Correct Performance Research Articles

Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: a case study of the Lake Hawassa watershed

Abstract A promising future development area to improve the accuracy of satellite rainfall estimates (SREs) is accessing merits from different sources of data through combining algorithms. The main objective of this study is to assess the accuracy and importance of the fused multistage approach of bias correction. Accordingly, two versions of resampled and spatially bias-corrected Climate Hazards Group Infrared Precipitation (CHIRP) estimates were merged with ground measurements using a conditional merging procedure. Results of applied performance measures (i.e. seven) on corrected and merged CHIRP SREs show that the Percent of Detection (POD) and Percent Volume Error (PVE) have improved. Depending on the combination of coupled stations for validation, up to 70 and 50% PVE improvement was achieved at some stations for wet and dry periods, respectively. Moreover, the bias-corrected and conditionally merged CHIRP SREs have outperformed the estimates by resampling CHIRP with station dataset (CHIRPS) over the sparsely populated western part of the watershed. However, the devised method was limited in considering dry-day events during bias correction, which in turn has affected the performance of the bias correction of the CHIRPS product. Finally, future research should concentrate on such methods of fusing to understand the benefits of various approaches and produce more precise rainfall records.

Pulse height estimation and pulse shape discrimination in pile-up neutron and gamma ray signals from an organic scintillation detector using multi-task learning

We developed a multi-tasking deep learning model for simultaneous pulse height estimation and pulse shape discrimination for pile-up n/γ signals. Compared with single-tasking models, our model showed better spectral correction performance with higher recall for neutrons. Further, it achieved more stable neutron counting with less signal loss and a lower error rate in the predicted gamma ray spectra. Our model can be applied to a dual radiation scintillation detector to discriminatively reconstruct each radiation spectrum for radioisotope identification and quantitative analysis.

Performance Indicators in the Tourism Sector: Comparative Analysis of the Visegrad Group

Dynamic shifts in society accelerate the need to measure performance and use the data to identify promising areas for continuous improvement and eliminate potential risk factors. The process of measuring and analysing performance isn't anymore an essential task of strategic management only from the perspective of individual enterprises but also from the perspective of other subjects. Countries evaluate the performance of various sectors to track and record the economic benefits of industries and calculate shares of industries in the country's gross domestic product and other macroeconomic results. Considering the effects of the COVID-19 pandemic and digital transformation on global economics, the stakeholders understand the need to be aware of potential risks and opportunities in the future. Detailed measuring of industry performance is a driving force for identifying critical factors in economic systems and effective strategic planning. Countries should design and use the correct metrics and performance measurements to achieve desired outcomes. In the tourism sector, the most important performance indicators and results at the country level are presented as a part of Tourism Satellite Accounts. In this paper, we compare the current use of performance indicators in the tourism sector in Visegrad Group countries based on information from national statistical offices and other relevant sources. We investigate the processes of collecting data from economic subjects used to evaluate industry performance. The findings of this research confirm slight differences in using tourism performance indicators among analysed countries and tools used for obtaining required data. Although we identify several shortcomings in the existing conditions in Slovakia, we also formulate practical recommendations based on examples from other analysed countries to improve the current state of measuring industry performance. This paper contributes to a better understanding of the correct use and visual presentation of performance indicators. Our findings can also be applied in other countries to identify the possibilities of better monitoring for taking appropriate strategic actions and measuring the impact of activities to support tourism at the level of individual destinations or regions.

Parietal/premotor lesions effects on visuomotor cognition in neuro-oncology patients: A multimodal study

