Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification Dynamic Parameters with a Novel Software Design for the M-DOF Collaborative Robot

The primary goal of this project is to develop a general identification method via software that can be applied to collaborative robots. To achieve this, collaborative ultralight robots Kinova Gen2 and Kuka LWR4+ with seven degrees of freedom (M-DOF) were used. Specifically, the "recursive Newton-Euler" formulation was used to provide a set of parameters that could describe the body structure and to create a general symbolic representation for collaborative robots. For parameter estimation "Least-Squares" method was used. Besides, trajectories generated with random numbers cannot give very consistent results; hence, verified trajectories were used. To verify the trajectories, real robots were simulated with V-Rep before being executed. Because when untested trajectories are first tested on robots, undesirable results may occur. Therefore, the created trajectories were first simulated with V-Rep, and the chance of damaging the robot was eliminated. After being satisfied with the simulation results and being sure that any trajectories may be harmful to the robots, the trajectories were executed on the real robot. This method not only provides great convenience in terms of parameter estimation, robot health, and saving time, but also increased the consistency of our results. During the execution of real robots, data were collected by ROS in realtime. The algorithms have been coded via Matlab, and ROS packages via Python. Matlab, ROS, V-Rep were work together in harmony under Ubuntu operating system. The identification methods were modeled, implemented, tested, and validated successfully. The results for both robots are given in this article.
Project Based Learning (Pjbl) dalam Proses Pembelajaran Daring pada Materi Hukum Bernoulli Menggunakan Botol Bekas

Covid-19 has an impact on education. In order to maintain the quality of learning, online mode learning innovation is carried out with the application of project based learning through experiments in the application of Bernoulli's Law on used bottle leaks. Online learning at SMAN 1 Metro usually uses Problem Based Learning. In this study will be used PjBl to increase the creativity of students. This research aims to explore students' ability in problem solving in dynamic fluid concepts. This type of research is quantitative with a Quasi Experimental approach, and the design used is Nonequivalent Group Design. The subject of the study was Sma N 1 Metro in class XI IPA which numbered 67 students. The analysis method uses the N-Gain Test and the different tests between the two sample groups. Results: N-Gain in experimental class by 65.74% and in control class by 25.04%. Based on hypothesis tests, it was found that the effect of applying madel project based learning in improving student learning outcomes in Bernoulli Law material. Project based learning model in online learning is also effective in developing 4Cs.
Bloodlines: National Border Crossings and Antisemitism in Weimar Germany

This article argues that national border crossings act as focal points for xenophobia. Two mechanisms converge to produce this pattern. First, when the nation-state is under pressure, border crossings make cross-national differences salient, producing a perceived link between international forces and socioeconomic problems of vulnerable social classes. Second, border crossings come to symbolize international threats and attract aggressive nationalist mobilization by radical movements who frame ethnic outsiders as an international evil. In this distinct spatial landscape, ethnic outsiders become scapegoats for broader social problems among individuals losing social status. I develop my argument through the study of local variation in antisemitism in Weimar Germany before the Holocaust. Statistical analysis of Jewish bogeymen and an in-depth exploration of local reports on antisemitism reveal how pluralism in the Weimar Republic started eroding among members of the lower-middle class living at the margins of the state. In doing so, I draw attention to the spatial sources of xenophobia and demonstrate that borders between nations activate borders within nations, shedding new light on the complicated relationship between pluralism and state formation.
FORMULATION DEVELOPMENT AND EVALUATION OF NIOSOMAL GEL OF COLLECTIVE ANTI-FUNGAL AGENTS

