Search Results (289)

Dynamic myocardial CT perfusion imaging enables functional assessment of coronary artery stenosis and myocardial microvascular disease. However, it is susceptible to residual motion artifacts arising from cardiac and respiratory activity. These artifacts introduce temporal misalignments, distorting Time-Enhancement Curves (TECs) and leading to inaccurate myocardial perfusion measurements. Traditional nonrigid registration methods can address such motion but are often computationally expensive and less effective when applied to low-dose images, which are prone to increased noise and structural degradation. In this work, we present a CNN-based motion-correction framework specifically trained for low-dose cardiac CT perfusion imaging. The model leverages spatiotemporal patterns to estimate and correct residual motion between time frames, aligning anatomical structures while preserving dynamic contrast behaviour. Unlike conventional methods, our approach avoids iterative optimization and manually defined similarity metrics, enabling faster, more robust corrections. Quantitative evaluation demonstrates significant improvements in temporal alignment, with reduced Target Registration Error (TRE) and increased correlation between voxel-wise TECs and reference curves. These enhancements enable more accurate myocardial perfusion measurements. Noise from low-dose scans affects registration performance, but this remains an open challenge. This work emphasizes the potential of learning-based methods to perform effective residual motion correction under challenging acquisition conditions, thereby improving the reliability of myocardial perfusion assessment. Full article

PredRNN is a spatiotemporal prediction model based on ST-LSTM units, capable of simultaneously extracting spatiotemporal features from ionospheric Total Electron Content (TEC). However, its internal convolutional operations require large kernels to capture low-frequency features, which can easily lead to model over-parameterization and consequently limit its performance. Although some studies have employed wavelet transform convolution (WTConv) to improve feature extraction efficiency, the introduced noise interferes with effective feature representation. To address this, this paper proposes a denoising wavelet transform convolution (DWTConv) and constructs the DWTPred-Net model with it as the key component. To systematically validate the model’s performance, we compared it with mainstream models (C1PG, ConvLSTM, and ConvGRU) under different solar activity conditions. The results show that both $MAE$ and $RMSE$ of DWTPred-Net are greatly reduced under all test conditions. In high solar activity, DWTPred-Net reduces $RMSE$ by 13.81%, 6.19%, and 9.28% compared to the C1PG, ConvLSTM, and ConvGRU, respectively. In low solar activity, the advantage of DWTPred-Net becomes even more pronounced, with $RMSE$ reductions further increasing to 19.39%, 11.51%, and 16.10%, respectively. Furthermore, in additional tests across different latitudinal bands and during geomagnetic storm events, the model consistently demonstrates superior performance. These multi-perspective experimental results collectively indicate that DWTPred-Net possesses obvious advantages in improving TEC prediction accuracy. Full article

Background: Due to the complexity of venous ulcer treatment, the role of nurses is critical, and artificial intelligence, particularly artificial neural networks of the Multilayer Perceptron type, can be effective tools that support professionals with objective, real-time evaluation. Thus, the present study aims to develop a network to assist in nurse decision-making regarding topical therapies for the treatment of venous ulcers. Methods: A methodological study with a technological focus and quantitative approach was conducted. The Unified Process methodology model was used, based on the Rational Unified Process strategy, following four phases: conception, elaboration, construction, and transition. Results: The development of the artificial neural network involved the collaboration of three specialists who evaluated clinical cases and images of venous ulcers to identify the topical therapies used in their clinical practice. A total of 23 dressings were selected, studied, and grouped into evaluation protocols to create the neural network flowchart, which defined the structure of the network. This network was then used by 13 nurses through the VenoTEC app (version 1.2, developed by the authors, Natal, Brazil). Conclusions: The software developed showed promising results in the initial evaluations conducted. The network achieved the highest accuracy in the initial tests and received a very good usability rating from the nurses who participated in the evaluation. The small dataset limits the generalization capability of the findings. Further studies are needed with additional datasets and populations. Full article

