Modulation Function Research Articles

Monolithically integrated mode converter from terahertz substrateless silicon guide to conductive slotline

Although substrateless micro-scale silicon waveguides are a useful and versatile platform for terahertz waves, the fact that modal fields occupy the volume of the core limits the potential to dynamically manipulate guided waves. To address this, we introduce an aperiodic lattice structure to enable the monolithic co-integration of a Vivaldi antenna-like mode converter with a substrateless silicon waveguide. This broadband transition is experimentally confirmed to exhibit ∼2.5 dB average loss for two couplers, from 220 GHz to 330 GHz, and enables a photoexcited variable attenuator as proof-of-concept demonstration. This is an important enabling step to incorporate general-purpose dynamic reconfigurability, sensing, and modulation functionality into terahertz-range silicon-based integrated circuits, which are currently limited to primarily all-passive structures.

GRB7 Plays a Vital Role in Promoting the Progression and Mediating Immune Evasion of Ovarian Cancer.

Despite breakthroughs in treatment, ovarian cancer (OC) remains one of the most lethal gynecological malignancies, with an increasing age-standardized mortality rate. This underscores an urgent need for novel biomarkers and therapeutic targets. Although growth factor receptor-bound protein 7 (GRB7) is implicated in cell signaling and tumorigenesis, its expression pattern and clinical implications in OC remain poorly characterized. To systematically investigate GRB7's expression in OC, our study utilized extensive datasets from TCGA, GTEx, CCLE, and GEO. The prognostic significance of GRB7 was evaluated by means of Kaplan-Meier and Cox regression analyses. Using a correlation analysis and gene set enrichment analysis, relationships between GRB7's expression and gene networks, immune cell infiltration and immunotherapy response were investigated. In vitro experiments were conducted to confirm GRB7's function in the biology of OC. Compared to normal tissues, OC tissues exhibited a substantial upregulation of GRB7. Reduced overall survival, disease-specific survival, and disease-free interval were all connected with high GRB7 mRNA levels. The network study demonstrated that GRB7 is involved in pathways relevant to the course of OC and has a positive connection with several key driver genes. Notably, GRB7's expression was linked to the infiltration of M2 macrophage and altered response to immunotherapy. Data from single-cell RNA sequencing data across multiple cancer types indicated GRB7's predominant expression in malignant cells. Moreover, OC cells with GRB7 deletion showed decreased proliferation and migration, as well as increased susceptibility to T cell-mediated cytotoxicity. With respect to OC, our results validated GRB7 as a viable prognostic biomarker and a promising therapeutic target, providing information about its function in tumorigenesis and immune modulation. GRB7's preferential expression in malignant cells highlights its significance in the biology of cancer and bolsters the possibility that it could be useful in enhancing the effectiveness of immunotherapy.

Merkel cell polyomavirus small tumor antigen contributes to immune evasion by interfering with type I interferon signaling.

Merkel cell polyomavirus (MCPyV) is the causative agent of the majority of Merkel cell carcinomas (MCC). The virus has limited coding capacity, with its early viral proteins, large T (LT) and small T (sT), being multifunctional and contributing to infection and transformation. A fundamental difference in early viral gene expression between infection and MCPyV-driven tumorigenesis is the expression of a truncated LT (LTtr) in the tumor. In contrast, sT is expressed in both conditions and contributes significantly to oncogenesis. Here, we identified novel functions of early viral proteins by performing genome-wide transcriptome and chromatin studies in primary human fibroblasts. Due to current limitations in infection and tumorigenesis models, we mimic these conditions by ectopically expressing sT, LT or LTtr, individually or in combination, at different time points. In addition to its known function in cell cycle and inflammation modulation, we reveal a fundamentally new function of sT. We show that sT regulates the type I interferon (IFN) response downstream of the type I interferon receptor (IFNAR) by interfering with the interferon-stimulated gene factor 3 (ISGF3)-induced interferon-stimulated gene (ISG) response. Expression of sT leads to a reduction in the expression of interferon regulatory factor 9 (IRF9) which is a central component of the ISGF3 complex. We further show that this function of sT is conserved in BKPyV. We provide a first mechanistic understanding of which early viral proteins trigger and control the type I IFN response, which may influence MCPyV infection, persistence and, during MCC progression, regulation of the tumor microenvironment.

