Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Framework of Curriculum Learning and Knowledge Distillation for Noise-Robust and Small-Footprint Keyword Spotting

Spoken keyword spotting, which is characterized by simplicity and low latency, has been widely used in consumer electronics to facilitate always-on voice interfaces. Small-footprint keyword spotting based on tiny convolutional neural networks can be implemented on resource-constrained, yet energy-efficient, microcontrollers in real time. However, it is difficult for tiny neural networks to learn the noise-robustness properties essential for successful voice interfaces. To overcome this problem, this study proposes a joint framework of curriculum learning and knowledge distillation for noise-robust small-footprint keyword spotting. The proposed joint framework applies noise-mixture curriculum learning to a network that is sufficiently large, to learn various noise situations. Subsequently, knowledge distillation is applied to compress the large network into a sufficiently small network for use in an onboard microcontroller. To enhance the effectiveness of the joint framework, a curriculum learning approach is proposed with a new noise mixture strategy along with knowledge distillation that employs an effective ensemble of neural network snapshots for each curriculum stage. The proposed methods enable large networks to effectively learn noisy situations, thereby transferring noise robustness to small networks. The effectiveness of the joint framework was illustrated on the Google Speech Commands dataset with noise mixtures incorporated from various public noise datasets. The performance of the joint framework was superior in noisy situations compared to that of state-of-the-art noise-robust keyword-spotting methods. Consequently, the proposed framework significantly improves the usability of voice interfaces in consumer electronics.
Survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people’s quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people’s daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the stateof-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR.
A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about 1 million cases and 784,000 deaths worldwide in 2018). GC has a poor prognosis (the 5-year survival rate is less than 20%), but there is an effort to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data were collected from the Gene Expression Omnibus (GEO) bank to obtain three dataset matrices analyzing gastric tumor tissue versus normal gastric tissue and involving microarray analysis performed using the GPL570 platform and different sources. The data were analyzed using the GEPIA tool for differential expression and KMPlot for survival analysis. For more robustness, GC data from the TCGA database were used to corroborate the analysis of data from GEO. The genes found in in silico analysis in both GEO and TCGA were confirmed in several lines of GC cells by RT-qPCR. The AlphaFold Protein Structure Database was used to find the corresponding proteins. Then, a structure-based virtual screening was performed to find molecules, and docking analysis was performed using the DockThor server. Our in silico and RT-qPCR analysis results confirmed the high expression of the AJUBA, CD80 and NOLC1 genes in GC lines. Thus, the corresponding proteins were used in SBVS analysis. There were three molecules, one molecule for each target, MCULE-2386589557-0-6, MCULE-9178344200-0-1 and MCULE-5881513100-0-29. All molecules had favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS and molecular docking analysis provided a suitable approach for finding genes involved in GC and working with the corresponding proteins to search for new molecules with anticancer properties.
The influence of body mass on posture, pressure distribution and discomfort during prolonged driving.

Abstract Background: Currently, if traveling the posted speed limit, the typical commuter driver in the Toronto Metropolitan area will travel round trip upwards of 60 minutes a day to work (Heisz and LaRochelle-Cote, 2005). As urban congestion continues to rise, commuting distances and times will progressively increase, placing commuter drivers at increased risk of developing musculoskeletal disorders (Porter and Gyi, 2002; Walsh et al., 1989; Chen et al., 2005; Sakakibara et al., 2006). As urban areas continue to expand, it is believed that a greater percentage of our urban populations will be defined as overweight or obese (Puska et al., 2003). To date the influence of body mass on driver posture, pressure distribution and discomfort during a prolonged driving situation has been left relatively untested. The purpose of this investigation is to determine the influence body mass has on driver posture, pressure distribution and discomfort during a prolonged driving situation. Methodology:
Fano and Lorentz Resonance Achieving Based on Dual-Injection Micro-Disk Resonator by Phase Relation

