Transmission Function Research Articles

Unraveling the Role of Wnt Signaling Pathway in the Pathogenesis of Autism Spectrum Disorder (ASD): A Systematic Review.

Autism spectrum disorder (ASD), or simply autism, is a neurodevelopmental disorder characterized by social communication deficit, restricted interests, and repetitive behavior. Several studies suggested a link between autism and the dysregulation of the Wnt signaling pathway which is mainly involved in cell fate determination, cell migration, cell polarity, neural patterning, and organogenesis. Despite the absence of effective therapy, significant progress has been made in understanding the pathogenesis of ASD. Neuropharmacological studies showed that drugs acting on the Wnt pathway like Canagliflozin can alleviate autistic-like behavior in animal models. Hence, this pathway could potentially be a futuristic therapeutic target to mitigate autism's symptoms. This systematic review aims to collect and analyze evidence that elucidates how alterations in the Wnt pathway may contribute to the pathogenesis of autism in animal models at the molecular, cellular, and physiological levels. Comprehensive searches were conducted across multiple databases, including PubMed, Web of Science, Embase, and Scopus to identify relevant studies up to March 2024. The inclusion criteria encompassed experimental studies that focused on the link between autism and this pathway, and the quality assessment was ensured by SYRCLE's risk of bias tools. Collectively, the included articles highlighted the possible implication of this pathway in the abnormalities found in autism, which impacted processes such as energy metabolism, oxidative stress, and neurogenesis. These alterations could underlie autistic behavior by affecting synaptic transmission and mitochondrial function.

Multifunctional Metasurface with PIN Diode Application Featuring Absorption, Polarization Conversion, and Transmission Functions

The objective of this paper is to explore the potential of integrating three distinct functionalities into a thin, single-layer metasurface. Specifically, the study introduces a metasurface design that combines absorption, polarization conversion, and transmission capabilities. The proposed structure consists of a double square loop disposed on a dielectric substrate, which is covered by a superstrate. In this study, the traditional ground plane was replaced with a periodic array, selectively reflecting frequencies of interest. Then, the absorption and polarization conversion characteristics were achieved by introducing the resonators in the front layer. By introducing asymmetry to the resonators and integrating PIN diodes for control, we demonstrated that the metasurface could efficiently absorb electromagnetic waves (with PIN diodes in the ON state), convert polarization (with PIN diodes in the OFF state), and enable signal transmission in a different frequency range. The numerical results indicated excellent performance in both absorption and polarization conversion. At a frequency of 3.05 GHz, the absorption rate reached 97%, while a polarization conversion rate of 98% was achieved at the resonance frequency of 4.37 GHz. Moreover, the proposed structure exhibited a thickness of λ/30.7 at the absorption peak.

The Mechanism of the Nucleus Accumbens–Ventral Pallidum Pathway Mediated by Drug Withdrawal-Induced High-Seeking Motivation in Cocaine Addiction

The reinforcement of drug-seeking motivation following drug withdrawal is recognized as a significant factor contributing to relapse. The ventral pallidum (VP) plays a crucial role in encoding and translating motivational aspects of reward. However, current research lacks a clear understanding of how the VP mediates drug-seeking motivation and the feedback modulation between the VP and the nucleus accumbens (NAc) following drug withdrawal. Therefore, utilizing a rat model of cocaine self-administration, we investigated the circuitry mechanisms underlying drug-seeking behavior post-drug withdrawal involving the NAc-VP pathway. Initially, we observed a significant enhancement in drug-seeking behavior 14 days after cocaine withdrawal. Subsequently, we identified the feedback mechanism through which the NAc-VP regulates this behavior. Immunofluorescence results indicated an increase in c-Fos expression levels in the ventral pallidum ventromedial (VPvm) and ventrolateral ventral pallidum (VPvl) following drug withdrawal. Calcium fiber photometry further elucidated that during the expression of high motivational drug-seeking behavior, there was a specific enhancement in VPvm neuronal activity, and retrograde tracing techniques suggested a weakened transmission function in the NAc-VPm pathway. Additionally, chemical genetic techniques demonstrated that inhibiting the activity of the NAc-VP pathway could increase the motivational level of drug-seeking behavior. These findings indicate that the reduced inhibitory function of the NAc-VP pathway following prolonged cocaine withdrawal forms the basis for heightened reactivity in VPvm neurons, thus regulating the expression of high motivational behavior triggered by drug-related cues. Our study results suggest that maintaining normal NAc-VP pathway functionality may decrease drug-seeking motivation post long-term drug withdrawal, offering new insights for interventions targeting relapse.