Model predictive control for single-phase cascaded H-bridge photovoltaic inverter system considering common-mode voltage suppression

In this article, a model predictive control (MPC) with common-mode voltage (CMV) suppression is proposed for single-phase cascaded H-bridge (CHB) inverters, which can also simultaneously achieve control objectives of grid-connected current tracking, voltages balancing of different H-bridge submodules on the DC-side and switching frequency reduction. To suppress high-frequency components of the common-mode voltage without additional switching devices, the algorithm proposed designs the predicted and reference values of the CMV and incorporates them in the cost function. At the same time, the capacitor voltages balancing control is integrated in the calculation of the optimal modulation function of the H-bridge, which reduces the complexity of control effectively. Besides, switching times of the MOSFETs are compared in two cycles. The cost function is constructed to represent comprehensive effect of the control. Finally, an experiment is performed on the hardware-in-the-loop experimental platform. The experimental results show that the proposed algorithm can offer a better voltage THD and reduce the times of switch action by nearly half while maintaining high-precision current tracking and maximum power point of photovoltaic modules, which alleviate the potential electromagnetic interference and cabling problem.

A Dual control strategy for improved power quality in grid-tied off-board bidirectional electric vehicle charger

Electric vehicle (EV) chargers face significant challenges in maintaining grid power quality (PQ) and ensuring efficient power management during grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. Additionally, they must seamlessly switch to vehicle-to-load (V2L) mode during grid disturbances to provide uninterrupted power supply (UPS) for domestic loads. This article explores a dual control approach incorporating adaptive model predictive direct power control (AMP-DPC) on the rectifier side and adaptive direct power control (ADPC) on the dual active bridge (DAB) side to address these challenges. The AMP-DPC employs a control strategy referred to the second-order generalized integrator (SOGI) which estimate synchronizing voltage templates specifically designed for single-phase systems. The proposed optimization control aims to mitigate the cost function value by selecting appropriate switching modes. The optimized function is independent of the weighting factor, expressing the optimal modulation function across various weighting factors without additional design or selection. The current reference used by the charger is designed to ensure that the power factor remains at closer unity throughout G2V and V2G modes. The proposed control algorithm ensures the grid current remains low in harmonics during G2V, V2G, and V2L modes of operation. The experimental validation of the proposed control strategy is performed in the laboratory on a 0.5 kW off-board electric vehicle charger (EVC) prototype under various conditions to demonstrate its effectiveness in compliance with the IEEE 519–2022 standard.

Efficient Broad-Spectrum Cyanophage Function Module Mining.

Cyanobacterial harmful algal blooms (CyanoHABs) cause health and environmental effects worldwide. Cyanophage is a virus that exclusively infects cyanobacteria. Using cyanophages to control blooms is the latest biological control method. However, little research on the genomics of cyanophages and the presence of numerous proteins with unidentified functions in cyanophage genomes pose challenges for their practical application and comprehensive investigation. We selected the broad-spectrum and efficient cyanophage YongM for our study. On the one hand, through rational analysis, we analyze essential genes, establish the minimal cyanophage genome and single essential gene modules, and examine the impact of essential modules on growth. Additionally, we conducted ultraviolet mutagenesis on YongM to generate more efficient cyanophages' critical modules through random mutagenesis. Then, we sequenced and analyzed the functionality of the mutational gene modules. These findings highlight several gene modules that contribute to a deeper understanding of the functional components within cyanophage genomes.

Performance of Adults With & Without Sensory Processing Challenges on the Sensory Processing 3 Dimensions Assessment

Abstract Date Presented 03/22/24 Scores from the Sensory Processing 3 Dimensions (SP3D) Assessment were used to examine sensory modulation, discrimination, praxis, and postural functions, along with associated occupational performance problems in a sample of adults with and without sensory processing challenges. Primary Author and Speaker: Shelley Mulligan Additional Authors and Speakers: Sarah A. Schoen

A Time-Series Feature-Extraction Methodology Based on Multiscale Overlapping Windows, Adaptive KDE, and Continuous Entropic and Information Functionals

