Power Pattern Research Articles

Analysis on Influencing Factors of Innovation Behavior of Specialized and New Enterprise Small and Micro Enterprises

As China continues to strengthen the cultivation of specialized and ultra-new small and micro enterprises, and promote the development of related enterprises in China's basic industries, now "specialization and innovation" enterprises have become a new power to enhance the industrial chain power and high-quality development pattern. However, the development of all kinds of "little giant" enterprises is still in the middle and low end of the industrial chain, the core creativity is still very lacking, and it is easy to be eliminated and replaced in the fierce competition of existing technology and innovation. In this regard, from the perspective of "innovation", this paper will use Probit model to empirically analyze the internal and external factors and constraints that affect the innovation behavior of small and medium-sized enterprises, among which government subsidies and human capital cultivation have significant negative and positive effects on enterprise innovation respectively. At the end of the paper, we try to find a way to solve the core problem of "innovation difficulty" of small, medium and micro enterprises under the background of "specialization and innovation" from multiple perspectives.

Can Empathy Provide a Route to Democratic Inclusivity?

How can democracies promote full consideration of all relevant interests in political decision-making? Is there a role for empathy, especially where there are obstacles to direct inclusion of relevant groups, as for example in the case of future generations and citizens of other countries? Critics of existing uses of empathy in political theory press that limits to our capacity to empathise can lead to bias and partiality. I argue instead for a more nuanced ‘holistic’ approach to the use of empathy into democratic design. The approach recommends, first, that we be sensitive to the potential consequences of catalysing empathy in specific decision-making contexts, rather than making general prescriptions. Second, it asks us to consider how different methods of empathic induction generate insight and motivation of different strength and degrees of generality. Third, the approach proposes not only that empathy be introduced into existing institutions and designs, but that we aim through democratic design to bring patterns of power into closer alignment with naturally occurring patterns of empathy. Fourth, the approach recommends taking a pragmatic view of which interventions might be most useful in any particular institutional context.

The effects of the antenna power pattern uncertainty within a global 21 cm experiment

ABSTRACT Measuring the redshifted sky-averaged neutral hydrogen 21 cm signal with a wide-band antenna operating at metre wavelengths can probe the thermal history of the Universe and the first star and galaxy formation during the Cosmic Dawn. Measurement of this ‘global 21 cm’ signal is extremely challenging due to foreground signals that are orders of magnitude brighter than the cosmological signal, which must be modelled and removed first. The Radio Experiment for the Analysis of Cosmic Hydrogen (REACH) aims to improve this process by simultaneously fitting the full posterior distribution of both the cosmological and foreground signals with Bayesian inference. The method, however, relies on an informed prior; partially derived from a simulated antenna power pattern. This simulated antenna power pattern will differ from the true antenna power pattern of the deployed instrument, and the impact of this uncertainty is unknown. We investigate this problem by forward modelling mock data with different levels of power pattern uncertainty through the REACH pipeline. We construct perturbed antenna power patterns through truncation of a singular-value-decomposed simulated power pattern; using one to generate mock observation data and the others to inform the prior. The power pattern uncertainty is quantified as ΔD, the absolute mean of the difference between the original and perturbed power patterns. Comparing the evidence and root-mean-square error we find that ΔD better than −35 dB, equivalent to millimetre accuracy in the antennas dimensions, is necessary for confident detection of the global signal. We discuss potential solutions to achieve this high level of accuracy.

Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Data Flows to Generate Data Flow Graphs in Dynamically-Typed Code

Data flow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically-typed programming languages like Python present implicit data flow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit data flow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs’ in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit data flow problems. To further enhance the accuracy of LLMs, we design a five-step Chain of Thought (CoT) and break it down into an AI chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach’s performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit data flow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve.

Design and analysis of a microstrip antenna for superior 5G communication performance

The continuous advancement in communication systems is propelled by the growing demand for fifth-generation (5G) technology, aiming to meet the growing demands of systems that are small in size, operate at high speeds, and have a wide bandwidth. In order to address these requirements, innovative and highly efficient antenna configurations are of paramount importance. This article introduces a microstrip antenna that has been meticulously designed for optimal performance 5G systems. Optimized for 5G communication systems, the research focuses on the design and simulation of microstrip patch antennas which is shaped like butterfly, resonating at a frequency of 50.5 GHz. A Rogers RT5880 (lossy) substrate with a dielectric constant of 2.2 was employed in the antenna design. The dimensions of the proposed antennas were 6 × 6.5 × 0.787 mm³, which resulted in a geometric configuration resembling a butterfly. A comprehensive performance assessment involves exhaustive simulations using the CST Studio Suite application suite. In order to optimize critical parameters such as the Voltage Standing Wave Ratio (VSWR), reflected power, gain, frequency range (BW), and radiation pattern, the dimensions were meticulously adjusted. Significantly, the antenna operating at 50.5 GHz demonstrated a gain of 6.8 dB, in addition to a broad bandwidth of 5.364 GHz and an outstanding VSWR of 1.0131. The outcomes successfully underscore the outstanding performance exhibited by the butterfly-shaped design, thereby establishing its suitability for implementation in 5G networks.

