Miracle-dz/UniFAR-FTUs huggingface dataset:
The 2018 Congressional Midterms, Symbolic Empowerment, and Ayanna Pressley’s Mobilizing Effect: A Case Study for Future Analysis of Historic Firsts

ABSTRACT Ayanna Pressley was elected in 2018, becoming the first Black woman from Massachusetts to serve in the US House of Representatives and the first person of color to represent its only majority-minority congressional district (which includes three-quarters of Boston, and most of Cambridge). While Pressley was no political newcomer to the city of Boston, her campaign garnered widespread media attention on account of her victory – that is, having defeated a ten-term incumbent, Michael Capuano, of the same political party. Here I argue that the electoral context mattered, given the historic nature of the campaign and its mobilizing effect on a racially and ethnically diverse electorate. Although highlighting qualifications and experience was the preferred tactic of her opponent, it was important for Pressley to choose a strategy that best fit the electoral context. Here I offer a case study, with information that will bear on the applicability of causal generalizations to be empirically tested via formal modeling or large-N analysis in future studies. The sort of specific, intensive, and detailed information provided is necessary in advance of forecasting results across similar subjects and comparable target populations using quantitative or statistical methods.
Pyramidal and conditional convolution attention network for hyperspectral image classification using limited training samples

ABSTRACT Hyperspectral image classification methods based on convolutional neural network (CNN) and attention mechanism have obtained satisfactory results. However, the currently applied convolution operations treat all inputs equally, and the multiscale information extraction of hyperspectral data is not sufficient. The used attention mechanism is also one-dimensional, two-dimensional, or a simple combination of them. Alongside these, the high-precision classification models rely on many training samples. Based on these, we propose a pyramidal and conditional convolution attention network (PCAN), which can fully and effectively capture the information of hyperspectral input data through convolution kernels with different weights and different scales. And for the first time, the attention of three-dimensional weights is applied to hyperspectral data, and satisfactory results are achieved on minimal training sets. Specifically, PCAN is composed of spectral branch and spatial branch. The spectral branch uses conditionally parameterized convolutions (CondConv) to customize the convolution kernels according to the input features. It directly generates three-dimensional weights to quantify the importance of the entire feature maps. The spatial branch uses the pyramidal convolution (PyConv), which includes convolution kernels of different spatial sizes and depths and can capture different levels of detailed information in hyperspectral images. At the same time, competition and cooperation between different channels are captured through gating adaptation and channel normalization. Experiments on three well-known hyperspectral data sets show that our model achieves higher accuracy and less training time than other advanced methods, especially with fewer training samples.
A Video Camera Road Sign System of the Early Warning from Collision with the Wild Animals

Abstract This paper proposes a camera road sign system of the early warning, which can help to avoid from vehicle collision with the wild animals. The system consists of camera modules placed down the particularly chosen route and the intelligent road signs. The camera module consists of the camera device and the computing unit. The video stream is captured from video camera using computing unit. Then the algorithms of object detection are deployed. Afterwards, the machine learning algorithms will be used to classify the moving objects. If the moving object is classified as animal and this animal can be dangerous for safety of the vehicle, warning will be displayed on the intelligent road sings.
How can the development of writing practices in the engineering curriculum be enabled

CONTEXT Competence in written communication is regarded as a critical requirement for engineering graduates and engineering educators alike, but the development of writing within the engineering curriculum is frequently invisible, and occasionally non-existent. This is despite repeated calls from EA and employer groups for Australian engineering faculties to improve the communication skills of their engineering graduates. It is also despite several decades of excellent initiatives and interventions to support and develop engineering students’ written and spoken communication, many of which have fallen into disuse once their champion has moved on.
The dynamic nature of the city image: Do image components evolve over time?

In this study, we propose that the city image is a multidimensional construct infl uenced by its image components which, together, aff ect tourist behaviour in a dynamic process. Th e general objective of this research is to understand the dynamic nature of a major tourism destination image and the relationships among its components from the tourists’ perspective. Th is study is exploratory and descriptive. Data was collected from tourists’ surveys applied in two diff erent moments at Istanbul: before the launching of the European Capital of Culture (ECoC) (Moment 1), and one year later, during the ECoC (Moment 2). Th e proposed model was estimated and tested using structural equation modelling (SEM). Th e comparison of data from the two moments indicates diff erent patterns of relationships. Findings contribute to a better understanding of the dynamic nature of a city image by investigating the relationships among diff erent image components in two diff erent moments, before and after a major cultural event. Future studies should investigate further the unique image construct given the importance of local identity in brand and event communication. Additionally, research should investigate the impact of events on the formation of the aff ective component of image and behavioural intentions among tourists.
The Diversity and Function of Soil Bacteria and Fungi Under Altered Nitrogen and Rainfall Patterns in a Temperate Steppe

