Miracle-dz/UniFAR-FTUs huggingface dataset:
Fabrication of Engineered Vascular Flaps using 3D Printing Technologies.

Engineering implantable, functional, thick tissues requires designing a hierarchical vascular network. 3D bioprinting is a technology used to create tissues by adding layer upon layer of printable biomaterials, termed bioinks, and cells in an orderly and automatic manner, which allows for creating highly intricate structures that traditional tissue engineering techniques cannot achieve. Thus, 3D bioprinting is an appealing in vitro approach to mimic the native vasculature complex structure, ranging from millimetric vessels to microvascular networks. Advances in 3D bioprinting in granular hydrogels enabled the high-resolution extrusion of low-viscosity extracellular matrix-based bioinks. This work presents a combined 3D bioprinting and sacrificial mold-based 3D printing approach for fabricating engineered vascularized tissue flaps. 3D bioprinting of endothelial and support cells using recombinant collagen-methacrylate bioink within a gelatin support bath is utilized for the fabrication of a self-assembled capillary network. This printed microvasculature is assembled around a mesoscale vessel-like porous scaffold, fabricated using a sacrificial 3D printed mold, and is seeded with endothelial cells. This assembly induces the endothelium of the mesoscale vessel to anastomose with the surrounding capillary network, establishing a hierarchical vascular network within an engineered tissue flap. The engineered flap is then directly implanted by surgical anastomosis to a rat femoral artery using a cuff technique. The described methods can be expanded for the fabrication of various vascularized tissue flaps for use in reconstruction surgery and vascularization studies.
Stop-Flow Lithography for the Continuous Production of Degradable Hydrogel Achiral Crescent Microswimmers

The small size of robotic microswimmers makes them suitable for performing biomedical tasks in tiny, enclosed spaces. Considering the effects of potentially long-term retention of microswimmers in biological tissues and the environment, the degradability of microswimmers has become one of the pressing issues in this field. While degradable hydrogel was successfully used to prepare microswimmers in previous reports, most hydrogel microswimmers could only be fabricated using two-photon polymerization (TPP) due to their 3D structures, resulting in costly robotic microswimmers solution. This limits the potential of hydrogel microswimmers to be used in applications where a large number of microswimmers are needed. Here, we proposed a new type of preparation method for degradable hydrogel achiral crescent microswimmers using a custom-built stop-flow lithography (SFL) setup. The degradability of the hydrogel crescent microswimmers was quantitatively analyzed, and the degradation rate in sodium hydroxide solution (NaOH) of different concentrations was investigated. Cytotoxicity assays showed the hydrogel crescent microswimmers had good biocompatibility. The hydrogel crescent microswimmers were magnetically actuated using a 3D Helmholtz coil system and were able to obtain a swimming efficiency on par with previously reported microswimmers. The results herein demonstrated the potential for the degradable hydrogel achiral microswimmers to become a candidate for microscale applications.
Tuning the Hyperparameters of the 1D CNN Model to Improve the Performance of Human Activity Recognition

activity raw data. This is done by empirically tuning the values of hyperparameters, such as kernel size, filter maps, number of epochs, batch size, and promoting an advanced multi-headed 1D CNN by employing each convolutional layer with a different kernel size to gain an ensemble–like results. The selected hyper parameter's impact is evaluated on a publicly available dataset named UCI HAR collected from smartphone sensors to perform six activities. A significant determinant of better results depends on the hyperparameter that has been chosen. The results demonstrated that tuning the hyperparameter of 1D CNN increased
History of Line Pipe Manufacturing in North America

This report was prepared as an account of work sponsored through the American Society of Mechanical Engineers Center for Research and Technology Development by Columbia Gas Transmission Corporation, El Paso Natural Gas Company, Gas Research Institute, Research Committee on Gas Pipelines Safety, and Washington Gas and Electric Company. This document’s purpose is to provide pipeline operators with historical data on line pipe, so that they will be able to operate their pipelines, particularly the older ones, with greater confidence in their safety and reliability. The document is comprised of four major sections. The first explains the manufacturing processes that have been and are being used to make line pipe. The second presents tables by type of pipe listing the manufacturers of line pipe, past and present, in North America. At the end of this section some techniques for identifying unknown pipe samples are presented. In the third section the API line pipe specifications as they have evolved since 1928 are reviewed. The fourth section is a glossary of terms frequently associated with line pipe manufacturing.
A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.
Problematic Use of the Internet Mediates the Association between Reduced Mentalization and Suicidal Ideation: A Cross-Sectional Study in Young Adults