BackgroundAssessing prior to surgery the functionality of brain areas exposed near the tumor requires a multimodal approach that combines the use of neuropsychological testing and fMRI tasks. Paradigms based on motor imagery, which corresponds to the ability to mentally evoke a movement, in the absence of actual action execution, can be used to test sensorimotor areas and the functionality of mental motor representations. MethodsThe most commonly used paradigm is the Limb Laterality Recognition Task (LLRT), requiring judgments about whether a limb belongs to the left or right side of the body. The group studied included 38 patients with high-grade (N = 21), low-grade (N = 11) gliomas and meningiomas (N = 6) in areas anterior (N = 21) and posterior (N = 17) to the central sulcus. Patients before surgery underwent neuropsychological assessment and fMRI. They performed the LLRT as an fMRI task. Accuracy, and neuroimaging data were collected and combined in a multimodal study. Structural MRI data analyses were performed by subtracting the overlap of volumes of interest (VOIs) plotted on lesions from the impaired patient group vs the overlap of VOIs from the spared group. The fMRI analyses were performed comparing the impaired patients and spared group. ResultsIn general, patients were within normal limits on many neuropsychological screening tests. Compared with the control group, 17/38 patients had significantly different performance. The subtraction between the VOIs overlay of the impaired patients' group vs. the VOIs overlay of the spared group revealed that the areas maximally involved by lesions in the impaired patients' group were the right postcentral gyrus, right inferior parietal lobe, right supramarginal gyrus, right precentral gyrus, paracentral lobule, left postcentral gyrus, right superior parietal lobe, left inferior parietal lobe, and left superior and middle frontal gyrus. Analysis of the fMRI data showed which of these areas contributes to a correct LLRT performance. The task (vs. rest) in the group comparison (spared vs. impaired patients) activated a cluster in the left inferior parietal lobe. ConclusionUnderlying the altered performance at LLRT in patients with lesions to the parietal and premotor areas of the right and left hemispheres is a difference in activation of the left inferior parietal lobe. This region is involved in visuomotor processes and those related to motor attention, movement selection, and motor planning.

LCAT: an isoform-sensitive error correction for transcriptome sequencing long reads.

As the carrier of genetic information, RNA carries the information from genes to proteins. Transcriptome sequencing technology is an important way to obtain transcriptome sequences, and it is also the basis for transcriptome research. With the development of third-generation sequencing, long reads can cover full-length transcripts and reflect the composition of different isoforms. However, the high error rate of third-generation sequencing affects the accuracy of long reads and downstream analysis. The current error correction methods seldom consider the existence of different isoforms in RNA, which makes the diversity of isoforms a serious loss. Here, we introduce LCAT (long-read error correction algorithm for transcriptome sequencing data), a wrapper algorithm of MECAT, to reduce the loss of isoform diversity while keeping MECAT's error correction performance. The experimental results show that LCAT can not only improve the quality of transcriptome sequencing long reads but also retain the diversity of isoforms.

The medical post-mortem examination - step by step

The physician has to make numerous and legally far-reaching determinations in the course of the post-mortem examination. These can have considerable consequences for relatives and, moreover, for society. Therefore, the correct performance of post-mortem examinations and the correct evaluation of the findings is an extremely responsible task that every physician should master.

Closed-Form Method for Atmospheric Correction (CMAC) of Smallsat Data Using Scene Statistics

High-cadence Earth observation smallsat images offer potential for near real-time global reconnaissance of all sunlit cloud-free locations. However, these data must be corrected to remove light-transmission effects from variable atmospheric aerosol that degrade image interpretability. Although existing methods may work, they require ancillary data that delays image output, impacting their most valuable applications: intelligence, surveillance, and reconnaissance. Closed-form Method for Atmospheric Correction (CMAC) is based on observed atmospheric effects that brighten dark reflectance while darkening bright reflectance. Using only scene statistics in near real-time, CMAC first maps atmospheric effects across each image, then uses the resulting grayscale to reverse the effects to deliver spatially correct surface reflectance for each pixel. CMAC was developed using the European Space Agency’s Sentinel-2 imagery. After a rapid calibration that customizes the method for each imaging optical smallsat, CMAC can be applied to atmospherically correct visible through near-infrared bands. To assess CMAC functionality against user-applied state-of-the-art software, Sen2Cor, extensive tests were made of atmospheric correction performance across dark to bright reflectance under a wide range of atmospheric aerosol on multiple images in seven locations. CMAC corrected images faster, with greater accuracy and precision over a range of atmospheric effects more than twice that of Sen2Cor.

Performance evaluation of procedures used to correct measured I‐V characteristics of photovoltaic modules for temperature and irradiance

AbstractIEC 60891 ed.3 published in 2021 has defined four standard I‐V characteristics correction procedures numbered 1 through 4. The aim of this work is to evaluate these four I‐V translation methods. The results show that correction procedure 1 (CP1) and 2 (CP2) work well over a broad range of irradiances and temperatures. However, CP1 requires I‐V curves being measured adequately down to negative current regime at low‐irradiance levels and CP2 is not so suitable for low shunt‐resistance modules; both also require a set of correction parameters. Based on our performance analysis, a new method based on CP2 is introduced to improve the correction performance for low shunt‐resistance modules; the mean bias error (MBE) value of maximum power (PMAX) improved from −10.26% to −1.32%. Correction procedure 3 (CP3) employs a drastically different correction procedure as compared with the other CPs. This work shows that CP3 works well over a broad range of irradiances and temperatures, but significant distortion of the corrected I‐V curves may occur when extrapolation is required. Correction procedure 4 (CP4) requires only a single I‐V curve and shows generally good results in short‐circuit current (ISC) but worse agreement in open‐circuit voltage (VOC) and PMAX.