Dynamic Analysis and FPGA Implementation of Fractional-Order Hopfield Networks with Memristive Synapse

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization.

Unraveling the Effects of SARS-CoV-2 on Dementia: A Comprehensive Study

Since late 2019, a pandemic caused by SARS-CoV-2, a highly contagious coronavirus, has jeopardized human and public health. Neurodegeneration and structural brain diseases produce dementia, which is developing rapidly. Strong evidence of COVID-19 brain abnormalities as neurotrophic viruses impair neurological systems. Neuron and glial cell degeneration can affect nerve transmission and brain function. Damage to these brain components raises dementia risk. Multiple studies linked SARS-CoV-2 to dementia. This manuscript contains selective data. This study aims to review the findings and suggest strategies to improve SARS-Covid dementia management. The study makes use of medical research databases like Google Scholar, PubMed, Medline, and Embase. Furthermore, information is also collected by recognized health organizations and government agencies. COVID-19 can produce neuropsychological deficits, agitation, confusion, inattention, and disorientation. Thus, SARS can worsen dementia and neurodegeneration. This article summarizes COVID-19's effects on dementia patients from several sources. The focus on existing treatments for affected patients ties the two.

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Structural health monitoring (SHM) is crucial for ensuring the safety and efficiency of engineering systems. Vibration-based signals have been widely employed in numerous studies for damage detection, localization and quantification, thanks to their high sensitivity to structural damages and versatile applicability across various structures. Among these, transmissibility functions (TFs) have emerged as particularly promising because they can be derived from output-only measurements, making them practical for real-world applications. Vibration-based features, including those captured by TFs, provide a wealth of damage-related information, but extracting these features can be challenging with traditional methods and often requires a priori knowledge. To address this challenge, deep learning has gained significant attention for its capacity to handle intricate patterns and extract hidden features. However, deep learning algorithms are often perceived as black-box models due to their complex and heterogeneous layers, which hinder transparency and raise concerns, especially in critical domains. Although numerous studies have highlighted the diagnostic potential of deep learning in SHM with vibration-based data, there has been limited exploration of the rationale behind using deep neural networks (DNNs) for processing TFs. This paper addresses this gap by introducing explainable artificial intelligence (XAI) methods in two distinct TF-based damage diagnostic case studies: a numerical aluminium structural beam and a large-scale experimental steel frame. In the former, damage is simulated as local stiffness reductions, while in the latter, damage is introduced by loosening joint bolts. In both cases, a convolutional neural network (CNN) is used to process raw TF data for damage detection, localization, and quantification in the beam, and for detection and localization in the frame. Subsequently, an XAI method based on the layer-wise relevance propagation (LRP) algorithm is employed to identify the most relevant input features. A pattern analysis is presented to explain recurrent patterns, and a comparison with other XAI methods is proposed for further validation. The results demonstrate that the CNN provides accurate predictions for both case studies, with its focus on damage-sensitive features aligning closely with findings in the existing literature.

