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Reconfigurable Manufacturing Systems (RMS) have gained attention in the aerospace industry in the past years, as post-pandemic context shows drastic production capacity changes and new environmental regulations to which it must adapt quicker than before to maintain competitiveness. Nevertheless, current RMS design methods have not thoroughly considered this industry specificities, nor industrial resources requirements in the current concurrent design practice. These limitations have been identified in several recent research works. Ontology-based Engineering (OBE) systems can stand overly complex collaborative design processes involving multidisciplinary stakeholders and various digital tools, integrating different levels of decision. Models for Manufacturing (MfM) is an OBE methodology aiming to enable industrial design and decision-making in manufacturing by preserving the company knowledge in ontology models, usable as knowledge base to generate and integrate the aircraft and manufacturing systems design. This paper presents an MfM application for RMS design in aerospace, introducing innovative design concepts that allow implementing RMS in a collaborative engineering process of an aerospace product. An implementation is shown designing the RMS of a model aircraft family to illustrate the concepts introduced and considerations are given to transfer this knowledge base into an OBE system to support complex real-life applications. [ABSTRACT FROM AUTHOR]

This paper introduces a novel autonomous vehicle-based storage and retrieval system that utilizes movable lifts (AVS/RS/ML), proposed as an alternative to the tier-captive shuttle-based storage and retrieval system (SBS/RS). The newly proposed system aims to provide an affordable solution with highly utilised AGVs, that can also perform operations out of warehouse. The performance of this novel system is compared with the equivalent tier-captive SBS/RS warehouse design, where each shuttle is dedicated in a specific tier in that design. The comparison is based on the initial system investments costs, throughput rates, and average utilisation of lifts/MLs in the system. Collision prevention rules are also applied to AVS/RS/ML, and its performance is tested through simulation. The results show that the tier-captive SBS/SR system becomes cost-efficient under high throughput rate requirements, while the AVS/RS/ML technology is preferred for relatively moderate and low process rate requirements. The unit-cost per month performance metric of AVS/RS/ML is less sensitive to an increase in number of tiers in the system, compared to the tier-captive SBS/RS case, indicating that AVS/RS/ML may be promising for high-tier warehouse system designs. [ABSTRACT FROM AUTHOR]

We consider the context of machining systems design where both equipment selection and line balancing decisions have to be taken in order to minimise the total cost of the system. The designed flow line employs multi-positional machines with rotary tables where vertical and horizontal machining modules can be used for the realisation of machining processes. For this challenging optimisation problem in production research, we develop an innovative mathematical model based on a mixed-integer linear programme and a heuristic algorithm for an approximate solution. An extensive numerical experiment is conducted in order to evaluate the performances of the proposed mathematical model and the developed heuristic. The obtained results show that the decision makers can use the elaborated methods for solving efficiently even large-scale industrial problems. [ABSTRACT FROM AUTHOR]

Industry 4.0 was presented more than a decade ago as the fourth industrial revolution, aiming to significantly raise the level of sophistication of interconnected technologies and thus increase manufacturing industries' profits. However, because the technology-driven narrow focus of Industry 4.0 on performance and profit fails to explain how to increase prosperity for all the stakeholders involved, the European Commission has introduced the concept of Industry 5.0. This vision overcomes the weaknesses of Industry 4.0 by paying explicit attention to outcomes for humans in the system and establishing an environment to create human-centric, resilient, and sustainable systems. Considering these developments, this position paper and editorial introducing the special issue of the International Journal of Production Research elaborates on the transition from Industry 4.0 to 5.0 through 10 papers focusing on the human-centric pillar of Industry 5.0 and its impacts on production and logistics system design and management. This work presents guidance for a more systemic approach needed in future research: to include empirically grounded works and data-driven multimethod approaches that consider diversity in system operators and human factors demands holistically in order to incorporate ethical implications missing from Industry 4.0 – in the pursuit of Industry 5.0 systems. [ABSTRACT FROM AUTHOR]

The quick-switch sampling system (QSS) and tightened-normal-tightened sampling system (TSS) are efficient schemes for dispositioning a series of lots. However, the QSS mechanism for switching decision rules is too simple to satisfy the requirements of suppliers and buyers. Conversely, the TSS is more flexible due to its adaptable switching mechanism. The TSS was recently developed based on process capability indices (PCIs) to help practitioners make more reliable and accurate decisions in practice. The existing PCI-based TSSs are the required sample-size type (TSS-n). However, the TSS-n requires a large sample size for the tightened inspection, which is costly and time-consuming. We propose the acceptance-benchmark type TSS (TSS-k) based on the most commonly used PCI, to improve the lot-disposition sampling efficiency. The TSS-k adjusts the acceptance benchmark instead of the sample size to constitute tightened and normal inspections. We investigated combinations of TSS-k switching mechanism parameters and provided managerial suggestions for practitioners. Compared with the existing TSS-n, the proposed TSS-k can reduce the average sample number by more than 60% and has superior discrimination power. Moreover, we developed a cloud-computing programme to calculate the optimal system design online. Finally, we illustrate an industrial case to demonstrate the applicability of the proposed TSS-k. [ABSTRACT FROM AUTHOR]

