Search Results (52)

Industry 5.0 places growing emphasis on intelligent and efficient design methodologies aiming to reduce development times, accelerate the time-to-market, and enhance human–machine collaboration in creating new products. This article proposes the use of a model-based design (MBD) approach to developing a detailed electro-thermal model (ETDM) of a Smart Latch Mechanism (SLM) used in automotive door automation systems. The proposed ETDM enhances the accuracy of the design and verification processes and enables the simulation of specific scenarios, such as fault conditions, within a virtual environment. The simulation-based framework presented in this article leverages partial knowledge of the system to enable rapid estimations of the performance and functional validation. It encompasses the injection of disturbances, the analysis of failure scenarios, and the use of processor-in-the-loop (PIL) procedures for validation purposes. This work aims to employ detailed modeling and simulation techniques and use publicly available technical data and work from the literature to eliminate the need for physical testing and instrumentation, enabling the development of models that accurately reflect the real-world behavior under defined operating conditions. The proposed framework has the potential to facilitate rapid prototyping and system reconfiguration, contributing to shorter development cycles and improved industrial efficiency by reducing both production times and the associated costs for established automotive subsystems where high precision is nonessential. Full article

In practical applications, the prediction of the explosive mass of an underwater explosion represents a crucial aspect for defining extreme scenarios and for assessing damage, implementing defensive and security strategies, and ensuring the structural integrity of marine structures. In this study, a deep neural network (DNN) was developed to predict the mass of an underwater explosive charge, by means of the transfer learning technique (TL). Both DNN and TL methods utilized data collected through coupled Eulerian–Lagrangian numerical simulations performed through the suite MSC Dytran. Different positions and masses of the charge, seabed typology, and distance between the structure and seabed have been considered within the dataset. All the features considered as input for the machine learning model are information that the crew is aware of through onboard sensors and instrumentations, making the framework extremely useful in real-world scenarios. TL involves reconfiguring and retraining a new DNN model, starting from a pre-trained network model developed in a past study by the authors, which predicted the spatial position of the explosive. This study serves as a proof of concept that using transfer learning to create a DNN model from a pre-trained network requires less computational effort compared to building and training a model from scratch, especially considering the vast amount of data typically present in real-world scenarios. Full article

Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS. Full article

In this paper, we focused on the advancement of Dam Monitoring Software that incorporates the Finite Element Method (FEM), as these large infrastructure constructions are crucial for ensuring a dependable water supply, irrigation, flood control, renewable electric energy generation, and safe operation, which is of utmost importance to any country. However, the material properties and geotechnical environments of dams can change (deteriorate) over time, while the standards and legal norms that govern them become more and more rigorous, so in order to accurately assess the state of a dam and detect any concerning behavior, the software must be updated as well. The custom-developed FEM solver, unlike many commercial alternatives, is adaptable and can be reconfigured to function within a Dam Monitoring System. In this paper, we present the procedure for interpolating numerical values at measurement points, when the position of the measurement point does not align with the node of the element, allowing for additional instrument locations to be added to the monitored system without the need for remeshing the numerical model. This procedure is used to compare the actual pore pressures and temperature values of the concrete dam structure with the prediction of the numerical model, and the agreement is much greater with the new interpolation algorithm in comparison to the nearest nodal values, with the average relative difference for pore pressure reduced from 8.89% to 8.10%, justifying this implementation. Full article

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver’s bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system. Full article

Based on data publicly available on various online platforms and in academic literature, this article analyzes the prominent role that the Christian Right has taken in the government of Jair Messias Bolsonaro (2019–2022), including the pandemic period) to strengthen its political–religious project. To this end, we present the ideological mechanisms that align neoconservative Catholics and Evangelicals with both the government’s neoliberal premises and Bolsonaro’s moral communities. We focus on the rhetorical updating of religious freedom to intensify the nationalist narrative of a Christian Brazil, highlighting the judicial expertise that the Christian Right has accumulated in its reactive activism against the pro-rights agenda of LGBTQI+ communities and the advancement of the Pro-Life, Pro-Family agenda. We discuss the anti-gender crusade and “gender ideology” as political instruments of the Evangelical leadership in the process of juridifying public policies related to sexuality, gender, and family, and as a defense of the Christian nation, which it also leads. However, we identify a reconfiguration of the balance in the correlation of forces within Brazilian Christianity. Full article