Structural damage detection based on transmissibility functions with unsupervised domain adaptation

Deep learning (DL) has been used for structural damage detection by training neural network models with a large amount of data. These trained models perform well when the test samples are from the data with an identical distribution of the training data. The distributions are always different due to numerical modelling errors and operational environmental varieties, and the data for damage scenarios is difficult to be obtained in practice. A novel method based on the joint maximum discrepancy and adversarial discriminative domain adaptation (JMDAD) for structural damage detection without labelled measurement data has been developed to address the above issues. To reduce the influence of external excitations in practice, the transmissibility function of measured structural responses is used for structural damage detection. The proposed network includes a feature generator, two classifiers and one discriminator. Firstly, the feature generator and domain discriminator are to extract features and merge their distributions at the domain level to overcome the issue of insufficient data from the target real structure. Secondly, the generator and two classifiers are optimised to align their distributions at the class level using the classification discrepancy between the two classifiers. As a result, the damage-sensitive features are extracted and aligned to eliminate modelling errors and operational environmental varieties between the source and target structures. Three case studies have been conducted in this paper, e.g., one case between two building structures with different storeys, another case between the numerical and experimental structures subjected to random and earthquake excitations and the application of Canton Tower. The results show that the proposed method is much robust to the measurement noise and operational environment and the structural damage is identified accurately without labelled measurement data from the target structure in operational environments.

Effects of insertion of an h-AlN monolayer spacer in Pt-WSe2-Pt field-effect transistors

The growth of two-dimensional hexagonal aluminum nitride (h-AlN) on transition metal dichalcogenide (TMD) monolayers exhibits superior uniformity and smoothness compared to HfO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{2}$$\\end{document} on silicon substrate. This makes an h-AlN monolayer an ideal spacer between the gate oxide material and the WSe2 monolayer in a two-dimensional field effect transistor (FET). From first principles approaches, we calculate and compare the transmission functions and current densities of Pt–WSe2–Pt nanojunctions without and with the insertion of an h-AlN monolayer as a spacer in the gate architecture. The inclusion of h-AlN can alter the characteristics of the Pt–WSe2–Pt FET in response to the gate voltage (Vg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_\ ext {g}$$\\end{document}). The FET without (or with) h-AlN exhibits the characteristics of a P-type (or bipolar) transistor: an on/off ratio of around 2.5×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.5 \ imes 10^{6}$$\\end{document} (or 1.7×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.7 \ imes 10^{6}$$\\end{document}); and an average subthreshold swing (S.S.) of approximately 109 mV/dec.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {dec.}$$\\end{document} (or 112 mV/dec.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {dec.}$$\\end{document}), respectively. We observe that Vg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_\ ext {g}$$\\end{document} shifts the profile of the transmission function by an energy of α(eVg)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha (e V_{\ ext {g}})$$\\end{document}, where α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} represents the gate-controlling efficiency. We observed that αin=83%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha _{\ ext {in}}=83\\%$$\\end{document} and αout=33%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha _{\ ext {out}}=33\\%$$\\end{document}, corresponding to whether the Fermi energy is located inside or outside the band gap. Therefore, we construct an effective gate model based on the Landauer formula, with the transmission function at Vg=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_\ ext {g}=0$$\\end{document} as the baseline. Our model generates results that are consistent with those obtained through first principles calculations. The relative error in current densities between model and first-principles calculations is within [ln(10)S.S.]|ΔVGeff|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[\\frac{ln(10)}{S.S.}] | \\Delta V_{\ ext {G}}^{\ ext {eff}} |$$\\end{document}. The 2D atomistic FETs show excellent device specifications and the ability to compete with existing transistors based on traditional silicon technology. Our findings could help advance the design of TMD-based FETs.

Analytical solution of a microrobot-blood vessel interaction model

AbstractThis study develops a dynamics model of a microrobot vibrating in a blood vessel aiming to detect potential cancer metastasis. We derive an analytical solution for microrobot’s motion, considering interactions with the vessel walls modelled by a linear spring-dashpot and a constant damping value for blood viscosity. The model facilitates instantaneous state transitions of the microrobot, such as contact with the vessel wall and free motion within the fluid. Amplitudes and phase angles from the transient solutions of dynamics model of the microrobot are solved at arbitrary moments, providing insights into its transient dynamics. The analytical solution of the proposed system is validated by experimental data, serving as a benchmark to examine the influence of pertinent parameters on microrobot’s dynamic response. It is found that the contact force transmitted to the vessel wall, assessed by system’s transmissibility function dependent on damping and frequency ratios, decreases with increasing damping ratio and intensifies when the frequency ratio is below $$\sqrt 2$$ 2 . At the frequency ratio is equal to 1, resonance phenomenon is dominated by the magnification factor linked to the damping ratio, increasing the amplitude of resonance as damping decreases. Finally, different sets of system parameters, including excitation frequency and magnitude, fluid damping, vessel wall’s stiffness and damping, reveal multi-periodic motions and fake collision of the microrobot with the vessel wall. Simulation results imply that these phenomena are minimally affected by vessel wall’s stiffness but are significantly influenced by other parameters, such as fluid damping coefficient and damping coefficient of the blood vessel wall. This research provides a robust theoretical foundation for developing control strategies for microrobots aimed at detecting cancer metastasis.

