Miracle-dz/UniFAR-FTUs huggingface dataset:
Costs of reproduction in common eiders ( Somateria mollissima ): An assessment of relationships between reproductive effort and future survival and reproduction based on observational and experimental studies

The two traditional approaches to the study of costs of reproduction, correlational and experimental, have been used in parallel in a breeding colony of common eiders ( Somateria mollissima ) and were compared in this paper. The analysis of the observational data was based on a two-strata capture-recapture model, the strata being defined on the basis of the clutch size laid by individual females in a given year. The best model according to AIC C indicated substantial variation in survival, recapture and transition rates, but overall a pattern emerged: females laying large clutches have a somewhat higher survival and much higher capture rate than females laying small clutches, and transition from large to small clutch size occurs much more frequently than the reverse transition. The analysis of the experimental data (adding/removing one egg) showed that no clear effect was found on either survival or transition rates. We conclude by suggesting (1) that condition should be included in multi-strata models in addition to reproductive effort; (2) that a specific study design for estimating the proportion of non-breeding females should be implemented, and (3) that non-breeding (a non-observable state in this study) may be influenced by previous reproduction events.
COVID-19 Pandemic Impact on the Electricity Demand of a Middle-Income Non-Industrial Country: Jordanian Case

The outbreak of the infamous Coronavirus disease (COVID-19) has widely affected every aspect and flow of the human daily lives. While this effect extended to the global electrical grid, it impacted every system distinctly owing to the divided global response to the pandemic, the diverse economic development of the countries as well as the different scale and responsiveness of each electrical grid. Jordan, a middle-income economy and one of the countries that were severely impacted by the disease, was known during the start of the pandemic for imposing one of the strictest curfews and nationwide lockdown around the world. These restrictions had significantly affected the electric load demand over the year 2020. With Jordan's high residential load share (exceeding 45%), increasing contribution of generation from renewable energy sources (more than 20% in 2020), alongside its unique pandemic response and its hot summers; the Jordanian electricity demand makes an interesting case to investigate. This paper provides a comparative analysis of the Jordanian load demand during the years 2019 and 2020 and includes insights on the effects of the imposed lockdown and the varying temperature in three different time frames; pre-lockdown, during lockdown and post lockdown.
A note on multivariate capability indices

In manufacturing industry, there is growing interest in quantitative measures of process variation under multivariate setting. This paper introduces a multivariate capability index and focuses its applications in geometric dimensioning and tolerancing. This index incorporates both the process variation and the process deviation from target. Two existing multivariate indices are compared with the proposed index.
Towards a Scalable Named Data Border Gateway Protocol

Named Data Networking (NDN) is a promising Internet architecture that exchanges data packets based on the contents' names regardless of the location of the data. Traffic routing and forwarding have scalability issues because of the gigantic number of Name-based prefixes. To address these issues, this paper proposes a new scheme for global routing optimization. This optimization is achieved by: 1) implementing a Peer Name Provider (PNP), 2) globally advertising PNP names and peer's names (PNAME) instead of content names, and 3) utilizing the NDN's Interest ForwardingHint to hold the producer's PNAME. The proposed scheme is implemented and tested in N-BGP, an interdomain routing protocol for routing NDN names and IP prefixes, using the Mininet emulator. The results show that the proposed scheme enhances the N-BGP scalability performance in terms of exchanged messages, and memory utilization.
Unsupervised Learning Algorithms for Denial of Service Detection in Vehicular Networks

Denial of Service ($D$ oS) attacks are a major threat for vehicular networks. Detecting and identifying the $D$ oS traffic is crucial for defending against such attacks. Machine Learning (ML) algorithms have been extensively adopted in traffic classification and detection of network attacks, namely the DoS attacks. Among and unlike different ML learning models, Unsupervised Learning (UL) algorithms have not being used in the literature for DoS detection. This paper shed the light on the feasibility of using unsupervised learning algorithms for detecting DoS attacks. It analyzes and compares the detection efficiency of selected UL algorithms using the Vehicular Reference Misbehavior (VeReMi) dataset [1]. Finally, simulation demonstrates the performance and efficiency of the used UL algorithms in $D$ oS detection; in particular, the Gaussian Mixture Model (GMM) algorithm demonstrates a detection accuracy with more than 95% for all $D$ oS attack traffic categories.
Molecular Dynamics Evolution for Thermal Conductivity of Complex Yukawa Liquids

