Reconfigurable computing (RC) theory aims to take advantage of the flexibility of general‐purpose processors (GPPs) alongside the performance of application specific integrated circuits (ASICs). Numerous RC architectures have been proposed since the 1960s, but all are struggling to become mainstream. The main factor that prevents RC to be used in general‐purpose CPUs, GPUs, and mobile devices is that it requires extensive knowledge of digital circuit design which is lacked in most software programmers. In an RC development, a processor cooperates with a reconfigurable hardware accelerator (HA) which is usually implemented on a field‐programmable gate arrays (FPGAs) chip and can be reconfigured dynamically. It implements crucial portions of software (kernels) in hardware to increase overall performance, and its design requires substantial knowledge of digital circuit design. In this paper, a novel RC architecture is proposed that provides the exact same instruction set that a standard general‐purpose RISC microprocessor (e.g., ARM Cortex‐M0) has while automating the generation of a tightly coupled RC component to improve system performance. This approach keeps the decades‐old assemblers, compilers, debuggers and library components, and programming practices intact while utilizing the advantages of RC. The proposed architecture employs the LLVM compiler infrastructure to translate an algorithm written in a high‐level language (e.g., C/C++) to machine code. It then finds the most frequent instruction pairs and generates an equivalent RC circuit that is called miniature accelerator (MA). Execution of the instruction pairs is performed by the MA in parallel with consecutive instructions. Several kernel algorithms alongside EEMBC CoreMark are used to assess the performance of the proposed architecture. Performance improvement from 4.09% to 14.17% is recorded when HA is turned on. There is a trade‐off between core performance and combination of compilation time, die area, and program startup load time which includes the time required to partially reconfigure an FPGA chip.
Micronization techniques like High pressure homogenization (HPH) and Disc milling (DM) are mechanical, high-shear processes used to reduce food particle size to the micron scale. These cell disruption technologies are widely used to complement green extraction processes for bioactive compound extraction, reducing the need for high temperature or organic solvents. However, while effective in disrupting cell structures and enhancing mass transfer, some techniques face scalability limitations. Identifying scalable alternatives is essential for reproducing small-scale results in industrial applications.
This study investigates DM as a viable technique for scaling up HPH and promoting the extraction in water of bioactive molecules from rucola. High shear mixing (HSM) was utilized as a benchmark. Rucola was treated with HPH and DM in distilled water at ratios of 1:10 and 1:3 (w/v), respectively. The number of passes varied from 5 to 15 for HPH and 1 to 5 for DM. Extraction efficiency was evaluated using ferric-reducing antioxidant power (FRAP) for the extracts collected in the supernatant, along with total phenolic compounds (TPC) and total flavonoid compounds (TFC) analyses. Particle size distribution and microscopy assessed the extent of cellular disruption.
Results indicate that DM up to five passes achieved significant particle size reduction, comparable to HPH, with d(0.1), d(0.5), and d(0.9) reduced to 70.3 µm, 334.2 µm, and 825.7 µm, respectively, suggesting complete cell disruption. Consequently, TPC, TFC, and FRAP values significantly increased compared to HSM- and HPH-treated samples. Moreover, DM's lower operational temperature and milder mechanical forces likely contributed to enhanced compound preservation and higher antioxidant activity. Overall, DM proves to be an effective technique for HPH scaling up, improving bioactive compound extraction and antioxidant activity in rucola, and offering a sustainable approach to agrifood residue valorization.
Chemical engineering, Computer engineering. Computer hardware
Salman Nisar, Josué Gonzalez-Camejo, Anna Laura Eusebi
et al.
Increasing global fish production demands sustainable waste management for the proper disposal of process leftovers. Fish waste stabilisation using pyrolysis has the potential to stabilise this putrescible waste, as well as production of biochar for sustainable agricultural applications. This study investigated the influence of residence times at a fixed temperature (400°C) on the yield and quality of biochar by co-pyrolysis of fish and pruning waste. Results showed a decreasing trend of biochar yield with a decrease in residence time for pruning waste (PW) tests, whereas fish waste (FW) and PW blend (30:70 w/w) resulted in a relatively stable trend. Biochar obtained at 30 minutes residence time accounted for 42.1%, with a higher carbon content of 62.8% and H/C of 0.69, indicating thermal conversion and stable biochar. Furthermore, biochar exhibits a low concentration of trace elements, complying with safety and quality regulations for biochar.
Chemical engineering, Computer engineering. Computer hardware
Noshina Tariq, Amjad Alsirhani, Mamoona Humayun
et al.
Abstract The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field.
