Computer Hardware Research Articles

National STEM Education Framework: Teachers’ Perspectives on the 2015-2022 Curriculum Cycle

Throughout the world, nations are busy investing in Science, Technology, Engineering and Mathematics (STEM) to prepare innovative minds that can spearhead the development and sustainable growth of their economies. Zimbabwe’s participation in the global economy is premised on the preference for STEM education system. The introduction of the Competence-Based Curriculum (CBC) in Zimbabwe meant a shift in the general aims and objectives of the education system. The transformation meant a move from an academic oriented and content-focused curriculum to one that is skills-based, giving prominence to continuous assessment over final examinations. To explore classroom practitioners’ perceptions of the first cycle of the CBC, 20 purposively selected STEM teachers participated in the study. A qualitative research approach and case study design were adopted as study methodology. Thematically analysed data were collected through focus group interviews with participants. The study focused on support systems available for STEM education in secondary schools and how the absence of the national framework affects its implementation. The study found that there were limited support systems for STEM education as some schools did not have adequate facilities and equipment such as science laboratories, apparatus, science consumables, computer laboratories, computer hardware and software among others. It came out from the study that the absence of national framework had negative effect on the implementation of STEM education in secondary schools. The study concluded that though the secondary schools experienced constraints in implementing STEM education there were noticeable pockets of good practices in some schools, for example, learners graduating with competences for life skills. The study recommended that the schools should engage stakeholders for support in provision of required resources. The Ministry of Primary and Secondary Education should expedite the development of a national framework for the effective implementation of STEM education.

A Knowledge Base Driven Task-Oriented Image Semantic Communication Scheme

With the development of artificial intelligence and computer hardware, semantic communication has been attracting great interest. As an emerging communication paradigm, semantic communication can reduce the requirement for channel bandwidth by extracting semantic information. This is an effective method that can be applied to image acquisition of unmanned aerial vehicles, which can transmit high-data-volume images within the constraints of limited available bandwidth. However, the existing semantic communication schemes fail to adequately incorporate the guidance of task requirements into the semantic communication process and are difficult to adapt to the dynamic changes of tasks. A task-oriented image semantic communication scheme driven by knowledge base is proposed, aiming at achieving high compression ratio and high quality image reconstruction, and effectively solving the bandwidth limitation. This scheme segments the input image into several semantic information unit under the guidance of task requirements by Yolo-World and Segment Anything Model. The assigned bandwidth for each unit is according to the task relevance scores, which enables high-quality transmission of task-related information with lower communication overheads. An improved metric weighted learned perceptual image patch similarity (LPIPS) is proposed to evaluate the transmission accuracy of the novel scheme. Experimental results show that our scheme achieves a notable performance improvement on weighted LPIPS while the same compression ratio compared with traditional image compression schemes. Our scheme has a higher target capture ratio than traditional image compression schemes under the task of target detection.

FLEXPART version 11: improved accuracy, efficiency, and flexibility

Abstract. Numerical methods and simulation codes are essential for the advancement of our understanding of complex atmospheric processes. As technology and computer hardware continue to evolve, the development of sophisticated code is vital for accurate and efficient simulations. In this paper, we present the recent advancements made in the FLEXible PARTicle dispersion model (FLEXPART), a Lagrangian particle dispersion model, which has been used in a wide range of atmospheric transport studies over the past 3 decades, extending from tracing radionuclides from the Fukushima nuclear disaster, to inverse modelling of greenhouse gases, and to the study of atmospheric moisture cycles. This version of FLEXPART includes notable improvements in accuracy and computational efficiency. (1) By leveraging the native vertical coordinates of European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) instead of interpolating to terrain-following coordinates, we achieved an improvement in trajectory accuracy, leading to a ∼8 %–10 % reduction in conservation errors for quasi-conservative quantities like potential vorticity. (2) The shape of aerosol particles is now accounted for in the gravitational settling and dry-deposition calculation, increasing the simulation accuracy for non-spherical aerosol particles such as microplastic fibres. (3) Wet deposition has been improved by the introduction of a new below-cloud scheme, by a new cloud identification scheme, and by improving the interpolation of precipitation. (4) Functionality from a separate version of FLEXPART, the FLEXPART CTM (chemical transport model), is implemented, which includes linear chemical reactions. Additionally, the incorporation of Open Multi-Processing parallelisation makes the model better suited for handling large input data. Furthermore, we introduced novel methods for the input and output of particle properties and distributions. Users now have the option to run FLEXPART with more flexible particle input data, providing greater adaptability for specific research scenarios (e.g. effective backward simulations corresponding to satellite retrievals). Finally, a new user manual (https://flexpart.img.univie.ac.at/docs/, last access: 11 September 2024) and restructuring of the source code into modules will serve as a basis for further development.

