Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of flexible metal wheel for pressurized lunar rover and traction performance prediction

The pressurized lunar rover has become one of the most important equipment in lunar exploration and resource utilization missions. On the terrain of soft lunar regolith, the rover wheels are easy to slip, sink, or even fail to move. To improve the traction performance of the rover on soft lunar soil, it is necessary to study the interaction between the wheels and the lunar soil. The deformation of the flexible wheel provides a larger contact area, which results in greater tractive force. Moreover, flexible wheels have better comfort and stability than rigid wheels, which are needed for manned rovers. However, there are few studies on the wheel soil interaction model of the heavy flexible wheel for the pressurized lunar rover. In this paper, a metal flexible wheel soil interaction model for pressurized lunar rovers was established, and the traction performance of the flexible wheel was predicted by using this model. Then, the accuracy of the wheel soil interaction model was verified by the soil bin test. The experimental results showed that the average error between the theoretical value of sinkage and the experimental value was 13.9%, and the average error between the theoretical and experimental value of drawbar pull was 11.5%, indicating that the model has high prediction accuracy. The new model can be used to predict the traction performance of flexible wheels and the experimental results can provide a reference for the flexible wheel design lunar rovers.
MSAF: A Multiple Self-Attention Field Method for Speech Enhancement

Speech enhancement (SE) systems, based on generative adversarial networks (GANs), are limited in improving speech quality and intelligibility. In this study, we propose a novel multiple self-attention field method for speech enhancement (MSAF). The models with different positions of the self-attention layers focus on different features. The output of each model is assigned a different feature weight, which is obtained by training. Then, we fuse the models according to the feature weights to obtain a clean speech signal. For speech quality, the proposed method improves by 8.22%, 8.52%, 9.28%, and 9.40% in CBAK, CSIG, COVL, and PESQ on average compared with the baseline SASEGANs. The results show that the MSAF comprehensively improves the performance of the baseline SASEGAN and performs better than the mainstream GAN-based SE meth-ods. Importantly, the proposed method can be extended to other GAN-based SE methods.
A Metric-Driven Approach to Conformer Layer Pruning for Efficient ASR Inference

Conformer-based end-to-end automatic speech recognition (ASR) models have gained popularity in recent years due to their exceptional performance at scale. However, there are significant computation, memory and latency costs associated with running inference on such models. With the aim of mitigating these issues, we evaluate the efficacy of pruning Conformer layers while fine-tuning only on 20% of the data used for the pre-trained model. We score Conformer layers using correlation, energy, and gradient-based metrics and rank them to identify candidate layers for pruning. We also propose an iterative pruning strategy which monitors and prunes layers that are consistently ranked low by the metrics during training. Using our methods, we prune large pre-trained offline and online (streaming) models by 20% and 40% with little impact on performance, while outperforming a strong knowledge distillation baseline.
Design, modeling, and control of a horizontal magnetic micromanipulator

Magnetic micromanipulators with a wide range of force generating capabilities are able to manipulate micron size particles for various applications and measurements. These magnetic particles can be coated with receptors to specifically bind to target biomolecules. In this work, a horizontal magnetic micromanipulator is designed, modeled and controlled for single micron size magnetic particle manipulations. A method is presented for dynamic modeling of magnetic micromanipulators. A feedback control method is designed that allows direct linearization of the system. It is shown that the proposed controller guarantees the stability of the closed-loop system, and yields zero steady-state error in a wide range of operation conditions. We show that the micromanipulator is able to generate a wide range of piconewton (pN) scale forces on a superparamagnetic particle for single molecule separation, and biosensor developments.
Helmet Shape and Phylogeography of the Treehopper Membracis mexicana

