Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Manipulation Mechanism and Analysis for Actively Assistive Pinch Movements

Pinching motions are important for holding and retaining objects with precision. Therefore, training exercises for the thumb and index finger are extremely important in the field of hand rehabilitation. Considering the need for training convenience, we developed a device and a driving system to assist pinching motions actively via a lightweight, simple, and wireless mechanism driven by the magnetic forces and torques generated by magnets attached to the tip of these two fingers. This device provides accurate pinching motions through the linking structures connecting the two magnets. The fabricated device has minimal mechanical elements with an ultralightweight of 57.2 g. The magnetic field, the intensity of which is based on the time variant, generates a pinching motion between the thumb and index finger, thus rendering it possible to achieve repetitive training. To verify the generation of an active pinching motion, we fabricated a finger model using a 3D printer and a rubber sheet and observed the active motions generated by the newly developed device. We also verified the performance of the proposed mechanism and driving method via various experiments and magnetic simulations. The proposed mechanism represents an important breakthrough for patients requiring hand rehabilitation and wearable assistive motion devices.
Light Scattering Calculations for Spherical Metallic Nanoparticles (Ag, Au) Coated by TCO (AZO, ITO, PEDOT:PSS) Shell

Ag and Au nanostructures became increasingly interesting due to their localized surface plasmon resonance properties. These properties can be successfully exploited in order to enhance the light trapping in solar cell devices by appropriate light scattering phenomena. In solar cell applications, the Ag or Au nanoparticles are, usually, supported on or embedded in a thin transparent conductive oxide layer, mainly AZO and ITO for inorganic solar cells and PEDOT:PSS for organic solar cells. However, the light scattering properties strongly depend on the shape and size of the metal nanostructures and on the optical properties of the surrounding environment. Therefore, the systems need to be well designed to maximize scattering and minimize the light absorption within the metal nanoparticles. In this regard, this work reports, in particular, results concerning calculations, by using the Mie theory, of the angle-dependent light scattering intensity (I(θ)) for spherical Ag and Au nanoparticles coated by a shell of AZO or ITO or PEDOT:PSS. I(θ) and scattering efficiency Qscatt for the spherical core–shell nanoparticles are calculated by changing the radius R of the spherical core (Ag or Au) and the thickness d of the shell (AZO, ITO, or PEDOT:PSS). For each combination of core–shell system, the evolution of I(θ) and Qscatt with the core and shell sizes is drawn and comparisons between the various types of systems is drawn at parity of core and shell sizes. For simplicity, the analysis is limited to spherical core–shell nanoparticles so as to use the Mie theory and to perform analytically exact calculations. However, the results of the present work, even if simplified, can help in establishing the general effect of the core and shell sizes on the light scattering properties of the core–shell nanoparticles, essential to prepare the nanoparticles with desired structure appropriate to the application.
Analysis of Nutrient Composition, Rumen Degradation Characteristics, and Feeding Value of Chinese Rye Grass, Barley Grass, and Naked Oat Straw

Simple Summary Limited studies have been reported so far on the rumen degradability characteristic of low-quality forage. In the current study, three low-quality forages (Chinese rye grass, barley grass, and naked oat straw) were selected. Various nutritional parameters such as rumen degradation characteristics, chemical composition and feeding value of three roughages were studied in detail. Among the three roughages, the Chinese rye grass (Leymus chinensis) was superior in terms of rumen degradability characteristics, chemical composition and feeding value. Abstract The current study was designed to investigate the chemical composition, rumen degradation characteristics, and feeding value of three roughages commonly used in Asia as ruminant feed, including Chinese rye grass (CRG), barley grass (BG), and naked oat straw (NO). Four Holstein Friesian cows equipped with permanent rumen fistulas were chosen for experimental trials in the current study. The nylon bag method was carried out to measure the crude protein (CP), acid detergent fiber (ADF), ruminal degradability of dry matter (DM), and neutral detergent fiber (NDF). Our analysis revealed that the contents of CP in the CRG (9.0%) and BG (8.9%) were higher than in the NO (5.94%). The contents of NDF in the CRG (64.97%) and NO (63.83%) were lower than in the BG (67.33%), and the content of ADF in the CRG (37.03%) was lower than in the BG (37.93%) and NO (38.28%). The ED values of DM in the NO and CRG were significantly higher (p < 0.001) than in the BG. The effective degradability (ED) values of NDF were the highest in the CRG and lowest in the NO (p < 0.001). In addition, the ED values of ADF were the highest in the CRG and lowest in the BG (p < 0.001). The ED value of CP in the CRG was significantly higher than that in the BG and NO (p < 0.001). The estimated total digestible nutrients (TDN) (54.56%) and DM degradation rate (DDM) (60.06%) of the CRG were higher than those of BG and NO. In addition, the expected DM intake (DMI), estimated relative feed value (RFV), and estimated relative feed quality (RFQ) of the BG were lower than those of the CRG and NO. Altogether, based on our findings, we concluded that the nutritional quality, feeding value and effective rumen degradation rate of CRG were better than of BG and NO.
Experimental Results and Performance Analysis of a 1 × 2 × 1 UHF MIMO Passive RFID System