A method to achieve Fano resonance and Lorentz resonance is theoretically proposed in this article, which is realized by changing the phase difference between the two input lights in the dual-injection structure. The method is analyzed by coupled-mode theory and exemplified by considering a dual-injection micro-disk resonator. Depending on the phase relationship between the two input lights in a dual-injection micro-disk resonator, two different resonances can be created. When the two input lights are out of phase by π/2, each whispering gallery mode (WGM) in the micro-disk resonator will generate Fano resonance. When the phase difference between the two input lights is zero, Lorentz resonances are observed, and some resonant modes disappear in the spectrum, which results in the free spectral range (FSR)of each mode being doubled
Effectiveness of cognitive-behavioural therapy plus pharmacotherapy in inpatient treatment of depressive disorders.

BACKGROUND Meta-analyses show benefits for patients from a combination of medication and cognitive-behavioural psychotherapy. However, it is still unclear whether or not additional cognitive-behavioural therapy (CBT) also produces a better treatment outcome in a naturalistic psychiatric setting. METHODS Two-hundred six consecutively registered acute psychiatric inpatients with a unipolar depressive disorder were treated with additional CBT. This combined therapy was then compared with psychiatric primary care in an inpatient setting (clinical management). In addition to pharmacological treatment, 105 of the 206 patients also received symptom-focused CBT after hospitalization. Seventeen-item Hamilton Rating Scale for Depression (HAMD, primary outcome criterion), Beck Depression Inventory (BDI), Dysfunctional Attitude Scale, Clinical Global Impression Scale and the Global Assessment of Functioning were performed with all patients. RESULTS Patients who were treated with additional CBT revealed a considerably greater reduction of depressive symptoms than in patients who received inpatient primary care only (HAMD: -22.21 versus -19.86, p = 0.027; BDI: 14.99 versus 11.36, p = 0.031). Moreover, remission rates were significantly higher (HAMD: 72% to 51%, p = 0.045; BDI: 58.8% versus 43.1%, p = 0.044) in the combined treatment group than in the primary care only group. LIMITATION The naturalistic design and the inconsistent pharmacological treatment are design flaws. CONCLUSION The results show that additional cognitive-behavioural treatment of depressive disorders notably improves outcome over standard procedure in acute psychiatric treatment. KEY PRACTITIONER MESSAGE There is a need for treatment strategies to accompany medication. In the Sequenced Treatment Alternatives to Relieve Depression trial (STAR*D), only 33% of the patients reached remission criteria after the first antidepressant treatment step and only 50% after the second step. The strict inclusion criteria of randomized controlled trials often render their patient populations unrepresented. For an accurate view of treatment effectiveness, their results need to be complemented with results gained from trials in clinical practice. Additional cognitive-behavioural treatment notably improves treatment outcomes compared with standard treatments in the acute psychiatric treatment of depressive disorders. The results of this study under naturalistic conditions are an important addition to findings from randomized and controlled studies.
Reactive Sulfur Species Produced by Cystathionine γ-lyase Function in the Establishment of Mesorhizobium loti–Lotus japonicus Symbiosis

Reactive sulfur species (RSS) are present in root nodules; however, their role in symbiosis and the mechanisms underlying their production remain unclear. We herein investigated whether RSS produced by the cystathionine γ-lyase (CSE) of microsymbionts are involved in root nodule symbiosis. A cse mutant of Mesorhizobium loti exhibited the decreased production of hydrogen sulfide and other RSS. Although the CSE mutation of M. loti did not affect the early stages of symbiosis, i.e., infection and nodulation, with Lotus japonicus, it reduced the nitrogenase activity of nodules and induced their early senescence. Additionally, changes in the production of sulfur compounds and an increase in reactive oxygen species (ROS) were observed in the infected cells of nodules induced by the cse mutants. The effects of CSE inhibitors in the L. japonicus rhizosphere on symbiosis with M. loti were also investigated. All three CSE inhibitors suppressed infection and nodulation by M. loti concomitant with decreased RSS levels and increased ROS and nitric oxide levels. Therefore, RSS derived from the CSE activity of both the microsymbiont and host plant are required for symbiosis, but function at different stages of symbiosis, possibly with crosstalk with other reactive molecular species.
Intelligent Detection of Road Cracks Based on Improved YOLOv5

