High Integration Research Articles

Giant electro-optic response in transparent rhombohedral ferroelectric Sm-PIN-PMN-PT crystal based on domain engineering

The relaxor ferroelectric crystal Pb(Mg1/3Nb2/3)O3-xPbTiO3 (PMN-PT), located near the morphotropic phase boundary (MPB), exhibits exceptionally high piezoelectric and electro-optic (EO) responses. Nevertheless, lower optical transparency and phase transition temperature of PMN-PT limit its optical applications. The ternary system Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 (PIN-PMN-PT) holds promise in addressing these challenges with a higher Curie temperature. Additionally, specific ferroelectric domain polarization techniques can eliminate domain scattering, substantially enhancing the transparency of the crystal. In this study, we explore the optical properties of Sm-doped PIN-PMN-PT. We achieve a 2R domain-engineered state by polarizing along the (110) direction of the crystal. The high transparency allows us to extract an effective EO coefficient of up to 431.5 pm/V from the Sm-PIN-PMN-PT crystal at the telecommunications wavelength. Second-harmonic generation (SHG) probing verified the domain-engineered state in Sm-PIN-PMN-PT. The temperature-dependent SHG reveals the ferroelectric phase transition process, laying the groundwork for studying the stability of the EO response. The Sm-PIN-PMN-PT crystal exhibits an exceptionally high EO coefficient, which is crucial for the development of enhanced EO devices with high integration and low driving voltages.

Multi-way modelling of oral microbial dynamics and host-microbiome interactions during induced gingivitis

Gingivitis—the inflammation of the gums—is a reversible stage of periodontal disease. It is caused by dental plaque formation due to poor oral hygiene. However, gingivitis susceptibility involves a complex set of interactions between the oral microbiome, oral metabolome and the host. In this study, we investigated the dynamics of the oral microbiome and its interactions with the salivary metabolome during experimental gingivitis in a cohort of 41 systemically healthy participants. We use Parallel Factor Analysis (PARAFAC), which is a multi-way generalization of Principal Component Analysis (PCA) that can model the variability in the response due to subjects, variables and time. Using the modelled responses, we identified microbial subcommunities with similar dynamics that connect to the magnitude of the gingivitis response. By performing high level integration of the predicted metabolic functions of the microbiome and salivary metabolome, we identified pathways of interest that describe the changing proportions of Gram-positive and Gram-negative microbiota, variation in anaerobic bacteria, biofilm formation and virulence.

Reconfigurable mode converter based on a Sb2Se3 phase change material and inverse design

In this paper, we combine the inverse design with a silicon-Sb2Se3 hybrid platform to design an on-chip mode converter that converts basic modes to higher-order modes. Firstly, we present a 1 × 2 mode converter with dimensions of 4.8 × 2.7 µm2 that enables TE0 mode input, TE0 or TE1 output in the C-band (1530 nm to 1565 nm) with an insertion loss (IL) of less than 0.8 dB and a crosstalk (CT) of less than -13 dB. Secondly, the device is extended to a 1 × 3 switchable three-mode converter. Using two controllable phase change regions as drivers, it can flexibly control the switching from TE0 mode input to three modes of TE0, TE1, or TE2 outputs, which enables mode switching and signal routing. The device can be switched between three modes and has broad application potential in broadband optical signal processing for mode division multiplexing systems, as well as optical interconnections. Finally, the device is extended to a 1 × 2 controllable (mode and power) beam splitter, which can control the power ratio between output modes. By modulating the crystallinity of Sb2Se3, the simulation achieves a multilevel switching of 36 levels (> 5-bit). These devices pave the way for high integration densities in future photonic chips.

Multifunctional plasmonic metamaterial absorber in the middle infrared range for polarization detection

It is significant to enhance the absorption rate, extend bandwidth and obtain the polarization information of electromagnetic radiation for detection technology. Here, we design and propose the simulation model of broadband and highly polarization-sensitive mid-IR metamaterial absorber by regulating the light field with the plasmonic response. The absorber is based on a simple subwavelength metal grating construction. Its absorption response can cover the 3–5 μm infrared atmospheric window. Detection technology in the mid-IR range has a strong ability to detect objects at high temperatures. In this waveband, the maximum absorption rate can reach more than 98% for TM polarization radiation. In comparison, it is less than 2% for TE polarization radiation, and the extinction ratio of 50 is concurrently achieved. The absorption spectrum can be broadened to the high and low-frequency direction by reasonably adding nanostrips in the unit cell. We also investigate the relationship between incident angle and spectral absorption and clarify the absorption stability of the absorber under oblique incidence. By integrating with the infrared focal plane detector, the device can make the infrared photodetector integrate and develop from a single sensor to multi-dimensional information and promote the development of polarization detector to miniaturization, high efficiency and high integration.

