Processing Capabilities Research Articles

Classical Inference of a New PCI CNpmkc for Logistic-Exponential Process Distribution

The process capability index (PCI) has been developed as a useful instrument for measuring how well a product matches customer expectations while also assessing the performance of a process. We are aware that when the quality characteristics of the processes follow normal distribution, conventional PCIs produces superior results. However, these traditional indices could not yield reliable findings when assessing nonnormally distributed processes, which could make decision-making more difficult. In this paper, we take into account the [Formula: see text] process capacity index, which may be used for both normally distributed and nonnormally distributed processes. We have employed 10 traditional methods of estimation to estimate the PCI [Formula: see text] when the process has a logistic-exponential distribution, and the performances of these traditional estimates of the index [Formula: see text] are compared in terms of their mean squared errors through a simulation exercise. Then, we create five PCI [Formula: see text] bootstrap confidence intervals and contrast them based on their average widths and related coverage probabilities. In order to demonstrate the applicability of the suggested methods of estimation, two data sets pertaining to the electronic industries are re-analyzed.

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of 74.68% in model storage requirements and 55.93% in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology.

Revealing the Origin of Graphene Anchored Single Palladium Atom for Electrochemical CO2 Reduction to Syngas with Tunable CO/H2 Ratios

Pd based catalysts are rare metal‐based catalyst to yield tunable CO/H2 ratios for Fischer‐Tropsch synthesis. How to achieve the co‐production of CO and H2 with as little Pd as possible is extremely meaningful for Cn industry. Recent experiment revealed single Pd atom anchored on graphene exhibits high activity for CO2 electroreduction to syngas, yet the origin of activity and controllable CO/H2 ratios, especially the exact Pd coordination structure, remains elusive. Here we employ grand‐canonical density functional theory to show that Pd‐N1, rather than the commonly accepted Pd‐N4, serves as the active center, and the charge‐carrying capability is an effective descriptor. The site with more Pd‑C coordination can better submerge in graphene's delocalized π electrons for higher charge‐carrying capacity to carry excess charges that occupy Pd 4dz2 orbital and promote electron injection. Importantly, the tunable CO/H2 ratio can be explained with difference in charge‐carrying capability of transition state for *COOH and *H2 formation. This work solves the puzzle of coordinating structure of Pd active site and demonstrates the important role of charge‐carrying capability in electrochemical process, which shall provide a reference for further exploration of efficient electrocatalysts.

Engineered 3D Vortex Array with Customized Energy and Topological Charges for Multi‐View Display

AbstractArrays of multiple vortex beams enhance parallel processing capabilities from particle manipulation to information communication. However, the spatial position, topological charge, and energy parameters of the vortex beams within the array have yet to be precisely controlled, posing significant challenges to the realization of multi‐view 3D Orbital Angular Momentum (OAM) display using vortex arrays as information carriers. To address this issue, a method for generating vortex arrays based on a phase‐only hologram, customizing the array by using the energy and OAM spectrum of each vortex beam as the loss function is developed. Experimental results indicate that a 2D array generates 100 vortex beams in the spatial frequency domain, while a 3D array in the Fresnel zone produces 252 vortex beams across 7 planes, each with 36 unprecedentedly controllable energy and topological charge vortex beams. Subsequently, images of 4 views using the 3D vortex arrays and decoded through coordinate transformation to achieve a multi‐view 3D OAM display are encoded. The results demonstrate imaging quality with Mean Peak Signal‐to‐Noise Ratio (MPSNR) of 12.7, 13.0, 13.4, and 12.2 dB in 4 views, respectively, offering promising opportunities for applications in 3D optical manipulation, high‐capacity communication, and 3D OAM holography.

Faster Evaluation of Dimensional Machine Performance in Additive Manufacturing by Using COMPAQT Parts

Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to Additive Manufacturing printers, while testing most of the printing area as recommended takes a very long time (nearly 1 month is common). This paper, by proposing a novel part design called COMPAQT (Component for Machine Performances Assessment in Quick Time), aims at giving the same level of printing area coverage, while keeping the manufacturing time below 24 h. The method was successfully tested on a material extrusion printer. It allowed the determination of potential and real machine tolerance interval capabilities. Independently of the feature size, those aligned with the X axis achieved lower TICs than those aligned with the Y axis, while the Z axis exhibited the best performance. The measurements specific to one part exhibited a systematic error centered around 0 mm ± 0.050 mm, while those involving two parts reached up to 0.314 mm of deviation. COMPAQT can be used in two applications: evaluating printer tolerance interval capabilities and tracking its long-term performance by incorporating it into batches of other parts.

