Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of signal processing strategy and stimulation type on speech and auditory perception in adult cochlear implant users

Abstract Objective: The objective of this study was to investigate the effects of signal processing strategy on speech understanding and auditory function for adult cochlear implant (CI) users with a focus on the effects of sequential versus paired stimulation. Design: Within-subjects, repeated measures design was utilised to compare performance between processing strategies and stimulation type on various measures of auditory function and subjective sound quality. Testing with subsequent strategies was completed after a total familiarisation time of two weeks. Study Sample: Ten post-lingually deafened adult CI users were recruited from a clinical population. Participants had a minimum of 13 months CI experience. Ages ranged from 25–78 years. All participants had long-term experience with the optima strategy; eight with sequential stimulation and two with paired stimulation. Results: We found no statistically significant effect of processing strategy. We observed an effect of stimulation type with sequential stimulation yielding significantly higher performance than paired stimulation for speech understanding in quiet and in noise, and subjective estimates of sound quality. No significant differences were noted across strategy or stimulation for music perception, spectral resolution or temporal resolution. Conclusions: Many patients utilise paired stimulation – the default stimulation type in the clinical software; however, sequential stimulation yielded significantly higher outcomes on multiple measures.
Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images

Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 2021) were employed in this study. The data were collected using an Intel RealSense D415 camera. The developed model has a two-stage CNN architecture based on ResNet50V2 layers. The developed model provided coefficients of determination from 0.88 to 0.95, with normalized root mean square errors of 6.09%, 6.30%, 7.65%, 7.92%, and 5.62% for fresh weight, dry weight, height, diameter, and leaf area, respectively, on unknown lettuce images. Using red, green, blue (RGB) and depth data employed in the CNN improved the determination accuracy for all five lettuce growth indices due to the ability of the stereo camera to extract height information on lettuce. The average time for processing each lettuce image using the developed CNN model run on a Jetson SUB mini-PC with a Jetson Xavier NX was 0.83 s, indicating the potential for the model in fast real-time sensing of lettuce growth indices.
Attentional Modulation of Visual Spatial Integration: Psychophysical Evidence Supported by Population Coding Modeling

Two prominent strategies that the human visual system uses to reduce incoming information are spatial integration and selective attention. Whereas spatial integration summarizes and combines information over the visual field, selective attention can single it out for scrutiny. The way in which these well-known mechanisms—with rather opposing effects—interact remains largely unknown. To address this, we had observers perform a gaze-contingent search task that nudged them to deploy either spatial or feature-based attention to maximize performance. We found that, depending on the type of attention employed, visual spatial integration strength changed either in a strong and localized or a more modest and global manner compared with a baseline condition. Population code modeling revealed that a single mechanism can account for both observations: Attention acts beyond the neuronal encoding stage to tune the spatial integration weights of neural populations. Our study shows how attention and integration interact to optimize the information flow through the brain.
Optimization of Time-Weighted Average Efficiency for Reconfigurable IPT Battery Charging System

Constant current (CC) and constant voltage (CV) combined charging is considered as the most popular and efficient method for charging batteries. There are a lot of works done to achieve CC/CV characteristics by utilizing reconfigurable circuits for easy control but without the consideration of efficiency optimization in the CV mode. In this paper, the optimization of mutual-inductance proportion between transmitter/receiver mutual inductance and transmitter/intermediate mutual inductance of the three-coil circuit is investigated to maximize the time-weighted average efficiency (TWAE). With two switches, a section of receiver coil of the series–series (SS) circuit, which corresponds to the optimized mutual-inductance proportion, can be converted into an intermediate coil of the three-coil circuit. Therefore, the system can be transferred from the CC mode to CV mode, and with the optimized mutual-inductance proportion, the efficiency in the CV mode can be improved. An experimental prototype is built to validate the feasibility of the proposed approach. In the CC (CV) mode, the impedance range is 30–<inline-formula> <tex-math notation="LaTeX">$108~\Omega $ </tex-math></inline-formula> (108–<inline-formula> <tex-math notation="LaTeX">$500~\Omega$ </tex-math></inline-formula>), and the maximum fluctuation for current (voltage) is 3.37% (4.55%). Besides, the TWAE is 94.5% (maximum 95.3% and minimum 94.49%) for the three-coil circuit, while that of SS circuit with control is 91.54% (maximum 95.20% and minimum 85.31%).
Construction of Diagnosis Model of Moyamoya Disease Based on Convolution Neural Network Algorithm