Suicide is a major public health problem, and it is urgent to investigate its underlying clinical and psychological concomitants. It has been suggested that low mentalization skills and problematic use of the internet (PUI) are factors that can play a role in suicidal behaviors. It is possible that poor mentalization skills contribute to leading to forms of PUI, which, in turn, can represent triggers for suicidal ideation (SI). We tested this hypothesis through a quantitative and cross-sectional study on a sample (n = 623) of young adults (age range: 18–34). Self-report measures investigating symptoms related to Social Media Addiction (SMA), Internet Gaming Disorder (IGD), mentalization capacity, and SI were used. A single mediation analysis with two mediators was carried out to evaluate the direct and indirect effects of mentalization on SI through the mediating role of SMA- and IGD-related symptoms, controlling for potential confounding factors (e.g., socio-demographic and addiction-related variables). The four explored variables were significantly associated with each other (all p < 0.001) across all subjects; the mediational model showed that the total effect of mentalization on SI was significant (B = −0.821, SE = 0.092 (95% CI: −1.001; −0.641)) and that both SMA- (B = −0.073, SE = 0.034 (95% CI: −0.145; −0.008)) and IGD-related symptoms (B = 0.046, SE = 0.027 (95% CI: −0.107; −0.001)) were significant mediators of such association. Our findings support the possibility that PUI severity plays a relevant role in mediating the association between low mentalization skills and levels of SI.
Delayed Ventricular Repolarization and Sodium Channel Current Modification in a Mouse Model of Rett Syndrome

Rett syndrome (RTT) is a severe developmental disorder that is strongly linked to mutations in the MECP2 gene. RTT has been associated with sudden unexplained death and ECG QT interval prolongation. There are mixed reports regarding QT prolongation in mouse models of RTT, with some evidence that loss of Mecp2 function enhances cardiac late Na current, INa,Late. The present study was undertaken in order to investigate both ECG and ventricular AP characteristics in the Mecp2Null/Y male murine RTT model and to interrogate both fast INa and INa,Late in myocytes from the model. ECG recordings from 8–10-week-old Mecp2Null/Y male mice revealed prolongation of the QT and rate corrected QT (QTc) intervals and QRS widening compared to wild-type (WT) controls. Action potentials (APs) from Mecp2Null/Y myocytes exhibited longer APD75 and APD90 values, increased triangulation and instability. INa,Late was also significantly larger in Mecp2Null/Y than WT myocytes and was insensitive to the Nav1.8 inhibitor A-803467. Selective recordings of fast INa revealed a decrease in peak current amplitude without significant voltage shifts in activation or inactivation V0.5. Fast INa ‘window current’ was reduced in RTT myocytes; small but significant alterations of inactivation and reactivation time-courses were detected. Effects of two INa,Late inhibitors, ranolazine and GS-6615 (eleclazine), were investigated. Treatment with 30 µM ranolazine produced similar levels of inhibition of INa,Late in WT and Mecp2Null/Y myocytes, but produced ventricular AP prolongation not abbreviation. In contrast, 10 µM GS-6615 both inhibited INa,Late and shortened ventricular AP duration. The observed changes in INa and INa,Late can account for the corresponding ECG changes in this RTT model. GS-6615 merits further investigation as a potential treatment for QT prolongation in RTT.
Crew Resource Management Training Attitudes of United States Coast Guard Aviators

Crew Resource Management Training Attitudes of United States Coast Guard Aviators Greg A Stewart Old Dominion University, 2014 Director: Dr. John Ritz This study presents an investigation of Coast Guard Crew Resource Management attitudes and beliefs. The purpose of this study was to determine if there is a significant difference in the Crew Resource Management attitudes and beliefs of pilots and crew members. The study was guided by two research questions. Research Question 1 asked if there was a significant difference between the CRM attitudes of enlisted flight crew members and pilots. Research Question 2 asked if there was a significant difference between the CRM attitudes of fixed wing HC-130 aviators and rotary wing H60 aviators. Three hundred forty-three aviators from Coast Guard Air Station Clearwater, Florida, were delivered the Cockpit Management Attitudes Questionnaire developed by Gregorich and Helmreich of which 172 responded representing 50.1% of the population. Data collected revealed that there was not a significant difference between pilots and enlisted aircrew members on the questionnaire as a complete instrument, however individual items did result in t-test analysis representing a significant difference. Investigation of Research Question 2 provided data proving that there is a significant difference between fixed-wing aviators and rotary-wing aviator’s attitudes and beliefs of Crew Resource Management, particularly in the field of recognition of stressor effects. These findings indicate that the current Crew Resource Management training program employed by the United States Coast Guard is not meeting the learning objectives of all members and requires revision.
A Mathematical Modeling and Analysis Method for the Kinematics of a Maglev Train

