Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of an Event-Driven Smart Sensor Node for Wireless Monitoring Systems

In this paper, an event-driven wireless sensor node is proposed and demonstrated. The primary design objective is to devise a wireless sensor node with miniaturization, integration, and high-accuracy recognition ability. The proposed wireless sensor node integrates two vibration-threshold-triggered energy harvesters that sense and power a threshold voltage control circuit for power management, a microcontroller unit (MCU) for system control, a one-dimensional convolutional neural network (1D-CNN) environment data analysis and vibration events distribution, and a radio frequency (RF) digital baseband transmitter with IEEE 802.15. 4-/. 6 protocols. The dimensions of the wireless sensor node are 4 × 2 × 1 cm3. Finally, the proposed wireless sensor node was fabricated and tested. The alarming time for detecting the vibration event is less than 6 s. The measured recognition accuracy of three events (knock, shake, and heat) is over 97.5%. The experimental results showed that the proposed integrated wireless sensor node is very suitable for wireless environmental monitoring systems.
Antimicrobial Activity of Stilbenes from Bletilla striata against Cutibacterium acnes and Its Effect on Cell Membrane

The abnormal proliferation of Cutibacterium acnes is the main cause of acne vulgaris. Natural antibacterial plant extracts have gained great interest due to the efficacy and safety of their use in skin care products. Bletilla striata is a common externally used traditional Chinese medicine, and several of its isolated stilbenes were reported to exhibit good antibacterial activity. In this study, the antimicrobial activity of stilbenes from B. striata (BSS) against C. acnes and its potential effect on cell membrane were elucidated by determining the minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), bacterial growth curve, adenosine triphosphate (ATP) levels, membrane potential (MP), and the expression of genes related to fatty acid biosynthesis in the cell membrane. In addition, the morphological changes in C. acnes by BSS were observed using transmission electron microscopy (TEM). Experimentally, we verified that BSS possessed significant antibacterial activity against C. acnes, with an MIC and MBC of 15.62 μg/mL and 62.5 μg/mL, respectively. The growth curve indicated that BSS at 2 MIC, MIC, 1/2 MIC, and 1/4 MIC concentrations inhibited the growth of C. acnes. TEM images demonstrated that BSS at an MIC concentration disrupted the morphological structure and cell membrane in C. acnes. Furthermore, the BSS at the 2 MIC, MIC, and 1/2 MIC concentrations caused a decrease in the intracellular ATP levels and the depolarization of the cell membrane as well as BSS at an MIC concentration inhibited the expression of fatty acid biosynthesis-associated genes. In conclusion, BSS could exert good antimicrobial activity by interfering with cell membrane in C. acnes, which have the potential to be developed as a natural antiacne additive.
Experimental Study on Thermoelectric Energy Harvesting From Indoor High-Voltage Disconnector

Thermoelectric energy harvesting is a promising power supply technology for low-power sensors installed on the high-voltage side. This article presents an experimental study on harvesting thermoelectric energy from an indoor high-voltage disconnector with commodity thermoelectric generators (TEGs). The main working conditions of the disconnector, including working current, ambient temperature, ambient airflow velocity, and ambient airflow direction, are controlled by an experimental platform, and their effects on the energy harvesting performance of TEG are analyzed in detail. A low-power wireless sensor with an energy management circuit is mounted on the switching blade of the disconnector to investigate the feasibility of TEG energy application. The experimental results show that the harvested power of TEG increases and decreases quadratically with the increasing disconnector current and ambient temperature, respectively. The increasing ambient airflow velocity causes the harvested power to increase first and then decrease. The airflow coming from the front of the disconnector is more conducive for energy harvesting than the airflow coming from the side and back. When the disconnector works under the rated current and standard indoor environmental conditions (20 °C and no airflow), the energy provided by a single TEG with an area of $90\times30$ mm2 can meet the energy consumption of the low-power wireless sensor working in the intermittent mode. When the disconnector works at a low current, a feasible way to improve the reliability of TEG energy application is changing the indoor environmental conditions, such as decreasing ambient temperature or adjusting airflow velocity and direction.
Relationships between the El Niño–Southern Oscillation, precipitation, and nitrogen wet deposition rates in the contiguous United States