The calculation of thermal properties of complex liquids is a significant work. Thermal conductivity (λ) of complex liquids (dusty plasmas) has been computed using Molecular Dynamics (MD) simulations by including polarization effect. The polarization effects on thermal conductivity along with different system sizes has been calculated for a wide series of Coulomb coupling (Γ) and Debye screening (κ) parameters using MD method. Accurate results are obtained using this algorithm with small dimension and quick convergence effects. The results have been contrasted with other accessible MD simulation results, theoretical assumptions, and with experimental findings of complex dusty plasma liquids. It is expected to demonstrate that it has better than those obtained in former known numerical methods. Moreover, it has been obtained that the present MD method has greater performance than previous numerical techniques. The newly proposed model checked in terms of accuracy and reliability.
A Privacy-Preserving Gait Recognition Scheme Under Homomorphic Encryption

In recent years, machine learning and deep neural networks have achieved remarkable results and have been widely used in different domains. Affected by COVID-19, the potential of gait feature recognition in biometric authentication has gradually emerged. However, machine learning algorithms are generally demanded in terms of computing power, sometimes need the support of cloud service providers, and require raw data, which is often sensitive, most privacy-preserving approaches only encrypted the trained model, and the data collected from users are unprotected. We propose a scheme for running deep neural networks on encrypted data using homomorphic encryption to address these issues.
Customers' perceptions and behavioral intentions regarding mobile banking usage

The Iraqi banking industry may benefit from the widespread usage of mobile phones and the government's promotion of cashless transactions to hasten the adoption of mobile banking. The aim of this study is to understand the behavioral intention to use mobile banking among mobile users. The study deployed a convenience sample to collect the data from the users in Iraq. Furthermore, 198 valid questionnaires were analyzed by Smart-PLS. the results indicated the perceived usefulness, perceived ease of use, social influence and self-efficacy statistically able to enhance the behavioral intention to use mobile banking among users. In contrast, awareness showed an insignificant impact on users' behavioral intention to use mobile banking.
>110 GHz High-Power Photodiode by Flip-Chip Bonding

We experimentally demonstrate >110 GHz charge-compensated modified uni-traveling carrier (CC-MUTC) photodiodes by flip-chip bonding on diamond submount. The typical dark current is ~200 nA at −3 V bias voltage and the measured responsivity reaches 0.15 A/W. Photodiodes with 6-μm, 8-μm, and 10-μm diameter exhibit 3-dB bandwidths of >110 GHz, 90 GHz, and 80 GHz, respectively. The RF output power reaches 8.4 dBm at 90 GHz at room temperature.
Transaction cost regularization for online portfolio selection

To further improve the performance of the online portfolio considering transaction costs, we propose a transaction cost regularization (TCR) model. This model minimizes the negative expected return and meanwhile incorporates the L1 norm of the difference between two consecutive allocations as a regularization term to control the transaction cost. We first apply the linearized augmented Lagrangian method to solve the proposed model and derive a convergent iterative algorithm whose update in each iteration has a closed form, which enables the computational efficiency of the algorithm. To further study the problem, the alternative direction method of multipliers is applied to solve the portfolio model and its convergence is theoretically guaranteed as well as guaranteeing that the portfolio variables for each iteration are constrained in simplex. Numerical experiments on benchmark datasets illustrate that our proposed algorithms are efficient and can achieve higher cumulative wealth than the compared state-of-the-art algorithms in most cases.
Direct evidence for degradation of polaron excited states in organic light emitting diodes