Simple Summary Membracis mexicana is a treehopper widely distributed in the neotropical region where it is a minor pest of some crops. In Mexico, it is found in at least four biogeographic provinces (abiotic subdivision of biogeographic realms where assemblages of biota share an evolutionary history), and few species of insects have such wide distributions. Field observations indicate that there are different forms of this species, but so far, how the different forms appear and how they are distributed have not been recorded. To test whether the distribution of morphological and genetic variation was consistent with the regionalization of the biogeographic provinces, we analyzed the morphological and genetic variation of this species sampling a total of 303 insects and conducted a geometric morphometric analysis from the body shape and phylogeographical analysis using five sequenced genes. We found three different morphotypes of these species and two genetic groups; this means that one of the genetic groups shows two forms. These findings show that M. mexicana is a very variable species geographically structured by the conformation of biogeographic provinces. Abstract Membracis mexicana (Hemiptera: Membracidae) is distributed in four biogeographic provinces of Mexico. Field observations indicate that there are different forms of this species, but the distribution of the phenotype and the genetic variation of this species have not been clarified. The aim of this study was to quantify the phenotypic and genetic variation of M. mexicana and determine whether the configuration of biogeographic provinces impacts the distribution of this variation. To achieve this, we analyzed 307 photographs using 19 landmarks and geometric morphometrics to quantify the phenotypic variation in helmets. We sequenced five molecular markers for 205 individuals to describe the phylogeographic pattern. As a result, we identified three morphological configurations of the helmet of M. mexicana and two genetic lineages. The morphotypes are (1) a large and wide helmet with small dorsal spots, (2) a small and narrow helmet with large dorsal spots, and (3) a small and narrow helmet with small spots. Genetic lineages are distributed in southeast and western Mexico. The western lineage corresponds to two helmet morphotypes (1 and 2) and the southeast lineage to morphotype 3. We found that the larger helmets correspond to the western lineage and are distributed in Trans-Mexican Volcanic Belt and Pacific lowlands provinces, whereas the smallest helmets correspond to the southeast lineage and are present in the Veracruzan and Yucatan Peninsula provinces.
Depression and Pseudodementia: Decoding the Intricate Bonds in an Italian Outpatient Setting

In spite of the uncertainties of its diagnostic framework, pseudodementia may be conceptualized as a condition characterized by depressive symptoms and cognitive impairment in the absence of dementia. Given the controversies on this topic, the aim of the present study was to assess neurological and cognitive dysfunctions in a sample of elderly depressed subjects, and the eventual relationship between cognitive impairment and depressive symptoms. Fifty-seven elderly depressed outpatients of both sexes were included in the study. A series of rating scales were used to assess diagnoses, depressive and cognitive impairment. Comparisons for continuous variables were performed with the independent-sample Student’s t-test. Comparisons for categorical variables were conducted by the χ2 test (or Fisher’s exact test when appropriate). The correlations between between socio-demographic characteristics and clinical features, as well as between cognitive impairment and depressive symptoms were explored by Pearson’s correlation coefficient or Spearman’s rank correlation. Our data showed the presence of a mild–moderate depression and of a mild cognitive impairment that was only partially related to the severity of depression. These dysfunctions became more evident when analyzing behavioral responses, besides cognitive functions. A high educational qualification seemed to protect against cognitive decline, but not against depression. Single individuals were more prone to cognitive disturbance but were similar to married subjects in terms of the severity of depressive symptoms. Previous depressive episodes had no impact on the severity of depression or cognitive functioning. Although data are needed to draw firm conclusions, our findings strengthen the notion that pseudodementia represents a borderline condition between depression and cognitive decline that should be rapidly identified and adequately treated.
VGNet: A Lightweight Intelligent Learning Method for Corn Diseases Recognition

