Miracle-dz/UniFAR-FTUs huggingface dataset:
Historical Demography and Genetic Population Structure of the Blackfin Tuna (Thunnus atlanticus) from the Northwest Atlantic Ocean and the Gulf of Mexico

Historical Demography and Genetic Population Structure of the Blackfin Tuna (Thunnus atlanticus) from the Northwest Atlantic Ocean and the Gulf of Mexico. (May 2009) Brandon LaRoy Saxton, B.S., Texas A&M University Galveston Chair of Advisory Committee: Dr. Jaime Alvarado-Bremer Little is known about the population structure and genetic variability of blackfin tuna despite catch increases over the past 25 years. In this thesis, levels of genetic variation contained in 323bp of the mitochondrial DNA (mtDNA) control region-I (CR-I) and in six microsatellite loci were characterized for two regions: the Gulf of Mexico (GoM) and the Northwest Atlantic. Large amounts of mtDNA diversity (h>0.99; =0.047) were observed in both regions. Mismatch distribution analysis of the CR-I sequence data, using a mutation rate of 1.6% Ma-1for scombroid fishes, indicate blackfin tuna underwent population expansion about 1.4 Ma, a timeline concordant with the expansion of other tunas and billfishes. Estimates of female effective population size were very large at 7.8 million and 12.8 million individuals for the NW Atlantic and the GoM, respectively. Both mtDNA and six microsatellite loci were used to determine blackfin tuna population structure. Microsatellite and mtDNA AMOVAs revealed significant differentiation (msat st=0.01, p=0.006 and mtDNA st=0.01, p=0.049) between the GoM and the NW Atlantic samples. Migration estimates using mtDNA data indicate
Future ozone air quality and radiative forcing over China owing to future changes in emissions under the Representative Concentration Pathways (RCPs)

We apply the nested grid version of the Goddard Earth Observing System (GEOS) chemical transport model (GEOS‐Chem) to assess 2000–2050 changes in O3 air quality and associated radiative forcing in China owing to future changes in emissions under the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Changes in surface layer O3 concentrations, numbers of O3 exceedance days (days with maximum daily 8 h average (MDA8) O3 exceeding 74.7 ppbv), and tropospheric O3 radiative forcing (RF) are simulated for 2000–2050. Over China, RCP8.5 is the worst scenario for near future (2020–2030) and RCP6.0 is the worst scenario over 2040–2050; the maximum increases in annual mean surface layer O3 concentrations of 6–12 ppbv relative to present day (year 2000) are found over southern China in 2020 and 2030 under RCP8.5 and in 2040 and 2050 under RCP6.0. The numbers of MDA8 O3 exceedance days are simulated to be 10, 0, 0, and 2 days over Beijing‐Tianjin‐Tanggu (BTT), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB), respectively, in the present day (year 2000). No exceedance days are simulated in year 2050 for all the four regions under RCP2.6 and RCP4.5, but extremely high numbers of exceedance days are found in 2050 under RCP6.0 (with 102, 75, 57, and 179 days in BTT, YRD, PRD, and SCB, respectively) and in 2030 under RCP8.5 (with 94, 60, 34, and 162 days in BTT, YRD, PRD, and SCB, respectively). The tropospheric O3 RF in 2050 relative to 2000 averaged over eastern China (18°–45°N, 95°–125°E) is simulated to be −0.11, 0.0, 0.01, and 0.14 W m−2 under RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. When we consider both the health and climate impacts of tropospheric O3 over China in 2050, RCP2.6 is a significantly improving scenario for both air quality and climate, RCP4.5 is a significantly improving scenario for air quality but has small consequences for climate, RCP6.0 is a significantly worsening scenario for air quality and a slightly worsening scenario for climate, and RCP8.5 is a slightly worsening scenario for air quality and a significantly worsening scenario for climate. These results indicate that to simultaneously abate air pollution and climate warming induced by O3 in China, both the anthropogenic emissions of NOx, CO, nonmethane volatile organic compounds (NMVOCs), and global CH4 levels should be reduced, as represented by RCP2.6.
Dynamic QSV-LES of hypersonic boundary layer transition delay via porous walls over a blunt cone

