Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyclic peptide-based nanostructures as efficient siRNA carriers

Abstract RNA interference shows a great strategy for biological studies; however, delivering of small interfering RNA (siRNA) remains challenging. Although several delivery vehicles, including cell-penetrating peptides, have been developed, their implementation is often restricted because of their endosomal entrapment. Herein, we report the formation of self-assembled nanostructures from rationally designed cyclic peptides and explore them for efficient delivery of functional biomacromolecules such as siRNA into mammalian cells. The newly obtained soft materials make stable complexes with siRNAs, thereby increasing their stability and deliver fluorescent labelled siRNA inside the cells as evident from confocal microscopy analysis. Flow cytometry analysis reveals that significant uptake of FAM-siRNA occurs in the presence of peptide nanostructures compared with siRNA alone. Peptide nanostructure-mediated delivery of very low concentration of siRNA causes significant knockdown of the target gene as observed at protein level by Western blot analysis, which is comparable to lipofectamine, commercially available transfection agent.
MARAAL: A Low Altitude Long Endurance Solar Powered UAV For Surveillance and Mapping Applications

This paper constitutes the detailed design, fabrication and validation of MARAAL: A low altitude long endurance solar powered UAV to be operated day-night in subtropical region. The SUAV prototype fabricated at Unmanned Aerial Laboratory IIT Kanpur has a wing span of 5.35m, maximum endurance of 18 hours and a maximum 6kg payload capability. The conceptual design process with simulation results are shown along with a detailed description of the solar cell installation process. The paper also highlights the construction efforts and details of the hardware and onboard electronics including surveillance equipment.
A methodology to Evaluate the Usability of Security APIs

Increasing number of cyber attacks demotivate people to use Information and Communication Technology (ICT) for industrial as well as day to day work. A main reason for the increasing number of cyber attacks is mistakes that programmers make while developing software applications that are caused by usability issues exist in security Application Programming Interfaces (APIs). These mistakes make software vulnerable to cyber attacks. In this paper, we attempt to take a step closer to solve this problem by proposing a methodology to evaluate the usability and identify usability issues exist in security APIs. By conducting a review of previous research, we identified 5 usability evaluation methodologies that have been proposed to evaluate the usability of general APIs and characteristics of those methodologies that would affect when using these methodologies to evaluate security APIs. Based on the findings, we propose a methodology to evaluate the usability of security APIs.
Sex, ADHD symptoms, and CHRNA5 genotype influence reaction time but not response inhibition

People showing symptoms of attention deficit hyperactivity disorder (ADHD) often present an impairment of reaction time and response inhibition. These executive functions are influenced by nicotinergic acetylcholine receptors (nAchr) as mediators of cholinergic signaling, and show differences between both sexes. We examined the effects of two functional polymorphisms rs3841324 (S/L) and rs16969968 (G/A) of the cholinergic gene CHRNA5, ADHD symptoms and sex on response inhibition/reaction time in the Stop Signal Task. In the analyses, 183 participants (52.4% females) were included. In participants carrying the diplotype (SS_GG), men with ADHD symptoms responded faster, while men without ADHD symptoms were slower than women (F = 5.313; p = 0.023; ηp² = 0.034). Although explorative, this threefold interaction on reaction time but not response inhibition extend previous findings, suggesting a moderating effect of ADHD symptoms in men carrying the CHRNA5 diplotype SS_GG and might inspire research on genotype‐ and gender‐specific ADHD medication.
Spatial spectrum for direction of arrival estimation of multiple non-stationary wideband sources in the time-frequency plane

A spatial spectrum function for estimating direction of arrival (DOA) of multiple and non-stationary wideband sources using a sensor array is proposed. The sensor array output signal is converted into the time–frequency (TF) array signal and the signal at each frequency bin is converted into the signal at the reference frequency using the focusing matrix. Then DOA is estimated by forming a novel spatial spectrum using the joint diagonalisation structure of spatial TF distribution matrices at auto-term points and by searching its peak points. Unlike the subspace-based high-resolution DOA estimation techniques, suggested method has no need of determining the number of sources and is applicable even when the number of sources is equal to the number of sensors.
Evolutionary clustering framework based on distance matrix for arbitrary-shaped data sets

