Miracle-dz/UniFAR-FTUs huggingface dataset:
Singular Boundary Value Problems for Ordinary Differential Equations

Singular boundary value problems for ordinary differential equations model many real world phenomena ranging from different physics equations to biological, physiological, and medical processes 1–3 . This special issue places its emphasis on the study, theory, and applications of boundary value problems involving singularities. It includes some review articles such as 4 , equations with discontinuous nonlinearities 5 , boundary value problems with uncertainty 6 , fractional differential equations 7 , periodic or antiperiodic solutions 8 , and biological 9 or medical applications 10 . Different methods and techniques are used ranging from variational methods 11 to bifurcation techniques 12 . The editors aimed at a volume that may serve as a reference in the topic of the special issue and collect twenty five original and cutting-edge research articles by some of the top researchers in boundary value problems for ordinary differential equations worldwide and from many different countries Algeria, Austria, Bulgaria, China, Czech Republic, Greece, Iran, Ireland, Italy, Japan, New Zealand, Pakistan, Saudi Arabia, South Korea, Spain, USA . We would like to thank the authors for their contributions, the Editor-in-Chief of the journal, Professor Ravi P. Agarwal, and the Editorial Office of the journal for their support.
Recent Progress of Carbon Dot Precursors and Photocatalysis Applications

Carbon dots (CDs), a class of carbon-based sub-ten-nanometer nanoparticles, have attracted great attention since their discovery fifteen years ago. Because of the outstanding photoluminescence properties, photostability, low toxicity, and low cost, CDs have potential to replace traditional semiconductor quantum dots which have serious drawbacks of toxicity and high cost. This review covers the common top-down and bottom-up methods for the synthesis of CDs, different categories of CD precursors (small molecules, natural polymers, and synthetic polymers), one-pot and multi-step methods to produce CDs/photocatalyst composites, and recent advances of CDs on photocatalysis applications mostly in pollutant degradation and energy areas. A broad range of precursors forming fluorescent CDs are discussed, including small molecule sole or dual precursors, natural polymers such as pure polysaccharides and proteins and crude bio-resources from plants or animals, and various synthetic polymer precursors with positive, negative, neutral and hydrophilic, hydrophobic, or zwitterionic feature. Because of the wide light absorbance, excellent photoluminescence properties and electron transfer ability, CDs have emerged as a new type of photocatalyst. Recent work of CDs as sole photocatalyst or in combination with other materials (e.g., metal, metal sulfide, metal oxide, bismuth-based semiconductor, or other traditional photocatalysts) to form composite catalyst for various photocatalytic applications are reviewed. Possible future directions are proposed at the end of the article on mechanistic studies, production of CDs with better controlled properties, expansion of polymer precursor pool, and systematic studies of CDs for photocatalysis applications.
A Mutation in Mouse MT-ATP6 Gene Induces Respiration Defects and Opposed Effects on the Cell Tumorigenic Phenotype

As the last step of the OXPHOS system, mitochondrial ATP synthase (or complex V) is responsible for ATP production by using the generated proton gradient, but also has an impact on other important functions linked to this system. Mutations either in complex V structural subunits, especially in mtDNA-encoded ATP6 gene, or in its assembly factors, are the molecular cause of a wide variety of human diseases, most of them classified as neurodegenerative disorders. The role of ATP synthase alterations in cancer development or metastasis has also been postulated. In this work, we reported the generation and characterization of the first mt-Atp6 pathological mutation in mouse cells, an m.8414A>G transition that promotes an amino acid change from Asn to Ser at a highly conserved residue of the protein (p.N163S), located near the path followed by protons from the intermembrane space to the mitochondrial matrix. The phenotypic consequences of the p.N163S change reproduce the effects of MT-ATP6 mutations in human diseases, such as dependence on glycolysis, defective OXPHOS activity, ATP synthesis impairment, increased ROS generation or mitochondrial membrane potential alteration. These observations demonstrate that this mutant cell line could be of great interest for the generation of mouse models with the aim of studying human diseases caused by alterations in ATP synthase. On the other hand, mutant cells showed lower migration capacity, higher expression of MHC-I and slightly lower levels of HIF-1α, indicating a possible reduction of their tumorigenic potential. These results could suggest a protective role of ATP synthase inhibition against tumor transformation that could open the door to new therapeutic strategies in those cancer types relying on OXPHOS metabolism.
Maternal Viral Infection and Risk of Fetal Congenital Heart Diseases: A Meta‐Analysis of Observational Studies