: Numerical simulations of hypersonic boundary layer transition delay on a 7 ˝ -half-angle cone with a 2.5 mm-nose tip radii via an impedance boundary condition that numerically replicates the effects of a porous carbon fibre reinforced carbon matrix ceramics (C/C) surface. The flow conditions studies are related to experiments performed at the High Enthalpy Shock Tunnel Göttingen (HEG) at the Reynolds numbers Re m “ 4 . 06 ¨ 10 6 and 6 . 40 ¨ 10 6 m ´ 1 with a freestream Mach number of M 8 “ 7 . 4 . The stability of the base flow is analyzed over impermeable and porous walls via both pulse-perturbed axisymmetric simulations and through solving linearized governing equations. Simulated results for second-mode spatial growth rates match linear predictions showing the dynamic large-eddy simulation (LES) quasi-spectral procedure’s capability of numerically preserving the instability wave dynamics. Three-dimensional transitional LES were then performed with the introduction of grid independent pseudorandom pressure perturbations. Numerical predictions were compared against experimental measurements regarding the frequency content of the disturbances in the transitional region with fairly good agreement capturing the shift to lower frequencies. Such shift is caused by the formation of near-wall low-temperature streaks that concentrate the pressure disturbances at locations with locally thicker boundary layers forming trapped wavetrains that can persist into the turbulent region. Additionally, results indicate that the presence of a porous surface representative of a C/C material does not affect turbulence significantly and simply shifts its onset downstream.
Fully-automated high-ﬁdelity LES around high-lift aircraft conﬁguration near stall

: This study presents the large-eddy simulation (LES) around a high-lift aircraft conﬁg-uration at near-stall conditions. In the present LES, we employ the non-body-ﬁtted hierarchical Cartesian grid and achieve fully automated grid generation for the complex aircraft conﬁguration. The wall modeling is also adopted to realize the LES of high-Reynolds-number ﬂows, and this study performs the wall-modeled LES around the complex aircraft conﬁguration by utilizing the wall modeling on non-body-ﬁtted Cartesian grids. Furthermore, we use the kinetic-energy and entropy preserving (KEEP) scheme, which is stable and non-dissipative, to achieve stable high-ﬁdelity simulations. The number of grid points is over 10 billion, and the state-of-the-art supercomputer Fugaku is used for massively parallel computations. The present wall-modeled LES successfully predicts the complex ﬂowﬁeld involving separation around the high-lift aircraft conﬁguration and aerodynamic forces at the near-stall conditions, without numerical instability.
How to predict community responses in the face of imperfect knowledge and network complexity

It is a challenge to predict the response of a large, complex system to a perturbation. Recent attempts to predict the behaviour of food webs have revealed that the eﬀort needed to understand a system grows quickly with its complexity, because increasingly precise information on the elements of the system is required. Here, we show that not all elements of the system need to be measured equally well. This suggests that a more eﬃcient allocation of eﬀort to understand a complex systems is possible. We develop an iterative technique for determining an eﬃcient measurement strategy. Finally, in our assessment of model food webs, we ﬁnd that it is most important to precisely measure the mortality and predation rates of long-lived, generalist, top predators. Prioritizing the study of such species will make it easier to understand the response of complex food webs to perturbations.
Fluid-structure interaction with multi-body collision: application to collective ﬁsh swimming in an impermeable or porous enclosure

: This work is dedicated to the numerical modeling of collective swimming in an enclosure. Collective swimming involves several complex physical phenomena such as ﬂuid-structure interactions and possible contacts and collisions between ﬁshes. Collective swimming in an enclosure necessarily leads to some collisions between ﬁshes and the enclosure boundary. We present here the whole numerical framework allowing to perform parallel numerical simulations of self pro-pelled locomotion with collisions. Numerical simulations show that diﬀerent swimming behaviors could be observed for porous and impermeable enclosures
Multidimensional aware Riemann solver for the Eulerian droplet equation system

: In clouds and under cold weather, water droplets impact and freeze on aircraft structures. The Eulerian model for the air-droplet flow predicts the droplet impingement. The model equations are close to the Euler equations but without a pressure term. Consequently, the resulting system is weakly hyperbolic and standard Riemann solvers cannot be used. To circumvent this problem, the system is modified to include the divergence of a particle pressure. The main purpose of this work is to implement a multidimensional HLLC Riemann solver for the modified formulation of the Eulerian droplet model. The method should preserve physical properties such as the density positivity and must produce accurate results compared to existing codes
ScholarWorks@UMass Amherst ScholarWorks@UMass Amherst

Pine and Gilmore (1998) set out the vision for a new economic era: the ‘experience economy’ in which consumers are now searching for extraordinary and memorable experiences. Since then, a rich body of research on applications of the experience economy concepts have appeared in the consumer literature. However, it is only recently (Oh, Fiore and Jeoung, 2007) that academic investigation on the measurement of tourism experiences have emerged in the literature. The purpose of the research is to replicate and validate Oh, Fiore and Jeoung (Measuring Experience Economy Concepts: Tourism Applications, Journal of Travel Research, 46, November 2007, pp. 119-132) proposed measurement scale in the context of cruising. The study further extends Oh, Fiore and Jeoung (2007) conceptualisations and investigates the relationship between experience evaluations, satisfaction and intention to recommend. Overall, findings of this study enhance theoretical progress on the experiential concept in tourism.
SCPD-IT: Smart COVID-19 Patient Detection Over IoT-WSN