In recent years, more and more countries are paying attention to maglev transportation because of its outstanding advantages. However, because the structure of a maglev train is more complex than a wheel-rail train, there is no systematic theory method for kinematic modeling of a maglev vehicle. Based on the screw theory and the product of exponentials (PoE) formula in the field of open-chain robotics, this paper presents a method of kinematic mathematical modeling and analysis for a maglev train and track. By analyzing the characteristics of the closed chain running mechanism of a maglev train, the kinematic mathematical model of a maglev train and track is established, and the motion state of each moving part of the vehicle on a typical track is calculated and analyzed. At last, compared with the results of experiment, the correctness of modeling method proposed in this paper is verified.
The Use of Prepositions among EFL Learners: A Systematic Review

As a second/foreign language learner, one should understand the rules and convention of the language s/he learns and how to use it. Therefore, s/he needs to master the language to be able to have good communication with others. One aspect that needs to be learned is parts of speech such as nouns, pronouns, verbs, adjectives, adverbs, conjunctions, and prepositions. Prepositions play a significant role in helping students increase their language skills. However, many students still face difficulties in learning its type of parts of speech. The purpose of this study is to examine the trends and main findings of the studies about the main error types and causes in using prepositions among second/foreign language learner. For this objective, articles from databases comprising Google Scholar, Eric, and Garuda Ristek were reviewed, and overall, selected articles were analysed. As the research methodology, systematic review was performed. A content analysis was used to evaluate the articles. According to the results of the study, the main error type in using prepositions among the learners is the wrong use of prepositions. In the meantime, the main cause of the errors made by the learners in using prepositions is the interlingual interference
Research of garbage salvage system based on deep learning

The increase in surface trash salvage tasks has led to problems such as high staff workload, high labor costs, and low work efficiency. A garbage salvage system with autonomous cruising, identification, and detection is designed for this problem. This paper is based on deep learning technology to detect surface garbage and salvage garbage by robotic arm to solve the problem of low intelligence of surface garbage salvage as well as the problem caused by rising labor cost and insufficient labor, and to improve the efficiency of surface garbage salvage task. The system is equipped with a ROS robot operating system and uses lidar to acquire environmental information, realize map construction and autonomous cruise of the salvage vessel, use the YOLO v4 target detection model to detect garbage, and then apply the detected target and location information to the intelligent garbage salvage system. Five common types of garbage on the water surface are collected, and the average detection speed reaches 45 FPS and the average recognition accuracy is up to 88% through experimental verification, meeting the real-time system application.
Exploring Relationships between Key Success Factors and Performance Metrics for Indian Express Delivery Service Providers

Studies on the global express industry are few and far between in the academic literature. The purpose of this paper is to identify from the extant literature the variables and dynamics significant for this industry and present a survey of Indian express delivery service providers in terms of these variables and their inter-relationships to assess the state of the sector, its dynamics, problems, and growth opportunities. The Indian express sector generates approximately USD 1.6 billion of revenues, growing at about 20% per annum, and employs close to one million people. The sector is fragmented, consisting of about 2,500 players with a few large players capturing about 80% of the market. The survey shows that small firms perform poorly with respect to on-time delivery and reliability, breadth of services, geographic coverage, investments in assets and information systems, and integration of services. However, all these factors are shown to be important determinants of success, and, therefore, small firms need to seriously concentrate on these factors, besides focusing on value-added services, global reach, computerization, and automation to improve their performance. The survey also shows that firms irrespective of their size do not place much emphasis on human resources. However, human resources turn out to be a significant predictor variable as far as firm performance is concerned. Therefore, it is imperative for firms to put a proper human resource management plan in place. It is expected that the findings of the survey would help managers make informed decisions in terms of the allocation of scarce resources. However, the government is expected to introduce policies conducive to the growth of express delivery services in India.
An End-to-End Atrous Spatial Pyramid Pooling and Skip-Connections Generative Adversarial Segmentation Network for Building Extraction from High-Resolution Aerial Images