Data encryption/decryption and medical image reconstruction based on a sustainable biomemristor designed logic gate circuit

Memristors are considered one of the most promising new-generation memory technologies due to their high integration density, fast read/write speeds, and ultra-low power consumption. Natural biomaterials have attracted interest in integrated circuits and electronics because of their environmental friendliness, sustainability, low cost, and excellent biocompatibility. In this study, a sustainable biomemristor with Ag/mugwort:PVDF/ITO structure was prepared using spin-coating and magnetron sputtering methods, which exhibited excellent durability, significant resistance switching (RS) behavior and unidirectional conduction properties when three metals were used as top electrode. By studying the conductivity mechanism of the device, a charge conduction model was established by the combination of F-N tunneling, redox, and complexation reaction. Finally, the novel logic gate circuits were constructed using the as-prepared memristor, and further memristor based encryption circuit using 3-8 decoder was innovatively designed, which can realize uniform rule encryption and decryption of medical information for data and medical images. Therefore, this work realizes the integration of memristor with traditional electronic technology and expands the applications of sustainable biomemristors in digital circuits, data encryption, and medical image security.

A note on limiting Calderon–Zygmund theory for transformed n$n$‐Laplace systems in divergence form

AbstractWe consider rotated ‐Laplace systems on the unit ball of the form where , , and for some . We prove that with estimates. As a corollary, we obtain that solutions to , where is the Hardy space, have a higher integrability, namely, .

Study of psychophysiological indicators of sensorimotor Integration in PTSD. Justification of the choice of targets for biofeedback

Post-traumatic stress disorder (PTSD) is an actual medico-social problem. The pathogenesis of PTSD closely relates to impairment of sensorimotor integration (SMI). The effectiveness of psychosomatic disorder therapy for PTSD rehabilitation can be improved by restoring normal sensorimotor integration. The review examines various autonomic, electro-physiological and postural markers of high sensory motor integration in highly skilled athletes and musicians, as well as impairment of these indices in persons with PTSD. We have established that the most accessible and informative indicators of SMI are: an increase in EEG power in the individually adjusted high-frequency alpha-band, a reduction in energy demands for posture control and a decrease in the electromyographic activity of redundant muscles, not involved in motor-cognitive dual task. In the future, it is planned to use these indicators to diagnose stress disorders and to develop sensorimotor integration recovery training in patients with PTSD.

Teaching foundational skills using strategic incremental rehearsal: Investigating effectiveness across varying school agents

AbstractStrategic incremental rehearsal (SIR) is an efficacious flashcard intervention shown to improve foundational skill acquisition. However, there is limited research examining the feasibility and effectiveness of SIR when used by school‐based implementers. We present two studies to address this limitation. Study 1 examined the effects of SIR implemented by teachers on the acquisition of grapheme‐phoneme correspondences with three at‐risk kindergarteners. Study 2 examined the effects of SIR implemented by a same‐age peer on the acquisition and fluency of addition math facts with 3 second‐grade students receiving special education services. Overall, both studies point to the effectiveness of using SIR with students requiring intensive intervention in an focused, short timeframe. The effectiveness of SIR is particularly salient given that teachers and peers were able to implement SIR with high treatment integrity and effectively improve outcomes for students with and at risk for disabilities.

Synthesis and Evaluation of 99mTc(CO)3 Complexes with Ciprofloxacin Dithiocarbamate for Infection Imaging.

