Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Bootstrapping in Multi-hop Harvesting-Based Networks

Short-range multi-hop communication is an energy-efficient way to collect, share, and distribute large amounts of data with Internet of Things (IoT) systems. Nevertheless, the resource demands of wireless communication impose a burden on battery-operated IoT nodes, limiting their lifetime. Energy harvesting can address the energy limitation but introduces significant power variability, which affects reliable operation causing nodes to disconnect and join the network repeatedly. To mitigate this, we present Dual-Range Bootstrapping (DRB), a new mechanism for bootstrapping harvesting-based IoT nodes in multi-hop networks. DRB has both a very low and predictable energy cost, key properties for efficient energy management of harvesting-based nodes. We demonstrate DRB’s effectiveness in numerous scenarios using LoRa and FSK modulations, leveraging the former’s long range and the latter’s high data rate. We experimentally evaluate DRB with energy measurements performed on a communication testbed. Lastly, we use real-world energy harvesting traces to simulate the long-term behavior of a harvesting-based network.
The Impact of High Protein Diets on Cardiovascular Outcomes: A Systematic Review and Meta-Analysis of Prospective Cohort Studies

High protein diets have gained increased popularity as a means of losing weight, increasing muscle mass and strength, and improving cardiometabolic parameters. Only a few meta-analyses have addressed their impact on cardiovascular morbidity and mortality and failed to show any significant associations without applying strict values to define high protein intake. Due to the conflicting research background, we conducted a meta-analysis to assess the impact of high protein diets compared to normal protein consumption on cardiovascular outcomes in adults without established cardiovascular disease. Fourteen prospective cohort studies were included. A total of 6 studies, including 221,583 participants, reported data about cardiovascular death, without showing a statistically significant difference in the random effect model (odds ratio: 0.94; confidence interval: 0.60–1.46; I2 = 98%; p = 0.77). Analysis of three studies, which included 90,231 participants showed that a high protein diet was not associated with a lower risk of stroke (odds ratio: 1.02; confidence interval: 0.94–1.10; I2 = 0%; p = 0.66). Regarding the secondary outcome of non-fatal myocardial infarction, stroke, or cardiovascular death, 13 studies that included 525,047 participants showed no statistically significant difference (odds ratio; 0.87; confidence interval: 0.70–1.07; I2 = 97%; p = 0.19). In conclusion, according to our study results, high protein consumption does not affect cardiovascular prognosis.
Development of New Aerial Image Datasets and Deep Learning Methods for Waterfowl Detection and Classification

Monitoring waterfowl populations and distribution is important for conservation. This paper presents our recent work on creating new aerial image datasets collected by drones and applying and evaluating state-of-the-art deep learning models for waterfowl detection and classification. We collected thousands of aerial images from 10 conservation areas in Missouri, labeled around 600 images with close to 300,000 bird labels, and created 9 datasets with different properties for training and evaluating deep neural network models. Among the models, YOLOv5 performed the best, outperforming Faster R-CNN and RetinaNet. To reduce the amount of labeled data needed for model training, we applied Soft Teacher, a semi-supervised learning method, and obtained slightly better detection performance than supervised learning methods, with just half of the labeled training examples. We trained generic detection models using all datasets containing diverse images and obtained accurate detection results in most cases. For waterfowl classification, we created a dataset of images containing individual waterfowl by cropping them from raw aerial images. We applied several deep learning models to the dataset and obtained promising results.
Measure-theoretic aspects of contrast production in medical images

An approach which-nominally springs from the same motivation as measure theory (e.g., the presumption of an absent metric on the domain of a mapping whose range is uncountable) is developed to create a class of generalized images in which the mechanism of contrast can be adjusted to desired combinations of metric and nonmetric sources. Images illustrating certain interesting features are presented. Examples of the approach are discussed. <<ETX>>
Anchorage Loss Evaluation during Maxillary Molars Distalization Performed by Clear Aligners: A Retrospective Study on 3D Digital Casts

The purpose of the research was to analyze the premolar and canine anchorage loss observed during maxillary molars distalization in subjects with Class II malocclusion treated with clear aligners. A total of 49 subjects (27 females, 22 males, mean age 14.9 ± 6 years) derived from the Department of Orthodontics of the University of Rome “Tor Vergata” were selected according to the following inclusion criteria: Caucasian ancestry, Class I or minor Class II skeletal malocclusion, Class II occlusal relationship, permanent dentition with fully erupted second molars, and good compliance with aligners. Each patient underwent the same non-extractive molar distalization protocol with no auxiliaries other than attachments and Class II elastics. Prior to treatment (T1) and at the end of the first maxillary molars distalization movement (T2), digital dental casts were taken by using an iTero intraoral scanner. Linear measurements were performed in order to evaluate the anchorage loss at the level of the second and first premolars and the canines. The statistical comparison of T2 and T1 values was obtained using a paired t-test (p < 0.05). A statistically significant distalization of the maxillary first permanent molars (2.5 mm) was observed; a slight and not significant anchorage loss of the first and second premolars was assessed, while a statistically significant mesial movement of upper canines (1.33 mm) was highlighted. Clear aligner treatment was effective in obtaining a molar distalization movement. However, during molar distalization, an anchorage loss at the level of upper canines was observed.
Development of multi-epitope vaccines against the monkeypox virus based on envelope proteins using immunoinformatics approaches

