Miracle-dz/UniFAR-FTUs huggingface dataset:
T2 Distribution in the Forearm Muscles and the T2 Threshold for Defining Activated Muscle

Purpose: In order to detect exercised muscles by the increase in T2, we have defined a Gaussian T2 distribution and reference values (T2r and SDr) in resting state muscles, and a threshold for detecting exercised muscles. Methods: The subjects were healthy adult volunteers (14 males and 12 females). Multiple-spin-echo (MSE) MR images were obtained with 10 TE values from 10 to 100 ms using a 0.2T MRI system. T2 values for 10 forearm muscles were obtained in the resting state and after isometric wrist flexion exercise with 5%, 15%, and 25% of the maximum voluntary contraction (MVC). Z values were obtained by (T2e − T2r)/SDr, where T2e was T2 after exercise. Based on sample size calculations, three thresholds (ZT = 1.00, 2.56, and 3.07) were applied to agonist and antagonist muscles. Results: A normal distribution of T2 was detected in resting muscles at 34 ± 3 ms (mean ± standard deviation [SD]) in 26 subjects using the Kolmogorov–Smirnov test, the Shapiro–Wilk test, and the Jarque–Bera test (P > 0.05). No gender differences were shown between the T2 or SD, and a similar result was obtained in 12 measurements on a single subject (P < 0.01). The T2r and SDr were used for reference values. The threshold ZT = 1.00 showed the highest sensitivity (0.86) even with 5% MVC, but it showed a lower specificity (0.85) than the other thresholds. ZT = 3.07 showed the highest specificity (1.0), but it showed a lower sensitivity (0.36) with the 5% MVC, compared with ZT = 2.56 (0.50). The receiver operating characteristics analysis also supported these results. Conclusion: We found that the T2 distribution in muscles was Gaussian, suggesting that a one-sample t-test can be applied, and that ZT = 2.56 could cover low-intensity exercise with high specificity and a low false-positive rate.
Unique morphogenetic signatures define mammalian neck muscles and associated connective tissues

In vertebrates, head and trunk muscles develop from different mesodermal populations and are regulated by distinct genetic networks. Neck muscles at the head-trunk interface remain poorly defined due to their complex morphogenesis and dual mesodermal origins. Here, we use genetically modified mice to establish a 3D model that integrates regulatory genes, cell populations and morphogenetic events that define this transition zone. We show that the evolutionary conserved cucullaris-derived muscles originate from posterior cardiopharyngeal mesoderm, not lateral plate mesoderm, and we define new boundaries for neural crest and mesodermal contributions to neck connective tissue. Furthermore, lineage studies and functional analysis of Tbx1- and Pax3-null mice reveal a unique genetic program for somitic neck muscles that is distinct from that of somitic trunk muscles. Our findings unveil the embryological and developmental requirements underlying tetrapod neck myogenesis and provide a blueprint to investigate how muscle subsets are selectively affected in some human myopathies.
Insinuation Anxiety: Concern That Advice Rejection Will Signal Distrust After Conflict of Interest Disclosures

When expert advisors have conflicts of interest, disclosure is a common regulatory response. In four experiments (three scenario experiments involving medical contexts, and one field experiment involving financial consequences for both parties), we show that disclosure of a financial or nonfinancial conflict of interest can have a perverse effect on the advisor–advisee relationship. Disclosure, perhaps naturally, decreases an advisee’s trust in the advice. But disclosure can also lead to concern that failure to follow advice will be interpreted as a signal of distrust. That is, rejecting the advice could suggest that the advisee is insinuating that the advisor could be biased by the conflict of interest. We show that this insinuation anxiety persists whether the disclosure is voluntary or required by law and whether the disclosed conflict is big or small, but it diminishes when the disclosure is made by an external source rather than directly by the advisor.
Stable Hierarchical Clustering Analysis Based on New Designed Cluster Validity Index

Cluster validity index (CVI) is an important method for evaluating the effect of clustering results generated by clustering algorithms. Currently, many CVIs have proposed, but they are suffering from issues of unstable and narrow range of applications. Therefore, a new clustering validity index-NCVI, is proposed in this paper. Firstly, the NCVI index combines the idea of maximum spanning tree and Euclidean distance formula. The clustering results are obtained by using the average of the sum of the weights of the maximum spanning trees of each cluster and the minimum distance between the clusters using the Euclidean distance between each cluster center point. At the same time, based on the underlying algorithm (average link hierarchical clustering algorithm) to determine the optimal cluster number, combined with the new cluster validity index NCVI designed a new K value optimization algorithm (KVOA). Finally, the paper evaluates the validity of the newly proposed index (NCVI) through four simulation data sets and two UCI real data sets, and compares it with other six classical indicators. The experimental results show that the proposed index has a good performance advantage over other indexes in the tested data set.
Low‐cost customizable microscale toolkit for rapid screening and purification of therapeutic proteins