Late ERP correlates of confidence for auditory categorization of complex sounds

Recent research suggests that confidence judgments relate to the quality of early sensory representations and later modality independent processing stages. It is not known whether the nature of this finding might vary based on task and/or stimulus characteristics (e.g., detection vs. categorization). The present study investigated the neural correlates of confidence using electroencephalography (EEG) in an auditory categorization task. This allowed us to examine whether the early event-related potentials (ERPs) related to confidence in detection also apply to a more complex auditory task. Participants listened to frequency-modulated (FM) tonal stimuli going up or down in pitch. The rate of FM tones ranged from slow to fast, making the stimuli harder or easier to categorize. Tone-locked late posterior positivity (LPP) but not N1 or P2 amplitudes were larger for (correct-only) trials rated with high than low confidence. These results replicated for trials presenting stimuli at individually identified threshold levels (rate of change producing ∼71.7% correct performance). This finding suggests that, in this task, neural correlates of confidence do not vary based on difficulty level. We suggest that the LPP is a task general indication of the confidence for an upcoming judgment in a variety of paradigms.

Morphology-Based Spell Checker for Dawurootsuwa Language

Processing of textual information by using word-processing tools is extremely increased due to the presence of misspelled or erroneous words. In order to minimize these misspelled words from digital information, different spellchecker tools are needed. A plenty of works are performed in technological favored languages like English and European languages but not for an underresourced language like Dawurootsuwa. The primary idea behind a morphology-based spellchecker is to use a dictionary lookup approach with morphological properties of the language to reduce dictionary size while also handling word inflection, derivation, and compounding. Two distinct tests were carried out in this work to evaluate the performance of a morphology-based spellchecker: error detection and error correction. The Hunspell dictionary format was utilized to construct the root words in this study, which included a total of 5,000 root words and more than 2,500 morphological rules along with 3,156 unique words for testing. The experimental result showed the overall spell error detection performance of 90.4% and the overall spell error correction performance of 79.31%. Moreover, we are working further towards developing a real word spelling checker that incorporate more numbers of language rules.

Channel Emulator Framework for Underwater Acoustic Communications

In this paper, we develop a tractable mathematical model and an emulation framework for communicating information through water using acoustic signals. Water is considered one of the most complex media to model due to its vastness and variety of characteristics, which depend on the scenario, the type of water body (lakes, rivers, tanks, sea, etc.), and the geographical location of the water body being considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water); Milne’s oscillator technique is used to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are employed to characterise the travelling acoustic signal. The signal strength, phase shift, and time delay generated from the mathematical model are then inputted into a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator utilises the wide sense stationary uncorrelated scattering (WSSUS) assumption and a finite sum-of-sinusoids (SOS) approach with a uniformly distributed phase to generate the channel samples. By utilising this emulator platform, it becomes feasible to generate profiles for amplitude variation, the Doppler shift, and spread experienced by any travelling signal in various underwater communication scenarios. Such a platform can be employed to simulate different communication scenarios, underwater network topologies, and data for training various learning models. Additionally, it can predict the performance of different modulation, multiplexing, error correction, and multi-access techniques for underwater acoustic communication (UWAC) systems.

Dual-mode anti-interference humidity detection: Differential microwave sensor based on microstrip circuit

Monitoring humidity is indispensable to ensure industry and health safety. However, humidity sensors usually suffer from interference by environmental variation. Differential measurement is an effective way to improve the anti-interference of devices in which the circuit design is the key point. The flexible circuit structures and unique dual port characteristics of microwave circuits make it candidates for differential measurement. Herein, a differential microwave humidity sensor based on microstrip circuit is proposed in this paper, which combines the anti-interference ability and the high sensitivity. Commercial polyvinyl alcohol (PVA) is applied as sensitive material. The proposed sensor exhibits a high dual-mode response for both frequency (1.5 MHz/%RH) and magnitude (0.39 dB/%RH) for the humidity range (25% RH – 85% RH). Moreover, the anti-interference measurement confirms the performance of baseline correction to enhance the sensor accuracy. Meanwhile, the sensor mechanism is proposed by the propagation of electromagnetic wave for the first time. This work provides a novel solution of anti-interference differential sensors for monitoring humidity.