We investigate the intrinsic degradation mechanisms of the prototypical phosphorescent emissive material fac-tris(2-phenylpyridine) iridium [Ir(ppy)3] doped into the host 4, 4′-bis(3-methylcarbazol-9-yl)-2,2′-biphenyl (mCBP) by separately evaluating the effects of unipolar current, optical excitation, and their combination. We find that the mCBP anion is unstable and becomes more so in its excited state. Degradation due to the formation of defect states is evident from changes in the capacitance-voltage characteristics and from increasing drive voltage over time of a unipolar test device. These changes are understood within the framework of trapped-charge-limited transport, allowing for the determination of rate constants for each degradation mechanism. We also observe degradation of the hole transport material 4, 4′-bis[N-(1-naphthyl)-N-phenyl-amino]-biphenyl under sub-energy-gap illumination and suggest that this instability may proceed through excitation of its cationic state. These results provide dir.
Modeling and MPC Based Control of Line Power Flow in Smart Grid

Constraints to expand the infrastructure of the transmission system, penetration of the renewable that has huge uncertainty in generation leading to congestion on the system. The conventional way of removing congestion on the line by the operator takes a long time, i.e., about 5–20 mim, which is too slow for the transmission network. One of the ways to eliminate the congestion automatically within less time is to use the controller. For real time line flow control, this paper proposes Model Predictive Controller (MPC) considering constraints on the SM (Synchronous Machine) and power system frequency deviation; SMs are appropriately dispatched based on Generation Shift Factor (GSF). The controller has been evaluated in the 68 bus test system. The controller showed better performance than the conventional techniques and could track the line flow in less time, i.e., about 60s.
Stereo vs. tri-stereo: Preliminary results on Anthropogenic-induced landform change detection using satellite derived remote sensing data

The Trans-Sulawesi railway construction runs for 144 km from South to North of the South Sulawesi's east coast. The railway line mostly was built on an embankment over agricultural flat, while on the hill, the railway line built in an incised slope. These actions essentially alter the surrounding landform. The changing landform could have its effect on how the surface runoff flow during the rainy season. In this study, we explore the potential of satellite-derived digital elevation models (DEMs) on detecting the changed landform over the Takkalasi Watershed. We employed the SPOT-7's tri-stereo acquisition mode, acquired on 17th April 2020. In these preliminary results, we compare the DEM results from each pair combination as well as the combination of all three pairs. The use of different tie-points (TP) and ground control points (GCP) were also investigated. Fast Fourier Phase Transformation algorithm were employed for the TP extraction, while the semi-global matching algorithm were employed for the epipolar DEM extractions. The produced DEMs then validated using the reference data taken from dGPS, UAV DEM, as well as the ICESat-2 ATL08 data. Our initial results shows that the best DEM generated is produced by the forward and backward stereo pair using only nine (9) GCP with the average vertical Root Mean Square Error (RMSE) of 4.4 meter. At 3 m ground sampling distance, the incised hill is easier to detect on SPOT-7 DEM compared to the embankment.
Spatial dynamics of the light emission from a microplasma array

The spatial dynamics of the optical emission from an array of 50×50 individual microplasma devices is reported. The array is operated in noble gas at atmospheric pressure with an ac voltage. The optical emission is analyzed with phase and space resolution. It has been found that the emission is not continuous over the entire ac period, it occurs only twice in each cycle. Each of the observed emission phases shows a self-pulsing of the discharge, with several bursts of emission of a fixed width and repetition rate. Cross-talk between the individual devices can be observed through spatially resolved measurements.The spatial dynamics of the optical emission from an array of 50×50 individual microplasma devices is reported. The array is operated in noble gas at atmospheric pressure with an ac voltage. The optical emission is analyzed with phase and space resolution. It has been found that the emission is not continuous over the entire ac period, it occurs only twice in each cycle. Each of the observed emission phases shows a self-pulsing of the discharge, with several bursts of emission of a fixed width and repetition rate. Cross-talk between the individual devices can be observed through spatially resolved measurements.
Tunable magnetic regenerator alloys with a giant magnetocaloric effect for magnetic refrigeration from ∼20 to ∼290 K

