Miracle-dz/UniFAR-FTUs huggingface dataset:
Using RAW as Control Plane for Wireless Deterministic Networks: Challenges Ahead

This paper provides an extensive analysis of Reliable and Available Wireless (RAW) enhancements and solutions needed to manage industrial environments more effectively. Starting from the description of a representative industrial use case, an analysis of gaps and promising new extensions is performed. Namely, the need to (i) support multi-domain operation, at both technology and administrative levels; (ii) integrate RAW with edge architectures; and, (iii) increase the mobility support in RAW networks. The identified gaps are indeed not yet tackled by the relevant standardization development organizations, mainly the Internet Engineering Task Force (IETF), and are thus object of our future work.
Unsupervised Cross-Domain Rumor Detection from Multiple Sources Based on RoBERTa and Multi-CNN

Internet rumors are prevalent and harmful to society. Hence, automatic rumor detection is essential. However, supervised learning methods are impractical due to the high cost of data labeling in the early stage of rumor propagation. Moreover, rumors can originate from multiple different source domains, and single-source domain adaptation methods cannot handle this scenario. To address these challenges, this paper proposes a rumor detection model based on RoBERTa pre-training model and multiple convolutional neural networks that combines unsupervised learning and multi-source domain adaptation. The model only uses the microblog text content to transfer the knowledge from multiple source domains to the target domain. We find that dynamic text representations can be effectively extracted by RoBERTa, and the discrepancy can be better reduced by aligning the distributions of each pair of source and target domains in multiple feature spaces. Additionally, according to F1 and time cost, dynamic distribution adaptation performs best on quantitative evaluation. Finally, extensive experiments demonstrate that the proposed model outperforms the baseline models based on transfer learning in rumor detection.
Audio Examination – Mental Health Diagnosis in Healthcare through Audio Analytics

There is an ever-increasing amount of audio content being created and shared online. With so much data available, it can be challenging to make sense of it all and extract useful information. The utilization of audio analytics tools can assist organizations in comprehending the vast quantities of audio data at their disposal, thereby acquiring valuable insights into customer behavior, market trends, and business operations. Therefore, this paper presents an Audio Analytics Tool for the healthcare industry to recognize patients’ mental health.
Multiwavelength campaign on Mrk 509 - XII. Broad band spectral analysis

The origin of the different spectral components present in the high-energy (UV to X-rays/gamma-rays) spectra of Seyfert galaxies is still being debated a lot. One of the major limitations, in this respect, is the lack of really simultaneous broad-band observations that allow us to disentangle the behavior of each component and to better constrain their interconnections. The simultaneous UV to X-rays/gamma rays data obtained during the multiwavelength campaign on the bright Seyfert 1 Mrk 509 are used in this paper and tested against physically motivated broad band models. Mrk 509 was observed by XMM-Newton and INTEGRAL in October/November 2009, with one observation every four days for a total of ten observations. Each observation has been fitted with a realistic thermal Comptonization model for the continuum emission. Prompted by the correlation between the UV and soft X-ray flux, we used a thermal Comptonization component for the soft X-ray excess. We also included a warm absorber and a reflection component, as required by the precise studies previously done by our consortium. The UV to X-ray/gamma-ray emission of Mrk 509 can be well fitted by these components. The presence of a relatively hard high-energy spectrum points to the existence of a hot (kT ∼ 100 keV), optically-thin (τ ∼ 0.5) corona producing the primary continuum. In contrast, the soft X-ray component requires a warm (kT ∼ 1 keV), optically-thick (τ ∼ 10−20) plasma. Estimates of the amplification ratio for this warm plasma support a configuration relatively close to the “theoretical” configuration of a slab corona above a passive disk. An interesting consequence is the weak luminosity-dependence of its emission, which is a possible explanation of the roughly constant spectral shape of the soft X-ray excess seen in AGNs. The temperature (∼ 3e V) and fl ux of the soft-photon field entering and cooling the warm plasma suggests that it covers the accretion disk down to a transition radius Rin of 10−20 Rg. This plasma could be the warm upper layer of the accretion disk. In contrast, the hot corona has a more photon-starved geometry. The high temperature (∼100 eV) of the soft-photon field entering and cooling it favors a localization of the hot corona in the inner flow. This soft-photon field could be part of the comptonized emission produced by the warm plasma. In this framework, the change in the geometry (i.e. Rin) could explain most of the observed flux and spectral variability.
Perturbation electric fields and disturbance currents investigated during the 25 September 1998 great storm