Oriented Growth of Highly Emissive Manganese Halide Microrods for Dual‐Mode Low‐Loss Optical Waveguides

Zero‐dimensional (0D) structured lead‐free metal halides have recently attracted widespread attention due to their high photoluminescence quantum yield (PLQY) and negligible self‐absorption, showing enormous potential as optical waveguides towards miniaturized photonic devices. However, due to the great difficulty in growth of rod‐like nano/micro‐sized morphologies, such applications have been less explored. Herein, a new‐type emissive organic‐inorganic manganese (II) halide crystal (TPS2MnCl4, TPS = C18H15S, triphenylsulfonium) in the form of microrods is synthesized via a facile chloride ion (Cl‐) induced oriented growth method. Due to a combination of attractive features such as a high PLQY of 86%, negligible self‐absorption and smooth crystal surface, TPS2MnCl4 microrods are well suited for use in optical waveguide with an ultra‐low optical loss coefficient of 1.20·10‐4 dB μm‐1, superior to that of most organic‐inorganic metal halide hybrids, organic materials, polymers and metal nanoclusters to the best of our knowledge. Importantly, TPS2MnCl4 microrods can further work as dual‐mode optical waveguides, combining active and passive light transmission functionalities in one single crystal. In addition, TPS2MnCl4 microrods also display remarkable performance in lighting and anti‐counterfeiting due to their distinct optical properties and commendable stability.

Oriented Growth of Highly Emissive Manganese Halide Microrods for Dual-Mode Low-Loss Optical Waveguides.

Zero-dimensional (0D) structured lead-free metal halides have recently attracted widespread attention due to their high photoluminescence quantum yield (PLQY) and negligible self-absorption, showing enormous potential as optical waveguides towards miniaturized photonic devices. However, due to the great difficulty in growth of rod-like nano/micro-sized morphologies, such applications have been less explored. Herein, a new-type emissive organic-inorganic manganese (II) halide crystal (TPS2MnCl4, TPS = C18H15S, triphenylsulfonium) in the form of microrods is synthesized via a facile chloride ion (Cl-) induced oriented growth method. Due to a combination of attractive features such as a high PLQY of 86%, negligible self-absorption and smooth crystal surface, TPS2MnCl4 microrods are well suited for use in optical waveguide with an ultra-low optical loss coefficient of 1.20·10-4 dB μm-1, superior to that of most organic-inorganic metal halide hybrids, organic materials, polymers and metal nanoclusters to the best of our knowledge. Importantly, TPS2MnCl4 microrods can further work as dual-mode optical waveguides, combining active and passive light transmission functionalities in one single crystal. In addition, TPS2MnCl4 microrods also display remarkable performance in lighting and anti-counterfeiting due to their distinct optical properties and commendable stability.

Fault Location Method of Distribution Network Based on VGAE-GraphSAGE

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario.

Flanking transmission from structure-borne sound sources in heavyweight buildings over multiple junctions

In many buildings, rooms that contain vibrationally-active machinery are not always adjacent to habitable rooms; hence it is useful to be able to predict vibration transmission over multiple junctions to non-adjacent rooms. In the first version of the European Standard EN 12354 Part 5 (2009) the equivalent vibration reduction index was introduced to account for transmission across multiple junctions. This was removed in the 2023 version but there remains a need for these estimates at the design stage of a building. This paper compares predictions of vibration transmission from a wall to different receiving rooms in a large heavyweight building. The SEA matrix solution is compared with SEA path analysis using different numbers of paths to assess whether path analysis is feasible, and if so, what errors can be expected when using a limited number of paths. An alternative approach to path analysis is to use transmission functions for heavyweight buildings, and an initial assessment is made to see whether these are expected to vary significantly depending on the position in the building.

