Input Phase Research Articles

Compact Topological Edge States in Flux-Dressed Graphenelike Photonic Lattices.

Flat band lattice systems promote the appearance of perfectly compact bulk states, whereas topology favors edge localization. In this work, we report the existence of compact topological edge states on flux-dressed photonic graphene ribbons. We found that robust localization is achieved through a synergy of Aharonov-Bohm caging and topological protection mechanisms. The topological nontriviality of the compact edge states is characterized through both theoretical derivations and experimental observations of an integer Zak phase obtained from the mean chiral displacement. Experiments are performed using direct laser writing of a graphene ribbon photonic lattice having 0 or π effective magnetic fluxes. Mode stability is demonstrated by the exceptional localization of the edge compact mode and its resilience to fabrication tolerances and input phase deviations. Our findings demonstrate the existence of perfectly compact topological edge states, as a concrete and promising example of synergy in between flat band physics and topology.

A Common Effort: New Divisions of Labor Between Journalism and OSINT Communities on Digital Platforms

This article explores the interactions between journalistic actors and emerging open-source intelligence and investigation (OSINT) communities. It employs qualitative content analysis of discourse from two OSINT communities surrounding three events following the Russian invasion of Ukraine in 2022, which received substantial coverage in news media. OSINT practices are rapidly becoming a mainstay of the contemporary political process by allowing ordinary citizens to verify information shared through digital platforms, which is traditionally the societal task assigned to journalism. In doing so, they provide a timely factual baseline for opinion formation and political decision-making. This research explores the role constellations resulting from this shift in verification duties from journalistic actors to amateur online communities on digital platforms and maps the fundamental dynamics involved in OSINT. We analyze how information is received and processed in OSINT communities, how digital platforms facilitate the fact-checking process, and how journalism and OSINT interact. Based on our findings, we develop a theoretical framework that distinguishes between the input, throughput, and output phases of OSINT. Our model contributes to a baseline understanding of the crucial and novel partnership between citizens and journalists on digital platforms.

Bandwidth enhancement of piezoelectric MEMS microspeaker via central diaphragm actuation and filter integration

Abstract This study presents the piezoelectric microspeaker design consisted of the central-diaphragm, connecting-spring, and cantilever-plate actuators to create two resonances in the desired frequency range. In addition to the cantilever-plate actuator, the electrical routing and piezoelectric film are designed to drive the central-diaphragm independently. According to the stress distributions on the microspeaker structure for both lower and higher modes, the all-pass filter circuit is designed and implemented to manage the phase of input signals to the central-diaphragm, thereby changing the motion of the proposed design. Thus, the sound pressure level (SPL) beyond 1 kHz is improved and the SPL zero at specific frequency range is avoided. As a result, the bandwidth enhancement is achieved by the proposed microspeaker. Measurements are conducted under 0.707 Vrms with 9 VDC driving voltage in standard ear simulator to evaluate the performances of the proposed design. A reference design without a piezoelectric film on the central-diaphragm is also implemented for comparison. Measurements indicate, in the low frequency range (before 4 kHz), the proposed designs have over 3 dB SPL enhancement due to the excitation of central-diaphragm. Moreover, compared to the reference design, proposed designs prevent the occurrence of an SPL zero near 10 kHz (between lower and higher modes) and achieve over 15 dB SPL enhancement. When the driving frequency exceeds the higher mode (14 kHz), the proposed design with the all-pass filter eliminates the SPL zero (at 16.8 kHz) with nearly 8 dB enhancement in the 15–18 kHz frequency range. Thus, this study demonstrates the bandwidth enhancement by the proposed microspeaker design with central-diaphragm actuation and all-pass filter integration.