From GPU to CPU (and Beyond): Extending Hardware Support in GPUSPH Through a SYCL‐Inspired Interface

ABSTRACTWhile most software is originally designed for serial or parallel execution on CPU, and porting to GPU comes later in its development, GPUSPH was designed from the ground up to run on GPUs using CUDA. Making it accessible to a wider audience by introducing support for other computational hardware, and in particular CPUs, poses challenges that are complementary to the ones normally faced when porting CPU code to GPU. We present the approach we have adopted to support CPUs as computational devices in GPUSPH with minimal code changes and low developer effort. Detailed benchmarks illustrating the performance of the implementation and its scalability across multiple cores in both single‐socket and NUMA configurations show good strong and weak scaling.

Fault3DNnet: A lightweight 3D seismic fault detection network with bidirectional decoding

Fault detection from seismic data is a critical component of hydrocarbon exploration and production. In recent years, deep-learning techniques have made tremendous achievements and breakthroughs in seismic fault detection. Especially U-nets, represented by FaultSeg3D and its variants, are a dominant method for 3D deep-learning-based seismic fault interpretation. The incorporation of state-of-the-art network modules or training strategies with U-nets continuously improves the performance of fault detection for field seismic data, usually at the cost of increased computational intensity and hardware consumption. We design a lightweight bidirectional decoding network, Fault3DNnet, for fault detection from 3D seismic data volume. To make it simple and concise, our Fault3DNnet is equipped with basic conventional convolution and regular operation modules. The two decoding branches in Fault3DNnet could provide complementary information and improve fault detection performance. The superiority of Fault3DNnet is verified using publicly available synthetic and four field seismic data sets. The experimental results indicate that the method predicts fault structures with improved completeness and continuity compared with FaultSeg3D. Meanwhile, the parameter and computational quantity of the network are only 1/7 and 1/5 of FaultSeg3D, respectively. With the same graphics processing unit memory consumption, Fault3DNnet can handle the size of approximately six times the volume of 3D seismic data during inferencing as that of FaultSeg3D. It helps to improve prediction stability and decrease discontinuous artifacts at the stitching boundaries when predicting the large field data in chunks. In general, our Fault3DNnet delivers superior fault detection results while greatly reducing computational intensity and memory usage.

How can artificial intelligence be used to detect and mitigate zero-day vulnerabilities?

A zero-day exploit is a cyberattack that uses unknown or unaddressed security flaws in computer software, hardware, or firmware. Zero-day vulnerabilities pose very significant threats to cyber security. While traditional methods have been effective, they are lacking in many aspects due to rapidly evolving cyber threats. Hence, this paper examines artificial intelligence techniques, including machine learning and their application in enhancing cyber security against zero-day vulnerabilities. The research delves into supervised and unsupervised models and algorithms like Naive Bayes. The findings suggest that effective solutions such as artificial intelligence-driven approaches are crucial in the face of rapidly evolving cyber threats.

GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show “GoToCloud”, a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD.

Hardware Design Strategies Oriented to Edge Computing and AgriFood Electronics for Image Processing using Cellular Automata

There has been a growing interest from academia and industry in developing circuits and systems for edge computing and quality control tasks in food production lines, where image-processing is frequently required. This paper outlines the required considerations for designing a fruit classification system based on image-processing using Cellular Automata (CA) models and integrating it into reconfigurable hardware (HW) such as Field Programmable Gate Arrays (FPGAs). Parallel processing in CA requires numerous processing elements to be implemented and mapping CA models to HW generally comes with limitations. Homogeneous CA arrays are easier to design and implement in HW but can be resource-demanding. To fill this gap, this study explores different alternatives for the HW implementation of CA models, particularly trading computational-parallelism for a more optimized use of the available HW resources. We conducted experimental tests of the designed HW system using the Digilent Nexys development board, and the operation was validated against software-based benchmarks for image-processing, particularly concerning edge-detection. The presented study provides a broader range of design solutions for the HW implementation of two-dimensional CA models and a better understanding of their advantages and disadvantages. The results show that solutions focusing on instruction-parallelism add some complexity to the conception and require more design effort, compared to homogeneous CA models composed of identical cells. However, the instruction-parallel design solutions can significantly improve the HW resource utilization, especially when implementing computationally intensive CA rules in FPGAs.

