Traditional Setup Research Articles

Immersive scene representation in human visual cortex with ultra-wide-angle neuroimaging

While human vision spans 220°, traditional functional MRI setups display images only up to central 10-15°. Thus, it remains unknown how the brain represents a scene perceived across the full visual field. Here, we introduce a method for ultra-wide angle display and probe signatures of immersive scene representation. An unobstructed view of 175° is achieved by bouncing the projected image off angled-mirrors onto a custom-built curved screen. To avoid perceptual distortion, scenes are created with wide field-of-view from custom virtual environments. We find that immersive scene representation drives medial cortex with far-peripheral preferences, but shows minimal modulation in classic scene regions. Further, scene and face-selective regions maintain their content preferences even with extreme far-periphery stimulation, highlighting that not all far-peripheral information is automatically integrated into scene regions computations. This work provides clarifying evidence on content vs. peripheral preferences in scene representation and opens new avenues to research immersive vision.

Versatile Chip-Scale Lasers : Bridging Near-Ultraviolet to Near-Infrared Spectra

Narrow-linewidth lasers that are widely tunable in the visible spectrum are crucial for various applications such as quantum optics, optical clocks, and atomic and molecular physics. However, current laser systems are typically bulky and confined to laboratory settings, limiting their practical use beyond research environments. In this study, we introduce a chip-scale visible laser platform capable of producing tunable and narrow-linewidth lasers spanning from near-ultraviolet to near-infrared wavelengths. By leveraging micrometer-scale silicon nitride resonators and off-the-shelf Fabry–Pérot laser diodes, we achieve significant coarse tuning capabilities of up to 12.5 nm, coupled with mode-hop-free fine tuning up to 33.9 GHz. Remarkably, our lasers exhibit intrinsic linewidths as low as a few kilohertz. Additionally, our platform demonstrates impressive fine-tuning speeds of up to 267 GHz per microsecond, alongside fiber-coupled powers reaching up to 10 mW and typical side-mode suppression ratios surpassing 35 dB. These remarkable specifications of our chip-scale lasers rival those previously attainable only with large, state-of-the-art benchtop laser systems. This breakthrough positions our lasers as powerful tools poised to drive the next generation of visible-light technologies, enabling practical applications in various fields beyond the confines of traditional laboratory setups.

Women academics’ motivation for higher education and empowerment: a qualitative case study in Pakistan

ABSTRACT Women’s empowerment is an important goal of the educational processes around the world. Women in developing countries need support and motivation for attaining higher education and empowerment. This qualitative-exploratory study sought to explore the perceptions of Pakistani female university academics living inside predominantly patriarchal social structures regarding their motivation for and issues in attaining higher education and empowerment. The study also aimed at understanding the notion of empowerment and areas where women academics have achieved empowered status. The study also investigated women academics’ access to higher education, and its impact on their current social positioning in a traditional social setup. A total of 30 purposefully selected women academics from public sector universities in Northwest Pakistan constituted the study sample. The data were collected through semi-structured interviews and were analysed using thematic analysis. The findings revealed that patriarchal social structures in and outside workplaces influence women academics’ motivation for higher education and empowerment. However, some motivational factors, including economic stability, professional identity in society, intellectual fulfilment and support of fathers and husbands seemed instrumental in contributing to women academics’ pursuits of higher education and attainment of positions in academia.

3D Printing-as-a-Service: An Economic Analysis of Pricing and Cocreation

Three-dimensional (3D) printing technology has opened up possibilities for product design collaborations between device providers and customers. To enable an environment of cocreation, device providers are now renting 3D printers via the 3D-as-a-Service (3DaaS) model. Although prior research has examined pricing and quality issues in the traditional manufacturing setup, these studies have not analyzed such decisions in the 3D printing supply chain setting, where end users possess the ability to customize product designs. Therefore, several important questions remain unanswered from the perspective of the 3D printing device provider. For example, what is the appropriate pricing model for providing 3DaaS? How do factors such as the extent of design customization and the complexity influence the pricing strategy of the 3DaaS firm? Our analysis shows that if the customers’ impact on the product quality is relatively high or low, the pay-per-build pricing model generates a higher profit than the fixed-fee pricing model. Interestingly, we also find that if customers frequently print highly intricate product designs, the firm might choose the pay-per-build pricing model, only if the likelihood of design failure for these complex structures is low. Otherwise, the firm might opt for the fixed-fee pricing model.