Ultra-high frequency (UHF) multiple input multiple output (MIMO) passive radio frequency identification (RFID) systems have attracted the attention of many researchers in the last few years. The system modeling and theoretical performance analysis of these systems have been well investigated and revealed in many studies, yet the system prototype and the corresponding experimental results are scarce. In this study, measurements of a 1 × 2 × 1 UHF passive RFID system, including a MIMO UHF passive RFID tag prototype and its corresponding software-defined radio-based reader, taken in a microwave anechoic chamber, are presented. The experimental results are compared with theoretical values and computer simulations. The overall results demonstrate the consistency and the feasibility of UHF MIMO passive RFID systems.
Are There Any Cognitive and Behavioral Changes Potentially Related to Quarantine Due to the COVID-19 Pandemic in People with Mild Cognitive Impairment and AD Dementia? A Longitudinal Study

The aim of the study was to examine potential cognitive, mood (depression and anxiety) and behavioral changes that may be related to the quarantine and the lockdown applied during the COVID-19 pandemic in Greek older adults with mild cognitive impairment (MCI), and AD dementia in mild and moderate stages. Method: 407 older adults, diagnosed either with MCI or AD dementia (ADD), were recruited from the Day Centers of the Greek Association of Alzheimer Disease and Related Disorders (GAADRD). Neuropsychological assessment was performed at baseline (at the time of diagnosis) between May and July of 2018, as well as for two consecutive follow-up assessments, identical in period, in 2019 and 2020. The majority of participants had participated in non-pharmacological interventions during 2018 as well as 2019, whereas all of them continued their participation online in 2020. Results: Mixed measures analysis of variance showed that participants’ ‘deterioration difference—D’ by means of their performance difference in neuropsychological assessments between 2018–2019 (D1) and 2019–2020 (D2) did not change, except for the FUCAS, RAVLT, and phonemic fluency tests, since both groups resulted in a larger deterioration difference (D2) in these tests. Additionally, three path models examining the direct relationships between performance in tests measuring mood, as well as everyday functioning and cognitive measures, showed that participants’ worsened performance in the 2019 and 2020 assessments was strongly affected by NPI performance, in sharp contrast to the 2018 assessment. Discussion: During the lockdown period, MCI and ADD patients’ neuropsychological performance did not change, except from the tests measuring verbal memory, learning, and phonemic fluency, as well as everyday functioning. However, the natural progression of the MCI as well as ADD condition is the main reason for participants’ deterioration. Mood performance became increasingly closely related to cognition and everyday functioning. Hence, the role of quarantine and AD progression are discussed as potential factors associated with impairments.
Innovative Joint for Cable Dome Structure Based on Topology Optimization and Additive Manufacturing

