Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband Sound Insulation and Dual Equivalent Negative Properties of Acoustic Metamaterial with Distributed Piezoelectric Resonators

Aiming at the unsatisfactory sound transmission loss (STL) of thin-plate structures in the low-mid frequency range, this paper proposes an acoustic insulation metamaterial with distributed piezoelectric resonators. A complete acoustic prediction model is established based on the effective medium method and classical plate theory, and the correctness is verified by the STL simulation results of the corresponding acoustic-structure fully coupled finite-element model. Moreover, the intrinsic relationship between the dual equivalent negative properties and STLs is investigated to reveal the insulation mechanisms of this metamaterial. Then, the influence of the geometric and material parameters on the double equivalent negative characteristics is studied to explore the broadband STL for distributed multi-modal resonant energy-dissipation modes in the frequency band of interest. The results show that the two acoustic insulation crests correspond to the dual equivalent negative performances, and the sound insulation in the low-mid frequency range is improved by more than 5 dB compared with that of the substrate, even up to 44.49 dB.
Potential eligibility for Aducanumab therapy in an Irish specialist cognitive service—Utilising cerebrospinal fluid biomarkers and appropriate use criteria

Aducanumab is a monoclonal antibody which has recently been licenced for use by the food and drug administration for treatment of patients with mild cognitive impairment due to Alzheimer's disease (AD) or mild AD dementia. Appropriate use criteria (AUC) for Aducanumab in clinical practice are available. We look to review patients in our specialist interdisciplinary cognitive service with positive cerebrospinal fluid (CSF) biomarkers for AD for their hypothetical eligibility for Aducanumab, or a similar anti‐amyloid agent.
Reducing the background in x-ray imaging detectors via machine learning

The sensitivity of astronomical x-ray detectors is limited by the instrumental background. The background is especially important when observing low surface brightness sources that are critical for many of the science cases targeted by future x-ray observatories, including Athena and future U.S.-led flagship or probe-class x-ray missions. Above 2 keV, the background is dominated by signals induced by cosmic rays interacting with the spacecraft and detector. We develop novel machine learning algorithms to identify events in next-generation x-ray imaging detectors and to predict the probability that an event is induced by a cosmic ray vs. an astrophysical x-ray photon, enabling enhanced filtering of the cosmic ray-induced background. We find that by learning the typical correlations between the secondary events that arise from a single primary, machine learning algorithms are able to successfully identify cosmic ray-induced background events that are missed by traditional filtering methods employed on current-generation x-ray missions, reducing the unrejected background by as much as 30 percent.
Aversive Learning Changes Face-Viewing Strategies, as Revealed by Model-Based Fixation-Pattern Similarity Analysis

To avoid costly situations animals must be able to rapidly predict imminent threat based on past experience and present noisy sensory evidence. We aimed to characterize to what extent active exploration strategies, can be adaptively tuned to achieve this goal. We measured how eye-movement patterns on 8 faces, organized along a circular similarity continuum, were modified after aversive learning and generalization. Using model-based Fixation Similarity Analysis, we characterized how similarity relationships between exploration strategies were modified after volunteers learnt to pair one face (CS+) with a mild electric shock. Initially, viewing patterns reflected the circular physical similarity structure of different faces, indicating that eye-movements were guided by subtle differences between faces. Following aversive learning, the similarity structure of exploration patterns became elliptical and stretched along the adversity gradient defined by the CS+ and the most dissimilar neutral face (CS-), indicating that exploration patterns on these faces were most dissimilar. These findings suggest that the need to predict adversity introduces substantial remodelling of exploration patterns and influences entire sensorimotor loops by selecting fixation locations that can help in categorizing stimuli as aversive or safe.
Detecting Compromised IoT Devices Through XGBoost

The evolution and rapid adoption of the Internet of Things (IoT) led to a rise in the number of attacks that target IoT environments. IoT environments are vulnerable to several attacks because many devices lack memory, processing power, and battery. Most of these vulnerabilities are relatively easy to mitigate when best practices are followed. However, even when best practices are followed, an attack to obtain a device credential and use it to generate false data is difficult to detect. Such an attack is called a replication attack and its impact can be catastrophic in crucial IoT scenarios such as smart transportation. In this sense, this paper proposes a solution to detect these attacks by analyzing abnormal network traffic through machine learning.
ECG Approximate Entropy in the Elderly during Cycling Exercise