Nonreciprocal synchronization in embryonic oscillator ensembles

Synchronization of coupled oscillators is a universal phenomenon encountered across different scales and contexts, e.g., chemical wave patterns, superconductors, and the unison applause we witness in concert halls. The existence of common underlying coupling rules defines universality classes, revealing a fundamental sameness between seemingly distinct systems. Identifying rules of synchronization in any particular setting is hence of paramount relevance. Here, we address the coupling rules within an embryonic oscillator ensemble linked to vertebrate embryo body axis segmentation. In vertebrates, the periodic segmentation of the body axis involves synchronized signaling oscillations in cells within the presomitic mesoderm (PSM), from which somites, the prevertebrae, form. At the molecular level, it is known that intact Notch-signaling and cell-to-cell contact are required for synchronization between PSM cells. However, an understanding of the coupling rules is still lacking. To identify these, we develop an experimental assay that enables direct quantification of synchronization dynamics within mixtures of oscillating cell ensembles, for which the initial input frequency and phase distribution are known. Our results reveal a "winner-takes-it-all" synchronization outcome, i.e., the emerging collective rhythm matches one of the input rhythms. Using a combination of theory and experimental validation, we develop a coupling model, the "Rectified Kuramoto" (ReKu) model, characterized by a phase-dependent, nonreciprocal interaction in the coupling of oscillatory cells. Such nonreciprocal synchronization rules reveal fundamental similarities between embryonic oscillators and a class of collective behaviors seen in neurons and fireflies, where higher-level computations are performed and linked to nonreciprocal synchronization.

Branched flow of light and interplay with input phase front curvature in a disordered photonic lattice

Branched flow refers to the bifurcation of waves as they propagate through customized disordered media, leading to the creation of multiple channels or branches. This dynamic wave phenomenon has been observed in various disordered systems under specific conditions. In our study, we demonstrate the branched flow of light within a disordered photonic lattice and establish that the intensity distribution of these branches follows Lévy statistics. This behavior is more pronounced when the wavefront of the input light beam is adjusted, resulting in the propagation of more stable branches through the structure. We also investigated the formation of light branches with varying input beam widths and the impact of two simultaneous input beams on the disordered photonic lattice. Our findings indicate that the distribution of branches in the disordered system can be controlled by shaping the wavefront of the input beam, which has potential applications in imaging, nano-lasing, and integrated photonic components. This research opens new avenues for fundamental studies on the manipulation of light through complex photonic systems.

High-power three-phase interleaved parallel NPC structure DC-DC converter modeling and simulation

Abstract In fields such as DC microgrids, renewable energy generation, and electric vehicle charging stations, three-phase NPC structure DC-DC converters play an irreplaceable role, constituting a significant part of modern power electronics technology. Mathematical models are established based on various operating modes of IGBT switching devices under PWM control, encompassing input voltage, output voltage, phase currents, average current, and duty cycle. Simulation models are constructed on the MATLAB platform to confirm the correctness of these mathematical models. Closed-loop control strategies are implemented using proportional-integral (PI) controllers. This control strategy serves to confirm the precision of the mathematical models.

Digital Twins-Based Cognitive Apprenticeship Model in Smart Agriculture

Applying digital technologies in education has been proven to have several positive impacts on teaching and learning. Digital twins have the potential to revolutionize education by offering immersive and interactive learning experiences. They can simulate virtual environments, enabling students to conduct experiments, practice techniques, and observe outcomes in a safe and cost-effective setting, particularly when access to physical laboratories is limited or expensive. This work proposes a learning management model that applies cognitive apprenticeship learning theory as a framework for learning digital twins to enhance the programming of a smart agriculture system and computational thinking skills. The model consists of three parts: input, learning process, and output with feedback. The input phase includes setting learning objectives, analyzing learners, determining learning content, planning learning activities, and utilizing a digital twin virtual laboratory space for experimenting in the development of a smart agriculture system. The learning process involves engaging students in programming and problem-solving tasks by offering expert guidance and support in computationally solving problems. The achievement of learning outcomes and practices is included in the output and feedback sections. After evaluation, five experts in this subject agreed that the learning management model was at the most appropriate level.

Tailoring nondiffracting fields with a non-Markovian phase imprint.

