Number Of Channels Research Articles

Translating Japanese Wordplay into English and Filipino A Linguistic Analysis of Joke Translation in Spy x Family Subtitles

Japanese animated shows called “anime” have seen increased popularity among Philippine audiences in the 2000s and beyond partly because of television broadcasts and online streaming platforms, but one possible hindrance to the understanding of the media is the language gap, since Japanese and the Philippine national lingua franca Filipino, along with the international lingua franca English, are unrelated languages. To bridge this gap, a number of channels and sites provide translations of the shows into th e languages spoken by Filipinos, and one of the forms that they take are subtitles: texts in the target language placed near the bottom of the screen, providing viewers with line-by-line translations of the dialogue. Through the analysis of three examples of Japanese linguistic humor and their corresponding translations into Filipino and English, this paper aimed to understand strategies translators use in presenting wordplay to unrelated languages. Results reveal that a word – by-word translation is not always followed, and translators put the target languages’ culture, structure, lexicon and patterns into consideration. Examples include the use of the figurative-literal dichotomy of the meanings of words in both Filipino and English when the original Japa nese material utilizes the exactly similar pronunciation between two words with entirely different meanings. Keywords : sociolinguistics, multilingualism, globalization, translation

A Thermodynamic Study on Information Power in Communication Systems.

Modern information theory pioneered by Shannon provides the mathematical foundation of information transmission and compression. However, the physical (and especially the energetic) nature of the information has been elusive. While the processing of information incurs inevitable energy dissipation, it is possible for communication systems to harness information to perform useful work. In this article, we prove that the thermodynamic cost (that is, the entropy production of the communication system) is at least equal to the information transmitted. Based on this result, a model of a communication heat engine is proposed, which can extract work from the heat bath by utilizing the transmission of information. The communication heat engine integrates the manipulation of both energy and information so that both information and power may be transmitted in parallel. The information transmission rate and the information power of the communication heat engine are derived from a pure thermodynamics argument. We find that the information power of the communication heat engine can be increased by increasing the number of communication channels, but the absolute energy efficiency of the heat engine first increases and then decreases after the number of channels of the system exceeds a threshold. The proposed model and definitions provide a new way to think of a classical communication system from a thermodynamic perspective.

Measurement and Feed Forward Induced Entanglement Negativity Transition.

We study the interplay between measurement-induced dynamics and conditional unitary evolution in quantum systems. We numerically and analytically investigate commuting random measurement and feed forward (MFF) processes and find a sharp transition in their ability to generate entanglement negativity as the number of MFF channels varies. We also establish a direct connection between these findings and transitions induced by random dephasing from an environment with broken time-reversal symmetry. In one variant of the problem, we employ free probability theory to rigorously prove the transition's existence. Furthermore, these MFF processes have dynamic circuit representations that can be experimentally explored on current quantum computing platforms.

The effect of pin-type flow field plate design on the current distribution in a H2-fed polymer electrolyte fuel cell

The flow field plate (FFP) design is a key factor for enhancing the electrochemical performance of Polymer Electrolyte Fuel Cells (PEFCs) through optimal reactants’ distribution, low-pressure drops, and efficient water removal. The effect of geometrical features of the pin-type FFP design on the electrochemical performance of a 48 cm2 H2-fed Fuel Cell (FC) was assessed through polarization curves recording and current and temperature distribution maps along the electrode active area. The study was performed by changing excess air stoichiometry and cathode relative humidity (RH). Low-pressure drops were measured using pin-type FFPs; regardless of geometrical features, they were one order of magnitude lower than those of a single-channel serpentine FFP. Channel width and number of pins greatly influenced the current distribution along the active area and, consequently, the electrochemical performance of FCs. In particular, the best electrochemical performance was reached at air stoichiometry 4 and cathode RH = 100 % by using FFP with the highest coverage factor (65 %) and the lowest channel width (1 mm). Current distribution maps demonstrated that this FFP geometry led to an almost homogeneous current distribution and efficient water removal also at high current density values. On the contrary, low coverage factor (45 %) and high channel width led to worse electrochemical performances due to an uneven reactants distribution and an inefficient water removal, causing cell flooding.

Hybrid algorithms for SAR matrix compression and the impact of post-processing on SAR calculation complexity.

