Similar Error Research Articles

WiTUnet: A U-shaped architecture integrating CNN and Transformer for improved feature alignment and local information fusion

Low-dose computed tomography (LDCT) has emerged as the preferred technology for diagnostic medical imaging due to the potential health risks associated with X-ray radiation and conventional computed tomography (CT) techniques. While LDCT utilizes a lower radiation dose compared to standard CT, it results in increased image noise, which can impair the accuracy of diagnoses. To mitigate this issue, advanced deep learning-based LDCT denoising algorithms have been developed. These primarily utilize Convolutional Neural Networks (CNNs) or Transformer Networks and often employ the Unet architecture, which enhances image detail by integrating feature maps from the encoder and decoder via skip connections. However, existing methods focus excessively on the optimization of the encoder and decoder structures while overlooking potential enhancements to the Unet architecture itself. This oversight can be problematic due to significant differences in feature map characteristics between the encoder and decoder, where simple fusion strategies may hinder effective image reconstruction. In this paper, we introduce WiTUnet, a novel LDCT image denoising method that utilizes nested, dense skip pathway in place of traditional skip connections to improve feature integration. Additionally, to address the high computational demands of conventional Transformers on large images, WiTUnet incorporates a windowed Transformer structure that processes images in smaller, non-overlapping segments, significantly reducing computational load. Moreover, our approach includes a Local Image Perception Enhancement (LiPe) module within both the encoder and decoder to replace the standard multi-layer perceptron (MLP) in Transformers, thereby improving the capture and representation of local image features. Through extensive experimental comparisons, WiTUnet has demonstrated superior performance over existing methods in critical metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean Square Error (RMSE), significantly enhancing noise removal and image quality. The code is available on github https://github.com/woldier/WiTUNet.

Modelling Blow Fly (Diptera: Calliphoridae) Spatiotemporal Species Richness and Total Abundance Across Land-Use Types.

Geographic Information Systems provide the means to explore the spatial distribution of insect species across various land-use types to understand their relationship with shared or overlapping spatiotemporal resources. Blow fly species richness and total fly abundance were correlated among six land-use types (residential, commercial, waste, woods, roads, and agricultural crop types) and distance to streams. To generate multivariate models of species richness and total fly abundance, blow fly trapping sites were chosen across the land-use gradient of Windsor-Essex County (Ontario, Canada) using a stratified random sampling approach. Sampling occurred in mid-June (spring), late August (summer), and late October (fall). Spring species richness correlated highest to residential (-), woods (-), distance to streams (+), and tomato fields (+) in models across all three land-use buffer scale distances (0.5, 1, 2 km), with waste (+/-), roads (-), wheat/corn (-), and commercial (-) correlating at only two of the three scales. Spring total fly abundance correlated with all but one land-use variable across all buffer scale distances, but the distance to streams (+), followed by orchards/vineyards (+) exhibited the greatest importance to these models. Summer blow fly species richness correlated with roads (-) and commercial (+) across all buffer distances, whereas at two of three buffer distances wheat/corn (-), residential (+), distance to streams (+), waste (-), and orchards/vineyards (+) were also important. Summer total fly abundance correlated to models with distance to streams (+), orchards/vineyards (+), and sugar beets/other vegetables (+) at the 2 km scale. Species richness and total abundance models at the 0.5 km buffer distance exhibited the highest correlation, lowest root mean square error, and similar prediction error to those derived at larger buffer distances. This study provides baseline methods and models for future validation and expansion of species-specific knowledge regarding adult blow fly relationships with spatiotemporal resources across land-use types and landscape features.

Phase contrast MRI with minimized background phase errors.