With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and mAP@0.5 compared with YOLOv5s, among which mAP@0.5 has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects.
WIDER Working Paper 2023/112 Returns to education and wage inequality in Namibia

: This paper estimates the returns to education and their implications for wage inequality using data from the 2015/16 Namibia Income and Expenditure Survey. The paper employs recentred influence function regression to analyse the impact of education across the wage distribution and uses a simulation approach to assess the impact on wage inequality of educational equalization. The results indicate higher returns to education among women compared with men. The quantile results further reveal that within the sample of women, higher returns to education are observed at the lower percentile compared with the upper end of the distribution, while within the sample of men, higher returns to education are observed at the upper end of the wage distribution. Simulation results suggest that educational expansion substantially widens inequality relative to equalization efforts for both men and women. The policy implications include the need to prioritize reducing educational disparities and ensuring equal access to quality education. Targeted interventions should address the specific needs of disadvantaged groups, such as women and individuals from lower-income households, by improving educational quality and supporting vocational training.
PHASE SENSITIVENESS TO SOIL MOISTURE IN CONTROLLED ANECHOIC CHAMBER: MEASUREMENTS AND FIRST RESULTS

in this work, we will present new Bistatic X-band (9.6 GHz) laboratory measurements, which have been carried out in the Bistatic Measurement Facility at the DLR Oberpfaffenhofen, Microwaves and Radar Institute in Germany. The Bistatic facility enables us to have full polarimetric data with different Bistatic angles and different soil moistures controlled by a TDR (Time Domain Reflectivity) system. After calibration of the measuring system using a large metal plate, the sensitivity of phase and reflectivity with regard to moisture variation and therefore the penetration depth was evaluated. Current results demonstrate a non-linear relationship between the signal phase and the soil moisture, as expected, confirming the possibility of using DInSAR to measure variations in soil moisture.
University student's mental stress detection using machine learning

University students are especially susceptible to the negative effects of mental stress in today's environment, which is a serious issue overall. A great deal of pressure is now being placed on a period of life that was traditionally considered to be the most carefree. People in today's culture are exposed to increasingly high levels of mental stress, which has been related to a broad variety of health problems, such as depression, suicide, heart attacks, and strokes. Because of this, in order to primarily extract, for the purposes of this research, the mental stress ratings of university students, we applied a total of six distinct machine learning methods. The Decision Tree Classifier, the Random Forest Classifier, the SVC, the KNN Classifier, the Multinomial NB, and the K-Nearest Neighbors Regressor are only some of the machine learning algorithms that are available. This investigation's principal objective is to determine the percentage of students who are struggling to deal with emotional pressure in their lives. The dataset was put together by hand with paper and manual information obtained from a survey. Out of the six distinct classification strategies, the Decision Tree Classifier and the Random Forest Classifier both achieved a test result of 0.99, which is the maximum score that can be achieved.
Simple net rainfall partitioning equations for nearly closed to fully closed canopy stands

Many net rainfall models have been developed, but they are often complex, data demanding and usable only for a specific vegetation type. The focus of this study was to develop and validate two simple equations (a two- and a three-coefficient equation) for nearly full canopies of oil palm, rubber and pine trees. Throughfall and stemflow data from seven past studies were used to determine the best-fit coefficients for the two equations. The three-coefficient equation was Pn = Pg x exp [- {0.3443 – (Pg / (58.9748 + Pg)} x 0.1639)] and the two-coefficient equation was Pn = 0.7724 x Pg – 0.5845 (R2 = 0.91), where Pn and Pg are the net and gross rainfall, respectively. To validate these two equations, field data collections were started. Thirteen rain gauges fit with data loggers were used for rainfall measurement. Three sampled trees were selected randomly for stemflow measurement and one rain gauge was installed at a nearby open area. Two error indices were used as a goodness-of-fit measure for equation accuracy: index of agreement and normalised mean absolute error. The results showed that the two- and three-equation equations performed nearly equally well. They predicted the net rainfall with an error of between 12 to 23% (ranked as "Fair" to "Good" in terms of overall equation accuracy) and with an index of agreement of more than 90%. The results showed that these two equations can be used fairly accurately to estimate throughfall and net rainfall, and, to a lesser degree, stemflow. Estimation errors occurred most probably because canopy and rainfall characteristics were not taken into account in the two equations.
Transcriptomic and physiological approaches to decipher cold stress mitigation exerted by brown-seaweed extract application in tomato