Surveillance System for Real Time High Precision Recognition of Criminal

In an era marked by escalating concerns over public safety and crime prevention, the demand for sophisticated surveillance technologies has surged. This paper presents a groundbreaking solution: a Surveillance System for Real-Time High-Precision Recognition of Criminals. Leveraging advancements in deep learning and facial recognition, our system transcends the limitations of traditional surveillance setups by autonomously identifying individuals of interest in real-time. By employing a meticulously designed deep neural network framework, our system detects and tracks criminal faces amidst dynamic, crowded environments, effectively enhancing public safety measures. Through a seamless integration of cutting-edge technology and meticulous methodology, our innovative system heralds a new era in proactive crime prevention, offering unparalleled precision and efficiency in identifying and apprehending high-risk individuals. Keywords — Real-time surveillance, High-precision facial recognition, Crime prevention and Deep learning-based system

Age of information minimization in UAV-assisted data harvesting networks by multi-agent deep reinforcement curriculum learning

Unmanned Aerial Vehicles (UAVs) are increasingly being used for data harvesting from Wireless Sensor Nodes (SNs). This study aims to minimize the Age of Information (AoI) during data collection, while also considering the energy sustainability of the UAVs. Addressing the challenge of optimizing performance in Multi-Agent Reinforcement Learning (MARL) systems as the number of agents increases, our study introduces a MARL approach combined with curriculum learning and evolutionary strategy. This innovation specifically targets the performance issue in traditional MARL setups when scaling up the number of agents. By applying curriculum learning and evolutionary strategies, our method not only enhances MARL scalability but also integrates energy-efficient charging mechanisms, effectively enhancing system performance in large-scale deployments. Numerical results show that our proposed algorithm outperforms baselines in terms of AoI and charging, proving its effectiveness in managing the complexities of large-scale MARL systems.

MAGPRIME: An Open‐Source Library for Benchmarking and Developing Interference Removal Algorithms for Spaceborne Magnetometers

AbstractMagnetometers are essential instruments in space physics, but their measurements are often contaminated by various external interference sources. In this work, we present a comprehensive review of existing magnetometer interference removal methods and introduce MAGPRIME (MAGnetic signal PRocessing, Interference Mitigation, and Enhancement), an open‐source Python library featuring a collection of state‐of‐the‐art interference removal algorithms. MAGPRIME streamlines the process of interference removal in magnetic field data by providing researchers with an integrated, easy‐to‐use platform. We detail the design, structure, and functionality of the library, as well as its potential to facilitate future research by enabling rapid testing and customization of interference removal methods. Using the MAGPRIME Library, we present two Monte Carlo benchmark results to compare the efficacy of interference removal algorithms in different magnetometer configurations. In Benchmark A, the Underdetermined Blind Source Separation (UBSS) and traditional gradiometry algorithms surpass the uncleaned boom‐mounted magnetometers, achieving improved correlation and reducing median error in each simulation. Benchmark B tests the efficacy of the suite of MAGPRIME algorithms in a boomless magnetometer configuration. In this configuration, the UBSS algorithm proves to significantly reduce median error, along with improvements in median correlation and signal to noise ratio. This study highlights MAGPRIME's potential in enhancing magnetic field measurement accuracy in various spacecraft designs, from traditional gradiometry setups to compact, cost‐effective alternatives like bus‐mounted CubeSat magnetometers, thus establishing it as a valuable tool for researchers and engineers in space exploration and magnetism studies.

