Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcranial Static Magnetic Field Stimulation of the Motor Cortex in Children

Background Non-invasive neuromodulation is an emerging therapy for children with early brain injury but is difficult to apply to preschoolers when windows of developmental plasticity are optimal. Transcranial static magnetic field stimulation (tSMS) decreases primary motor cortex (M1) excitability in adults but effects on the developing brain are unstudied. Objective/Hypothesis We aimed to determine the effects of tSMS on cortical excitability and motor learning in healthy children. We hypothesized that tSMS over right M1 would reduce cortical excitability and inhibit contralateral motor learning. Methods This randomized, sham-controlled, double-blinded, three-arm, cross-over trial enrolled 24 healthy children aged 10–18 years. Transcranial Magnetic Stimulation (TMS) assessed cortical excitability via motor-evoked potential (MEP) amplitude and paired pulse measures. Motor learning was assessed via the Purdue Pegboard Test (PPT). A tSMS magnet (677 Newtons) or sham was held over left or right M1 for 30 min while participants trained the non-dominant hand. A linear mixed effect model was used to examine intervention effects. Results All 72 tSMS sessions were well tolerated without serious adverse effects. Neither cortical excitability as measured by MEPs nor paired-pulse intracortical neurophysiology was altered by tSMS. Possible behavioral effects included contralateral tSMS inhibiting early motor learning (p < 0.01) and ipsilateral tSMS facilitating later stages of motor learning (p < 0.01) in the trained non-dominant hand. Conclusion tSMS is feasible in pediatric populations. Unlike adults, tSMS did not produce measurable changes in MEP amplitude. Possible effects of M1 tSMS on motor learning require further study. Our findings support further exploration of tSMS neuromodulation in young children with cerebral palsy.
Comparisons Between Successful Versus Unsuccessful Switches From Benzodiazepines or Zolpidem (Z-Drug) to Controlled-Release Melatonin in Patients With Major Depressive Disorder and Insomnia

Objectives Chronic use of benzodiazepines and zolpidem (BDZ/Z-drugs) leads to dependency, cognitive impairment, and falls. Although controlled-release melatonin (Melatonin CR) may be an alternative, a switch in clinical settings has proven difficult. We investigated the factors associated with successful switch to Melatonin CR in patients with major depressive disorder (MDD) and insomnia. Methods This retrospective observational study involved 170 patients diagnosed with MDD and insomnia, and aged older than 55 years, who were treated with BDZ/Z-drug for at least the past 90 days and never before exposed to Melatonin CR. All patients were treated with a standard antidepressant therapy and Melatonin CR for their insomnia. A successful switch was defined as three or more consecutive prescriptions of Melatonin CR separated by at least one-month interval, after discontinuation of the BDZ/Z-drug. Results Seventy-nine patients (46.5%) who switched successfully showed no significant differences in sex, age, education, and occupational status compared with the unsuccessful group. The types of antidepressants used or BDZ/Z-drug treatment did not differ significantly between the two groups. Fewer somatic symptoms were observed in the successful group. In the multivariate analysis, the successful group showed significantly less somatic anxiety, shorter duration from first BDZ/Z-drug use to the first antidepressant use, and shorter total duration of BDZ/Z-drug therapy. Conclusions A successful switch from BDZ/Z-drug to Melatonin CR was associated with less baseline somatic anxiety, earlier use of antidepressants, and shorter total duration of BDZ/Z-drug use, but was less strongly associated with the type of antidepressants in MDD patients with insomnia.
Impact Factors of Empathy in Mainland Chinese Youth