Background Since May 2022, cases of monkeypox, a zoonotic disease caused by the monkeypox virus (MPXV), have been increasingly reported worldwide. There are, however, no proven therapies or vaccines available for monkeypox. In this study, several multi-epitope vaccines were designed against the MPXV using immunoinformatics approaches. Methods Three target proteins, A35R and B6R, enveloped virion (EV) form-derived antigens, and H3L, expressed on the mature virion (MV) form, were selected for epitope identification. The shortlisted epitopes were fused with appropriate adjuvants and linkers to vaccine candidates. The biophysical andbiochemical features of vaccine candidates were evaluated. The Molecular docking and molecular dynamics(MD) simulation were run to understand the binding mode and binding stability between the vaccines and Toll-like receptors (TLRs) and major histocompatibility complexes (MHCs). The immunogenicity of the designed vaccines was evaluated via immune simulation. Results Five vaccine constructs (MPXV-1-5) were formed. After the evaluation of various immunological and physicochemical parameters, MPXV-2 and MPXV-5 were selected for further analysis. The results of molecular docking showed that the MPXV-2 and MPXV-5 had a stronger affinity to TLRs (TLR2 and TLR4) and MHC (HLA-A*02:01 and HLA-DRB1*02:01) molecules, and the analyses of molecular dynamics (MD) simulation have further confirmed the strong binding stability of MPXV-2 and MPXV-5 with TLRs and MHC molecules. The results of the immune simulation indicated that both MPXV-2 and MPXV-5 could effectively induce robust protective immune responses in the human body. Conclusion The MPXV-2 and MPXV-5 have good efficacy against the MPXV in theory, but further studies are required to validate their safety and efficacy.
Invasive Species Change Plant Community Composition of Preserved Prairie Pothole Wetlands

Plant communities in North American prairie pothole wetlands vary depending on hydrology, salinity, and anthropogenic disturbance in and around the wetland. We assessed prairie pothole conditions on United States Fish and Wildlife Service fee-title lands in North Dakota and South Dakota to improve our understanding of current conditions and plant community composition. Species-level data were collected at 200 randomly chosen temporary and seasonal wetland sites located on native prairie remnants (n = 48) and previously cultivated lands that were reseeded into perennial grassland (n = 152). The majority of species surveyed appeared infrequently and were low in relative cover. The four most frequently observed species were introduced invasive species common to the Prairie Pothole Region of North America. Our results suggested relative cover of a few invasive species (i.e., Bromus inermis Leyss., Phalaris arundinacea L., and Typha ×glauca Godr. (pro sp.) [angustifolia or domingensis × latifolia]) affect patterns of plant community composition. Wetlands in native and reseeded grasslands possessed distinct plant community composition related to invasive species’ relative cover. Invasive species continue to be prevalent throughout the region and pose a major threat to biological diversity, even in protected native prairie remnants. Despite efforts to convert past agricultural land into biologically diverse, productive ecosystems, invasive species continue to dominate these landscapes and are becoming prominent in prairie potholes located in native areas.
Evaluation of a scattering liquid flow velocity profile using Doppler optical coherence tomography and dynamic stochastic interference fringe processing

Flow velocity of scattering intralipid mixture within the glass capillary is measured using Doppler Optical Coherence Tomography (DOCT) technique. In the DOCT system, interference fringe frequency shift contains information about velocity of mixture. Due to scattering, interference fringe parameters are disturbed and stochastic fringe processing method should be used to evaluate fringe frequency. Interferometric signal frequency and other parameters have been estimated dynamically using discrete nonlinear Kalman filtering method. The stochastic filtering methodology and nonlinear Kalman filtering method are considered with application to experimental DOCT data processing.
Transformer-Based Fire Detection in Videos