We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Density Function (PDF) associated with a time signal in the context of non-parametric Kernel Density Estimation (KDE). We illustrate the applicability of this method for anomaly detection in time signals. Specifically, our approach combines a non-parametric kernel density estimator with overlapping windows of various scales. Regarding the parameters involved in the KDE, it is well-known that bandwidth tuning is crucial for the kernel density estimator. To optimize it for time-series data, we introduce an adaptive solution based on Jensen–Shannon divergence, which adjusts the bandwidth for each window length to balance overfitting and underfitting. This solution selects unique bandwidth parameters for each window scale. Furthermore, it is implemented offline, eliminating the need for online optimization for each time-series window. To validate our methodology, we designed a synthetic experiment using a non-stationary signal generated by the composition of two stationary signals and a modulation function that controls the transitions between a normal and an abnormal state, allowing for the arbitrary design of various anomaly transitions. Additionally, we tested the methodology on real scalp-EEG data to detect epileptic crises. The results show our approach effectively detects and characterizes anomaly transitions. The use of overlapping windows at various scales significantly enhances detection ability, allowing for the simultaneous analysis of phenomena at different scales.

Multi-shaker time-varying root mean square non-stationary random vibration environmental control test

The current multi-shaker random control methods concentrate on the simulation of stationary vibrations. This work presents an innovative technique for multi-shaker non-stationary random vibration environmental control test. Firstly, an amplitude modulation method is introduced to generate multi-input non-stationary random signals with time-varying root mean square values, whose kurtoses and moving RMS values are specified by amplitude modulation functions. Then, the generated signals are applied as excitation signals for the non-stationary random vibration environmental control test. The response signals of a linear vibration system under non-stationary excitations are derived in the frequency domain. The derived formulas for kurtoses and moving RMS values are also applicable to non-stationary response signals. Meanwhile, the stationary power spectral densities of response signals are put forward. When the amplitude modulation functions are zero mean Gaussian distributions, the power spectral densities of the response signals can be directly calculated. Ultimately, the control of kurtoses, moving RMS values and stationary power spectral densities of response non-stationary random signals is realized. Simulation and experimental examples both adopt a two-input two-output beam vibration system and the results prove the correctness and practicability of the proposed method. The presented control procedure is concise and efficient in simulating multi-shaker non-stationary random vibration environments.

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Abstract Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.

Modulating Neuromorphic Behavior of Organic Synaptic Electrolyte-Gated Transistors Through Microstructure Engineering and Potential Applications.

Organic synaptic transistors are a promising technology for advanced electronic devices with simultaneous computing and memory functions and for the application of artificial neural networks. In this study, the neuromorphic electrical characteristics of organic synaptic electrolyte-gated transistors are correlated with the microstructural and interfacial properties of the active layers. This is accomplished by utilizing a semiconducting/insulating polyblend-based pseudobilayer with embedded source and drain electrodes, referred to as PB-ESD architecture. Three variations of poly(3-hexylthiophene) (P3HT)/poly(methyl methacrylate) (PMMA) PB-ESD-based organic synaptic transistors are fabricated, each exhibiting distinct microstructures and electrical characteristics, thus serving excellent samples for exploring the critical factors influencing neuro-electrical properties. Poor microstructures of P3HT within the active layer and a flat active layer/ion-gel interface correspond to typical neuromorphic behaviors such as potentiated excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-term potentiation (STP). Conversely, superior microstructures of P3HT and a rough active layer/ion-gel interface correspond to significantly higher channel conductance and enhanced EPSC and PPF characteristics as well as long-term potentiation behavior. Such devices were further applied to the simulation of neural networks, which produced a good recognition accuracy. However, excessive PMMA penetration into the P3HT conducting channel leads to features of a depressed EPSC and paired-pulse depression, which are uncommon in organic synaptic transistors. The inclusion of a second gate electrode enables the as-prepared organic synaptic transistors to function as two-input synaptic logic gates, performing various logical operations and effectively mimicking neural modulation functions. Microstructure and interface engineering is an effective method to modulate the neuromorphic behavior of organic synaptic transistors and advance the development of bionic artificial neural networks.