Empathy was investigated in 592 Mainland Chinese youth using the Interpersonal Reactivity Index. Participants’ empathy-related information covering demographic traits, emotional wellness, as well as academic and social problems were recorded. Results of Classification and Regression Tree (CART) analysis showed that emotional empathy, cognitive empathy, and empathy-related personal distress was impacted by inherited traits (e.g., sex), acquired traits (e.g., study major), and a combination of both aspects, respectively. Moreover, empathy was found to be higher in youth in a vulnerable social position (i.e., outlander, female, and ethnic minority) than those in a dominant one (i.e., local, male, and ethnic majority). It was also found that personal distress, rather than empathy, was significantly correlated with academic, social, and emotional problems in the youth cohort. Hence, the current study provided an innovative observation of the relationships between empathy, personal distress, ethnicity, social vulnerability, wellness, study major, and other key characteristics in Mainland Chinese youth.
Development of a Novel Lateral Flow Biosensor Combined With Aptamer-Based Isolation: Application for Rapid Detection of Grouper Nervous Necrosis Virus

Nervous necrosis virus (NNV) has infected more than 50 fish species worldwide, and has caused serious economic losses in the aquaculture industries. However, there is no effective antiviral therapy. The development of a rapid and accurate point-of-care diagnostic method for the prevention and control of NNV infection is urgently required. Commonly used methods for NNV detection include the cell culture-based assay, antibody-based assay and polymerase chain reaction (PCR)-based assay. However, these methods have disadvantages as they are time-consuming and complex. In the present study, we developed a simple and sensitive aptamer-based lateral flow biosensor (LFB) method for the rapid detection of red-spotted grouper nervous necrosis virus (RGNNV). An aptamer is a single-stranded nucleotide, which can specifically bind to the target and has many advantages. Based on a previously selected aptamer, which specifically bound to the coat protein of RGNNV (RGNNV-CP), two modified aptamers were used in this study. One aptamer was used for magnetic bead enrichment and the other was used for isothermal strand displacement amplification (SDA). After amplification, the product was further tested by the LFB, and the detection results were observed by the naked eye within 5 min with high specificity and sensitivity. The LFB method could detect RGNNV-CP protein as low as 5 ng/mL or 5 × 103 RGNNV-infected GB (grouper brain) cells. Overall, it is the first application of a LFB combined with aptamer in the rapid diagnosis of virus from aquatic animals, which provides a new option for virus detection in aquaculture.
Optimal Synthesis and Evaluation of Tri-Amine Modified Ordered Mesoporous Carbon (TriFeOMC) and Its Application for the Adsorption of Arsenic and Lead From Aqueous Solution

Heavy metal pollution has become a serious problem due to increasing industrial waste, causing damage to water ecological environment, economic development and human health through their toxic effects. A unique Triamine-Iron-ordered mesoporous carbon (TriFeOMC) material was produced via a soft template approach. The ensuing sample of TriFeOMC was tested for the adsorption of arsenic (As) and Lead (Pb) along with its comparison with other two materials, which are Ordered mesoporous carbon (OMC) and Iron-ordered mesoporous carbon (FeOMC). The adsorption efficiency of TriFeOMC was excellent (92%) for the adsorption of As whereas, OMC has shown higher efficiency (88%) for the adsorption of Pb. The interaction between amino group and acidic groups on the surface of carbon formed a strong layer during Pb ions adsorption process, which resulted decreased adsorption of Pb on TriFeOMC. The Langmuir model fit for OMC, while Freundlich model fit for FeOMC. Both models fit for TriFeOMC for As whereas, Langmuir model showed comparatively better fit for Pb. The adsorption process followed an almost similar way of both pseudo models for Arsenic and Lead adsorption for OMC, FeOMC, and TriFeOMC and the adsorption process has been followed by pseudo-second-order for the adsorption of Pb. The novel material TriFeOMC with large pore volume and large surface area showed excellent results for the adsorption of arsenic from the aqueous solution. The results show the highest efficiency of TriFeOMC for adsorption of arsenic from arsenic-polluted water due to higher adsorption capacity with total pore volume 1.70 to 300.00 nm along with highly suitable pore width of 0.0680 cm3/g for adsorption, and therefore, it is highly recommended to use TriFeOMC for the adsorption of arsenic from polluted water.
The Effectiveness of Trauma-Focused Psychodrama in the Treatment of PTSD in Inpatient Substance Abuse Treatment