Background At present, the association between maternal viral infection and risk of congenital heart diseases (CHD) in offspring is uncertain; additionally, a complete overview is missing. A meta‐analysis of observational studies was performed to address the question of whether women who had a history of viral infection in early pregnancy were at an increased risk of CHD in offspring, compared with mothers without viral infection. Methods and Results Unrestricted searches were conducted, with an end date parameter of July 15, 2018, of PubMed, Embase, Google Scholar, Cochrane Libraries, and Chinese databases, to identify studies that met prestated inclusion criteria. Seventeen case‐control studies involving 67 233 women were included for analysis. Both fixed‐effects models (odds ratio [OR], 1.83; 95% CI, 1.58–2.12; P<0.0001) and random‐effects models (OR, 2.28; 95% CI, 1.54–3.36; P<0.0001) suggested that mothers who had a history of viral infection in early pregnancy experienced a significantly increased risk of developing CHD in offspring. For specific viral infections, the risk of developing CHD in offspring was significantly increased among mothers with rubella virus (OR, 3.49, 95% CI, 2.39–5.11 in fixed‐effects models; and OR, 3.54; 95% CI, 1.75–7.15 in random‐effects models) and cytomegalovirus (OR, 3.95; 95% CI, 1.87–8.36 in fixed‐effects models) in early pregnancy; however, other maternal viral infections in early pregnancy were not significantly associated with risk of CHD in offspring. Sensitivity analysis yielded consistent results. No evidence of publication bias was observed. Conclusions Although the role of potential bias and evidence of heterogeneity should be carefully evaluated, the present study suggests that maternal viral infection is significantly associated with risk of CHD in offspring.
Importance of quadrupolar ordering in antiferroelectric liquid crystal devices

A model of switching in antiferroelectric liquid crystals is presented which predicts both helix unwinding in the pretransitional regime and a field induced transition to the ferroelectric state. When the ratio of the coefficients of the quadrupolar and dipolar ordering terms in the bulk free energy expression exceeds a critical value, the latter transition becomes first order, in agreement with experimental observation. We have determined these coefficients in the commercial mixture CS4001. The ratios of the coefficients imply that all three states (antiferroelectric and two ferroelectric) are stable in the ground state, i.e., antiferroelectric liquid crystals are truly tristable.
Semantic SLAM-based Autonomous Tributary Navigation System Using 3D LiDAR Point Cloud for UAV

Remote sensing methodologies are useful for managing natural systems. Among them, we focused on a methodology for collecting data by attaching three-dimensional (3D) light detection and ranging (LiDAR) sensors to unmanned aerial vehicles (UAVs) that can perform detection over a wide area with high mobility. In this study, we propose a semantic simultaneous localization and mapping (SLAM)-based autonomous driving system for tributary mapping by using a UAV equipped with a 3D LiDAR. As a natural system, tributaries are not studied extensively for diagnosing polluted watersheds and managing water quality. Therefore, it is necessary to develop a robust autonomous driving system for mapping tributary environments.
High-Fidelity Specular SVBRDF Acquisition From Flash Photographs