One molecule to couple them all: Toward realistic numbers of molecules in multiscale molecular dynamics simulations of exciton-polaritons.

Collective strong coupling of many molecules to the confined light modes of an optical resonator can influence the photochemistry of these molecules, but the origin of this effect is not yet fully understood. To provide atomistic insights, several approaches have been developed based on quantum chemistry or molecular dynamics methods. However, most of these methods rely on coupling a few molecules (or sometimes only one) to a single cavity mode. To reach the strong coupling regime with such a small number of molecules, much larger vacuum field strengths are employed than in experiments. To keep the vacuum field realistic and avoid potential artefacts, the number of coupled molecules should be significantly increased instead, but that is not always possible due to restrictions on computational hardware and software. To overcome this barrier and model the dynamics of an arbitrarily large ensemble of molecules coupled to realistic cavity fields in atomistic molecular dynamics simulations, we propose to coarse-grain subsets of molecules into one or more effective supermolecules with an enhanced dipole moment and concerted dynamics. To verify the validity of the proposed multiscale model, we performed simulations in which we investigated how the number of molecules that are coupled to the cavity affects excited-state intra-molecular proton transfer, polariton relaxation, and exciton transport.

VR Simulation: Advancing Practical Skills in Computer Science Education

This study presents the successful integration of Virtual Reality (VR) tools as an element in the computer science curriculum at the German University of Technology, Oman. The research study investigates the use of MyScore, a VR application developed by RWTH Aachen University - Teaching & Research Field Engineering Hydrology as an environment for computer hardware assembly and troubleshooting exercises. The VR simulation utilized in this study acts as a gamified environment, incorporating elements like scorekeeping and interactive challenges to enhance practical learning in computer science, akin to game-based strategies that engage and motivate students. Over some weeks, the project involved seven students and one tutor in immersive learning sessions to develop applied skills in computer hardware assembly with VR technology over conventional teaching methods. Structured surveys and direct observations were used for data collection through students' interactions with the VR sessions. We also evaluated the student's learning experiences as well as the usability of technology. The results showed that the user-friendliness of the VR interface and its ability to accurately simulate real-world tasks associated with computer assembly received both positive and negative reactions. While some students praised the technology for being pretty cutting-edge, some criticized it as unrealistic or non-interactive. We also received complaints that ranged from actually moving around in the virtual space to navigating only through teleportation spark points. Specifically, two students got motion sickness, which is a well-known obstacle associated with VR environments, and consequently left the sessions prematurely. The feedback of the instructor pointed out that setting up the VR equipment is straightforward and described some potential methods. Still, a call for more reliability in simulations and better interfaces began to surface as nevertheless less controversial agenda items. However, the instructor saw how VR has the potential to revolutionize educational practices. Providing interactive views between the VR device and students that can act as new learning methods. The present study extends the discussion on educational technology by providing empirical evidence of the benefits and challenges experienced in deploying VR for technical education implementation. These findings thus support the continued development and validation of VR technologies in pursuit of improved commitment, cost-effectiveness, immersion, and environment quality that may maximize learning outcomes in future educational applications.

The Method of Diversity-Based Ensuring the Reliability of the Router in the IIoT System

The architecture of the Industrial Internet of Things (IIoT) system assumes the presence of a router as the main subsystem, which will allow connecting the subsystems into a single network and connecting them to the equipment of the Internet service provider. Failure of the router can lead to downtime of industrial equipment and the whole workshop at the industrial enterprise, and to the failure of the IIoT system as a whole. Control processing units of industrial routers (RCPU) are complex microprocessor systems (MS). Violation of their operability can be caused by both hardware (HW) and software (SW) failures. Therefore, to increase the reliability of RCPU it is reasonable to use such methods of reliability assurance as HW redundancy (duplication of processors with comparison of results), as well as the use of modern means of control and diagnostics. However, the number of HW and SW failures in RCPUs, is still quite high. In this connection, the solution of issues related to the development of methods for ensuring high reliability of RCPU is of great urgency. This paper proposes a method of ensuring the reliability of RCPU using SW diversity on the basis of priority series. The method let to choose the variant of redundancy of HW and the number of versions of SW as diversity in RCPU.