This single group pretest-posttest study explores the effectiveness of trauma-focused psychodrama in the treatment of post-traumatic stress disorder (PTSD) at an inpatient addiction treatment center. The results contribute to the limited research bases of both psychodrama and PTSD treatment outcomes in inpatient addiction treatment. The present study supports the potential effectiveness of two trauma-focused psychodrama models, the Therapeutic Spiral Model and the Relational Trauma Repair Model. Findings of the research demonstrate clinically significant reductions in overall PTSD symptoms (over 25% change) and each PTSD symptom cluster (i.e., re-experiencing and intrusion, avoidance and numbing, and hyper-arousal). Additionally, patient satisfaction exit survey data support overall treatment effectiveness and highlight its tolerability, and capacity for establishing emotional safety, connection, and group cohesion. Patients even described the trauma-focused psychodrama treatment approach as enjoyable and helpful. Overall, the results of this study are promising, and support continued clinical application of trauma-focused psychodrama treatment with other subpopulations diagnosed with PTSD. However, the ability to isolate effects of trauma-focused psychodrama in this study is compromised due to the absence of a control group and participants’ involvement in other inpatient treatment services.
Subjective Cognitive Decline and Related Cognitive Deficits

Since late stage dementia, including Alzheimer's disease (AD), cannot be reversed by any available drugs, there is increasing research interest in the preclinical stage of AD, i.e., subjective cognitive decline (SCD). SCD is characterized by self-perceptive cognitive decline but is difficult to detect using objective tests. At SCD stage, the cognitive deficits can be more easily reversed compared to that of mild cognitive impairment (MCI) and AD only if accurate diagnosis of SCD and early intervention can be developed. In this paper, we review the recent progress of SCD research including current assessment tools, biomarkers, neuroimaging, intervention and expected prognosis, and the potential relevance to traumatic brain injury (TBI)-induced cognitive deficits.
Adaptive Direction Control for UAV Full-Duplex Relay Networks Using Multiple Directional Antennas

We consider a UAV (Unmanned Aerial Vehicle) relay network whereby each UAV senses data and forwards it to dedicated ground stations by means of multi-hop relaying. In particular, we focus on a UAV relay network with a simple yet realistic linear topology for which we propose an adaptive direction control scheme to achieve a high throughput performance. In the proposed scheme, each UAV, equipped with multiple directional antennas, selects either the Decode-and-Forward (DF) straight relaying method or the orthogonal relaying method, for full-duplex data transfer, i.e., data is transmitted and received simultaneously on the same frequency, but at different antennas. The originality of our proposed method is to make each UAV rotate relatively to the position of its neighbouring UAVs, in order to optimize its antenna radiation direction, according to the selected relaying method. The key advantage of the proposed method is that signal direction can be controlled without the need of heavy adaptive signal processing as in conventional beamforming techniques. In order to clarify the decision parameters for selecting a relaying method, we first evaluate the throughput performance of the two-hop relay network under the severe interference conditions of UAV networks, and next, propose a procedure for selecting a multihop relaying method. Numerical experiments show that the proposed scheme enables to achieve a high throughput performance, with low computational costs.
Defining Social Engineering in Cybersecurity

Social engineering has posed a serious security threat to infrastructure, user, data and operations of cyberspace. Nevertheless, there are many conceptual deficiencies (such as inconsistent conceptual intensions, a vague conceptual boundary, confusing instances, overgeneralization and abuse) of the term making serious negative impacts on the understanding, analysis and defense of social engineering attacks. In this paper, an in-depth literature survey is conducted, the original meaning of social engineering in cybersecurity is traced, the conceptual evolution and technical development are analysed systematically, and the conceptual problems are discussed. Based on above work, this paper attempts to address these conceptual deficiencies by proposing a more compatible and precise definition of social engineering in cybersecurity (SEiCS). This definition eliminates the conceptual inconsistencies, covers the mainstream conceptual connotations, clarifies the conceptual boundary, mitigates the overgeneralization and abuse, etc. Five analysis tables (i.e., the comparative analysis of the SEiCS definition vs. mainstream conceptual intensions in the conceptual evolution, the comparative analysis of the SEiCS definition vs. typical definitions in the literature, the analysis of confusing “social engineering cases”, the analysis of popular social engineering attack scenarios, and the analysis of social-engineering-based attacks) are provided to illustrate the performance of the proposed definition.
Use of synthetic soil aggregates as a containerized growth medium component to substitute peat in the ornamental plant production