Aiming at the problems of a low material utilization rate and uneven stress distribution of cast-steel support joints in cable dome structures, topology optimization and additive manufacturing methods are used for optimization design and integrated manufacturing. First, the basic principle and calculation process of topology optimization are briefly introduced. Then, the initial model of the support joint is calculated and analyzed by using the universal software ANSYS Workbench 2020R2 and Altair OptiStruct, and the optimized joint is imported into Discovery Live to smooth the surface. The static behaviors of three types of joints (topology-optimized joints, joints after the smoothing treatment, and joints from practical engineering) are compared and analyzed. Finally, the joints are printed by using fused deposition modeling (FDM) technology and laser-based powder bed fusion (LBPBF) technology in additive manufacturing. The results show that the new support joint in the cable dome structure obtained by the topology optimization method has the advantages of a novel shape, a high material utilization rate, and a uniform stress distribution. Additive manufacturing technology can allow the manufacture of complex shape components with high precision and high speed. The combination of topology optimization and additive manufacturing effectively realizes the advanced design and integrated manufacturing of support joints for cable dome structures.
(−)-6-epi-Artemisinin, a Natural Stereoisomer of (+)-Artemisinin in the Opposite Enantiomeric Series, from the Endemic Madagascar Plant Saldinia proboscidea, an Atypical Source

Chemical and biological investigation of the Madagascar endemic plant Saldinia proboscidea led to the isolation of an isomer of artemisinin, (−)-6-epi-artemisinin (2). Its structure was elucidated using a combination of NMR and mass spectrometry. The absolute configuration was established by chemical syntheses of compound 2 as well as a new stereoisomer (3). The comparable bioactivities of artemisinin (1) and its isomer (−)-6-epi-artemisinin (2) revealed that this change in configuration was not critical to their biological properties. Bioactivity was assessed using an apoptosis induction assay, a SARS-CoV-2 inhibitor assay, and a haematin polymerization inhibitory activity (HPIA) assay. This is the first report of an artemisinin-related compound from a genus not belonging to Artemisia and it is the first isolation of an artemisinin-related natural product that is the opposite enantiomeric series relative to artemisinin from Artemisia annua.
Feature Pyramid Network Based Efficient Normal Estimation and Filtering for Time-of-Flight Depth Cameras

In this paper, an efficient normal estimation and filtering method for depth images acquired by Time-of-Flight (ToF) cameras is proposed. The method is based on a common feature pyramid networks (FPN) architecture. The normal estimation method is called ToFNest, and the filtering method ToFClean. Both of these low-level 3D point cloud processing methods start from the 2D depth images, projecting the measured data into the 3D space and computing a task-specific loss function. Despite the simplicity, the methods prove to be efficient in terms of robustness and runtime. In order to validate the methods, extensive evaluations on public and custom datasets were performed. Compared with the state-of-the-art methods, the ToFNest and ToFClean algorithms are faster by an order of magnitude without losing precision on public datasets.
Urban Nature and Public Health: How Nature Exposure and Sociocultural Background Relate to Depression Risk

As the world’s population becomes more urbanized, there is an associated decrease in nature exposure and a rise in noncommunicable diseases, including depression. Previous cross-sectional studies examining urban nature exposure and depression have reported favorable associations. However, many of these studies rely primarily on nature exposure metrics that measure the intensity of nature exposure, while other dimensions of urban nature exposure remain understudied. Therefore, in a cross-sectional, questionnaire-based case study targeting a general urban population (n = 282), we examined the relationship between two less commonly studied urban nature exposure variables (i.e., gardening behavior and greenspace visit frequency) and depression risk while also considering sociocultural background (multivariate logistic regression model). Results indicated that being a gardener was significantly associated with a reduced odds of being at risk of depression and that having a family migration history, but not a self-migration history, was associated with increased odds of being at risk of depression. In the examination of neighborhood socialization frequency and depression risk, we did not determine any significant association. The results of this study, therefore, highlight the importance of considering both people’s sociocultural backgrounds and urban nature exposure in more detail to help plan for and support healthier cities in the future.
Metagenomic Analysis of Bacterial Communities in Agricultural Soils from Vietnam with Special Attention to Phosphate Solubilizing Bacteria