Obtaining accurate SVBRDFs from 2D photographs of shiny, heterogeneous 3D objects is a highly sought-after goal for domains like cultural heritage archiving, where it is critical to document color appearance in high fidelity. In prior work such as the promising framework by Nam et al., the problem is simplified by assuming that specular highlights exhibit symmetry and isotropy about an estimated surface normal. The present work builds on this foundation with several significant modifications. Recognizing the importance of the surface normal as an axis of symmetry, we compare nonlinear optimization for normals with a linear approximation proposed by Nam et al. and find that nonlinear optimization is superior to the linear approximation, while noting that the surface normal estimates generally have a very significant impact on the reconstructed color appearance of the object. We also examine the use of a monotonicity constraint for reflectance and develop a generalization that also enforces continuity and smoothness when optimizing continuous monotonic functions like a microfacet distribution. Finally, we explore the impact of simplifying from an arbitrary 1D basis function to a traditional parametric microfacet distribution (GGX), and we find this to be a reasonable approximation that trades some fidelity for practicality in certain applications. Both representations can be used in existing rendering architectures like game engines or online 3D viewers, while retaining accurate color appearance for fidelity-critical applications like cultural heritage or online sales.
Towards On-device Deep Neural Network Inference and Model Update for Real-time Gesture Classification

Deep learning resurgence ushered in the application of pattern recognition algorithms in high-impact research fields with impressive accuracy. In addition, deep neural networks (DNN) have recently been used to classify gestures for rehabilitation device control utilizing raw electromyography data. However, the computational resources required by a convolution neural network (CNN) are a constraint that often limits deployment to embedded devices for real-time inference. An optimized edge adaptive convolutional neural network using a short-time Fourier transform (STFT) spectrogram input was proposed in this study. The model’s classification accuracy was evaluated offline and on-device for inter-subject accuracy. Furthermore, an adaptive weight update approach was implemented to improve inference model accuracy due to degradation. The proposed model and optimization technique achieved offline accuracy of 92.19 % and 94.29 % for the raw and STFT input, respectively. However, the on-device accuracy for raw and STFT input to the model was 82.26 % and 85.19 %, respectively. On the other hand, the adaptive model update increased the respective accuracy by an average of 7% on-device. Finally, our study demonstrates the deployment of DNN on-device for real-time gesture classification inference.
Proof-of-Monitoring (PoM): A Novel Consensus Mechanism for Blockchain-Based Secure Service Level Agreement Management

In the current 5th Generation (5G) networking paradigm, the enforcement of Service Level Agreements (SLAs) is a non-trivial measure to ensure the scope and the quality of services and standards between tenants and service providers (SPs). On top of this, Secure Service Level Agreements (SSLA) are introduced to ensure that SPs deliver the most critical and required security-related standards defined in the contract, such as integrity, confidentiality, availability, non-repudiation, and privacy assurance. However, with the tendency for more distributed and multi-stakeholder networking architectures in next-generation networks, the management process of such SSLAs will be challenging due to the diversified security vulnerabilities and complexity of underlying technologies. Although blockchain is emerging as a platform to facilitate such distributed SSLA/SLA management frameworks, its currently available consensus mechanisms are more generic. Still, they need to improve in terms of applying in multi-stakeholder networks. Therefore, this paper presents a novel consensus mechanism called Proof-of-Monitoring (PoM) for a blockchain-based novel SSLA management framework. Moreover, we provide details about the prototype implementation of our proposed consensus algorithm and SSLA management framework. It is proven by comparing our proposal with the other existing solutions that our solution outperforms in many aspects, such as energy consumption, computation cost, and security features.
The growth and characterization of ? single crystals

GalnSe(2) single crystals have been grown and characterized by experimental techniques such as high-resolution transmission electron microscopy, x-ray diffraction, x-ray photoelectron spectroscopy and optical and electrical measurements. The samples were prepared in single-crystal form from a melt. The structural analysis indicates that GalnSe(2) has a hexagonal structure, and confirms the high quality of the produced single crystals. Quantitative information on electrical and optical properties of single-crystalline GalnSe(2) was obtained by investigating the resistivity and photoluminescence as a function of the temperature and excitation intensity.
Masking Patterns of Tones