The automatic recognition of crop diseases based on visual perception algorithms is one of the important research directions in the current prevention and control of crop diseases. However, there are two issues to be addressed in corn disease identification: (1) A lack of multicategory corn disease image datasets that can be used for disease recognition model training. (2) The existing methods for identifying corn diseases have difficulty satisfying the dual requirements of disease recognition speed and accuracy in actual corn planting scenarios. Therefore, a corn diseases recognition system based on pretrained VGG16 is investigated and devised, termed as VGNet, which consists of batch normalization (BN), global average pooling (GAP) and L2 normalization. The performance of the proposed method is improved by using transfer learning for the task of corn disease classification. Experiment results show that the Adam optimizer is more suitable for crop disease recognition than the stochastic gradient descent (SGD) algorithm. When the learning rate is 0.001, the model performance reaches a highest accuracy of 98.3% and a lowest loss of 0.035. After data augmentation, the precision of nine corn diseases is between 98.1% and 100%, and the recall value ranges from 98.6% to 100%. What is more, the designed lightweight VGNet only occupies 79.5 MB of space, and the testing time for 230 images is 75.21 s, which demonstrates better transferability and accuracy in crop disease image recognition.
Managing Occupational Health Issues through Coaching, Emerging Perspectives from Emergency and Intensive Care Nurses: A Mixed-Method Study

(1) Background: Emergency and intensive care nurses are among the health professionals most exposed to occupational health issues such as stress and burnout, etc. Coaching has been considered a useful preventative strategy to provide better support for professionals. This study has two objectives: the first objective is to identify the coaching needs of emergency and intensive care nurses, and the second is to propose a coaching model that addresses the needs and helps manage occupational health issues. (2) Methods: this study followed a mixed-method design, and it included thirty nurses working in the emergency and intensive care unit from two public hospitals in Morocco. The study entailed semi-structured interviews transcribed verbatim until data saturation, guided by the grounded theory approach in order to explore the coaching requirements of emergency and intensive care nurses, and the measurement of the three dimensions of burnout with the Maslach Burnout Inventory (MBI). (3) Results: the results reveal three main conceptualizing explanatory categories of the nurses’ coaching requirements: Steps of a coaching action; topics for a coaching action related to occupational health issues such as stress and burnout (it is shown that the prevalence of burnout in our sample is 66.7%); barriers to a coaching action. (4) Conclusions: by investigating the coaching requirements of the nursing staff, a transtheoretical coaching model with a theoretical and ethical basis was suggested in this regard for their occupational health issues management.
Time Domain Characteristic of Dual-mode Whispering-gallery Mode Lasers

We studied the time domain characteristic in whispering-gallery mode laser and whispering-gallery Fabry-Perot hybrid laser. In the hybrid cavity laser, mode competition at mode hopping position is intense, and the time domain signals of the competition modes oscillate alternately and complementarily. However, the time domain signals of whispering-gallery mode laser are relatively stable.
Integration of an Ultra-Short Polarization Rotator on an Active-Passive Indium Phosphide Membrane

We present the technology development for the integration of an ultra-short polarization rotator on an active-passive indium phosphide membrane platform. The technology is proven with two demonstrator circuits: a polarization-independent semiconductor optical amplifier and a dual-polarization light source. Experimental results for both circuits are presented in this paper.
Multimode demultiplexer for space division multiplexing by using nano-pixel

Multimode demultiplexer is proposed by using nano-pixel configuration in a square of 3.0 × 3.0 μm$^{2}$. The simulated results demonstrate mode XT of 13.6 dB and 11.4 dB for 0$^{th}$ and 1$^{st}$ order modes, respectively.
Optimal Endmember-Based Super-Resolution Land Cover Mapping

Super-resolution mapping (SRM) aims to determine the spatial distribution of the land cover classes contained in the area represented by mixed pixels to obtain a more appropriate and accurate map at a finer spatial resolution than the input remotely sensed image. The image-based SRM models directly use the observed images as input and can mitigate the uncertainty caused by class fraction errors. However, existing image-based SRM models always adopt a fixed set of endmembers used in the entire image, ignoring the spatial variability and spectral uncertainty of endmembers. To address this problem, this letter proposed an optimal endmember-based SRM (OESRM) model, which considers the spatial variations in endmembers, and determines the best-fit one for each coarse resolution pixel using the spectral angle and the spectral distance as the spectral similarity indexes. A Sentinel-2A and a Landsat-8 multispectral images were used to analyze the performance of OESRM, by comparing with three other SRM methods which adopt a fixed endmember set or multiple endmember sets. The results showed that OESRM generated resultant land cover maps with more spatial detail, and reduced the confusion between land cover classes with similar spectral features. The proposed OESRM model produced the results with the highest overall accuracy in both experiments, showing its effectiveness in reducing the effect of endmember uncertainty on SRM.
Synthetic CsCEP3 Peptide Attenuates Salinity Stress via ROS and ABA Signaling in Cucumber Primary Root