Bacterial communities can promote increased phosphorus (P) availability for plants and microbes in soil via various mechanisms of phosphate solubilization. The production of extracellular phosphatases releases available P through the hydrolysis of organic P. Examining the abundance and diversity of the bacterial community, including phosphate solubilizing bacteria in soil, may provide valuable information to overcome P scarcity in soil ecosystems. Here, the diversity and relative abundance of bacterial phyla and genera of six agricultural soil samples from Vietnam were analysed by next generation sequencing of the 16S rRNA gene. Phosphatase activities of each soil were compared with physico-chemical parameters and the abundance of the alkaline phosphatase gene phoD. We showed the dominance of Chloroflexi, Proteobacteria, Actinobacteria, Acidobacteria and Firmicutes. Total nitrogen positively correlated with phyla Proteobacteria, Acidobacteria, Firmicutes and Planctomycetes. The abundance of several genera of Proteobacteria showed positive relationship with the copy number of the phoD gene. The abundance of several taxa positively correlated with silt content, while a negative relationship of Proteobacteria was found with sand content. Our results demonstrated the clear influence of soil physico-chemical properties on the abundance of various bacterial taxa including those potentially involved in phosphate solubilization.
Compound Prioritization through Meta-Analysis Enhances the Discovery of Antimicrobial Hits against Bacterial Pathogens

The development of informatic tools to improve the identification of novel antimicrobials would significantly reduce the cost and time of drug discovery. We previously screened several plant (Xanthomonas sp., Clavibacter sp., Acidovorax sp., and Erwinia sp.), animal (Avian pathogenic Escherichia coli and Mycoplasma sp.), and human (Salmonella sp. and Campylobacter sp.) pathogens against a pre-selected small molecule library (n = 4182 SM) to identify novel SM (hits) that completely inhibited the bacterial growth or attenuated at least 75% of the virulence (quorum sensing or biofilm). Our meta-analysis of the primary screens (n = 11) using the pre-selected library (approx. 10.2 ± 9.3% hit rate per screen) demonstrated that the antimicrobial activity and spectrum of activity, and type of inhibition (growth versus virulence inhibitors) correlated with several physico-chemical properties (PCP; e.g., molecular weight, molar refraction, Zagreb group indexes, Kiers shape, lipophilicity, and hydrogen bond donors and acceptors). Based on these correlations, we build an in silico model that accurately classified 80.8% of the hits (n = 1676/2073). Therefore, the pre-selected SM library of 4182 SM was narrowed down to 1676 active SM with predictable PCP. Further, 926 hits affected only one species and 1254 hits were active against specific type of pathogens; however, no correlation was detected between PCP and the type of pathogen (29%, 34%, and 46% were specific for animal, human foodborne and plant pathogens, respectively). In conclusion, our in silico model allowed rational identification of SM with potential antimicrobial activity against bacterial pathogens. Therefore, the model developed in this study may facilitate future drug discovery efforts by accelerating the identification of uncharacterized antimicrobial molecules and predict their spectrum of activity.
Biometrics for Internet-of-Things Security: A Review

The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.
EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.
The Role of Information in Knowledge-How

Knowledge-how is the kind of knowledge implicated in skill employment and acquisition. Intellectualists claim that knowledge-how is a special type of propositional knowledge. Anti-intellectualists claim that knowledge-how is not propositional. We argue that both views face two open challenges. The first challenge pertains to the relationship between informational states and motor variability. The second pertains to the epistemic function of practice that leads to skill (and knowledge-how). The aim of this paper is to suggest a general conceptual framework based on functional information with both intellectualist and anti-intellectualist features. Our proposal, we argue, avoids the above challenges, and can further the debate on knowledge-how and skill.
Method for QCM Resonator Device Equivalent Circuit Parameter Extraction and Electrode Quality Assessment

Quartz crystal microbalance (QCM) resonators are used in a wide range of sensors. Current QCM resonators achieve a simultaneous measurement of multiple physical quantities by analyzing lumped-element equivalent parameters, which are obtained via the introduction of external devices. This introduction of external devices will probably increase measurement error. To realize the measurement of multiple physical quantities while eliminating the measurement error caused by external devices, this paper proposes a measurement method for the lumped-element equivalent parameters of QCM resonators without the need for extra external devices. Accordingly, a numerical method for solving nonlinear equations with fewer data points required and a higher accuracy was adopted. A standard crystal resonator parameter extraction experiment is described. The extracted parameters were consistent with the nominal parameters, which confirms the accuracy of this method. Furthermore, six QCM resonator device samples with different electrode diameters and materials were produced and used in the parameter measurement experiment. The linear relationship between the electrode material conductivity and motional resistance R1 is discussed. The ability of this method to characterize the electrode material and to detect the rust status of the electrode is also demonstrated. These abilities support the potential utility of the proposed method for an electrode quality assessment of piezoelectric devices.