Representations of imaginary scenes and their properties in cortical alpha activity

Imagining natural scenes enables us to engage with a myriad of simulated environments. How do our brains generate such complex mental images? Recent research suggests that cortical alpha activity carries information about individual objects during visual imagery. However, it remains unclear if more complex imagined contents such as natural scenes are similarly represented in alpha activity. Here, we answer this question by decoding the contents of imagined scenes from rhythmic cortical activity patterns. In an EEG experiment, participants imagined natural scenes based on detailed written descriptions, which conveyed four complementary scene properties: openness, naturalness, clutter level and brightness. By conducting classification analyses on EEG power patterns across neural frequencies, we were able to decode both individual imagined scenes as well as their properties from the alpha band, showing that also the contents of complex visual images are represented in alpha rhythms. A cross-classification analysis between alpha power patterns during the imagery task and during a perception task, in which participants were presented images of the described scenes, showed that scene representations in the alpha band are partly shared between imagery and late stages of perception. This suggests that alpha activity mediates the top-down re-activation of scene-related visual contents during imagery.

Functional changes in sleep-related arousal after ketamine administration in individuals with treatment-resistant depression

The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in arousal-related sleep metrics between 36 individuals with treatment-resistant major depression (TRD) and 25 healthy volunteers (HVs). It also sought to determine whether ketamine normalizes arousal in individuals with TRD and whether ketamine’s effects on arousal mediate its antidepressant and anti-SI effects. This was a secondary analysis of a biomarker-focused, randomized, double-blind, crossover trial of ketamine (0.5 mg/kg) compared to saline placebo. Polysomnography (PSG) studies were conducted one day before and one day after ketamine/placebo infusions. Sleep arousal was measured using spectral power functions over time including alpha (quiet wakefulness), beta (alert wakefulness), and delta (deep sleep) power, as well as macroarchitecture variables, including wakefulness after sleep onset (WASO), total sleep time (TST), rapid eye movement (REM) latency, and Post-Sleep Onset Sleep Efficiency (PSOSE). At baseline, diagnostic differences in sleep macroarchitecture included lower TST (p = 0.006) and shorter REM latency (p = 0.04) in the TRD versus HV group. Ketamine’s temporal dynamic effects (relative to placebo) in TRD included increased delta power earlier in the night and increased alpha and delta power later in the night. However, there were no significant diagnostic differences in temporal patterns of alpha, beta, or delta power, no ketamine effects on sleep macroarchitecture arousal metrics, and no mediation effects of sleep variables on ketamine’s antidepressant or anti-SI effects. These results highlight the role of sleep-related variables as part of the systemic neurobiological changes initiated after ketamine administration. Clinical Trials Identifier: NCT00088699.

Aperiodic and periodic components of oscillatory brain activity in relation to cognition and symptoms in pediatric ADHD

Abstract Children with attention-deficit/hyperactivity disorder show deficits in processing speed, as well as aberrant neural oscillations, including both periodic (oscillatory) and aperiodic (1/f-like) activity, reflecting the pattern of power across frequencies. Both components were suggested as underlying neural mechanisms of cognitive dysfunctions in attention-deficit/hyperactivity disorder. Here, we examined differences in processing speed and resting-state-Electroencephalogram neural oscillations and their associations between 6- and 12-year-old children with (n = 33) and without (n = 33) attention-deficit/hyperactivity disorder. Spectral analyses of the resting-state EEG signal using fast Fourier transform revealed increased power in fronto-central theta and beta oscillations for the attention-deficit/hyperactivity disorder group, but no differences in the theta/beta ratio. Using the parameterization method, we found a higher aperiodic exponent, which has been suggested to reflect lower neuronal excitation-inhibition, in the attention-deficit/hyperactivity disorder group. While fast Fourier transform–based theta power correlated with clinical symptoms for the attention-deficit/hyperactivity disorder group only, the aperiodic exponent was negatively correlated with processing speed across the entire sample. Finally, the aperiodic exponent was correlated with fast Fourier transform–based beta power. These results highlight the different and complementary contribution of periodic and aperiodic components of the neural spectrum as metrics for evaluation of processing speed in attention-deficit/hyperactivity disorder. Future studies should further clarify the roles of periodic and aperiodic components in additional cognitive functions and in relation to clinical status.