Objective The convolutional neural network (CNN) was used to improve the accuracy of digital subtraction angiography (DSA) in diagnosing moyamoya disease (MMD), providing a new method for clinical diagnosis of MMD. Methods A total of 40 diagnosed with MMD by DSA in the neurosurgery department of our hospital were included. At the same time, 40 age-matched and sex-matched patients were selected as the control group. The 80 included patients were divided into training set (n = 56) and validation set (n = 24). The DSA image was preprocessed, and the CNN was used to extract features from the preprocessed image. The precision and accuracy of the preprocessed image results were evaluated. Results There was no significant difference in baseline data between the training set and validation set (P > 0.05). The precision and accuracy of the images before processing were 79.68% and 81.45%, respectively. After image processing, the precision and accuracy of the model are 96.38% and 97.59%, respectively. The area under the curve of the CNN algorithm model was 0.813 (95% CI: 0.718-0.826). Conclusion This diagnostic method based on CNN performs well in MMD detection.
Modeling of Soft Sensor Based on DBN-ELM and Its Application in Measurement of Nutrient Solution Composition for Soilless Culture

At present, the detection of important components of nutrient solution in soilless culture is high cost, difficult and low precision. A soft measurement method of nutrient solution component based on deep belief network and extreme learning machine (DBN-ELM) is proposed. The component concentration in nutrient solution was selected as the dominant variable, and the variables that were easy to be measured and correlated with the ion concentration were the auxiliary variables, including PH value, conductivity, nutrient solution circulation speed and temperature. The deep belief network is used to extract the features of the auxiliary variables, then the extracted features are input into the ultimate learning machine for training, and the soft measurement model is obtained. Finally, the data of soil - free tomato culture nutrient solution was used to verify the experiment. The results show that this method has higher comprehensive measurement accuracy than the method using the extreme learning machine or the least square method, and is of great significance for improving the yield and quality of the soilless cultivated crops.
Climate models can correctly simulate the continuum of global-average temperature variability

Significance Climate models are foundational to formulations of climate policy and must successfully reproduce key features of the climate system. The temporal spectrum of observed global surface temperature is one such critical benchmark. This spectrum is known to obey scaling laws connecting astronomical forcings, from orbital to annual scales. We provide evidence that the current hierarchy of climate models is capable of reproducing the increase in variance in global-mean temperature at low frequencies. We suggest that successful climate predictions at decadal-to-centennial horizons hinge critically on the accuracy of initial and boundary conditions, particularly for the deep ocean state. Climate records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer timescales. It is unclear whether climate models are capable of simulating these fluctuations, which draws into question their ability to simulate such variability in the coming decades and centuries. Using the latest simulations and data syntheses, we find agreement for spectra derived from observations and models on timescales ranging from interannual to multimillennial. Our results confirm the existence of a scaling break between orbital and annual peaks, occurring around millennial periodicities. That both simple and comprehensive ocean–atmosphere models can reproduce these features suggests that long-range persistence is a consequence of the oceanic integration of both gradual and abrupt climate forcings. This result implies that Holocene low-frequency variability is partly a consequence of the climate system’s integrated memory of orbital forcing. We conclude that climate models appear to contain the essential physics to correctly simulate the spectral continuum of global-mean temperature; however, regional discrepancies remain unresolved. A critical element of successfully simulating suborbital climate variability involves, we hypothesize, initial conditions of the deep ocean state that are consistent with observations of the recent past.
Testing whether the combination of victimization and minority stressors exacerbate PTSD risks in a diverse community sample of sexual minority women

ABSTRACT Informed by minority stress and intersectionality frameworks, we examined 1) associations of sexual identity and race/ethnicity with probable diagnosis of post-traumatic stress disorder (PTSD-PD) among sexual minority women (SMW; e.g. lesbian, bisexual) and 2) potential additive and interactive associations of minority stressors (discrimination, stigma consciousness and internalised homonegativity) and potentially traumatic childhood and adulthood events (PTEs) with PTSD-PD. The data come from a large and diverse community sample in the United States of SMW (N=662; age range: 18–82; M=40.0, SD=14.0). The sample included 35.8% Black, 23.4% Latinx and 37.2% White participants. More than one-third of SMW (37.2%) had PTSD-PD with significantly higher prevalence among bisexual, particularly White bisexual women, than lesbian women. Discrimination, stigma consciousness and internalised homonegativity were each associated with higher odds of PTSD-PD, but only internalised homonegativity was additively associated with PTSD-PD above and beyond effects of PTEs. We found no evidence for interactive effects between PTEs and minority stressors. PTSD was strongly associated with childhood PTEs and with minority stressors above and beyond associations with adulthood PTEs and stressors. Our findings suggest a strong need to address effects of marginalization in treatment for PTSD, as minority stressors likely maintain and exacerbate effects of past traumas.
Woad extract containing cream improves significantly dry, irritated, and pruritic skin