Biopharmaceutical separations require tremendous amounts of optimization to achieve acceptable product purity. Typically, large volumes of reagents and biological materials are needed for testing different parameters, thus adding to the expense of biopharmaceutical process development. This study demonstrates a versatile and customizable microscale column (µCol) for biopharmaceutical separations using immobilized metal affinity chromatography (IMAC) as an example application to identify key parameters. µCols have excellent precision, efficiency, and reproducibility, can accommodate any affinity, ion‐exchange or size‐exclusion‐based resin and are compatible with any high‐performance liquid chromatography (HPLC) system. µCols reduce reagent amounts, provide comparable purification performance and high‐throughput, and are easy to automate compared with current conventional resin columns. We provide a detailed description of the fabrication methods, resin packing methods, and µCol validation experiments using a conventional HPLC system. Finite element modeling using COMSOL Multiphysics was used to validate the experimental performance of the µCols. In this study, µCols were used for improving the purification achieved for granulocyte colony stimulating factor (G‐CSF) expressed using a cell‐free CHO in vitro translation (IVT) system and were compared to a conventional 1 ml IMAC column. Experimental data revealed comparable purity with a 10‐fold reduction in the amount of buffer, resin, and purification time for the μCols compared with conventional columns for similar protein yields.
An Association Rule Mining-Based Framework for Profiling Regularities in Tactics Techniques and Procedures of Cyber Threat Actors

Tactics Techniques and Procedures (TTPs) in cyber domain is an important threat information that describes the behavior and attack patterns of an adversary. Timely identification of associations between TTPs can lead to effective strategy for diagnosing the Cyber Threat Actors (CTAs) and their attack vectors. This study profiles the prevalence and regularities in the TTPs of CTAs. We developed a machine learning-based framework that takes as input Cyber Threat Intelligence (CTI) documents, selects the most prevalent TTPs with high information gain as features and based on them mine interesting regularities between TTPs using Association Rule Mining (ARM). We evaluated the proposed framework with publicly available TTPbased CTI documents. The results show that there are 28 TTPs more prevalent than the other TTPs. Our system identified 155 interesting association rules among the TTPs of CTAs. A summary of these rules is given to effectively investigate threats in the network.
Current Cyber Security Challenges in ICS

After a cyber-incident on an Industrial Control System (ICS), there is frequently an immediate incident response. However, after restoring the critical service, there is often little in the way of forensics to discover the source, impact, or events of the attack. In part, this is due to the challenge areas in ICSs cybersecurity outlined in this document. This paper discusses common challenges, some mitigations to those challenges, and applies them briefly to past attacks.
Island Biogeography of Cryoconite Hole Bacteria in Antarctica's Taylor Valley and Around the World

Cryoconite holes are holes in a glacier’s surface caused by sediment melting into the glacier. These holes are self-contained ecosystems that include abundant bacterial life within their sediment and liquid water, and have recently gained the attention of microbial ecologists looking to use cryoconite holes as “natural microcosms” to study microbial community assembly. Here, we explore the idea that cryoconite holes can be viewed as “islands”, in the same sense that an island in the ocean is an area of habitat surrounded by a barrier to entry. In the case of a classic oceanic island, the ocean is a barrier between islands, but in the case of cryoconite holes, the ocean is comprised of impermeable solid ice. We test two hypotheses, born out of island biogeographic theory, that can be readily applied to cryoconite hole bacteria. First, we ask to what extent the size of a cryoconite hole is related to the amount of bacterial diversity found within it. Second, we ask to what extent cryoconite holes exhibit distance decay of similarity, meaning that geographically close holes are expected to harbor similar bacterial communities, and distant holes are expected to harbor more different bacterial communities. To test the island size hypothesis, we measured the sizes of cryoconite holes on three glaciers in Antarctica’s Taylor Valley and used DNA sequencing to measure diversity of bacterial communities within them. We found that for two of these glaciers, there is a strong relationship between hole size and bacterial phylogenetic diversity, supporting the idea that cryoconite holes on those glaciers are “islands. ” The high biomass dispersing to the third glacier we measured could explain the lack of size-diversity relationship, remaining consistent with island biogeography. To test the distance decay of similarity hypothesis, we used DNA sequence data from several previous studies of cryoconite hole bacteria from across the world. Combined with our Taylor Valley data, those data showed that cryoconite holes have strong spatial structuring at scales of one to several hundred kilometers, also supporting the idea that these dirty holes on glaciers are really islands in the cryosphere.
Prioritizing challenges of agile process in distributed software development environment using analytic hierarchy process