We experimentally generate nondiffracting speckles that carry non-Markovian properties by encoding the wavefront of a monochromatic laser beam with ring-shaped non-Markovian phases. The resulting non-Markovian nondiffracting fields present a ring-shaped pattern and central dark notches, which are analyzed with an expression of the orbital angular momentum spectra of the wavefront possessing ring-shaped non-Markovian phases. Furthermore, we demonstrate that the intensity profiles of these non-Markovian nondiffracting fields exhibit stability over multiple Rayleigh ranges, and their statistical properties could be controlled with the non-Markovianity of the input phase masks. This work presents an approach for simultaneously tailoring the diffracting property and non-Markovianity of optical fields and provides a deeper understanding of non-Markovian processes.

AI covers: legal notes on audio mining and voice cloning

Abstract This article explores the impact of Artificial Intelligence (AI) on the music industry, particularly focusing on the case of AI-generated covers. The emergence of AI technologies has been raising concerns not just about the originality and protection of AI-generated outputs but also about the complex input and training phase of those systems. The focus of this contribution is the latter, analysing the case of AI covers from the perspective of copyright and image rights. In the first part, an overview of the text and data mining (TDM) exception found in Article 4 of Directive 2019/790 is presented, with a primary focus on the opt-out mechanism in connection with the three-step test. Moving to the second part, the analysis delves into the complexities of voice cloning, highlighting the absence of a comprehensive European Union regime for image rights. By addressing these issues, this contribution unveiled two crucial points. First, AI models trained on various artists’ works to create and spread deepfake covers not only violate copyright but also reveal shortcomings in the TDM exception. Second, while the multifaceted image right regime may not be as exhaustive as necessary, it proves to be a viable solution against voice cloning with anticipated advancements in the future.

All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30–40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

Design and Simulation of MEMS Surface Acoustic Wave Biosensor Based on PVDF Thin Film

Abstract: Surface acoustic wave sensors, as a class of MEMS, are widely used recently. The sensor can transform an input electrical signal into a mechanical wave which can be easily influenced by physical phenomena. Then, the changed mechanical wave is transduced back into an electrical signal. The presence of the desired phenomenon can be detected through the difference between the input and output electrical signal (amplitude, phase, frequency, or time delay). The basic surface acoustic wave device consists of a piezoelectric substrate, an input interdigitated transducer (IDT) on one side of the surface of the substrate, and a second output interdigitated transducer on the other side of the substrate.

Investigation the thermal performance of Nano-Enhanced Phase Change Material (NEPCM) in an enclosure

Phase change materials (PCMs) play a vital role in thermal energy storage applications, as they provide efficient and dependable heat storage and release capabilities. This study provides a comprehensive evaluation of the thermal performance of Nano-Enhanced Phase Change Material (NEPCM) in an enclosure, with a focus on the effect of temperature variation and the role of nanoparticles. Variable temperature regimes significantly influence the physical and thermal properties of the NEPCM, as revealed by numerical simulations. The enthalpy-porosity approach for the phase change process and the solution of the Navier-Stokes equations for fluid flow serve as the primary foundations for the numerical model that will be covered in this study. With only a single phase of user input, ANSYS facilitates the creation of three-dimensional models and solid geometry meshes. Notably, an increase in the temperature of the heat-supplying wall resulted in substantial changes to the NEPCM, which we have thoroughly investigated and quantified. It has been demonstrated that the incorporation of various nanoparticles (Al2O3, CuO, and ZnO) into paraffin enhances the heating process, thereby enhancing the thermal conductivity, heat transfer coefficient, and surface tension of the NEPCM. The three nanoparticles decrease the mass fraction of paraffin in the range of 0.00 to 0.960 at the same concentration and testing temperature. In addition, increasing nanoparticle concentration under higher temperature regimes resulted in a faster and more uniform nanoparticle synthesis, significantly enhancing the NEPCM's thermal performance. Particularly, as the temperature increased, the NEPCM near the wall melted more rapidly. The study reveals the crucial role that temperature plays in modulating the behavior and performance of NEPCM, thereby providing valuable insights for thermal management systems. These results demonstrate the significance of temperature control for maximizing the potential of NEPCM in a variety of applications. As the temperature rises, one side becomes covered in the blue color (solid), while in the standard case, the melting curve shows a little red region (melting) at the other side that starts to increase when the nanomaterial's are added.

Common-Unique Decomposition Driven Diffusion Model for Contrast-Enhanced Liver MR Images Multi-Phase Interconversion.