Fire detection in videos forms a valuable feature in surveillance systems, as its utilization can prevent hazardous situations. The combination of an accurate and fast model is necessary for the effective confrontation of this significant task. In this work, a transformer-based network for the detection of fire in videos is proposed. It is an encoder–decoder architecture that consumes the current frame that is under examination, in order to compute attention scores. These scores denote which parts of the input frame are more relevant for the expected fire detection output. The model is capable of recognizing fire in video frames and specifying its exact location in the image plane in real-time, as can be seen in the experimental results, in the form of segmentation mask. The proposed methodology has been trained and evaluated for two computer vision tasks, the full-frame classification task (fire/no fire in frames) and the fire localization task. In comparison with the state-of-the-art models, the proposed method achieves outstanding results in both tasks, with 97% accuracy, 20.4 fps processing time, 0.02 false positive rate for fire localization, and 97% for f-score and recall metrics in the full-frame classification task.
Post-glitch relaxation in pulsars

The glitch model of Ruderman, Zhu & Chen is applied to the problem of post-glitch relaxation. The energy dissipated in a glitch is such that motion of both solid and liquid phases during and after spin-up is strictly constrained to surfaces of constant chemical composition. The amorphous and heterogeneous nature of the solid phase of neutron star matter is used to deduce the qualitative form of the dissipative force acting on unit length of a neutron vortex as a function of its velocity. The analysis of post-glitch relaxation given here is necessarily qualitative owing to the stochastic nature of the model, but is consistent with both the normal and anomalous cases observed.
A regularity result for the singular values of a transfer matrix and a quadratically convergent algorithm for computing its L/sub infinity /-norm

The ith singular value of a transfer matrix, sigma /sub i/(H(j omega )), need not be a differential function of omega at frequencies where its multiplicity is greater than one. However, near a local maximum the largest singular value sigma /sub 1/(H(j omega )) has a Lipschitz second derivative, but need not have a third derivative. On the basis of this regularity result, the authors obtain a quadratically convergent algorithm for computing the L/sub infinity /-norm of a transfer matrix. <<ETX>>
Two degree-of-freedom dual-stage actuator controller design for hard disk drives

This paper presents a new controller design method for dual-stage actuators. The controller consists of two stabilizing controllers for VCM and MEMS actuators and two main controllers (slave and master). The slave and master controllers are used to shape the sensitivity of the closed loop system at low and high frequency, respectively. With the proposed method satisfactory sensitivity and complementary sensitivity are achieved.
Study of SiGeSn-based multiple quantum well laser for photonics integrated circuits

As one of two mainstream platforms, photonics integrated circuits (PICs) on Si photonics platform benefits from the mature complementary metal-oxide-semiconductor (CMOS) manufacturing capabilities and allows for the processing of Si-based PICs with ultra-high volume and low cost. Recent studies of SiGeSn materials, which yield true direct bandgap with sufficient Sn incorporation, hold great promise for PICs featuring scalable, cost-effective, and power-efficient. While the exciting developments in bulk devices including lasers, light emitting diodes (LEDs), and photodetector were reported, the quantum wells (QWs) structure and devices have been investigated targeting the dramatically improved and/or novel device performance via variety of quantum confinement effects. In this work, we report the recent progress on SiGeSn QW development. Particularly, the study of MQW laser is presented. Devices with higher optical confinement factors exhibit clear lasing confirmed by the threshold characteristic and the emission spectra below and above threshold. Only spontaneous emission was observed with the thinner cap layer samples. On the other hand, samples with thicker cap layers of 250 and 290 nm exhibit clear lasing at 77 K with thresholds of 214 and 664 kW/cm2, respectively. These promising results establish the guidance for the device design and pave the way for the SiGeSn QW devices towards future high-performance PICs on Si platform.
Remote Sensing Image Classification Based on Lightweight Network and Pruning

In recent years, deep neural network has continuously improved the performance of remote sensing image classification. Though The deep neural network model is powerful, it is difficult to deploy on resource-constrained hardware platforms such as mobile terminal devices and embedded devices due to the large number of network parameters. In this paper, a novel lightweight network (LW-Net) model and a network pruning method are used for remote sensing image classification. This LW-Net model adopts a net block unit to obtain more characteristic graphs with less computational complexity. The proposed network pruning method uses the sparsity regularization on the influence factor in BN layer to automatically identified and pruned unimportant channels to make the model structure simpler. Experimental results demonstrate compared with traditional deep neural networks, the proposed model with the network pruning method can greatly reduce the computational complexity and model parameters with a little accuracy loss.
Multifractal structure of the interplanetary magnetic field: Voyager 2 observations near 25 AU, 1987‐1988