The response of soil microorganisms to altered nitrogen (N) and rainfall patterns plays an important role in understanding ecosystem carbon and nitrogen cycling processes under global change. Previous studies have separately focused on the effects of N addition and rainfall on soil microbial diversity and community composition. However, the combined and interactive impact of N addition and rainfall on soil microbial diversity and function mediated by plant and soil processes have been poorly investigated for grassland ecosystems. Here, we conducted a field experiment with simulated N addition (N addition: 10 g N m–2yr–1) and altered rainfall pattern [control, rainfall reduction (compared to control –50%); rainfall addition (compared to control + 50%)] to study their interactive effects on soil microbial diversity and function in a temperate steppe of Inner Mongolia. Our results showed that N addition and rainfall addition significantly increased soil bacterial diversity, and the bacterial diversity was positively correlated with soil microbial biomass nitrogen, inorganic nitrogen, and Stipa krylovii root exudate C:N ratio, Allium polyrhizum root exudate C and N, and A. polyrhizum root exudate C:N ratio. N addition and rainfall reduction significantly reduced fungal diversity, which correlated closely with soil microbial biomass carbon and the C:N ratio of A. polyrhizum root exudates. Bacteria were mainly eutrophic r-strategists, and the responses of bacterial function guilds to the interaction between N addition and rainfall pattern were not significant. However, the arbuscular mycorrhizal fungi (AMF), in the functional classification of fungi, were significantly reduced under the condition of N addition and rainfall reduction, and the absolute abundance of the phylum Glomeromycota increased under rainfall addition, suggesting that AMFs are sensitive to altered N and rainfall patterns over short timescales (1 year). Collectively, our results have important implications for understanding the plant–soil–microbe system of grasslands under climate change.
Exocytosis Proteins: Typical and Atypical Mechanisms of Action in Skeletal Muscle

Insulin-stimulated glucose uptake in skeletal muscle is of fundamental importance to prevent postprandial hyperglycemia, and long-term deficits in insulin-stimulated glucose uptake underlie insulin resistance and type 2 diabetes. Skeletal muscle is responsible for ~80% of the peripheral glucose uptake from circulation via the insulin-responsive glucose transporter GLUT4. GLUT4 is mainly sequestered in intracellular GLUT4 storage vesicles in the basal state. In response to insulin, the GLUT4 storage vesicles rapidly translocate to the plasma membrane, where they undergo vesicle docking, priming, and fusion via the high-affinity interactions among the soluble N-ethylmaleimide sensitive factor attachment protein receptor (SNARE) exocytosis proteins and their regulators. Numerous studies have elucidated that GLUT4 translocation is defective in insulin resistance and type 2 diabetes. Emerging evidence also links defects in several SNAREs and SNARE regulatory proteins to insulin resistance and type 2 diabetes in rodents and humans. Therefore, we highlight the latest research on the role of SNAREs and their regulatory proteins in insulin-stimulated GLUT4 translocation in skeletal muscle. Subsequently, we discuss the novel emerging role of SNARE proteins as interaction partners in pathways not typically thought to involve SNAREs and how these atypical functions reveal novel therapeutic targets for combating peripheral insulin resistance and diabetes.
A Recombinant MVA-Based RSV Vaccine Induces T-Cell and Antibody Responses That Cooperate in the Protection Against RSV Infection

Respiratory syncytial virus (RSV) causes a respiratory disease with a potentially fatal outcome especially in infants and elderly individuals. Several vaccines failed in pivotal clinical trials, and to date, no vaccine against RSV has been licensed. We have developed an RSV vaccine based on the recombinant Modified Vaccinia Virus Ankara-BN® (MVA-RSV), containing five RSV-specific antigens that induced antibody and T-cell responses, which is currently tested in clinical trials. Here, the immunological mechanisms of protection were evaluated to determine viral loads in lungs upon vaccination of mice with MVA-RSV followed by intranasal RSV challenge. Depletion of CD4 or CD8 T cells, serum transfer, and the use of genetically engineered mice lacking the ability to generate either RSV-specific antibodies (T11µMT), the IgA isotype (IgA knockout), or CD8 T cells (β2M knockout) revealed that complete protection from RSV challenge is dependent on CD4 and CD8 T cells as well as antibodies, including IgA. Thus, MVA-RSV vaccination optimally protects against RSV infection by employing multiple arms of the adaptive immune system.
Multi‐parametric quantitative magnetic resonance imaging of the upper arm muscles of patients with spinal muscular atrophy