Human activities have significantly increased reactive nitrogen (N) in the environment, leading to adverse effects on various ecosystems. We used 1979–2012 seasonal inorganic N wet deposition data from the National Atmospheric Deposition Program to evaluate the relationship between the El Niño–Southern Oscillation (ENSO) and N wet deposition in the contiguous U.S. The correlations between precipitation and inorganic N wet deposition were the strongest and most spatially extensive during winter; up to 62% and 53% of the 2 to 6 year variations of precipitation and N wet deposition rates, respectively, in the Rocky Mountains, along the coast of the Gulf of Mexico and near the Great Lakes, can be explained by variation in the NINO3.4 climate index, which was used as a measure of ENSO activity. During El Niño winters, precipitation and N wet deposition rates were higher than normal in the southern U.S., while La Niña events brought higher precipitation and N wet deposition to the Rocky Mountains and Great Lakes regions. Wintertime N wet deposition correlations held through springtime in the Great Lakes and the northeast; however, correlations between NINO3.4 and precipitation were not significant at most sites, suggesting factors besides precipitation amount contributed to the 2 to 6 year variation of N wet deposition in these regions. As the frequency, strength, and types of ENSO change in the future, interannual variability of N wet deposition will be affected, indirectly affecting spatial distribution of dry N deposition and potentially changing the overall spatial patterns of N deposition.
Overhead Transmission Line Sag Monitoring Using a Chirped-Pulse Phase-Sensitive OTDR

The capacity of overhead transmission lines is fixed and determined by the physical properties of the line, which are estimated based on worst-case weather scenarios. Exceeding the capacity limit can cause the conductor to increase the sag excessively, bringing it too close to the ground and creating a safety risk. Real-time monitoring of the sag value enables dynamic capacity configuration, thereby reducing safety risks and improving the efficiency of the transmission line. Distributed acoustic sensing based on chirped-pulse phase-sensitive optical time-domain reflectometry (CP- $\Phi $ OTDR) can be used to measure the vibration induced by wind on the cable line taking advantage of the optical fiber already deployed in the power line. An analysis of the recovered strain in the frequency domain reveals multiple frequency components related to the cable’s current mechanical state. By identifying the fundamental frequency, it becomes possible to quantitatively calculate the sag. Tracking the central frequency of each span allows for the monitoring of the sag for each span of the line using just one single-end interrogator.
Severe food insecurity and mental health among women living in extreme poverty in Ceara, Brazil

Backgroung: the prevalence of moderate or severe food insecurity (FI) in the world will reach 2.4 billion people in 2020. Common Mental Disorders (CMDs) affect one in five people, reaching all social classes. Objective: to examine the associations between CMDs and FI in the most vulnerable population, mothers of young children. Methods: population-based cross-sectional observational study, interviewing 484 families with children under six years of age, beneficiaries of the cash transfer program Cartão Mais Infância Ceará (CMIC), in 24 municipalities of Ceara. Poisson regression models were used, adjusted for the sampling effect. Results: 86% of the families were in a situation of FI, with 36% expressing a severe FI, a condition compatible with hunger. In relation to CMDs, it was observed that the prevalence of severe FI increased significantly, affecting 53% of mothers with CMDs against 31% of those without the disorder (p<0.001). Regression analysis showed that CMDs remained the risk factor most associated with severe FI, with mothers with CMDs having a 64% higher adjusted risk compared to other mothers (p=0.002). In the final model, the condition of not having treated water at home was associated with a 55% higher adjusted risk of severe FI (p=0.011), and two factors were close to statistical significance, namely: not feeling safe at home (possibility of domestic violence) and growing edible plants at home, with adjusted measures of 48% risk and 13% protection, respectively. Conclusion: 13% of this population live with very high rates of severe FI, compatible with hunger, concomitant with CMDs. As aggravating factors of severe FI and CMDs, public social support programs are necessary to have a real positive impact on the quality of life of this population.
BER estimation for STBC-MC-DS-CDMA-4 antennas system by varied wavelet-carriers features via AWGN-flat channels

This paper improves the bit error rate (BER) of the modern communication system by taking into account the effect of the wavelet shape and the number of carriers on the performance of the space time block code multi-carrier direct sequence code division multiple access (STBC-MC-DS-CDMA). Here the transmitter is moved with speeds 2 km/hr, 45 km/hr and 100 km/hr via Rayleigh flat fading channel. Here, 2 antennas are employed at the receiver to mitigate the multipath signal influence. The system’s orthogonal frequencies are generated using Haar, Daubechies 4, Symlets 4, Cohen-Daubechies-Feauveau 1.1 with 9.7 and B-spline 3. The number of used carriers is 128, 512, and 1,024. Quadrature phase shift key (QPSK) is used with cyclic prefix 1/16 and a bandwidth of 20 MHz. traditional fast fourier transform (FFT) system is compared to the proposed discrete wavelet packet transform (DWPT) to show the BER enhancements. The space-time block coding (STBC) is used to enhance system cabability in error corection. The proposed system shows significant improvement in BER, so that, it reaches to the same BER when using FFT but with less signal to noise ratio (SNR), which interns reduce the power consumed within the system and the cost.
Compost Amendment to a Grazed California Annual Grassland Increases Gross Primary Productivity Due To a Longer Growing Season