Data clustering plays a key role in both scientific and real-world applications. However, current clustering methods still face some challenges such as clustering arbitrary-shaped data sets and detecting the cluster number automatically. This study addresses the two challenges. A novel clustering analysis method, named automatic evolutionary clustering method based on distance (AED) matrix, is proposed to determine the proper cluster number automatically, and to find the optimal clustering result as well. In AED, a distance matrix is first obtained by using a specific distance metric such as Euclidean distance metric or path distance metric, and then this distance matrix is partitioned by an evolutionary clustering framework. In this framework, a fixed-length representation scheme is implemented to represent the clustering result, a novel cross-over scheme is introduced to increase the convergence speed, and a validity index is proposed to evaluate the intermediate clustering results and the final clustering results. AED is systematically compared with some state-of-the-art clustering methods on both hyper-spherical and irregular-shaped data sets, and the experimental results suggest that the authors approach not only successfully detects the correct cluster numbers but also achieves better accuracy for most of test problems.
Walking with a Weighted Pelvic Belt or with an Equivalent Pure Downward Force on the Pelvis: Are These Different?

In a previous study, a Tethered Pelvic Assist Device (TPAD) was used to successfully retrain crouch gait of children with Cerebral Palsy by applying a downward force on the pelvis during walking on a treadmill. While the results of this study were promising, an important issue was translating these results to special needs children with crouch gait using simpler alternative procedures. This motivates the present study to compare the biomechanical differences in walking under two conditions: (i) the TPAD applies a pure downward force on the pelvis using tethers, and (ii) a weighted pelvic belt is used to apply the same downward force on the pelvis. In the second case the weight belt also increases the mass at the pelvis. Ten healthy subjects performed two separate experiments while walking on an instrumented treadmill. The whole-body kinematics was recorded using a motion capture system and the ground reaction forces were measured by force plates embedded in the treadmill. We found no significant difference in the actual gait parameters of healthy subjects when the downwards force, equivalent to 15% body weight, applied by the TPAD was replaced by a weighted pelvic belt of 15% body weight. However, the estimated maximum ankle torque, predicted by an inverted pendulum mathematical model, during the single support phase showed a higher increase during walking with the weight belt when compared to a pure downward force. This suggests that the weight belt, due to its simplicity, may be a better medium to translate the results of TPAD in children with cerebral palsy who have a crouch gait.
The Inverted Muscle Skeleton Approach: Moving Beyond Rigid Exoskeletons

Exoskeleton technology has improved significantly over the past decades, yet devices are still not user-friendly. Recently, so-called exosuits-soft wearable suits that assist walking-have been proposed, but their torques are limited. In this paper we propose the inverted Muscle Skeleton (iMS) approach; this method allows for the design of wearable robotics that are less rigid and more lightweight than exoskeletons, but more powerful than exosuits. We have applied our approach to the design of a low-power knee orthosis that assists knee extension. Evaluation of the prototype during one-legged squats shows that the device can successfully generate knee extension torques that reduce muscle activity in the knee extensor muscles. The approach is promising and we believe that higher joint torques can be generated with more powerful actuators. Future work includes testing the device during walking and applying the method to the design of orthoses for other joints.
Maximizing Profit of Cloud Service Brokerage with Economic Demand Response

Cloud service brokerage (CSB), which procures cloud services from multiple cloud service providers (CSPs) and resells them to cloud customers, has been put forward to facilitate the delivery of cloud services. However, it is challenging to address the economic issues of CSB incurred by insufficient provisioning problem in response to dynamic conditions, for example, dynamic customer demands, dynamic cloud service prices and different availabilities of CSPs. In this paper, we propose a novel mechanism called CSB Demand Response (DR-CSB), which aims to maximize the profit of CSB under dynamic customer demands with respect to the capacity and availability constraints, to mitigate the insufficient provisioning problem. To this end, we formulate an optimization problem of profit maximization for the CSB, and employ economic demand response mechanism to allow cloud customers to adjust their consumptions with dynamic cloud service prices. Our evaluations driven by Google cluster-usage traces have verified that the DR-CSB not only can help the CSB to achieve the profit maximization, but also can handle the impact of the dynamic conditions in CSB. As the result shows, the profit of CSB with implementing DR-CSB can increase by up to 20%, and customers also achieve a 37% aggregated cost saving, compared with the scenario without DR-CSB.
Study of Work-Function Variation Effects in Multigate Tunneling Field Effect Transistors

The impacts of metal-gate work-function variation (WFV) effects in Fin tunnel field effect transistors (TFETs) and gateall-around (GAA) TFETs are investigated with 3-D technology computer aided design (TCAD) simulations. Metal grains generated with Voronoi method and square method are also compared. The impacts of WFV effect on fin TFETs and GAA TFETs increase with metal grain size increase in terms of threshold voltage $(\mathrm {V}_{th})$ and subthreshold swing (SS).
SMART – Not Always Only “Stroke‐Like”