Phase contrast MRI (PC-MRI) is used clinically to measure velocities in the body, but systematic background phase errors caused by magnetic field imperfections corrupt the velocity measurements with offsets that limit clinical utility. This work aims to minimize systematic background phase errors in PC-MRI, thereby maximizing the accuracy of velocity measurements. The MRI scanner's background phase errors from eddy currents and mechanical oscillations were modeled using the gradient impulse response function (GIRF). Gradient waveforms were then numerically optimized using the GIRF to create pulse sequences that minimize the background phase errors. The pulse sequences were tested in a static phantom where the predicted response could be compared directly to the measured background velocity. The optimized acquisitions were then tested in human subjects, where flow rates and background errors were compared to conventional PC-MRI. When using the GIRF-optimized gradient waveforms, the predicted background phase was within 0.6 [95% CI = -3.4, 5.4] mm/s of the measured background phase in a static phantom. Excellent agreement was seen for in vivo blood flow values (flow rate agreement = 0.96), and the background phase was reduced by 78.8 18.7%. This work shows that using a GIRF to model the effects of magnetic field imperfections combined with numerically optimized gradient waveforms enables PC-MRI waveforms to be designed to produce a minimal background phase in the most time-efficient manner. The methodology could be adapted for other MRI sequences where similar magnetic field errors affect measurements.

Compact lossy compression of tensors via neural tensor-train decomposition

AbstractMany real-world datasets are represented as tensors, i.e., multi-dimensional arrays of numerical values. Storing them without compression often requires substantial space, which grows exponentially with the order. While many tensor compression algorithms are available, many of them rely on strong data assumptions regarding its order, sparsity, rank, and smoothness. In this work, we propose TensorCodec, a lossy compression algorithm for general tensors that do not necessarily adhere to strong input data assumptions.TensorCodec incorporates three key ideas. The first idea is neural tensor-train decomposition (NTTD) where we integrate a recurrent neural network into Tensor-Train Decomposition to enhance its expressive power and alleviate the limitations imposed by the low-rank assumption. Another idea is to fold the input tensor into a higher-order tensor to reduce the space required by NTTD. Finally, the mode indices of the input tensor are reordered to reveal patterns that can be exploited by NTTD for improved approximation. In addition, we extend TensorCodec to enable the lossy compression of tensors with missing entries, often found in real-world datasets. Our analysis and experiments on 8 real-world datasets demonstrate that TensorCodec is (a) Concise: it gives up to $$7.38 \times $$ 7.38 × more compact compression than the best competitor with similar reconstruction error, (b) Accurate: given the same budget for compressed size, it yields up to $$3.33\times $$ 3.33 × more accurate reconstruction than the best competitor, (c) Scalable: Its empirical compression time is linear in the number of tensor entries, and it reconstructs each entry in logarithmic time. Our code and datasets are available at https://github.com/kbrother/TensorCodec.

Gradient boosting: A computationally efficient alternative to Markov chain Monte Carlo sampling for fitting large Bayesian spatio-temporal binomial regression models

Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly used for such data. When the spatio-temporal support of the model is large, implementing an MCMC algorithm becomes a significant computational burden. This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatio-temporal mixed effects binomial regression model. We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach. Both methods are used to produce monthly forecasts for Lyme disease, anaplasmosis, ehrlichiosis, and heartworm disease in domestic dogs for the contiguous United States. The data have a spatial support of 3108 counties and a temporal support of 108–138 months with 71–135 million test results. The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm, with a similar mean absolute prediction error.

Seating Accuracy of Prefabricated Interim Crowns on Immediate and Delayed Implants: A Laboratory Study.

Evaluation of seating accuracy of interim crowns for the immediate restoration of immediate implants (I-Imp) and delayed implants (D-Imp) placed via static computer-assisted implant surgery (sCAIS). A maxillary training model was modified by removing the central incisors and simulating fresh extraction socket in one site and healed ridge on the other site. An I-Imp was planned in the extraction socket and D-Imp was planned in the healed site. The planned implants were used to design sCAIS surgical template and interim crowns for immediate restoration of the implants. Fourteen surgical models received sCAIS implants after which the interim crowns were inserted. Subsequently, the models with the seated crowns were scanned by a laboratory scanner. The planning virtual model was superimposed against every surgical model to measure vertical, horizontal and proximal contact errors of each crown. All the crowns were positioned more incisally than the planned crowns. This was significantly more noticeable for the D-Imp crowns (0.81 mm) than the I-Imp crowns (0.55 mm). The 2 crown groups had similar horizontal errors (I-Imp =0.35 mm, D-Imp = 0.36 mm). The D-Imp crowns had minimal proximal contact error (0.14 mm), but the I-Imp crowns had significantly greater proximal contact error (0.74 mm) in the form of open distal contacts. This pattern of error appears related to the relationship between the socket morphology and the planned implant position. Prefabricated interim I-Imp crowns suffered from greater errors that affected the proximal contact quality than DImp crowns. The observed deviation of the I-Imp crowns can be attributed to the socket morphology and its relation to the planned implant position. The deviations of the I-Imp crowns are clinically significant and will require clinical adjustments. Thus, caution is needed before routine use of prefabricated interim crowns on I-Imp.

Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions

BackgroundCancer prognosis-related signatures have traditionally been constructed based on gene expression profiles derived from tumor or normal tissues. However, the potential benefits of incorporating gene expression profiles from both tumor and normal tissues to improve signature performance have not been explored. MethodsIn this study, we developed three prognostic models for lung adenocarcinoma (LUAD) using gene expression profiles from tumor tissues, normal tissues, and a combination (COM) of both, sourced from The Cancer Genome Atlas (TCGA). To ensure comparability, the same workflow was followed for all three models. ResultsWhen applied to the TCGA LUAD dataset, the tumor-derived model exhibited the best overall performance, except in calibration analysis, where the normal-derived model performed better. The COM-derived model demonstrated intermediate performance. Validation on three independent test datasets revealed that the COM-derived model showed the best performance, while the normal-derived model showed the worst. In overall survival (OS) analysis, the low-risk group defined by the COM-derived model consistently exhibited longer mean survival times. The tumor-derived model did not consistently show this trend, and the normal-derived model produced opposite results. In discrimination analysis, no significant differences were observed. The COM-derived model demonstrated good discrimination ability for short periods, while the tumor-derived model performed better for longer periods. In calibration analysis, both the COM and tumor-derived models had similar absolute prediction errors, which were better than those of the normal-derived model. However, the tumor-derived model tended to underestimate survival rates. The clinical feature analysis and validation in GSE229705 indicate that the risk score (RS) from the COM model is the most clinically significant. These results demonstrate that the COM model's RS aligns more closely with clinical data, maintaining stable performance and the strongest generalizability. ConclusionsOverall, the COM-derived model demonstrated the best generalization ability. The superior performance of the tumor-derived model in the TCGA LUAD dataset might be due to overfitting. Our results suggest that appropriate combinations of gene expression data from tumor and normal tissues can enhance the predictive power of prognostic signatures.

Shock Me The Way: Directional Electrotactile Feedback under the Smartwatch as a Navigation Aid for Cyclists

Cycling navigation is a complex and stressful task as the cyclist needs to focus simultaneously on the navigation, the road, and other road users. We propose directional electrotactile feedback at the wrist to reduce the auditory and visual load during navigation-aided cycling. We designed a custom electrotactile grid with 9 electrodes that is clipped under a smartwatch. In a preliminary study we identified suitable calibration settings and gained first insights about a suitable electrode layout. In a subsequent laboratory study we showed that a direction can be encoded with a mean error of 19.28\,° (σ = 42.77°) by combining 2 adjacent electrodes. Additionally, by interpolating with 3 electrodes a direction can be conveyed with a similar mean error of 22.54° (σ = 43.57°). We evaluated our concept of directional electrotactile feedback for cyclists in an outdoor study, in which 98.8% of all junctions were taken correctly by eight study participants. Only one participant deviated substantially from the optimal path, but was successfully navigated back to the original route by our system.

Beyond Simple Tapping: Is Timed Body Movement Influenced When Balance Is Threatened?