Software organizations are increasingly combining agile methodologies and distributed software development (DSD) for efficiently and effectively built software products. There are numerous challenges associated with scaling agile methods in a distributed environment. Our study is intended to explore and prioritize the challenges for scaling agile practices in the DSD environment. This study was divided into 3 stages. In the first stage, 22 challenges were identified from literature review and grouped into 4 categories: management, team, technology, and process based on discussion with experts. In the second stage, an online questionnaire study was conducted to validate the identified challenges. Finally, analytic hierarchy process method was used to prioritize challenges and their categories based on their relative importance. The results highlighted that management is a most significant category as compared with the other categories. Similarly, lack of management commitments, lack of effective communication, lack of knowledge sharing, etc are identified as the most significant challenges that need to be focused by the organizations for scaling agile methodologies. On the basis of the research findings, we could conclude that the identified challenges along with their categories provide a robust framework to scale agile methodologies in the DSD environment.
Design Automation for Micro-Electro-Mechanical Systems

This paper demonstrates and discusses a highly automated approach for the design of micro-elec-tro-mechanical systems (MEMS) and system-level multidomain reduced order model generation. The presented techniques in form of rigid body models (RBM) and modal superposition models (MSUP) in conjunction with component mode synthesis (CMS) enable fast and efficient model adaption and optimization of components in the different phases of the MEMS design process by providing sufficiently fast and accurate modeling solutions. Different aspects and requirements of individual methods are discussed and compared.
Interference Detection of Spacecraft Before Deck Docking Based on 3D Scanner

Spacecraft contains many flexible components and sampling parts, there are often many differences between real product and three-dimensional design models, so spacecraft component sometimes can't be installed due to component interference or small safety distance in assembly and integration process of spacecraft. Non-contact interference detection based on 3D scanner Handyscan 700 and Geomagic Control is designed, The Handyscan 700 which is a portable scanner takes into account the measurement speed and accuracy. The experiment shows that this detection method can achieve flexible components interference analysis and quantitative measurement before the cabin board installation through virtual assembly.
Microwave and Induced Thermoacoustic Dual Imaging for Potential Breast Cancer Detection

Microwave imaging (MWI) and microwave thermoacoustic imaging (MTAI) show great potential for early- stage breast tumor detection. MWI suffers from low spatial resolution for accurate diagnosis, while MTAI is unable to provide quantitative dielectric information and its image quality degrades due to acoustic heterogeneity of breast tissue. To compensate for these drawbacks, we propose a microwave-based dual imaging modality, by combining the conventional MWI and MTAI, for breast tumor detection. First, a low-resolution MWI image will be utilized to provide a rough estimate of the high-dielectric region in breast. Then, the high-dielectric region will be incorporated in MTAI reconstruction to mitigate the acoustic heterogeneity effect. Finally, medical images with ultrasonic resolution and quantitative dielectric information of breast tissue can be ob- tained. We verified this idea by imaging anatomically realistic numerical breast phantoms. Simulation results demonstrate the feasibility of the proposed dual imaging modality by significantly improving the image fidelity and providing quantitative dielectric information.
Efficient Holistic Control over Industrial Wireless Sensor-Actuator Networks

Process automation is embracing wireless sensor-actuator networks (WSANs) in the era of Industrial Internet. Despite the success of WSANs for monitoring applications, feedback control poses significant challenges due to data loss and stringent energy constraints in WSANs. Holistic control adopts a cyber-physical system approach to overcome the challenges by orchestrating network reconfiguration and process control at run time. In this paper, we explore efficient holistic control designs to maintain control performance while reducing the communication cost. The contributions of this work are four-fold: (1) We introduce a holistic control architecture that integrates low-power wireless bus (LWB) and two control strategies, rate adaptation and self-triggered control, specifically proposed to reduce communication cost; (2) We design novel wireless network mechanisms to support rate adaptation and self-triggered control, respectively, in a multi-hop WSAN; (3) We build a real-time network-in-the-loop simulator that integrates MATLAB/Simulink and a three-floor WSAN testbed to evaluate wireless control systems; (4) We empirically explore the tradeoff between communication cost and control performance under alternative holistic control approaches. Our case studies show that rate adaptation and self-triggered control offer advantages in control performance and energy efficiency, respectively, in normal operating conditions. The advantage in energy efficiency of self-triggered control, however, may diminish under harsh physical and wireless conditions due to the cost of recovering from data loss and physical disturbances.
Comparison of S-band Analog and Dual-Input Digital Doherty Power Amplifiers

