Search Results (74)

Mechanical defects and sensor failures can substantially undermine the reliability of low-cost sensors, especially in applications where measurement inaccuracies or malfunctions may lead to critical outcomes, including system control disruptions, emergency scenarios, or safety hazards. To overcome these challenges, this paper presents a novel Self-X architecture with sensor redundancy, which incorporates dynamic calibration based on multidimensional mapping. By extracting reliable sensor readings from imperfect or defective sensors, the system utilizes Self-X principles to dynamically adapt and optimize performance. The approach is initially validated on synthetic data from tunnel magnetoresistance (TMR) sensors to facilitate method analysis and comparison. Additionally, a physical measurement setup capable of controlled fault injection is described, highlighting practical validation scenarios and ensuring the realism of synthesized fault conditions. The study highlights a wide range of potential TMR sensor failures that compromise long-term system reliability and demonstrates how multidimensional mapping effectively mitigates both static and dynamic errors, including offset, amplitude imbalance, phase shift, mechanical misalignments, and other issues. Initially, four individual TMR sensors exhibited mean absolute error (MAE) of 4.709°, 5.632°, 2.956°, and 1.749°, respectively. To rigorously evaluate various dimensionality reduction (DR) methods, benchmark criteria were introduced, offering insights into the relative improvements in sensor array accuracy. On average, MAE was reduced by more than 80% across sensor combinations. A clear quantitative trend was observed: for instance, the MAE decreases from 4.7°–5.6° for single sensors to 0.111° when the factor analysis method was applied to four sensors. This demonstrates the concrete benefit of sensor redundancy and DR algorithms for creating robust, fault-tolerant measurement systems. Full article

The two-phase ejector has gained prominence in heat pump systems as a device that effectively mitigates throttling losses through expansion work recovery. This investigation employs three-dimensional computational fluid dynamics (CFD) simulations to analyze the parametric effects of the mixing channel geometry on the entrainment characteristics in an R410A ejector. After validating the model according to the experimental data, the parameter analysis was carried out, and four key geometric parameters were changed within a certain range: the nozzle exit position (NXP = 13–19 mm), the pre-mixing channel convergent angle (CA = 20–60°), the diameter ratio (DDR = 5.0–7.1), and the length-to-diameter ratio (LDR = 8.9–12.4). Multi-variable optimization studies revealed optimal geometric configurations at NXP = 17 mm (about 3.5D_mix), CA = 30°, DR = 6.4, and LDR = 11.1, yielding an optimized mass entrainment ratio enhancement of 23.6% compared to baseline designs. This research provides actionable guidelines for the design of high-efficiency ejector components for heat pump applications. Full article

The widespread presence of pesticides—especially malathion—in aquatic environments presents a major obstacle to conventional remediation strategies, while the ongoing global energy crisis underscores the urgency of developing renewable energy sources such as hydrogen. In this context, photocatalytic water splitting emerges as a promising approach, though its practical application remains limited by poor charge carrier dynamics and insufficient visible-light utilization. Herein, we report the design and evaluation of a series of TiO₂-based ternary nanocomposites comprising commercial P25 TiO₂, reduced graphene oxide (rGO), and molybdenum disulfide (MoS₂), with MoS₂ loadings ranging from 1% to 10% by weight. The photocatalysts were fabricated via a two-step method: hydrothermal integration of rGO into P25 followed by solution-phase self-assembly of exfoliated MoS₂ nanosheets. The composites were systematically characterized using X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), UV-Vis diffuse reflectance spectroscopy (DRS), and photoluminescence (PL) spectroscopy. Photocatalytic activity was assessed through two key applications: the degradation of malathion (20 mg/L) under simulated solar irradiation and hydrogen evolution from water in the presence of sacrificial agents. Quantification was performed using UV-Vis spectroscopy, gas chromatography–mass spectrometry (GC-MS), and thermal conductivity detection (GC-TCD). Results showed that the integration of rGO significantly enhanced surface area and charge mobility, while MoS₂ served as an effective co-catalyst, promoting interfacial charge separation and acting as an active site for hydrogen evolution. Nearly complete malathion degradation (~100%) was achieved within two hours, and hydrogen production reached up to 6000 µmol g⁻¹ h⁻¹ under optimal MoS₂ loading. Notably, photocatalytic performance declined with higher MoS₂ content due to recombination effects. Overall, this work demonstrates the synergistic enhancement provided by rGO and MoS₂ in a stable P25-based system and underscores the viability of such ternary nanocomposites for addressing both environmental remediation and sustainable energy conversion challenges. Full article

