span, dense distribution, and overhead imaging and complex backgrounds, which result in high target feature similarity and serious occlusion. In order to address the above issues in a comprehensive manner, within this paper, we first propose a Centralised Visual Processing Center (CVPC), this structure is a parallel visual processing center for Transformer encoder and CNN, employing a lightweight encoder to capture broad, long-range interdependencies. Pixel-level Learning Center (PLC) module is used to establish pixel-level correlations and improve the depiction of detailed features. CVPC effectively improves the detection efficiency of remote sensing targets with high feature similarity and severe occlusion. Secondly, we propose a centralised feature cross-layer fusion pyramid structure to fuse the results with the CVPC in a top-down manner to enhance the detailed feature representation capability at each layer. Ultimately, we present a Context Enhanced Adaptive Sparse Convolutional Network (CEASC), which improves the accuracy while ensuring the detection efficiency. Based on the above modules, we designed and conducted a series of experiments. These experiments are conducted on three challenging public datasets, DOTA-v1.0, DIOR, and RSDO, showing that our proposed 3CNet achieves a more advanced detection accuracy while balancing the detection speed (78.62% mAP for DOTA-v1.0, 79.12% mAP for DIOR, and 95.50% mAP for RSOD).

Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.

ProteinFlow: An advanced framework for feature engineering in protein data analysis.

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data.

Understanding invisible pain in the older adult: a content analysis of social media's representation of herpes zoster vaccine.

Herpes zoster (HZ), a common disease in older adults, affects their quality of life. Therefore, this study aimed to examine the blog posts of HZ-related information on social media platforms to analyze the attitudes and behaviors of residents toward the dissemination of health information. This research used content analysis to focus on Weibo, a representative social media in China, to analyze the content of 1866 blog posts related to herpes zoster (HZ) and herpes zoster vaccine (HZV). According to the consistency test by Cohen's Kappa, four themes were identified: (a) sources, (b) tones, (c) epidemiological information, and (d) extended parallel process model elements. The findings showed that most information on Weibo came from non-professionals, with a neutral tone, and showed the invisible pain of HZ and the effectiveness of HZV through the two largest aspects of prevention and aged protection in epidemiological information. However, current blog posts treat the older adult as invisible individuals, failing to acknowledge them as recipients of the information. Additionally, the cost of the vaccine acts as an invisible economic barrier, contributing to the dissemination of incorrect information about folk remedies. This impacts the older adult's acceptance of health information related to HZV. Thus, the way to share health information with the older adult needs to be improved in the future, and attention should be paid to the transmission of incorrect information to improve their vaccination rates and awareness of health management.