Production scheduling that involves distributed factories, machine maintenance, and resource constraints plays a crucial role in manufacturing. However, these realistic constraints have rarely been considered simultaneously in the hybrid flow shop (HFS). To address this issue, a distributed resource-constrained hybrid flow shop scheduling problem with machine breakdowns (DRCHFSP-MB) is studied. There are two optimization objectives, i.e., makespan and total energy consumption (TEC). To solve the strongly NP-hard problem, a mathematical model is established and a block–neighborhood-based multi-objective evolutionary algorithm (BNMOEA) is developed. In the proposed algorithm, an efficient hybrid initialization method is adopted to obtain high-quality individuals to participate in the evolutionary process of the population. Next, to enhance the search capability of the BNMOEA, three well-designed crossover operators are used in the global search. Then, the convergence of the proposed algorithm is improved by utilizing eight critical factory-based local search operators combined with block–neighborhood. Finally, the BNMOEA is compared with several of the most advanced multi-objective algorithms; the results indicate that the BNMOEA has an outstanding performance in solving DRCHFSP-MB. Full article

Transcription–replication conflicts (TRCs) often occur in cells and cause DNA replication fork stalling. In this study, we investigated the interplay of RNA polymerase (RNAP), DNA polymerase, and RNase H1 (RH1) during head-on TRC in vitro with precise control over the reaction conditions. We show that it is a catalytically competent transcription elongation complex (TEC) that interferes with the action of both the Klenow fragment and full-length DNA Pol I. An incompetent RNAP complex with an R-loop stimulates the 3′→5′ exonuclease activity and pauses the DNA polymerase during head-on TRC. As RNAP advances along the DNA template, elongating the RNA, the head-on TRC is slowly overcome in our model system, likely through the reassociation of the displaced DNA polymerase with the nontemplate DNA strand upstream of RNAP. An isolated R-loop containing an 11-nt heteroduplex (R-loop-11) does not interfere with DNA replication by the Klenow fragment. For DNA Pol I, such an R-loop also does not stall replication but stimulates its 3′→5′ exonuclease activity. We demonstrate that a stalled Klenow fragment does not interfere with transcription, whereas a Klenow fragment moving along the TRC substrate towards RNAP alters the kinetics of RNAP. Stalled DNA Pol I does not stop RNAP but stimulates its endonuclease activity. We find that RH1 alone does not displace stalled RNAP from a competent TEC containing R-loop-11 and does not resolve the head-on TRC. On the other hand, RH1 displaces RNAP from the incompetent complex with the TRC substrate. This eliminates the stimulation of the 3′→5′ exonuclease activity of DNA polymerase during head-on TRC. Full article

The phase-pure cubic pyrochlore of the Bi₂Cr_0.5Co_0.5Nb₂O_9+Δ composition can be successfully synthesized by a modified sol–gel method (Pecini method-PM) and a traditional solid-phase method (SPM). A feature of the solid-phase synthesis method is the formation of bismuth(VI) chromates as intermediate synthesis products, which is confirmed by X-ray spectroscopy data (NEXAFS). During the sol–gel synthesis method, bismuth chromates are not formed due to the formation of the Bi₂₈O₃₂(SO₄)₁₀ salt, which is thermally stable up to 880 °C, preventing the interaction of bismuth(III) and chromium(III) oxides. The temperature of the final pyrochlore calcination during sol–gel synthesis is reduced by 100 °C (950 °C) compared to the solid-phase method. The crystal structure of pyrochlore (sp. gr. Fd-3m, PM, a = 10.49360(5) Å, Z = 4) was refined by the Rietveld method based on X-ray powder diffraction (XRD) data. NEXAFS Cr2p and Co2p spectra of ceramics synthesized at 1050 °C correspond to the charge states of Cr(III), Co(II) and Co(III) ions. The thermal expansion coefficient of the cell was calculated from high-temperature X-ray diffraction measurements in the range of 20–1200 °C. The thermal expansion coefficient (TEC) monotonically increases from 3.92 × 10⁻⁶ °C⁻¹ (20 °C) to 9.89 × 10⁻⁶ °C⁻¹ (1020 °C). Above 1110 °C, TEC decreases due to thermal dissociation of Bi₂Cr_0.5Co_0.5Nb₂O_9+Δ with the formation of CoNb₂O₆, Bi₂O₃. The mixed pyrochlore (PM) exhibits a moderately high permittivity of ∼97, and low dielectric losses of ∼2 × 10⁻³ at 1 MHz and ∼30 °C. The activation energy of conductivity of the high-temperature region is 0.89 eV. The electrical properties of pyrochlore were synthesized by the ceramic (SPM) and Pechini methods (PM) were analyzed. The electrical properties of the samples up to 400 °C were modeled using equivalent electrical circuits Full article