Under accelerated global warming, frequent droughts pose mounting threats to vegetation productivity, yet the spatiotemporal patterns and primary controls of drought resilience (DR) in China remain insufficiently quantified. This study aimed to characterize DR trends across Köppen–Geiger climate zones in China from 2001 to 2020 and to identify the dominant drivers and their interactions. We constructed a hazard–exposure–adaptability framework, combining multi-source satellite observations and the station data. A Bayesian-optimized Light Gradient Boosting Machine (LightGBM, version 4.3.0) model was trained under five-fold cross-validation. Shapley Additive exPlanations (SHAP) analysis decomposed each driver’s main and interaction effects on DR. The results indicated that DR was better in tropical regions, whereas arid and polar regions require more attention. From 2001 to 2020, 45.3% of China’s land area saw DR increases, while 36.4% declined. The key drivers influencing DR were temperature, sunlight hours, potential evapotranspiration, and precipitation. Notably, an increase in sunlight hours was often accompanied by a decrease in precipitation, resulting in suboptimal DR in China. When the normalized precipitation fell within the range of 0.12 to 0.65, elevated temperature exhibited an inhibitory effect on DR. Overall, this study established a DR assessment framework, elucidated its spatiotemporal dynamics, and revealed key driver interactions, offering timely insights for ecosystem research and management in the face of climate change. Full article

Traffic flow prediction, crucial for intelligent transportation systems, has seen advancements with graph neural networks (GNNs), yet existing methods often fail to distinguish between the importance of different intersections. These methods usually model all intersections uniformly, overlooking significant differences in traffic flow characteristics and influence ranges between ordinary and important nodes. To tackle this, this study introduces a dynamic regional-aggregation-based heterogeneous graph neural network (DR-HGNN). This model categorizes intersections into two types—ordinary and important—to apply tailored feature aggregation strategies. Ordinary intersections aggregate features based on local neighborhood information, whereas important intersections utilize deeper neighborhood diffusion and multi-hop dependencies to capture broader traffic influences. The DR-HGNN model also employs a dynamic graph structure to reflect temporal changes in traffic flows, alongside an attention mechanism for adaptive regional feature aggregation, enhancing the identification of critical traffic nodes. Demonstrating its efficacy, the DR-HGNN achieved 19.2% and 15.4% improvements in the RMSE over 50 min predictions in the METR-LA and PEMS-BAY datasets, respectively, offering a more precise prediction method for traffic management. Full article

To effectively account for the impact of fluctuations in the power generation efficiency of renewable energy sources such as photovoltaics (PVs) and wind turbines (WTs), as well as the uncertainties in load demand within an integrated energy system (IES), this article develops an IES model incorporating power generation units such as PV, WT, microturbines (MTs), Electrolyzer (EL), and Hydrogen Fuel Cell (HFC), along with energy storage components including batteries and heating storage systems. Furthermore, a demand response (DR) mechanism is introduced to dynamically regulate the energy supply–demand balance. In modeling uncertainties, this article utilizes historical data on PV, WT, and loads, combined with the adjustability of decision variables, to generate a large set of initial scenarios through the Monte Carlo (MC) sampling algorithm. These scenarios are subsequently reduced using a combination of the K-means clustering algorithm and the Simultaneous Backward Reduction (SBR) technique to obtain representative scenarios. To further manage uncertainties, a distributionally robust optimization (DRO) approach is introduced. This method uses 1-norm and ∞-norm constraints to define an ambiguity set of probability distributions, thereby restricting the fluctuation range of probability distributions, mitigating the impact of deviations on optimization results, and achieving a balance between robustness and economic efficiency in the optimization process. Finally, the model is solved using the column and constraint generation algorithm, and its robustness and effectiveness are validated through case studies. The MC sampling method adopted in this article, compared to Latin hypercube sampling followed by clustering-based scenario reduction, achieves a maximum reduction of approximately 17.81% in total system cost. Additionally, the results confirm that as the number of generated scenarios increases, the optimized cost decreases, with a maximum reduction of 1.14%. Furthermore, a comprehensive cost analysis of different uncertainties modeling approaches is conducted, demonstrating that the optimization results lie between those obtained from stochastic optimization (SO) and robust optimization (RO), effectively balancing conservatism and economic efficiency. Full article