Power flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach

Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

Impact of Sliding Window Variation and Neuronal Time Constants on Acoustic Anomaly Detection Using Recurrent Spiking Neural Networks in Automotive Environment

Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in the context of ultra-low power sensory edge devices. Spiking architectures leverage biological plausibility to achieve computational capabilities, accurate performance, and great compatibility with neuromorphic hardware. In this work, we explore the depths of spiking neurons and feature components with the acoustic scene analysis task for siren sounds. This research work aims to address the qualitative analysis of sliding windows’ variation on the feature extraction front of the preprocessing pipeline. Optimization of the parameters to exploit the feature extraction stage facilitates the advancement of the performance of the acoustics anomaly detection task. We exploit the parameters for mel spectrogram features and FFT calculations, prone to be suitable for computations in hardware. We conduct experiments with different window sizes and the overlapping ratio within the windows. We present our results for performance measures like accuracy and onset latency to provide an insight on the choice of optimal window. The non-trivial motivation of this research is to understand the effect of encoding behavior of spiking neurons with different windows. We further investigate the heterogeneous nature of membrane and synaptic time constants and their impact on the accuracy of anomaly detection. On a large scale audio dataset comprising of siren sounds and road traffic noises, we obtain accurate predictions of siren sounds using a recurrent spiking neural network. The baseline dataset comprising siren and noise sequences is enriched with a bird dataset to evaluate the model with unseen samples.

Epidemiology of complications after non-compulsory planned hardware-removal after limbs fracture

IntroductionRemoval of hardware (HR) following a fracture is a frequent question from patients. The incidence of this kind of intervention remains very variable depending on the healthcare systems and its interest is debated in view of the benefits and associated risks that remain poorly defined. Mandatory preoperative information cannot be given optimally in this context. ObjectiveTo determine the rate of complications (major and minor) after non-compulsory planned hardware-removal following a limb fracture. HypothesisThe rate of major complications was greater than 1%. MethodsA 10-year retrospective single-center study included 1990 patients who had undergone routine HR. Analysis of medical records, with a minimum of one year of follow-up, allowed us to collect: patient data, the type and anatomical location of the osteosynthesis material, as well as the occurrence of a postoperative complication, categorized as a major complication (resulting in either a new surgical procedure, re-hospitalization, or lasting functional impairment) or a minor complication. ResultsOverall, 4.1% (79/1990) of patients experienced postoperative complications, including 1.56% (31/1990) major complications and 21 surgical revisions (1.06%). The time to onset of complications was 9.1 +/- 8.4 days. The most common complications were deep infections and impaired skin healing with superficial infection (55/79, 69.6%). Locations "around the knee" and "around the ankle" were at higher risk of complications (p < 0.01). Smoking was identified as a significant risk factor for complications, particularly deep infection (p = 0.004, OR = 8.7 [1.98; 38.11]). DiscussionNon-mandatory routine RH has a significant complication rate even in a healthy population. Preoperative information of the patient and the assessment of the benefit/risk balance are essential in this indication. This study also raises the question of mandatory smoking cessation preoperatively. Level of evidenceIV; retrospective study.

Parent GAN: image generation model for creating parent’s images using children’s images

AbstractIn the past decade, several applications have emerged in predicting children’s images using their parents via Generative Adversarial Networks (GANs). However, no one has tackled the problem of predicting one of the parents using the other parent and their children or answering the question of the possibility of deducing the parent images from the children and other parent image features. It could be used in parental identification cases. Moreover, it could help children who don’t know one of their parents to have a visual representation of their images. To perform this task, several obstacles were overcome, like the small number of parent pairs in the dataset and stabilizing the GANs to produce good-looking images. The proposed method depends on dual GAN architecture in addition to adaptive instance normalization layers and introducing a triple loss function to stabilize further and improve the resulting images. The results were proven using a kinship verification model, a face verification model, and other well-known evaluation metrics, which showed that the generated parent images are of decent quality compared to real parents’ images with affordable computational hardware. As a result, a novel method is developed that could produce unknown parent images.