Chilling temperatures represent a challenge for crop species originating from warm geographical areas. In this situation, biostimulants serve as an eco-friendly resource to mitigate cold stress in crops. Tomato (Solanum lycopersicum L.) is an economically important vegetable crop, but quite sensitive to cold stress, which it encounters in both open field and greenhouse settings. In this study, the biostimulant effect of a brown-seaweed extract (BSE) has been evaluated in tomato exposed to low temperature. To assess the product effects, physiological and molecular characterizations were conducted. Under cold stress conditions, stomatal conductance, net photosynthesis, and yield were significantly (p ≤ 0.05) higher in BSE-treated plants compared to the untreated ones. A global transcriptomic survey after BSE application revealed the impact of the BSE treatment on genes leading to key responses to cold stress. This was highlighted by the significantly enriched GO categories relative to proline (GO:0006560), flavonoids (GO:0009812, GO:0009813), and chlorophyll (GO:0015994). Molecular data were integrated by biochemical analysis showing that the BSE treatment causes greater proline, polyphenols, flavonoids, tannins, and carotenoids contents.The study highlighted the role of antioxidant molecules to enhance tomato tolerance to low temperature mediated by BSE-based biostimulant.
Consensus Control of Multi-Agent Systems With Different State Constraints and Event-Triggered Communication

This brief considers the problem of achieving consensus in multi-agent systems subject to different state constraints. To address this issue, we present a new consensus protocol that involves using an event-triggered condition and projection operator to reduce communication burden while ensuring state constraint satisfaction. We also introduce an auxiliary state into the Lyapunov function, which enables us to prove that asymptotic consensus can be achieved without Zeno behavior. A key advantage of our method is that it only requires the last neighbor broadcast information and does not necessitate continuous communication or prior knowledge of global network parameters. Finally, we provide simulations to demonstrate and validate our proposed protocols.
Optimizing University Admissions: A Machine Learning Perspective

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.
Evaluation of Body Image in Patients Diagnosed with Fibromyalgia

Objective: The aim of this study was to determine the extent to which body image is affected by pain severity in patients diagnosed with fibromyalgia (FM). Method: A total of 51 patients who were selected from the psychiatry and algology outpatient clinics and had their informed consent obtained were included in the study. All patients were evaluated using the numeric rating scale, body image scale (BIS), Rosenberg self-esteem scale (RSES), Beck depression inventory (BDI)
A Nanosecond Pulsed Generator With Fast-Solid-State Switch for Synchronous Discharge in Plasma Synthetic Jet Actuators

The plasma produced by a pulsed generator is an efficient tool for an airflow control. In this paper, a nanosecond pulsed generator constructed with a fast solid-state switch is designed for triggering synchronous discharges in plasma synthetic jet (PSJ) actuators connected in series. The generator delivers high-voltage pulses with a peak voltage varying from 0 to 20 kV, a rise time of 20 ns, a full-width at half-maximum of 200 ns, and a wide range of pulse-repetition rate varying from 0 to 5.5 kHz. It is proved that this generator is able to trigger synchronous discharges in three PSJ actuators in atmospheric air with a peak discharge current more than 90 A and a deposited energy per pulse of 39 mJ. A schlieren system is used to observe the airflow dynamics, which shows the high synchronization among the three PSJ actuators with a time triggering delay of about 20 ns. The synchronous operation of three PSJ actuators produces a plasma jet with a maximum velocity of 60 m/s and a precursor wave with a velocity of 350 m/s.