Enhancing Wind Farm Efficiency Through Active Control of the Atmospheric Boundary Layer’s Vertical Entrainment of Momentum

In contemporary wind farm design, the primary focus has traditionally been on reducing wake interference to optimize energy capture from horizontal wind flows. However, with the scaling up of wind farms, their interaction with the Atmospheric Boundary Layer (ABL) evolves, making vertical entrainment the main mechanism for the exchange of momentum and energy. This study introduces a methodical approach to augment the efficiency of large-scale offshore wind farms by actively controlling this vertical entrainment of momentum within the ABL. The strategy involves the precise engineering of advection fluxes to alter wind flow dynamics, utilizing turbines as effective vortex generators, toward a process of ”regenerative wind farming.” This setup aims to create a vorticity and vertical flux system akin to those observed in highly unstable ABLs. Expanding upon previous studies that focused on single Vertical Axis Wind Turbines (VAWTs), our research explores the implementation of multi-rotor systems equipped with lift-generating wings. These systems are designed to exert forces perpendicular to the prevailing wind direction, thus creating trailing vortices and directing the flow orthogonally for improved vertical advection. This research is part of a comprehensive investigative framework that combines experiments and multifidelity simulations. The current study extends those findings to wind farm simulations, aiming to assess the impact of ABL control on a full wind farm scale. The first part of the work validates an established analytical wind farm performance model against real wind farm data for thirty-one wind farms in the North Sea and Baltic Sea. The results confirm the predicted trend of decreased performance with increased wind farm size and density. The model is used to calculate the performance of a wind farm for varying regimes of vertical entrainment due to the creation of large-scale circulatory systems. The results are compared against 3D vortex simulations of the full wind farm in ”regenerative wind farming” mode. Our results demonstrate a notable improvement in wind speeds at the turbine hub height and the potential to double the feasible density of wind farms without compromising efficiency compared to traditional setups. These findings suggest a promising pathway towards a more sustainable and profitable future in wind energy, achieved through the strategic manipulation of ABL momentum, regenerating the energy in the wind farm.

ChARpack: The Chemistry Augmented Reality Package.

Off-loading visualization and interaction into virtual reality (VR) using head-mounted displays (HMDs) has gained considerable popularity in simulation sciences, particularly in chemical modeling. Because of its unique way of soft immersion, augmented reality (AR) HMD technology has even more potential to be integrated into the everyday workflow of computational chemists. In this work, we present our environment to explore the prospects of AR in chemistry and general molecular sciences: The chemistry in Augmented Reality package (chARpack). Besides providing an extensible framework, our software focuses on a seamless transition between a 3D stereoscopic view with true 3D interactions and the traditional desktop PC setup to provide users with the best setup for all tasks in their workflow. Using feedback from domain experts, we discuss our design requirements for this kind of hybrid working environment (AR + PC), regarding input, features, degree of immersion, and collaboration.

Estimating the parameter of a geometric distribution from series system data

In a traditional setup of estimation of an unknown parameter of component lifetime distribution, system’s continuous lifetime data is used. In this paper, we propose a simple and competitive estimator that is based on discrete lifetime data, i.e., the number of failed components at the time when the system fails. In particular, we consider the estimation of the parameter of a geometric distribution based on the system’s lifetime data, and the number of failed components upon the failure of the system when the system has a series structure. Two moment estimators that are based on the system lifetime data and the number of failed components at the moment of system failure are obtained and their performances are compared in terms of the mean square error. The associated Bayesian estimators with non-informative priors are also discussed.

"Utilizing Layer-2 Blockchain and IPFS Innovations for Establishing a Robust System for Secure Document Signing and Management"

Data holds immense significance within any information system. In traditional setups, data typically funnels into a central server for processing and storage, rendering the entire system reliant on this singular point. The ongoing digitalization trend, particularly accelerated by the global pandemic, has ushered in many transformative shifts in various operational aspects, including the validation, storage, and electronic signing of many documents. This surge in digital transactions has amplified both the data storage requirements and the volume of data traversing the Internet, thereby heightening the potential risks associated with fraud and document tampering. Consequently, an imperative need for a robust and secure document management system arises. Blockchain technology emerges as an innovative solution poised to address these challenges. In this study, we explore the compelling advantages that Blockchain technology offers to the realm of digital document storage, culminating in the development of a secure, web-based solution for document storage and processing.

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

Support vector regression-based state of charge estimation for batteries: cloud vs non-cloud

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology’s performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives.