A giant magnetocaloric effect (ΔSmag) has been discovered in the Gd5(SixGe1−x)4 pseudobinary alloys, where x⩽0.5. For the temperature range between ∼50 and ∼280 K it exceeds the reversible (with respect to alternating magnetic field) ΔSmag for any known magnetic refrigerant material at the corresponding Curie temperature by a factor of 2–10. The two most striking features of this alloy system are: (1) the first order phase transformation, which brings about the large ΔSmag in Gd5(SixGe1−x)4, is reversible with respect to alternating magnetic field, i.e., the giant magnetocaloric effect can be utilized in an active magnetic regenerator magnetic refrigerator; and (2) the ordering temperature is tunable from ∼30 to ∼276 K by adjusting the Si:Ge ratio without losing the giant magnetic entropy change.
Equivariant $s$-cobordism theorems

The classical h-cobordism theorem and the s-cobordism theorem have played an important role in studying differential topology [15], [16]. In the present paper, we discuss equivariant versions of these theorems. Let $G$ be a compact Lie group and $X$ a finite G-CW-complex. In 1974, S. Illman [6] defined the equivariant Whitehead group $Wh_{G}(X)$ of $X$ and the equivariant Whitehead torsion $\tau_{G}(f)$ for a G-homotopy equivalence $f:Xarrow Y$ between finite G-CW-complexes $X$, $Y$ as an element of $Wh_{G}(X)$ . When $\tau_{G}(f)=0,$ $f$ is called a simple $G$ -homotopy equivalence. In this paper, we deal with only smooth G-manifolds. Let $(W;X, Y)$ be a smooth G-h-cobordism. Namely $W$ is a compact Gmanifold with boundary $\partial W=X\perp Y$ (disjoint union) and the inclusions
Constraint Sets of Geometric Programs Characterized by Auxiliary Problems

This paper refines a previous classification scheme by using auxiliary problems of the dual geometric program. This new scheme provides a one-to-one correspondence between the possible states of primal and dual geometric programs. Moreover, a characterization of superconsistency, strong inconsistency and subconsistency without consistency is obtained in terms of properties of the dual problem.
Heterogeneous Remote Sensing Image Fusion Based on Homogeneous Transformation and Target Enhancement

Heterogeneous remote sensing (HRS) image fusion effectively improves image interpretability to better achieve the subsequent target detection tasks. Current HRS image fusion methods own limited target improvement performance leading to degraded target detection performance because they mainly focus on enhancing the geometric structure and texture details. In this paper, a novel homogeneous transformation and target enhancement (HTTE) module-based HRS image fusion method is designed. In HTTE, deep homogeneous feature fusion is used to transform HRS images into homogeneous images with similar image styles and then the proposal-copula-based target enhancement strategy is utilized to fuse homogeneous images, enhance target information, and relatively suppress background clutter therein. Experiments using measured high-resolution spaceborne and airborne inshore HRS data show that our proposed HTTE-based method improves the image fusion quality in comparison with current commonly used methods.
Design Flexible Microstrip Antenna for Non-Invasive Blood Glucose Detection

Diabetes is a chronic disease characterized by high blood sugar (glucose) levels. This research was conducted in line with the high urgency to no longer use invasive techniques in checking blood sugar levels. Therefore, it is necessary to design a tool that can detect blood sugar levels without injuring the body by non-invasive methods. This paper creates a flexible antenna sensor using a flexible substrate of Polyethylene terephthalate (PET) with a thickness of 0.135 mm. Silver nano-ink with a thickness of 0.01 mm was used as a patch, ground, and feedline material. The feeding technique used in the design of this antenna model is a coplanar waveguide (CPW). The antenna is proposed as a sensor for blood glucose detection using saliva samples. In this paper, the antenna is modeled with a circular patch form. The simulation shows that the proposed antenna works well at 2.45 GHz with a bidirectional radiation pattern.
Noncoeval star formation, starbursts, and the growth of supershells in Ob associations