Salinity stress prominently limits cucumber growth and productivity. However, the mechanism underlying salinity response mediated by the C-TERMINALLY ENCODED PEPTIDE (CEP) peptide in cucumber primary root remains largely unclear. In this study, we show that salinity prominently inhibits cucumber primary root growth, and CsCEP gene expression is differentially induced by salinity. We further demonstrate that the exogenous application of synthetic CsCEP3 peptide partially suppresses salinity-triggered growth inhibition in cucumber primary root, although CsCEP3 peptide itself shows no obvious effect on cucumber primary root growth under normal conditions. Our transcriptomic and qRT-PCR data further reveal that CsCEP3 peptide may modulate gene expression related to abscisic acid (ABA) signaling pathway, reactive oxygen species (ROS) production, and slat-responsive transcription factors to attenuate the inhibitory effect of salinity on cucumber primary root growth. Taken together, our work provides a fundamental insight into CEP peptide-mediated cucumber salinity adaptation.
Whisper Encoder features for Infant Cry Classification

Identifying the pathology in infant cry is an important and socially relevant research problem, as it can save the lives of many infants. This study proposes the use of transfer learning based approach using Whisper Encoder Module which is compared against state-of-the art MFCC feature set for classification of normal vs. pathological infant cry. Moreover, we also present multi-class pathological infant cry classification using CNN and Bi-LSTM networks. Our study finds that whisper encoder module coupled with DNN classifiers such as CNN and Bi-LSTM outperform MFCC features with absolute increment of 4% and 1% on CNN and Bi-LSTM respectively. Furthermore, whisper encoder features are analysed using statistical parameters and t-SNE plots. The experiments are performed using the 10 -fold cross-validation on Baby Chillanto dataset, In-house DA-IICT dataset, and also on the datasets formed by combining these two datasets.
Combination of clinical, radiomic, and “delta” radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma

Objectives To identify combined clinical, radiomic, and delta-radiomic features in metastatic gastroesophageal adenocarcinomas (GEAs) that may predict survival outcomes. Methods A total of 166 patients with metastatic GEAs on palliative chemotherapy with baseline and treatment/follow-up (8–12 weeks) contrast-enhanced CT were retrospectively identified. Demographic and clinical data were collected. Three-dimensional whole-lesional radiomic analysis was performed on the treatment/follow-up scans. “Delta” radiomic features were calculated based on the change in radiomic parameters compared to the baseline. The univariable analysis (UVA) Cox proportional hazards model was used to select clinical variables predictive of overall survival (OS) and progression-free survival (PFS) (p-value <0.05). The radiomic and “delta” features were then assessed in a multivariable analysis (MVA) Cox model in combination with clinical features identified on UVA. Features with a p-value <0.01 in the MVA models were selected to assess their pairwise correlation. Only non-highly correlated features (Pearson’s correlation coefficient <0.7) were included in the final model. Leave-one-out cross-validation method was used, and the 1-year area under the receiver operating characteristic curve (AUC) was calculated for PFS and OS. Results Of the 166 patients (median age of 59.8 years), 114 (69%) were male, 139 (84%) were non-Asian, and 147 (89%) had an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1. The median PFS and OS on treatment were 3.6 months (95% CI 2.86, 4.63) and 9 months (95% CI 7.49, 11.04), respectively. On UVA, the number of chemotherapy cycles and number of lesions at the end of treatment were associated with both PFS and OS (p < 0.001). ECOG status was associated with OS (p = 0.0063), but not PFS (p = 0.054). Of the delta-radiomic features, delta conventional HUmin, delta gray-level zone length matrix (GLZLM) GLNU, and delta GLZLM LGZE were incorporated into the model for PFS, and delta shape compacity was incorporated in the model for OS. Of the treatment/follow-up radiomic features, shape compacity and neighborhood gray-level dependence matrix (NGLDM) contrast were used in both models. The combined 1-year AUC (Kaplan–Meier estimator) was 0.82 and 0.81 for PFS and OS, respectively. Conclusions A combination of clinical, radiomics, and delta-radiomic features may predict PFS and OS in GEAs with reasonable accuracy.
Estimation of 3D human hand poses with structured pose prior