Cellulose derivatives are promising raw material as emulsifier, thickening or suspending agent, excipient, drug release agent, adhesive or water-based coating for food, building or pharmaceutical industries. Additives such as plasticizers or surfactants are frequently incorporated into such biopolymers to improve their properties. The aim of this work is to formulate, by introducing dedicated additives, biopolymer films based on cellulose derivatives and to explore their surface properties. The study investigates the influence of hydrophilic and hydrophobic additives on the surface properties of hypromellose films. The surface structure and morphology of hypromellose films shows the presence of nano-clusters, the which disappear as surfactant content increases. So does the surface average roughness and the surface free energy of the formulated films, suggesting the formation of a weak boundary layer at the film surface. As a consequence, a sharp decrease of nano-adhesion and nanofriction forces is observed. Hydrophilic additive induces the swelling of hypromellose clusters and an increase of the film surface free energy as well as nano-friction and nano-adhesion forces. The present study clearly underlines the strong dependence of the surface properties of the formulated films on additive nature and concentration as well as the interplay with additive-biopolymer matrix compatibility. Formulation appears then as an original and simple way to tune and target surface morphology and surface properties of biobased polymer films.
Chemical engineering, Computer engineering. Computer hardware
As quantum computers mature, the complexity of quantum software increases. As we move from the initial standalone quantum algorithms toward complex solutions combining quantum algorithms with traditional software, new software engineering methods and abstractions are needed. Nowadays, quantum computers are usually offered in the cloud, under a pay-per-use model, leading to the adoption of the service-oriented good practices that dominate the cloud today. However, specific adaptations are needed to reap the benefits of service-oriented computing while dealing with quantum hardware limitations. In this article, we propose the Quantum API Gateway—an adaptation of the API Gateway pattern that takes into account the fact that quantum services cannot be deployed as traditional services. Instead, the Quantum API Gateway recommends the best quantum computer to run a specific quantum service at run time. As proof of concept, we provide an implementation of the Quantum API Gateway for the Amazon Braket platform.
. The implementations of matrix multiplication on contemporary, vector-oriented, and multicore-oriented computer hardware are very carefully designed and optimized with respect to their efficiency, due to the essential significance of that operation in other science and engineering domains. Consequently, the available implementations are very fast and it is a natural desire to take advantage of the efficiency of those implementations in other problems, both matrix and nonmatrix. Such an approach is often called a black box matrix computation paradigm in the literature on the subject. In this article, we gathered a broad series of algorithms taking advantage of the efficiency of fast matrix multiplication algorithms in other mathematical and computer science operations.
Eszter Szabó-Szentgróti, Márta Konczos-Szombathelyi, Szabolcs Rámháp
et al.
Digitalization and technological innovation have revolutionized the retail sector. In recent years, a new trend has emerged in the form of unmanned stores, pioneered by Amazon Go. Unmanned solutions using artificial intelligence are beginning to enter the public consciousness and represent a new sustainability perspective (such as lowering paper waste, packaging or using sustainable construction materials) in trade. Although it is not yet widespread and is still a new solution for consumers, the global market dynamics suggest that it will expand in the future. Unmanned shops pose some challenges, but these can be effectively addressed by the appropriate introduction of new technology. To identify or filter out potential shortcomings of this technology on the consumer side, it is also necessary to examine the acceptance of this technology by customers. In this paper, the internationally accepted Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model was modified and used to examine how consumers accept this technology. For data analysis, Partial Least Squares-Structural Equation Modelling method was applied. In the proposed model six constructs were examined on how they influence the intention to use. In the performed query, Hungarian university students’ behavioural intention is influenced by performance expectancy, effort expectancy, and hedonic motivation. However social influence, atmosphere, and price sensitivity have no significant influence on use intention.
Chemical engineering, Computer engineering. Computer hardware
Elsy Rodríguez Revelo, Wellington Rafael Cruz Salavarria, Jair Josue Seme Márquez
Este trabajo tiene como objetivo validar la plataforma SECOED V2, desarrollada para mejorar la formación docente en la Universidad de Guayaquil. Aunque inicialmente diseñada para una facultad específica, la falta de una evaluación adecuada generó fallos técnicos y problemas en su escalabilidad. Con este trabajo se identificaron y resolvieron estos problemas mediante un análisis detallado, priorizando áreas críticas y proponiendo soluciones a corto y largo plazo. Se empleó una metodología mixta que combina enfoques cualitativos y cuantitativos, permitiendo obtener una visión completa del funcionamiento interno de la plataforma. Además, se llevó a cabo un análisis exploratorio y descriptivo para familiarizar a los usuarios con el manual de parametrización, que registra los componentes de la plataforma. Como resultado fundamental se logró garantizar una experiencia de formación docente en línea de alta calidad a través de SECOED V2.