Approximate entropy (ApEn) is used as a nonlinear measure of heart-rate variability (HRV) in the analysis of ECG time-series recordings. Previous studies have reported that HRV can differentiate between frail and pre-frail people. In this study, EEGs and ECGs were recorded from 38 elderly adults while performing a three-stage cycling routine. Before and after cycling stages, 5-min resting-state EEGs (rs-EEGs) and ECGs were also recorded under the eyes-open condition. Applying the K-mean classifier to pre-exercise rs-ECG ApEn values and body weights revealed nine females with EEG power which was far higher than that of the other subjects in all cycling stages. The breathing of those females was more rapid than that of other subjects and their average heart rate was faster. Those females also presented higher degrees of asymmetry in the alpha and theta bands (stronger power levels in the right frontal electrode), indicating stressful responses during the experiment. It appears that EEG delta activity could be used in conjunction with a very low ECG frequency power as a predictor of bursts in the heart rate to facilitate the monitoring of elderly adults at risk of heart failure. A resting ECG ApEn index in conjunction with the subject’s weight or BMI is recommended for screening high-risk candidates prior to exercise interventions.
Multispecies study of patterns and drivers of wildlife impacts on human livelihoods in communal conservancies

Farmers in developing countries often work in challenging environments with poor infrastructure, marginal agricultural potential, and limited economic opportunities. These challenges are exacerbated when wildlife impact human livelihoods. Here, we analyze data quantifying the type and frequency of human‐wildlife impacts within communal conservancies across Namibia and explore possible drivers of temporal and spatial variation of these data. A total of 112,165 human‐wildlife impacts were reported between 2001 and 2019 at the national level, with livestock depredation the most common. Marked regional variation was however evident with crop raiding and attacks on humans more prevalent in the mesic North‐East, and both livestock depredation and infrastructure damage highest in the arid north‐western regions. Elephant, jackal, hyena, cheetah, and leopard (in descending order) were the species most frequently linked to reported damage. Distance to the nearest protected area and river, terrain ruggedness, conservancy size, and annual rainfall (amongst others) all had a significant impact on both the distribution and extent of human‐wildlife impact reports. Reports did not vary significantly with years but were significantly influenced by average monthly rainfall. Understanding spatial and temporal patterns of human‐wildlife impacts at a national scale, in addition to their potential drivers, allows for the identification of conflict hotspots and the allocation of resources and expertise to mitigate them. Ultimately, mitigating negative interactions between people and wildlife will allow for the continued sharing of space and with that the sustainability of a model that has seen a dramatic increase in both the distribution and abundance of wildlife in Namibia.
Assessing quantum error correction: fidelity and entanglement measures with application to photonic codes

By interpreting the well-known, qualitative criteria for the existence of quantum error correction (QEC) codes by Knill and Laflamme from a quantitative perspective, we propose a figure of merit for assessing a QEC scheme based on the average fidelity between codewords. This enables us to quantify the performance of a protocol as a whole, including errors beyond the correctable set. Various examples are calculated for photonic qubit codes dealing with the experimentally relevant case of photon loss, demonstrating the advantages of the new measure. In the context of continuous-variable QEC, our codeword-overlap measure can be used to reproduce, in a different way with no need for calculating entanglement measures, a previous result concerning the impossibility of improving transmission of Gaussian states through Gaussian channels via Gaussian operations alone.
Spatio-Temporal Variation of Precipitation in the Qinling Mountains from 1970 to 2100 Based on CMIP6 Data

Estimating future precipitation changes in the Qinling Mountains has significance, for understanding how to reveal the basic characteristics of the atmospheric water cycle in mountainous areas under the action of monsoons and the temporal- and spatial-variation mechanism of water resources in the ‘central water tower’, under the background of climate change. Based on four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) of the CMIP6 model, the Taylor diagram method was used to select the best regional simulation model, according to historical observation data (1970–2014). On this basis, the precipitation change and circulation background of the Qinling Mountains over the next 86 years (2015–2100) were analyzed. The results show that the simulation effect of the optimal mode is better than that of the single mode. Under the four scenarios, the variation trends of the annual precipitation in the Qinling Mountains from 2015 to 2100 were 4.4 mm/10a, 18.5 mm/10a, 18.1 mm/10a, and 19.1 mm/10a, respectively. By the middle of this century (2041–2060), compared with the reference period of 1995–2014, the average annual precipitation in the Qinling Mountains under the four scenarios will increase by 64.1 mm, 7 mm, 28.8 mm, and −51 mm, respectively. By the end of this century (2081–2100), the average annual precipitation under the four scenarios will increase by 29.5 mm, 77.2 mm, 82.9 mm, and 21.2 mm, respectively. The abnormal increase (decrease) of water vapor, transported northward from the western Pacific and the Bay of Bengal, is the main reason for the abnormal increase (decrease) of precipitation in the flood season in the Qinling Mountains. With the increase in emission scenarios, the influence of the South Asian summer monsoon on precipitation in the Qinling Mountains becomes more significant.
​Effect of Breed, Season and Stage of Lactation on Different Milk Parameters at Organized Farm