Original Equipment Manufacturers (OEMs) have sought new supply chain paradigms that allowed them to focus on core activities, i.e. overall product design and commercialisation. This pursuit led to partnerships with a new generation of tier-1 strategic suppliers acting as integrators. Integrators are not only responsible for system supply, but also for system design. However, critical integrators were not able to live up to their new roles, which led to costly delays in development and production. These failures highlight the ineptitude of current risk management practices employed by OEMs. To support OEMs in implementing a more differentiated and suitable approach to the use of integrators, this paper proposes a mathematical programming model for Supply Chain Design (SCD). Instead of looking at the introduction of integrators as a dichotomous decision, the model suggests the optimal number of integrators, i.e. systems, and individual part suppliers. We propose new measures for integration risk, which build upon current risk assessment practices. Robust optimisation is used to study the effect of uncertainty over baseline risk values. All approaches were tested using both randomly generated instances and real data from a large European OEM in the aerospace industry. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the efficacy of a combined training program (CTP) in reducing the effects of dual tasking on the temporal parameters and kinematics of gait, as compared with single-task gait. A controlled, randomized, intervention study was performed in an intervention group and a control group. The intervention group attended three weekly CTP sessions for 24 weeks. Gait pattern was evaluated prior to the baseline intervention, at 12 weeks, and at 24 weeks (Repost). The sample was composed of 22 subjects diagnosed with multiple sclerosis with an Expanded Disability Status Scale score of 0–5.5. A total of 12 patients were allocated to the intervention group and another 10 to the control group. A three-dimensional photogrammetry scanner was connected to a selective attention system designed to present a dual-task gait condition. Dual tasking had an impact on all spatiotemporal parameters of gait, and the most remarkable effect of dual tasking was on double-support time, which increased by 9% with respect to normal walking. In contrast, dual tasking had a trivial effect on single-support time. The CTP was effective in reducing the effects of dual tasking on stride length and velocity of the center of mass after Repost of training (p <.05). The CTP reduced time in double-support phase, whereas single-support time increased after Repost of intervention. The application of the CTP had no effect on the cost of the double task after 12 weeks of intervention. It is suggested to increase the application time over Repost. [ABSTRACT FROM AUTHOR]

Recent developments in artificial intelligence (AI) have made AI applications in corporate governance an area of increasing interest to researchers and practitioners. Augmented intelligence, also known as advisory intelligence, is a form of collaboration between humans and AI, where the goal is to enhance human capabilities. It represents the second stage of AI development in corporate governance. In the realm of boards of directors, augmented intelligence offers a unique opportunity to enable machines to effectively collaborate with board members. This collaboration allows boards to harness the expertise of AI without supplanting the essential human element. In contrast, there exist more autonomous AI systems designed to operate independently, which could potentially lead to the replacement of humans on the board. This study seeks to assess the suitability of augmented intelligence for corporate boards, particularly in comparison to these more autonomous AI systems. Furthermore, it addresses the application of augmented intelligence in boardroom decision-making, its role in improving directors' decision-making abilities, and the associated risks and challenges. [ABSTRACT FROM AUTHOR]

Fire is a serious threat to people's lives and property. Be aimed at the low detection accuracy of the current fire detection system, a fire detection system based on one-chip computer is proposed in this work. To achieve fire detection and warning, the system collects these signals such as smoke and temperature from the surrounding environment, and processes them with a one-chip computer. The simulation experiments of the system are conducted with Proteus, and the experimental results indicate that the system can detect accurately. [ABSTRACT FROM AUTHOR]

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system. [ABSTRACT FROM AUTHOR]

One of the major study areas in VLSI system design is the creation of an area-efficient, high-speed adder. So this paper focused on an effective adder design as an Area Effective Novel Sum Generator (AENSG) to make it suitable for real-time applications. The major operations involved in this adder are Brent Kung addition, group kill and generate (KG) signal generation, Kill Generate Propagate (KGP) signal generation, and various sum generation categories. In the sum-generating block, AENSG is utilized to minimize the space by a reduction in the number of logic gates. Xilinx ISE 13.1 is used to synthesize and validate the suggested adder design. The 64-bit AENSG adder has 37.98%, 9.09% lesser number of logic gates and 34.81%, 28.40% lesser delay when compared with PPF/CSSA_4 and hybrid adder respectively. [ABSTRACT FROM AUTHOR]

Interest in automated building analytics, including fault detection and diagnostics has been increasing; however, developers of these solutions have lacked access to ground-truth-validated data across a wide range of weather conditions for algorithm development and performance assessment. This study presents the development, and validation of faulted and fault-free models for air handling units (AHUs) – a common HVAC system design. Detailed models for the single-duct AHU (Modelica) and dual-duct AHU (HVACSIM+) were used to conduct annual simulations, for common sensor, mechanical, and control sequence faults. We report lessons learned during the efforts, including challenges and insights regarding how these simulation models, typically used for design applications, can be purposed to accurately reflect real-world system operational behaviours. Finally, we highlight considerations for researchers and FDD developers who may wish to leverage this dataset to assess the performance of their algorithms, and evolving performance of FDD solutions over time. [ABSTRACT FROM AUTHOR]

Plastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in the transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, a lift platform, and a macro/micromanipulator. The force‐controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes convolutional neural networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm's effectiveness was validated during an autonomous plastering trial wherein a large‐scale wall was processed without human involvement. [ABSTRACT FROM AUTHOR]

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The functional integration of biological switches with synthetic building blocks enables the design of modular, stimulus‐responsive biohybrid materials. By connecting the individual modules via diffusible signals, information‐processing circuits can be designed. Such systems are, however, mostly limited to respond to either small molecules, proteins, or optical input thus limiting the sensing and application scope of the material circuits. Here, a highly modular biohybrid material is design based on CRISPR/Cas13a to translate arbitrary single‐stranded RNAs into a biomolecular material response. This system exemplified by the development of a cascade of communicating materials that can detect the tumor biomarker microRNA miR19b in patient samples or sequences specific for SARS‐CoV. Specificity of the system is further demonstrated by discriminating between input miRNA sequences with single‐nucleotide differences. To quantitatively understand information processing in the materials cascade, a mathematical model is developed. The model is used to guide systems design for enhancing signal amplification functionality of the overall materials system. The newly designed modular materials can be used to interface desired RNA input with stimulus‐responsive and information‐processing materials for building point‐of‐care suitable sensors as well as multi‐input diagnostic systems with integrated data processing and interpretation. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the diverse demands of mobile users and vertical industries, it is crucial for 5G-advanced (5G-A) networks to exhibit flexibility in system design across both the physical and higher layers. A single-frame structure alone cannot provide the necessary flexibility to accommodate multiple traffic modes. Therefore, we investigate a heterogeneous frame structure that meets the requirements. However, this structure will cause severe cross-link interference (CLI) and degrade the performance. To solve this problem, four CLI mitigation methods are proposed: slot level adaptive modulation and coding (AMC) scheduling (method 1), interference mitigation algorithm (method 2), adaptive slot turn-off (method 3), and edge user adaptive scheduling (method 4). The experimental results show that the CLI problem can be completely mitigated by deployed method 3 in a macro-base station (BS) with a low load scenario, the micro-BS uplink (UL) throughput was improved by 75.8%. Otherwise, with macro-BS in the high load scenario, when method 4 was implemented in macro-BS and methods 1 and 2 were deployed in micro-BS, a nearly complete CLI mitigation effect was obtained, reflecting that the micro-BS UL throughput increased to 57.4%. These methods are elaborated upon and evaluated within practical 5G-A networks, and the experimental results not only demonstrate the viability and efficacy of the above methods but also highlight their potential applications in future usage scenarios. [ABSTRACT FROM AUTHOR]

AbstractEnvironment plays an important role in non-verbal communication for human-virtual agent interaction. Existing research explores the influence of an agent’s appearance and attributes to enhance human-virtual agent communication. However, there is no common practice for dynamically adjusting the surrounding environments of the virtual agent. In this paper, we introduce a real-time virtual agent environment generation system (VAEnvGen), which contributes to the field by enhancing users’ content perception and improving task performance through dynamic environment adjustment. The system dynamically analyzes both the appropriate communication environment and filters the key information according to the current context. Leveraging Large Language Models, it generates a pseudo-3D background space to create an engaging atmosphere and a dynamic foreground content space for vivid key information display, thereby significantly enhancing content perception. For widespread adoption and flexibility, VAEnvGen is developed as a web application. We further evaluate the impact of VAEnvGen on content perception, user attention, and subjective satisfaction through a mixed-design user study with 50 participants. Quantitative and qualitative results reveal significant improvements in content perception, task completion time, and user satisfaction when using VAEnvGen. The system effectively redistributes user attention from subtitles and the virtual agent itself to the dynamically generated background and key foreground information, leading to a more immersive and less fatiguing user experience. [ABSTRACT FROM AUTHOR]

A lot of infrastructure upgrade and algorithms have been developed for the information technology driven smart grids over the past twenty years, especially with increasing interest in their system design and real-world implementation. Meanwhile, the study of detecting and preventing intruders in ubiquitous smart grids environment is spurred significantly by the possibility of access points on various communication equipment. As a result, there are no comprehensive security protocols in place preventing from a malicious attacker's accessing to smart grids components, which would enable the interaction of attackers and system operators through the power grid control system. Recently, dynamics of time-extended interactions are believed to be predicted and solved by reinforcement learning technology. As a descriptive advantage of the approach compared with other methods, it provides the opportunities of simultaneously modeling several human continuous interactions features for decision-making process, rather than specifying an individual agent's decision dynamics and requiring others to follow specific kinematic and dynamic limitations. In this way, a machine-mediated human-human interaction's result is determined by how control and physical systems are designed. Technically, it is possible to design dedicated human-in-the-loop societal control systems that are attack-resistant by using simulations that predict such results with preventive assessment and acceptable accuracy. It is important to have a reliable model of both the control and physical systems, as well as of human decision-making, to make reliable assumptions. This study presents such a method to develop these tools, which includes a model that simulates the attacks of a cyber-physical intruder on the system and the operator's defense, demonstrating the overall performance benefit of such framework designs. [ABSTRACT FROM AUTHOR]