During the COVID-19 pandemic, antibiotic production and consumption are increasing dramatically, and exploring the effective treatment of medical wastewater methods has become a major challenge to environmental protection. SMX as an important sulfonamide antibiotic (SA) has been frequently observed worldwide in contaminated water. CuFe2O4, as a class of promising efficient catalysts to activated PMS has been widely used to degrade organic pollutants. To maximize the catalytic performance of none-support-CuFe2O4, a single-needle electrospinning approach in conjunction with the sol-gel process at 500 °C is employed to synthesize CuFe2O4¬-500 (CFO-500), which was used for PMS activation to degrade SMX. The results indicate that CFO-500 exhibited an excellent degradation rate on SMX in 90 min, which exceeded 94.63 %. The outstanding properties could be mainly ascribed to two aspects: (i) Successfully obtained almost singular copper ferrite phases, which have higher specific surface area and more electron transfer path; (ii) The cooperation of free radical and singlet oxygen, abundant active sites, and excellent electron transfer capability make it a leap in the ability to produce active substances. ·SO4- and singlet oxygen 1O2 played a dominant role according to EPR and quenching tests, especially 1O2. This work may provide a new idea and mechanism for activating PMS to degrade wastewater.
Chemical engineering, Computer engineering. Computer hardware
The ability to create low-cost, high-availability, moderate-performance, low-power, sustainable file storage clusters that may be organically distributed throughout an organization would allow organizations to bring data back from cloud-based providers, provide local backup solutions, create local distributed storage pods, and allow remote developing countries to have access to information and other compute resources. The Internet of Things has driven much of the development in low-power ecological systems. The emergence of these devices allowed for the creation of this research project. This research utilized the design science method to create an instantiation of this concept as a demonstrative artifact that could be powered on USB power provided from almost any source. This includes the ability for small solar arrays to provide adequate power to charge the onboard power banks, allowing for continual use over periods of power loss or darkness. This artifact was evaluated using real-time direct download from up to twentyfour workstations. During the course of the research for a period of over approximately 400 days, the artifact performed without interruption. This could be an indication that it may be possible to replace cloud-based storage with organically-distributed sustainable systems for enterprise-level use.
Bio-inspired neuron models are the key building blocks of brain-like neural networks for brain-science exploration and neuromorphic engineering applications. The efficient hardware design of bio-inspired neuron models is one of the challenges to implement brain-like neural networks, as the balancing of model accuracy, energy consumption and hardware cost is very challenging. This paper proposes a high-accuracy and energy-efficient Fast-Convergence COordinate Rotation DIgital Computer (FC-CORDIC) based Izhikevich neuron design. For ensuring the model accuracy, an error propagation model of the Izhikevich neuron is presented for systematic error analysis and effective error reduction. Parameter-Tuning Error Compensation (PTEC) method and Bitwidth-Extension Error Suppression (BEES) method are proposed to reduce the error of Izhikevich neuron design effectively. In addition, by utilizing the FC-CORDIC instead of conventional CORDIC for square calculation in the Izhikevich model, the redundant CORDIC iterations are removed and therefore, both the accumulated errors and required computation are effectively reduced, which significantly improve the accuracy and energy efficiency. An optimized fixed-point design of FC-CORDIC is also proposed to save hardware overhead while ensuring the accuracy. FPGA implementation results exhibit that the proposed Izhikevich neuron design can achieve high accuracy and energy efficiency with an acceptable hardware overhead, among the state-of-the-art designs.
Using the traditional single image encryption algorithm for video images is time-consuming and inefficient. To improve the efficiency of video image encryption, combined with the Cellular Neural Network(CNN) hyper-chaotic system and Logistic chaos mapping, an algorithm combining single frame encryption one by one and multi-frame combination encryption is proposed.According to the video frame, SHA-256 generates the initial value of logistic, and the logistic chaotic sequence is obtained through Logistic mapping iteration.The generated chaotic sequence diffuses the video frame by frame.The video frames are combined into a matrix in binary form, and the initial value generated by using the hash function according to the combination matrix is substituted into CNN hyper-chaotic system.The obtained chaotic sequence scrambles the combination matrix, and the diffusion and scrambling of each pixel of all video frames are completed in one step to shorten the encryption time.Simultaneously, the combination matrix is re-decomposed into a single frame image to obtain the final encrypted video image.Experiments show that using a high-dimensional hyper-chaotic system in the algorithm has higher security, effectively shortens the time spent encrypting video images, and can resist statistical attacks, differential attacks, and violent attacks.
