Miracle-dz/UniFAR-FTUs huggingface dataset:
Where Did You Come From? Where Did You Go? Investigating the Origin of Invasive Leptocybe Species Using Distribution Modelling

Research Highlights: We present the first attempts to model the distributions of the two cryptic and globally invasive species of Leptocybe invasa sensu lato (Fisher & LaSalle) (Hymenoptera: Eulophidae) in its purported country of origin, namely Australia. Background and Objectives: Leptocybe invasa is an invasive eucalypt-galling wasp that spread quickly all over the world in the early to mid-2000’s, achieving significant pest status through its severe impacts on the growth and productivity of extra-limital eucalypt plantations. Until its discovery in Europe and the Middle East, the genus was undescribed, and its native range remains unclear. Molecular studies indicate the globally invasive population comprises two cryptic species with variable modes of reproduction. Collection records from Australia, the purported origin, represent only one of the invasive lineages, restricted to subtropical and tropical Queensland and northern New South Wales. To date, the original invasive lineage has not been found in Australia, despite searches over the seventeen years that it has been spreading overseas. Materials and Methods: To understand the distributions of the invasive populations, and to infer Leptocybe spp. native ranges within Australia, we used correlative niche modelling in Maximum Entropy (MaxEnt) and multivariate analysis, and created a CLIMEX model based on development rates of an invasive population. Results: We used the environmental conditions in the extra-limital range to infer possible origins, with our findings supporting the possibility that the invasive populations may have originated from different populations in Australia. Conclusions: We highlight the need for better understanding of the distribution, genetic diversity, and reproductive mode of the species within Australia. The variety of climatic niches occupied by invasive lineages of the wasp potentially present new threats to eucalypts in previously uninfested habitats.
A Comparison of Marine and Estuarine Algal Populations from Downeast Maine and Eight Contiguous Northwest Atlantic Areas

ABSTRACT. Downeast ME, which is located within the northeastern-most part of the state, extends from the mouth of the Bay of Fundy (NS/NB/ME) to Mount Desert Island, ME. Its flora consists of 208 major macroscopic, multicellular, marine and estuarine algae, including 59 green, 70 brown, and 79 red algae. The number of species at 42 Downeast towns was highly variable, ranging from 118-128 taxa/town at open coastal and tidal rapid sites to 3–12 taxa at sheltered coastal and inner estuarine areas. Detailed studies at 27 individual sites between Eastport and Bar Harbor, ME identified 154 species. Seven open coastal and a Cobscook Bay tidal rapids within these 27 individual sites had the highest number of taxa (141 taxa, 91.6%). Fewer species (93 taxa, 60.4%) were found between Moose Neck and the Narraguagus River that had more sheltered coastal, estuarine, or riverine habitats with reduced salinities and higher surface water temperatures. Eight freshwater taxa were identified at the tidal headwaters of several rivers, including five green (Bolbochaete repanda, Mougeotia sp. Spirogyra sp., Stigeoclonium sp., and Zygnema sp.) and three red algae (Audouinella hermannii, Batrachospermum gelatinosum, and Lemanea fucina). The total flora from Downeast ME and eight other areas from the Bay of Fundy to the Great Bay Estuary consisted of 336 taxa. The number of total seaweed taxa and their occurrence values were highly variable, with the Bay of Fundy having the highest values (i.e. 253 taxa or 75.3%), Downeast the second highest (208 taxa or 61.9%), and Penobscot Bay the lowest (98 taxa or 29.1%). The percent of shared Downeast seaweeds varied from 62.7% (Penobscot Bay) to 85.9% (Bold Coast), while the number of shared taxa varied from 96 (Penobscot Bay) to 170 (Bay of Fundy). The seaweed flora from Downeast represents ~38% of the 535 seaweeds currently recorded from the NW Atlantic. The floristic ratios for the 42 Downeast towns and eight contiguous NW Atlantic areas had a cold-temperate ratio of 2.0. However, a few towns and individual sites had warm temperate ratios of 3.0–5.0 due to few brown algae resulting from low salinities and variable water temperatures. Nine warm-water or disjunct estuarine seaweeds were recorded from a few Downeast estuarine sites. In addition, eight introduced seaweeds were identified from Downeast ME or 25% of the 32 known introductions for the NW Atlantic, while 17 introduced taxa (53.1%) are known for Downeast ME plus the other eight areas.
Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification

Remote sensing image scene classification is an important means for the understanding of remote sensing images. Convolutional neural networks (CNNs) have been successfully applied to remote sensing image scene classification and have demonstrated remarkable performance. However, with improvements in image resolution, remote sensing image categories are becoming increasingly diverse, and problems such as high intraclass diversity and high interclass similarity have arisen. The performance of ordinary CNNs at distinguishing increasingly complex remote sensing images is still limited. Therefore, we propose a feature fusion framework based on hierarchical attention and bilinear pooling called HABFNet for the scene classification of remote sensing images. First, the deep CNN ResNet50 is used to extract the deep features from different layers of the image, and these features are fused to boost their robustness and effectiveness. Second, we design an improved channel attention scheme to enhance the features from different layers. Finally, the enhanced features are cross-layer bilinearly pooled and fused, and the fused features are used for classification. Extensive experiments were conducted on three publicly available remote sensing image benchmarks. Comparisons with the state-of-the-art methods demonstrated that the proposed HABFNet achieved competitive classification performance.
Secrecy Capacity via Cooperative Transmitting under Rayleigh and Nakagami-m Fading Channel

In a wireless network, it is necessary to make the security of the network so high that eavesdroppers cannot intercept the messages. If a key message is divided into several blocks and sent to different transmitters around the receiver, then the eavesdroppers cannot retrieve the actual key in case of missing any blocks of the key messages. Cooperating transmitting provides a strategy to avoid overhearing of messages by any eavesdropper while transmitting them over a communication channel. This paper presents a network which consists of a number of transmitters, a number of eavesdroppers at unknown locations and a receiver at a random location. There are secured channels between the transmitters. The receiver exchanges a secret key with the transmitters. Poisson distribution and independently uniformly distribution (IUD) for the spatial distribution of the transmitters and eavesdroppers are used. In this paper, we analyze the variation of secrecy capacity of the network in the real-time environment using the Rayleigh and Nakagami-$m$ fading channel. Network under Nakagami-$m$ fading case shows little bit better performance compared to that of Rayleigh fading environment.
High-sigma analysis of DRAM write and retention performance: a TCAD-to-SPICE approach

This paper presents a TCAD-to-SPICE high-sigma analysis of DRAM write and retention performance. Both statistical and process-induced variability are taken into- account. We highlight that the interplay between discrete traps and discrete dopants is ruling the leakage statistical tails and therefore can play a fundamental role in determining yield and reliability of ultra-scaled DRAMs.
Organizational Climate Theoretical Model and its Relationship with the Occupational Safety and Health Management

This article provides the relationship theoretical model between Occupational Health and Safety Management (OHSM) and organizational climate in order to develop the reference framework for the construct’s evaluation mentioned in the organizational environment. The above was carried out based on a study process that gave rise to the relationship model between the variables. The process consisted of three stages: bibliographic retrieval, model coding, and model formulation. Stage one yielded 66 documents (hermeneutic unit - HU) associated with definition, conceptualization, constructs relationship and associated models. The next stage corresponds to the coding that was carried out through the ATLAS.ti qualitative analysis software. The coding associated with the analysis HU was open, axial and selective. The model consists of 10 dimensions that describe the individual and joint evaluation of the constructs. The results are the constructs conceptualization and definition, the theoretical relationship and finally the theoretical relationship model. It is concluded that the organizational climate dimensions related to the OHSM are: reward / salary, risk, standards, communication and participation, performance and improvement objectives, security, integration, organizational orientation, resources availability and personal development.
Properties of Conductive Oxygen Vacancies and Compact Modeling of IV Characteristics in HfO2 Resistive Random-Access-Memories

The HfO2-based resistive random-access-memory (RRAM) is studied. In the first part, two parameters of oxygen vacancies are extracted. The migration barrier of the oxygen vacancy (or the extended Frenkel pair) is calculated. The resistivity of a filament is also calculated. In the second part, an existing compact model for the RRAM is implemented and its results are compared with the experimental data
How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study

