Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Transcranial Magnetic Therapy In Combination With Electrostimulation For Correcting Neuroendocrine-Immune Disorders In Obese Boys

Objective — To justify the use of low-intensity transcranial magnetic therapy (TCMT) in combination with electrostimulation (TES) for the correction of neuroendocrine-immune disorders. Material and Methods — Fifty adolescent boys with exogenous constitutional obesity 13-15 years of age were examined. The control group consisted of 30 boys aged 13-15 years without obesity and concomitant pathology. The examination included an assessment of complaints, anamnesis of the disease, life history, objective data, hormonal status, neurotransmitters and cytokines. By random sampling, the group of obese children was divided into two subgroups: subgroup 1a (n=24), who received TCMT in combination with TES, and subgroup 1b – comparison group (n=22), who received placebo therapy with disconnected electrodes. Both groups of children received identical recommendations regarding the nutrition. The effectiveness of the therapy was evaluated after 3 months. The treatment course comprised 10 procedures. Statistical processing of the obtained data was carried out using the statistical software package Statistica 8. Results — An increase in the levels of testosterone and β – endorphin as well as a decrease in estradiol and kisspeptin, which was accompanied by a decrease in body weight was observed in children of subgroup 1a (n=24) under the influence of TCMT in combination with TES. In subgroup 1b (n=22) receiving placebo therapy with disconnected electrodes, there was also a decrease in body weight, but at a much lower level. Conclusion — The use of TCMT in combination with TES led to the normalization of neuroendocrine-immune disorders and contributed to weight loss.
Fundamentals of Renewable Energy and Applications

The simple water splitting electrolysis cell has been shown that can easily be used to assess iteratively changed aspects of design and operation for the water splitting process and the design concepts for water splitting devices. The design characteristics and materials have been discussed such that a cheap and easy starting point for the assessment of design and process modification can be fully assessed. Concentration of electrolyte, and distance between electrodes have been shown to be key to the resistance of the cell and therefore to the efficiency of the process. This test cell will form the basis for comparison for future research regarding a number of aspects of potential improvements to the water splitting process.
Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble

ABSTRACT Effective and timely forecasting of daily tourism volume is an important topic for tourism practitioners and researchers, which can reduce waste and promote the sustainable development of tourism. Several studies are based on the decomposition-ensemble model to forecast the time series of high volatility in tourism volume, but ignore different forecasting methods suitable for different subseries. This study provides an adaptive decomposition-ensemble hybrid forecasting approach. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to effectively decompose the original time series into multiple relatively easy subseries, which reduces the complexity of the data. Secondly, sample entropy calculates the complexity of a sequence, and then adopts the elbow rule to adaptively divide them into different complex sets. Finally, multi-kernel extreme learning machine (KELM) models are used to forecast the components of different sets and integrate them. This hybrid approach makes full use of the advantages of different models, which enables effective use of data. The empirical results demonstrate that the approach can both produce results that are close to the actual values and be utilized as a strategy for forecasting daily tourism volume.
Concept of Placement of Fiber-Optic Sensor in Smart Energy Transport Cable under Tensile Loading

