Mode Output Research Articles

The effect of inverting decades and units on the retention of two-digit numbers in working memory: a matter of the output mode

AbstractNumber-word structure affects transcoding and relates to working memory capacity (WMc). We tested the relationship between the structure of number-words (inverted “Units-Decades (UD) format” or not “Decades-Units (DU) format”) and performance on the digit span task for recalling two-digit number-words presented verbally, using two modes of output. University students, native speakers of Arabic, who were also highly proficient in Hebrew, and native speakers of Hebrew, were asked to response by typing Arabic digits or recalling numbers verbally. Arabic and Hebrew speakers were equal in WMc for one-digit numbers in the two modes. However, when inversion was presented, performance was related to output and participants habits. In the typing response, both groups showed better performance for the DU format. In the native language (standard format), Arabic speakers (UD) remembered fewer numbers than Hebrew speakers (DU). Arabic speakers show inversion errors even in the noninverted format. For verbal responses, there were no differences between the groups in their standard format, and native speakers of Arabic weren’t affected by the format, while Hebrew speakers still preferred the DU format. Experience with inverted and noninverted language affected WMc but is also related to the output demands.

GPe Projections to the Retrorubral Field Give Rise to Diverging GABAergic and DAergic Circuits.

The external segment of the globus pallidus (GPe) is involved in the modulation of motor output and limbic components of behavior. The circuitry by which the GPe contributes to limbic components of behavior remains unknown. While many investigations have focused on the local circuitry within the basal ganglia, numerous GPe projections to target regions outside of the basal ganglia including the midbrain, thalamus, and cortex exist and remain largely unexplored. Here, by anatomical and electrophysiological means we investigate the projections of the GPe to the Retrorubral Field (RRF), a dopaminergic nucleus in the caudal midbrain, in both male and female mice. We find that the GPe targets both GABAergic and DAergic neurons in the RRF (RRFDA and RRFGABA). Viral-assisted circuit mapping to trace the cell-type specific projection patterns of RRFDA and RRFGABA populations revealed diverging pathways. The connectivity of GPe-recipient RRFDA neurons followed the traditional medial forebrain bundle path, innervating the ventral striatum and to a lesser extent the dorsal striatum. GPe-recipient RRFGABA neurons, on the other hand, displayed broad projections targeting numerous brain regions involved in limbic function including the parabrachial nucleus, amygdala, and hypothalamus. These contrasting projection profiles reveal multiple nodes by which the GPe is disynaptically connected to the limbic system, representing novel access points for the GPe to modulate limbic components of behavior.Significance Statement Progress in our understanding of GPe composition, circuits, and functions has allowed for a greater appreciation of the role of the GPe in shaping behavior. Here, we elaborate on the connectivity of the GPe with the RRF, the third-largest dopaminergic nucleus of the ventral midbrain. We reveal the targeting of both RRFDA and RRFGABA neurons by the GPe and demonstrate the vast non-overlapping projection profiles of RRFDA and RRFGABA Via the multi-level limbic connectivity of the RRF, these results add a novel branch of circuitry to the GPe, which may represent the foundation for some of its non-motor functions.

Self-mixing thinly sliced ruby laser for laser Doppler velocimetry with high optical sensitivity

In self-mixing laser Doppler velocimetry (LDV), the motion of a moving target is observed by using intensity-modulated laser light detected by a simple photodetector. Here, the self-mixing laser output modulation takes place, reflecting the pronounced effective loss modulation index, which is proportional to the fluorescence-to-photon lifetime ratio. The fluorescence lifetime of a ruby laser is extremely long, so if a ruby crystal can be used as a laser light source for a self-mixing LDV system, high-sensitivity LDV measurements can be performed with it. We describe a method for velocimetry of moving targets using self-mixing LDV in which a CW oscillating ruby laser is the light source. The oscillation mechanism of the thin-slice ruby laser with a large fluorescence-to-photon lifetime ratio, which is suitable for LDV measurements, is clarified and the results of highly sensitive LDV measurements are presented, featuring nonlinear dynamics observed associated with the self-mixing velocimetry experiment. The measurement accuracy is clarified by measuring the rotating disc with various conditions using self-mixing LDV.