Compost amendment to rangelands is a proposed nature‐based climate solution to increase plant productivity and soil carbon sequestration. However, it has not been evaluated using quasicontinuous ecosystem‐scale measurements. Here, we present the first study to utilize eddy covariance and footprint partitioning to monitor carbon exchange in a grassland with and without compost amendment, monitoring for 1 year before and 1 year after treatment. Compost amendment to an annual California grassland was found to enhance net ecosystem removal of carbon. Our study confirmed that compost‐amended grasslands, similar to nonamended grasslands, are net carbon sources to the atmosphere; however, the amendment appears to be slowing down the rate at which these ecosystems lose carbon by 0.71 Mg C ha−1 per growing season. Digital repeated imagery of the canopy revealed that compost‐amended grasslands experienced an earlier green‐up, resulting in an overall longer growing season by >60 days. Notably, we did not detect significantly higher amounts of soil carbon in compost‐amended soils. High variability in soil carbon demands greater sampling replication in future studies. A longer growing season and higher productivity are hypothesized to be a result of greater availability of macronutrients and micronutrients in the top layer of soil (specifically nitrogen, phosphorus, and zinc).
Research on variable stiffness asymmetrical resonant linear piezoelectric actuator based on multi-modal drive

In this paper, a linear piezoelectric motor with variable stiffness and asymmetric resonance is proposed, which is driven by a single harmonic signal. Working in the resonant state improve the output performance of the motor. Motor control is relatively simple and can realize reverse movement under the driving of second-order single harmonic signal. At the same time, the new motor can obtain different operating speed and step distance by changing the clamping position in front and back to meet the requirements of different loads and different working conditions and has strong applicability. By experiment, the first-order optimal operating frequency of the motor prototype at three different stiffness adjustment positions is 88 Hz, 90 Hz and 92 Hz respectively. Under the excitation of 240 Vp–p first-order resonance signal, the corresponding output speed of the motor prototype is 16.116 mm s−1, 20.457 mm s−1 and 25.015 mm s−1 respectively, and the corresponding displacement resolution is 0.18 mm, 0.22 mm and 0.27 mm respectively. When the stiffness adjustment positions is 2 mm, the maximum load of the motor prototype reaches 450 g. The second-order optimal operating frequency at the stiffness adjustment positions 1 mm is 601 Hz. Under the excitation of a 240 Vp–p second-order resonant signal, the reverse output speed of the motor prototype is 13.126 mm s−1, and the corresponding displacement resolution is 0.02 mm.
The Effect of Homework on Student Achievement: A Meta-Analysis Study/Učinak domaće zadaće na postignuća učenika: metaanaliza

AbstractThe aim of the present study was to combine the findings of experimental studies conducted between 2000 and 2019 on the effect of homework on students' academic achievements using the meta-analysis method. Ten papers that were reported by Google Scholar and Higher Education Institution (YÖK) National Thesis Center databases between 2000 and 2019 were included in the study based on certain criteria. The study also included subgroup analyses, as well as the determination of the general effect of homework on academic achievement. The findings of the present study revealed that the Hedges’ g value was significant; it was calculated as 0.970, which indicated the general effect of homework on student achievement. Furthermore, the analysis demonstrated that the effect of homework on student achievement differed significantly based on school level and various sub-dimensions reported previously.Key words: homework; achievement; meta-analysis---SažetakCilj ovoga istraživanja bio je primjenom metaanalize objediniti rezultate eksperimentalnih istraživanja provedenih od 2000. do 2019. godine koja su se bavila učinkom domaće zadaće na akademska postignuća učenika. Deset radova prijavljenih u bazama Google Scholar i Nacionalni centar za disertacije Vijeća za visoko obrazovanje (YÖK) između 2000. i 2019. uključeno je u analizu na temelju određenih kriterija. U istraživanje je također uključena analiza podskupina te utvrđivanje općega učinka domaće zadaće na akademsko postignuće. Rezultati su pokazali da je Hedgesova g vrijednost bila značajna; iznosila je 0,970, što otkriva opći učinak domaće zadaće na postignuća učenika. Nadalje, analiza je pokazala da se učinak domaće zadaće na postignuća učenika značajno razlikuje ovisno o školskoj razini i različitim poddimenzijama koje su prethodno prijavljene. Ključne riječi: domaća zadaća; metaanaliza; postignuće
Adaptive Wind Gust and Associated Gust-factor Model for the Gust-producing Weather over the Northern South China Sea