Background: The accurate diagnosis of bacterial infections remains a critical challenge in clinical practice. Traditional imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) often fail to distinguish bacterial infections from sterile inflammation. Nuclear medicine, such as technetium-99m (99mTc) radiopharmaceuticals, offers a promising alternative due to its ideal characteristics. Methods: This study explores the development of [2 + 1] mixed-ligand 99mTc-labeled ciprofloxacin dithiocarbamate (Cip-DTC) complexes combined with various phosphine ligands, including triphenylphosphine (PPh3), tris(4-methoxyphenyl)phosphine (TMPP), methyl(diphenyl)phosphine (MePPh2), dimethylphenylphosphine (DMPP), and 1,3,5-triaza-7-phosphaadamantane (ADAP). The characterization of 99mTc-complexes was conducted using rhenium analogs as structural models to ensure similar coordination. Results: Stability studies demonstrated the high integrity (97-98%) of the complexes under various conditions, including cysteine and histidine challenges. Lipophilicity studies indicated that complexes with higher logD7.4 values (1.6-2.7) exhibited enhanced tissue penetration and prolonged circulation. Biodistribution studies in Swiss Albino mice with induced infections and aseptic inflammation revealed distinct patterns. Specifically, the complex fac-[99mTc(CO)3(Cip-DTC)(PPh3)] (2') showed high infected/normal muscle ratios (4.62 at 120 min), while the complex fac-[99mTc(CO)3(Cip-DTC)(TMPP)] (3') demonstrated delayed but effective targeting (infected/normal muscle ratio of 3.32 at 120 min). Conclusions: These findings highlight the potential of 99mTc-labeled complexes as effective radiopharmaceuticals for the differential diagnosis of bacterial infections, advancing nuclear medicine diagnostics. Future studies will focus on optimizing molecular weight, lipophilicity, and stability to further enhance the diagnostic specificity and clinical utility of these radiopharmaceuticals.

Higher integrability and the number of singular points for the Navier–Stokes equations with a scale-invariant bound

Higher integrability and the number of singular points for the Navier–Stokes equations with a scale-invariant bound

A Review of Current Differencing Buffered Amplifiers: Performance Metrics and Technological Advances

Current Differencing Buffered Amplifiers (CDBAs) are a critical class of analog circuit components capable of handling both current and voltage signals with minimal power consumption. Due to their low impedance voltage output, they play a significant role in modern electronics for developing high-performance, high-precision analog and mixed-signal circuits. But, designing and characterizing CDBAs pose several challenges, such as ensuring stability at high frequencies, minimizing noise impact for high-precision applications, and enhancing adaptability. Integrating CDBAs with other analog components to create multifunctional integrated circuits opens many opportunities in the analog signal-processing domain. This paper reviews the evolution and applications of CDBAs in analog signal processing. Various implementation schemes, including those using commercial Current Feedback Amplifiers (CFAs) and novel CMOS configurations, are analyzed for their performance metrics such as supply voltage, power dissipation, input/output impedances, and technology node. Future trends and challenges in advancing CDBA technology towards higher integration and lower-voltage operation are discussed, highlighting potential applications in next-generation electronics.

Towards characterization of cell culture conditions for reliable proteomic analysis: in vitro studies on A549, differentiated THP-1, and NR8383 cell lines

Proteomic investigations result in high dimensional datasets, but integration or comparison of different studies is hampered by high variances due to different experimental setups. In addition, cell culture conditions can have a huge impact on the outcome. This study systematically investigates the impact of experimental parameters on the proteomic profiles of commonly used cell lines—A549, differentiated THP-1 macrophage-like cells, and NR8383—for toxicity studies. The work focuses on analyzing the influence at the proteome level of cell culture setup involving different vessels, cell passage numbers, and post-differentiation harvesting time, aiming to improve the reliability of proteomic analyses for hazard assessment. Mass-spectrometry-based proteomics was utilized for accurate protein quantification by means of a label-free approach. Our results showed that significant proteome variations occur when cells are cultivated under different setups. Further analysis of these variations revealed their association to specific cellular pathways related to protein misfolding, oxidative stress, and proteasome activity. Conversely, the influence of cell passage numbers on the proteome is minor, suggesting a reliable range for conducting reproducible biological replicates. Notable, substantial proteome alterations occur over-time post-differentiation of dTHP-1 cells, particularly impacting pathways crucial for macrophage function. This finding is key for the interpretation of experimental results. These results highlight the need for standardized culture conditions in proteomic-based evaluations of treatment effects to ensure reliable results, a prerequisite for achieving regulatory acceptance of proteomics data.

Assessment of the Renewable Energy Consumption Capacity of Power Systems Considering the Uncertainty of Renewables and Symmetry of Active Power

The rapid growth of renewable energy presents significant challenges for power grid operation, making the efficient integration of renewable energy crucial. This paper proposes a method to evaluate the power system’s capacity to accommodate renewable energy based on the Gaussian mixture model (GMM) from a symmetry perspective, underscoring the symmetrical interplay between load and renewable energy sources and highlighting the balance necessary for enhancing grid stability. First, a 10th-order GMM is identified as the optimal model for analyzing power system load and wind power data, balancing accuracy with computational efficiency. The Metropolis–Hastings (M-H) algorithm is used to generate sample spaces, which are integrated into power flow calculations to determine the maximum renewable energy integration capacity while ensuring system stability. Short-circuit ratio calculations and N-1 fault simulations validate system robustness under high renewable energy integration. The consistency between the results from the M-H algorithm, Gibbs sampling, and Monte Carlo simulation (MCS) confirms the approach’s accuracy.