Software design is an important concern in modern code review through which multiple developers actively discuss and improve each single code change. However, there is little understanding of the impact of such developers’ reviews on continuously reducing design degradation over time. It is even less clear to what extent and how design degradation is reversed during the process of each single code change’s review. In summary, existing studies have not assessed how the process of design degradation evolution is impacted along: (i) within each single review, and (ii) across multiple reviews. As a consequence, one cannot understand how certain code review practices consistently contribute to either reduce or further increase design degradation as the project evolves. We aim at addressing these gaps through a multi-project retrospective study. By investigating 14,971 code reviews from seven software projects, we report the first study that characterizes how the process of design degradation evolves within each review and across multiple reviews. Moreover, we analyze a comprehensive suite of metrics to enable us to observe the influence of certain code review practices on combating or even accelerating design degradation. Our results show that the majority of code reviews had little to no design degradation impact in the analyzed projects. Even worse, this observation also applies, to some extent, to reviews with an explicit concern on design. Surprisingly, the practices of long discussions and high proportion of review disagreement in code reviews were found to increase design degradation. Finally, we also discuss how the study findings shed light on how to improve the research and practice of modern code review.
Human Umbilical Cord Mesenchymal Stem Cell-Derived Extracellular Vesicles Inhibit Endometrial Cancer Cell Proliferation and Migration through Delivery of Exogenous miR-302a

MicroRNAs (miRNAs) are potential therapeutic targets in endometrial cancer, but the difficulties associated with their delivery to tumor target cells have hampered their applications. Human umbilical cord mesenchymal stem cells (hUCMSCs) have a well-recognized tumor-homing ability, emphasizing the capacity of tumor-targeted delivery of extracellular vesicles. hUCMSCs release extracellular vesicles rich in miRNAs, which play a vital role in intercellular communication. The purpose of this study was to verify a potential tumor suppressor microRNA, miR-302a, and engineered hUCMSC extracellular vesicles enriched with miR-302a for therapy of endometrial cancer. Here, we observed that miR-302a was significantly downregulated in endometrial cancer tissues when compared with adjacent tissues. Overexpression of miR-302a in endometrial cancer cells robustly suppressed cell proliferation and migration. Meanwhile, the proliferation and migration were significantly inhibited in endometrial cancer cells when cultured with miR-302a-loaded extracellular vesicles derived from hUCMSCs. Importantly, our data showed that engineered extracellular vesicles rich in miR-302 significantly inhibited the expression of cyclin D1 and suppressed AKT signaling pathway in endometrial cancer cells. These results suggested that exogenous miR-302a delivered by hUCMSC-derived extracellular vesicles has exciting potential as an effective anticancer therapy.
Design and Implementation of a Smart Bike Accident Detection System

Nowadays overpopulated countries like Bangladesh have become a warzone where thousands of people lose their lives and even more become handicapped due to road accidents. With the recent popularity of ride sharing apps like Pathao, Uber Moto, Shohoz etc. the number of bikes in Dhaka city has increased drastically. As bike accidents are becoming quite an alarming issue, we felt the need of an improved bike safety system. In this paper, we have proposed a system that detects bike accidents using MPU6050 (gyro sensor and accelerometer), SIM808 (GPS+GPRS+GSM), Raspberry Pi 3 Model B and Arduino Uno. We have placed the proposed system on the surface of the bike. If an accident occurs, the sensors will trigger and send a message containing the number and location of the biker to the nearest hospitals, police stations and registered family members.
Kinetic Studies of the Microbiological Conversion of Sulphate to Hydrogen Sulphide and their Relevance to Sulphide Generation within Sewers

The relevance of the studies carried out, to the important practical problem of predicting sulphide generation rates within sewers, is discussed in the introduction section of the paper. The predictive equations presently in use are compared, and the desirability of replacing these empirical equations by a more scientific approach based on an analysis of mass transport and biochemical reaction is stressed. A theoretical analysis of mass transport and metabolism of sulphate during laminar flow in a model sewer is then described. This theoretical section is followed by the description of the laboratory experimental studies. These studies consisted of flowing simulated sewage through tubes containing biofilms of mixed cultures of sulphate reducing bacteria. The results obtained in the laboratory studies showed that the diffusional resistance in the liquid phase was negligible and that the biological conversion of sulphate to sulphide followed zero order kinetics when mass transfer was not rate limiting. The observed sulphate removal rates gave good agreement with Australian field data for sulphide generation in sewers. The value of the zero order rate constant (measured at 41 °C) was 30 mg cm −3 h −1 .
Polynomial Controller Design and Its Application: Experimental Validation on a Laboratory Setup of Nonideal DC–DC Buck Converter