Interlocking Compressed Earth Blocks (ICEBs) offer a sustainable alternative to conventional fired-clay bricks but remain hindered by inconsistent geometric designs and limited standardization. This study develops a stakeholder-weighted Analytic Hierarchy Process (AHP) framework to evaluate and select the most suitable ICEB geometry for sustainable and seismic-ready construction in developing regions. Five evaluation criteria—size, weight, interlocking effectiveness, reinforcement/grout provision, and handling ergonomics—were prioritized based on expert input from masons, engineers, architects, and researchers. The synthesized results ranked the HiLo-Tec-type geometry highest, followed by Thai-Rhino, Auram, and Hydraform designs. Unit weight (0.289) and reinforcement capacity (0.261) emerged as dominant decision factors. Sensitivity analysis confirmed the robustness of rankings under varying weight perturbations. The AHP framework identifies the top-ranked geometry, whose structural performance was examined experimentally through a full-scale cyclic test on a grouted double-wythe ICEB wall, revealing enhanced ductility and residual strength compared with traditional brick masonry. The proposed framework demonstrates that selected ICEB geometry can balance ergonomic and structural performance while meeting seismic resilience demands. Beyond geometry selection, the model provides a replicable decision-support tool adaptable for regional material innovations in sustainable construction. Full article

We present a high-speed, miniaturized, Peltier-driven thermocycler for Polymerase Chain Reaction (PCR) with heating rates of 22.25 °C/s and cooling rates of 5.30 °C/s, using a standard aluminum block (a four-well section of a 96-well plate) and laterally arranged micro-thermoelectric coolers (TECs) to induce predominantly horizontal heat flow. Simulations without copper preheating predict a cooling rate of 5.70 °C/s. Finite-element thermoelectric modeling (COMSOL 6.2) closely matches measurements. The selection of materials is guided by the introduction of the merit number G_β that balances thermal diffusivity and volumetric heat capacity, enabling consistent comparison across candidate block materials. The performance of this system is evaluated against data reported in scientific literature, encompassing both recent academic developments and selected commercial systems that employ silver components to enhance thermal conductivity. Despite aluminum’s lower thermal conductivity, our device achieves superior thermal cycling rates, demonstrating that with innovative design, less expensive materials can compete with higher-performing ones. This work includes detailed numerical simulations, comparative analyses of block materials, design considerations, fabrication methods, and experimental validation. By integrating insights from current scientific research, this study contributes to the advancement of accessible and high-performance diagnostic technologies. Full article

This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder (ED) architecture enhanced with residual connections and convolutional channel projection, which collectively improve the synergy among its core components. Based on this framework, we developed ED-ConvLSTM-Res, a multi-channel feature-based global ionospheric TEC prediction model. Comprehensive accuracy evaluation and comparative tests were carried out using datasets from the solar minimum year of 2019 and the current solar maximum year of 2024. The results indicate that the proposed model consistently achieves strong predictive performance compared with other models, along with a significantly enhanced feature representation capability. Specifically, the Root Mean Square Errors (RMSE) of the ED-ConvLSTM-Res model’s predictions in 2019 and 2024 are 1.28 TECU and 5.28 TECU, respectively, while the corresponding Mean Absolute Errors (MAE) are 0.87 and 3.87, and the coefficients of determination (R²) are 0.95 and 0.94. In the current high solar activity year 2024, the proposed model achieves error reductions of 13.6% in MAE and 11.6% in RMSE compared with the Center for Orbit Determination in Europe (CODE)’s one-day-ahead forecast product, c1pg. These results confirm that the proposed model not only outperforms the ConvLSTM model without additional indices and c1pg but also exhibits strong generalization capability, maintaining stable performance with low errors under both high and low solar activity conditions. Full article