This study investigates the ionosphere's response to the 25 September 1998 great storm and utilizes multi‐instrument observations covering the Australian (140°E; geographic) and Indian (75°E) longitude sectors. Results show the domination of eastward (westward) electrojet at 140°E (75°E). Its causative net eastward (westward) perturbation electric (E) field drove the forward (reverse) plasma fountain, caused the presence (absence) of the equatorial ionization anomaly (EIA). These strong longitudinal differences were due to a combination of various LT‐dependent, E field‐driven, and competing mechanisms. Perturbation E fields are identified as prompt penetration E field (PPEF) and disturbance dynamo E field (DDEF). Due to the later (earlier) local time at 140°E (75°E), the undershielding PPEF was eastward (westward) directed early in the main phase. A series of periodic substorms occurred during the recovery phase. The substorm‐related eastward PPEFs became overpowered by westward DDEFs over India but remained dominant in the Australian sector. Thus, eastward PPEFs (westward DDEFs) dominated at 140°E (75°E). At 140°E these eastward PPEFs exhibited a strong positive correlation with the variations of both the cross polar cap potential drop and the asymmetric ring current, significantly increased the net equatorial upward E × B drift and thus caused EIA development with plasma bubbles scintillating GPS signals. Based on the strong and independent correlation of these asymmetric ring current events with both the EIA development and the scintillation activity, we propose that the ASY‐H index could provide a natural tool for modeling EIA development and scintillation episodes during severe storms characterized by periodic substorm‐related eastward PPEFs.
In silico optimization of DNA codons in genes encoded by various strains of Ebola Virus

Ebola hemorrhagic fever (HF) is a severe and often lethal disease that occurs in primates, including humans. It was first identified in 1976 during two outbreaks of fatal haemorrhagic fever in Central Africa. Due to its high fatality rate and lack of a widespread cure, Ebola HF poses a considerable challenge in treatment, making Ebola virus disease (EVD) one of the deadliest zoonotic diseases. The viral genome is ∼19kb long, linear, non-segmented, and negative single-stranded (-SS) RNA. The genome of the Ebola virus (EBOV) comprises seven genes, namely NP, GP, L, and VP (VP30, VP24, VP40, VP35), which encode nucleoprotein, glycoprotein, RNA polymerase, and viral proteins. By optimizing the DNA sequence through codon adaptation, we observed significant enhancements in the codon adaptation index (CAI) and the GC content compared to the wild-type strain. These findings demonstrate that optimized genes hold the potential for improved expression in the host organism without the production of truncated proteins. Further, these optimized genes can facilitate proper protein folding and function. In conclusion, these results have implications for vaccine production, as higher codon optimization enhances the expression of the genes, making them appropriate amounts for vaccine development.
Cybersecurity Simulator for Connected and Autonomous Vehicles

In recent years, we have witnessed a significant rise in both the popularity and capability of Connected and Autonomous Vehicles (CAVs). This progress has been facilitated, in part, by advancements in CAV simulators that enable researchers to efficiently, safely, and cost-effectively test their vehicles. However, many current simulators do not directly address one of the most pressing challenges that CAVs encounter: cybersecurity. In this paper, we present a step towards resolving this issue. We have developed a simulator with the ability to simulate various Vehicle-to-Everything (V2X) attacks in real time. Our approach involves co-simulation of three simulators: CARLA, SUMO, and Artery. Utilizing the V2X communication capabilities of these simulators, our attacks involve injecting malicious Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) into the simulation.
Cyber Security in Internet of Things using Optimization algorithms: A Systematic Mapping of Literature