Tibial and femoral articular cartilage exhibit opposite outcomes for T1ρ and T2* relaxation times in response to acute compressive loading in healthy knees

Abnormal loading is thought to play a key role in the disease progression of cartilage, but our understanding of how cartilage compositional measurements respond to acute compressive loading in-vivo is limited. Ten healthy subjects were scanned at two timepoints (7 ± 3 days apart) with a 3 T magnetic resonance imaging (MRI) scanner. Scanning sessions included T1ρ and T2* acquisitions of each knee in two conditions: unloaded (traditional MRI setup) and loaded in compression at 40 % bodyweight as applied by an MRI-compatible loading device. T1ρ and T2* parameters were quantified for contacting cartilage (tibial and femoral) and non-contacting cartilage (posterior femoral condyle) regions. Significant effects of load were found in contacting regions for both T1ρ and T2*. The effect of load (loaded minus unloaded) in femoral contacting regions ranged from 4.1 to 6.9 ms for T1ρ, and 3.5 to 13.7 ms for T2*, whereas tibial contacting regions ranged from −5.6 to −1.7 ms for T1ρ, and −2.1 to 0.7 ms for T2*. Notably, the responses to load in the femoral and tibial cartilage revealed opposite effects. No significant differences were found in response to load between the two visits. This is the first study that analyzed the effects of acute loading on T1ρ and T2* measurements in human femoral and tibial cartilage separately. The results suggest the effect of acute compressive loading on T1ρ and T2* was: 1) opposite in the femoral and tibial cartilage; 2) larger in contacting regions than in non-contacting regions of the femoral cartilage; and 3) not different visit-to-visit.

A revised double-slit experiment to explore the mechanism of photon wavefunction collapse by numerical design

The double-slit experiment has long been pivotal in understanding matter’s wave–particle duality. A central question revolves around Born’s interpretation of wavefunction whether a single photon demonstrates a 50% probability of passing through each slit individually as particles or simultaneously traverses both as waves. Experimentally, once the photon’s path is detected, the observer effect causes its wavefunction to collapse, rendering the results inconclusive. Designing an experiment to minimize instrumental involvement during the wavefunction collapse of photons, while aiming to gain insight into its collapse mechanism, becomes necessary. We propose a revised experiment that replaces the traditional setup with two Au nanoparticles acting as observers, triggering photon collapse before spectrum collection. In single-photon scenarios, we consider two assumptions: first, the photon wavefunctions collapse into a particle and transfer energy to one of the nanoparticles exclusively, and second, the photon acts as a wave, splitting and transferring its energy to two nanoparticles simultaneously, which does not align well with Born’s interpretation of wavefunction as spatial probabilities. These two assumptions would generate distinctly different spectra. Conversely, in high-intensity experiments, both nanoparticles collectively undergo excitation, regardless of the collapse mechanism. A comparative analysis of scattering spectra under the two conditions reveals crucial insights into the genuine nature of photon collapse. We also proposed using two molecules attached to a metal nanoparticle as an alternative design. Whether affirming or refuting the observer effect, this research holds promise for resolving the theoretical debate surrounding the collapse of wavefunctions and advancing quantum computing and communication fields.

Utility of a skin marker-less setup procedure using surface-guided imaging: a comparison with the traditional laser-based setup in extremity irradiation.

This study aimed to assess the feasibility of a skin marker-less patient setup using a surface-guided radiotherapy (SGRT) system for extremity radiotherapy. Twenty-five patients who underwent radiotherapy to the extremities were included in this retrospective study. The first group consisted of 10 patients and underwent a traditional setup procedure using skin marks and lasers. The second group comprised 15 patients and had a skin marker-less setup procedure that used an SGRT system only. To compare the two setup procedures for setup accuracy, the mean 3D vector shift magnitude was 0.9mm for the traditional setup procedure and 0.5mm for the skin marker-less setup procedure (p < 0.01). In addition, SGRT systems have been suggested to improve the accuracy and reproducibility of patient setups and consistently reduce interfractional setup errors. These results indicate that a skin marker-less patient setup procedure using an SGRT system is useful for extremity irradiation.