This study proposes faster virtual observation point (VOP) compression as well as post-processing algorithms for specific absorption rate (SAR) matrix compression. Furthermore, it shows the relation between the number of channels and the computational burden for VOP-based SAR calculation. The proposed new algorithms combine the respective benefits of two different criteria for determining upper boundedness of SAR matrices by the VOPs. Comparisons of the old and new algorithms are performed for head coil arrays with various channel counts. The new post-processing algorithm is used to post-process the VOP sets of nine arrays, and the number of VOPs for a fixed median relative overestimation is compared. The new algorithms are faster than the old algorithms by a factor of two to more than 10. The compression efficiency (number of VOPs relative to initial number of SAR matrices) is identical. For a fixed median relative overestimation, the number of VOPs increases logarithmically with the number of RF coil channels when post-processing is applied. The new algorithms are much faster than previous algorithms. Post-processing is very beneficial for online SAR supervision of MRI systems with high channel counts, since for a given number of VOPs the relative SAR overestimation can be lowered.

EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.

In recent years, electroencephalography (EEG) has been investigated for identifying brain disorders. This technique involves placing multiple electrodes (channels) on the scalp to measure the brain's activities. This study focuses on accurately detecting mild cognitive impairment (MCI) from the recorded EEG signals. To achieve this, this study first introduced discrete wavelet transform (DWT)-based approaches to generate reliable biomarkers for MCI. These approaches decompose each channel's signal using DWT into a set of distinct frequency band signals, then extract features using a non-linear measure such as band power, energy, or entropy. Various machine learning approaches then classify the generated features. We investigated these methods on EEGs recorded using 19 channels from 29 MCI patients and 32 healthy subjects. In the second step, the study explored the possibility of decreasing the number of EEG channels while preserving, or even enhancing, classification accuracy. We employed multi-objective optimization techniques, such as the non-dominated sorting genetic algorithm (NSGA) and particle swarm optimization (PSO), to achieve this. The results show that the generated DWT-based features resulted in high full-channel classification accuracy scores. Furthermore, selecting fewer channels carefully leads to better accuracy scores. For instance, with a DWT-based approach, the full-channel accuracy achieved was 99.84%. With only four channels selected by NSGA-II, NSGA-III, or PSO, the accuracy increased to 99.97%. Furthermore, NSGA-II selects five channels, achieving an accuracy of 100%. The results show that the suggested DWT-based approaches are promising to detect MCI, and picking the most useful EEG channels makes the accuracy even higher. The use of a small number of electrodes paves the way for EEG-based diagnosis in clinical practice.

CIBORG: CIrcuit-Based and ORiented Graph theory permutation routing protocol for single-hop IoT networks

The Internet of Things (IoT) has emerged as a promising paradigm which facilitates the seamless integration of physical devices and digital systems, thereby transforming multiple sectors such as healthcare, transportation, and urban planning. This paradigm is also known as ad-hoc networks. IoT is characterized by several pieces of equipment called objects. These objects have different and limited capacities such as battery, memory, and computing power. These limited capabilities make it difficult to design routing protocols for IoT networks because of the high number of objects in a network. In IoT, objects often have data which does not belong to them and which should be sent to other objects, then leading to a problem known as permutation routing problems. The solution to that problem is found when each object receives its items. In this paper, we propose a new approach to addressing the permutation routing problem in single-hop IoT networks. To this end, we start by representing an IoT network as an oriented graph, and then, based on a reservation channel protocol, we first define a permutation routing protocol for an IoT in a single channel. Secondly, we generalize the previous protocol to make it work in multiple channels. Routing is done using graph theory approaches. The obtained results show that the wake-up times and activities of IoT objects are greatly reduced, thus optimizing network lifetime. This is an effective solution for the permutation routing problem in IoT networks. The proposed approach considerably reduces energy consumption and computation time. It saves 5.2 to 32.04% residual energy depending on the number of items and channels used. Low energy and low computational cost demonstrate that the performance of circuit-based and oriented graph theory is better than the state-of-the-art protocol and therefore is a better candidate for the resolution of the permutation routing problem in single-hop environment.