The flexible job shop scheduling problem (FJSP) with transportation resources such as automated guided vehicles (AGVs) is prevalent in manufacturing enterprises. Multi-type AGVs are widely adopted to transfer jobs and realize the collaboration of different machines, but are often ignored in current research. Therefore, this paper addresses the FJSP with multi-type AGVs (FJSP-MTA). Considering the difficulties caused by the introduction of transportation and the NP-hard nature, the artificial bee colony (ABC) algorithm is adopted as a fundamental solution approach. Accordingly, a Q-learning hybrid multi-objective ABC (Q-HMOABC) algorithm is proposed to deal with the FJSP-MTA. First, to minimize both the makespan and total energy consumption (TEC), this paper proposes a novel mixed-integer linear programming (MILP) model. In Q-HMOABC, a three-layer encoding strategy based on operation sequence, machine assignment, and AGV dispatching with type selection is used. Moreover, during the employed bee phase, Q-learning is employed to update all individuals; during the onlooker bee phase, variable neighborhood search (VNS) is used to update nondominated solutions; and during the scout bee phase, a restart strategy is adopted. Experimental results demonstrate the effectiveness and superiority of Q-HMOABC. Full article

Global Navigation Satellite System (GNSS) spoofing, which manipulates PVT solutions through false measurements, increasingly threatens GNSS reliability and user safety. However, most existing simulator-based spoofers, constrained by their inability to access real-time ionospheric information (e.g., Global Ionosphere Maps, GIMs) from external sources, struggle to replicate authentic total electron content (TEC) along each signal propagation path accurately and in a timely manner. In contrast, widespread dual-frequency (DF) receivers with access to the internet can validate local TEC measurements against external references, establishing a pivotal spoofing detection distinction. Here, we propose an Ionospheric Information-Assisted Spoofing Detection Technique (IIA-SDT), exploiting the inherent consistency between TEC values derived from DF pseudo-range measurements and external references in spoofing-free scenarios. Spoofing probably disrupts this consistency: in simulator-based full-channel spoofing where all channels are spoofed, the inaccuracies of the offline ionospheric model used by the spoofer inevitably cause TEC mismatches; in partial-channel spoofing where the spoofer fails to control all channels, an unintended PVT deviation is induced, which also causes TEC deviations due to the spatial variation of the ionosphere. Basic principles and theoretical analysis of the proposed IIA-SDT are elaborated in the paper. Simulations using ionospheric data collected from 2023 to 2024 at a typical mid-latitude location are conducted to evaluate IIA-SDT performance under various parameter configurations. With a window length of 5 s and satellite number of 8, the annual average detection probability approximates 75% at a false alarm rate of $1\times {10}^{-3}$, with observable temporal variations. Field experiments across multiple scenarios further validate the spoofing detection capability of the proposed method. Full article

The Denoising Wavelet Transform Convolutional Long Short-Term Memory Network (DWTConvLSTM) is a novel ionospheric total electron content (TEC) spatiotemporal prediction model proposed in 2025 that can simultaneously consider high-frequency and low-frequency features while suppressing noise. However, it also has flaws as it only considers unidirectional temporal features in spatiotemporal prediction. To address this issue, this paper adopts a bidirectional structure and designs a bidirectional DWTConvLSTM model that can simultaneously extract bidirectional spatiotemporal features from TEC maps. Furthermore, we integrate a lightweight attention mechanism called Convolutional Additive Self-Attention (CASA) to enhance important features and attenuate unimportant ones. The final model was named CASA-BiDWTConvLSTM. We validated the effectiveness of each improvement through ablation experiments. Then, a comprehensive comparison was performed on the 11-year Global Ionospheric Maps (GIMs) dataset, involving the proposed CASA-BiDWTConvLSTM model and several other state-of-the-art models such as C1PG, ConvGRU, ConvLSTM, and PredRNN. In this experiment, the dataset was partitioned into 7 years for training, 2 years for validation, and the final 2 years for testing. The experimental results indicate that the RMSE of CASA-BiDWTConvLSTM is lower than those of C1PG, ConvGRU, ConvLSTM, and PredRNN. Specifically, the decreases in RMSE during high solar activity years are 24.84%, 16.57%, 13.50%, and 10.29%, respectively, while the decreases during low solar activity years are 26.11%, 16.83%, 11.68%, and 7.04%, respectively. In addition, this article also verified the effectiveness of CASA-BiDWTConvLSTM from spatial and temporal perspectives, as well as on four geomagnetic storms. Full article