This research focuses on the development and implementation of an Internet of Things based Wireless sensor network (IoT-WSN) in healthcare for detecting and assessing the mobility pattern of the COVID-19 patient within the coverage region. Signal processing applications of Pyroelectric Infrared sensors (PIR) are being investigated to detect non-contact human body infrared radiation. Human body temperature's time-varying sensor signal is utilized to infer infrared radiation. Finding an appropriate design circuit required a significant design procedure. Pyroelectric infrared (PIR) sensors, micro controllers, and communication units are tested in IoT-WSN research. The customized GUI lets users monitor and manage the entire IoT-WSN on a PC functioning as a base station. The IoT-WSN system presented has higher potential in the healthcare system with more cost-effective outcome.
Systematic Evaluation of a Knee Exoskeleton Misalignment Compensation Mechanism Using a Robotic Dummy Leg

The objective and quantitative assessment of physical human-exoskeletons interaction (pHEI) represents a pressing necessity in the wearable robots field. This process remains of difficult execution, especially for early stage devices, in which the inclusion of human testing could pose ethical and safety concerns. This manuscript proposes a methodology for pHEI assessment based on an active dummy leg named Leg Replica, which is able to sense interaction forces while wearing an exoskeleton. We tested this methodology on a wearable active knee exoskeleton prototype, with the goal to evaluate the effects of a misalignment compensation mechanism. Through this methodology, it was possible to show how the misalignment compensation mechanism was able to reduce the interaction forces during passive exoskeleton motion. Such reduction was less evident when the exoskeleton was active. The tests allowed to identify specific points of improvements for the exoskeleton, enabling a more specific upgrade of the device based on these experimental results. This study demonstrates the ability of the proposed methodology to objectively benchmark different aspects of pHEI, and to accelerate the iterative development of new devices prior to human testing.
Dynamic Graph Neural Networks-Based Alert Link Prediction for Online Service Systems

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as “alert storm”. In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert $a$ would lead to the occurrence of alert $b$. Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.
Restoration of fire in National Parks

Over the past century, policies related to the management of fire in U.S. National Parks have evolved fiom efforts to eliminate all fire to recognition of the importance of restoring and maintaining fire as a natural ecological process. Prior to their formal designation by Congress, most National Parks had experienced thousands of years of periodic fire. Long-term interactions of climate, vegetation, and fire are largely responsible for shaping the ecosystems that most parks were established to protect.
Research on the Validity of Network Security Model Based on WOA-BP Neural Network

In the field of network security, using machine learning to detect network anomalies and attacks is becoming increasingly popular. However, the availability of a security model based on machine learning may vary depending on the dataset and the setting of security features. In this article, we consider introducing a binary classification model for anomaly detection and a multi category classification model for various types of network attacks. Machine learning algorithms used to implement the models above include Naive Bayes, Random Forest, and BP artificial neural network security models, as well as WOA optimized by BP neural network. Firstly, we conducted a series of comparative experiments by using UNSW-NB15, the most popular security data sets. On this basis, we investigated the effectiveness of the network security model. Finally, conclusions about the effectiveness of network anomaly detection are drawn, which can provide certain model ideas and data support for current network security analysis.
Sensitivity Analysis of Novel PolyMUMPs-Based Ultrasonic MEMS Microphones

MEMS (micro-electro-mechanical-systems) microphones have been widely studied over the past decades. Studies have focused on the audible frequency range, while microphones operating in the ultrasonic range beyond 20 kHz (inaudible range) have rarely been explored. The aim of this work is to design a capacitive-based ultrasonic MEMS microphone having a flat response of up to 100 kHz. One major application for this is leak detection from highly pressurized pipes. The proposed microphone structure accounts for the limitations associated to the PolyMUMPs standard surface micro-machining process to enable its potential for future fabrication. Equations governing static and dynamic behavior are derived and solved using MATLAB by implementing the Galerkin method. The finite element simulations are conducted using COMSOL to present the sensitivity analysis while the model is further validated by obtaining the sensitivity curve of an audible MEMS microphone available in the literature. The sensitivity of the proposed microphone is -78 dB with a flat response within ±2 dB at up to 100 kHz. Considering the sensitivity of the benchmark ultrasonic bulk microphone B&K 4138 being equal to -60 dB, it can be concluded that although the proposed PolyMUMPs-based device has lower sensitivity, it eliminates the need for the costly additional back-etch processing required to create a larger back chamber.
When Carbon Footprint Meets Data Transportation in IoT Networks