Calibration HPGe detector using IAEA-U source for CBRNe

Detecting and preventing the illicit movement of radioactive materials within a country is crucial, requiring the identification of radiologic anomalies against the normal radiation background. High-purity germanium (HPGe) detectors, known for their precision and sensitivity, have become popular for analyzing radioactive materials in CBRNe scenarios. This study focused on calibrating an HPGe detector for CBRNe applications, using gamma-ray spectra from an IAEA-U reference source. Energy calibration involved identifying peaks in the spectra and creating a calibration curve for energy and channel number data. Efficiency calibration, determined using the known activity of the source, revealed a linear relationship between energy and detector response. Over four years, systematic efficiency calibrations showed a deviation of only 3% well below the recommended limit of 5%. These findings underscore the reliability of the system as a reference spectrometry method for accurate detection of radioactive materials. Graphical abstract

Liquid bath-assisted combustion activation preparation of nitrogen/sulfur-doped porous carbon for sodium-ion battery applications

A liquid-bath-assisted salt-template combustion activation method has been developed to overcome the shortcomings of the conventional template method, including elaborate processes and environmental pollution. Herein, N/S double-doped porous carbon material (ECSK-15) with high specific surface area (1148.60 m2 g−1) is synthesized through it for sodium-ion batteries (SIBs). Benefitting from this approach, the number of ion diffusion channels and the ion exchange rate of the product are significantly increased. When using ether-based electrolyte, after 1000 cycles at a large current density of 2 A g−1, the reversible capacity could still be maintained at 135.60 mA h g−1. The assembled full cell exhibits a high reversible capacity of 480.49 mAh g−1 at a current rate of 0.5 A g−1. The energy density is calculated to be 204.11 Wh kg−1 at a power density of 160.71 W kg−1. This work provides a porous carbon anode material with good sodium-ion storage capacity and rate performance for application in SIBs.

Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a paucity of raw data. To leverage the data characteristics and enhance the classification accuracy, we propose and evaluate a novel dual-link deep neural network model, 'DoubleLinkSleepCLNet'. The DoubleLinkSleepCLNet model performs feature extraction and efficient classification on both the raw EEG and the EEG processed with the Hilbert transform. It leverages the frequency domain and time domain feature modules, resulting in superior performance compared to other models. The DoubleLinkSleepCLNet model, using the 2 Raw/2 Hilbert data modes, achieved the highest classification performance with an accuracy of 88.47%. The average accuracy of the EEG was improved by approximately 4.08% after the application of the Hilbert transform. Additionally, Convolutional Neural Network (CNN) demonstrated superior performance in processing phase information, whereas Long Short-Term Memory (LSTM) excelled in handling time series data. The application of the Hilbert transform to EEG data, followed by processing it with a convolutional neural network, enhances the accuracy of the model. These findings introduce novel concepts for accelerating sleep stage prediction research, suggesting potential applications of these methods to other EEG analyses.

Mode-folded thin-film lithium niobate phase shifter for channel scalable optical interconnect with high switching efficiency

Paralleled optical interconnection has been widely used in optical transceivers. Reconfigurable photonic integration with scalable channel numbers is thus highly useful in timely adjusting the link capacity to the changing traffic patterns in data centers or high-performance computing (HPC) systems. In this paper, a 1×8 Mach–Zehnder switch (MZS) over thin-film lithium niobate is proposed and experimentally demonstrated for 1-to-8 channel scalable optical interconnects, with high switching efficiency through utilizing the mode-folded phase shifter. The three-mode phase shifter recirculates the light three times, undergoing phase changing with different waveguide modes. Simultaneously, the multimode waveguiding within the phase shifter results in a pronounced enhancement of the optical field confinement, further improving the Pockels effect for all modes. A 3.5-time improvement of the switching efficiency is experimentally demonstrated, exhibiting a low Vπ·L of 0.6 V⋅cm. The proposed MZS also features low channel crosstalk (below -20 dB over 1530-1565 nm) and nanosecond-order switching time, paving the way towards channel scalable and versatile MSA-compatible optical interconnect.

Large area tiles of position-sensitive silicon photomultipliers

Silicon photomultipliers (SiPMs) are highly sensitive photodetectors emerging as the technology of choice for many applications. In FBK, we developed a position sensitive SiPM technology, called “linearly graded” (LG-SiPM), which is based on an avalanche-current position-weighted partitioning approach. In particular, we recently developed a new version of LG-SiPM with a larger chip area (10 × 10 mm2) and based on NUV-HD technology, having a peak photon detection efficiency at 420 nm. These SiPMs have been characterized in terms of noise, detection efficiency and position resolution.We also developed tiles of 2x2 and 3x3 elements with such SiPMs, reaching very large sensitive areas of about 20 × 20 mm2 and 30 × 30 mm2. In such large area tiles we implemented a “smart channel” configuration, being able to have just 6 output channels and 8 channels respectively, with a position resolution below 0.5 mm. These detectors provide great advantages in all application requiring position resolution but with a small number of readout channels and reduced system complexity.