A Lightweight Future Skeleton Generation Network(FSGN) Based on Spatio-Temporal Encoding and Decoding

Since early warning in industrial applications is far more valuable than post-event analysis, human activity prediction based on partially observed skeleton sequences has become a popular research area. Recent studies focus on building complex deep learning networks to generate accurate future skeleton data, but overlook the requirement for timeliness. Different from such frame-by-frame generation methods, we propose a Future Skeleton Generation Network (FSGN) based on spatio-temporal encoding and decoding framework. Firstly, we design a dynamically regulated input module to ensure equal-length input of partially observed data, and set modules like discrete cosine transform(DCT) and low-pass filtering(LPF) to filter important information. Then, we employ an improved multi-layer perceptron(MLP) structure as the basic computational unit for the encoding and decoding framework to extract spatio-temporal information, and propose using multi-dimensional motion error of human skeleton to form the loss function. Finally, we use an output module symmetrical to the input module to achieve the generation of future activity data. Results show that the proposed FSGN achieves fewer parameters(0.12M) and higher generation accuracy, which can effectively provide future information for human activity prediction tasks.

Arch dam point cloud segmentation based on deep feature learning and normal vector data optimization

Separating the dam body, spillway, and other structures from the point cloud in the dam area is an important step in dam deformation monitoring. Manual segmentation is time consuming and inaccurate. This study proposes a point cloud segmentation neural network model based on normal vector optimization suitable for dam environment: (1) This model utilizes the voxel uniform sampling method of equal length cubes to solve the problem of uneven point cloud density caused by wide range and long-distance measurement during point cloud measurement in dam areas. (2) Designed block input and combined output modules in the model, achieving efficient input of large volume point cloud and eliminating the impact of interpolation points offset during seq2seq model decoding process. (3) In response to the diverse characteristics of point cloud normal vectors presented by vegetation, rock mass, and complex dam structures in the dam area, this paper proposes an adaptive radius plane fitting vector estimation method based on eigenvalue method to improve the accuracy of segmentation. The experiment on the prototype arch dam shows that the proposed normal estimation method improves the classification accuracy of PointNet + + from 96.26 to 98.27%. Compared with the other three normal estimation methods (2-jets, Hough CNN, iterative PCA), the overall accuracy is improved by 0.82%, 1.22%, and 0.22%, and the joint average intersection is improved by 0.0293, 0.0325, and 0.0104. The prototype arch dam experiment shows that our proposed method has a segmentation accuracy of 98.27%. Compared with 2-jets, Hough CNN, and iterative PCA, the overall accuracy has been improved by 0.82%, 1.22%, and 0.22%. This study provides a high-precision segmentation scheme for applications such as deformation detection of dam components based on point clouds.

Spatially controllable stimulate Brillouin scattering in the diamond Raman laser

Diamond Raman lasers offer a significant advantage over conventional inversion lasers in achieving high-power single-longitudinal mode (SLM) output, making them highly suitable for various potential applications. However, the presence of stimulated Brillouin scattering (SBS) introduces instability and degrades the SLM mode into a multimode longitudinal mode (MLM) as power scaling increases. In this research article, we present an analytical model that elucidates the transverse-mode behavior of the SBS field within the resonator to explore the mechanism of SBS inhibition. We further investigate the relationship between the cavity ABCD matrix and the transverse mode distribution of the SBS. To validate the model, we design and build laser cavities to observe the SBS beam profile. By utilizing this model, we achieve the controllable TEMmn (m + n ≤ 9) transverse modes of SBS. This model not only acts as a guide for suppressing SBS in diamond Raman lasers, thereby increasing the power limit of SLM output, but also provides a novel approach to attain flexible transverse modes with high power, narrow linewidth, and adjustable wavelength without the need for additional modulation elements.