Estimating the effective fracture toughness of a variety of materials using several machine learning models

Since conducting laboratory tests to obtain the fracture toughness of materials is time-consuming and costly, it is necessary to provide tools to estimate this property with high accuracy quickly and without the need for such a high cost. This study considered using machine learning (ML)-based models as a suitable option to address such problems. For this purpose, twelve ML-based models were presented using 1715 datasets generated from five experimental tests to estimate the adequate fracture toughness (Keff) of 44 different materials. The behavior of the ML models compared to the practice tests was investigated, and the correct and acceptable performance of each of them in estimating the Keff of different materials was confirmed. Among the twelve ML-based models, extreme tree regressor (ETR) and Gaussian process regression (GPR) models provided the highest and lowest accuracies in estimating the Keff of different materials, respectively. To further aid in the estimation of the Keff of different materials for engineering challenges, a graphical user interface (GUI) for the ML-based models was developed.

Construction of protograph LDPC codes based on the convolution neural network

This paper presents an intelligent protograph construction algorithm. Protograph LDPC codes have shown excellent error correction performance and play an important role in wireless communications. Random search or manual construction are often used to obtain a good protograph, but the efficiency is not high enough and many experience and skills are needed. In this paper, a fast searching algorithm is proposed using the convolution neural network to predict the iterative decoding thresholds of protograph LDPC codes effectively. A special input data transformation rule is applied to provide stronger generalization ability. The proposed algorithm converges faster than other algorithms. The iterative decoding threshold of the constructed proto-graph surpasses greedy algorithm and random search by about 0.53 dB and 0.93 dB respectively under 100 times of density evolution. Simulation results show that quasi-cyclic LDPC (QC-LDPC) codes constructed from the proposed algorithm have competitive performance compared to other papers.

Symbol-level iterative information set decoding of RS codes

This paper presents the implementation of a low-complex iterative symbol-level decoding scheme for Reed-Solomon codes. Most soft-decision iterative decoders for Reed-Solomon codes work on a bit level due to the efficient passing of soft information due to the sparsity of the binary parity check matrix. These decoders yield a good error correction performance, but this comes at the cost of an increased computational complexity resulting from working at a bit-level. This study aims to lower the computational complexity of iterative decoding of Reed-Solomon codes by introducing a high-performance soft-decision iterative Reed-Solomon decoder that works on a symbol-level, in contrast to the bit-level implementation used in most iterative Reed-Solomon decoders. The proposed algorithm utilises soft information to decode while applying information set decoding techniques to an extended parity check matrix. The use of the extended parity check matrix provides additional parity check equations which assist the algorithm in determining the correct information set of symbols used in the decoding process. For a given (n,k) Reed-Solomon code, the algorithm converges to a valid codeword by correctly decoding a specified information set of k symbols from the received vector. Simulations run show the proposed algorithm performs favourably when compared to other symbol-level iterative decoders while working at a relatively lower complexity.

Charge trapping correction and energy performance of the Majorana Demonstrator

P-type point contact (PPC) high-purity germanium detectors are an important technology in astroparticle and nuclear physics due to their superb energy resolution, low noise, and pulse shape discrimination capabilities. Analysis of data from the MAJORANA DEMONSTRATOR, a neutrinoless double-beta decay experiment deploying PPC detectors enriched in $^{76}$Ge, has led to several novel improvements in the analysis of PPC signals. In this work we discuss charge trapping in PPC detectors and its effect on energy resolution. Small dislocations or impurities in the crystal lattice result in trapping of charge carriers from an ionization event of interest, attenuating the signal and degrading the measured energy. We present a modified digital pole-zero correction to the signal energy estimation that counters the effects of charge trapping and improves the energy resolution of the MAJORANA DEMONSTRATOR by approximately 30% to around 2.4 keV FWHM at 2039 keV, the $^{76}$Ge $Q$-value. An alternative approach achieving similar resolution enhancement is also presented.