Quantitative magnetic resonance imaging (qMRI) is frequently used to map the disease state and disease progression in the lower extremity muscles of patients with spinal muscular atrophy (SMA). This is in stark contrast to the almost complete lack of data on the upper extremity muscles, which are essential for carrying out daily activities. The aim of this study was therefore to assess the disease state in the upper arm muscles of patients with SMA in comparison with healthy controls by quantitative assessment of fat fraction, diffusion indices, and water T2 relaxation times, and to relate these measures to muscle force. We evaluated 13 patients with SMA and 15 healthy controls with a 3‐T MRI protocol consisting of DIXON, diffusion tensor imaging, and T2 sequences. qMRI measures were compared between groups and related to muscle force measured with quantitative myometry. Fat fraction was significantly increased in all upper arm muscles of the patients with SMA compared with healthy controls and correlated negatively with muscle force. Additionally, fat fraction was heterogeneously distributed within the triceps brachii (TB) and brachialis muscle, but not in the biceps brachii muscle. Diffusion indices and water T2 relaxation times were similar between patients with SMA and healthy controls, but we did find a slightly reduced mean diffusivity (MD), λ1, and λ3 in the TB of patients with SMA. Furthermore, MD was positively correlated with muscle force in the TB of patients with SMA. The variation in fat fraction further substantiates the selective vulnerability of muscles. The reduced diffusion tensor imaging indices, along with the positive correlation of MD with muscle force, point to myofiber atrophy. Our results show the feasibility of qMRI to map the disease state in the upper arm muscles of patients with SMA. Longitudinal data in a larger cohort are needed to further explore qMRI to map disease progression and to capture the possible effects of therapeutic interventions.
Implementation and Calibration of the 3GPP Industrial Channel Model for ns-3

The Fifth Generation (5G) of mobile communications has brought a change of paradigm in the way cellular technologies are conceived. 5G has been designed to provide services not only to people, but also to industries and verticals. One of the verticals that will benefit most from the 5G is the Industry 4.0. Channel modeling for this vertical is receiving significant attention, mainly due to the increased complexity that comes with multipath fading scenarios. In order to overcome this problem, the Third Generation Partnership Project (3GPP) defined a new stochastic channel model as part of Release-16, called Indoor Factory (InF). This paper describes the implementation process of this channel model as an extension tool for one of the most well-known open-source software simulators, ns-3. Calibration results have been obtained and compared with other 3GPP references. This work permits to use ns-3 as a reliable tool for evaluating new industrial scenarios and use cases.
A Sigfox Module for the Network Simulator 3

In recent years, Sigfox has earned a prominent place in the Low-Power Wide-Area Network arena, attracting the attention of industry, academia, and standards development organizations. As sensors and actuators used in the Sigfox devices aim to be operated for a long time with no maintenance, Sigfox’s low energy consumption characteristics are part of its appeal to real-world applications. Prior to the work described in this paper, Network Simulator 3 had no support for the Sigfox protocol, and this paper presents a module to simulate the Sigfox protocol. The module is parametric with respect to the energy consumed by devices in different states, and we present simulation results obtained by calibrating the model using measurements obtained from a real Sigfox hardware module. Moreover, this work models a battery that is affected by self-discharge current. By means of this ns-3 module, it is possible to simulate a scenario prior to its deployment, thus helping with planning the deployment itself. To showcase the benefits of our work, we designed a behavioral model of devices using different communication strategies, and we present results on how long a device lasts and how much data delivery costs for different strategies. We plan to contribute the module to the ns-3 App Store.
Performance Analysis of Internet of Things Protocols Based Fog/Cloud over High Traffic

The Internet of Things (IoT) becomes the future of a global data field in which the embedded devices communicate with each other, exchange data and making decisions through the Internet. IoT could improves the qualityoflife in smart cities, but a massive amount of data from different smart devices could slow down or crash database systems. In addition, IoT data transfer to Cloud for monitoring information and generating feedback thus will lead to highdelay in infrastructure level. Fog Computing can help by offering services closer to edge devices. In this paper, we propose an efficient system architecture to mitigate the problem of delay. We provide performance analysis like responsetime, throughput and packet loss for MQTT (Message Queue Telemetry Transport) and HTTP (Hyper Text Transfer Protocol) protocols based on Cloud or Fog serverswith large volume of data form emulated traffic generator working alongsidewith one real sensor. We implement both protocols in the same architecture, with low cost embedded devices to local and Cloud servers with different platforms. The results show that HTTP response time is 12.1 and 4.76 times higher than MQTT Fog and cloud based located in the same geographical area of the sensors respectively. The worst case in performance is observed when the Cloud is public and outside the country region. The results obtained for throughput shows that MQTT has the capability to carry the data with available bandwidth and lowest percentage of packet loss. We also prove that the proposed Fog architecture is an efficient way to reduce latency and enhance performance in Cloud based IoT. Keywords —Internet of Things; Web of Things; Fog Computing; Cloud Computing; Edge Computing; MQTT; HTTP; Tsung.
Implementation of mmWave-energy Module and Power Saving Schemes in ns-3