MMLmiRLocNet: miRNA Subcellular Localization Prediction based on Multi-view Multi-label Learning for Drug Design.

Identifying subcellular localization of microRNAs (miRNAs) is essential for comprehensive understanding of cellular function and has significant implications for drug design. In the past, several computational methods for miRNA subcellular localization is being used for uncovering multiple facets of RNA function to facilitate the biological applications. Unfortunately, most existing classification methods rely on a single sequencebased view, making the effective fusion of data from multiple heterogeneous networks a primary challenge. Inspired by multi-view multi-label learning strategy, we propose a computational method, named MMLmiRLocNet, for predicting the subcellular localizations of miRNAs. The MMLmiRLocNet predictor extracts multi-perspective sequence representations by analyzing lexical, syntactic, and semantic aspects of biological sequences. Specifically, it integrates lexical attributes derived from k-mer physicochemical profiles, syntactic characteristics obtained via word2vec embeddings, and semantic representations generated by pre-trained feature embeddings. Finally, module for extracting multi-view consensus-level features and specific-level features was constructed to capture consensus and specific features from various perspectives. The full connection networks are utilized as the output module to predict the miRNA subcellular localization. Experimental results suggest that MMLmiRLocNet outperforms existing methods in terms of F1, subACC, and Accuracy, and achieves best performance with the help of multi-view consensus features and specific features extract network.

Performance analysis of photovoltaic module using eutectic phase change material

ABSTRACT Geographical factors and environmental variables affect the performance of the solar photovoltaic (SPV) system. The incident solar radiation on the SPV will generate power and increase the surface temperature. Thermal regulation is needed for the SPV system to enhance its performance. In this work, a passive cooling approach is adopted to reduce the surface temperature of the SPV system. The eutectic phase change material is synthesized with the composition of RT35 paraffin wax and sodium sulfate decahydrate. The thermophysical properties of the eutectic mixture are analyzed using a differential scanning calorimeter, and thermal conductivity is measured. The developed eutectic PCM is integrated into the SPV modules’ rear-side packing in the aluminum container. The output of the PV module is increased by 10% and up to 6.1°C reduces the surface temperature of the SPV module compared to the conventional SPV system.

Fixed-time second-order sliding mode output feedback trajectory tracking control for an autonomous surface vehicle with model uncertainties and prescribed performance

This paper investigates the fixed-time trajectory tracking control problem of autonomous surface vehicle system with asymmetric performance constraints. The external disturbances, model uncertainties and the difficulty of velocity measurement are all taken into account in the controller design. Asymmetric performance constraints are firstly studied to improve the transient and steady-state performance of the tracking error system. Next, a fixed-time extended state observer is developed to estimate the unknown velocity and external disturbances. Both the fixed-time first-order and the second-order PID-type sliding surfaces are designed. Further, two continuous output feedback controllers are built to achieve high control accuracy and low energy consumption and to achieve fixed-time stability of the closed-loop system. Finally, abundant simulations for CyberShip II demonstrate the effectiveness of the proposed algorithms.

Two‐Photon Pumped Wavelength‐Tunable Single‐Mode Plasmonic Nanolaser with Ultralow Threshold

AbstractNonlinear optics play an important role in laser technology, optical communication, integrated optics, and other fields. However, conventional two‐photon lasing faces challenges such as high thresholds and large size, which hinder the miniaturization of lasers. In this study, the structure of single‐crystal Au/Al2O3/CsPbBr3 (ScAu/Al2O3/CPB) is constructed to achieve two‐photon pumped frequency upconversion single‐mode plasmonic lasing. The strong spatial confinement and near‐field enhancement of surface plasmons in metals enable the plasmonic lasing mode output in a hybrid nanocavity, significantly reducing the lasing threshold. Additionally, by applying external mechanical strain, the resonant wavelength of the lasing mode is dynamically regulated, further reducing the threshold to 0.48 mJ cm−2 based on piezo‐electronic effect. These results provide an effective strategy for all‐optical integration and the development of smaller, faster, and more efficient nanophotonics devices.