Monaural masking patterns were obtained for pure tones spaced by octaves from 250 cps to 8000 cps at 20–100 db SL on three listeners in an attempt to provide extensive data assessing the relative importance of aural harmonics and cochlear spread of masking tone activity in the extension of masking to frequencies above the masking tone. The masking patterns confirm and extend the results of others, but especially indicate that second peaks do not necessarily occur at the second harmonic of the masking tone. The masking patterns are explained in terms of (1) the activity pattern of the masking tone in the cochlea, (2) beats between signal and masking tones, (3) aural harmonics, and (4) suppression of cochlear response to the signal.
Modeling of Errors in a Digital Energy Detector for On-Off Keying Receivers

A method of computing the error probability in a digital energy detection OOK receiver with a moving sum and hysteretic thresholds (<tex>$MV_{\mathbf{th}1} > MV_{\mathbf{th}0}$</tex>) is presented. The model considered samples from both previous and current bits as the window for the moving sum is evaluated per new sample. Errors when consecutive bits are the same or different were analyzed separately as <tex>$ER_{\mathbf{B}(\mathbf{same})}$</tex> and <tex>$ER_{\mathbf{B}(\mathbf{diff})}$</tex> and were characterized based on the number of transitions of the majority vote output. <tex>$ER_{\mathbf{B}(\mathbf{same})}$</tex> and <tex>$ER_{\mathbf{B}(\mathbf{diff})}$</tex> were verified to have opposite behavior with <tex>$\Delta MV_{\mathbf{th}}$</tex>. Optimal thresholds for the smallest bit error rate were then identified given a fixed number of samples N, error probability per sample <tex>$ER_{\mathbf{S}}$</tex>, and probability of having bit transitions. The optimal thresholds for <tex>$\mathbf{N}=5,\ 10,\ 15$</tex>, and 20, were found to be (2,3), (4,6), (6,9), and (8,12) when the sample error rate is from 0.01 to 0.16 and when the probability of bit transition is 50%. The computed error probabilities had %-errors of −6.91%, −0.40%, and −1.42% for <tex>$ER_{\mathbf{B}(\mathbf{same})},\ ER_{\mathbf{B}(\mathbf{diff})}$</tex>, and <tex>$ER_{\mathbf{B}}$</tex> when compared with behavioral simulations. <tex>$ER_{\mathbf{B}}$</tex> was observed to be approximately proportional to <tex>$(ER_{\mathbf{S}})^{k}$</tex>, where k is positive, and so improvements in <tex>$ER_{\mathbf{S}}$</tex> will result in k-fold improvements in <tex>$ER_{\mathbf{B}}$</tex>. Based on the results of the model, the k values for the N's mentioned were 2.45, 4.78, 6.60, and 8.40 and were noted to be slightly below N/2.
The Evaluation of the Damping Characteristics of a Hard Coating on Titanium

Engine failures due to fatigue have cost the Air Force an estimated d400 million dollars per year over the past two decades. Damping treatments capable of reducing the internal stresses of fan and turbine blades to levels where fatigue is less likely to occur have the potential for reducing cost while enhancing reliability. This research evaluates the damping characteristics of magnesium aluminate spinel, MgO+Al2O3, (mag spinel) on titanium plates from an experimental point of view. The material and aspect ratio were chosen to approximate the low aspect ratio blades found in military gas turbine fans. In the past, work has generally been performed on cantilever supported beams, and thus the two-dimensional features of damping were lost. In this study plates were tested with a cantilevered boundary condition, using electrodynamic shaker excitation. The effective test area of each specimen was 4.5 in × 4.5 in. The nominal plate thickness was 0.125 in. Mag spinel was applied to both sides of the plate, at a thickness of 0.01 in, and damping tests were run at room temperature. The effect of the coating was evaluated at the 2nd bending mode (mode 3) and the chord wise bending mode (mode 4). A scanning laser vibrometer revealed the frequency and shape of each mode for the plates. Sine sweeps were used to characterize the damping of the coated and uncoated specimens for the modes tested. The coating increased damping nonlinearly for both modes tested in which the general outcome was similar to that found in beams.
A NOTE ON MULTIVARIATE LOGISTIC MODELS FOR CONTINGENCY TABLES