Land consolidation (LC) plays an important role in disturbing carbon storage (CS) change. Evaluating how LC affects CS is crucial for mitigating global climate change. However, existing research often overlooks differences in various aspects of land remediation, making it challenging to propose targeted policy adjustments to enhance CS effectiveness. This study presents a framework to assess the effectiveness of CS changes throughout the LC process, encompassing policy formulation stages (PF), construction stages (CO), and post-management stages (PM). Carbon density, a key factor in measuring CS changes, is adjusted using biomass model-integrated empirical measurements with dynamic growth coefficients calibrated through phenological monitoring. The Guangdong Demolition and Reclamation (D&R) project, a specific type of LC, serves as a case study. The findings are as follows: (1) D&R increased forest and garden land by 1420 hm² and 1674 hm², respectively, leading to a regional CS increase of 359,000 t, a five-fold rise per hectare. (2) The effectiveness of PF is 5.81%, with a discrepancy of over 36 million tons. The policy content’s adaptability is low, indicating significant room for improvement in CS outcomes at this stage. (3) The effectiveness of CO is 24.71%, with considerable variation between counties, ranging from 1.26% to 97.55%, due to the varying capabilities of executors and the diverse regional topographical features. Refining implementation content and encouraging collaborative efforts are effective strategies to enhance CS. (4) The effectiveness of PM is 65.03%, and the counties in the east are lower than the west. Scientific post-care is essential for improving CS. This framework provides theoretical support for optimizing LC to enhance regional CS and lays the groundwork for future investigations into the long-term impacts of LC on CS, as well as the potential for applying the methods used in this study to other regions and types of land consolidation projects. Full article

The eco-friendly flame retardancy of polycarbonate (PC) was achieved by blending with phosphorylated-PC in the range of 1–5% w/w. Dynamic properties were characterized using broadband dielectric relaxation spectroscopy (DRS), while structural and thermal properties were investigated using Fourier transform infrared spectroscopy, wide-angle X-ray diffraction, small-angle X-ray scattering, differential scanning calorimetry, and thermogravimetric analysis. A reduction in the single glass transition temperature with increasing phosphorylated-PC content was observed, indicating that the blends were miscible. No crystalline phases were detected in any of the samples. The thermo-oxidative stability and UL-94 ratings of flame-retardant polycarbonates (FRPCs) improved compared to neat PC, with char residue increasing as the phosphorylated-PC content rose. DRS analysis revealed the formation of a well-defined local (β) relaxation in the FRPC samples, originating from the motion of phosphorylated branches. All samples exhibited the segmental (α) relaxation of PC chains above the glass transition temperature. The size of the cooperatively rearranging domain played a significant role in the dynamic fragility of the rigid FRPCs. Additionally, DRS analysis highlighted the presence of physical crosslinks from nanoclusters of phosphorylated polar groups, approximately 14 nm in size. Full article

High-dynamic-range (HDR) technology enhances the capture of luminance beyond the limits of traditional images, facilitating the capture of more nuanced and lifelike visual effects. This advancement has profound implications across various sectors, such as medical imaging, augmented reality (AR), virtual reality (VR), and autonomous driving systems. The evolution of complementary metal-oxide semiconductor (CMOS) image sensor (CIS) manufacturing techniques, particularly through backside illumination (BSI) and advancements in three-dimensional (3D) stacking architectures, is driving progress in HDR’s capabilities. This paper provides a review of the technologies developed over the past six years that augment the dynamic range (DR) of CIS. It systematically introduces and summarizes the implementation methodologies and distinguishing features of each technology. Full article