The transient response control is one of the most important tasks in the control system design. The transient response has characterized based on the relationship between characteristic ratios and time domain specifications. The characteristic ratios are the design parameters of the characteristic ratio assignment (CRA) approach. This article presents a novel polynomial controller for all types of linear second-order systems. The proposed controller is designed using CRA. The important features of this controller are that it has simple design steps, set-point tracking, and disturbance rejection capabilities. Initially, the proposed controller is designed for an unstable nonminimum-phase second-order system to demonstrate the performance and robustness. Subsequently, the presented controller is analyzed and validated on the nonideal dc–dc buck converter. The buck converter is highly sensitive to the suddenly changing load conditions. The major issue of output voltage control in the dc–dc buck converter is addressed in this article. In addition, the robustness and performance of the proposed controller are verified for the sudden change in supply input voltage, the sudden change in the reference voltage, the parametric uncertainty in inductor ($L$) and capacitor ($C$), and the sudden change in load. Furthermore, the efficacy of the proposed controller is evaluated by comparing it with the recently published control schemes for this converter. The proposed controller is implemented using dSPACE 1104 real-time controller. Applicability of the presented controller is corroborated experimentally on the nonideal dc–dc buck converter. The simulation as well as hardware results recommend that the proposed controller is suited for the nonideal dc–dc buck converter under such conditions.
Dual-Objective Control Using an SMC-Based CW Current Controller for Cascaded Brushless Doubly Fed Induction Generator

This article proposes a dual-objective control method using a sliding-mode control (SMC)-based control winding (CW) current controller for cascaded brushless doubly fed induction generator (CBDFIG), where the smooth grid synchronization and flexible grid-connected power regulation are achieved with the same CW current controller. A so-called virtual grid flux method is introduced to obtain the CW current reference value for the grid synchronization. In addition, according to the relationship between the power winding (PW) power and CW current, a novel direct power control method is proposed for CBDFIG. Furthermore, a unified form of the CW current references is investigated to achieve the dual-objective control for CBDFIG, where no mode switching operation is required and constant switching frequency can be achieved in both the control processes with space vector pulsewidth modulation. The proposed control method shows a strong robustness against the grid voltage disturbances, which is necessarily required in both the grid synchronization and grid-connected power regulation. The simulation and experiments are conducted to validate the effectiveness of the proposed control method.
Sparse Identification of Volterra Models for Power Amplifiers Without Pseudoinverse Computation

We present a new formulation of the doubly orthogonal matching pursuit (DOMP) algorithm for the sparse recovery of Volterra series models. The proposal works over the covariance matrices by taking advantage of the orthogonal properties of the solution at each iteration and avoids the calculation of the pseudoinverse matrix to obtain the model coefficients. A detailed formulation of the algorithm is provided along with a computational complexity assessment, showing a fixed complexity per iteration compared with its previous versions in which it depends on the iteration number. Moreover, we empirically demonstrate the reduction in computational complexity in terms of runtime and highlight the pruning capabilities through its application to the digital predistortion of a class J power amplifier operating under 5G-NR signals with the bandwidth of 20 and 30 MHz, concluding that this proposal significantly outperforms existing techniques in terms of computational complexity.
Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy

The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people’s mobility.
Simultaneous Tracking and Separation of Multiple Sources Using Factor Graph Model

In this article, we present an algorithm for direction of arrival (DOA) tracking and separation of multiple speakers with a microphone array using the factor graph statistical model. In our model, the speakers can be located in one of a predefined set of candidate DOAs, and each time-frequency (TF) bin can be associated with a single speaker. Accordingly, by attributing a statistical model to both the DOAs and the associations, as well as to the microphone array observations given these variables, we show that the conditional probability of these variables given the microphone array observations can be modeled as a factor graph. Using the loopy belief propagation (LBP) algorithm, we derive a novel inference scheme which simultaneously estimates both the DOAs and the associations. These estimates are used in turn for separating the sources, by directing a beamformer towards the estimated DOAs, and then applying a TF masking according to the estimated associations. A comprehensive experimental study demonstrates the benefits of the proposed algorithm in both simulated data and real-life measurements recorded in our laboratory.
PET Parametric Imaging: Past, Present, and Future

Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g., standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this article, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.