There is strong evidence that the epoch of massive-star formation in OB associations is spread over intervals of 10-20 Myr as a result of subclustering, continuous or sequential star formation, or delayed collapse of the most massive stars. The growth of supershells is strongly influenced by this star formation history and by mass deposition to the hot bubble interior ('radiative poisoning'). Even in cases of coeval or continuous star formation, the rising energy input from massive stars, as they evlolve off the main sequence, may be observed as a 'starburst' with peak energy deposition 3-10 times that of the late-time rate due to supernovae and an excess of Wolf-Rayet stars (WR/O ratio approximately equal 0.1). The peak in energy deposition results in faster shell growth and velocity evolution than in constant-luminosity models. At finite bubble ages, this mechanism can alter the covering factor of superbubbles in the disk by factors of 2-3 and can accelerate the dynamical effects of galactic 'superwinds. ' In contrast, if sufficient matter is entrained in the hot bubble, radiative poisoning will stall its growth.
Using Topic Modelling for Analyzing Panamanian Parliamentary Proceedings with Neural and Statistical Methods

This work used statistical and neural topic modelling algorithms to perform an unsupervised analysis on a Panamanian parliamentary proceedings corpus of 2086 Spanish language texts. The statistical algorithm known as Latent Dirichlet Allocation (LDA) was employed and its performance compared to BERTopic, a neural-based method that encompasses deep neural document embeddings, non-linear dimensionality reduction, and hierarchical density clustering. Both models achieved topic diversity and coherence, yielding interpretable results with different levels of semantic abstraction. Furthermore, taking advantage of BERTopic’s features, Dynamic Topic Modelling analysis showed global and evolutionary word frequency of health-related topics. Results can be used for in-depth analysis of political trends, even though more complex hyperparameter tuning might be necessary to achieve higher topic coherence and interpretability.
Eady Instability of Isolated Baroclinic Jets with Meridionally Varying Tropopause Height.

Abstract An efficient solution procedure is developed for a nonseparable semigeostrophic Eady problem on a semi-infinite f plane. A model is designed to study the effects of meridionally isolated jets and tropopause morphology on baroclinic instability. Potential vorticity (PV) is assumed to be piecewise constant and discontinuous at the tropopause, whose height is allowed to vary with latitude. The computational domain is discretized in a stretched coordinate to maintain adequate resolution in the vicinity of the tropopause and axis of the jet. The basic state is numerically inverted from the two PV values with the specification of only the meridional profiles of potential temperature (PT) at the surface and (unspecified) tropopause height. The nonseparable eigenvalue problem about this basic state is solved for the discrete (zero-PV) normal modes. Since continuum modes are not sought, the size of the problem is reduced greatly, keeping the storage and CPU requirements moderate even at relatively high sp.
A Dynamically Weighted Loss Function for Unsupervised Image Segmentation

Image segmentation is the foundation of several computer vision tasks, where pixel-wise knowledge is a pre-requisite for achieving the desired target. Deep learning has shown promising performance in supervised image segmentation. However, supervised segmentation algorithms require a massive amount of data annotated at a pixel level, thus limiting their applicability and scalability. Therefore, there is a need to invest in unsupervised learning for segmentation. This work presents an improved version of an unsupervised Convolutional Neural Network (CNN) based algorithm that uses a constant weight factor to balance between the segmentation criteria of feature similarity and spatial continuity, and it requires continuous manual adjustment of parameters depending on the degree of detail in the image and the dataset. In contrast, we propose a novel dynamic weighting scheme that leads to a flexible update of the parameters and an automatic tuning of the balancing weight between the two criteria above to bring out the details in the images in a genuinely unsupervised manner. We present quantitative and qualitative results on four datasets, which show that the proposed scheme outperforms the current unsupervised segmentation approaches without requiring manual adjustment.