: Wind gusts are common environmental hazards that can damage buildings, bridges, aircraft, and cruise ships and interrupt electric power distribution, air traffic, waterway transport and port operations. Accurately predicting peak wind gusts in numerical models is essential for saving lives and preventing economic losses. This study investigates the climatology of peak wind gusts and their associated gust factors (GFs) using observations in the coastal and open ocean of the northern South China Sea (NSCS), where severe gust-producing weather occurs throughout the year. The stratified climatology demonstrates that the peak wind gust and GF vary with seasons and particularly with weather types. Based on the inversely proportional relationship between the GF and mean wind speed (MWS), a variety of GF models are constructed through least squares regression analysis. Peak gust speed (PGS) forecasts are obtained through the GF models by multiplying the GFs by observed wind speeds rather than forecasted wind speeds. The errors are thus entirely due to the representation of the GF models. The GF models are improved with weather-adaptive GFs, as evaluated by the stratified MWS. Nevertheless, these weather-adaptive GF models show negative bias for predicting stronger PGSs due to insufficient data representation of the extreme wind gusts. The evaluation of the above models provides insight into maximizing the performance of GF models. This study further proposes a stratified process for forecasting peak wind gusts for routine operations.
Enhancing the Localization Accuracy of UAV Images under GNSS Denial Conditions

Unmanned aerial vehicles (UAVs) are widely used in many industries. The use of UAV images for surveying requires that the images contain high-precision localization information. However, the accuracy of UAV localization can be compromised in complex GNSS environments. To address this challenge, this study proposed a scheme to improve the localization accuracy of UAV sequences. The combination of traditional and deep learning methods can achieve rapid improvement of UAV image localization accuracy. Initially, individual UAV images with high similarity were selected using an image retrieval and localization method based on cosine similarity. Further, based on the relationships among UAV sequence images, short strip sequence images were selected to facilitate approximate location retrieval. Subsequently, a deep learning image registration network, combining SuperPoint and SuperGlue, was employed for high-precision feature point extraction and matching. The RANSAC algorithm was applied to eliminate mismatched points. In this way, the localization accuracy of UAV images was improved. Experimental results demonstrate that the mean errors of this approach were all within 2 pixels. Specifically, when using a satellite reference image with a resolution of 0.30 m/pixel, the mean error of the UAV ground localization method reduced to 0.356 m.
Using Wearable and Structured Emotion-Sensing-Graphs for Assessment of Depressive Symptoms in Patients Undergoing Treatment

Depression, as a common mental illness, has become a significant public health issue, and the recurrence rate for patients with depression who have been treated is relatively high. In this study, a mental health monitoring system based on wearable sensing wristbands with sensors for voice, activity, and heart rate has been developed. Using this system, we perform a therapeutic monitoring study for hospitalized patients with depression and healthy controls to investigate multimodal changes before, during, and after a course of treatment. The obtained results demonstrate that there are significant changes in multimodal features such as audio short-time energy and angular velocity shape skewness with the remission of depressive symptoms. According to Mikels’ emotion wheel, a day’s data for subjects is defined as three types of emotional units and the emotional state of each emotional unit is recognized as positive or negative emotions. With this, emotion-sensing-graphs guided by Mikels’ emotion wheel theory are constructed. The analysis of emotion-sensing-graphs reveals that the same emotions are more closely linked to each other and the average degree and proportion of positive emotion nodes after a course of treatment have increased significantly. Finally, an emotion-sensing-graph graph convolutional network (ESG-GCN) model fused three types of emotion-sensing-graphs with emotion labels has been developed to assess the levels of depression, thereby monitoring the changes in depressive symptoms. Compared with classical machine learning models, the accuracy, F1 score, and recall rate of the model perform best and the model achieves a verification accuracy of 0.83.
Design and feasibility analysis of personal rapid transit network for an Indian heritage city

Personal Rapid Transit (PRT) is a state-of-art amalgamation of automotive, computer, network and transit technologies which could prove to be very economical if properly managed. In this study, a PRT network is proposed for the city of Varanasi in India which is one of the oldest cities in the world. The study area constitutes of narrow streets having mix traffic with a predominance of pedestrians and slow moving vehicles. The proposed track connects the primary tourist and heritage spots of Varanasi to various centers of crowd generation. Proposed PRT network will provide the last mile connectivity to targeted spots which are otherwise tough to provide with conventional means of transit due to their higher need for space, infrastructure and due to a large amount of generated air and noise pollution. Various primary and secondary data were collected from different sources for efficient planning of PRT system. Travel demand assessment for PRT network was done after calculating the base year trips for the study area. Apart from capacity analysis, the financial viability of proposed project was analyzed based on financial analysis of 30 years and by calculating Internal Rate of Return (IRR). The capacity of proposed PRT network was found to accommodate successfully the net demand arise during the peak and non-peak hours. This will ensure the reduced congestion on the roads and subsequently faster movement of traffic. After the systematic analysis of various parameters and by charging a reasonable fare based on the opinion of targeted population, an optimum IRR of 17% can be gained which is excellent for any business model. This study concludes that PRT could bridge the gap between traditional modes of transit that we have inherited and transit focused urban forms to ensure fulfillment of sustainability goals.