Due to the exponential growth in offshore renewable energies and structures such as floating offshore wind turbines and wave power converters, the research and engineering in this field is experiencing exceptional development. This emergence of offshore renewable energy requires power cables which are usually made up of copper to transport this energy ashore. These power cables are critical structures that must withstand harsh environmental conditions, handling, and shipping, at high seas which can cause copper wires to deform well above the limit of proportionality and consequently break. Copper, being an excellent electric conductor, has, however, very weak mechanical properties. If plasticity propagates inside copper not only will the mechanical properties be affected, but the electrical properties are also disrupted. Constantly monitoring such large-scale structures can be carried out by providing continuous strain using fiber-optic sensors (FOSs). The embedding of optical fibers within the cables (not within the phase) is practiced. Nevertheless, these optical fibers are first introduced into a cylinder of larger diameter than the optical fiber before this same fiber is embedded within the insulator surrounding the phases. Therefore, this type of embedding can in no way give a precise idea of the true deformation of the copper wires inside the phase. In this article, a set of numerical simulations are carried-out on a single phase (we are not yet working on the whole cable) with the aim of conceptualizing the placement of FOSs that will monitor strain and temperature within the conductor. It is well known that copper wire must never exceed temperatures above 90 °C, as this will result in shutdown of the whole system and therefore result in heavy maintenance, which would be a real catastrophe, economically speaking. This research explores the option of embedding sensors in several areas of the phase and how this can enable obtaining strain values that are representative of what really is happening in the conductor. It is, therefore, the primary objective of the current preliminary model to try to prove that the principle of embedding sensors in between copper wires can be envisaged, in particular to obtain an accurate idea about strain tensor of helical ones (multi-parameter strain sensing). The challenge is to ensure that they are not plastically deformed and hence able to transport electricity without exceeding or even becoming closer to 90 °C (fear of shutdown). The research solely focuses on mechanical aspects of the sensors. There are certainly some others, pertaining to sensors physics, instrumentation, and engineering, that are of prime importance, too. The upstream strategy of this research is to come up with a general concept that can be refined later by including, step by step, all the aspects listed above.
Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain Patients

Low back pain (LBP) obviously reduces the quality of life but is also the world’s leading cause of years lived with disability. Alterations in motor response and changes in movement patterns are expected in LBP patients when compared to healthy people. Such changes in dynamics may be assessed by the nonlinear analysis of kinematical time series recorded from one patient’s motion. Since sample entropy (SampEn) has emerged as a relevant index measuring the complexity of a given time series, we propose the development of a clinical test based on SampEn of a time series recorded by a wearable inertial measurement unit for repeated bending and returns (b and r) of the trunk. Twenty-three healthy participants were asked to perform, in random order, 50 repetitions of this movement by touching a stool and another 50 repetitions by touching a box on the floor. The angular amplitude of the b and r movement and the sample entropy of the three components of the angular velocity and acceleration were computed. We showed that the repetitive b and r “touch the stool” test could indeed be the basis of a clinical test for the evaluation of low-back-pain patients, with an optimal duration of 70 s, acceptable in daily clinical practice.
Legged and Flying Robots for Disaster Response

Disaster response is among the most important tasks in need for autonomous robots, but also one of the most challenging for robots operation. While robots are already doing an effective job as factory work-horses or floor cleaning devices, operations in highly unstructured and unknown environments, as encountered after disasters, is still a major challenge. Within this paper we will present and discuss our latest research results with legged, tracked, and flying robot systems designed to support disaster response operations. We will elaborate on the design of our quadruped walking robots, our solar airplane as well as our findings in localization, mapping and path planning for disaster response.
Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available.
Salivary and Lacrimal Gland Alterations of the Epidermal Fatty Acid-Binding Protein (E-FABP) in Non-Obese Diabetic Mice

The purpose of this study was to investigate the changes in E-FABP in the salivary and lacrimal glands of the Sjögren syndrome (SS) model non-obese diabetic mice (NOD). Cotton thread and ocular vital staining tests were performed on 10-week NOD male mice (n = 24) and age- and sex-matched wild-type (WT) mice (n = 25). Tear and saliva samples were collected at sacrifice for E-FABP ELISA assays. Salivary and lacrimal gland specimens underwent immunohistochemistry stainings for E-FABP. Real-time RT-PCR was also performed for the quantification of mRNA expression levels in the salivary and lacrimal glands. Corneal vital staining scores in the NOD mice were significantly higher compared with those for the wild-type mice (p = 0.0001). The mean tear E-FABP level showed a significantly lower concentration in the NOD mice (p = 0.001). The mean saliva E-FABP level also showed a significantly lower concentration in the NOD mice (p = 0.04). Immunohistochemistry revealed intense E-FABP staining in the LG acinar epithelium and less intense staining in the acinar epitheliae of the SGs in the NOD mice compared to the WT mice. Real-time RT-PCR for the mRNA expression of E-FABP showed a significantly decreased expression in the SG and a significant increase in the LG of the NOD mice compared to the WT mice. In conclusion, the E-FABP showed marked alterations in the tear film, saliva, lacrimal, and salivary glands of the NOD mouse, which may help explain the ocular surface changes in relation to the dry eye disease in this SS model mouse and keratoconjunctivitis sicca in SS patients.
Fed_ADBN: An efficient intrusion detection framework based on client selection in AMI network