A possible path to persistent re-entry waves at the outlet of the left pulmonary vein

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia, often evolving from paroxysmal episodes to persistent stages over an extended timeframe. While various factors contribute to this progression, the precise biophysical mechanisms driving it remain unclear. Here we explore how rapid firing of cardiomyocytes at the outlet of the pulmonary vein of the left atria can create a substrate for a persistent re-entry wave. This is grounded in a recently formulated mathematical model of the regulation of calcium ion channel density by intracellular calcium concentration. According to the model, the number of calcium channels is controlled by the intracellular calcium concentration. In particular, if the concentration increases above a certain target level, the calcium current is weakened to restore the target level of calcium. During rapid pacing, the intracellular calcium concentration of the cardiomyocytes increases leading to a substantial reduction of the calcium current across the membrane of the myocytes, which again reduces the action potential duration. In a spatially resolved cell-based model of the outlet of the pulmonary vein of the left atria, we show that the reduced action potential duration can lead to re-entry. Initiated by rapid pacing, often stemming from paroxysmal AF episodes lasting several days, the reduction in calcium current is a critical factor. Our findings illustrate how such episodes can foster a conducive environment for persistent AF through electrical remodeling, characterized by diminished calcium currents. This underscores the importance of promptly addressing early AF episodes to prevent their progression to chronic stages.

Thermal performance of a two-pass microchannel evaporator

Microchannel evaporator is one of the key components in many thermal systems such as heat exchanger of HAVC and refrigeration system. The evaporation heat transfer performance of microchannel evaporator, which usually has many flow paths, is highly affected by the distribution of liquid flow that will seriously deteriorate the flow boiling performance due to the lack of liquid supply, local dryout, etc. In this study, a novel two-pass microchannel evaporator (L × W × H = 103 × 64 × 20 mm) is designed and fabricated. The width, height and length of each individual channel are 0.6, 1 and 65 mm, respectively. To significantly improve liquid redistribution after the first-pass, evenly distributed spacers with different diameter holes were placed in the connecting flow path. Additionally, the channel number ratio between the first and second pass plays an important role. Therefore, three different channel ratios of 1:1, 1:2, and 3:5 were selected in this study. The heat transfer and flow characteristics of the two-pass microchannel evaporator using working fluid HFE-7100 were experimentally studied for flow rate varying from 30 ml/min to 110 ml/min. The inlet subcooling keeps about 30 K. Also, visualization studies were conducted for the explanations. The experimental results indicate that the thermal performance is superior in microchannel evaporator with channel ratio of 1:2. By considering Reynolds number of each channel in the first pass, microchannel evaporator with channel ratio of 1:1 outperforms other two evaporators. Furthermore, the wall temperature uniformity of this evaporator is better than others. The achieved heat flux of 20 W/cm2 is much higher than the reported studies. The overall two-phase HTC of this microchannel evaporator is about 4.5 kW/m2K.

Performance analysis of the dual-hop mixed RF/UWOC system with fog channel and turbulence based on diversity

ABSTRACT In this paper, the performance of a dual-hop mixed radio frequency (RF)/underwater wireless optical communication (UWOC) system with amplifying and forwarding (AF) relaying is investigated. The diversity combining technology were combined with unifying Fisher-Snedecor ( F ), extended generalized- K (EGK), and α − μ distribution for atmospheric turbulence in foggy conditions to analyze the probability density function (PDF) of RF links. The UWOC link experiences mixed exponential generalized gamma (EGG) fading. By utilizing diversity combination methods and considering the statistical effects of atmospheric turbulence, random fog, and underwater turbulence, we derive the expression for the PDF of the RF/UWOC system. In addition, the tight asymptotic expression for the outage probability and average bit error rate under high signal-to-noise ratio (SNR) is provided. The analytical expressions are verified by Monte Carlo method, and the effects of channel number on RF/UWOC system performance under various atmospheric, underwater and weather conditions are demonstrated.