From port to disposal: a strategic view to Australia’s O&G decommissioning success across the value chain

Australia’s decommissioning portfolio is forecasted to increase substantially over the next 50 years amounting to US$40.5 billion nationally, with 90% of decommissioning by 2060. High costs, the complexity of our infrastructure geography, ensuring circular economy and carbon efficient principles, will be challenges the industry will need to collaborate through. But Australia has a skilled workforce, a strong sustainability commitment, a mindset of partnership and collaboration as well as a tradition for innovation. These strategic advantages will enable the establishment of new industries early to be a regional player in decommissioning. As the assets are decommissioned offshore, travel to the beach, transported to identified waste providers and go through final end-of-life cycle management including recycling and repurposing, strategic linkages of the value chain will be key for a successful decommissioning industry set up. Understanding the oil and gas (O&G) decommissioning industry’s needs, opportunities and challenges early will strategically enable Australia to pave the way for building decommissioning expertise, critical infrastructure, and sustainable practices. This includes understanding where ports should intersect with waste storage, processing, recycling and disposal capabilities, as we have an opportunity to think differently about turning waste into new resources. And most importantly, how do we ensure availability of a skilled workforce in an already resource-crunched market? As we analyse the port to disposal value chain, it is important to overlay the carbon footprint, regulation, and socio-economic lenses on the journey to support the industry and to ensure long-term viability to minimise conflict with other competing industries or activities.

Compact multichannel reconfigurable mode demultiplexer enabled by phase change material

The reconfigurable mode demultiplexer is a crucial component for flexibly routing modes into different channels in on-chip multimode photonic systems with enhanced information processing capabilities. In this paper, we present a multichannel reconfigurable mode demultiplexer enabled by ultralow-loss phase-changing Sb2Se3. By harnessing the phase-change-mediated mode coupling in asymmetric directional couplers (ADCs), one or more of the higher-order modes including TE1, TE2 and TE3 modes could be selectively dropped from the bus waveguide with low losses. With an optimized ADCs structure, the proposed mode demultiplexer demonstrates insertion loss less than 0.227 dB in the ON (amorphous) state and the extinction ratios large than 23.28 dB over the C-band. By coupling the access waveguides of the higher-order mode in parallel on both sides of the bus waveguide, the device size can be compact with a footprint of ∼ 7 × 75 µm2, and this design approach can be further extended to enable more higher-order mode multiplexing.

Optimized Design of EdgeBoard Intelligent Vehicle Based on PP-YOLOE+

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system’s ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles’ operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model’s performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms.

Textual similarity for legal precedents discovery: Assessing the performance of machine learning techniques in an administrative court

The importance of legal precedents in ensuring consistent jurisprudence is undisputed. Particularly in jurisdictions following the Common law, but even in Civil law systems, uniformity in case law requires adherence to precedents. However, with the growing volume of cases, manual identification becomes a bottleneck, prompting the need for automation. Leveraging the capabilities of natural language processing (NLP) and machine learning (ML), our study delves into the potential of automation in identifying similar cases indicative of precedents. Drawing from a unique, substantial dataset of legal cases from an administrative court in Brazil, we extensively evaluated over one hundred combinations of document representations and text vectorizations. Contrary to earlier studies that relied on minimal validation samples, ours employed a statistically significant sample vetted by legal experts. Our findings reveal that models focusing on granular text representations perform optimally, especially when extracting concepts and relations. Notably, while intricate models may not always guarantee superior outcomes, the importance of refining textual features cannot be understated. These findings pave the way for creating efficient decision support systems in judicial contexts and set a direction for future research aiming to integrate technology in legal decision-making.

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

A Historical Building Information Modeling-Based Framework to Improve Collaboration and Data Security in Architectural Heritage Restoration Projects

With the increasing awareness of architectural heritage conservation and the development of digital technology, there is an urgent need in the field of architectural heritage restoration for a novel solution that can enhance data security, collaboration efficiency, and file management capabilities. This study proposes an Architectural Heritage Restoration Distributed Common Data Environment (AHR-DCDE) framework based on blockchain and IPFS technologies to address the above challenges. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. In this study, the practicality and effectiveness of the AHR-DCDE framework is verified by taking the heritage restoration design project of Pinghe Packing Factory in Wuhan, Hubei Province, as an example. Evaluation of the framework’s network latency, throughput, and storage costs indicates that AHR-DCDE can meet the requirements of architectural heritage restoration projects, possessing efficient capabilities for handling and sharing project data. Furthermore, the implementation of the AHR-DCDE framework also facilitates efficient collaboration among interdisciplinary teams, providing robust technical support for the protection and restoration of architectural heritage.