Bioinspired Artificial Intelligent Nociceptive Alarm System Based on Fibrous Biomemristors.

With the advancement of modern medical and brain-computer interface devices, flexible artificial nociceptors with tactile perception hold significant scientific importance and exhibit great potential in the fields of wearable electronic devices and biomimetic robots. Here, a bioinspired artificial intelligent nociceptive alarm system integrating sensing monitoring and transmission functions is constructed using a silk fibroin (SF) fibrous memristor. This memristor demonstrates high stability, low operating power, and the capability to simulate synaptic plasticity. As a result, an artificial pressure nociceptor based on the SF fibrous memristor can detect both fast and chronic pain and provide a timely alarm in the event of a fall or prolonged immobility of the carrier. Further, an array of artificial pressure nociceptors not only monitors the pressure distribution across various parts of the carrier but also provides direct feedback on the extent of long-term pressure to the carrier. This work holds significant implications for medical support in biological carriers or targeted maintenance of electronic carriers.

Design and analysis of large displacement bidirectional device based on multi-electrothermal co-actuation

Abstract As an important part of the MEMS safety system of fuze, the micro-actuator is responsible for the important functions of energy conversion, motion, force transmission and response to system information, etc. However, due to the size limitation of MEMS and the characteristics of electrothermal actuator itself, it is difficult for a single electrothermal actuator to drive the flameproof slider to achieve the target displacement. This paper aims to study and analyze the performance of the large displacement bidirection device based on multi-electrothermal co-actuation. After finite element analysis, the results show that the large displacement bidirection device can drives the flameproof slider to achieve the displacement up to 162.6μm within one cycle time, which is about 1.4 times displacement of the single V-shaped actuator outputs at 1.5V voltage. The error of response time between the theoretical calculation and the simulation analysis is less than 5.88%. The correctness of simulation and the feasibility of the large displacement bidirection device based on multi-electrothermal co-actuation is verified.

Wearable and Multiplexed Biosensors based on Oxide Field-Effect Transistors.

Wearable sensors designed for continuous, non-invasive monitoring of physicochemical signals are important for portable healthcare. Oxide field-effect transistor (FET)-type biosensors provide high sensitivity and scalability. However, they face challenges in mechanical flexibility, multiplexed sensing of different modules, and the absence of integrated on-site signal processing and wireless transmission functionalities for wearable sensing. In this work, a fully integrated wearable oxide FET-based biosensor array is developed to facilitate the multiplexed and simultaneous measurement of ion concentrations (H+, Na+, K+) and temperature. The FET-sensor array is achieved by utilizing a solution-processed ultrathin (≈6nm thick) In2O3 active channel layer, exhibiting high compatibility with standard semiconductor technology, good mechanical flexibility, high uniformity, and low operational voltage of 0.005V. This work provides an effective method to enable oxide FET-based biosensors for the fusion of multiplexed physicochemical information and wearable health monitoring applications.

Design and Implementation of Image Acquisition and Signal Transmission Functions based on FPGA

Abstract In recent years, video network monitoring systems have evolved rapidly, from the first generation of digital-analogue systems to the fully IP-based systems. Based on the FPGA core, this design focuses on image acquisition and information transmission. The FPGA expansion board contains several peripheral modules, including the OV7670 camera captures images, DRAM stores data, and FIFO handles cross-clock domain signal transmission. While the reference clock is 40ns, in this system, the clock period is 20ns, making two clock cycles equal to one reference clock cycle. Then, two counters are used, one is the reference clock and another is the line synchronization (cnt1) to count 800 clock cycles per line. The end of a line is indicated by end_cnt0, and FIFO data is stored in SDRAM. Configuring the VGA display driver allows image data to be transmitted and displayed on the VGA screen. The system features a simple structure, high resource utilization, and strong reliability.