The Optimization Research on RF Chip Signal Processing Based on Deep Learning Algorithms

With the rapid development of wireless communication technology, radio frequency (RF) chips, as the core components of communication systems, directly influence the overall system performance through their signal processing capabilities. Traditional signal processing methods often struggle to achieve optimal results in the face of complex and variable communication environments. In recent years, deep learning technology, due to its powerful feature learning and pattern recognition capabilities, has shown tremendous potential in various fields. This paper addresses the signal processing issues of RF chips and proposes an optimization method based on deep learning algorithms. Firstly, the status and challenges of RF chip signal processing are analyzed, followed by the design of a deep learning model suitable for RF signal processing. Through data pre-processing and feature extraction, the generalization ability of the model is enhanced. Experimental results demonstrate that the proposed method outperforms traditional methods in signal noise suppression, interference cancellation, and signal distortion compensation, significantly improving the signal processing performance of RF chips. This research provides new ideas and methods for the application of deep learning in the field of RF chip signal processing and holds significant theoretical and practical importance for the future development of wireless communication systems.

Design of Clustered Phased Arrays by Means of an Innovative Power Pattern Matching-Driven Method—The Linear Array Case

Design of Clustered Phased Arrays by Means of an Innovative Power Pattern Matching-Driven Method—The Linear Array Case

Analysis of Performance Improvement of Planar Antenna Arrays With Optimized Square Rings for Massive MIMO Applications

The power pattern of the conventional fully optimized planar arrays can be properly reshaped according to the required user-defined constraints. However, the practical implementation of such fully optimized large planar arrays is complex and expensive. This paper introduces a new and efficient technique that is capable of providing better performance and almost the same power pattern shapes as that of the conventional fully optimized planar arrays by optimally adjusting the element amplitude and phase excitations of the outer-square rings instead of all elements' excitations. The proposed technique starts with a massive fully planar array then divides it into two contiguous sub-planar arrays which are both symmetric about the original array center. The elements excitation amplitudes or phases of the outer sub-planar array are only adjusted to form the desired power pattern shapes, while the amplitudes or phases of the central sub-planar array elements which have usually higher weights than the outer elements are made constants (i.e., they made ones for the case of amplitude-only control and zeros for the phase-only control). The results demonstrate the capability of the proposed planar array to form the required power patterns with far less number of the adjustable elements.

Stimulus Complexity Can Enhance Art Appreciation: Phenomenological and Psychophysiological Evidence for the Pleasure-Interest Model of Aesthetic Liking.

We tested predictions deriving from the "Pleasure-Interest Model of Aesthetic Liking" (PIA Model), whereby aesthetic preferences arise from two fluency-based processes: an initial automatic, percept-driven default process and a subsequent perceiver-driven reflective process. One key trigger for reflective processing is stimulus complexity. Moreover, if meaning can be derived from such complexity, then this can engender increased interest and elevated liking. Experiment 1 involved graffiti street-art images, pre-normed to elicit low, moderate and high levels of interest. Subjective reports indicated a predicted enhancement in liking across increasing interest levels. Electroencephalography (EEG) recordings during image viewing revealed different patterns of alpha power in temporal brain regions across interest levels. Experiment 2 enforced a brief initial image-viewing stage and a subsequent reflective image-viewing stage. Differences in alpha power arose in most EEG channels between the initial and deliberative viewing stages. A linear increase in aesthetic liking was again seen across interest levels, with different patterns of alpha activity in temporal and occipital regions across these levels. Overall, the phenomenological data support the PIA Model, while the physiological data suggest that enhanced aesthetic liking might be associated with "flow-feelings" indexed by alpha activity in brain regions linked to visual attention and reducing distraction.