Background: The current study had been conducted to understand the impact of breed as well as season and stage of lactation on various milk constituents viz. freezing point, SNF, pH, lactose, protein, fat and electric conductivity of Sahiwal and Kankrej cows maintained at LRS”Livestock Research Station”, College of Veterinary and Animal Science, RAJUVAS, Bikaner, Rajasthan. Methods: Milk samples from 45 each Sahiwal and Kankrej cows were collected and analyzed for milk composition parameters. Data have been classified as per breed along with season and stage of lactation. Result: The analysis of variance for breed demonstrated highly significant impacts of fat and SNF whereas non-significant effect was found on pH, freezing point, lactose and protein.The analysis of variance demonstrated highly significant (P less than 0.01) impact of season on fat, SNF, freezing point and significant effect on protein whereas non-significant effect on lactose, pH and Electric conductivity in Kankrej cattle however highly significant (P less than 0.01) effect was found on fat in Sahiwal cattle.The analysis of variance presented significant (P less than 0.05) effect of lactation stage on fat and Protein in Sahiwal cattle whereas on fat and SNF in Kankrej cattle.
A review on interaction control for contact robots through intent detection

Interaction control presents opportunities for contact robots physically interacting with their human user, such as assistance targeted to each human user, communication of goals to enable effective teamwork, and task-directed motion resistance in physical training and rehabilitation contexts. Here we review the burgeoning field of interaction control in the control theory and machine learning communities, by analysing the exchange of haptic information between the robot and its human user, and how they share the task effort. We first review the estimation and learning methods to predict the human user intent with the large uncertainty, variability and noise and limited observation of human motion. Based on this motion intent core, typical interaction control strategies are described using a homotopy of shared control parameters. Recent methods of haptic communication and game theory are then presented to consider the co-adaptation of human and robot control and yield versatile interactive control as observed between humans. Finally, the limitations of the presented state of the art are discussed and directions for future research are outlined.
Development of active optics for thin meniscus mirrors in 1-meter-class telescopes

This paper presents the ongoing project of implementing an active optics system in an 1-meter telescope to build a lightweight, cost-effective system with high imaging quality. A systematic design methodology for meniscus mirrors based on thin plate theory and finite element analysis enables target-oriented engineering of a support system that takes the application-dependent optical mirror requirements into account. The actuators for the lateral and axial system are based on stepper motors and designed in a modular manner, adjustable for a wide range of nominal force values. The axial actuators integrate an optical force measurement to achieve a compact design. Laser triangulation sensors placed in the mirror cell measure the lateral mirror position in 3 degrees of freedom with respect to its cell, providing position data that is used in a feedback control to compensate for finite stiffness of the support structure and thermal disturbances. Tests of the lateral system’s performance show maximum displacements of 1.4 μm in both lateral directions, keeping the according wavefront RMS error below 5 nm.
Deep Learning: Differential Privacy Preservation in the Era of Big Data

ABSTRACT In recent years, deep learning (DL) has been ubiquitous in several areas, such as text recognition and data analysis, limited by this and increasingly used in security and data protection applications. Thus, the DL method has achieved remarkable big data analysis growth to avoid different attacks. This paper presents different methods for protecting privacy for DL in big data analysis. First, some possible attacks are explained, and then some basic approaches to protecting privacy in big data platforms are explained. In each section, drawbacks of the corresponding attacks are elaborated, and DL-based methods’ effectiveness in privacy preservation has been discussed. Finally, an effective solution for enhancing privacy preservation in DL models is given. The several DL-based privacy preservation methods for big data analysis and their advantages and disadvantages are elaborated. At last, drawbacks of DL based methods are highlighted, and future scope is given to address these issues.
NUMERICAL ANALYSIS OF EFFECTIVENESS FOR VEHICLE NET SYSTEMS PROTECTING AGAINST SHAPED CHARGE PROJECTILES

Streszczenie. W artykule przedstawiono analizę skuteczności dwóch typów systemów siatkowych chroniących pojazdy przed pociskami z głowicą kumulacyjną. Zbudowano modele numeryczne pocisku PG7-WM i systemów ekranów siatkowych, które użyto do wykonania symulacji penetracji ekranu przez pocisk. Za pomocą analiz numerycznych wyznaczono maksymalną odległość od osi pocisku na kierunku promieniowym, w której powinno dojść do kontaktu pocisku z elementami ekranów w celu jego neutralizacji. Wyznaczone odległości użyto do obliczenia prawdopodobieństwa neutralizacji pocisku przez systemy siatkowe. Obliczenia wykonano dla kątów elewacji i azymutu w zakresie α = 0÷45°. Wyliczone w taki sposób wartości posłużyły do sporządzenia wykresów przedstawiających zależności prawdopodobieństwa neutralizacji pocisku PG-7WM od rozmiaru oczka w przypadku obu typów systemów siatkowych oraz zależność prawdopodobieństwa neutralizacji pocisku PG-7WM od kątów elewacji i azymutu w przypadku różnych rozmiarów oczek obu typów systemów siatkowych. Wyniki mogą zostać wykorzystane do optymalizacji geometrii systemów siatkowych pod kątem efektywności neutralizacji pocisków.
Enhancing BDI Agents Using Fuzzy Logic for CPS and IoT Interoperability Using the JaCa Platform

Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS’s network and communication capabilities. In the literature, software agents, particularly belief–desire–intention (BDI) agents, are considered options to program these heterogeneous and complex systems in various domains. Moreover, fuzzy logic is a method for handling uncertainties. Therefore, the enhancement of BDI with fuzzy logic can also be employed to improve the abilities, such that autonomy, pro-activity, and reasoning, which are essentials for intelligent systems. These features can be applied in CPSs and IoT interoperable systems. This study extends the CPSs and IoT interoperable systems using fuzzy logic and intelligent agents as symmetric paradigms that equally leverage these domains as well as benefit the agent & artifact approach. In this regard, the main contribution of this study is the integration approach, used to combine the CPS and IoT augmented with fuzzy logic using BDI agents. The study begins with constructing the design primitives from scratch and shows how Jason BDI agents can control the distributed CPS. The study then performs the artifact approach by encapsulating a fuzzy inference system, utilizing time-based reasoning, and benefiting from symmetric fuzzy functions. Lastly, the study applies the self-adaptiveness method and flexibility plan selection, considering the run-time MAPE-K model to tackle run-time uncertainty.
Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Human activity recognition (HAR) can effectively improve the safety of the elderly at home. However, non-contact millimeter-wave radar data on the activities of the elderly is often challenging to collect, making it difficult to effectively improve the accuracy of neural networks for HAR. We addressed this problem by proposing a method that combines the improved principal component analysis (PCA) and the improved VGG16 model (a pre-trained 16-layer neural network model) to enhance the accuracy of HAR under small-scale datasets. This method used the improved PCA to enhance features of the extracted components and reduce the dimensionality of the data. The VGG16 model was improved by deleting the complex Fully-Connected layers and adding a Dropout layer between them to prevent the loss of useful information. The experimental results show that the accuracy of our proposed method on HAR is 96.34%, which is 4.27% higher after improvement, and the training time of each round is 10.88 s, which is 12.8% shorter than before.
Semantic Grasping Via a Knowledge Graph of Robotic Manipulation: A Graph Representation Learning Approach

Semantic grasping aims to make stable robotic grasps suitable for specific object manipulation tasks. While existing semantic grasping models focus only on the grasping regions of objects based on their affordances, reasoning about which gripper to use for grasping, e.g., a rigid parallel-jaw gripper or a soft gripper, and how strongly to grasp the target object allows more sophisticated robotic manipulation. In this letter, we create a knowledge graph of robotic manipulation named roboKG to represent information about objects (e.g., the material and the components of an object), tasks, and appropriate robotic manipulation such as which component of an object to grasp, which gripper to use, and how strongly to grasp. Using knowledge graph embedding, we generate semantic representations of the entities and relations in roboKG, enabling us to make predictions on robotic manipulation. Based on the predicted gripper type, grasping component, and grasping force, a real robot performs seven different real-world tasks on 42 household objects, achieving an accuracy of 95.21%.
An Added-Mass Measurement Technique for Transducer Parameter Estimation

We present a methodology to measure transducers with a dual-added-mass technique in order to extract the motional impedance ZM(ω) and force factor B` from the total impedance. This methodology is suitable for determining moving mass and compliance for motional impedance models that include viscoelasticity and frequency-dependent damping by curve fitting, and is applicable to classical transducers for which adding mass to the moving parts is possible without introducing significant artefacts. Furthermore, we discuss techniques to assess the quality of the results. Finally, the quality of the proposed measurement technique is verified with an ANOVA Gage R&R measurement system analysis.
Depression Detection on Social Media Using Text Mining

Depression is regarded as the major cause of disability and a leading cause of suicide. Social media has recently risen to prominence as a leading means of disseminating information online. The majority of people use the internet to express their opinions, ideologies, and personal experiences. Depression has an effect on how people communicate on social media as evidenced by the language they use. The detection of mental illness has become a major concern as a result of the massive increase in mental health awareness. In this paper, we examine Twitter users’ tweets to detect any factors that may reveal depression. For this purpose, we use Text Mining and Natural Language Processing techniques. We employed CNN + LSTM classification technique and obtained an accuracy of 92%. We have also compared our model with Logistic Regression and TF-IDF classifiers.