The mast of the rover is a device used to carry precision instruments such as cameras on the rover, and its performance directly affects the working quality of these devices. The replaceable interface mast can effectively solve the problems of a single structure and poor maintenance of ordinary masts, so we study the deployment process dynamics properties and vibration for replaceable interface mast. Firstly, we analyzed the spatial motion of the reconfigurable rigid body module by the absolute node coordinate method and the natural coordinate method, and established the dynamic equation of the interface, the reconfigurable module without external constraints. Then, we established a dynamic model of the mast system deployment process. We analyzed the dynamic behavior of the replaceable interface mast and studied and compared the differences in the deployment behavior of the replaceable interface mast under different system configurations, flexible interface geometric parameters, and different driving rules. Finally, we built a model of the mast system and experimentally analyzed the deployment process of the replaceable interface mast. Using numerical solution and experimental verification, we proved that the established dynamic model of the mast system can correctly analyze the deployment behavior dynamics of the replaceable interface mast, and the study can provide a reference for the design and behavior analysis of the mast system. Full article

The diversity of students reaching higher education, the skills required of the 21st-century citizen, the Bologna Declaration, and the pressure exerted by international organizations impose a pedagogical reconfiguration of teaching, learning, and assessment through the recognition of the pedagogical dimension as a component of teacher professional development. We present the results of a study conducted at a university in Portugal with the following objectives: identifying conceptions and practices of pedagogical assessment and determining the influence of pedagogical training on these conceptions and practices. An online questionnaire (pre- and post-test) was administered to 31 teachers who had taken part in a training course on pedagogical assessment. It was found that: nearly half of the teachers experience difficulties in pedagogical assessment, with fairness being the main issue; the most commonly used instruments are written tests, research assignments, and reports; around two-thirds of teachers change the way they assess students, with the nature of the curricular units being the most influential factor in this decision; and there has been a change in the concept of assessment, in which the strict idea of testing, measuring, and classifying students’ knowledge has been replaced by the gathering of information for decision-making about the teaching and learning process. Full article

Realistic sensory feedback is paramount for amputees as it improves prosthetic limb control and boosts functionality, safety, and overall quality of life. This sensory restoration relies on the direct electrostimulation of residual peripheral nerves. Computational models are instrumental in simulating these neurostimulation effects, offering solutions to the complexities tied to extensive animal/human trials and costly materials. Central to these models is the detailed mapping of nerve geometry, necessitating the delineation of internal nerve structures, such as fascicles, across various cross-sections. In our modeling process, we faced the challenge of organizing an originally unstructured set of points into coherent contours. We introduced a parameter-free curve-reconstruction algorithm that combines valley-seeking clustering, an adaptive Kalman filter, and the nearest neighbor classification technique. While intuitively simple for humans, the task of reconstructing multiple open and/or closed lines with pronounced corners from a nonuniform point set is daunting for many algorithms. Additionally, the precise differentiation of adjacent curves, commonly encountered in realistic nerve models, remains a formidable challenge even for top-tier algorithms. Our proposed method adeptly navigates the complexities inherent to nerve structure reconstruction. While our algorithm is chiefly designed for closed curves, as dictated by nerve geometry, we believe it can be reconfigured with appropriate code adjustments to handle open curves. Beyond neuroprosthetics, our proposed model has the potential to be applied and spark innovations in biomedicine and a variety of other fields. Full article