Objectives: Tea tree oil (TTO) and Eucalyptus oil (EO) are essential oils derived from the leaves and terminal branches of Melaleuca alternifolia and Eucalyptus globulus. Both oils have narrative topical antifungal agents. Niosomal vesicles were chosen for Tea tree and EO dispersion in this investigation because of their ability to protect enclosed drugs, reduce drug dose amount, target drug delivery, increase residence time and penetration. Methods: TTO and EO containing niosomes were made using a modified thin-film hydration process and Carbopol 934 as a gelling agent to produce a smooth antifungal niosomal gel. Results: TTO and EO entrapment efficiency was found to be 84.89±0.19% and 86.86±0.57%, respectively, and percent cumulative drug diffusion of TTO and EO was found to be 84.21% and 85.22% in the prepared optimized batch [N9]. Transmission electron microscopy revealed vesicular, spherical particles in the nano range with a smooth surface. The optimum batch [N9] of niosomal gel was made with 1% w/w carbopol 934. TTO 84.9% and EO 86.89%, respectively, were found to have prolonged drug release in an in vitro release investigation utilizing the dialysis bag method. The G9 batch niosomal gel was found to be stable by performing an accelerated stability study for 3 months. Conclusion: It was concluded that the best formulation batch G9 shows better-sustained release, enhanced residence time, and stability.
First Principles Study of Structural, Electronic and Magnetic Properties of Defected (Monovacant) Hexagonal Boron Nitride Sheet

The structural, electronic, and magnetic properties of vacancy structures with triangular shape are related to the defect in single hexagonal boron nitride (h-BN) sheet. It is investigated by using density functional theory calculations. The first-principles calculations based GGA functionals have been implemented to study the structural, electronic and magnetic properties of pure and defected hexagonal boron nitride (h-BN) monolayer sheet using Quantum ESPRESSO (QE) package, 6.5 version. The calculated values of formation energy reveal the structural stability of the defected systems. The formation energies for B and N vacant system are found to be 16.45 eV and 12.87 eV respectively. This predicts that the N vacant system is more preferable with lower formation energy. The defect in h-BN seems to be changing its band gap and magnetic properties of h-BN system. The 6.25 % B-vacancy results h-BN to be half metallic ferromagnetic with total magnetization of 2. 74µB/cell. Further, 6.25 % N-vacancy causes it to be magnetic semiconductor with total magnetization 1. 00µB /cell.
Bridging the Last-Mile Gap in Network Security via Generating Intrusion-Specific Detection Patterns through Machine Learning

With successful machine learning applications in many fields, researchers tried to introduce machine learning into intrusion detection systems for building classification models. Although experimental results showed that these classification models could produce higher accuracy in predicting network attacks on the offline datasets, compared with the operational intrusion detection systems, machine learning is rarely deployed in the real intrusion detection environment. This is what we call the last mile problem with the machine learning approach to network intrusion detection, the discrepancy between the strength and requirements of machine learning and network operational semantics. In this paper, we aim to bridge the aforementioned gap. In particular, an LCC-RF-RFEX feature selection approach is proposed to select optimal features of the specific type of attacks from dataset, and then, an intrusion-specific approach is introduced to convert them into detection patterns that can be used by the nonmachine-learning detector for the corresponding specific attack detection in the real-world network environment. To substantiate our approach, we take Snort, KDDCup’99 dataset, and Dos attacks as the experimental subjects to demonstrate how to close the last-mile gap. For the specific type of Dos attacks in the KDDCup’99 dataset, we use the LCC-RF-RFEX method to select optimal feature subset and utilize our intrusion-specific approach to generate new rules in Snort by using them. Comparing performance differences between the existing Snort rule set and our augmented Snort rule set with regard to Dos attacks, the experimental results showed that our approach expanded Snort’s detection capability of Dos attacks, on average, reduced up to 25.28% false-positive alerts for Teardrop attacks and Synflood attacks, and decreased up to 98.87% excessive alerts for Mail bomb attacks.
An Application of Boundary-scan in Testing Digital Circuit Board with DSP Chips

A method integrating boundary scan and traditional vector inputting technique is introduced to test the non-boundary-scan devices in digital circuit board with DSP chips in the paper.The results of function tests to such device indicate that faults can be effectively detected and isolated to chip level using this method.The results prove that using the method integrating boundary scan and traditional vector inputting technique can effectively solve the problems of function test of partial non-boundary-scan devices in PCB with DSP chips.
An RDMA-enabled In-memory Computing Platform for R-tree on Clusters