For RSSI measurement of RF systems, a wide band, large dynamic range (DR), parallel-summation logarithmic amplifier is presented in this paper. The circuit adopts an 8-stage DC-coupled cascaded limiting amplifier structure. The output voltage of the limiting amplifier is converted into current through a rectifier to realize parallel summation. In order to reduce offset, this paper introduces offset reduction circuits in the gain and output stage, respectively. In addition, a log slope adjuster is proposed, which can achieve log slope control of different frequency inputs. The post-simulation results show that at a power supply voltage of 5 V, the 3 dB gain bandwidth is 4.5 GHz, the dynamic range reaches 64 dB, and the log error is less than ±1 dB. The overall circuit consumes 21 mA of current. Full article

This study presents an automated circuit design approach using neural networks to optimize the dynamic range (DR) of active filters, illustrated through the design of a 7th-order Chebyshev low-pass filter. Traditional design methods rely heavily on designer expertise, often resulting in time-intensive and energy-consuming processes. Two techniques are proposed: inverse modeling and forward modeling. In inverse modeling, artificial neural networks (ANNs) predict circuit parameters to meet specific performance goals. A randomly selected subset, comprising 0.05% of the 1,953,125 possible circuit configurations, was used to train and validate the model, providing an accurate representation of the entire dataset without requiring full-scale data analysis. In forward modeling, the same subset was used to train the network, which was then used to predict DR values for the remaining dataset. This approach enabled the identification of circuit parameters that resulted in optimal DR values. The results confirm the effectiveness of these techniques, with both inverse modeling and forward modeling outperforming the standard circuit design. At 160 kHz, a critical frequency for the operation of the designed filter, inverse modeling achieved a DR of 140.267 dB and forward modeling reached 136.965 dB, compared to 132.748 dB for the standard circuit designed using the traditional approach. These findings demonstrate that ANN-based methods can significantly enhance design accuracy, reduce time requirements, and improve energy efficiency in analog circuit optimization. Full article

Over the past two decades, our understanding of star formation has undergone a major shift, driven by a wealth of data from infrared, submillimeter and radio surveys. The emerging view depicts star formation as a hierarchical process, which predominantly occurs along filamentary structures in the interstellar medium. These structures span a wide range of spatial scales, ultimately leading to the birth of young stars, which distribute in small groups, clusters and OB associations. Given the inherently complex and dynamic nature of star formation, a comprehensive understanding of these processes can only be achieved by examining their end products—namely, the distribution and properties of young stellar populations. In the Gaia era, the nearby OB associations are now characterised with unprecedented detail, allowing for a robust understanding of their formation histories. Nevertheless, to fully grasp the mechanisms of star formation and its typical scale, it is essential to study the much larger associations, which constitute the backbones of spiral arms. The large catalogues of young open clusters that have emerged from Gaia DR3 offer a valuable resource for investigating star formation on larger spatial scales. While the cluster parameters listed in these catalogues are still subject to many uncertainties and systematic errors, ongoing improvements in data analysis and upcoming Gaia releases promise to enhance the accuracy and reliability of these measurements. This review aims to provide a comprehensive summary of recent advancements and a critical assessment of the datasets available. Full article