All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology.

Theoretical and Experimental Analysis of a 44 Recongurable MZI-Based Linear Optical Processor

A 4 4 reconfigurable Mach-Zehnder interferometer (MZI)-based linear optical processor is investigated through its theoretical analyses and characterized experimentally. The linear transformation matrix of the structure is theoretically determined using its building block, which is a 2 2 reconfigurable MZI. To program the device, the linear transformation matrix of a given application is decomposed into that of the constituent MZIs of the structure. Thus, the required phase shifts for implementing the transformation matrix of the application by means of the optical processor are determined theoretically. Due to random phase offsets in the MZIs resulting from fabrication process variations, they are initially configured through an experimental protocol. The presented calibration scheme allows to straightforwardly characterize the MZIs to mitigate the possible input phase errors and determine the bar and cross states of each MZI for tuning it at the required sate before programming the device. After the configuration process, the device can be programmed to construct the linear transformation matrix of the application. In this regard, using the required bias voltages, the phase shifts obtained from the decomposition process are applied to the phase shifters of the MZIs in the device.

Application of an artificial intelligence segmentation for deep hyperthermia treatment planning in the pelvic region

During a microwave hyperthermia oncology treatment, the target region temperature is elevated to the temperatures of 40–44 °C, which improves the therapeutic effect of a standard radiotherapy and/or chemotherapy treatments. Amplitudes and phases of antenna input signals in the phased array setup surrounding the 3D patient model are optimised with respect to maximise the energy deposition in the target region. In this study, we successfully integrated an automatic artificial intelligence segmentation routine, used for patient-specific 3D model generation, into the hyperthermia treatment planning process. This allows us to apply more realistic patient 3D model for the online hyperthermia guidance including detailed retrospective analyses of the overall treatment quality, possibly leading to a widespread clinical use of the hyperthermia treatment planning.

Revolutionizing the Developmental Processing of Short-Bodied Mackerel Fish (Rastrelliger Brachysoma) Through Advanced Thermal Techniques: Unveiling A Pathway to Unprecedented Quality Enhancement

This study focuses on the thermal processing of short-bodied mackerel fish (Rastrelliger brachysoma) through a bottling method, exploring its quality attributes such as appearance, taste, aroma, and texture. The conceptual framework involves input (materials, tools, and equipment), process (descriptive-quantitative research method with different sample treatments), and output (bottled short-bodied mackerel in three treatments). The study aims to determine the optimal processing time and assess the product's acceptability among food technology students and experts. The input phase details the materials used, including short-bodied mackerel, various ingredients, tools, and equipment. The process involves three treatments with different cooking durations. The output is the bottled short-bodied mackerel, pasteurized for extended shelf life and stable flavor. The study's significance lies in benefiting local fishermen, vendors, consumers, and future researchers. The research objectives include evaluating whether short-bodied mackerel can be effectively processed using thermal application and assessing its acceptability based on aroma, taste, texture, and color. The hypothesis suggests no significant differences in assessment between food experts and technology students. The study's significance extends to supporting local fishermen, vendors, and consumers while providing insights for future research. The methodology encompasses a quantitative research design, utilizing a checklist questionnaire and taste testing with third-year food technology students and experts. The study is conducted at Aurora State College of Technology, with thirty respondents. The experimental design involves three treatments with varying cooking durations, analyzed using ANOVA for statistical significance. Results from each treatment indicate that the processed short-bodied mackerel is generally acceptable, with variations in attributes across treatments. The study contributes valuable insights for the optimal processing of short-bodied mackerel, offering potential benefits to the fisheries industry and culinary preferences.