R-tree is a foundational data structure used in spatial databases and scientific databases. With the advancement of networks and computer architectures, in-memory data processing for R-tree in distributed systems has become a common platform. We have observed new performance challenges to process R-tree as the amount of multidimensional datasets become increasingly high. Specifically, an R-tree server can be heavily overloaded while the network and client CPU are lightly loaded, and vice versa. In this article, we present the design and implementation of Catfish, an RDMA-enabled R-tree for low latency and high throughput by adaptively utilizing the available network bandwidth and computing resources to balance the workloads between clients and servers. We design and implement two basic mechanisms of using RDMA for a client-server R-tree data processing system. First, in the fast messaging design, we use RDMA writes to send R-tree requests to the server and let server threads process R-tree requests to achieve low query latency. Second, in the RDMA offloading design, we use RDMA reads to offload tree traversal from the server to the client, which rescues the server as it is overloaded. We further develop an adaptive scheme to effectively switch an R-tree search between fast messaging and RDMA offloading, maximizing the overall performance. Our experiments show that the adaptive solution of Catfish on InfiniBand significantly outperforms R-tree that uses only fast messaging or only RDMA offloading in both latency and throughput. Catfish can also deliver up to one order of magnitude performance over the traditional schemes using TCP/IP on 1 and 40 Gbps Ethernet. We make a strong case to use RDMA to effectively balance workloads in distributed systems for low latency and high throughput.
LAMP: Load-Balanced Multipath Parallel Transmission in Point-to-Point NoCs

Network-on-chip (NoC) is an emerging paradigm that is able to connect a significant amount of processing elements (PEs). However, as a distributed subsystem, NoC resources have not been exploited to the fullest. Multipath parallel transmission, which splits one message into multiple parts and sends them simultaneously, shows its efficiency in utilizing NoC resources and further reducing the transmission latency. However, this method is not fully optimized in previous works, especially for emerging point-to-point NoCs due to the following reasons: 1) only limited shortest paths are chosen; 2) static message splitting strategy without considering NoC utilization state increases contentions; and 3) the optimization of hardware that supports multipath parallel transmission is missing, resulting in additional overheads. Thus, we propose LAMP, a software and hardware collaborated design to efficiently utilize resources and reduce latency in point-to-point NoCs through the load-balanced multipath parallel transmission. Specifically, we propose a reinforcement learning-based algorithm to decide when and how to split messages, and which path should be used according to traffic loads. Also, the temporal and spatial load-balancing algorithms are proposed so that the message size is adjusted properly to utilize NoC resources. Moreover, we revise the hardware design to support multipath parallel transmission efficiently. Extensive experiments show that our algorithm achieves a remarkable performance improvement (+18.0% to +29.9%) when compared with the state-of-the-art dual-path algorithm. Our hardware design decreases power and area consumption by 23.2% and 10.3% over the dual-path hardware.
Glycoprotein Acetyls: A Novel Inflammatory Biomarker of Early Cardiovascular Risk in the Young