Single-Input Multiple-Output (SIMO) Cascode Low-Noise Amplifier with Switchable Degeneration Inductor for Carrier Aggregation.

This paper presents a single-input multiple-output (SIMO) cascode low-noise amplifier with inductive degeneration for inter- and intra-band carrier aggregation. The proposed low-noise amplifier has two output ports for flexible operation in carrier aggregation combinations for band 30 and band 7. However, during inter- and intra-band operation, gain variation occurs depending on the output mode. To compensate for this, a switching circuit is proposed to adjust the degeneration inductor, optimizing gain performance for both modes. The switching operation can minimize the control for the dynamic range in the receiver system to support carrier aggregation. The designed low-noise amplifier was fabricated using a 65 nm CMOS process, occupying an area of 2.1 mm2. In inter-band operation, the small-signal gain was measured by 18.9 dB for band 30 and 18.6 dB for band 7, with the noise figures of 1.03 dB and 1.07 dB, respectively. For intra-band operation, the small-signal gain was 17.3 dB and 17.2 dB, with the noise figures of 1.3 dB and 1.41 dB. The IIP3 values were measured by -7.6 dBm and -6.7 dBm for inter-band, and -6.3 dBm and -6.2 dBm for intra-band. Power consumption was 8.04 mW and 7.68 mW in inter-band, and 17.04 mW and 17.64 mW in intra-band depending on the output configuration.

Efficient and cost-effective maximum power point tracking technique for solar photovoltaic systems with Li-ion battery charging

This paper presents an effective approach to achieve maximum power point tracking (MPPT) in photovoltaic (PV) systems for battery charging using a single-sensor incremental conductance (InC) method. The objective is to optimize the MPPT process while minimizing the number of sensors required. The suggested technique leverages the relationship between the PV module's output voltage and the duty cycle to automatically adjust and reach the MPP, resulting in optimal power generation. By eliminating the PV current sensor from the control circuit, the developed method reduces both the cost and size of the MPPT circuit. Compared to the conventional InC method, the developed approach demonstrates improved response speed and accuracy in steady-state operation, along with the ability to damp oscillations near the MPP. Extensive simulations using MATLAB/Simulink validate the performance of the developed technique across various environmental conditions. The results highlight the recommended method's realistic and effective MPP tracking capabilities, achieving higher efficiency (99.12 %) compared to the classical method (97.8 %) under high irradiance levels.

Recent Advances in Developing Optical and Electrochemical Sensors for Monitoring Thiram and Future Perspectives.

Thiram, as one widely used dithiocarbamate pesticide, has been considered seriously detrimental to food safety and human health because of poor efficiency, nonstandard/superfluous usage, and lack of a targeting effect. Developing high-performance sensors for thirams is strongly needed. With the rapid development of chemistry, biology, and materials science, many sensors have been constructed for thiram with high sensitivity and selectivity. Regarding the energy form of the signal, recognition mode, and detection principle, recent advances in the design and construction of optical and electrochemical sensors for thiram are summarized in this review, including colorimetric, luminescent, chemiluminescent, and electrochemical sensors. The advantages and disadvantages of the sensors for thiram including sensitivity, ability to avoid interference, recognition mechanism, signal output mode, and practicability are clarified in detail. Furthermore, the challenges faced, effective restrictions, and next direction of development are proposed for achieving more sensitive and selective analysis of thiram with less interference. We desire that this review will supply a solid theoretical basis and inspiration to generate innovative thinking for achieving new progress on thiram assays and the commercialization of the developed sensors in the future.

Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection

Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes for maturity. Recently, Depression arises as a common mental health issue in adolescents and this affects the daily life of the person. This leads to educational and social impairments and this acts as a major risk for suicide. As a result, the identification and treatment for this disorder are essential. By applying Deep learning (DL) algorithms to medical data, the mental condition of a person can be predicted. However, the traditional deep learning models face the challenge in processing the huge sized data. Hence, FL has emerged as an efficient solution for addressing the data size issue of DL. Here, Depression detection in adolescents is carried out by considering the FL framework, which comprises two modules, namely the local module and the Global module. The detection process is done in the local module using the proposed Exponential African Pelican Optimization based Deep Convolutional Neural Network (ExpAPO-DCNN), whereas the Global module produces the aggregated output of the local module. In this research, FL utilizes the DL model in producing the output, where the DL model considered two modalities of inputs, such as speech signal and Magnetic Resonance Imaging (MRI) image. The processing steps used for this research are pre-processing, feature extraction and detection. For MRI and speech signals, all the above processes are carried out individually. Finally, both the outputs are fused utilizing the overlap coefficient. The ExpAPO-DCNN obtained accuracy, Loss, Root mean Squared error (RMSE), Mean Squared error (MSE), True Negative rate (TNR), and True Positive rate (TPR) of 98.00 %, 0.023, 0.058, 0.240, 97.90 %, and 96.30 %, respectively.

Boost clustering with Gaussian Boson Sampling: a quantum-classical hybrid approach

Gaussian Boson Sampling (GBS) is a recently developed paradigm of quantum computing consisting of sending a Gaussian state through a linear interferometer and then counting the number of photons in each output mode. When the system encodes a symmetric matrix, GBS can be viewed as a tool to sample subgraphs: the most sampled are those with a large number of perfect matchings, and thus are the densest ones. This property has been the foundation of the novel clustering approach we propose in this work, called GBS-based clustering, which relies solely on GBS, without the need of classical algorithms. The GBS-based clustering has been tested on several datasets and benchmarked with two well-known classical clustering algorithms. Results obtained by using a GBS simulator show that on average, our approach outperforms the two classical algorithms in two out of the three chosen metrics, proposing itself as a viable quantum-classical hybrid clustering option.

Nonlinear amplification of nano bowl surface concavity on the critical response threshold to biosignals

Polymer nanoparticles that can sharply sense and detect biological signals in cells are promising candidates for biomedical and theranostic nanomaterials. However, the response ability of current polymer assemblies poorly matches the requirement of trace concentration level (10−6 ~ 10−9 mol/L) of cellular biosignals due to their linear signal input-to-function output mode, which impedes their practical applications in vivo. Here we report a kind of nanobowl system with pH-tunable invaginated morphology that can nonlinearly amplify the response abilities toward biosignals by modulating the surface concavity. Compared to conventional spherical nanoparticles, nonspherical nanobowls with a specific concave structure reduce the critical response threshold of polymers by up to 5 orders of magnitude, from millimole to nanomole level, covering most of biosignal concentration windows. Moreover, we find that this nonlinear signal gain effect is originated from the collective impact of a single signal on transitioning the polymer chain aggregation state of individual assemblies, rather than just altering a certain unit or chain. This nonlinear signal-to-response mechanism is potential to solve the tricky problems of probing and sensing endogenous signals with trace physiological concentration.

Multimodal vehicle trajectory prediction and integrated threat assessment algorithm based on adaptive driving intention

As autonomous driving and connected communication technologies advance swiftly, vehicle trajectory prediction has become increasingly significant. The motion of a vehicle is contingent not only on its historical trajectory but is also subject to the influence of surrounding vehicles, thereby exhibiting intricate social and temporal interdependencies. Furthermore, the inherent randomness and uncertainty in driver behavior render vehicle trajectory prediction inherently multimodal, a factor that is frequently neglected in current research. Against this backdrop, a multimodal vehicle trajectory prediction (MTP) model based on an encoder-decoder architecture is proposed to hierarchically extract historical features of vehicles. The model consists of five key components: temporal feature encoder module, spatial interaction module, spatial-temporal dependence module, driving intention fusion module and multimodal trajectory output module. Experiments on the NGSIM dataset show that the predictive performance of the model has been improved to varying degrees, especially at 3–5 s, where the improvement is more significant. Compared with state-of-the-art models, the Root Mean Square Error (RMSE) error at 5 s time horizon is 3.38 m on NGSIM dataset, which represents a 25 % improvement. To measure the safety of predicted trajectories, we propose a comprehensive threat assessment model that combines collision time (TTC), headway (TH) and time to lateral collision (TLC) metrics based on safe distance theory. This model not only evaluates the longitudinal collision threat in the following state, but also evaluates the lateral collision threat during driving maneuvers in multi lane scenarios, thereby comprehensively improving the safety of vehicle driving. This research also offers new perspectives and insights for the development of autonomous driving.

Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images

Breast cancer (BC) is the most common type of cancer among women globally and is one of the leading causes of cancer-related deaths among women. In the diagnosis of BC, histopathological assessment is the gold standard, where automated tumor detection technologies play a pivotal role. Utilizing Convolutional Neural Networks (CNNs) for automated analysis of image patches from Whole Slide Images (WSIs) enhances detection accuracy and alleviates the workload of pathologists. However, CNNs often face limitations in handling pathological patches due to a lack of sufficient contextual information and limited feature generation capabilities. To address this, we propose a novel Multi-scale Multi-head Self-attention Ensemble Network (MMSEN), which integrates a multi-scale feature generation module, a convolutional self-attention module, and an adaptive feature integration and output module, effectively optimizing the performance of classical CNNs. The design of MMSEN optimizes the capture of key information and the comprehensive integration of features in WSIs pathological patches, significantly enhancing the precision of tumor detection. Validation results from a five-fold cross-validation experiment on the PatchCamelyon (PCam) dataset demonstrate that MMSEN achieves a ROC-AUC of 99.01%±0.02%, an F1 Score of 98.00%±0.08%, a Balanced Accuracy (B-Acc) of 98.00%±0.08%, and a Matthews Correlation Coefficient (MCC) of 96.00%±0.16% (p<0.05). These results demonstrate the effectiveness and potential of MMSEN in detecting tumors from pathological patches in WSIs for BC.

Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditioning with addictiveness and time memory for automatic learning. The circuit is mainly composed of hunger output module, neuron module, excitement output module, memristor-based decision module, and memory and feedback generation module. In the circuit, the process of output excitement and addiction in stochastic feedback is achieved. The memory of interval between the two rewards is formed. The circuit can adapt to complex scenarios with these functions. In addition, hunger and satiety are introduced to realize the interaction between biological behavior and exploration desire, which enables the circuit to continuously reshape its memories and actions. The process of operant conditioning theory for automatic learning is accomplished. The study of operant conditioning can serve as a reference for more intelligent brain-inspired neural systems.

Comparative experimental analysis of monocrystalline and polycrystalline photovoltaic panel through hybrid phase change material

Photovoltaics thermal management is utmost necessary as continuous heating of the module decreases the performance. This paper presents a comparative study in terms of performance of two photovoltaic modules namely monocrystalline and polycrystalline using hybrid phase change materials (PCMs). The hybrid PCMs of RT31 and RT 44HC were prepared by method of stirring. The final prepared hybrid PCMs were poured at the back of panel and panel was covered. The purpose of these experiments is to improve the efficiency of polycrystalline and monocrystalline photovoltaic modules and comparing which technology is more efficient having hybrid PCM. The experiments were performed under the room conditions in Taxila, Pakistan. The output parameters of the photovoltaic module appeared to be strongly dependent upon the solar irradiance. The power output and efficiency of monocrystalline and polycrystalline photovoltaic module were measured. The monocrystalline panel maximum temperature was 36.59 °C as compared to polycrystalline panel which was 40.23 °C. As the radiation intensity increased till noon, electrical power was improved, and it was 6.52 W in monocrystalline panel as compared to polycrystalline panel which was 6.32 W. Electrical conversion efficiency was also observed, and it was maximum up to 11.84 % in monocrystalline panel. The higher performance was observed in case of monocrystalline panel having hybrid in terms of performance as compared to polycrystalline panel.