Miracle-dz/UniFAR-FTUs huggingface dataset:
Upregulation of YKL-40 Promotes Metastatic Phenotype and Correlates with Poor Prognosis and Therapy Response in Patients with Colorectal Cancer

YKL-40 is a heparin- and chitin-binding glycoprotein that belongs to the family of glycosyl hydrolases but lacks enzymatic properties. It affects different (patho)physiological processes, including cancer. In different tumors, YKL-40 gene overexpression has been linked to higher cell proliferation, angiogenesis, and vasculogenic mimicry, migration, and invasion. Because, in colorectal cancer (CRC), the serological YKL-40 level may serve as a risk predictor and prognostic biomarker, we investigated the underlying mechanisms by which it may contribute to tumor progression and the clinical significance of its tissue expression in metastatic CRC. We demonstrated that high-YKL-40-expressing HCT116 and Caco2 cells showed increased motility, invasion, and proliferation. YKL-40 upregulation was associated with EMT signaling activation. In the AOM/DSS mouse model, as well as in tumors and sera from CRC patients, elevated YKL-40 levels correlated with high-grade tumors. In retrospective analyses of six independent cohorts of CRC patients, elevated YKL-40 expression correlated with shorter survival in patients with advanced CRC. Strikingly, high YKL-40 tissue levels showed a predictive value for a better response to cetuximab, even in patients with stage IV CRC and mutant KRAS, and worse sensitivity to oxaliplatin. Taken together, our findings establish that tissue YKL-40 overexpression enhances CRC metastatic potential, highlighting this gene as a novel prognostic candidate, a predictive biomarker for therapy response, and an attractive target for future therapy in CRC.
Gaps in the spectrum of the Neumann Laplacian generated by a system of periodically distributed traps

The article deals with a convergence of the spectrum of the Neumann Laplacian in a periodic unbounded domain Ωϵ depending on a small parameter ϵ > 0. The domain has the form Ωϵ=Rn∖Sϵ , where Sϵ is an ϵZn ‐periodic family of trap‐like screens. We prove that, for an arbitrarily large L, the spectrum has precisely one gap in [0,L] when ϵ is small enough; moreover, when ϵ → 0, this gap converges to some interval whose edges can be controlled by a suitable choice of geometry of the screens. An application to the theory of 2D photonic crystals is discussed. Copyright © 2014 John Wiley & Sons, Ltd.
PIE-ARNet: Prior Image Enhanced Artifact Removal Network for Limited-Angle DECT

Dual-energy computed tomography (DECT) is of great clinical significance because it can simultaneously visualize the internal structure of the scanned object and provide material-specific information. DECT obtains two attenuation measurements of the same object at two different X-ray spectra, resulting in obvious redundant information. In this context, this article suggests acquiring dual-energy projection data using two complementary incomplete scans and utilizes a pretrained Prior-Net to generate the artifact-free prior image. Then the prior image is fed into the proposed prior image enhanced artifact removal network (PIE-ARNet) together with the degraded DECT images to improve the artifact removal performance. The generator of PIE-ARNet has two encoders and two decoders, with each component being responsible for a specific task. Two encoders extract and fuse prior information and image features, while two decoders perform differential learning for data in different energy channels. The discriminator of PIE-ARNet is dedicated to transferring the real statistical properties to the generated images, producing results with enhanced visual perception. Please note that Prior-Net could be trained using the freely available conventional single-energy CT data, which will not bring extra demand for DECT data. Experiments based on the simulated data and real rat data have demonstrated the promising performance of the proposed PIE-ARNet in removing artifacts, recovering image details, and preserving reconstruction accuracy.
Using ANN to Predict Gender-Based Violence in Iraq: How AI and data mining technologies revolutionized social networks to make a safer world

With prevalence of social network and invasion of internet, a new type of violence is threatened the world and especially the women. Gender-Based Violence considered a new generation of threat which results unsafe environment for our societies. Therefore, countries, humanitarian organizations and data scientists are considered this threat is a critical problem and resources have to be allocated to eliminate it. Artificial Intelligence becomes the most impactful science that plays major role in our daily lives. AI evolves remarkably and it involved into many industries to contribute and build creative products. Deep Learning techniques is a subset of AI family. Deep Learning has the ability to predict new results based on input data. Therefore, data scientists are highly encouraged to use Deep Learning techniques to predict Gender-Based Violence from social network datasets.
Brain Tumor Detection Using Convolutional Neural Network