The tapping paradigm offers valuable insights into movement timing; however, it simplifies mechanics by minimizing force, restricting motion, and relying on a clear contact endpoint. Thus, it may not fully capture the complexity of larger-scale multi-segmental (or single-segment) timed body movements. The aim of this study was to extend beyond the tapping paradigm by examining the timing of two large-scale movements commonly performed in physical fitness or rehabilitation modalities, with varying inherent balance threats: two-legged squatting (low balance threat) and standing hip abduction (higher balance threat) paced by a metronome set at the participants’ preferred tempo (N = 39, all physically active). In synchronization with the metronome audio signal, the trunk and shank angular velocities were also recorded to extract the entrainment, synchronization, and pace stability metrics. Paired t-tests indicated similar entrainment in both movements (p > 0.05 for IRI match) but significant differences in timing metrics’ manifestations (p ≤ 0.05, standing hip abduction: 50% greater IRI error, 30% lower synchronization error, 2.6% units lower pace stability). The similar entrainment but different synchronization error and pace stability highlight a complex timing interplay between balance threat/challenges and movement complexity concerning the two large-scale movements employed in physical fitness and rehabilitation modalities.

S2HConv-Caps-BiGRU: Deep Learning-Based Heterogeneous Face Recognition Model with Divergent Stages

Real-time images of faces captured in different spectrum bands are considered heterogeneous images. Heterogeneous Face Recognition (HFR) matches faces across domains and is crucial to public safety. This paper proposes an HFR approach based on Deep Neural Networks (DNN). Feature maps are extracted from two images, such as gallery and sketch images, using Squirrel Search Heterogeneous Convolutional-Capsule- Bidirectional Gated Recurrent Unit (S2HConv-Caps-BiGRU). As a method of efficiently recognizing faces, coupled representation similarity metric (CRSM) will use the measure for the similarity of two feature maps. The experimental results will be evaluated to state-of-the-art (SOTA) statistical measures in terms of accuracy, recall, Jaccard score, dice score, mean square error (MSE), image similarity, performance and root mean square error (RMSE). Compared to other SOTA, the model produces the best results. The accuracy value of a CUFS dataset is 98.7%.

Hemoglobin-based transfusion strategies for cardiovascular and other diseases: restrictive, liberal, or neither?

AbstractA “restrictive” red blood cell transfusion threshold, a hemoglobin concentration <7 to 8 g/dL, has long been recommended for most hospitalized patients including anemic patients with stable cardiovascular disease (CVD). Although no threshold recommendation is given for acute coronary syndromes (ACSs), recent evidence suggests that “liberal” rather than “restrictive” transfusion strategies are associated with significantly improved safety for hospitalized patients with stable CVD and/or ACS. This finding suggests that previously available data were misinterpreted. Conclusions drawn from earlier transfusion trigger trials have been confounded by unintentional trial design and analysis flaws that have contributed to erroneous recommendations regarding the safety of a restrictive threshold. Subsequently, these conclusions have been incorporated into widely accepted guidelines and clinical practice. Management with a restrictive vs liberal transfusion strategy (<10 g/dL) increases the risk of new-onset ACS in patients with CVD by ∼2%. We estimate that since 2019, using hospital databases and a recent meta-analysis, this practice may have resulted in ∼700 excess ACS events per year in orthopedic surgical patients. Given these findings, transfusion practices in other clinical conditions, particularly those derived from similar transfusion trigger trials, should be questioned. Restrictive and liberal transfusion policies merit a general reconsideration. Rather than a single numerical transfusion trigger, transfusion therapy should be personalized. Consideration of an individual patient’s age, clinical status, and comorbidities is integral to transfusing. To avoid making similar errors, future trials of transfusion therapy should determine common practices before study inception and incorporate them as a usual-care “control” comparator arm into the trial design. Such studies should more reliably improve current transfusion practice.

Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual Simulation

This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, our system facilitates information gathering from multiple users and executes 17 administrative tasks for group requests adeptly by leveraging a state machine-based framework for complete control over dialogue flow and a large language model (LLM) for robust context understanding. Assessing such a unique dialogue system poses challenges, as it requires many groups of users to interact with the system concurrently for an extended duration. To address this, we simulate various virtual groups using an LLM, each comprising 10–30 users who may belong to multiple groups, in order to evaluate the efficacy of our system; each user is assigned a persona and allowed to interact freely without scripts. As a result, our system shows average success rates of 87% for task completion and 89% for natural language understanding. Comparatively, our virtual simulation, which has an average success rate of 80%, is juxtaposed with a group of 15 human users, depicting similar task diversity and error trends. To our knowledge, it is the first work to show the LLM’s potential in both task execution and the simulation of a synchronous dialogue system to fully automate administrative tasks.