Objective of this open, noninterventional, noncontrolled study was to investigate the tolerability and performance of a woad extract containing cream in subjects with dry, pruritic skin after twice daily application over 2 weeks. Assessments included sensorial characteristics, skin condition, pruritus (numeric rating scale [NRS], dynamic pruritus score [DPS]), skin dryness (itch‐controlled days [ItchCD], overall dry skin [ODS]), transepidermal water loss (TEWL), skin hydration and quality of life (5PLQ, DLQI). All sensorial characteristics were well perceived (>4), with tolerability (4.77 ± 0.47) and no negative skin feelings (4.73 ± 0.74) achieving the highest scores (0–5 scale). Dry skin and symptoms improved significantly (p < .001) for all variables: NRS (from 4.8 ± 1.74 to 1.83 ± 2.18), ItchCD (from 8.23 ± 4.40 to 4.81 ± 4.07), ODS (from 1.80 ± 0.85 to 0.65 ± 0.65), hydration (23.33 ± 1.33 to 40.70 ± 1.86), and quality of life (5PLQ: from 7.12 ± 4.25 to 4.24 ± 3.67; DLQI: from 5.29 ± 4.79 to 3.00 ± 3.98). The DPS revealed a moderate antipruritic effect. The TEWL decreased slightly (p = .511). No side effects were reported. The study suggests that, the cream is effective, and well tolerated in treating dry, irritated, and pruritic skin.
Concurrent Data Dissemination at Intersections in mmWave for Cooperative Perceptions

Cooperative perceptions for autonomous vehicles by sharing image sensor data enhance traffic security. For sharing a large amount of sensor data, millimeter-wave (mmWave) communication is expected to be an enabler of high-throughput communications because of its wide band width and its efficiency in spatial reuse. This paper proposes a concurrent scheduling for sensor data dissemination in mmWave vehicular networks at an intersection. The proposed algorithm improves the region covered by shared data in situations where dissemination time is limited and not all data are disseminated. Improvement is realized by prioritizing data to be forwarded considering the geographical information. The priority control enlarges the average of the area of covered region by 15% at maximum. Meanwhile, it is proved that when sufficient time is available for dissemination, the proposed algorithm guarantees that all vehicles can share their data with each other.
Integrated Bragg Grating Filter With Reflection Light Dropped via Two Mode Conversions

We propose an integrated Bragg grating (BG) filter with reflection light dropped in a silicon-on-insulator platform. Most of the light reflected by the BG is coupled to a branch waveguide, while some residual reflected light radiates rather than propagating back to the input port. The structure is mainly composed of an antisymmetric BG and an asymmetric directional coupler. Two mode conversions between the fundamental transverse electric mode and the first higher order transverse electric mode are applied to control the on-chip light route. The proposed structure is investigated via simulation and experiment. Two integrated BGs—a π phase-shifted grating and a chirped grating—are successfully fabricated. The proposed structure is expected to aid practical applications of BGs to various photonic integrated devices.
Experimental study and mathematical modeling for encapsulation of fentanyl citrate drug in nanostructured lipid carrier

Abstract The aim of this study is to prepare a nanostructured lipid carrier (NLC) containing Fentanyl Citrate drug. The materials were selected in a way to achieve a nanostructure with lower particle size and higher drug entrapment efficiency. For this purpose, we used two mathematical models, Van Krevelen-Hoftyze and Hoy’s methods, which are based on the calculation of solubility parameters. Various NLC formulations are prepared experimentally to validate the mathematical modeling results. Hot homogenization method was used for NLC preparation. DLS, HPLC, TEM and DSC analyses are performed to calculate the size, drug entrapment efficiency, morphology and thermal behavior of particles, respectively. Experimental results suggest that the best NLC formulation has a particle size of 90 nm with a spherical morphology and drug entrapment efficiency of about 82%. A comparison of the mathematical and experimental results exhibits that Van Krevelen-Hoftyzer method is unable to provide an accurate estimation of the decreasing trend of particle size by chaining the components of NLC. However, Hoy’s method seems to be suitable for this purpose. Moreover, both mathematical methods could successfully estimate variation trend of drug entrapment efficiency by chaining the NLC components. Results show that surfactants-lipids solubility parameter has a bearing on the nanoparticle size while drug-lipid solubility parameter affects drug entrapment efficiency. Communicated by Ramaswamy H. Sarma
Optimization of PLA parts manufactured by the Fused Deposition Modeling Technology

this work presents the optimization of the process parameters for the production of PolyLactic Acid (PLA) parts by 3d printing. First of all, the standard specimens are printed by varying tree parameters, namely the layer thickness, the infill degree and the deposition orientation. Then, the obtained samples are characterized by tensile tests according to the associated standards. The results showed the significant effect of infill degree and orientation in the increasing of the ultimate tensile strength, and the influence of the layer thickness on the mechanical properties of the prints including how it dramatically changes the filling rate impact. The maximum resistance obtained is around 64 MPa. These tests demonstrated the potential of 3D printing to be exploited in fabrication of functional parts as other convential processes.
Student Performance Prediction Using Algorithms of Data Mining