The Application of Deep Learning to Accurately Identify the Dimensions of Spinal Canal and Intervertebral Foramen as Evaluated by the IoU Index.

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

An efficient XOR-free implementation of polar encoder for reconfigurable hardware

— This paper presents a novel approach to implementing an XOR-Free architecture of the non-systematic polar encoder (NSPE) for 5G radio. The optimization of XOR logic for hardware (HW) implementation is essential to reduce delay and power consumption. The proposed architecture for NSPE replaces XOR operations with combinational logical patterns, and some redundant patterns are removed with the help of bit manipulation to make it more efficient. The design infers multiplexers (2:1 or 4:1) and inverters as its functional units, making the design adequate and effective in alleviating HW complexity. The XOR-Free encoder performs the same functionality as the XOR-based conventional encoder. We have written a MATLAB script that generates Verilog hardware description language (HDL) code for fully or partially parallel polar encoders tailored to specific code lengths (N) and degrees of parallelism (M). A comparative analysis of various fully and partially parallel encoders with the XOR-Free algorithm is presented. The implementation results show that the proposed architectures are more efficient in terms of HW cost, power consumption, throughput, and latency.

Computational Study of Aminoglycoside and Fluoroquinolone Antibiotics

Molecular Docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. In this research Molecular Docking performed on Ciprofloxacin and Streptomycin by using Auto dock and Discovery Studio Software. QSAR study revealed that substitution of different electron donating or withdrawing group at different position on Ciprofloxacin and Streptomycin. Lead nucleus elaborate change in pharmacological activity. Molecular docking done by substituting or replacing different group at different position affected the potency of drug. On addition of methyl and nitro group decreases the activity of Ciprofloxacin while replacement of Guanidino groups from streptidine ringincreases its antibiotic activity.

KEGIATAN MASA PENGENALAN LINGKUNGAN SEKOLAH DENGAN PENDEKATAN SOFT SKILL DALAM MENGEMBANGAN KETERAMPILAN DAN EMOSIONAL SISWA BARU DI SD NEGERI KADUBERUK CIOMAS – KAB. SERANG

Computer training in Cibadak Village aims to improve students' technological skills in hardware, applications, and creativity. The training was held to prepare students for the challenges of the digital era by providing them with relevant and practical capabilities. The training method involved both practical and theoretical approaches, which included hands-on sessions for hardware assembly and maintenance, the use of various productivity and creative applications, and projects that encourage innovation. The results of the training showed significant success in three key areas. First, students demonstrated improved proficiency in handling computer hardware, including basic skills in assembly and troubleshooting. Second, application training helped students master software such as word processing, worksheets, graphic design and basic programming tools. Third, students' creativity thrives through technology-based projects that encourage them to create innovative solutions, such as video creation, animation, and simple application development. In conclusion, the computer training in Cibadak Village successfully improved students' technical and creative skills, demonstrating the importance of integrating technology education in the school curriculum to prepare the younger generation for the digital world.

PENDAMPINGAN PENGGUNAAN KOMPUTER ANAK DI DESA CIBADAK KECAMATAN CIBADAK KABUPATEN LEBAK-BANTEN

Computer training in Cibadak Village aims to improve students' technological skills in hardware, applications, and creativity. The training was held to prepare students for the challenges of the digital era by providing them with relevant and practical capabilities. The training method involved both practical and theoretical approaches, which included hands-on sessions for hardware assembly and maintenance, the use of various productivity and creative applications, and projects that encourage innovation. The results of the training showed significant success in three key areas. First, students demonstrated improved proficiency in handling computer hardware, including basic skills in assembly and troubleshooting. Second, application training helped students master software such as word processing, worksheets, graphic design and basic programming tools. Third, students' creativity thrives through technology-based projects that encourage them to create innovative solutions, such as video creation, animation, and simple application development. In conclusion, the computer training in Cibadak Village successfully improved students' technical and creative skills, demonstrating the importance of integrating technology education in the school curriculum to prepare the younger generation for the digital world.