Background Low‐grade inflammation in the young may contribute to the early development of cardiovascular disease. We assessed whether circulating levels of glycoprotein acetyls (GlycA) were better able to predict the development of adverse cardiovascular disease risk profiles compared with the more commonly used biomarker high‐sensitivity CRP (C‐reactive protein). Methods and Results A total of 3306 adolescents and young adults from the Avon Longitudinal Study of Parents and Children (mean age, 15.4±0.3; n=1750) and Cardiovascular Risk in Young Finns Study (mean age, 32.1±5.0; n=1556) were included. Baseline associations between inflammatory biomarkers, body composition, cardiovascular risk factors, and subclinical measures of vascular dysfunction were assessed cross‐sectionally in both cohorts. Prospective risk of developing hypertension and metabolic syndrome during 9‐to‐10‐year follow‐up were also assessed as surrogate markers for future cardiovascular risk. GlycA showed greater within‐subject correlation over 9‐to‐10‐year follow‐up in both cohorts compared with CRP, particularly in the younger adolescent group (r=0.36 versus 0.07). In multivariable analyses, GlycA was found to associate with multiple lifestyle‐related cardiovascular disease risk factors, cardiometabolic risk factor burden, and vascular dysfunction (eg, mean difference in flow‐mediated dilation=−1.2 [−1.8, −0.7]% per z‐score increase). In contrast, CRP levels appeared predominantly driven by body mass index and showed little relationship to any measured cardiovascular risk factors or phenotypes. In both cohorts, only GlycA predicted future risk of both hypertension (risk ratio [RR], ≈1.1 per z‐score increase for both cohorts) and metabolic syndrome (RR, ≈1.2–1.3 per z‐score increase for both cohorts) in 9‐to‐10‐year follow‐up. Conclusions Low‐grade inflammation captured by the novel biomarker GlycA is associated with adverse cardiovascular risk profiles from as early as adolescence and predicts future risk of hypertension and metabolic syndrome in up to 10‐year follow‐up. GlycA is a stable inflammatory biomarker which may capture distinct sources of inflammation in the young and may provide a more sensitive measure than CRP for detecting early cardiovascular risk.
Coastal communities’ responses to climate change and variability impacts: a threat to coastal and marine resources?

ABSTRACT Climate change is real and has been causing significant effects on livelihood activities of coastal communities of Tanzania. Nevertheless, coastal communities have been resorting to various response options in trying to address such effects. Hitherto, diminutive consideration has been paid on the resulting effects of autonomous, anticipatory, or reactive adaptation strategies in practice on coastal and marine resources, which is a gap this article addressed. A total of 150 survey respondents were randomly selected from village registers for household survey. Purposive sampling procedures were used to identify key informants, discussants for focus group discussions, and selection of the study area. Findings showed that communities have been changing and diversifying their livelihood activities significantly as reported by 71% and 64% (p<0.0001) of respondents from Mlingotini and Kondo villages respectively. Most respondents acknowledged that their responses to adverse impacts of climate change was a problem to coastal and marine resources (p<0.041). Respondents affirmed to have been involved in decision-making on the use, conservation and management of aquatic ecosystems (p<0.0001). Despite their participation, degradation of ecosystems was increasing, signifying ineffective resource conservation and management. Thus, care should be taken when choosing responses to climate change impacts in coastal areas to avoid maladaptation practices.
Interactions between nuclear and mitochondrial SNPs and Parkinson's disease risk

Interactions between the products of the nuclear and mitochondrial genomes are critical for the function of most eukaryotic cells. Recently the introduction of mitochondrial replacement therapy has raised the question of incompatibilities between mitochondrial and nuclear variants, and their potential influence on the genetic makeup of human populations. Such interactions could also contribute to the variability of the penetrance of pathogenic DNA variants. This led us to investigate the frequencies of combinations of nuclear and mitochondrial SNP alleles (mitonuclear combinations) in healthy individuals (n=5375) and in a cohort of patients with Parkinsons disease (PD, n=2210). In the unaffected population, we were not able to find associations between nuclear and mitochondrial variants with a false discovery rate below 0.05 after accounting for multiple testing (i.e., the number of combinations examined). However, in the PD cohort, five combinations surpassed this threshold. Next, combining both cohorts, we investigated whether these associations were modulated by disease status. Significant results were achieved by all five combinations (p<10-3 for all tests). These combinations also showed significant evidence for an effect of the interaction between the mitochondrial and nuclear variants on disease risk. Their nuclear components mapped to TBCA, NIBAN3 and GLT25D1 and an uncharacterised intergenic region. Starting from a single cohort design we identified combinations of nuclear and mitochondrial variants affecting PD disease risk.
RINet: Efficient 3D Lidar-Based Place Recognition Using Rotation Invariant Neural Network