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future. [ABSTRACT FROM AUTHOR]

Many online monitoring devices in substations are placed at high potential differences from the ground, and it is not possible to supply power to these devices directly from the ground through wires. Placing magnetically coupled structures inside the substation pillar insulators for inductive power transfer is considered a feasible solution, and at this stage of research, systems that add multi‐stage repeating coil structures are mostly used to achieve stable power supply for individual online monitoring devices. However, the monitoring point generally requires multiple devices to monitor together, and the power requirements of different devices are different, so adding multiple repeating coils at the same time will increase the complexity of the system design, which is not conducive to engineering applications. Therefore, this paper designs and optimizes a multi‐stage multi‐load wireless power supply system magnetic coupling structure, using a three‐dimensional hollow solenoid combined with a ferrite core as the system repeating coil structure and an embedded magnetic coupling structure as the receiver end structure to reduce the number of system repeating coils while supplying power to devices with different power requirements. A prototype with a transmission distance of 1.02 m was built in the experiment, and the experimental results showed that the highest transmission efficiency of the system was 44.3% and 39.2% under single load and double load conditions, respectively. As the load resistance increases, the system can achieve constant voltage output under both single‐load and dual‐load operating conditions. Adjusting the number of turns of the receiving coil can change the output characteristics of the system and achieve constant voltage output of different voltage levels or constant current output of different current levels, which can meet the power supply needs of different loads. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment. [ABSTRACT FROM AUTHOR]

The exploitation of insects as protein sources in the food industry has had a strong impact in recent decades for many reasons. The emphasis for this phenomenon has its primary basis on sustainability and also to the nutritional value provided. The gender of the insects, specifically Acheta domesticus, is strictly related to their nutritional value and therefore the availability of an automatic system capable of counting the number of Acheta in an insect farm based on their gender will have a strong impact on the sustainability of the farm itself. This paper presents a non-contact measurement system designed for gender counting and recognition in Acheta domesticus farms. A specific test bench was designed and realized to force the crickets to travel inside a transparent duct, across which they were framed by means of a high-resolution camera able to capture the ovipositor, the distinction element between male and female. All possible sources of uncertainty affecting the identification and counting of individuals were considered, and methods to mitigate their effect were described. The proposed method, which achieves 2.6 percent error in counting and 8.6 percent error in gender estimation, can be of significant impact in the sustainable food industry. [ABSTRACT FROM AUTHOR]

Today, there is an ever-increasing number of workloads pushed and executed on the Cloud. Data center operators and Cloud providers have embraced application co-location and multi-tenancy as first-class system design concerns to effectively serve and manage these huge computational demands. In addition, the continuous advancements in the computers' hardware technology have made it possible to seamlessly leverage heterogeneous pools of physical machines in data center environments. Even though current modern Cloud schedulers and orchestrators adopt application-aware policies to achieve automation of time-consuming management tasks at scale, e.g., resource provisioning, they still rely on coarse-grained system metrics, such as CPU and/or memory utilization to place incoming applications, thus, not considering (1) interference effects that are provoked by co-located tasks, and (2) the impact on performance caused by the diversity of heterogeneous systems' characteristics. The lack of such knowledge in existing state-of-the-art orchestration solutions results in their inability to perform efficient allocations, which negatively impacts the overall latency distribution delivered by the infrastructure. In this paper, to alleviate this inefficiency, we present a machine learning (ML) based Cloud orchestration extension that takes into account both resource interference and heterogeneity. The framework adequately schedules data-analytics applications on a pool of heterogeneous resources. We evaluate our proposed solution on different application mixes and co-location scenarios. We show that the proposed framework improves the tail latency of the distribution of the deployed applications by up to 3.6x compared to the state-of-the-art Kubernetes scheduler. [ABSTRACT FROM AUTHOR]

The failures of high-consequence systems can cause serious harm to humans, including loss of human health, life security, finance, and even social chaos. To protect high-consequence systems, both optimal system design and maintenance activities contribute to improving system reliability and social safety. The existing works generally optimize these two problems sequentially and assume that the degradation process of components is precisely known. However, sequential optimization often results in significant losses due to redundancies, and such a presumption usually cannot be guaranteed in practice, due to limited historical data or a lack of expert knowledge, referred to as epistemic uncertainty. To fill this gap, in this article, we consider an integrated optimization of system design and maintenance scheduling for multi-state high-consequence systems in which the component's degradation is known with limited distributional information. To address this issue, we utilize the framework of distributionally robust optimization to provide a risk-averse decision to decision-makers even under the worst realizations of random parameters, and develop a two-stage integer distributionally robust model with moment-based ambiguity set to determine the system design and maintenance scheduling simultaneously. The proposed model can be converted to a tractable approximation as an integer linear stochastic programming problem. In order to solve large-scale problems, we develop a sample-based adaptive large neighborhood search algorithm to find the optimal system designs. In the numerical experiments, we present a case study on feedwater heating systems in nuclear power plants and demonstrate that an integrated optimization consideration creates significant benefits in profitability. We also present the out-of-sample performance of the distributionally robust design to avoid extreme risk. [ABSTRACT FROM AUTHOR]