High-Speed Controller to Enhance Responsiveness and Stability of Dynamic Characteristics for DC–DC Converter

In general, the DC load is electrically sensitive to the input voltage and current compared to the AC load. As the demand for digital systems that require DC power increases rapidly, the importance of stable DC power supply methods is increasing. Various elements such as renewable energy and load are configured in the power grid that supplies DC, and they are connected in various forms and power patterns. These elements have different starting and operating characteristics, which can be disadvantageous to the design of controllers for power converters to maintain a stable bus voltage in the DC power grid. Therefore, in a DC power system, an energy storage system with active bi-directional power control technology that can minimize transient conditions, such as overshoot, which can occur when a load is connected or power accelerates rapidly, is significantly important. This paper proposes a novel power control method capable of compensating for the instability of the DC bus voltage owing to the transient state in the DC power supply system. This method minimizes overshoot and ensures fast responsiveness and system stability by calculating the flux linkage of an inductor in a bi-directional DC/DC converter and applying an excitation voltage without pulse-width modulation (PWM) switching. The performance and validity of the proposed transient high-speed controller (THSC) were verified through simulations and experiments.

Improvement Method of Antenna Negative Sidelobes on Cross Beam Correlation Microwave Radiometer

A weighting method is proposed for the correlation radiation measurement system based on Mills cross array. The Mills cross array achieves high spatial resolution by the product of two orthogonal fan beams. However, the product power pattern of the Mills cross array lacks a square operation, resulting in sidelobe deterioration and the presence of negative sidelobes. Therefore, a window function is necessary to improve beam performance. However, because of the negative sidelobes, the antenna array is sensitive to the weighting function, and common window functions such as Hanning and Hamming are not suitable for achieving optimal antenna performance. A weighting method is proposed to fully utilize the feature that the received energy of positive and negative sidelobes cancel each other out and to reduce the influence of antenna sidelobe errors in radiation measurement. Such a weighting function can be obtained by combining the combined cosine window function with the existing particle swarm optimization (PSO). The optimized window function is evaluated through numerical simulation and compared with the typical window function weighting results. The results show that this weighting method can minimize the impact of negative sidelobes and reduce the loss of spatial resolution.

Reduced frontocingulate theta connectivity during emotion regulation in major depressive disorder

BackgroundCognitive reappraisal is an essential emotion regulation skill for social life and psychological health. However, individuals with major depressive disorder (MDD) cannot use this skill effectively. Successful cognitive reappraisal in healthy controls (HC) has been shown to be associated with theta activity in a frontal and subcortical network. In the present study, we investigated whether MDD patients are characterized by altered theta power and connectivity pattern during cognitive reappraisal compared to HC. MethodsUsing EEG and eLORETA, we examined both theta activity and connectivity when 25 controls and 24 patients with MDD were asked to complete the emotion cognitive reappraisal task of viewing neutral and negative pictures and reappraise negative pictures. Habitual use of emotion regulation skills was collected using the Cognitive Emotion Regulation Questionnaire (CERQ). ResultsThe results showed that MDD patients had (1) reduced theta activity in the left dorsolateral (dlPFC), dorsomedial prefrontal (dmPFC), and rostral-ventral cingulate cortices (rvACC), as well as (2) reduced dlPFC-rvACC theta connectivity than HC during reappraisal. In addition, left dlPFC-rvACC theta connectivity was positively correlated with self-reported cognitive reappraisal in HC. This relation was not observed in MDD. In contrast, CERQ revealed significantly greater use of inadequate regulations skills and significantly lower use of adaptive skills in MDD. LimitationSample size, limited solution space to cortical grey matter excluding regions such as the amygdala. ConclusionThis study may indicate a putative frontocingulate dysfunction leading either to an increased use of inadequate emotion regulation or a decreased use of skills that serve to boost positive emotion.

Quantitative characteristics and influencing factors of Tibetan Buddhist religious space with monasteries as the carrier: a case study of U-Tsang, China

Tibetan Buddhism has an inseparable connection with the spatial characteristics, development process, and human-natural environment of the Tibetan Plateau. This paper takes monasteries as carriers of Tibetan Buddhist culture in U-Tsang region which is one of the traditional geographical units of Tibet. Using geospatial analysis methods, this study explores the spatial quantification characteristics of Tibetan Buddhist monasteries and qualitatively and quantitatively analyzes the factors that influence the spatial distribution of monasteries. The results indicate that: (1) Political activities of human society influence the pattern of political power. Under the unique context of Tibetan theocracy system, the scope of political authority has a significant influence and affects the religious spatial pattern in the U-Tsang region throughout history. The distribution of monasteries in the U-Tsang region shows significant spatial differences at three sub-regional scales. (2) The religious spatial pattern in the U-Tsang is the result of the diverse interaction of human-natural factors. The results indirectly endorse that religious space is an inevitable product of the interaction between humans and the environment. (3) The religious spatial distribution patterns in the three major Tibetan regions have distinct characteristics, closely related to the superior conditions of their respective historical and geographical environments.