This study investigates characteristics and utilization of synthetic soil aggregates (SA) formed by low productive acidic soil with paper and starch waste for production of French marigold (Tagetes patula) as a partial peat substitution in growing substrate. Five different growth substrates utilized in this study were: (i) Peat only, (ii) peat 75%: SA 25%, (iii) peat 50%: SA 50%, (iv) peat 25%: SA 75%, and (v) SA only. Peat 75%: SA 25% enhanced substrate physical and chemical properties into the established ideal substrate range. Plant height, numbers of flowers, fresh shoot weight, dry shoot weight, root length, fresh root weight and dry root weight of French marigold grown in the substrate of peat 75%: SA 25% increased by 13.28, 23.07, 28.51, 27.41, 6.66, 68.33 and 7.40%, respectively, compared with peat substrate. Peat 50%: SA50% gave similar growth parameters to peat only substrate. Nitrogen (N) content of plants grown in peat 75%: SA25% was higher than peat substrate. Cu, Fe, Mn and Zn concentrations in all plant shoots were in the normal range and well below the phytotoxic range. Therefore, growth substrates with 25% and 50% SA can be recommended as the most effective substrates to substitute expensive and less available peat from an environmental point of view.
A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

Hydrogen is considered to be a hazardous substance. Hydrogen sensors can be used to detect the concentration of hydrogen and provide an ideal monitoring means for the safe use of hydrogen energy. Hydrogen sensors need to be highly reliable, so fault identification and diagnosis for gas sensors are of vital practical significance. However, traditional machine learning methods for fault diagnosis are based on features extracted by experts, prior knowledge requirements and the sensitivity of system changes. In this study, a new convolutional neural network (CNN) using the random forest (RF) classifier is proposed for hydrogen sensor fault diagnosis. First, the 1-D time-domain data of fault signals are converted into 2-D gray matrix images; this process does not require noise suppression and no signal information is lost. Secondly, the features of the gray matrix images are automatically extracted by using a CNN, which does not rely on expert experience. Dropout and zero-padding are used to optimize the structure of the CNN and reduce overfitting. Random forest, which is robust and has strong generalization ability, is introduced for the classification of gas sensor signal modes, in order to obtain the final diagnostic results. Finally, we design and implement a prototype hydrogen sensor array for experimental verification. The accuracy of fault diagnosis in hydrogen sensors is 100% under noisy environment with the proposed method, which is superior of CNN without RF and other methods. The results show that the proposed CNN with RF method provides a good solution for hydrogen sensor fault diagnosis.
Wideband Low-Profile Luneburg Lens Based on a Glide-Symmetric Metasurface

This paper presents a two-dimensional low-profile Luneburg lens that is designed by using a glide-symmetric metasurface. The entire lens consists of two mirrored metal plates with periodic metal pins, and the pins on one plate are glided exactly a half period of the unit cell compared with the pins on the other plate. The proposed design has not only a stable refractive index over a wide operating band but also no dielectric loss compared to other metamaterial-based lenses. In addition, it is easy to manufacture and can also largely reduce the production cost. The complete Luneburg lens is simulated in CST Microwave Studio, and the results demonstrate that the lens can work within 14 GHz–22 GHz, which is greater than 44% of the bandwidth. The measured results show that the lens also agrees well with the simulated lens. This lens antenna would be an excellent candidate for satellite communication applications.
Bayesian Cooperative Localization With NLOS and Malicious Vehicle Detection in GNSS-Challenged Environments

