Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated computer-aided architectural and structural design

A programme of research was carried out to provide an integrated design tool for interactive design of architectural layout and structural system of regular multistory buildings. The outline of the system is as follows. The user works with architectural spaces and defines the final arrangement of spaces in terms of geometry by drawing them in AutoCAD. The plan of designed spaces or "blocks" is used to generate a set of gridlines the intersections of which produce "nodes". Certain rules are applied to render the nodes into structural nodes forming the end points of structural members. Once the geometry of structure is defined, data is passed to a structural steel design module using an approximate method which generates the initial sizing of members. For a more accurate result, two sophisticated analysis programs are used for strength and serviceability. Conformance to standards is carried out by another module and due modifications are made to the structure. A cost estimate of the building structure is finally produced.
SMPG: Secure Multi Party Computation on Graph Databases

: In this position paper, we outline how secure multi-party querying can be brought to graph databases. Such a system will allow multiple users to jointly query federated graph databases that consist of several private parts. We have provided a proof of concept. Our prototype implementation for the SMPG system builds on top of Conclave (Volgushev et al., 2019), which was originally proposed and implemented for multi-party computation and querying on relational databases. We describe the templates of queries that are currently supported by our prototype and discuss current limitations as well as the extensions planned to tap the conceptual beneﬁts.
Use of Support Vector Machines to Assign Short-Term Counts to Seasonal Adjustment Factor Groups

The traditional method of estimating annual average daily traffic (AADT) involves several steps, with the most critical being the assignment process that involves allocating short-term counts to groups of seasonal adjustment factors (SAFs). The accuracy of AADT estimates highly depends on the assignment step, which is subject to errors stemming from human judgment. Support vector machines (SVMs), a supervised learning and statistical method, are employed to construct a series of assignment models that are compared with the traditional method and discriminant analysis (DA) models. Traffic volume data obtained from permanent traffic recorders are used to train and validate the models. The analysis is conducted with SAFs calculated for each direction of travel and separately for two-way traffic. The results reveal that the Gaussian kernel-based SVM model yields the lowest errors, improving AADT accuracy by 65% and decreasing the standard deviation of absolute percentage error by 73.7% over the traditional method. Another finding is that the assignment errors of the directional volume-based analysis are lower than those of the total volume-based analysis by 41.8%. The comparison between the two model parameters examined—the average daily traffic and the hourly factors—indicates that the combined use of both parameters in SVMs is more effective than when hourly factors are used alone. However, in the case of DA, the opposite results are obtained. A possible explanation is that the SVM kernels transfer data from the input space to a feature space and thus provide the ability to assign counts effectively with different types of data within the same model.
FineCodeAnalyzer: Multi-Perspective Source Code Analysis Support for Software Developer Through Fine-Granular Level Interactive Code Visualization

Source code analysis is one of the important activities during the software maintenance phase that focuses on performing the tasks including bug localization, feature location, bug/feature assignment, and so on. However, handling the aforementioned tasks on a manual basis (i.e. finding the location of buggy code from a large application) is an expensive, time-consuming, tedious, and challenging task. Thus, the developers seek automated support in performing the software maintenance tasks through automated tools and techniques. However, the majority of the reported techniques are limited to textual analysis where the real developers’ concerns are not properly considered. Moreover, existing solutions seem less useful for the developers. This work proposes a tool (called as FineCodeAnalyzer) that supports an interactive source code analysis grounded on structural and historical relations at fine granular-level between the source code elements. To evaluate the performance of FineCodeAnalyzer, we consider 74 developers that assess three main facets: (i) usefulness, (ii) cognitive-load, and (iii) time efficiency. For usefulness concern, the results show that FineCodeAnalyzer outperforms the developers’ self-adopted strategies in locating the code elements in terms of Precision, Recall, and F1-Measure of accurately locating the code elements. Specifically, FineCodeAnalyzer outperforms the developers’ strategies up to 47%, 76%, and 61% in terms of Precision, Recall, and F1-measure, respectively. Additionally, FineCodeAnalyzer takes 5% less time than developers’ strategies in terms of minutes of time. For cognitive-load, the developers found FineCodeAnalyzer to be 72% less complicated than manual strategies, in terms of the NASA Tool Load Index metric. Finally, the results indicate that FineCodeAnalyzer allows effectively locating the code elements than the developer’s adopted strategies.
In Silico Analysis and Synthesis of Nafamostat Derivatives and Evaluation of Their Anti-SARS-CoV-2 Activity