Using optically pumped magnetometers to replicate task-related responses in next generation magnetoencephalography

Optically pumped magnetometers (OPMs) offer a new wearable means to measure magnetoencephalography (MEG) signals, with many advantages compared to conventional systems. However, OPMs are an emerging technology, thus characterizing and replicating MEG recordings is essential. Using OPM-MEG and SQUID-MEG, this study investigated evoked responses, oscillatory power, and functional connectivity during emotion processing in 20 adults, to establish replicability across the two technologies. Five participants with dental fixtures were included to assess the validity of OPM-MEG recordings in those with irremovable metal. Replicable task-related evoked responses were observed in both modalities. Similar patterns of oscillatory power to faces were observed in both systems. Increased connectivity was found in SQUID-versus OPM-MEG in an occipital and parietal anchored network. Notably, high quality OPM-MEG data were retained in participants with metallic fixtures, from whom no useable data were collected using conventional MEG.

Mitigation of bio-corrosion characteristics of coronary artery stent by optimising fs-laser micromachining parameters

Cardiovascular diseases, particularly coronary artery disease, pose big challenges to human life. Deployment of the stent is a preferable treatment for the above-mentioned disease. However, stents are usually made up of shape memory alloy called Nitinol. The poorer surface finish on the machined nitinol stents accelerates the migration of Nickel ions from the implanted nitinol stent, which is considered toxic and can lead to stenosis. The current study deals with controlling surface quality by minimising surface roughness and improving corrosion resistance. Femtosecond laser (fs-laser 10−15 s) micromachining was employed to machine the Nitinol surface to achieve sub-micron surface roughness. The Grey relational analysis (GRA)-coupled design of the experimental technique was implemented to determine optimal levels of four micromachining parameters (laser power, pulse frequency, scanning speed, and scanning pattern) varied at three levels to achieve minimum surface roughness and to maximise the volume ablation. The results show that to yield minimum surface roughness and maximum volume ablation, laser power and scanning speed are in a higher range. In contrast, the pulse frequency is lower, and the scanning pattern is in a zig-zag manner. ANOVA results manifest that scanning speed is the predominant factor in minimising surface roughness, followed by pulse frequency. Furthermore, the corrosion behaviour of the machined nitinol specimens was evaluated, and the results show that specimens with lower surface roughness had lower corrosion rates.

Chip-level mass detection for micro-LED displays based on regression analysis and deep learning.

Though micro-light-emitting diode (micro-LED) displays are regarded as the next-generation emerging display technology, challenges such as defects in LED's light output power and radiation patterns are critical to the commercialization success. Here we propose an electroluminescence mass detection method to examine the light output quality from the on-wafer LED arrays before they are transferred to the display substrate. The mass detection method consists of two stages. In the first stage, the luminescent image is captured by a camera by mounting an ITO (indium-tin oxide) transparent conducting glass on the LED wafer. Due to the resistance of the ITO contact pads and on-wafer n-type electrodes, we develop a calibration method based on the circuit model to predict the current flow on each LED. The light output power of each device is thus calibrated back by multi-variable regression analysis. The analysis results in an average variation as low as 6.89% for devices predicted from luminescent image capturing and actual optical power measurement. We also examine the defective or non-uniform micro-LED radiation profiles by constructing a 2-D convolutional neural network (CNN) model. The optimized model is determined among three different approaches. The CNN model can recognize 99.45% functioning LEDs, and show a precision of 96.29% for correctly predicting good devices.

Three‐dimensional omni‐directional power pattern using rotating electric current sphere via exact maxwell solution

AbstractThe author reveals that the power pattern for a particular selected rotating spherical electric current density profile exhibits the following two properties simultaneously: (a) fully omnidirectional in three dimensions (3‐D) and (b) invariant with regards to radio frequency (RF). Specifically, most known antenna designs exhibit either nodal lines or planes for at least some RF frequencies. In contrast, the primary innovation of the subject rotating electric current sphere is that it generates a power pattern that is characterised by no nodal lines nor nodal planes for any RF frequency. In the present analysis, the electro‐magnetic (EM) fields are calculated as an exact solution of Maxwell's equations for the subject electric current density that rotates azimuthally on a spherical surface. As expected, the spatial structure of the resulting EM fields also rotates azimuthally. More surprisingly, this rotating electric current density generates pure magnetic dipole radiation exactly, with the absence of any higher order multipole moments. This proposed antenna concept could offer utility in various applications, including communications beaconing and radar surveillance.