The minimum multiplex arrangement problem locates each vertex of a graph on an integer point on a line, with each integer point being assigned at most $ \mathfrak {m} $ m vertices. The case with $ \mathfrak {m}=2 $ m = 2 , known as the minimum duplex arrangement (MinDA) problem, is investigated in this study. MinDA is $ \mathscr {N}\!\!\mathscr {P} $ N P -hard and is related to applications such as manufacturing systems design and VLSI layout. A mixed-integer linear programming formulation of MinDA is presented. The capabilities of the proposed formulation are assessed on 63 problem instances with varying densities and sizes up to 25 vertices. Additionally, an examination of the structure of an optimal MinDA solution is conducted. Based on this analysis, an iterated local search (ILS) algorithm is established, and novel strategies are developed to enhance its effectiveness. In contrast to traditional ILS approaches, where the local search phase invariably reaches a local optimum, the local search phase in the proposed algorithm may converge to a point in the vicinity of a local optimum, thereby reducing the computational burden for large-scale instances. Extensive computational experiments employing the ILS algorithm are conducted on instances with sizes n up to 10,240 vertices. [ABSTRACT FROM AUTHOR]

The advancement of traditional sample preparation techniques has brought about miniaturization systems designed to scale down conventional methods and advocate for environmentally friendly analytical approaches. Although often referred to as green analytical strategies, the effectiveness of these methods is intricately linked to the properties of the sorbent utilized. Moreover, to fully embrace implementing these methods, it is crucial to innovate and develop new sorbent or solid phases that enhance the adaptability of miniaturized techniques across various matrices and analytes. Graphene-based materials exhibit remarkable versatility and modification potential, making them ideal sorbents for miniaturized strategies due to their high surface area and functional groups. Their notable adsorption capability and alignment with green synthesis approaches, such as bio-based graphene materials, enable the use of less sorbent and the creation of biodegradable materials, enhancing their eco-friendly aspects towards green analytical practices. Therefore, this study provides an overview of different types of hybrid graphene-based materials as well as their applications in crucial miniaturized techniques, focusing on offline methodologies such as stir bar sorptive extraction (SBSE), microextraction by packed sorbent (MEPS), pipette-tip solid-phase extraction (PT-SPE), disposable pipette extraction (DPX), dispersive micro-solid-phase extraction (d-µ-SPE), and magnetic solid-phase extraction (MSPE). [ABSTRACT FROM AUTHOR]

The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time data analysis, using a combination of first-order difference functions and sliding window techniques. The method is specifically designed to accurately separate and analyze key gait events such as heel strike (HS), toe-off (TO), walking start (WS), and walking pause (WP) from a continuous stream of inertial measurement unit (IMU) signals. The core innovation of DGEI is the application of its dynamic feature extraction strategies, including first-order differential integration with positive/negative windows, weighted sleep time analysis, and adaptive thresholding, which together improve its accuracy in gait segmentation. The experimental results show that the accuracy rate of HS event detection is 97.82%, and the accuracy rate of TO event detection is 99.03%, which is suitable for embedded systems. Validation on a comprehensive dataset of 1550 gait instances shows that DGEI achieves near-perfect alignment with human annotations, with a difference of less than one frame in pulse onset times in 99.2% of the cases. [ABSTRACT FROM AUTHOR]

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious actors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evaluation, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multi-state honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or non-lethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, reliability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment. [ABSTRACT FROM AUTHOR]