Here, the authors present multistage estimation model embedding with structured pose prior (SPP), a novel coarse-to-fine framework for real-time 3D hand estimation from single depth image. Authors’ main contributions can be summarised as follows: (i) The authors proposed SPP to enforce constraints of canonical hand pose instead of original hand pose. (ii) The authors are the first to adopt under-complete stacked denoising auto-encoder (SDA) to construct pose prior by mapping canonical hand pose to latent representation. In the case of enforcing constraints of canonical hand pose, the authors empirically validate that under-complete SDA outperforms over-complete SDA in improving the hand estimation accuracy. (iii) The authors propose candidate keypoints patches (CKP) as intermediate data to conduct further hand pose refinement. Experimental evaluation on two publically available datasets shows that authors’ model is competitive both in accuracy and computation time. Especially, authors’ method placed first in the location of palm key-point on both two datasets, and the high accuracy of hand palm key-point plays an important role in many applications, such as that manipulator can grasp objects to specific coordinates with the guiding of human hand palm.
Detection of Laughter and Screaming Using the Attention and CTC Models

This study aimed to detect social signals, such as laughter and screams, in real environments. Social signals inﬂuence human-to-human communication. To effectively apply these signals in various systems, computer systems must appropriately detect social signals. In this study, social signal detection (SSD) experiments were conducted to demonstrate which of three feature sets, i.e., a spectral feature set, prosodic feature set, and spectral and prosodic feature set, was best for detecting laughter and screaming. The results showed that using both the spectral and prosodic feature sets yielded the best performance, with 81.83% accuracy for laughter and 81.68% accuracy for screams. More-over, the detection model comparison results revealed that the bidirectional long short-term memory (BiLSTM)-connectionist temporal classiﬁcation (CTC) yielded the best laughter detection performance, while attention-CTC was best for scream detection. These results suggest that CTC is effective for SSD.
Controlling Matter-Wave Smooth Positons in Bose-Einstein Condensates

In this investigation, we explore the existence and intriguing features of matter-wave smooth positons in a non-autonomous one-dimensional Bose–Einstein condensate (BEC) system with attractive interatomic interactions. We focus on the Gross–Pitaevskii (GP) equation/nonlinear Schrödinger-type equation with time-modulated nonlinearity and trap potential, which govern nonlinear wave propagation in the BEC. Our approach involves constructing second- and third-order matter-wave smooth positons using a similarity transformation technique. We also identify the constraints on the time-modulated system parameters that give rise to these nonlinear localized profiles. This study considers three distinct forms of modulated nonlinearities: (i) kink-like, (ii) localized or sech-like, and (iii) periodic. By varying the parameters associated with the nonlinearity strengths, we observe a rich variety of captivating behaviors in the matter-wave smooth positon profiles. These behaviors include stretching, curving, oscillating, breathing, collapsing, amplification, and suppression. Our comprehensive studies shed light on the intricate density profile of matter-wave smooth positons in BECs, providing valuable insights into their controllable behavior and characteristics in the presence of time-modulated nonlinearity and trap potential effects.