Next-generation 5G New Radio (NR) cellular networks operating at mmWave frequencies are targeted to support diverse use cases, such as enhanced Mobile Broadband (eMBB), massive machine-type communications (mMTC), ultra-reliable and low latency communications (URLLC), etc. Energy-Efficiency is one of the key performance indicators for NR technology. User Equipment (UE) battery life significantly impacts the Quality of Experience (QoE) of the UE. Thus 5G NR standard is designed to have great flexibility on network operation modes to adapt to different requirements and trade-offs. 3GPP, in its 5G technical specification release, has proposed various power-saving schemes such as connected mode Discontinuous Reception (cDRX), RRC INACTIVE state, etc. In this work, we discuss the implementation and analysis of UE RRC state-based energy consumption module, including different power saving schemes in ns-3. We have thoroughly evaluated the module with the simulation study and validated the implementation with the 3GPP standards. The implementation source code is publicly available as open-source.
Effects of pore geometry on face seal performance

Surfaces covered with rmcropores have recently become a topic of interest as an effective means for better lubrication and friction reduction in thrust bearings, mechanical seals, piston rings, and other machine parts. The surface structure of the sliding faces plays a vital role in their performances. Surface pores on face seal surfaces are developed during different kinds of manufacturing operations and surface treatments and their shapes may be different. Moreover, different kinds of surface textures may be deliberately machined on the seal surface through modem techniques. It is, therefore, essential to investigate how pores! asperities of different shapes affect the hydrodynamic behavior of face seal In the present work, performance of mechanical face seals with pores! asperities of square and exponential shapes is investigated. Mathematical models are developed for different pore geometry to allow the performance prediction of liquid non-contacting face seals with regular microsurface structure in the forms of rectangular and exponential pores. Seal performance such as equilibrium face separation, friction torque and leakage across the seal are calculated and presented for a range of sealed pressure, pore size and pore ratio of the annular ring surface area. An optimum pore size is found that depend on corresponding to the maximum axial stiffness and minimum friction torque.
Stimulation-Evoked Effective Connectivity (SEEC): An in-vivo approach for defining mesoscale corticocortical connectivity

Background Cortical electrical stimulation has been a versatile technique for examining the structure and function of cortical regions as well as for implementing novel therapies. While electrical stimulation has been used to examine the local spread of neural activity, it may also enable longitudinal examination of mesoscale interregional connectivity. Recent studies have used focal intracortical microstimulation (ICMS) with optical imaging to show cross-region spread of neural activity, but techniques are limited to utilizing hemodynamic responses within anesthetized preparations. Objective Here, we sought to use ICMS in conjunction with recordings of multi-unit action potentials to assess the mesoscale effective connectivity within sensorimotor cortex. Methods Neural recordings were made from multielectrode arrays placed into sensory, motor, and premotor regions during surgical experiments in three squirrel monkeys. During each recording, single-pulse ICMS was repeatably delivered to a single region. Mesoscale effective connectivity was calculated from ICMS-evoked changes in multi-unit firing. Results Multi-unit action potentials were able to be detected on the order of 1 ms after each ICMS pulse. Across sensorimotor regions, short-latency (< 2.5 ms) ICMS-evoked neural activity strongly correlated with known anatomic connections. Additionally, ICMS-evoked responses remained stable across the experimental period, despite small changes in electrode locations and anesthetic state. Conclusions These results show that monitoring ICMS-evoked neural activity, in a technique we refer to as Stimulation-Evoked Effective Connectivity (SEEC), is a viable way to longitudinally assess effective connectivity enabling studies comparing the time course of connectivity changes with the time course of changes in behavioral function. Highlights Short-latency neural responses to ICMS were evaluated in multiple cortical regions. Neural responses strongly correlated with known anatomical connections. Stimulation-evoked neural responses were maintained across repeated tests. ICMS-evoked activity can show longitudinal changes in effective connectivity.
The Cdc42 effector and non-receptor tyrosine kinase, ACK defines a distinct STAT5 transcription signature in a chronic myeloid leukaemia cell model

Activated Cdc42-associated kinase (ACK) is a Rho family effector that is widely implicated in cancer. Here, we describe new roles for ACK in transcriptional regulation mediated by its relationship with the signal transducer and activators of transcription (STAT) family. We show that ACK can interact with STAT3, STAT5A and STAT5B, and augments phosphorylation at the conserved activation tyrosine on these STAT members. ACK stimulates oncogenic STAT nuclear relocation and transcriptional activation. We also identify endogenous relationships between ACK and STAT family members in haematopoietic disease cell lines. In the K562 chronic myeloid leukaemia cell line, we confirm that ACK contributes to the pool of active, nuclear STAT5. By interrogating ACK knock out cells we describe an ACK-driven STAT5 transcriptional signature in K562s. We propose ACK as a contributor to hyperactivated STAT5 signalling in this CML cell line and reveal a new route for therapeutic intervention.