In this paper, we propose an integrated sensing and communication (ISAC) base station (BS) system designed for applications by multiple users in complex offshore environments. We begin by modeling the communication and detection functions of the dual-function BS in the corresponding maritime channel environment, as well as the sea clutter influence of multiple-input-multiple-output (MIMO) radar in the marine environment. To evaluate the performance of our dual-function system, we measure the communication reachable rate and sensing signal-clutter-noise ratio (SCNR) of the offshore system separately for communication and radar functions, respectively. We then develop two optimization schemes to maximize the system's performance under power constraint of radar SCNR and communication rate constraint, respectively, and solve them using convex optimization methods such as the successive convex approximation (SCA) method. We also model the beamforming and provide simulation results that demonstrate the effectiveness of our proposed maritime integrated sensing and communication (MISAC) system. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology is a revolutionary paradigm that aims to transform physical objects into connected devices that can be accessed ubiquitously. Due to heterogeneous manufacturers and supported infrastructures, IoT technology introduces a wide range of communication protocols. Device interoperability is a significant problem when two or more IoT applications are developed using different application-layer protocols. Conspicuously, a formalization of the system design is proposed using the Model Driven Architecture (MDA) approach for IoT interoperability. Specifically, a comprehensive MDA-based approach is proposed that reduces the processing time and effort to develop IoT interoperable systems. Metamodel, UML profile, and transformation rules are developed to make heterogeneous application-layer protocols interoperable using devices as intermediates. The objective is to elucidate the extent to which various layers of the MDA framework are encompassed. Furthermore, the smart home-based power consumption analysis environment is presented as the application scenario. For validation purposes, the proposed model is simulated using a challenging dataset obtained from an online UCI repository. Results depict that the proposed technique is more effective as compared to state-of-the-art techniques in terms of Temporal Delay (109.68s), Data Classification Accuracy (92.06%), Statistical Efficiency (92.36%), Reliability (86.23%), and Stability (71.23%). [ABSTRACT FROM AUTHOR]

Background: Individuals with high risk for lung cancer may benefit from lung cancer screening, but there are associated risks as well as benefits. Shared decision‐making (SDM) tools with personalized information may provide key support for patients. Understanding patient perspectives on educational tools to facilitate SDM for lung cancer screening may support tool development. Aim: This study aimed to explore patient perspectives related to a SDM tool for lung cancer screening using a qualitative approach. Methods: We elicited patient perspectives by showing a provider‐facing SDM tool. Focus group interviews that ranged in duration from 1.5 to 2 h were conducted with 23 individuals with high risk for lung cancer. Data were interpreted inductively using thematic analysis to identify patients' thoughts on and desires for a patient‐facing SDM tool. Results: The findings highlight that patients would like to have educational information related to lung cancer screening. We identified several key themes to be considered in the future development of patient‐facing tools: barriers to acceptance, preference against screening and seeking empowerment. One further theme illustrated effects of patient–provider relationship as a limitation to meeting lung cancer screening information needs. Participants also noted several suggestions for the design of technology decision aids. Conclusion: These findings suggest that patients desire additional information on lung cancer screening in advance of clinical visits. However, there are several issues that must be considered in the design and development of technology to meet the information needs of patients for lung cancer screening decisions. Patient or Public Contribution: Patients, service users, caregivers or members of the public were not involved in the study design, conduct, analysis or interpretation of the data. However, clinical experts in health communication provided detailed feedback on the study protocol, including the focus group approach. The study findings contribute to a better understanding of patient expectations for lung cancer screening decisions and may inform future development of tools for SDM. [ABSTRACT FROM AUTHOR]

Reliability-based control co-design (RBCCD) formulations have been developed for the design of stochastic dynamic systems. To address the limitations of their current formulations, and to enable higher-fidelity solutions for complex problems, a novel reliability-based multidisciplinary feasible (MDF) formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) and a new reliability analysis method using generalized polynomial chaos (gPC) expansion for RBCCD were developed in previous work. Although the gPC expansion method was initially selected for the reliability analysis of simulation-based RBCCD, its performance against state-of-the-art, the most-probable-point (MPP) method, has not been established yet. Therefore, in this work, the first-ever MPP-based formulations of RB-MDF-MDSDO are developed, and using two engineering test problems, the new formulations' solution efficiency and accuracy are compared to those from the gPC-based formulation. Numerical results reveal that the gPC expansion method is marginally more accurate than the MPP algorithms, and therefore, it is more suitable for accuracy-sensitive applications. Conversely, the MPP algorithms are much more efficient, and thus, are more attractive for problems where solution efficiency is the priority. [ABSTRACT FROM AUTHOR]

The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. First, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Second, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems. [ABSTRACT FROM AUTHOR]

In this paper, we contribute to research on enterprise artificial intelligence (AI), specifically to organizations improving the customer experiences and their internal processes through using the type of AI called machine learning (ML). Many organizations are struggling to get enough value from their AI efforts, and part of this is related to the area of explainability. The need for explainability is especially high in what is called black-box ML models, where decisions are made without anyone understanding how an AI reached a particular decision. This opaqueness creates a user need for explanations. Therefore, researchers and designers create different versions of so-called eXplainable AI (XAI). However, the demands for XAI can reduce the accuracy of the predictions the AI makes, which can reduce the perceived usefulness of the AI solution, which, in turn, reduces the interest in designing the organizational task structure to benefit from the AI solution. Therefore, it is important to ensure that the need for XAI is as low as possible. In this paper, we demonstrate how to achieve this by optimizing the task structure according to sociotechnical systems design principles. Our theoretical contribution is to the underexplored field of the intersection of AI design and organizational design. We find that explainability goals can be divided into two groups, pattern goals and experience goals, and that this division is helpful when defining the design process and the task structure that the AI solution will be used in. Our practical contribution is for AI designers who include organizational designers in their teams, and for organizational designers who answer that challenge. [ABSTRACT FROM AUTHOR]