Fingerprint localisation for fine‐scale wildlife tracking using automated radio telemetry

Abstract Automated radio telemetry systems are one of the most widely applicable methods for tracking wildlife at fine spatiotemporal scales. A growing trend is for these systems to apply large networks of receivers to detect radio signal strength (RSS) from radio‐tagged individuals. Analytical methods to derive position estimates using RSS localisation, however, are relatively underdeveloped in the field of wildlife tracking. Here, we apply approaches from indoor positioning systems to develop a new method, radio fingerprinting, for localising radio‐tagged animals in structurally complex, outdoor environments. This method characterises the RSS patterns at known locations to generate a radio map of the study area, which is then used to predict the location of new RSS patterns. We conducted field tests to evaluate the localisation accuracy of radio fingerprinting relative to multilateration, a commonly used method for RSS localisation. To do so, we established an experimental receiver network covering multiple habitat types and compared the localisation accuracy of radio fingerprinting to multilateration. Additionally, we evaluated how a variety of features characteristic of typical tracking data sets affected the accuracy of both methods. While both methods had a similar median error under ideal conditions (~30 m), radio fingerprint localisation method offered several advantages over multilateration. Multilateration localisation estimates were highly affected by missed detections from the nearest receiver to the test point, which occurred in 30% of cases. In these cases, the median error was 103 m, over a 3.5‐fold increase. Distance to the nearest receiver also biased multilateration estimates with error increasing as the distance increased. Additionally, errors from multilateration estimates were higher in more densely vegetated areas. In contrast, fingerprinting position estimates were largely robust to each of these scenarios. Automated radio telemetry enables the fine‐scale, continuous tracking of range‐resident animals. We present radio fingerprinting as a localisation method in outdoor environments where standard localisation methods are error‐prone due to habitat heterogeneity and missed detections. This approach can be applied to any automated radio telemetry hardware and to any study system where a radio map can be generated.

Stress intensity factor models using mechanics-guided decomposition and symbolic regression

The finite element method can be used to compute accurate stress intensity factors (SIFs) for cracks with complex geometries and boundary conditions. In contrast, handbook solutions act as surrogate SIF models that provide significantly faster evaluation times. However, the development of conventional surrogate SIF models relies on manual development based on low-order parameterizations. This limits surrogate model accuracy and generalizability. In this paper, we develop a framework for the automated development of mechanics-guided handbook SIF solutions by using interpretable machine learning via genetic programming for symbolic regression (GPSR). Formalizing the mechanics-based approach of Raju and Newman, SIF training data is decomposed into multiple subsets. This decomposition enables parallel GPSR model development of subfunctions, each of which accounts for specific geometrical corrections with respect to a known analytical model. Using this mechanics-based approach with GPSR allows for equations to be learned with improved accuracy and reduced complexity relative to the Raju Newman equations while maintaining the inherent interpretability of mathematical expressions. In this paper, we present equations that match the complexity of the Raju Newman equations while having reduced error, as well as equations with similar errors and reduced complexity.