Lateral Electronic Junction of a Single Ultrathin Silicon Induced by Interfacial Dipole of Self-Assembled Monolayer.

Interface engineering is pivotal for enhancing the performance and stability of devices with layered structures, including solar cells, electronic devices, and electrochemical systems. Incorporating the interfacial dipole between the bulk layers effectively modulates the energy level difference at the interface and does not significantly influence adjacent layers overall. However, interfaces can drastically affect adjoining layers in ultrathin devices, which are essential for next-generation electronics with high integrity, excellent performance, and low power consumption. In particular, the interfacial effect is pronounced in ultrathin semiconductors, which have a weak electric field screening effect. Herein, the substantial interfacial impact on the ultrathin silicon is shown, the p- to n-type inversion of the semiconductor solely through the deposition of a self-assembled monolayer (SAM) without external bias. The effects of SAMs with different interfacial dipoles are investigated by using Hall measurement and surface analytic techniques, such as UPS, XPS, and KPFM. Furthermore, the lateral electronic junction of the ultrathin silicon is engineered by the regioselective deposition of SAMs with opposite dipoles, and the device exhibits rectification behavior. When the interfacial dipole of SAM is manipulated, the rectification ratio changes sensitively, and thus the fabricated diode shows potential to be developed as a sensing platform.

Low detection limit of uric acid based on Prussian blue-enhanced photothermal effect with microfiber knot resonator

The human serum uric acid (UA) level is a crucial indicator for diagnosing dementia in middle-aged and elderly individuals, with decreased levels being expressed in patients. Therefore, developing a high-performance sensor for online uric acid detection is of significant research interest. Herein, a microfiber knot resonator (MKR) sensor for quantitative detection of UA levels is reported. Combining polydimethylsiloxane (PDMS) with an adiabatic MKR, the specificity of UA detection in serum is improved using the photothermal effect of Prussian blue-enhanced uricase reaction. During the sensor operation, UA generates the photothermal effect under 650 nm laser irradiation, causing the PDMS film to expand with heat absorption, thereby shifting the resonance spectrum of the PDMS-MKR sensor. Experimental results demonstrate a sensitivity of 0.0559 nm/(μmol/L) within a UA concentration range of 40–120 μmol/L and a detection limit as low as 0.3578 μmol/L. The proposed sensor shows potential applications in clinical point-of-care early diagnosis and prognosis of dementia due to its specificity, fast detection speed, robustness, and high integration.

Hybrid Fuzzy Method for Performance Evaluation of City Construction

Evaluating the performance of city construction not only helps optimize city functions and improve city quality, but it also contributes to the development of sustainable cities. However, most of the scoring rules for evaluating the performance of city construction are overly cumbersome and demand very high data integrity. Moreover, the properties, change scale, and scope of different evaluation indicators of city construction often lead to uncertain and ambiguous results. In this study, a hybrid fuzzy method is proposed to conduct the performance evaluation of city construction in two phases. Firstly, a city performance index (CPI) was developed by combining the means and standard deviations of indicators of city construction to address the volatility of historical statistical data as well as different types of data. Considering the sampling errors in data analysis, the parameter estimation method was used to derive the 100% × (1 − α) confidence interval of the CPI. Buckley’s fuzzy approach was then adopted to extend the statistical estimators from the CPI into fuzzy estimators, after which a fuzzy CPI was proposed. To identify the specific improvement directions for city construction, the fuzzy axiom design (fuzzy AD) method was applied to explore the relationship between the targets set by city managers and actual performance. Finally, an example of six cities in China is provided to illustrate the effectiveness and practicality of the proposed method. The results show that the performance of Chongqing on several evaluation indicators is lower than that of other cities. The proposed method takes into account the issues of uniformity and diversity in the performance evaluation of city construction. It can enable a quantitative assessment of the city construction level in all cities and provide theoretical support and a decision-making basis for relevant government departments to optimize city construction planning and scientifically formulate city construction policies.