This work aims at assessing the performance improvement offered by a dual-input digitally driven Doherty power amplifier (DPA) with respect to the single-input topology. To this aim, an analog DPA is designed and characterized. The equivalent digital version, which only differs by the absence of the input power divider, is also realized and analyzed. This ensures a fair comparison of the two topologies. Power-dependent input signal splitting between the main and auxiliary branches as well as adaptive phase alignment are adopted in the digital version to compensate for the back-off efficiency degradation as well as other shortcomings typically shown by traditional analog DPAs. For the first time, the performance figures of merit of the equivalent analog and digital DPAs are compared one-to-one systematically. The comparison over the 3.1-3.7 GHz range shows a superior efficiency performance of the digital DPA over the analog one, both at saturation and in back-off. Furthermore, the dual-input control also ensures higher gain and saturated output power.
Anomaly detection in cyber security attacks on networks using MLP deep learning

Malicious traffic has garnered more attention in recent years, owing to the rapid growth of information technology in today’s world. In 2007 alone, an estimated loss of 13 billion dollars was made from malware attacks. Malware data in today’s context is massive. To understand such information using primitive methods would be a tedious task. In this publication we demonstrate some of the most advanced deep learning techniques available, multilayer perceptron (MLP) and J48 (also known as C4.5 or ID3) on our selected dataset, Advanced Security Network Metrics & Non-Payload-Based Obfuscations (ASNM-NPBO) to show that the answer to managing cyber security threats lie in the fore-mentioned methodologies.
Millimeter Wave Channel Modeling – Present Development and Challenges in Tropical Areas

Millimeter wave is the key enablers for modern wireless applications. The Utilization of millimeter wave technology becomes a key factor in solving the problem of bandwidth shortage. Millimeter wave leads to delivering much higher throughput, faster data rate, and capacity. However, it is still facing some technical challenges’. The main objective of this paper is to identify research challenges in the mm-wave channel and activities on modeling. A brief analysis of rain fading was presented based on simultaneously measurement of one-minute rain rate and its effects on a short experimental link of 38 GHz and rain fade is observed as high as 15 dB for 300 m path at about 125 mm/hr rain intensity. Hence rain attenuation at 0.001% and 0.01% of outages must be considered for 38 GHz. The statistical spatial channel mode simulation software was utilized for an operating frequency of 38 GHz. To observe the received power and path loss, power delay profile (PDP) was generated. The omnidirectional and directional received power and path loss have been estimated using environmental parameters of Kuala Lumpur city which illustrates the theoretical performances of 5G in Malaysia. As the receiver moves from line-of-sight (LOS) to non-line-of-sight (NLOS) scenarios, a drop of 33.1 dB omnidirectional power is found.
Evaluating the suitability of state‐based formal methods for industrial deployment

After a number of success stories in safety‐critical domains, we are starting to witness applications of formal methods in contemporary systems and software engineering. However, one thing that is still missing is the evaluation criteria that help software practitioners choose the right formal method for the problem at hand. In this paper, we present the criteria for evaluating and comparing different formal methods. The criteria were chosen through a literature review, discussions with experts from academia and practitioners from industry, and decade‐long personal experience with the application of formal methods in industrial and academic projects. The criteria were then evaluated on several model‐oriented state‐based formal methods. Our research shows that besides technical grounds (eg, modeling capabilities and supported development phases), formal methods should also be evaluated from social and industrial perspectives. We also found out that it is not possible to generate a matrix that renders the selection of the right formal method an automatic process. However, we can generate several pointers, which make this selection process a lot less cumbersome.
Clone refactoring inspection by summarizing clone refactorings and detecting inconsistent changes during software evolution

It has been broadly assumed that removing code clones by refactorings would solve the problems of code duplication. Despite recent empirical studies on the benefit of refactorings, contradicting evidence shows that it is often difficult or impossible to remove clones by using standard refactoring techniques. Developers cannot easily determine which clones can be refactored or how they should be maintained scattered throughout a large code base in evolving systems. We propose pattern‐based clone refactoring inspection (PRI), a technique for managing clone refactorings. PRI summarizes refactorings of clones and detects clones that are not consistently refactored. To help developers refactor these anomalies, PRI also visualizes clone evolution and refactorings and fixes refactoring anomalies to prevent the clone group from being left in an inconsistent state. We evaluated PRI on 6 open‐source projects and showed that it identifies clone refactorings with 94.1% accuracy and detects inconsistent refactorings with 98.4% accuracy, tracking clone change histories. In a study with 10 student developers, the participants reported that flexible PRI's summarization and detection features can be valuable for novice developers to learn about refactorings to clones. These results show that PRI should improve developer productivity in inspecting clone refactorings distributed across multiple files in evolving systems.