A practical methodology for estimating regionally optimized Klobuchar coefficients using only International GNSS Service (IGS) Global Ionosphere Map (GIM) data is proposed. The method preserves computational simplicity, enabling near-real-time corrections suitable for accurate GNSS positioning. Utilizing both slant and vertical total electron content (STEC and VTEC) values extracted from GIM as inputs to estimate eight Klobuchar coefficients, robust parameter sets were obtained. Root mean square error (RMSE) analysis was used to compare these models to the standard Klobuchar model. Comprehensive performance evaluations using STEC-derived parameters, encompassing both seasonal and spatial analyses across South Korea, demonstrated significant reductions in ionospheric delay errors, with improvements reaching up to 57% compared to the conventional Klobuchar model. The far less computationally intensive VTEC-based model was applied over a wider region with 120 grid points. Continuous testing of this model over an entire year confirmed consistent enhancements in correction accuracy every day, demonstrating stable performance throughout the period. The developed regional Klobuchar models were further validated indirectly through satellite positioning performance, demonstrating daily RMSE improvements over the standard Klobuchar model ranging from 17.3% to 44.6%. Full article

The paper presents the development and experimental verification of an electrothermal model of a heatsink-less thermoelectric generator (TEG) implemented in the LTspice simulator. The model incorporates key physical phenomena, including the Seebeck effect, the Peltier effect, and Joule heating. It also takes into account a variable convective thermal resistance to the environment that depends on the temperature of the thermoelectric module’s cold side. The model was calibrated using experimental measurements of the open-circuit Seebeck voltage and the output voltage under resistive load connected to the TEC1-12706-SR thermoelectric module (TEM), under controlled temperature gradients. The model’s accuracy was validated by comparing simulation results with measured output voltages and power generated by the TEG for various load resistances and ambient temperatures. The simulations showed good agreement with the experimental data. The analysis and tests also confirmed the existence of an optimal load resistance that maximizes power transfer from the module, which is consistent with the principle of matching the load to the TEG’s internal resistance. We present the comparison between the theoretical model of a TEG and its physical properties. We used the results of the measurements to tailor the model, so finally we were able to achieve consistency of measurements with experiment within 10–17%. The developed model is a useful tool for rapid design and optimization of energy-harvesting systems using TEGs, enabling the integration of these generators into autonomous IoT systems and wearable electronics, without the need for a traditional heatsink. Full article

The efficacy of indoor residual spray (IRS) products is affected by various factors, such as the substrate on which they are sprayed and the surface concentration and bioavailability of the insecticide. This study investigated the potential of bespoke silica particles (hereafter referred to as ‘X-tec silica’) as a unique carrier for insecticides to reduce the insecticide content in an IRS formulation by improving pickup by mosquitoes and optimising the physical state of the insecticide while maintaining its residual biological activity on a surface. Molecular computer modelling was used to define the critical crystallisation size of clothianidin, and silica particles were manufactured with pore diameters smaller than this length to maintain the insecticide in an amorphous state. Silica carriers were then formulated to incorporate clothianidin inside their pores, and a full material characterisation was conducted to assess the clothianidin coating position on/in the silica particles, their concentration, and their physical form. The clothianidin-formulated silica (10%) was sprayed at three different application rates (30, 60, and 90 mg active ingredient (a.i.)/m²) onto two surfaces: glazed and unglazed tiles. The tiles were tested for bioefficacy against the insecticide-susceptible Anopheles gambiae s.s. Kisumu mosquito strain at 1 week and 8 months post-spray application. At 1 week post-spray application, at 60 and 90 mg a.i./m² application rates, 100% mortality was observed on both surfaces within 48 h. For the lowest concentration (30 mg a.i./m²), 100% mortality was reached within 72 h on glazed tiles; however, for unglazed tiles, due to the surface irregularity and porosity, it remained below 60%. At 8 months post-spray application, on glazed tiles, 100% mortality was reached within 24 h at 60 and 90 mg a.i./m² application rates and within 48 h at 30 mg a.i./m². On unglazed tiles, 96 h mortality was not measured; however, 100% mortality was reached within 72 h (90 mg a.i./m²) and 120 h (60 mg a.i./m²) at higher concentrations. At the lowest concentration (30 mg a.i./m²) at 120 h, mortality only reached 25%. The lowest application rate tested (30 mg a.i./m²) is ten times lower than that of current products on the market and demonstrates the potential of this approach. Preliminary findings from this study suggest that X-tec silica particles may enhance the effectiveness of IRS using clothianidin. However, further extensive research is needed to confirm this. Full article