Heat flux partition based on onset of significant void

The thermal log-law Θ+(y+)=β+2.12log(y+) is valid in flow boiling with a value of β that evolves as the flow develops. Using a multiphase flow cross-literature database, this constant is shown to be βOSV=−7 at the point of onset of significant void (OSV). This means that at the OSV the liquid is at saturation temperature up to y+≃30. The OSV predictions using this model have a similar mean average error as the Saha and Zuber 1974 correlation for one less fitted constant for channel, pipe and annular flows for pressure from 1 to 147 bar and Peclet numbers from 3.5⋅103 to 4⋅105. This model is used to build a heat flux partitioning (HFP) inspired from system-scale codes (Lahey 1978). It predicts the distribution of the heat flux between the liquid phase and the evaporation term when the total heat flux is known. It does not give information on the total heat flux as a function of wall temperature and cannot be used to draw a boiling curve. In imposed flux conditions, this partition provides more coherent flux distribution between the evaporation and liquid terms than Kurul and Podowski (1990) based approaches and improves void fraction predictions in high-subcooling regions on the DEBORA database (Garnier et al. 2001) and on experiments by Bartolomei and Chanturiya (1967) and Bartolomei et al. (1982). When the wall temperature is imposed, it must be coupled with an empirical boiling total heat flux correlation to replace a traditional HFP. The prediction of the total heat flux is then as good as that of the correlation.

HDCCT: Hybrid Densely Connected CNN and Transformer for Infrared and Visible Image Fusion

Multi-modal image fusion is a methodology that combines image features from multiple types of sensors, effectively improving the quality and content of fused images. However, most existing deep learning fusion methods need to integrate global or local features, restricting the representation of feature information. To address this issue, a hybrid densely connected CNN and transformer (HDCCT) fusion framework is proposed. In the proposed HDCCT framework, the network of the CNN-based blocks obtain the local structure of the input data, and the transformer-based blocks obtain the global structure of the original data, significantly improving the feature representation. In the fused image, the proposed encoder–decoder architecture is designed for both the CNN and transformer blocks to reduce feature loss while preserving the characterization of all-level features. In addition, the cross-coupled framework facilitates the flow of feature structures, retains the uniqueness of information, and makes the transform model long-range dependencies based on the local features already extracted by the CNN. Meanwhile, to retain the information in the source images, the hybrid structural similarity (SSIM) and mean square error (MSE) loss functions are introduced. The qualitative and quantitative comparisons of grayscale images with infrared and visible image fusion indicate that the suggested method outperforms related works.

Improving medication safety in a Latin American hospital: Examination of medication errors and the role of pharmacists.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Prospective medication order review by a clinical pharmacist is uncommon in many South and Central American countries. Voluntary error reporting and analysis are similarly uncommon. This paper describes the results of pharmacist prospective order review, medication error reporting, and quality improvement activities in a Latin American hospital. On January 1, 2020, the hospital initiated prospective review of all medication orders in both the hospital and clinic setting by pharmacists. Health professionals were encouraged to report errors identified to the hospital's voluntary reporting program. Data collected included the medication name and dose, stage of the medication use process, error severity, and error cause. Error reports were periodically reviewed by pharmacy staff. In the 402,100 orders reviewed, errors were found in 605 inpatient orders and 405 clinic orders (0.25%). Most errors were identified before they reached the patient (69.9% of inpatient errors and 81.0% of clinic errors). The prescribing phase was associated with the highest proportion of errors (50.8% of inpatient errors and 41.7% of clinic errors). The most common reasons for prescribing errors were confusing orders and wrong doses. Analgesics (22.7%) and antibiotics (21.3%) were the medication classes most frequently identified. After aggregated review, pharmacists generated 19 clinical alerts leading to system changes and staff education. This study demonstrates the impact of prospective order review by pharmacists on patient safety. In addition to preventing errors from reaching the patient, voluntary error reporting and evaluation led to system changes intended to reduce the likelihood that similar errors would occur again.

Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery

Generating granular-scale surface albedo data is extremely important for solar photovoltaic site planning and to optimise renewable energy yield of bifacial panel installations. The albedo effect brings about a significant increase in power in bifacial photovoltaic systems, compared to their mono-facial counterparts, since the spectral response of bifacial solar panels correlates with the incident solar radiation wavelength on the back of the panel, to provide additional power generation capacity. Thus, harnessing the albedo data at relatively local scales is critical towards boosting solar power generation and providing greater power density in local electricity grids. This paper develops novel modelling approaches to produce high-resolution spectral albedo imagery across the Visible and Near Infrared (VNIR) bands, using the Wavelet-Fusion super-resolution model (i.e., Wavelet-FusionSR) trained with the Learned Gamma Correction approach by applying satellite image enhancement methodology. The proposed Wavelet-FusionSR model utilises the low-resolution moderate-resolution Imaging Spectroradiometer (MODIS) as well as high-resolution multi-spectral Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) satellite images, as critical inputs and ground-truth imagery, respectively, in order to perform sensor-to-sensor deep downscaling, without employing any synthetic or low-resolution satellite imagery data pairs. To augment the proposed deep learning algorithm across the decomposed sub-images of low-resolution inputs, we integrate local and global feature representation learning to train the proposed Wavelet-FusionSR model with Cauchy loss functions. In comparison with five competing benchmark models, the proposed Wavelet-FusionSR model demonstrates performance superiority using quantitative image downscaling metrics and visual assessments of the downscaled images for the visible band of solar radiation. The proposed Wavelet-FusionSR model yielded a Mean Square Error (MSE) of 0.00017, Signal-to-noise-ratio (PSNR) of 37.80, Structural Similarity Index (SSIM) of 0.999 and combined loss, MS-SSIMLoss, based on Multi Structural Similarity and Mean Absolute Error of 2.354 for the Visible Band images, and an MSE of 0.0014, PSNR of 28.43, SSIM of 0.999 and MS-SSIMLoss of 7.426 for the NIR spectral bands, demonstrating high efficacy of the proposed Wavelet-FusionSR method. The Wavelet-FusionSR method therefore attains high-resolution spectral albedo imagery outputs.

Design and Analysis of the Effect of the Number of Membership Functions on the Accuracy of the Fuzzy Controller in a Sun Tracking System

Purpose: To investigate how the number of rules in the fuzzy controller affects the single-axis solar tracking system's orientation accuracy. Methodology: Design and compare FLC49 and FLC25 controllers, each with different organic rules and functions. To improve the system efficiency by accurately measuring the maximum power point, the controller receives comparison results from the light sensors installed on the solar panels. We study and analyze the controllers, selecting the best one based on the accuracy of the solar panel orientation. Using the FLC output to operate a two-phase SM. Findings: This study developed a sun tracking system using Matlab/Simulink and compared FLC49 and FLC25 controllers. FLC49 performed better in simulations, with lower settling time and overshoot. The number of rules significantly influenced accuracy, and it also showed lower transient ripple magnitudes, accelerating time response. Unique Contribution to Theory, Practice and Policy: The research investigates the effectiveness of rules in a fuzzy controller and recommends the optimal number of windings to achieve precision in controlling a stepper motor. The study explores the use of 49-rule and 25-rule fuzzy logic controllers to control a stepper motor based on LDR sensor inputs. The researchers found that while both controllers had similar steady time error and overshoot, they differed in orientation accuracy, with the 49-roll fuzzy controller being more accurate in orientation. This single-axis solar tracking system optimizes solar energy conversion into electricity.

Audiomotor prediction errors drive speech adaptation even in the absence of overt movement.

Observed outcomes of our movements sometimes differ from our expectations. These sensory prediction errors recalibrate the brain's internal models for motor control, reflected in alterations to subsequent movements that counteract these errors (motor adaptation). While leading theories suggest that all forms of motor adaptation are driven by learning from sensory prediction errors, dominant models of speech adaptation argue that adaptation results from integrating time-advanced copies of corrective feedback commands into feedforward motor programs. Here, we tested these competing theories of speech adaptation by inducing planned, but not executed, speech. Human speakers (male and female) were prompted to speak a word and, on a subset of trials, were rapidly cued to withhold the prompted speech. On standard trials, speakers were exposed to real-time playback of their own speech with an auditory perturbation of the first formant to induce single-trial speech adaptation. Speakers experienced a similar sensory error on movement cancelation trials, hearing a perturbation applied to a recording of their speech from a previous trial at the time they would have spoken. Speakers adapted to auditory prediction errors in both contexts, altering the spectral content of spoken vowels to counteract formant perturbations even when no actual movement coincided with the perturbed feedback. These results build upon recent findings in reaching, and suggest that prediction errors, rather than corrective motor commands, drive adaptation in speech.