Data transmission between smart meters and data center is facing network security threats in advanced metering infrastructure of smart grid. The traditional solution is to move the data to the data center to build a centralized attack detection model, or divide the collected data into several independent and identically distributed datasets to build a distributed attack detection model. However, the long‐distance transmission and the centralized storage of data not only increase the communication overhead and time overhead, but also increase the risk of being attacked, causing privacy disclosure during the process of building the model. In this paper, we propose an efficient intrusion detection framework Fed_ADBN based on federated attention deep belief network and client selection. Clients cooperate with the data center to jointly build a horizontal federated learning framework. Under the premise of protecting data security by keeping data on the clients, we design a client selection algorithm based on client computing power, communication quality and security risks, which can improve the operating efficiency of federated learning. We also deploy a deep belief neural network with attention mechanism in each client to accurately detect possible network attacks in AMI network in real time. Experimental results show that compared with state‐of‐the‐art methods, the proposed framework can not only maintain good detection accuracy but also protect privacy.
Exploring the Distinct Distribution of Archaeal Communities in Sites Contaminated with Explosives

Most of the research on bioremediation and estimation of microbial diversity in waste contaminated sites is focused on the domain Bacteria, whereas details on the relevance of Archaea are still lacking. The present study examined the archaeal diversity and predicted metabolic pathways in two discrete sites (SITE1 and SITE2) contaminated with explosives (RDX and HMX) by amplicon-targeted sequencing of 16S rRNA genes. In total, 14 soil samples were processed, and 35,758 OTUs were observed, among which 981 OTUs were classified as Archaea, representing ~2.7% of the total microbial diversity in our samples. The majority of OTUs belonged to phyla Euryarchaeota (~49%), Crenarchaeota (~24%), and Thaumarchaeota (~23%), while the remaining (~4%) OTUs were affiliated to Candidatus Parvarchaeota, Candidatus Aenigmarchaeota, and Candidatus Diapherotrites. The comparative studies between explosives contaminated and agricultural soil samples (with no history of explosives contamination) displayed significant differences between the compositions of the archaeal communities. Further, the metabolic pathways pertaining to xenobiotic degradation were presumably more abundant in the contaminated sites. Our data provide a first comprehensive report of archaeal communities in explosives contaminated sites and their putative degradation role in such ecosystems which have been as yet unexplored.
Noncontact Sensing of Heart Rate Variability from Facial Video Using the Topology Algorithm

: This study proposes a noncontact, accurate sensing method for heartbeat R–R intervals and heart rate variability from the video of a target person’s face. The proposed method comprises three steps: (1) automatic face recognition and tracking, (2) the detection of skin-tone pixels in the face region, and (3) heart rate estimation using the topology algorithm. We applied the proposed method to facial video recordings of 55 participants and evaluated its accuracy. The results demonstrate that the root-mean-square error in estimating the heart rate variability was 2.7 ms using the proposed method, demonstrating the eﬀectiveness of the proposed approach.
Obstacle Avoidance and Boundary Following Behavior of the Echolocating Bat

Developing a robust and rapid system for obstacle avoidance and boundary following has long been a goal of robotics research. The echolocating bat, Eptesicus fuscus, has evolved to navigate through complex environments in pursuit of evasive prey. Given its robust flight behavior, active sensing through sonar, small size, and low power consumption, E. fuscus has much potential as a source of inspiration for bio-inspired engineering, especially in the context of unmanned aerial vehicles. In this paper, we take steps toward such engineering by investi- gating the obstacle avoidance and boundary interaction behavior of E. fuscus. The bat's obstacle avoidance behavior is compared to the neurally-inspired open space algorithm, a winner-take-all based heading direction selection method. Boundary interaction by the bat is examined by testing a hypothesis: in following a boundary, the bat's flight path curvature is a time-delayed version of the boundary curvature. Initial results are promising; however, future work will include a more rigorous statistical analysis of observed behavior.
High intraspecific trait variation results in a resource allocation spectrum of a subtropical pine across an elevational gradient