The United Nations established the 17 Sustainable Development Goals (SDGs) in 2015 to address issues like gender equality, clean water, health, education, and hunger by 2030. Of the 17 SDGs, health and education have an outsized impact on countries’ socioeconomic development, so providing insights into progress on these two goals is crucial. Machine learning can help solve many real-world problems, including working towards the SDGs. This paper proposes using a metaheuristic ensemble of Cat Swarm Optimization algorithms with Feed Forward Extreme Learning Machines, called Sustainable Health And Educational Goal Development (SHEGD) Prediction, to effectively contribute to countries’ economic growth by achieving health and education SDGs through machine learning. The model is assessed using UN SDG datasets and performance metrics like accuracy, precision, recall, specificity, and F1-score. Comparisons to other machine learning models demonstrate this model’s superiority in designing a recommendation system for progressing towards the health and education SDGs. The proposed model outperforms the other approaches, proving its value for an SDG recommendation system design. [ABSTRACT FROM AUTHOR]

Power system state estimation (PSSE) is a control center application that comprises a collection of algorithms aimed at providing essential information about the current operating condition of the power grid. As such, PSSE plays a vital role in the real-time operation of power systems. Accuracy and reliability of the estimator are closely connected to both quality and quantity of the real-time data that feed it. Therefore, such applications can benefit from functions that pre-filter the dataset, removing obvious spurious measurements or complementing it with reliable information. Moreover, some functionalities intended to analyze the dataset are desirable, since they indicate if state estimation can be performed with the currently available measurement set and if its vulnerabilities can be identified. Such analysis tools can assist in the design of measurement sets, or strengthen an existing one. They constitute preceding steps for PSSE and have drawn growing interest in recent years, with the evolution of computational techniques and the advent of new technologies. This paper provides a review of the literature on such preceding steps, namely pre-filtering, observability analysis, redundancy/criticality analysis, and metering system design, along with a market evaluation of existing solutions, with main focus on transmission systems. [ABSTRACT FROM AUTHOR]

This study provides a detailed analysis of bidirectional inductive power transfer (IPT) systems using LCC-LCC compensation topology. It aims to contribute to the field by offering a unified framework that includes mathematical models for both time and frequency domains, accounting for the resistance of all passive components. This research introduces a concise state-space model with voltage inputs and develops conductance equations related to transferred power and losses. These conductances are used to determine the inductor currents, capacitor voltages, active and reactive powers, and efficiency relative to the primary and secondary voltages. The effectiveness of the proposed models is demonstrated through a comparison with simulation results based on a 1.5-kW LCC-LCC IPT system design, showing precise agreement. [ABSTRACT FROM AUTHOR]

The present study developed a Detection Android cybercrime Model (DACM), deploying the design science approach to detect different Android-related cybercrimes. The developed model consists of five stages: problem identification and data collection, data preprocessing and feature extraction, model selection and training, model evaluation and validation, and model deployment and monitoring. Compared to the existing cybercrime detection models on the Android, the developed DACM is comprehensive and covers all the existing detection phases. It provides a robust and effective way to spot cybercrime in the Android ecosystem by following Machine Learning (ML) technology. The model covers all the detection stages that are normally included in similar models, so it provides an integrated and holistic approach to combating cybercrime. [ABSTRACT FROM AUTHOR]

Near-space high-altitude balloon-based platforms have a series of advantages and provide superior conditions for optical observation. In order to ensure the stability of the optical axis of the optical detection load and stable tracking of the target, a near-space high-altitude balloon-based high-precision pointing and tracking system was designed, which can compensate for changes in the pitch angle and azimuth angle of the platform during flight. The system includes a primary platform stable pointing system and a secondary precise tracking system. In the finished flight experiment, the primary platform pointing system and secondary precise tracking system on the balloon-based observation platform worked normally, providing a guarantee for the coronagraph's stable tracking and detection of the sun. The primary platform pointing system can realize ±1° pointing accuracy, and the simulated accuracy of the secondary precise tracking system is 4″, which guaranteed that the coronagraph obtained more than 20,000 images. In subsequent works, we will upgrade and optimize the whole system and conduct our next flight experiment in the future. [ABSTRACT FROM AUTHOR]

Optimizing heat generation capacity is crucial for geothermal system design and evaluation. Computer simulation is a valuable approach for determining the influence of various parameter combinations on a geothermal system's ability to produce heat. However, computer simulation evaluations are often computationally demanding since all potential parameter combinations must be examined, posing significant hurdles for heat generation performance evaluation and optimization. This research proposes an adaptive Kriging-based heat generation performance optimization method. Firstly, a two-horizontal-well geothermal system with rectangular multi-parallel fractures is constructed. The heat production performance optimization problem is then established, and the temperature and enthalpy of the outlet water are calculated using computer simulation and Kriging. A parameterized lower confidence bounding sampling scheme (PLCB) is developed to adaptively update Kriging in order to strike a compromise between optimization accuracy and computation burden. The outcomes of the optimization are compared to those of the Kriging-based optimization approach and other common infill options to demonstrate the efficiency of the proposed method. The outlet temperature curve obtained with PLCB-AKO-1 rose for a longer time and the heat generation power curve reached a stable output without a downward trend. According to the Friedman and Wilcoxon signed ranks tests, the PLCB-1-AKO technique is statistically superior to alternative strategies. [ABSTRACT FROM AUTHOR]