Stroke‐like migraine attacks after radiation therapy (SMART) is a syndrome observed in a delayed fashion in patients following brain radiation therapy. It is a rare but important differential diagnosis of migraine in these patients, often presenting with migraine‐like headache. In addition, there may be focal neurological deficits or seizures. In general, it is regarded as a reversible condition, therefore termed stroke‐like. Our case illustrates that there may also be ischemic stroke associated with SMART leading to an unfavorable course, even after prior remission.
Edge Detection and Image Segmentation on Encrypted Image with Homomorphic Encryption and Garbled Circuit

Edge detection is one of the most important topics of image processing. In the scenario of cloud computing, performing edge detection may also consider privacy protection. In this paper, we propose an edge detection and image segmentation scheme on an encrypted image with Sobel edge detector. We implement Gaussian filtering and Sobel operator on the image in the encrypted domain with homomorphic property. By implementing an adaptive threshold decision algorithm in the encrypted domain, we obtain a threshold determined by the image distribution. With the technique of garbled circuit, we perform comparison in the encrypted domain and obtain the edge of the image without decrypting the image in advanced. We then propose an image segmentation scheme on the encrypted image based on the detected edges. Our experiments demonstrate the viability and effectiveness of the proposed encrypted image edge detection and segmentation.
CCoLBP: Chromatic Co-Occurrence of Local Binary Pattern for Face Presentation Attack Detection

To counter face presentation attack in face recognition system, the chromatic facial texture differences between the real faces and the facial artefacts are fully analyzed, and chromatic co-occurrence of local binary pattern (CCoLBP) is proposed to investigate the inter-channel based information. Based on the principle of presentation attack and its influence on color component of the face image, a face presentation attack detection (PAD) scheme based on CCoLBP is proposed. By combining intra-channel based facial texture and CCoLBP feature, the differences of color distortion and texture distribution between the real faces and the artefacts are characterized. With these features, the detection is accomplished by using a Softmax classifier. Experiments are done with 5 public databases, and the experimental results and analysis indicate the effectiveness of CCoLBP, and it can achieve good performance in cross-database testing. It has great potential in the application of face PAD with real-time requirement.
Noise-Aided Demodulation With One-Bit Comparator for Multilevel Pulse-Amplitude-Modulated Signals

This letter proposes a demodulation method using a one-bit comparator for signals processed by multilevel pulse amplitude modulation (PAM). The proposed method is simple and provides an alternative to using an analog-to-digital converter to describe multilevel input signals. Because of the noise present in the transmitted multilevel PAM signal, the two-level output of the one-bit comparator shows different statistical behavior for each level of the signal. Thus, it is possible to detect the signal level, or perform symbol decision, based on the maximum likelihood criterion. The present theoretical analysis reveals that reliable demodulation is possible even with a one-bit comparator if the probability mass function of the two-level outputs of each received symbol plus intentionally added noise is known.
Performance Analysis of NDT-based Graph SLAM for Autonomous Vehicle in Diverse Typical Driving Scenarios of Hong Kong

Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (LiDAR) can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based simultaneous localization and mapping (SLAM) demonstrations tend to be studied in light traffic and less urbanized area. However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings. This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions. The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds. Then, the LiDAR odometry is performed based on the calculated continuous transformation. The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization. The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed. Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions. The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle.
Accuracy assessment of a novel image-free handheld robot for Total Knee Arthroplasty in a cadaveric study

Abstract Surgical navigation has been shown to improve the accuracy of bone preparation and limb alignment in total knee arthroplasty (TKA). Previous work has shown the effectiveness of various types of navigation systems. Here, for the first time, we assessed the accuracy of a novel imageless semiautonomous handheld robotic sculpting system in performing bone resection and preparation in TKA using cadaveric specimens. In this study, we compared the planned and final implant placement in 18 cadaveric specimens undergoing TKA using the new tool. Eight surgeons carried out the procedures using three types of implant designs. A quantitative analysis was performed to determine the translational, angular, and rotational differences between the planned and achieved positions of the implants. The mean femoral flexion, varus/valgus, and rotational error was −2.0°, −0.1°, and −0.5°, respectively. The mean tibial posterior slope, and varus/valgus error was −0.2°, and −0.2°, respectively. We obtained higher flexion errors for the femoral implant when using cut-guides as compared to using a bur for cutting the bones. The image-free robotic sculpting tool achieved accurate implementation of the surgical plan with small errors in implant placement. Future studies will focus on determining how well the accurate implant placement translates into a clinical and functional benefit for the patient.
Person Retrieval in Surveillance Video using Height, Color and Gender