Inhibition of transmembrane serine protease 2 (TMPRSS2) is expected to block the spike protein-mediated fusion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nafamostat, a potent TMPRSS2 inhibitor as well as a candidate for anti-SARS-CoV-2 drug, possesses the same acyl substructure as camostat, but is known to have a greater antiviral effect. A unique aspect of the molecular binding of nafamostat has been recently reported to be the formation of a covalent bond between its acyl substructure and Ser441 in TMPRSS2. In this study, we investigated crucial elements that cause the difference in anti-SARS-CoV-2 activity of nafamostat and camostat. In silico analysis showed that Asp435 significantly contributes to the binding of nafamostat and camostat to TMPRSS2, while Glu299 interacts strongly only with nafamostat. The estimated binding affinity for each compound with TMPRSS2 was actually consistent with the higher activity of nafamostat; however, the evaluation of the newly synthesized nafamostat derivatives revealed that the predicted binding affinity did not correlate with their anti-SARS-CoV-2 activity measured by the cytopathic effect (CPE) inhibition assay. It was further shown that the substitution of the ester bond with amide bond in nafamostat resulted in significantly weakened anti-SARS-CoV-2 activity. These results strongly indicate that the ease of covalent bond formation with Ser441 in TMPRSS2 possibly plays a major role in the anti-SARS-CoV-2 effect of nafamostat and its derivatives.
Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic

Abstract In early 2020, as diagnostic and surveillance responses for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the USA by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single or small number of introductions from Asia three weeks prior to the state’s first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely related viruses in multiple US states by the end of March 2020. Our findings indicate that the exclusive focus on identifying SARS-CoV-2 in returning international travelers early in the pandemic may have led to a failure to recognize locally circulating infections for several weeks and point toward a critical need for implementing rapid, broadly targeted surveillance efforts for future pandemics.
Progress in Topological Mechanics

Topological mechanics is rapidly emerging as an attractive field of research where mechanical waveguides can be designed and controlled via topological methods. With the development of topological phases of matter, recent advances have shown that topological states have been realized in the elastic media exploiting analogue quantum Hall effect, analogue quantum spin Hall effect, analogue quantum valley Hall effect, higher-order topological physics, topological pump, topological lattice defects and so on. This review aims to introduce the experimental and theoretical achievements with defect-immune protected elastic waves in mechanical systems based on the abovementioned methods, respectively. From these discussions, we predict the possible perspective of topological mechanics.
Vehicle load simulation for long-span bridge based on merging real-monitored data and cellular automaton model

Vehicular load has significant impact on long-span railway-highway bridges and highway bridges.Compared with short and medium span bridges,the traffic volume is much denser and the occurrence probabilities of speed variation and lane changing on the long-span bridge are larger,which results in the distribution variation of vehicle loads on the bridge. The current traffic monitoring system cannot provide the detailed information of the whole process of vehicle movement on the bridge. In order to determine the vehicle distribution on the long-span bridge,the traffic model merging the real-monitored data and cellular automation( CA) model was developed. This model could consider the traffic properties in different regions,traffic rule,roadway situation and driver's behavior. Based on the traffic monitoring data in a heavy-traffic roadway in Hebei province,the vehicle load properties' variation in space and time domain on the middle span of a long-span cable-stayed bridge was analyzed. The load impact was evaluated through the comparison with the stipulated vehicle load.
Molecular detection of Haemophilus influenzae isolated from eye swabs of patients with conjunctivitis in Hilla Province, Iraq

Jundi Alak Mahdi Al-Buhilal1, Abdulkadir Kareem Rhumaid1, Alyaa Mohammed H. Al-Tabtabai2, Niran Kadhim F. AL-Rubaey3* 1Department of Technical Medical Laboratories, Institute of Medical Technology Al-Mansour, Middle Technical University, Baghdad, Iraq. 2Department of Biotechnology, College of Science, University of Baghdad, Baghdad, Iraq. 3Department of Microbiology, Hammurabi College of Medicine, University of Babylon, Babylon, Iraq.
Who Watches the Watchers: A Multi-Task Benchmark for Anomaly Detection