Synchrophasor-driven smart grid applications aiming to orchestrate a diverse set of Distributed Energy Resources (DERs) require extensive infrastructure including substantial instrumentation hardware, communication network extensions and controller installations for coordinated operation. This can make the overall installation expensive. Additionally, due to the computational complexity and data-intensive nature of the PDC functionality, most of the existing PDC implementations are on a purely software level, making them unsuitable for the real-time applications. To address this, the current paper proposes an alternate architecture for the real-time synchrophasor-based control of DER applications (e.g., microgrids) incorporating a centralized synchronization hardware designed to replace aggregation Phasor Data Concentrators (PDCs) and supplementary control algorithms into a singular reconfigurable hardware. This particular hardware is termed a Synchrophasor Synchronization Gateway and Controller (SSGC). The robustness of the proposed architecture is tested by using real-time (RT) Controller Hardware-In-the-Loop (CHIL) simulation-based experiments by manipulating the communication network that connects the SSGC with multiple Phasor Measurement Unit (PMU) streams broadcasting data through the IEEE C37.118.2 protocol in real time. These PMU streams were generated by using a real-time microgrid model running on a Typhoon HIL 604 simulator. To manipulate the communication interface between the proposed SSGC hardware and the PMU streams, a configurable Wide Area Network (WAN) emulator and communication network impairment appliance deployed in the Candela Technologies CT910 external hardware was utilized. The real-time control system was expanded by incorporating a low-pass filter to eliminate the potential overswitching of a Battery Energy Storage System (BESS). The proposed architecture demonstrated a reliable performance under ideal to moderately tampered communication networks. However, under a significantly corrupted network, the performance of this architecture is acutely affected. Full article

The paper examines how Hindu nationalist social service organizations, specifically the Deseeya Seva Bharathi (DSB), reconfigured the religious conception of ‘Seva’ to advance the project of constructing a Hindu social identity during the COVID-19 pandemic in the state of Kerala. The southern Indian state of Kerala has remained an exception in the story of the rise of the Hindu nationalist movement in contemporary India, which has repeatedly failed to make any considerable political inroads in the state. However, the disastrous economic consequences and livelihood challenges during the COVID-19 pandemic in the state, which was heavily dependent on foreign remittance and service industries, have opened up new spaces of engagement for Hindu nationalists. Drawing on the fieldwork conducted in central Kerala during the pandemic, this paper will elaborate on how the DSB used the crisis moment of the pandemic to reach out to economically and socially disadvantaged communities using the language of ‘Seva’ to build a Hindu social identity, which imbues the influence of majoritarian Hindu nationalist politics. The paper argues that the DSB’s articulation of ‘Seva’ as a distinct and superior form of social service that is ‘self-less’, ‘non-instrumental’ and ‘non-reciprocal’ is significant in understanding the growing appeal of Hindu nationalist social service in the contested political sphere of Kerala, which is marked by competing social provisions by the state as well as other secular and religious groups. The paper notes that the reconfiguration of ‘Seva’ as a continuous religious concept enables Hindu nationalists to attain greater acceptance and legitimacy that even the secular state welfare could not achieve, while also concealing the inherent instrumental nature of its social service towards the construction of a Hindu social identity in the region. Full article

This paper presents a practical implementation and measurement results of power-efficient chip performance optimization, utilizing low-cost indirect measurement methods to support self-X properties (self-calibration, self-healing, self-optimization, etc.) for in-field optimization of analog front-end sensory electronics with XFAB 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The reconfigurable, fully differential indirect current-feedback instrumentation amplifier (CFIA) performance is intrinsically optimized by employing a single test sinusoidal signal stimulus and measuring the total harmonic distortion (THD) at the output. To enhance the optimization process, the experience replay particle swarm optimization (ERPSO) algorithm is utilized as an artificial intelligence (AI) agent, implemented at the hardware level, to optimize the performance characteristics of the CFIA. The ERPSO algorithm extends the selection producer capabilities of the classical PSO methodology by incorporating an experience replay buffer to mitigate the likelihood of being trapped in local optima. Furthermore, the CFIA circuit has been integrated with a simple power-monitoring module to assess the power consumption of the optimization solution, to achieve a power-efficient and reliable configuration. The optimized chip performance showed an approximate 34% increase in power efficiency while achieving a targeted THD value of −72 dB, utilizing a 1 Vp-p differential input signal with a frequency of 1 MHz, and consuming approximately 53 mW of power. Preliminary tests conducted on the fabricated chip, using the default configuration pattern extrapolated from post-layout simulations, revealed an unacceptable performance behavior of the CFIA. Nevertheless, the proposed in-field optimization successfully restored the circuit’s performance, resulting in a robust design that meets the performance achieved in the design phase. Full article