Effect of rapid changes in environmental conditions on canine detection of methyl benzoate

Detection canines are utilized worldwide in some of the most challenging field conditions for the detection of narcotics, explosives, and other targets. Much remains unknown, however, how these challenging conditions impact detection canine performance. Anecdotal evidence suggests that detection dogs require a “start-up” period – a short duration of time working – before performing optimally. These dogs commonly rest in climate-controlled settings (such as in a climate-controlled vehicle) and quickly transition to searches in contrasting conditions, which may exacerbate the issue. Accordingly, this study sought to quantify the magnitude and duration of the start-up period, and to identify any further effects on performance due to rapid transitions into extreme temperature/humidity conditions. The detection threshold of seven dogs was established for methyl benzoate (an odor simulant of illicit cocaine) under standard conditions using an air dilution olfactometer. A series of evaluations were then conducted to determine changes in this threshold when the dogs were rapidly transitioned from standard conditions to one of six temperature/humidity conditions - hot-humid, warm-humid, hot-dry, standard, cold-dry and cold-humid. Temperatures ranged from 0 to 40 ºC and relative humidity ranged from <40 % to >85 %. Changes in detection threshold were measured via a series of three “probes” of six trials, with a 2 min inter-probe interval to habituate to environmental conditions. Probes started at the dogs’ estimated threshold and decreased in concentration based on correct performance. Overall, dogs showed substantial decrements in the hot-humid condition followed by moderate decrements in hot-dry and warm-humid conditions. Cold conditions did not produce statistically significant decrements. In addition, the data indicate that a start-up period does exist when canines transition from a state of rest directly into a search assignment. The duration of this start-up period was initially measured to be several minutes long; but after the first series of testing (approximately a couple months of training/testing), the dogs only exhibited a decrement in performance on the first trial of a session. Overall, the results suggest that environmental conditions and a brief “start-up” effect should be considered as important variables that can impact detection canine performance.

Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement.

Functional near-infrared spectroscopy (fNIRS) is an optical non-invasive neuroimaging technique that allows participants to move relatively freely. However, head movements frequently cause optode movements relative to the head, leading to motion artifacts (MA) in the measured signal. Here, we propose an improved algorithmic approach for MA correction that combines wavelet and correlation-based signal improvement (WCBSI). We compare its MA correction accuracy to multiple established correction approaches (spline interpolation, spline-Savitzky-Golay filter, principal component analysis, targeted principal component analysis, robust locally weighted regression smoothing filter, wavelet filter, and correlation-based signal improvement) on real data. Therefore, we measured brain activity in 20 participants performing a hand-tapping task and simultaneously moving their head to produce MAs at different levels of severity. In order to obtain a "ground truth" brain activation, we added a condition in which only the tapping task was performed. We compared the MA correction performance among the algorithms on four predefined metrics (R, RMSE, MAPE, and ΔAUC) and ranked the performances. The suggested WCBSI algorithm was the only one exceeding average performance (p < 0.001), and it had the highest probability to be the best ranked algorithm (78.8% probability). Together, our results indicate that among all algorithms tested, our suggested WCBSI approach performed consistently favorably across all measures.

Defect Detection and Depth Estimation in Composite Materials for Pulsed Thermography Images by Nonuniform Heating Correction and Oriented Gradient Information.

Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing. Nonuniform heating correction and the gradient direction information combined with a local and global segmentation phase are used to analyze carbon fiber-reinforced plastic (CFRP) thermal images with Teflon inserts with different length/depth ratios. Additionally, a comparison between the actual depths and estimated depths of detected defects is performed. The performance of the nonuniform heating correction proposed method is superior to that obtained on the same CFRP sample analyzed with a deep learning algorithm and the background thermal compensation by filtering strategy.

Using tail current phase shift technique to improve locking range injection‐locked frequency divider

SummaryIn this article, an injection‐locked frequency divider (ILFD) is presented. By assuming the maximum phase of the tank current, we obtain a relation for the lock range. Besides, the effects of parasitic capacitors on the lock range have been probed. The analysis shows that the greater the phase difference between the tail current and injected signal, the phase of the tank current increases and, consequently, the lock range also increases. The accomplished simulations confirm the correct performance of the proposed design and the extracted relationships. The simulations have been CMOS technology and with a 0.9‐V supply voltage. The results show that the current consumption equals 3.7 mA and the power consumption equals 3.33 mW. The proposed ILFD lock range with phase shift technique with power input signal 0 dBm is equal to 5.1 GHz (3.6–8.7 GHz), and the relative percentage of the lock range is also 83%.