Research effort in IoT network architectures for new technologies is directed towards improving quality of service (QoS) or reducing energy consumption to extend networks lifetime. Simultaneously, the environmental impact of services is of crucial importance. In this paper, we propose a new approach to analyse the data transportation carbon footprint induced by different IoT architectures with the same infrastructure. The proposed method considers an end-to-end energy consumption model as well as the carbon emissions due to data transportation, which depend on the route taken by the data from the access network to the edge, and within the core network. We introduce this method by comparing three existing architectures: a centralized architecture, a semi-distributed architecture and a locally-distributed architecture. The performance evaluation is conducted using emulation tools on three scenarios: Unicast, Geocast and Broadcast. We used two metrics: the carbon footprint and the QoS in terms of delay. The obtained results show the benefits and concerns of each architecture evaluated.
Breast Tumor Monitoring Vest with Embedded Flexible UWB Antennas - the Proof-of-Concept Evaluations with Realistic Phantoms

This paper presents a proof-of-concept for a breast tumor monitoring vest designed to operate in the ISM 2.5 GHz and ultrawideband (UWB) frequency range (3.1-10.6 GHz). The vest incorporates three different types of antennas, enabling a comparative evaluation of their performance. To assess the effectiveness of the vests, realistic breast tissue and tumor phantoms are utilized, accommodating various breast sizes and densities. Tumor detection relies on a microwave technique, with the presence of tumors observed through measured channel transfer parameters $(\mathrm{S}21\mathrm{s})$. The study reveals promising results, indicating that even the small breast abnormalities (1 cm in size) can be detected using the vests across different breast densities. The observed differences in S21 values range from 0.5-20dB demonstrating a significant level of detectability.
Deep Learning-Based Cramér-Rao Bound Optimization for Integrated Sensing and Communication in Vehicular Networks

Integrated sensing and communication (ISAC) is capable of achieving both heterogeneous connectivity and highly accurate sensing performance in vehicular networks through effective beamforming design at the roadside unit (RSU). In the traditional paradigm, the first step is predicting the kinematic parameters of each vehicle and then designing the optimal beamforming matrix, which requires excessively large computational complexity. To tackle this issue, this paper proposes a deep learning (DL)-based method that bypasses explicit channel estimation and directly optimizes beamformers to minimize the Cramér-Rao Bound (CRB) of radar sensing while guaranteeing an acceptable level of achievable communication rate. This is achieved by leveraging the convolutional and long short-term memory (CLSTM) neural networks to implicitly capture the features of historical channels, thereby improving the ISAC system performance. Finally, simulation results demonstrate that the proposed approach can satisfy the pre-defined requirement of achievable rate, while simultaneously achieving sensing performance that approaches the perfect beamforming bound.
LITE: Light Inception with boosTing tEchniques for Time Series Classification

Deep learning models have been shown to be a powerful solution for Time Series Classification (TSC). State-of-the-art architectures, while conducting promising results on the UCR archive, present a high number of trainable parameters. This can lead to long training with a high CO2, Power consumption and possible increase in the number of FLoat-point Operation Per Second (FLOPS). In this paper, we present a new architecture for TSC, the Light Inception with boosTing tEchnique (LITE) with only 2.34% of the state-of-the-art model InceptionTime’s number of parameters, while preserving performance. This architecture, with only 9, 814 trainable parameters due to the usage of DepthWise Separable Convolutions (DWSC), is boosted by three techniques: multiplexing, custom filters, and dilated convolution. The LITE architecture, trained on the UCR, is 2.78 times faster than InceptionTime and consumes 2.79 times less CO2 and Power.
Information Plane Analysis Visualization in Deep Learning via Transfer Entropy

In a feedforward network, Transfer Entropy (TE) can be used to measure the influence that one layer has on another by quantifying the information transfer between them during training. According to the Information Bottleneck principle, a neural model's internal representation should compress the input data as much as possible while still retaining sufficient information about the output. Information Plane analysis is a visualization technique used to understand the trade-off between compression and information preservation in the context of the Information Bottleneck method by plotting the amount of information in the input data against the compressed representation. The claim that there is a causal link between information-theoretic compression and generalization, measured by mutual information, is plausible, but results from different studies are conflicting. In contrast to mutual information, TE can capture temporal relationships between variables. To explore such links, in our novel approach we use TE to quantify information transfer between neural layers and perform Information Plane analysis. We obtained encouraging experimental results, opening the possibility for further investigations.