Automatic building extraction based on high-resolution aerial imagery is an important challenge with a wide range of practical applications. One of the mainstream methods for extracting buildings from high-resolution images is deep learning because of its excellent deep feature extraction capability. However, existing models suffer from the problems of hollow interiors of some buildings and blurred boundaries. Furthermore, the increase in remote sensing image resolution has also led to rough segmentation results. To address these issues, we propose a generative adversarial segmentation network (ASGASN) for pixel-level extraction of buildings. The segmentation network of this framework adopts an asymmetric encoder–decoder structure. It captures and aggregates multiscale contextual information using the ASPP module and improves the classification and localization accuracy of the network using the global convolutional block. The discriminator network is an adversarial network that correctly discriminates the output of the generator and ground truth maps and computes multiscale L1 loss by fusing multiscale feature mappings. The segmentation network and the discriminator network are trained alternately on the WHU building dataset and the China typical cities building dataset. Experimental results show that the proposed ASGASN can accurately identify different types of buildings and achieve pixel-level high accuracy extraction of buildings. Additionally, compared to available deep learning models, ASGASN also achieved the highest accuracy performance (89.4% and 83.6% IoU on these two datasets, respectively).
Prevention Techniques against Distributed Denial of Service Attacks in Heterogeneous Networks: A Systematic Review

The Distributed Denial of Service (DDoS) attack is one of the most critical issues in network security. These sorts of attacks pose a noteworthy danger to the accessibility of network services for their legitimate users by flooding the bandwidth or network service using various infected computer systems. The targeted servers are overwhelmed with malicious packets or connection requests, causing them to slow down or even crash the server operations which results in preventing genuine users from accessing the service. In this paper, we discussed the detailed classification of DDoS attacks and identified attackers’ motivations behind them and their consequences. Further, the DDoS attacks on IoT devices are elaborated based on applications and network layers. A comprehensive literature review has been conducted on cutting-edge defense techniques to defend against such attacks. An in-depth analysis of each mechanism has been carried out to find the optimal solutions. We fairly evaluated the existing defense techniques for DDoS attacks and presented key findings in comparison tables. Furthermore, this paper provides recommendations for future work for new researchers.
Morphology-Controlled Synthesis of Polyphosphazene-Based Micro- and Nano-Materials and Their Application as Flame Retardants

Common flame retardants, such as halogen-based materials, are being phased-out owing to their harmful environmental and health effects. We prepared poly-(cyclotriphosphazene-co-4,4′-sulfonyldiphenol) (PZS) microspheres, nanotubes, capsicum-like nanotubes, and branched nanotubes as flame retardants. An increase in reaction temperature changed the morphology from nanotubes to microspheres. A PZS shape had a positive effect on the flame retardancy of polyethylene terephthalate (PET). The PZS with a capsicum-like nanotube morphology had the best flame retardancy, and the PET limiting oxygen index increased from 25.2% to 34.4%. The flame retardancy capability was followed by PZS microspheres (33.1%), branched nanotubes (32.8%), and nanotubes (32.5%). The capsicum-like nanotubes promote the formation of highly dense and continuous carbon layers, and they release a non-combustible gas (CO2). This study confirms polyphosphazene-based flame retardants as viable and environmentally-friendly alternatives to common flame retardants. It also presents a novel and facile design and synthesis of morphology-controlled nanomaterials with enhanced flame retardant properties.
Ultra‐wideband low‐scattering metamaterial based on combination of water absorber and polarization rotation metasurface

In this paper, an ultra‐wideband low scattering metamaterial based on the combination of water absorber and polarization rotation metasurface (PRM) is proposed. The water absorber and PRM realize low backscattering in higher and lower frequency bands, respectively. The mutual influence between the water absorber and PRM is insignificant due to their different design strategies of absorbing and diffusion respectively. Therefore, a continuous low‐scattering bandwidth is obtained by designing the water absorber and PRM operating in two adjacent frequency bands. The simulated results show that the radar cross section (RCS) of the proposed metamaterial can be reduced by more than 10 dB in the ultra‐wideband band of 2.8–80 GHz at room temperature of 25°C. Moreover, excellent thermal and angle stabilities are obtained for temperatures from 10 to 90°C and incident angles from 0 to 40°, respectively. A prototype of the proposed metamaterial with 12 × 12 unit cells was fabricated and measured. The measured and simulated results were in good agreement, which validated the correctness of the design concept. The proposed metamaterial has promising potential applications for low‐observable platforms and antennas.