: A driver in the rise of IoT systems has been the relative ease with which it is possible to create specialized-but-adaptable deployments from cost-effective components. Such components tend to be relatively unreliable and resource poor, but are increasingly widely connected. As a result, IoT systems are subject both to component failures and to the attacks that are an inevitable consequence of wide-area connectivity. Anomaly detection systems are therefore a cornerstone of effective operation; however, in the literature, there is no established common basis for the evaluation of anomaly detection systems for these environments. No common set of benchmarks or metrics exists and authors typically provide results for just one scenario. This is profoundly unhelpful to designers of IoT systems, who need to make a choice about anomaly detection that takes into account both ease of deployment and likely detection performance in their context. To address this problem, we introduce Aftershock, a multi-task benchmark. We adapt and standardize an array of datasets from the public literature into anomaly detection-speciﬁc benchmarks. We then proceed to apply a diverse set of existing anomaly detection algorithms to our datasets, producing a set of performance baselines for future comparisons. Results are reported via a dedicated online platform located at https://aftershock. dev, allowing system designers to evaluate the general applicability and practical utility of various anomaly detection models. This approach of public evaluation against common criteria is inspired by the immensely useful community resources found in areas such as natural language processing, recommender systems, and reinforcement learning. We collect, adapt, and make available 10 anomaly detection tasks which we use to evaluate 6 state-of-the-art solutions as well as common baselines. We offer researchers a submission system to evaluate future solutions in a transparent manner and we are actively engaging with academic and industry partners to expand the set of available tasks. Moreover, we are exploring options to add hardware-in-the-loop. As a community contribution, we invite researchers to train their own models (or those reported by others) on the public development datasets available on the online platform, submitting them for independent evaluation and reporting results against others.
A Computer Science Perspective on Digital Transformation in Production

The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production, two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI. Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure. Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.
Investigation of Ventilated Cavity Shapes of a High Speed Underwater Vehicle

The ventilated cavitating flow in a longitudinal gravity force field was studied by using numerical simulation method during vertical launch submarine missile rising.Based on the Mixture multiphase model and coupled with the natural cavitation model,the Reynolds-averaged Navier-Stokes equations were solved and the mathematical model of multiphase cavitating flow of gas,vapor and water was established.A dynamic mesh technique was utilized,combined with UDF program,the underwater motion process of a submarine vertically launched missile was simulated,and the evolution of the ventilated cavity and the effect of the partial cavity on the hydrodynamics were achieved.
The Ionic Product of Water in the Eye of the Quantum Cluster Equilibrium

The theoretical description of water properties continues to be a challenge. Using quantum cluster equilibrium (QCE) theory, we combine state-of-the-art quantum chemistry and statistical thermodynamic methods with the almost historical Clausius–Clapeyron relation to study water self-dissociation and the thermodynamics of vaporization. We pay particular attention to the treatment of internal rotations and their impact on the investigated properties by employing the modified rigid-rotor–harmonic-oscillator (mRRHO) approach. We also study a novel QCE parameter-optimization procedure. Both the ionic product and the vaporization enthalpy yield an astonishing agreement with experimental reference data. A significant influence of the mRRHO approach is observed for cluster populations and, consequently, for the ionic product. Thermodynamic properties are less affected by the treatment of these low-frequency modes.
The Potential Roles of Unique Leaf Structure for the Adaptation of Rheum tanguticum Maxim. ex Balf. in Qinghai–Tibetan Plateau

Leaves are essential plant organs with numerous variations in shape and size. The leaf size is generally smaller in plants that thrive in areas of higher elevation and lower annual mean temperature. The Qinghai–Tibetan Plateau is situated at an altitude of >4000 m with relatively low annual average temperatures. Most plant species found on the Qinghai–Tibetan Plateau have small leaves, with Rheum tanguticum Maxim. ex Balf. being an exception. Here, we show that the large leaves of R. tanguticum with a unique three-dimensional (3D) shape are potentially an ideal solution for thermoregulation with little energy consumption. With the increase in age, the shape of R. tanguticum leaves changed from a small oval plane to a large palmatipartite 3D shape. Therefore, R. tanguticum is a highly heteroblastic species. The leaf shape change during the transition from the juvenile to the adult phase of the development in R. tanguticum is a striking example of the manifestation of plant phenotypic plasticity. The temperature variation in different parts of the leaf was a distinct character of leaves of over-5-year-old plants. The temperature of single-plane leaves under strong solar radiation could accumulate heat rapidly and resulted in temperatures much higher than the ambient temperature. However, leaves of over-5-year-old plants could lower leaf temperature by avoiding direct exposure to solar radiation and promoting local airflow to prevent serious tissue damage by sunburn. Furthermore, the net photosynthesis rate was correlated with the heterogeneity of the leaf surface temperature. Our results demonstrate that the robust 3D shape of the leaf is a strategy that R. tanguticum has developed evolutionarily to adapt to the strong solar radiation and low temperature on the Qinghai–Tibetan Plateau.
Effects of Transcutaneous Electrical Nerve Stimulation on Pain and Chemotherapy-Induced Peripheral Neuropathy in Cancer Patients: A Systematic Review