Orbitally-paced climate change during the Carnian Pluvial Episode

Global climate change has profound implications for human survival and prosperity. The Carnian (early Late Triassic, ∼233 Ma) was a time of global environmental perturbations, climatic change, and biotic turnover, commonly known as the Carnian Pluvial Episode (CPE). However, the understanding of possible triggering mechanisms of the CPE is still partially untraveled due to the lack of a high-resolution chronostratigraphic framework. Here we present an astrochronology of episodic negative carbon isotope excursions (NCIEs) observed in the shallow marine sediments of the Bagong Formation in the Qiangtang Basin (Tibetan Plateau, China) to explore the role of orbital forcing during the CPE. This study marks the first identification of five episodic NCIEs in the Carnian strata of the Bagong Formation. These NCIEs correspond with five distinct periods of increased detrital influx. The coupling relationship between each phase of NCIE and related terrigenous input indicates that episodic NCIEs may be influenced by pulses of active continental weathering. Mercury isotope is contemporaneous with the active volcanism that triggered the onset of the NCIEs; however, the impact of volcanism in the Qiangtang Basin was weak and almost non-existent at this time. An anchored floating astronomical timescale (ATS) for the episodic NCIEs is established, revealing evidence for a 405 kyr eccentricity in high-resolution gamma ray data series using time series analysis. A ∼13.16 Myr-long ATS of the Bagong Formation is developed by astronomical tuning of gamma ray logs to the stable 405-kyr long-eccentricity cycles. This floating ATS takes the U-Pb zircon as its anchor point and establishes an anchored floating ATS of the Bagong Formation from 233.16 ± 1.37 Ma to 220.4 ± 1.1 Ma. The innovative astrochronology approach, employing a sedimentary noise model, has successfully reconstructed sea-level changes during the Late Triassic period. These findings align well with the documented global sea-level fluctuations of the same era. The antiphase relationship of the filtered ∼1.2 Myr cycles between the sedimentary noise model sea-level curve and the obliquity modulation cycles demonstrates that the ∼1.2 Myr modulation cycles may be the main driver of sea level changes during the Late Triassic. The ∼1.2 Myr obliquity modulation maxima correlate well with the high sea level, episodic NCIEs, global warming, and marine life crisis, suggesting that obliquity forcing could have played a prominent role during the CPE. Our results reveal that the orbital forcing enhanced the hydrological cycle during the CPE, which provides a broader perspective of the CPE-related to the astronomical forcing.

Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study.

Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection. To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection. Multicenter, longitudinal. Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort. Center A: single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath-hold examination sequence. Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant. In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709). The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer. 1 TECHNICAL EFFICACY: Stage 4.

Cavity soliton formation in Kerr comb oscillators via input phase and amplitude modulation in the presence of high order dispersion

The generation of dissipative cavity solitons (CSs) in Kerr microresonators through pulsed driving is an effective method for generating coherent optical microcombs. Yet, the underlying physics has not been fully investigated. Our study investigates the effect of high-order dispersion on the generation of Kerr frequency combs in Si3N4 microresonator driven by phase- and amplitude-modulated inhomogeneities of the pump which had not been studied before. Here, we present a numerical and theoretical analysis of the effects of desynchronization and the pulsed driving chirp parameter on the train of solitons circulating in the cavity for various detunings. In this case, deterministic production of a K-band single soliton, as well single breather soliton has been investigated. Our results show that high-order dispersion can also affect CSs positioning, stability, and existence.

A feature-based restoration dynamic interaction network for multimodal sentiment analysis

Multimodal sentiment analysis aims to infer the sentiment of video bloggers from the features of multiple input modalities. However, there are problems such as signal noise and signal loss in the input phase and inefficient utilization of features in the modality fusion phase. To address these issues, this study proposes a feature-based restoration dynamic interaction network for multimodal sentiment analysis. Firstly, the idea of resampler and integration is employed to enhance visual and textual features during the input phase. Secondly, in the modal interaction phase, a dynamic routing network is employed. The network is centered on text modality and dynamically fuses visual and audio features. Finally, in the classification phase, multimodal representations are united to provide guidance for multimodal sentiment analysis. This study conducted experiments on the datasets MOSI, MOSEI and UR-FUNNY, which have 2199, 22856 and 16514 video segments respectively. The results show that the proposed method achieves an average improvement of about 1 point for three metrics on MOSI and 0.5 points for individual metrics on MOSEI compared to the state-of-the-art methods. Compared to other methods, the proposed approach achieve about 1 point improvement for individual metrics on UR-FUNNY dataset.