Software engineering architecture and its promising opportunities

The evolution of software architecture has witnessed the transition from monolithic to microservices, offering enhanced scalability, maintainability, and flexibility. With the rise of Microservices Architecture (MA), containerization has emerged as a pivotal technology to encapsulate microservices in isolated environments, ensuring consistent deployment. This paper delves into the intricate relationship between Microservices Architecture and containerization, focusing on the benefits, challenges, and practical implications of integrating both. Through a comprehensive experimental setup simulating an e-commerce platform, we quantitatively evaluate the performance metrics of a containerized microservices system versus a traditional monolithic setup. Our findings accentuate the performance gains achieved through MA and containerization, while also shedding light on areas that demand caution and further research. The insights presented serve as a beacon for organizations aiming to transition to or optimize their microservices and containerization practices.

Minimizing total annualized cost per tonne of feed processed of a semicontinuous distillation process utilizing data-driven model predictive control

Semicontinuous distillation is a separation technique used to purify multicomponent mixtures with low to medium throughput. This research addresses the problem of designing a Data-driven Model Predictive Control (MPC) approach that enables minimizing the Total Annualized Cost (TAC) of the semicontinuous process per tonne of feed processed while maintaining the required product purity. In lieu of typically unavailable first principles models, the manuscript demonstrates the implementation of data-driven technique using data collected from an Aspen Plus Dynamics simulation as a test bed. A subspace model identification technique is adapted to develop a multi-model framework to capture the dynamic behavior of the process and then utilized within a Shrinking Horizon MPC (SHMPC) scheme, to achieve the required objective. The simulation results demonstrate a lowering of the TAC/tonne of feed by 11.4% compared to the traditional PI setup used in the previous studies.

The pecking order: a Bourdieusian look at authority in virtual peer crits

AbstractCultural capital having sway in establishing authority in educational fields, including architecture, has been prevalent in scholarly work discussing the traditional studio setup. With the growing use of multi-user virtual environments (MUVEs) in architectural education, some studios, their occupants, and artifacts moved to the new medium. Such change places those studios in a precarious position vis-a-vis traditional architectural pedagogy, problematizing cultural capital and authority. This research examines the relationship between cultural capital and authority, focusing on MUVE-mediated studio peer crits. It adopts a quasi-experimental approach, where twenty-four participants with varying design proficiencies in diverse peer compositions completed a timed design task. The research employs linkography for analysis and Pierre Bourdieu's theoretical framework for interpretation. The findings suggest that MUVEs have a transformative effect on exogenous cultural capital, potentially disrupting previously established norms and hierarchies in architectural pedagogy and creating new hierarchical models, which add nuances to the existing models in the literature. A MUVE-mediated studio has the potential to present the studio as a new exploratory ground not weighed down by pre-established notions of studio culture "habitus."

Pure sine wave generation in battery-less solar system using advanced control through single machine

Due to the reduction of conventional energy sources, humanity faces a critical challenge, forcing innovative solutions to bridge the growing gap between energy demand and supply. Solar energy has emerged as a promising alternative, owing to its efficiency and renewable nature. Despite their benefits, conventional solar power systems face many challenges, including their reliance on battery banks, which introduce complexities and inefficiencies, such as voltage fluctuations and harmonic content. To overcome these challenges, researchers propose a novel solution: the Dual Winding Machine (DWM). This innovative system integrates motor and generator functionalities within a single machine, operating on a dual stator core to achieve synchronous operation. In contrast to traditional setups, the DWM eliminates the need for batteries during periods of low solar energy. A crucial component in this novel approach is the utilization of a Zeta converter, which adjusts the switching frequency to regulate the DC voltage and machine terminals. By doing so, the DWM maintains a constant voltage supply without relying on a battery, mitigating the fluctuations observed in traditional solar power systems. The proposed solution offers several benefits over existing strategies. Firstly, by eliminating the need for a battery, the DWM reduces system complexity and enhances overall efficiency. The absence of a battery backup also minimizes the risk of fluctuations in the inverter's input voltage and mitigates potential increases in output harmonic content. This not only improves the reliability of the solar power system but also extends the lifespan of connected machinery, such as motors and electrical devices. Furthermore, simulations conducted using MATLAB and MAXWELL programs validate the effectiveness of the DWM, confirming its ability to generate harmonic-free sine waves without relying on a battery, thus representing a significant advancement towards more sustainable and reliable solar energy solutions. In summary, the DWM presents a promising solution to the challenges faced by conventional solar power systems, offering improved efficiency, reliability, and longevity.