Two-Stage Robust Optimization of Integrated Energy Systems Considering Uncertainty in Carbon Source Load

Integrated Energy Systems (IESs) interconnect various energy networks to achieve coordinated planning and optimized operation among heterogeneous energy subsystems, making them a hot topic in current energy research. However, with the high integration of renewable energy sources, their fluctuation characteristics introduce uncertainties to the entire system, including the corresponding indirect carbon emissions from electricity. To address these issues, this paper constructs a two-stage, three-layer robust optimization operation model for IESs from day-ahead to intra-day. The model analyzes the uncertainties in carbon emission intensity at grid-connected nodes, as well as the uncertainty characteristics of photovoltaic, wind turbine, and cooling, heating, and electricity loads, expressed using polyhedral uncertainty sets. It standardizes the modeling of internal equipment in the IES, introduces carbon emission trading mechanisms, and constructs a low-carbon economic model, transforming the objective function and constraints into a compact form. The column-and-constraint generation algorithm is applied to transform the three-layer model into a single-layer main problem and a two-layer subproblem for iterative solution. The Karush–Kuhn–Tucker (KKT) condition is used to convert the two-layer subproblem into a linear programming model. A case study conducted on a park shows that while the introduction of uncertainty optimization increases system costs and carbon emissions compared to deterministic optimization, the scheduling strategy is more stable, significantly reducing the impact of uncertainties on the system. Moreover, the proposed strategy reduces total costs by 5.03% and carbon emissions by 1.25% compared to scenarios considering only source load uncertainty, fully verifying that the proposed method improves the economic and low-carbon performance of the system.

Unsupervised end-to-end multiscale neural network for multi-focus MicroLED image fusion

MicroLED has a broad application prospect in visible light communication, medical detection, and other fields, owing to its small size, high integration, and long service life. However, capturing a full-focus image during microscopic visual inspection of MicroLED is challenging due to the significant thickness of the chip. To address this problem, an end-to-end neural network named MMLFuse is proposed for MicroLED image fusion, which uses unsupervised learning to directly generate fused images from two original images. Firstly, we introduce the Spatial Pyramid Pooling Mixing (SPPM) module for rapid extraction of partially focused image features. The extracted features are then used to obtain a weight map, which is further refined using a moving window smoothing technique. This refined weight map is employed for feature fusion, and the fused image is reconstructed based on the fused features. Specifically, the network uses a two-stage training strategy with different loss functions for each stage to improve the convergence speed of the model and the quality of the fused image. In particular, mask loss is designed in the second stage to ensure that the network pays more attention to the focus area during training to accurately match the corresponding input image. Experimental results demonstrate that MMLFuse achieves superior performance on the MicroLED dataset compared to other classical methods, highlighting its effectiveness and potential in the field.

Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.

Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain-computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain-computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%.

Optimization of Ni-Cu binder composition and sintering time for enhanced mechanical properties of Mo alloys via liquid phase sintering

Liquid phase sintering of refractory alloys offers a promising approach to enhance mechanical properties while ensuring cost-effectiveness and near-net shaping capabilities. This study systematically explores the impact of sintering time and Ni-Cu binder composition on densification, shape retention, and mechanical properties of Mo alloys processed via liquid phase sintering. The aim is to identify optimal binder composition and sintering time for low-cost production of dense, near-net-shape Mo alloys with superior mechanical properties. The sinterability of Mo compacts is enhanced as the Ni-Cu ratio increases. However, excessive Ni content induces geometric distortion during sintering. Alloys with Ni-rich binders exhibit two liquids at the sintering temperature, each with differing Mo solubility, resulting in a dual-phase matrix on solidification. Both Cu-rich and Ni-rich binder compositions exhibit brittle δ-MoNi intermetallic compound formation, whose quantity increases with prolonged sintering times. Alloys characterized by low Mo solubility in the matrix, high Mo volume fraction, high Mo grain contiguity, and large dihedral angles demonstrate high structural integrity and resistance to geometric distortion. Conversely, alloys featuring high matrix volume fraction, low grain contiguity, elevated Mo solubility in the binder, and strong Mo-matrix interfaces exhibit enhanced strength, hardness, and ductility. Notably, an alloy with a Ni-Cu ratio of 3:1 sintered for just 30 min at 1420°C demonstrates outstanding mechanical properties—hardness of 410 HV, tensile strength of 920 MPa, and elongation of 9 %—comparable to or better than many previously reported refractory alloys.