The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in important domains such as computer vision, speech recognition, and natural language processing. The most popular class of techniques used in these domains is called deep learning, and is seeing significant attention from industry. However, these models require incredible amounts of data and compute power to train, and are limited by the need for better hardware acceleration to accommodate scaling beyond current data and model sizes. While the current solution has been to use clusters of graphics processing units (GPU) as general purpose processors (GPGPU), the use of field programmable gate arrays (FPGA) provide an interesting alternative. Current trends in design tools for FPGAs have made them more compatible with the high-level software practices typically practiced in the deep learning community, making FPGAs more accessible to those who build and deploy models. Since FPGA architectures are flexible, this could also allow researchers the ability to explore model-level optimizations beyond what is possible on fixed architectures such as GPUs. As well, FPGAs tend to provide high performance per watt of power consumption, which is of particular importance for application scientists interested in large scale server-based deployment or resource-limited embedded applications. This review takes a look at deep learning and FPGAs from a hardware acceleration perspective, identifying trends and innovations that make these technologies a natural fit, and motivates a discussion on how FPGAs may best serve the needs of the deep learning community moving forward.
El presente artículo muestra el origen del desarrollo sostenible en forma breve, así como las proyecciones futuras de los problemas que pueden ocasionar el crecimiento demográfico y el impacto del avance industrial en los países. También se analiza sobre los conceptos y la caracterización que se da al desarrollo sostenible, sus alcances y las ventajas que pueden proporcionar las políticas establecidas en las regiones que implementan dichas normativas. Asimismo, las Tecnologías Limpias (TL), que aportan a la mejora de las emisiones y desechos que emiten las industrias al ambiente, son tratadas en la dimensión de la eficacia de las industrias. Se determina además la forma que pueden aportar ciertos indicadores en la evaluación del desarrollo y cómo estos contribuyen a la resolución de varios problemas relacionados al medio ambiente y la energía tales como: Sustentabilidad ecológica que se relaciona con el mantenimiento de los ecosistemas, Sustentabilidad social, que propone mejorar la calidad de vida de las personas y, Sustentabilidad económica.
Quantum computers promise to solve certain problems that are intractable for classical computers, such as factoring large numbers and simulating quantum systems. To date, research in quantum computer engineering has focused primarily at opposite ends of the required system stack: devising high-level programming languages and compilers to describe and optimize quantum algorithms, and building reliable low-level quantum hardware. Relatively little attention has been given to using the compiler output to fully control the operations on experimental quantum processors. Bridging this gap, we propose and build a prototype of a flexible control microarchitecture supporting quantum-classical mixed code for a superconducting quantum processor. The microarchitecture is based on three core elements: (i) a codeword-based event control scheme, (ii) queue-based precise event timing control, and (iii) a flexible multilevel instruction decoding mechanism for control. We design a set of quantum microinstructions that allows flexible control of quantum operations with precise timing. We demonstrate the microarchitecture and microinstruction set by performing a standard gate-characterization experiment on a transmon qubit. CCS CONCEPTS. • General and reference → General conference proceedings; • Computer systems organization → Quantum computing; • Hardware → Quantum technologies;
The structural information of retinal vesselsassists in the diagnosis of ophthalmic diseases,and thus efficient and accurate segmentation of retinal vessel images has become an urgent clinical demannd.The traditional artificial segmentation methods are time-consumingand frequently affected by personal subjective factors,leading to a decline in segmentation quality.To address the problem,thispaper proposes an automatic image segmentation algorithm based on dense attention network.The algorithm combines the basic structure of the encoder-decoder fully convolutional neural network with the densely connected network to fully extract the features of each layer.Then the attention gate module on the decoder side of the network is introduced to suppress unnecessary features and thus improve the segmentation accuracy of retinal vessel segmentation.Experimental results on DRIVE and STARE fundus image datasets show that compared with other algorithms based on deep learning,the proposedalgorithm has excellent segmentation performance with the sensitivity,specificity,accuracy and AUC value all improved.
Frederic Heymes, Pol Hoorelbeke, Dirk Roosendans
et al.
A series of large-scale tests were carried out to evaluate the effectiveness of using extinguishing powder (Purple K) to supress propane or petrol fire or to reduce emitted radiative heat flux. Three sets of different fire were carried out: a petrol leakage fire, a petrol pool fire and a liquid propane jet fire impinging a horizontal cylinder. In these tests, the powder was not able to extinguish the liquid hydrocarbons fire, but in some cases was able to extinguish the propane jet fire. In all cases, powder spray had excellent properties to reduce radiative heat flux.
Chemical engineering, Computer engineering. Computer hardware