Soil-dependent stochastic optimization of base isolation system with negative stiffness inerter damper subjected to non-stationary seismic excitation

The negative stiffness inerter damper (NSID) has demonstrated excellent performance in mitigating vibrations in frame structures. However, analysis and optimization of BIS-NSID considering both soil conditions and non-stationary seismic excitation are rarely reported. This study proposes a multi-objective performance optimization procedure (MPOP) to investigate the optimal parameters of BIS-NSID under non-stationary seismic excitation considering firm, medium and soft soil conditions. The non-stationary seismic excitation is expressed as Clough-Penzien model with time modulation function. The analytical solution for the root mean square responses of BIS-NSID are derived considering non-stationary seismic excitation. The pareto optimal fronts is utilized to minimize the BIF displacement and superstructure acceleration. The optimal design is further validated under artificial seismic records. The results illustrate the validity of the MPOP and highlight the advantage of the BIS-NSID for all soil conditions. The demand for inertance and damping values are greater in soft soil conditions, compared to firm and medium soil condition.

Research on Security Detection of Embedded Terminals in Power Grids Under Artificial Intelligence

Artificial intelligence (AI) has an important significance in the construction of smart grids. There is a problem of low accuracy of behavior detection due to a large number of attacks and long data fragments in the power grid data. In this paper, a behavior detection method based on a generative adversarial network (GAN) is proposed. The method focuses on optimizing the learning strategies of the generator and discriminator based on a GAN. In this regard, the loss function is converged by maximizing the root mean square error of the attack in the discriminator, and the linear activation function is used as the modulation function before the output in the generator. Therefore, the improved GAN can utilize the data enhancement features in the original mode and ensure the stability of attack behavior recognition, and then, effectively detect the attack information in the grid. The experimental results show that the method in this paper can accurately and effectively monitor abnormal attack behavior in the embedded terminals of the smart grid, and its effective detection rate is 97.6%.

Multilevel Conductance States of Vapor-Transport-Deposited Sb2S3 Memristors Achieved via Electrical and Optical Modulation.

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

Fuzzy Socle-Jacobson–Two-Absorbiang Submodules

Background: The concept has been studied fuzzy- Socle- acobson–Two-Absorbiang submodules and some fuzzy relationships According to the concept above. Where the new definition was named fuzzy Socle- acobson–Two-Absorbiang, we used a few algebraic techniques to determine the rapport between fuzzy Two-Absorbing and fuzzy Socle- acobson–Two-Absorbiang. In addition to studying functions and some other related concepts. Our study's ramifications can be utilized to identify novel ideas that rely on hazy Socle- acobson–Two-Absorbiang concepts. In addition, we used some of the basics of modules and partial modules to prove that the intersection process in the new concept does not necessarily have to be achieved. In this study, the functions in modules and their relationship with the same concept and investigated some of the properties of those functions. The properties of the distributive module were also used in this research to achieve some theorems. In this research, it was also proven that the process of combining does not necessarily occur between two sub modules that bear the characteristics of the studied concept. Results: Using previous properties to find a new fuzzy sub module and a new definition. Conclusions: In this work, we presented a new type of fuzzy sub modules using the properties of addition and intersection between them and some properties of the new fuzzy submodule.

The distribution and evolutionary dynamics of dopaminergic neurons in molluscs.

Dopamine is one of the most versatile neurotransmitters in invertebrates. It's distribution and plethora of functions is likely coupled to feeding ecology, especially in Euthyneura (the largest clade of molluscs), which presents the broadest spectrum of environmental adaptations. Still, the analyses of dopamine-mediated signaling were dominated by studies of grazers. Here, we characterize the distribution of dopaminergic neurons in representatives of two distinct ecological groups: the sea angel - obligate predatory pelagic mollusc Clione limacina (Pteropoda, Gymnosomata) and its prey - the sea devil Limacina helicina (Pteropoda, Thecosomata) as well as the plankton eater Melibe leonina (Nudipleura, Nudibranchia). By using tyrosine hydroxylase-immunoreactivity (TH-ir) as a reporter, we showed that the dopaminergic system is moderately conservative among euthyneurans. Across all studied species, small numbers of dopaminergic neurons in the central ganglia contrast to significant diversification of TH-ir neurons in the peripheral nervous system, primarily representing sensory-like cells, which predominantly concentrated in the chemotactic areas and projecting afferent axons to the central nervous system. Combined with α-tubulin immunoreactivity, this study illuminates the unprecedented complexity of peripheral neural systems in gastropod molluscs, with lineage-specific diversification of sensory and modulatory functions.