Plant functional traits are broadly used to quantify and predict impacts of climate change on vegetation. However, high intraspecific trait variation can bias mean values when few measurements are available. Here, we determine the extent of individual leaf trait variation and covariation across a highly heterogeneous environmental gradient for a widely distributed subtropical pine. We demonstrate the implications of trait variation for characterising species by assessing data availability and variability across the Pinus genus.
Electricity sector transformation in New Zealand: A sustainability assessment approach

New Zealand has a long history in producing a major part of its electricity from low-carbon resources. A major transition from a state owned and operated electricity sector to an open electricity market model and the policies adopted during this period have shaped the generation mix of New Zealand to its current form. Currently, domestic fossil fuel resources offer opportunity for economic growth, while renewable energy resources are significant and present opportunities for green growth in both domestic and global markets. What is particular about the New Zealand case is the “middle out” approach of the national renewable energy strategy, rather than the common top-down approach of many other countries. Consenting is done on a case by case basis at regional level, allowing for wide community and stakeholder participation. This paper describes the transitions in the electricity sector and renewable energy policy of New Zealand, and presents an assessment using sustainability indicators to study the impact of this transition on the level of sustainability of the country's electricity supply system. Although the electricity mix has seen a transition from hydropower towards gas, geothermal, and wind energy, there has been no significant change in the overall sustainability index of the New Zealand electricity supply. This is because the system remains based on renewable sources and gas, which scores relatively high in the model we have applied. Using the country-specific values for the indicators would give a more reliable model to assess the sustainability of the electricity system in detail from its economic, social, and environmental aspects.
Anisotropic Generalized Bayesian Coherent Point Drift for Point Set Registration

Registration is highly demanded in many real-world scenarios such as robotics and automation. Registration is challenging partly due to the fact that the acquired data is usually noisy and has many outliers. In addition, in many practical applications, one point set (PS) usually only covers a partial region of the other PS. Thus, most existing registration algorithms cannot guarantee theoretical convergence. This article presents a novel, robust, and accurate three-dimensional (3D) rigid point set registration (PSR) method, which is achieved by generalizing the state-of-the-art (SOTA) Bayesian coherent point drift (BCPD) theory to the scenario that high-dimensional point sets (PSs) are aligned and the anisotropic positional noise is considered. The high-dimensional point sets typically consist of the positional vectors and normal vectors. On one hand, with the normal vectors, the proposed method is more robust to noise and outliers, and the point correspondences can be found more accurately. On the other hand, incorporating the registration into the BCPD framework will guarantee the algorithm’s theoretical convergence. Our contributions in this article are three folds. First, the problem of rigidly aligning two general PSs with normal vectors is incorporated into a variational Bayesian inference framework, which is solved by generalizing the BCPD approach while the anisotropic positional noise is considered. Second, the updated parameters during the algorithm’s iterations are given in closed-form or with iterative solutions. Third, extensive experiments have been done to validate the proposed approach and its significant improvements over the BCPD. Note to Practitioners—This paper was motivated by the problem of 3D rigid PSR for computer-assisted surgery (CAS), especially in orthopedic applications. The proposed algorithm is also suitable for other scenarios where the initial coarse registration is conducted. The traditional registration methods are susceptible to noise (especially anisotropic noise), outliers, and incomplete partial data. This paper generalizes the recently proposed BCPD method to the six-dimensional scenario where anisotropic positional noise is considered and normal vectors are incorporated. The proposed noise model is decomposed into three parts to be solved alternately: the membership probability of mixture distributions, the soft correspondence estimation, and the model parameters (i.e., the rotation matrix, translation vector, the covariance matrix with the anisotropic positional error, and the concentration parameter with the estimation of the normal vectors). Especially, the convergence is guaranteed at the theoretical level using the variational inference theory. The experimental results demonstrate the superiority of our algorithm on registration accuracy, convergence speed, and robustness to noise, outliers, and partial data.