LiDAR-based place recognition (LPR) is one of the basic capabilities of robots, which can retrieve scenes from maps and identify previously visited locations based on 3D point clouds. As robots often pass the same place from different views, LPR methods are supposed to be robust to rotation, which is lacking in most current learning-based approaches. In this paper, we propose a rotation invariant neural network structure that can detect reverse loop closures even training data is all in the same direction. Specifically, we design a novel rotation equivariant global descriptor, which combines semantic and geometric features to improve description ability. Then a rotation invariant siamese neural network is implemented to predict the similarity of descriptor pairs. Our network is lightweight and can operate more than 8000 FPS on an i7-9700 CPU. Exhaustive evaluations and robustness tests on the KITTI, KITTI-360, and NCLT datasets show that our approach can work stably in various scenarios and achieve state-of-the-art performance. Our code will be available at: https://github.com/lilin-hitcrt/RINet.
Making collaboration work: application of a conceptual design stages protocol for pre-BIM stages.

This paper presents the application of a Conceptual Design Stages Protocol during feasibility stages within a multidisciplinary design team, for the purpose of bridging the gap between ideas generation and their representation in later and more advanced design stages. Increased effort during the early and conceptual design is a prerequisite for effective and sustainable overall design and construction. Consequently, shift of the effort towards feasibility stages aims to tackle problems with workflow, education and organisation of design teams at the very beginning of a project. Furthermore, the application of an organised process during conceptual design could further support a multi-party agreement, hence enhancing the potentials of collaboration and coordination for the entirety of a project. This paper demonstrates the Conceptual Design Stages Protocol as an organised process encompassing different professionals, technologies and means for communication that has been tested in a conceptual stage study and the outcomes have been incorporated within BIM. The impact of this Protocol on designers cognitive, physical, perceptual and collaborative actions is presented and the first pre-BIM Protocol is established. This was a paper presented at the International Conference on Building Information Modelling (BIM) in Design, Construction and Operations, held in Bristol, 9-11 September 2015.
Bearing Remaining Useful Life Prediction Based on Regression Shapalet and Graph Neural Network

Remaining useful life (RUL) prediction of bearing is essential to guarantee its safe operation. In recent years, deep learning (DL)-based methods attract a lot of research attention for accurate RUL prediction. However, the weak interpretability of the DL models prevents their wide use in practical systems. In this article, the graph is used to represent the degradation state of bearings, and a graph neural network (GNN) is applied for their RUL prediction. Specifically, regression shapelet is proposed to transform the bearings time series data into graph structure first. Then, with the proposed distance matrix/adjacency matrix as the input and smoothed nonlinear health index (SNHI) as the output, a deep GNN model combining graph convolutional neural network (GCN) and gate recurrent unit (GRU) is set up in both spatial and temporal perspectives to predict the bearing RUL. Meanwhile, graph evolution is adopted to monitor the graph changes with time and offer an explanation for the bearing degradation procedure. The experiment study on the PRONOSTIA platform is used to evaluate the proposed method. The results show that the proposed method can well explain the bearing degradation process from the graph perspective and will achieve superior performance to the existing methods.
Reliability among School-Based Professionals: Using a Feature-Matching Screening Checklist to Identify Communication Systems

Students with complex communication needs may require augmentative and alternative communication systems to supplement or replace their speech abilities. To effectively identify a communication system, a feature-matching process should be implemented as it considers the student’s present levels of performance. Due to the unique communication characteristics of students with complex communication needs, informal assessment tools are often used to help determine the students’ skills in natural contexts. A challenge often faced with informal assessment tools is the reliability among evaluators. As such, this pilot study attempted to evaluate the reliability of a feature-matching screening checklist and its corresponding matrices among potential professionals who would be part of the educational team. Results indicated that (a) pre-service and in-service special education teachers were the most reliable combination when completing the screening checklist, (b) exposure to the screening checklist was an influencing factor on reliability, and (c) in-service speech-language pathologists made the most errors while completing the screening checklist. Implications for practices and future research directions are discussed.