Cybersecurity has emerged as one of the most crucial facets of the Internet of Things (IoT) due to the increased possibility of cyberattacks. IoT cybersecurity aims to lower cybersecurity risk for businesses and users by safeguarding IoT resources and privacy. IoT security management could be improved by using new cybersecurity technology and solutions. However, efficient IoT cyber risk management frameworks are lacking for organizations. This paper presents the most significant proposals of cybersecurity in IoT by describing their objectives, working principles, applications, features, and approaches referred. The paper also presents cybersecurity in IoT using five optimization algorithms such as particle swarm optimization, ant colony optimizations, an artificial bee colony, genetic algorithm, and AdaBoost algorithm. The paper concludes with research gaps and limitations, which urge for further study for subsequent research work.
ARID3A coordinates the proliferation-differentiation switch of transit-amplifying cells in the intestine

Intestinal stem cells (ISCs) at the crypt base divide and give rise to progenitor cells that have the capacity to proliferate and differentiate into various mature epithelial cell types in the transit-amplifying (TA) zone. Here, we identified the transcription factor ARID3A as a novel regulator of intestinal epithelial cell proliferation and differentiation at the TA compartment. We show that ARID3A forms an expression gradient from villus tip to the early progenitors at the crypts mediated by TGF-β and WNT signalling. Intestinal epithelial-specific deletion of Arid3a reduces proliferation of TA cells. Bulk and single cell transcriptomic analysis shows increased enterocyte differentiation and reduced secretory cells in the Arid3a cKO intestine. Interestingly, upper-villus gene signatures of both enterocytes and secretory cells are enriched in the mutant intestine. We find that the enhanced enterocyte differentiation in the Arid3a cKO intestine is caused by increased binding of HNF1 and HNF4. Finally, we show that loss of Arid3a impairs irradiation-induced regenerative process by altering the dynamics of proliferation and apoptosis. Our findings imply that ARID3A may play a gatekeeping role in the TA compartment to maintain the “just-right” proliferation-to-differentiation ratio for tissue homeostasis and plasticity.
Semiparametric Bayesian analysis: Selection models and meteorological applications

ii To Minhye, Sunnie, and April iii ACKNOWLEDGMENTS I would like to thank my advisor, Professor James O. Berger, for his support and guidance. His thoughts and insight in statistics helped me in writing this thesis and nurtured my research in many ways. I am grateful to my committee members, Professor Anirban Dasgupta, Professor Chong Gu, and Professor Thomas Sellke for their support and help. Many thanks go to faculty, staa and friends in the Statistics Department at Purdue University and in ISDS at Duke University for their support and friendship. Special thanks go to my colleagues, Sarat C. Dass, Jose M. Perez, Dejun Tang and Nitai Mukhopadhyay for the good time and the valuable discussions we had together during my years as a Ph.D. student. I would like to thank my mother, my brother and his family for their constant love. Last, but not least, I sincerely appreciate the support and love of my wife, Minhye, and my two little daughters, Sunnie and April, who were always by my side.
Qualitative rather than quantitative phosphoregulation shapes the end of meiosis I in budding yeast

Exit from mitosis is brought about by dramatic changes in the phosphoproteome landscape. A drop in Cyclin-dependent kinase (Cdk), the master regulatory kinase, and activation of counteracting phosphatases such as Cdc14 in budding yeast, results in ordered substrate dephosphorylation, allowing entry into a new cell cycle and replication licensing. In meiosis however, two cell divisions have to be executed without intermediate DNA replication, implying that global phosphorylation and dephosphorylation have to be adapted to the challenges of meiosis. Using a global time-resolved phosphoproteomics approach in budding yeast, we compared the phosphoproteome landscape between mitotic exit and the transition from meiosis I to meiosis II. We found that unlike exit from mitosis, Cdk phosphomotifs remain mostly stably phosphorylated at the end of meiosis I, whereas a majority of Cdk-unrelated motifs are reset by dephosphorylation. However, inducing an artificial drop of Cdk at metaphase of meiosis I leads to ordered substrate dephosphorylation, comparable to mitosis, indicating that phosphoregulation of substrates at the end of meiosis I is thus mainly qualitatively rather than quantitatively ordered.
Modifications to gene body methylation do not alter gene expression plasticity in a reef‐building coral