Data mining is mainly used to get information from a large amount of data. Process it to convert this information into understanding form predictions. Data minings algorithms have immense importance and chore in predictions. The data mining methods can uncover the unseen patterns (unsupervised), associations, and oddity from collected data. This information can enhance the decision-making processes for predictions. Data mining can be considered as a most suitable technology for predictions especially in predictions of students performance. The primary aim of this paper is to give a precise review on Applications of algorithms of data mining for prediction of students performance. In this paper, we presented a methodology in which we applied pre-processing on data to select best attributes in order to increase its accuracy then we applied different Algorithms to measure its accuracy and found out that Random Forest algorithm gives best results.
Cancelable Permutation-Based Indexing for Secure and Efficient Biometric Identification

Cancelable biometric schemes have been widely studied to protect templates in biometric authentication over networks. These schemes transform biometric features and perform pattern matching without restoring the original features. Although they strongly prevent the leakage of the original features, the response time can be very long in a large-scale biometric identification system. Most of the existing indexing schemes cannot be used to speed up the biometric identification system over networks since a biometric index leaks some information about the original feature. Secure and efficient indexing is a major challenge in large-scale biometric identification over networks. In this paper, we propose a novel indexing scheme that is promising with regard to both security and efficiency. The proposed indexing scheme transforms a permutation-based index, which is the state-of-the-art index in the field of similarity search, and performs a query search without recovering the original index. We also propose a method to artificially generate biometric features necessary to generate an index (which are called “pivots”) based on GANs (Generative Adversarial Networks). We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and comprehensively show that the proposed indexing scheme has the irreversibility, unlinkability, and revocability. We then demonstrate that the proposed indexing scheme significantly outperforms the existing indexing schemes using three real datasets (face, fingerprint, and finger-vein datasets), and is very promising with respect to the accuracy and response time.
Simulation Framework for Evaluating Video Delivery Services Over Vehicular Networks

Vehicular Ad-hoc Networks contribute to the Intelligent Transportation Systems by providing a set of services related to traffic, mobility, safe driving, and infotainment applications. One of the most challenging applications is video delivery, since it has to deal with several hurdles typically found in wireless communications, like high node mobility, bandwidth limitations and high loss rates. In this work, we propose an integrated simulation framework that will provide a multilayer view of a particular video delivery session with a bunch of simulation results at physical (i.e., collisions), MAC (i.e., packet delay), application (i.e., % of lost frames), and user levels (i.e., perceptual video quality). With this tool, we can analyze the performance of video streaming over vehicular networks with a high level of detail, giving us the keys to better understand and, as a consequence, improve video delivery services.
Entity-Dependent Long-Short Time Memory Network for Semantic Relation Extraction

Semantic relatedness between context information of two different entities, which are one of the most easily accessible features, have been shown to be very useful for detecting the semantic relations held in the text segments. However, these semantic relatedness information is often ignored by some researcher in the study of the problem of relation extraction. In this work, we propose a novel neural network for semantic relation extraction called entity-dependent long short-term memory network (ED-LSTM). This network can extract the relatedness of entity with the context, and select the relevant parts of contexts to infer the semantic relation towards the entity. We do some experiments on the SemEval-2010 Task 8 dataset. Extensive experiment and the results demonstrate that the proposed methods are effective for relation extraction, which can obtain state-of-the-art F1 result just with minimal feature engineering.
Application of a modified hyperbolic sine creep rate equation to correlate uniaxial creep data vs. stress and temperature for creep resistant low chrome steel alloys

ABSTRACT Modified hyperbolic sine minimum creep rate equations were used to correlate uniaxial minimum creep strain rate data of 1/2Cr-1/2Mo-1/4 V and Grade 22 steel alloys and to correlate creep life data of Grade 22 steel when coupled with the Monkman-Grant equation. Hyperbolic sine equations were selected because of their physical basis in dislocation creep and numerical stability. Both equations provided very good fits to minimum strain rate data over a wide range of temperatures and stresses. Reasonable correlations of a large Grade 22 creep life data set were obtained with goodness of fit nearly equivalent to those obtained with the Larson-Miller and Wilshire equations, and with more conservative predictions of long-term creep strength. However, all these equations provided a poor fit to the entire data set when only short-term data (tr < 5,000 h) were correlated, and they predicted lower long-term creep rupture strengths vs. the full data set correlations.