Summary The log-linear model is a tool widely accepted for modelling discrete data given in a contingency table. Although its parameters reflect the interaction structure in the joint distribution of all variables, it does not give information about structures appearing in the margins of the table. This is in contrast to multivariate logistic parameters, recently introduced by Glonek & McCullagh (1995), which have as parameters the highest order log odds ratios derived from the joint table and from each marginal table. Glonek & McCullagh give the link between the cell probabilities and the multivariate logistic parameters, in an algebraic fashion. The present paper focuses on this link, showing that it is derived by general parameter transformations in exponential families. In particular, the connection between the natural, the expectation and the mixed parameterization in exponential families (Barndorff-Nielsen, 1978) is used; this also yields the derivatives of the likelihood equation and shows properties of the Fisher matrix. The paper emphasises the analysis of independence hypotheses in margins of a contingency table.
Tube Mechanism With 3-Axis Rotary Joints Structure to Achieve Variable Stiffness Using Positive Pressure

Studies on soft robotics have explored mechanisms for switching the stiffness of a robot structure. The hybrid soft-rigid approach, which combines soft materials and high-rigidity structures, is commonly used to achieve variable stiffness mechanisms. In particular, the positive-pressurization method has attracted significant attention in recent years as it can eliminate the constraints on driving pressure. Moreover, it can change the shape holding force according to internal pressure. In this study, a variable stiffness mechanism, comprising 3-axis rotary ball joints and a single chamber, was devised via frictional force using positive pressure. The prototype can change joint angles arbitrarily when no pressure is applied and can hold joint angles when positive pressure is applied. Using a theoretical model of the torque required to hold the joint angle, we simulated the holding torque using finite element modeling analysis and measured the holding torque in the pitch and roll directions when internal pressure was applied. Based on the interaction of the theoretical model, measurement, and FEM analysis, it was confirmed that the value of the holding torque in the roll direction was approximately π/2 times larger than that in the pitch direction for each value of the internal pressure. Further, we evaluated the FEM value, theoretical value, and measured value of the holding torque by performing pairwise numerical comparisons. Our approach will aid the design of effective stiffening mechanisms for soft robotics applications.
An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm.
Cones and Iterative Methods for Best Least Squares Solutions of Linear Systems

The concept of a regular splitting of a real nonsingular matrix, introduced by R. Varga, is used in iterative methods for solving linear systems. The purpose of this paper is to extend this concept to rectangular linear systems \[ ( * )\qquad Ax = b, \] in two directions. Firstly, this is accomplished by replacing $A^{ - 1} $ by $A^\dag $, the Moore–Penrose inverse of A, and, secondly, by considering matrices that leave a cone invariant.Let $A \in R^{m \times n} $. The splitting $A = M - N$ is called proper if $R(A) = R(M)$ and $N(A) = N(M)$. Such is the case when A and M are nonsingular. Consider the iteration \[ ( * * )\qquad x^{i + 1} = M^ \dag Nx^i + M^ \dag b. \] It is shown that for a proper splitting, $\rho (M^ \dag N)$ (the spectral radius of $M^ \dag N$) is less than l, if and only if the iteration (**) converges to $A^ \dag b$, the best least squares approximate solution of the system (*). This approach has the advantage of avoiding the normal system $A^T Ax = A^T b$ in solving (*). Necessary an.
Attitude Control of Spacecraft Multibody System by Optimal Nonholonomic Motion Planning

The optimal nonholonomic motion planning for spacecraft multibody system is researched. The attitude motion equations of spacecraft multibody system take on nonholonomic constraint without outside force. The control of system can be converted to the nonholonomic motion planning problem for a driftless system. Based on modified-Newton method, the optimal control algorithm of controlling spacecraft to desired attitude is accepted by optimal control algorithm and Ritz approximation theory. Quaternion is used to describe the kinematics equation, which avoids the singularity and has the benefit of small calculation and high precision. At last, numerical simulations of spacecraft with two reaction fly wheels has proved of the approach to be effective.