In order to better understand the complex pooling and vaporization of a liquid hydrogen spill, Sandia National Laboratories is conducting a highly instrumented, controlled experiment inside their Shock Tube Facility. Simulations were run before the experiment to help with the planning of experimental conditions, including sensor placement and cross wind velocity. This paper describes the modeling used in this planning process and its main conclusions. Sierra Suite’s Fuego, an in-house computational fluid dynamics code, was used to simulate a RANS model of a liquid hydrogen spill with five crosswind velocities: 0.45, 0.89, 1.34, 1.79, and 2.24 m/s. Two pool sizes were considered: a diameter of 0.85 m and a diameter of 1.7. A grid resolution study was completed on the smaller pool size with a 1.34 m/s crosswind. A comparison of the length and height of the plume of flammable hydrogen vaporizing from the pool shows that the plume becomes longer and remains closer to the ground with increasing wind speed. The plume reaches the top of the facility only in the 0.45 m/s case. From these results, we concluded that it will be best for the spacing and location of the concentration sensors to be reconfigured for each wind speed during the experiment. Full article

Plants undergo metabolic perturbations under various abiotic stress conditions; due to their sessile nature, the metabolic network of plants requires continuous reconfigurations in response to environmental stimuli to maintain homeostasis and combat stress. The comprehensive analysis of these metabolic features will thus give an overview of plant metabolic responses and strategies applied to mitigate the deleterious effects of stress conditions at a biochemical level. In recent years, the adoption of metabolomics studies has gained significant attention due to the growing technological advances in analytical biochemistry (plant metabolomics). The complexity of the plant biochemical landscape requires sophisticated, advanced analytical methods. As such, technological advancements in the field of metabolomics have been realized, aided much by the development and refinement of separatory techniques, including liquid and gas chromatography (LC and GC), often hyphenated to state-of-the-art detection instruments such as mass spectrometry (MS) or nuclear resonance magnetic (NMR) spectroscopy. Significant advances and developments in these techniques are briefly highlighted in this review. The enormous progress made thus far also comes with the dawn of the Internet of Things (IoT) and technology housed in machine learning (ML)-based computational tools for data acquisition, mining, and analysis in the 4IR era allowing for broader metabolic coverage and biological interpretation of the cellular status of plants under varying environmental conditions. Thus, scientists can paint a holistic and comprehensive roadmap and predictive models for metabolite-guided crop improvement. The current review outlines the application of metabolomics and related technological advances in elucidating plant responses to abiotic stress, mainly focusing on heavy metal toxicity and subsequent osmotic stress tolerance. Full article

Microfluidics technology plays an important role in modern analytical instruments, while the modular design of microfluidics facilitates the reconfiguration of analytical instrument functions, making it possible to deploy on-demand systems in the field. However, modular design also faces the challenges such as connection reliability and reconfiguration convenience. Inspired by the self-locking structure of the Rubik’s cube, a modular, reconfigurable microfluidic instrument architecture is proposed in this paper. The system has a self-locking structure of Rubik’s cube components and an O-ring-based alignment and sealing mechanism, which enables reliable interconnection and rapid rearrangement of microfluidic modules by simply rotating the faces of the microfluidic cube. In addition, the system is capable of integrating a variety of customized modules to perform analysis tasks. A proof-of-concept application of detecting multiple pollutants in water is demonstrated to show the reconfigurable characteristics of the system. The findings of this paper provide a new idea for the design of microfluidic analytical instrument architectures. Full article