This paper analyzes the large-scale fluctuations of the interplanetary magnetic field strength observed by Voyager 2 from 23.3 AU to 27.8 AU during the period from day 190, 1987 to day 345, 1988. The q-th moments of B{sub {tau}} show scaling behavior in the range of scales from 16 hours to 21 days for {minus}10 {le} q {le} 10, i.e., {approximately} {tau}{sup s(q)} in this range. s(q) is approximately a quadratic function of q for {minus}4 {le} q {le} 4, as one expects for a distribution that is approximately lognormal, but the higher moments diverge from those of a lognormal distribution. The function D{sub q}(q) = 1 + s(q)/(q {minus} 1) has the form that is characteristic of multifractals. For a multifractal magnetic field, the moments of the field should scale as {tau}{sup {alpha}} on a set with fractal dimension f({alpha}), where {alpha} has a continuum of values over some limited range. For the large-scale magnetic field fluctuations, the function f({alpha}) computed from D{sub q}(q) is approximately a fourth order polynomial for {minus}10 {le} q {le} 10, and positive values of f({alpha}) occur for 0.8 {le} {alpha} {le} 1.2. The multifractional character of the magnetic field strength fluctuations generalizes themore » concept that the magnetic field is organized into interaction regions (regions in which the magnetic field strength and pressure are higher than average for several hours) and rarefaction regions (regions in which the magnetic field strength and pressure are lower than average). Near solar maximum the interaction regions in the distant heliosphere might be viewed as cluster of strong disturbed fields with considerable fine structure on various scales. « less
Temperature-Based State-of-Charge Estimation Using Neural Networks, Gradient Boosting Machine and a Jetson Nano Device for Batteries

Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops because of their environmentally friendly nature, high energy density, and long lifespan. Despite these advantages, lithium-ion batteries may experience overcharging or discharging if they are not continuously monitored, leading to fire and explosion risks, in cases of overcharging, and decreased capacity and lifespan, in cases of overdischarging. Another factor that can decrease the capacity of these batteries is their internal resistance, which varies with temperature. This study proposes an estimation method for the state of charge (SOC) using a neural network (NN) model that is highly applicable to the external temperatures of batteries. Data from a vehicle-driving simulator were used to collect battery data at temperatures of 25 °C, 30 °C, 35 °C, and 40 °C, including voltage, current, temperature, and time data. These data were used as inputs to generate the NN models. The NNs used to generate the model included the multilayer neural network (MNN), long short-term memory (LSTM), gated recurrent unit (GRU), and gradient boosting machine (GBM). The SOC of the battery was estimated using the model generated with a suitable temperature parameter and another model generated using all the data, regardless of the temperature parameter. The performance of the proposed method was confirmed, and the SOC-estimation results demonstrated that the average absolute errors of the proposed method were superior to those of the conventional technique. In the estimation of the battery’s state of charge in real time using a Jetson Nano device, an average error of 2.26% was obtained when using the GRU-based model. This method can optimize battery performance, extend battery life, and maintain a high level of safety. It is expected to have a considerable impact on multiple environments and industries, such as electric vehicles, mobile phones, and laptops, by taking advantage of the lightweight and miniaturized form of the Jetson Nano device.
An Analytical Approach for Optimal DG Placement in Distribution Network Contaminated With Harmonics

This paper has proposed an analytical approach to determine the optimal size and location of a distributed generation unit in a distribution network to minimize the power loss in the network. The suggested method is applicable irrespective of whether harmonic sources are present in the network or not. The analytical methods available in the existing literature are suitable only when no harmonic source is present. The main strength of the proposed method, however, lies in its capability of finding near optimal solutions with very little computational effort even when harmonic sources are present in the network. The method utilizes the concept of B- coefficient loss formula. The conventional method for determining the B-coefficients are not only computation intensive but it is also not applicable for harmonic polluted network. An indirect method is introduced in this work which determines the B-coefficients with very small amount of computation. The proposed method is tested on IEEE 33- bus radial distribution network with the load data suitably modified to incorporate different degrees of nonlinearity. The DG has been assumed to be interfaced with the network through 6-pulse inverter. The test results demonstrate the efficacy of the proposed method for distribution network containing harmonic sources.
Building the Bridge to a Participatory Citizenship: Curricular Integration of Communal Environmental Issues in School Projects Supported by the Internet of Things

Generally, there is much to praise about the rise in acknowledging the need for young citizens to exercise their rights and duties, but the belief remains that this is not yet entrenched in young citizens’ overall democratic involvement. A lack of citizenship and engagement in community issues was revealed by a recent study conducted by the authors in a secondary school from the outskirts of Aveiro, Portugal, during the 2019/2020 school year. Under the umbrella of a Design-Based Research methodological framework, citizen science strategies were implemented in the context of teaching, learning, and assessment, and at the service of the educational project of the target school, in a STEAM approach, and under Domains of Curricular Autonomy activities. The study’s findings suggest that to build the bridge for participatory citizenship, teachers should engage students in collecting and analyzing data regarding communal environmental issues in a Citizen Science approach supported by the Internet of Things. The new pedagogies addressing the lack of citizenship and engagement in community issues promoted students’ involvement at school and in the community, contributed to inform municipal education policies, and promoted dialogue and communication between local actors.