DELIVERY AUTOMATION ROBOT USING ARDUINO MICROCONTROLLER

The Delivery Automation Robot project aims to develop a robust and efficient autonomous delivery robot for last-mile logistics operations. Built upon a foundation of sturdy hardware components including a four-wheel metal chassis and large wheels, the Delivery Automation Robot ensures stability and durability while navigating diverse terrains. Integrated with advanced control systems such as the L298n motor driver shield, NODEMCU, and Arduino Uno, the robot offers precise movement control and data processing capabilities. Equipped with essential sensors like the magnetometer HMC5883 and NEO 6m GPS module, the MDAR achieves accurate localization and navigation, crucial for optimizing delivery routes. Additionally, wireless communication facilitated by the HC06 Bluetooth module enables remote monitoring and control. Overall, the Delivery Automation Robot represents an innovative solution poised to revolutionize last-mile delivery processes, enhancing efficiency and reliability in logistics operations. Keywords: four-wheel chassis, L298n motor driver, NODEMCU, Arduino Uno, magnetometer HMC5883, NEO 6m GPS module, HC06 Bluetooth module.

Evolving Generative AI: Entangling the Accountability Relationship

Since ChatGPT's debut, generative AI technologies have surged in popularity within the AI community. Recognized for their cutting-edge language processing capabilities, these excel in generating human-like conversations, enabling open-ended dialogues with end-users. We consider that the future adoption of generative AI for critical public domain applications transforms the accountability relationship. Previously characterized by the relationship between an actor and a forum, the introduction of generative systems complicates accountability dynamics as the initial interaction shifts from the actor to an advanced generative system. We conceptualise a dual-phase accountability relationship involving the actor, the forum, and the generative AI as a foundational approach to understanding public sector accountability in the context of these technologies. Focusing on integrating generative AI for assisting healthcare triaging, we identify potential challenges introduced for maintaining effective accountability relationships, highlighting concerns that these technologies relegate actors to a secondary phase of accountability and creates a disconnect between government actors and citizens. We suggest recommendations aimed at disentangling the complexities generative systems bring to the accountability relationship. As we speculate on the technologies disruptive impact on accountability, we urge public servants, policymakers, and system designers to deliberate on the potential accountability impact generative systems produce prior to their deployment.

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning

Understanding the unique properties of perovskite materials is crucial in advancing solar energy technologies. Factors like heat of formation and bandgap significantly influence the light absorption capability and stability of perovskite solar cells. However, it is time-consuming and labor-intensive to obtain the properties of perovskites using traditional experimental or high-throughput computational methods. As a prospective method, machine learning can find regularities in the given training data and give accurate prediction results. In this article, we use deep learning models based on attention mechanisms and elemental features to predict the heat of formation and bandgap of perovskite materials. Random Forest and Gradient Boosted Regression Tree models have also been used for interpretable predictions of properties. The compositionally restricted attention-based network was improved by introducing a densely connected network and optimizing the network structure to increase data processing capabilities. The experiment results show that the mean absolute errors of the heat of formation and bandgap on the test sets are decreased by 5.77% and 3.37% respectively. The optimized model also shows better performance when used for classification tasks. In addition, we use the gradient boosting regression tree model and the shapley additive explanations tool to conduct an interpretable analysis, explaining the impact of different features on the predictions of the properties.

Attack on optical cryptosystems by skip connection networks

Optical encryption methods, due to their efficient operation speed and parallel processing capabilities, hold significant importance in securing multidimensional and large-volume data. Enhancing the security of optical cryptosystems from the perspective of cryptanalysis holds significant importance currently. Presently, attack methods against optical encryption are complex, and the effectiveness of these attacks is insufficient. Security analysis solutions face limitations in both breadth and depth. Therefore, this paper proposes an attack on optical cryptosystems based on a skip connection network, demonstrating the susceptibility of optical cryptosystems to attacks based on neural network algorithms. The network model is trained on plaintext-ciphertext pairs, fitting equivalent keys without various additional conditions. It approximates plaintext information in high-dimensional space, directly obtaining corresponding plaintext through ciphertext information, expanding the applicability and enhancing the effectiveness of the attack scheme. Finally, the feasibility and effectiveness of the attack scheme were verified through computer simulations. The experiments indicate that the method proposed in this paper has low computational complexity, wide applicability, produces high-quality decrypted images, and high decipherment accuracy. This provides a universal approach for analyzing the security of various optical cryptosystems from the perspective of chosen plaintext attacks.

PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios

Evolutionary algorithms exhibit flexibility and global search advantages in multi-UAV path planning, effectively addressing complex constraints. However, when there are numerous obstacles in the environment, especially narrow passageways, the algorithm often struggles to quickly find a viable path. Additionally, collaborative constraints among multiple UAVs complicate the search space, making algorithm convergence challenging. To address these issues, we propose a novel hybrid particle swarm optimization algorithm called PPSwarm. This approach initially employs the RRT* algorithm to generate an initial path, rapidly identifying a feasible solution in complex environments. Subsequently, we adopt a priority planning method to assign priorities to UAVs, simplifying collaboration among them. Furthermore, by introducing a path randomization strategy, we enhance the diversity of the particle swarm, thereby avoiding local optimum solutions. The experimental results show that, in comparison to algorithms such as DE, PSO, ABC, GWO, and SPSO, the PPSwarm algorithm demonstrates significant advantages in terms of path quality, convergence speed, and runtime when addressing path planning issues for 40 UAVs across four different scenarios. In larger-scale experiments involving 500 UAVs, the proposed algorithm also exhibits excellent processing capability and scalability.

Evaluating the strengths and weaknesses of large language models in answering neurophysiology questions

Large language models (LLMs), like ChatGPT, Google’s Bard, and Anthropic’s Claude, showcase remarkable natural language processing capabilities. Evaluating their proficiency in specialized domains such as neurophysiology is crucial in understanding their utility in research, education, and clinical applications. This study aims to assess and compare the effectiveness of Large Language Models (LLMs) in answering neurophysiology questions in both English and Persian (Farsi) covering a range of topics and cognitive levels. Twenty questions covering four topics (general, sensory system, motor system, and integrative) and two cognitive levels (lower-order and higher-order) were posed to the LLMs. Physiologists scored the essay-style answers on a scale of 0–5 points. Statistical analysis compared the scores across different levels such as model, language, topic, and cognitive levels. Performing qualitative analysis identified reasoning gaps. In general, the models demonstrated good performance (mean score = 3.87/5), with no significant difference between language or cognitive levels. The performance was the strongest in the motor system (mean = 4.41) while the weakest was observed in integrative topics (mean = 3.35). Detailed qualitative analysis uncovered deficiencies in reasoning, discerning priorities, and knowledge integrating. This study offers valuable insights into LLMs’ capabilities and limitations in the field of neurophysiology. The models demonstrate proficiency in general questions but face challenges in advanced reasoning and knowledge integration. Targeted training could address gaps in knowledge and causal reasoning. As LLMs evolve, rigorous domain-specific assessments will be crucial for evaluating advancements in their performance.

Graph Analysis of the Visual Cortical Network during Naturalistic Movie Viewing Reveals Increased Integration and Decreased Segregation Following Mild TBI

Traditional neuroimaging methods have identified alterations in brain activity patterns following mild traumatic brain injury (mTBI), particularly during rest, complex tasks, and normal vision. However, studies using graph theory to examine brain network changes in mTBI have produced varied results, influenced by the specific networks and task demands analyzed. In our study, we employed functional MRI to observe 17 mTBI patients and 54 healthy individuals as they viewed a simple, non-narrative underwater film, simulating everyday visual tasks. This approach revealed significant mTBI-related changes in network connectivity, efficiency, and organization. Specifically, the mTBI group exhibited higher overall connectivity and local network specialization, suggesting enhanced information integration without overwhelming the brain’s processing capabilities. Conversely, these patients showed reduced network segregation, indicating a less compartmentalized brain function compared to healthy controls. These patterns were consistent across various visual cortex subnetworks, except in primary visual areas. Our findings highlight the potential of using naturalistic stimuli in graph-based neuroimaging to understand brain network alterations in mTBI and possibly other conditions affecting brain integration.

Entangled photon pair generation in an integrated SiC platform

Entanglement plays a vital role in quantum information processing. Owing to its unique material properties, silicon carbide recently emerged as a promising candidate for the scalable implementation of advanced quantum information processing capabilities. To date, however, only entanglement of nuclear spins has been reported in silicon carbide, while an entangled photon source, whether it is based on bulk or chip-scale technologies, has remained elusive. Here, we report the demonstration of an entangled photon source in an integrated silicon carbide platform for the first time. Specifically, strongly correlated photon pairs are efficiently generated at the telecom C-band wavelength through implementing spontaneous four-wave mixing in a compact microring resonator in the 4H-silicon-carbide-on-insulator platform. The maximum coincidence-to-accidental ratio exceeds 600 at a pump power of 0.17 mW, corresponding to a pair generation rate of (9 ± 1) × 103 pairs/s. Energy-time entanglement is created and verified for such signal-idler photon pairs, with the two-photon interference fringes exhibiting a visibility larger than 99%. The heralded single-photon properties are also measured, with the heralded g(2)(0) on the order of 10−3, demonstrating the SiC platform as a prospective fully integrated, complementary metal-oxide-semiconductor compatible single-photon source for quantum applications.