Research on temperature field and thermal deformation characteristics of casting rollers in twin-roll casting process

The twin-roll casting (TRC) process is considered a revolutionary technology in the metallurgical field and has garnered significant attention and research in recent years. The casting rollers used in the TRC process are crucial for achieving the desired shape of the casting strip. This study investigates the temperature distribution and thermal deformation of casting rollers in the TRC process using numerical simulation. A novel model for predicting the thermal resistance distribution at the circumferential interface between the casting roller and the molten pool has been introduced. The study examined the effects of various factors including casting roller diameter, water channel position, number, and diameter on temperature, thermal deformation, and heat flux. The findings reveal that increasing the casting roller diameter from 460 mm to 500 mm or augmenting the distance from the water channel to the casting roller surface from 35 mm to 55 mm resulted in a 37 % and 30 % increase in the maximum surface temperature of the casting roller, respectively. Additionally, an increase in the number of water channels from 30 to 50 and a growth in their diameter from 20 mm to 28 mm resulted in the average heat flux of the casting roller remaining stable, with fluctuations less than 1 MW/m2. Leveraging the insights gained from the thermal deformation behavior of the casting roller and the ideal shape of the casting strip, a comprehensive control approach for the casting roller profile was developed. The outcomes of this research provide a valuable theoretical framework for enhancing the design of casting roller structures in the TRC process.

Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data

Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.

Welding Seam Tracking and Inspection Robot Based on Improved YOLOv8s-Seg Model.

A weld is the main connection form of special equipment, and a weld is also the most vulnerable part of special equipment. Therefore, an effective detection of a weld is of great significance to improve the safety of special equipment. The traditional inspection method is not only time-consuming and labor-intensive, but also expensive. The welding seam tracking and inspection robot can greatly improve the inspection efficiency and save on inspection costs. Therefore, this paper proposes a welding seam tracking and inspection robot based on YOLOv8s-seg. Firstly, the MobileNetV3 lightweight backbone network is used to replace the backbone part of YOLOv8s-seg to reduce the model parameters. Secondly, we reconstruct C2f and prune the number of output channels of the new building module C2fGhost. Finally, in order to make up for the precision loss caused by the lightweight model, we add an EMA attention mechanism after each detection layer in the neck part of the model. The experimental results show that the accuracy of weld recognition reaches 97.8%, and the model size is only 4.88 MB. The improved model is embedded in Jetson nano, a robot control system for seam tracking and detection, and TensorRT is used to accelerate the reasoning of the model. The total reasoning time from image segmentation to path fitting is only 54 ms, which meets the real-time requirements of the robot for seam tracking and detection, and realizes the path planning of the robot for inspecting the seam efficiently and accurately.

Exploring the potential of water channels for developing genetically encoded reporters and biosensors for diffusion-weighted MRI

Genetically encoded reporters for magnetic resonance imaging (MRI) offer a valuable technology for making molecular-scale measurements of biological processes within living organisms with high anatomical resolution and whole-organ coverage without relying on ionizing radiation. However, most MRI reporters rely on synthetic contrast agents, typically paramagnetic metals and metal complexes, which often need to be supplemented exogenously to create optimal contrast. To eliminate the need for synthetic contrast agents, we previously introduced aquaporin-1, a mammalian water channel, as a new reporter gene for the fully autonomous detection of genetically labeled cells using diffusion-weighted MRI. In this study, we aimed to expand the toolbox of diffusion-based genetic reporters by modulating aquaporin membrane trafficking and harnessing the evolutionary diversity of water channels across species. We identified a number of new water channels that functioned as diffusion-weighted reporter genes. In addition, we show that loss-of-function variants of yeast and human aquaporins can be leveraged to design first-in-class diffusion-based sensors for detecting the activity of a model protease within living cells.

Design and performance analysis of double gate vertically stacked MoS2 nanosheet field effect transistor

In this work we report a vertically stacked nanosheet Field Effect Transistor (NSFET) in double gate configuration using transition metal dichalcogenide (TMD) based molybdenum disulphide (MoS2) as the conducting channel. The performance of the NSFET is analysed for number of channels, different channel thickness, different source/drain contacts. The performance of the device at different temperatures (T) also analysed. The proposed NSFET with three vertically stacked channels, exhibits a ON current (ION) of 30.6 μA μm−1, Subthreshold swing (SS) of 69 mV/dec and ON to OFF current ratio of more than 108 at Vds = 1V. Further the ION can be improved with multi-layer channel thickness. The performance of the vertically stacked MoS2 NSFET in junction less (JL) and inversion mode (IM) is compared, it is concluded from the simulations that JL vertically stacked MoS2 NSFET more immune to short channel effects such as threshold voltage (Vth) roll-off and drain induced barrier lowering (DIBL).