As coral reefs continue to decline due to climate change, the role of coral epigenetics (specifically, gene body methylation, GBM) in coral acclimatization warrants investigation. The evidence is currently conflicting. In diverse animal phyla the baseline GBM level is associated with gene function: continuously expressed “housekeeping” genes are typically highly methylated, while inducible context-dependent genes have low or no methylation at all. Some authors report association between GBM and the environment, and interpret this observation as evidence of GBM’s role in acclimatization. Yet, others argue that correlation between GBM change and gene expression change is typically absent or negligible. Here, we used the reef- building coral, Acropora millepora, to test whether environmentally driven changes in GBM are associated with a gene’s ability to respond to environmental changes (plasticity) rather than expression level. We analyzed two cases of modified gene expression plasticity observed in a three-week-long heat acclimatization experiment. The first one was a group of heat-induced genes that failed to revert their expression after the coral was translocated back to the control tank. The second case involved genes that changed the magnitude of their response to the daily temperature fluctuations over the course of the experiment. In both cases we found negligible or no association with GBM change. We conclude that although both gene expression plasticity and GBM can change during acclimatization, there is no direct association between the two. This adds to the increasing volume of evidence that the function of GBM in invertebrates is unrelated to acclimatization on physiological timescales.
Intuitive Human-Swarm Interaction with Gesture Recognition and Machine Learning

Unmanned Aerial Systems (UAS), commonly known as drones, have revolutionized various industries with their diverse applications. As the demand for seamless and intuitive drone control grows, researchers are exploring innovative approaches to improve human-swarm interaction. This paper presents a novel method for operating a swarm of drones in real time using wearable technology and machine learning. Through the integration of motion capture data and classification algorithms, we strive to achieve an intuitive level of control that is accessible to users with varying skill levels. While the full realization of this approach remains a work in progress, our research lays the groundwork for future endeavors in this domain. In this paper, we discuss the limitations of existing control methods and present our methodology for data preprocessing, model training and testing, and result analysis. Our findings indicate the potential of this approach and open avenues for refining the interaction between humans and drone swarms.
Heart arrhythmia detection based on ECG wave classification using neural network and Fuzzy Inference system

Detecting the Arrhythmia correctly is an important task in cardiovascular disease detection. Identifying the correct type of arrhythmia provides a fast and effective way for diagnosis. In this paper a neural network based (ANN & CNN) and fuzzy logic based classifiers (ANFIS) are presented and their performance is evaluated using performance evaluation parameters. For this study, MITBIH and PTB diagnostic database are used which provided the total sample size of 1,23,998 ECG waveforms. For classification purpose, features are extracted such as Skewness, Kurtosis, Mean, Standard Deviation, Shannon Entropy, and Energy. In this study, it has been observed that ANFIS based classifier performs better than neural network based classifier.
Cryptanalysis of an Authentication Scheme for WSN within an IoT Environment

Across the world, intruders are constantly on the lookout to exploit security weaknesses which brings the risk of jeopardy to the data of companies, governments, monitoring, and healthcare organizations. With this arises the demand for the Internet of Things (IoT) to authenticate users using unique signatures generated by each device. In this paper, we investigate the RCBE-AS technique for wireless sensor networks (WSN) in an IoT scenario, as described by Singh et al. The model is discovered to be vulnerable to a wide range of attacks, including user tracing, smart card (SC) loss attacks, denial-of-service attacks, attacks on sensor nodes that are not anonymous, hostile and ineffective password changes, and a problem with perfect forward secrecy.
Elevated dissolved organic carbon biodegradability from thawing and collapsing permafrost