Cooking from ancient times has evolved from using open fires to wood, gas cookers, using liquefied petroleum gas (LPG). This has also come with various adverse effects ranging from gas leakages to burnt food due to absent-mindedness, thereby creating a significant disaster that could lead to loss of life and property damage. The study aimed to reduce the rate of liquefied petroleum gas related accidents in domestic usage and improve the safety of domestic gas users. An automated method to enforce safety was proposed to avoid unwanted cooking gas flow consequences, especially in homes. The paper presents a control system using an Arduino Uno with a control design interfaced with a utensil sensor, solenoid valve, and a timer circuit to allow gas flow to commence and ignite a flame automatically. The automatic ignition apparatus, which has a high-voltage electric circuit, begins to function once the utensil detector comes in contact with silverware. The system is designed to function in different modes to ensure safety and prevent gas flow. The prototype serves as a means of curbing gas wastage and increasing the safety of people who use LPG as a source of fuel for cooking. [ABSTRACT FROM AUTHOR]

Accessibility research has matured over the last three decades and developed a better understanding of accessibility technologies, design and evaluation methods, systems and tools as well as empirical studies in accessibility. We envision how progress in new contexts over the next decade can be made to develop stronger links to other areas in Human-Centered Computing and address the research communities. A human-centered perspective on disability needs to develop from a medical model to a social model. New methods will utilize generative AI in design and development processes that address accessibility from the start of system design. We build on AI embedded into future design processes to address participation of small numbers of users better, and new technologies to allow for personalization of multi-modal interaction to improve verbal and non-verbal communication, making body-centric computing and natural interaction truly accessible. [ABSTRACT FROM AUTHOR]

People are increasingly eager to know more about themselves through technology. To date, technology has primarily provided information on our physiology. Yet, with advances in wearable technology and artificial intelligence, the current advent of consumer neurotechnology will enable users to measure their cognitive activity. We see an opportunity for research in Human-Computer Interaction (HCI) in the development of these devices. Neurotechnology offers new insights into user experiences and facilitates the development of novel methods in HCI. Researchers will be able to create innovative interactive systems based on the ability to measure cognitive activity at scale in real-world settings. In this paper, we contribute a vision of how neurotechnology will transform HCI research and practice. We discuss how neurotechnology prompts a discussion about ethics, privacy, and trust. This trend highlights HCI's crucial role in ensuring that neurotechnology is developed and utilised in ways that truly benefit people. [ABSTRACT FROM AUTHOR]

With the development of simulation technology, more and more manufacturers have begun to use the digital twin to design workshops and factories. For these design scenarios under real-time interaction requirements with an excessive amount of model data, if the rendering is stuck, it will reduce the work efficiency. It is a key enabling technology to simplify and switch the geometry models with different resolutions, according to the distance of the viewpoint or the motion state to reduce the computational complexity. Existing model simplification methods emphasize the universality and efficiency under various scenarios, while the simplification performance in the 3D geometry models of industrial mechanisms is poor. This paper proposes a rapid simplification approach to the 3D geometry model of mechanisms in the digital twins-driven manufacturing system design context. A novel Vertex Saliency-oriented Classified Edge Collapse (VS-CEC) algorithm is proposed to simplify the shape feature of the 3D geometry model of mechanisms. It especially emphasizes solving the sharp shape preservation issues in the mechanical design scenario rather than a universal things design scenario. A vertex saliency factor is defined and integrated with the region boundary information obtained from the processing of detailed features to ensure visual fidelity as well as shape preservation such as sharp edges. Experiments show that this approach reduces the data model complexity more reasonably to speed up the rendering. It ensures that the digital twin model interacts quickly with the physical manufacturing system, and thus realizes the low-latency visual effect of cyber-physical synchronization. [ABSTRACT FROM AUTHOR]

Microaerial vehicles (MAVs) have achieved rapid advancement and vast applications. Energy efficiency and friction loss are common problems of MAVs, and exploring novel structural design and enhancing flight performance are critical research directions. Here, a bionic‐inspired single‐rotor MAV with a foldable wing is fabricated by imitating winged achene in nature. The structural design of the wing based on origami principle, overall aerodynamic modeling analysis, kinematic analysis, and control system design is carried out. The prototype weighs 75 g and equipped with only one motor, achieved the desired flight attitude of vertical takeoff and landing, hovering and horizontal flight, and so forth. During nonflight, the wings fold down can reduce 70% storage area for easier transportation. The vehicle combines high flight efficiency with compactness, superior stability, simple structure, economical manufacturing cost, low flight noise, and spin deceleration safety protection, which has good potential for application in the aerial vehicle fields. [ABSTRACT FROM AUTHOR]