Transcutaneous electrical nerve stimulation (TENS) is the usage of a mild electrical current through electrodes that stimulate nerves. Patients with malignancies experience pain and chemotherapy-induced peripheral neuropathy. A systematic review was performed to find research evaluating the effect of TENS on these two common symptoms decreasing the quality of life in cancer patients. PubMed, the Cochrane Central Register of Controlled Trials and EMBASE were searched. Original studies, namely randomized controlled trials, quasi-randomized controlled trials and controlled clinical trials, published between April 2007 and May 2020, were considered. The quality of the selected studies was assessed. Seven papers were incorporated in a qualitative synthesis, with 260 patients in total. The studies varied in terms of design, populations, endpoints, quality, treatment duration, procedures and follow-up period. Based on the results, no strict recommendations concerning TENS usage in the cancer patient population could be issued. However, the existing evidence allows us to state that TENS is a safe procedure that may be self-administered by the patients with malignancy in an attempt to relieve different types of pain. There is a need for multi-center, randomized clinical trials with a good methodological design and adequate sample size.
Measurement of the surface excitation parameter of Kapton, polyethylene (PE), polymethyl methacrylate (PMMA), polystyrene (PS) and polytetrafluoroethylene (PTFE)

Reflection electron energy loss spectra (REELS) were measured for five insulating organic compounds: Kapton, polyethylene (PE), poly(methyl methacrylate) (PMMA), polystyrene (PS) and polytetrafluoroethylene (PTFE), as well as for Ni and Si, in the energy range between 200 and 1600 eV. The average number of surface excitations for a single surface crossing were determined from the experimental data and were found to be considerably smaller than for earlier studied materials, which mainly consisted of elemental metals [Surf. Sci. 486(2001)L461]. The surface excitation parameter, a material parameter used to quantify the relative intensity of surface losses in (photo)electron spectroscopy, was extracted from the data and compared with values found in the literature. The results indicate that surface excitations only have a minor influence on quantification of XPS spectra of polymers. On the other hand, a correction for surface excitations turns out to be essential for measurements of the electron inelastic mean free path of polymers when a metal is used as reference material.
TALBOT: A Track-Leg Transformable Robot

This article introduces a tracked-leg transformable robot, TALBOT. The mechanical and electrical design, control method, and environment perception based on LiDAR are discussed. The original tracked-leg transformable structure allows the robot to switch between the tracked and legged mode to achieve all-terrain adaptation. In the tracked mode, TALBOT is controlled by the method of differential speed between the two tracked feet. In the legged mode, TALBOT is controlled based on a bionic control strategy of the central pattern generator to realize the generation and conversion of gait. In addition, the robot is equipped with a LiDAR, through sensor preprocessing and optimization of the slam mapping algorithm, so that the robot achieves a better mapping effect. We tested the robot’s motion performance and the slam mapping effect, including going straight and turning in tracked and legged modes and building a map in an indoor environment.
ANALYSIS OF POWER SPECTRUM OFA DUAL RANDOMIZED MODULATION (DRM)

Randomized pulse width modulation schemes in power converters have shown potential for relaxing filtering requirements and reducing electromagnetic interference. Existing schemes invariably randomize one modulation parameter to disperse the harmonic contents of the output of the power converter. This paper proposes the investigation of a new dual randomized modulation (DRM or RCFPM that is pulse position with carrier frequency modulation) scheme using a statistics communication theory. Analytical and experiment results show the new scheme leads to a further degree of dispersion of harmonic contents when compared with the single randomization schemes, and is propitious to improve electro- magnetic compatibility of the power converter system.