As high latitudes warm, a portion of the large organic carbon pool stored in permafrost will become available for transport to aquatic ecosystems as dissolved organic carbon (DOC). If permafrost DOC is biodegradable, much will be mineralized to the atmosphere in freshwater systems before reaching the ocean, accelerating carbon transfer from permafrost to the atmosphere, whereas if recalcitrant, it will reach marine ecosystems where it may persist over long time periods. We measured biodegradable DOC (BDOC) in water flowing from collapsing permafrost (thermokarst) on the North Slope of Alaska and tested the role of DOC chemical composition and nutrient concentration in determining biodegradability. DOC from collapsing permafrost was some of the most biodegradable reported in natural systems. However, elevated BDOC only persisted during active permafrost degradation, with a return to predisturbance levels once thermokarst features stabilized. Biodegradability was correlated with background nutrient concentration, but nutrient addition did not increase overall BDOC, suggesting that chemical composition may be a more important control on DOC processing. Despite its high biodegradability, permafrost DOC showed evidence of substantial previous microbial processing, and we present four hypotheses explaining this incongruity. Because thermokarst features form preferentially on river banks and lake shores and can remain active for decades, thermokarst may be the dominant short‐term mechanism delivering sediment, nutrients, and biodegradable organic matter to aquatic systems as the Arctic warms.
High‐resolution mapping of motor vehicle carbon dioxide emissions

A fuel‐based inventory for vehicle emissions is presented for carbon dioxide (CO2) and mapped at various spatial resolutions (10 km, 4 km, 1 km, and 500 m) using fuel sales and traffic count data. The mapping is done separately for gasoline‐powered vehicles and heavy‐duty diesel trucks. Emission estimates from this study are compared with the Emissions Database for Global Atmospheric Research (EDGAR) and VULCAN. All three inventories agree at the national level within 5%. EDGAR uses road density as a surrogate to apportion vehicle emissions, which leads to 20–80% overestimates of on‐road CO2 emissions in the largest U.S. cities. High‐resolution emission maps are presented for Los Angeles, New York City, San Francisco‐San Jose, Houston, and Dallas‐Fort Worth. Sharp emission gradients that exist near major highways are not apparent when emissions are mapped at 10 km resolution. High CO2 emission fluxes over highways become apparent at grid resolutions of 1 km and finer. Temporal variations in vehicle emissions are characterized using extensive day‐ and time‐specific traffic count data and are described over diurnal, day of week, and seasonal time scales. Clear differences are observed when comparing light‐ and heavy‐duty vehicle traffic patterns and comparing urban and rural areas. Decadal emission trends were analyzed from 2000 to 2007 when traffic volumes were increasing and a more recent period (2007–2010) when traffic volumes declined due to recession. We found large nonuniform changes in on‐road CO2 emissions over a period of ~5 years, highlighting the importance of timely updates to motor vehicle emission inventories.
Towards extreme network KPIs with programmability in 6G

6G's superpower must be simplicity, which should not be viewed as a constraint, but rather as the organic outcome of using the most advanced technologies at our disposal. Programmability in the data plane, cloud-native features like automatic scaling and failover, transparent acceleration of both network functions and applications and AI-driven optimizations are already present. We only need to integrate these different innovations into a consistent architecture and offer a simple yet powerful solution for the very different applications that would use future mobile networks. The application space is getting more and more heterogeneous, e.g., legacy Internet-based services still using the good old TCP protocol, future media services relying on multipath transport - always utilizing the best available connection, or control applications of robots or drones requiring extreme low and stable latency. The different applications will require very different Key Performance Indicators (KPIs) from the network. In this paper, we present a novel architecture called DESIRE6G (D6G) architecture that aims to fulfill these requirements by integrating the key technological innovations mentioned above. Besides supporting the diverse KPIs of future applications, the novel architecture should also simplify the mobile network itself by promoting modularity and service-based network function selection which can replace traditional control plane centric solutions, e.g., for handover.