Anharmonic incommensurate structure modulation in Ni-Mn-Ga martensite exhibiting highly mobile twin boundaries

We perform neutron diffraction on a bulk Ni50.0Mn27.5Ga22.5 single crystal to investigate the evolution of its five-layered modulated (10M) martensite structure, from 300 K down to 10 K. Close to martensite transformation, the modulation is nearly commensurate, with q = 0.402 in a modulation vector (q, q, 0). Upon cooling, the shift in diffraction satellites indicates a transition to an incommensurate modulation with increasing q. However, the observed fifth-order diffraction satellites below 260 K and even more complex satellite landscape at 10 K cannot be explained by incommensurability alone. Our analysis reveals that the modulation function is highly anharmonic, encompassing Fourier components up to the eighth order. We have developed a single-parameter description of the evolution of modulation across the entire temperature interval of the 10M phase existence. Surprisingly, the structure evolution from commensurate to incommensurate modulation has no significant effect on twin boundary mobility.

Manipulating circular Airy beam dynamics with quadratic phase modulation in fractional systems under some diffraction modulations and potentials.

Based on a split-step Fourier algorithm, the transmission of circular Airy beams with quadratic phase modulation (QPM) is investigated in the fractional Schrödinger equation (FSE) under diffraction modulations (periodic modulation, linear modulation and power function modulation) and external potentials (parabolic potential and linear potential). The results show that QPM is able to change the focusing position and intensity, as well as the transmission trajectory of the beam. In a periodic modulation, the circular Airy beam (CAB) exhibits periodic variation characteristics, and the beam splitting is retarded under the action of the QPM. The self-focusing distance of the beam is significantly reduced, and its transmission trajectory and beam width are altered by the QPM under the linear modulation. The CAB progressively evolves into a non-diffraction beam under the power function modulation, and the QPM is able to reduce the light intensity and increase the beam width as the Lévy index decreases. In a parabolic potential, CABs display autofocusing and defocusing behavior, and the QPM affects the intensity distribution and optical width of the beam. The CAB is deflected and evolves periodically in a linear potential. The beam width increases and gradually stabilizes with the addition of the QPM. The propagation of CABs controlled with QPM in parabolic and linear potentials is also analyzed in the frequency domain. The results demonstrate that we can control the transmission of CABs in an FSE optical system by rationally setting parameters such as QPM, modulation coefficients, and external potentials.

Understanding complex effects on clinical outcomes and biomarker activities of food supplementation in heavy smokers: an approach using bayesian networks

BACKGROUND: Antioxidant administration can significantly alleviate smoking-mediated oxidative stress. In our previous research, using a concentrated mixed juice powder led to significant reduction in oxidative status markers and improved lung function among smokers. OBJECTIVE: To compare the effectiveness of different antioxidant formulations of the retail product Juice Plus+® on oxidative status and lung function in healthy smokers via a a Bayesian Network (BN) analysis. METHODS: At baseline and after 3 months’ supplementation, pulmonary function, cardiovascular risk factors, and oxidative status were assessed in 101 smokers, randomized into three groups: placebo (P), fruit/vegetable (FV), and fruit/vegetable/berry (FVB) group. BN analysis was performed via Monte Carlo simulations. Statistical significance was assigned as p < 0.05. RESULTS: No significant interaction was observed in P (network score –101.4, p = ns), whereas overall fit significantly improved in both FV (network score –185.7, p = 0.01) and FVB (–293.7, p = 0.003). Moreover, P group’s respiratory parameters appeared unrelated to the biochemical structure, whereas those of the FV and, to a greater extent, FVB groups were definitely related to it CONCLUSIONS: BN analysis demonstrated that the FV and FVB formulations contain nutraceutical compounds capable of synergistically acting and improving antioxidant modulation and respiratory function in smoking subjects

Integrity Testing Performance Analysis in Embedded Systems using IDI

Multiple modules that interact with one another to exchange data make up embedded systems. Incorrect resource data exchanged between modules might cause anomalies or mistakes in operation. Resources that interact create dependencies between two modules, such that modifications made to one module's resources will impact modifications made to another module's functionality. Interaction errors between modules are one of the main causes of severe software failures, according to several embedded system investigations, such as those conducted on aerospace or automotive systems. Interaction testing is therefore a crucial step in minimizing risk and reducing interaction errors. When modules interact both directly and indirectly, interaction failures arise. Indirect interactions occur beneath the interface, where a data reliance relationship with resources may lead to an unexpected result