Dynamic digital radiography (DDR) is a recent imaging technique that allows for real-time visualization of thoracic and pulmonary movement in synchronization with the breathing cycle, providing useful clinical information. A 46-year-old male, a former smoker, was evaluated for unexplained dyspnea and reduced exercise tolerance. His medical history included a SARS-CoV-2 infection in 2021. On physical examination, decreased breath sounds were noted at the right-lung base. Spirometry showed results below predicted values. A standard chest radiograph revealed an elevated right hemidiaphragm, a finding not present in a previous CT scan performed during his SARS-CoV-2 infection. To better assess the diaphragmatic function, a posteroanterior DDR study was performed in the standing position with X-ray equipment (AeroDR TX, Konica Minolta Inc., Tokyo, Japan) during forced breath, with the following acquisition parameters: tube voltage, 100 kV; tube current, 50 mA; pulse duration of pulsed X-ray, 1.6 ms; source-to-image distance, 2 m; additional filter, 0.5 mm Al + 0.1 mm Cu. The exposure time was 12 s. The pixel size was 388 × 388 μm, the matrix size was 1024 × 768, and the overall image area was 40 × 30 cm. The dynamic imaging, captured at 15 frames/s, was then assessed on a dedicated workstation (Konica Minolta Inc., Tokyo, Japan). The dynamic acquisition showed a markedly reduced motion of the right diaphragm. The diagnosis of diaphragm dysfunction can be challenging due to its range of symptoms, which can vary from mild to severe dyspnea. The standard chest X-ray is usually the first exam to detect an elevated hemidiaphragm, which may suggest motion impairment or paralysis but fails to predict diaphragm function. Ultrasound (US) allows for the direct visualization of the diaphragm and its motion. Still, its effectiveness depends highly on the operator’s experience and could be limited by gas and abdominal fat. Moreover, ultrasound offers limited information regarding the lung parenchyma. On the other hand, high-resolution CT can be useful in identifying causes of diaphragmatic dysfunction, such as atrophy or eventration. However, it does not allow for the quantitative assessment of diaphragmatic movement and the differentiation between paralysis and dysfunction, especially in bilateral dysfunction, which is often overlooked due to the elevation of both hemidiaphragms. Dynamic Digital Radiography (DDR) has emerged as a valuable and innovative imaging technique due to its unique ability to evaluate diaphragm movement in real time, integrating dynamic functional information with static anatomical data. DDR provides both visual and quantitative analysis of the diaphragm’s motion, including excursion and speed, which leads to a definitive diagnosis. Additionally, DDR offers a range of post-processing techniques that provide information on lung movement and pulmonary ventilation. Based on these findings, the patient was referred to a thoracic surgeon and deemed a candidate for surgical plication of the right diaphragm. Full article

This paper presents the CMOS circuit realization of a low-pass Inverse Chebyshev fractional-order filter (FOF) of order (1 + α) using the inverse-follow-the-leader feedback (IFLF) topology. A nonlinear least squares optimization routine is used to determine the coefficients of the fractional-order transfer function to approximate the stop-band characteristics. The Inverse Chebyshev FOF of orders 1.3, 1.6, and 1.9 using cross-coupled operational transconductance amplifier (OTA) was designed in united microelectronics corporation (UMC) 180 nm complementary metal–oxide–semiconductor process. The MATLAB and Cadence Spectre simulations are used to validate the implementation of the fractional-order filter of orders 1.3, 1.6 and 1.9. The dynamic range (DR) of the filter is found to be 83.04 dB, 86.13 dB, and 84.71 dB, respectively, for order of 1.3, 1.6, and 1.9. The simulation results such as magnitude response, transient plot, Monte Carlo, and PVT plots, have justified the design accuracy. Full article

Battery-powered Internet-of-Things applications require high-resolution, energy-efficient analog-to-digital converters (ADCs). There are still limited works on sub-MHz-bandwidth ADC designs. This paper presents a sturdy multi-stage shaping (SMASH) discrete-time (DT) delta-sigma modulator (DSM) structure using a self-cascoded floating-inverter-based dynamic amplifier (FIA). The proposed structure removes the explicit quantization error extraction of the first loop and all the feedback DACs in the cascaded loop, decreasing the design complexity of the circuit. This enables the proposed DT DSM to operate at a higher speed, which is suitable for achieving high-order noise at a low oversampling ratio (OSR). The proposed self-cascoded FIA is more power-efficient and can acquire more than 45 dB DC gain under a 1.2 V supply. The DT DSM implemented in a piece of 55 nm CMOS technology measures an 88.0 dB peak signal-to-noise-and-distortion ratio (SNDR) in a 100 kHz bandwidth (BW) and an 85.3 dB dynamic range (DR), consuming 249.1 μW from a 1.2 V supply at 10 MS/s. The obtained 174.0 dB SNDR-based Schreier figure-of-merit (FoMs) is competitive within state-of-art high-resolution (SNDR > 85 dB) and general-purpose (sub-MHz-bandwidth) ΔΣ ADCs. Full article