Owing to the inter-vehicle non-line-of-sight (NLOS) measurement and malicious attack in global navigation satellite system (GNSS) challenged environment, the vehicle position precision is seriously damaged. In order to improve the vehicle position accuracy, we propose a new Bayesian cooperative localization scheme which tackles this problem by combining the vehicle position measurements and inter-vehicle distance measurements. In the proposed scheme, an abnormal vehicle detection algorithm (AVDA) is presented to eliminate the impacts of NLOS and malicious attack. Simulation results demonstrate that the proposed scheme can achieve excellent localization performance in the presence of NLOS and malicious attacks. Based on these results, the abnormal and normal detection rates of AVDA are approximate and the root mean square error (RMSE) is reduced to the sub-meter level. The performances of the proposed scheme are also verified in real environmental conditions by using the simulation of urban mobility (SUMO).
Towards Eidetic Blockchain Systems with Enhanced Provenance

Modern blockchain systems with smart contract support are continuing to be rapidly adopted across various industry sectors and are increasingly used to manage valuable assets. As the size and complexity of smart contract applications increases, so are the coding errors, exploit potential, and regulation requirements. For these reasons, it has become necessary to efficiently manage the system’s historic execution information, or provenance, to enable efficient analysis. Existing approaches facilitate historic data access, however, they do not support tracking what initiated the changes or why the states mutated. To address this, we propose a system that enables efficient management of historic smart contracts calls, their parameters, and the blockchain state before and after a call. We further explore how querying this historic data in different granularity levels can facilitate the analysis of a use case example comprised of multiple smart contract calls across different entities.
Discovery of Loose Group Companion From Trajectory Data Streams

The general usability of location tracking devices has been generated a high volume of spatial-temporal data in the form of trajectory. Exploring useful knowledge from these trajectory data can contribute to understanding many real-world applications, such as traffic monitoring and weather forecasting. The main task of trajectory data analysis is the tracking of an object group movement pattern. Existing algorithms, studying the evolving structure of moving object trajectories, have high computational complexity, particularly when tracking loose group companions. To address this problem, we describe a loose group companion tracking framework over trajectory data streams in an incremental manner, which reduces computational time. Loose group companion is the moving objects group that travels together. However, some members are allowed to leave at some timestamp. A crucial part of our framework is the micro-group based loose group companion discovery. It follows a moving object group and then incrementally detects the loose group companions. We validated our techniques using two real vehicle data sets and one synthetic data set. Our approach was, on average, 45% faster than previous algorithms.
Sensor Fault Diagnosis and Fault Tolerant Control for Forklift Based on Sliding Mode Theory

For the forklift equipped with electrical sensors, a fault tolerant control (FTC) strategy is proposed. First, considering the uncertainty of forklift cargo and the external output disturbance, the equivalent sensor fault model of forklift is constructed. Then, a sliding mode observer (SMO) with adaptive regulation law is proposed to solve the problem that some fault reconstruction methods demand the upper bound of faults. Based on the fault value reconstructed by SMO, a sliding mode fault-tolerant controller is designed. It can realize active FTC of typical sensor faults of forklift in the presence of uncertainties and output disturbance. Finally, experiment is given to verify the effectiveness of the proposed FTC strategy.
Channel Estimation for Large-Scale Multiple-Antenna Systems Using 1-Bit ADCs and Oversampling

Large-scale multiple-antenna systems with large bandwidth are fundamental for future wireless communications, where the base station employs a large antenna array. In this scenario, one problem faced is the large energy consumption as the number of receive antennas scales up. Recently, low-resolution analog-to-digital converters (ADCs) have attracted much attention. Specifically, 1-bit ADCs are suitable for such systems due to their low cost and low energy consumption. This paper considers uplink large-scale multiple-antenna systems with 1-bit ADCs on each receive antenna. We investigate the benefits of using oversampling for channel estimation in terms of the mean square error and symbol error rate performance. In particular, low-resolution aware channel estimators are developed based on the Bussgang decomposition for 1-bit oversampled systems and analytical bounds on the mean square error are also investigated. Numerical results are provided to illustrate the performance of the proposed channel estimation algorithms and the derived theoretical bounds.