A person is commonly described by attributes like height, build, cloth color, cloth type, and gender. Such attributes are known as soft biometrics. They bridge the semantic gap between human description and person retrieval in surveillance video. The paper proposes a deep learning-based linear filtering approach for person retrieval using height, cloth color, and gender. The proposed approach uses Mask R-CNN for pixel-wise person segmentation. It removes background clutter and provides precise boundary around the person. Color and gender models are fine-tuned using AlexNet and the algorithm is tested on SoftBioSearch dataset. It achieves good accuracy for person retrieval using the semantic query in challenging conditions.
Engaging Research Scholars in Social Media A New Way to Innovative Research

Social media has emerged as a significant communication tool that has remarkably changed the way research is being conducted. Social Media Tools (SMT) help researchers to increase the visibility of their research and also promote interactive open access environment. For research scholars, social media serves as a platform where they can engage in informal communications, perceive and share ideas, review, collaborate and find helpful critique to their work, thereby improving quality of research and developing standards. Keeping in view the social media trend and its wider acceptability in academic environment, this paper aims to explore the usage of select Academic Social Media Sites among research scholars. The results of the study reveal that research scholars are active users of Academic Social Media Sites. They engage in various activities on social media that include formal and informal interaction, organizing research related work online and making their research more visible, thereby increasing research visibility and credibility.
Identifying Learning Styles from Chat Conversation using Ontology-Based Dynamic Bayesian Network Model

One of the commonly adopted parameters in personalized e-learning is learning style. As an individual trait, this parameter indicates the preferable learning object for a specific type of a learner. When considering collaborative learning environment through online chat and discussion, the learners can express their opinion on a shared learning object. Besides, level of interaction with the object is also important to identify the learning style. The nature of generated contents during discussion makes it difficult to extract the required information. In this paper we discuss how to identify the learning style from the learning object preferences expressed by the learner via the online discussion in a collaborative learning platform. To do so, the paper proposes an ontology-based Dynamic Bayesian Network (DBN) model to represent the relationship between the learning style and preferable learning object. The model also obtains the learner's opinion more than one time by using time slice to make the indication of learning styles more accurate. Consequently, providing the learner the appropriate personalized learning package.
Distinctive structural motifs co‐ordinate the catalytic nucleophile and the residues of the oxyanion hole in the alpha/beta‐hydrolase fold enzymes

The alpha/beta‐hydrolases (ABH) are among the largest structural families of proteins that are found in nature. Although they vary in their sequence and function, the ABH enzymes use a similar acid–base‐nucleophile catalytic mechanism to catalyze reactions on different substrates. Because ABH enzymes are biocatalysts with a wide range of potential applications, protein engineering has taken advantage of their catalytic versatility to develop enzymes with industrial applications. This study is a comprehensive analysis of 40 ABH enzyme families focusing on two identified substructures: the nucleophile zone and the oxyanion zone, which co‐ordinate the catalytic nucleophile and the residues of the oxyanion hole, and independently reported as critical for the enzymatic activity. We also frequently observed an aromatic cluster near the nucleophile and oxyanion zones, and opposite the ligand‐binding site. The nucleophile zone, the oxyanion zone and the residue cluster enriched in aromatic side chains comprise a three‐dimensional structural organization that shapes the active site of ABH enzymes and plays an important role in the enzymatic function by structurally stabilizing the catalytic nucleophile and the residues of the oxyanion hole. The structural data support the notion that the aromatic cluster can participate in co‐ordination of the catalytic histidine loop, and properly place the catalytic histidine next to the catalytic nucleophile.
Hardware Implementation of SM4 Based on Composite Filed S-box and It's Security against Machine Learning Attack

In this paper, we implemented the SM4 block cipher algorithm based on both composite filed S-box and look up tables (LUTs) S-box. We also explored the performance of SM4 against machine learning attack, including conventional classifiers, SVM for example, and convolutional neural network (CNN). Implementation of SM4 based on composite filed S-box has a great advantage on area consumption compared with conventional implementation based on LUTs, which can be widely used in resource constrained applications. On the other hand, power attack based on CNN poses a serious threat to the security of block cipher using S-box in LUTs, while experiment show that SM4 using composite filed S-box has a better security against linear classifiers and CNN attack.