PdMoPtCoNi High Entropy Nanoalloy with d Electron Self-Complementation-Induced Multisite Synergistic Effect for Efficient Nanozyme Catalysis.

Engineering multimetallic nanocatalysts with the entropy-mediated strategy to reduce reaction activation energy is regarded as an innovative and effective approach to facilitate efficient heterogeneous catalysis. Accordingly, conformational entropy-driven high-entropy alloys (HEAs) are emerging as a promising candidate to settle the catalytic efficiency limitations of nanozymes, attributed to their versatile active site compositions and synergistic effects. As proof of the high-entropy nanozymes (HEzymes) concept, elaborate PdMoPtCoNi HEA nanowires (NWs) with abundant active sites and tuned electronic structures, exhibiting peroxidase-mimicking activity comparable to that of natural horseradish peroxidase are reported. Density functional theory calculations demonstrate that the enhanced electron abundance of HEA NWs near the Fermi level (EF) is facilitated via the self-complementation effect among the diverse transition metal sites, thereby boosting the electron transfer efficiency at the catalytic interface through the cocktail effect. Subsequently, the HEzymes are integrated with a portable electronic device that utilizes Internet of Things-driven signal conversion and wireless transmission functions for point-of-care diagnosis to validate their applicability in digital biosensing of urinary biomarkers. The proposed HEzymes underscore significant potential in enhancing nanozymes catalysis through tunable electronic structures and synergistic effects, paving the way for reformative advancements in nano-bio analysis.

Transmission and Agreement: Reading and the Contemporary Northern Irish Novel

Transmission and Agreement: Reading and the Contemporary Northern Irish Novel. This paper evaluates the capacity of the contemporary Northern Irish novel to act as an agent of transmission for a ‘post-Troubles’ readership, one distanced by a generation from the 1998 Good Friday Agreement that symbolised the partial end of sectarian violence. The critic Liam Harte suggests that “no part of modern Europe (if not the world) has greater claim than Northern Ireland to the mantle of most-narrativized region”, but the transmissive function of fictional works in this context remain under-critiqued. Here, I assess the aspirations of novelistic narrative to cater for what Premesh Lalu has described, in the context of post-apartheid South African art forms, as ‘psychic repair’ (Undoing Apartheid, 2023). The essay engages the 2018 novel Milkman, by Northern Irish author Anna Burns, to ask how fictional narrative transmits the detail of the past across a generation first, in parallel to other literary and artistic genres, and second, in relation to an expanding archive of ongoing legal tribunals and government incentives directed towards the production of an ‘official history’ for Northern Ireland. Through attention to the self-conscious foregrounding of forensic reading practice by Burns and fellow writers, I show the necessary place of fictional recovery in the field of political and cultural memory. Keywords: Troubles, Northern Irish fiction, transmission, memory, generation

Electromagnetically induced grating via amplitude and phase modulations in a three-level V-type atomic medium

Abstract A probe laser beam can be completely absorbed in an atomic resonance frequency region, however, if a coupling laser beam is further applied to the atom, the atomic medium can exhibit electromagnetically induced transparency (EIT), i.e, the probe beam can be completely transmitted through the atomic medium. Thus, if the coupling beam is a standing wave field with nodes and antinodes, it will cause in space a periodic modulation of the transmitted spectrum of the probe field. This means that the probe field propagates through the atomic medium just as it passes through a diffraction grating which is called electromagnetically induced grating (EIG). Based on amplitude or phase modulation of transmission function, the absorption grating or the phase grating can be formed, respectively. In this work, based on controllable absorption and dispersion properties of a three-level V-type atomic system, we study the control of the absorption grating and the phase grating with respect to the intensity and the frequency of the laser fields. The absorption diffraction pattern at the two-photon resonance is obtained, while the phase diffraction pattern appears when there is a frequency shift of either the coupling frequency or the probe frequency compared to the corresponding atomic resonance frequency. By adjusting the coupling or/and probe laser frequency, the absorption grating can be converted into the phase grating and the high-order diffraction efficiency can also be improved.