The extraction of tumor areas from images is challenging since brain tumors have a diverse range of appearances and share many characteristics with normal tissues. Further-more, handling a sizable amount of data manually requires a time-consuming effort. This study developed a model for detecting brain tumors from two-dimensional MRI scans using the data augmentation method, CNN model, and pre-trained model. The experimental effort utilized the Kaggle dataset of tumors in varying sizes, shapes, and intensities. Deep learning, one of the most advanced technical methods for classifying and detecting tumors, has long been used to remove abnormal tumor areas from the brain. Advanced Artificial Intelligence and Neural Network classification algorithms may be useful in the early detection of brain cancers. To find brain tumors, CNN models were built using the VGG 16, and parameters were chosen to train this model to assess the literature solutions. VGG16 is one of the most effective CNN models due to its simplicity. Furthermore, the study discovered a way to use MRI for quick, efficient, and precise diagnose brain cancer. Using the Faster CNN, VGG 16 was used as the leading network to create feature maps that were then classified into tumor regions. The accuracy of the predictions was used to evaluate performance. The proposed technique was tested on a dataset of 253 MRI brain scans, with 155 revealing tumors. In MRI scans of the brain, the technique could be able to spot malignancies. The accuracy of VGG 16 was 99.94%, CNN was 97%, RestNet50 was 45.75%, and InceptionV3 was 48.85%.
Compact frequency reconfigurable MIMO antenna pair with high isolation for 5G mobile applications

This article presents a design of compact frequency reconfigurable dual‐port antenna pair with high isolation for 5G mobile devices. Two feeding ports are located side by side and share a common radiator within a small area of 11.5 mm × 6 mm in the proposed antenna pair. The feeding ports achieve a self‐decoupling characteristic without any additional decoupling structure based on a synthesis of common and differential modes in the shared radiator. Moreover, the antenna pair has a frequency reconfigurable property and can operate in both 3.5 GHz and 5 GHz bands by switching PIN diodes. A prototype multiple‐input‐multiple‐output (MIMO) antenna composed of four designed antenna pair is fabricated and tested. The experimental results show that the antenna pair can operate in both the frequency bands and has peak isolations of 26 dB in 3.5 GHz band and 27 dB in 5 GHz band. The antenna efficiencies are around 60% at 3.5 GHz and about 80% at 5 GHz respectively. The proposed antenna pair can be a promising candidate for 5G applications.
Adaptive image processing: A bilevel structure learning approach for mixed-order total variation regularizers

A class of mixed-order \emph{PDE}-constraint regularizer for image processing problem is proposed, generalizing the standard first order total variation $(TV)$. A semi-supervised (bilevel) training scheme, which provides a simultaneous optimization with respect to parameters and the new class of regularizers, is studied. Also, A finite approximation method, which used to solve the global optimization solutions of such training scheme, is introduced and analyzed.
Trash Classification Network Based on Attention Mechanism

The classification and recycling of garbage can greatly improve the utilization of garbage resources. This paper proposes a new convolutional neural network that fuses a multi-branch Xception network with an attention mechanism module. The effective feature information is emphasized and the invalid information is suppressed to overcome the problem caused by the small data set. To verify the usefulness of this network structure in the field of garbage images, this paper uses a widely used data set in the field of garbage image classification. For any network without pre-trained weights, the network proposed in this paper outperforms all other methods by 94.4%.
Assessing concrete strength loss at elevated temperatures as a function of dielectric variation measured by GPR: an empirical study

ABSTRACT Reinforced concrete (RC) parts, which are exposed to high temperatures for a certain period, undergo physical-chemical changes that deteriorate their mechanical properties. To this end, this study investigates the efficiency of using ground-penetrating radar (GPR) as a non-destructive testing (NDT) method in assessing the core compressive strength loss, elastic modulus decrease, and tensile strength loss of concrete under extreme temperatures. RC specimens are fabricated and exposed to elevated temperatures (300°C, 400°C, 500°C, 600°C, and 700°C). GPR measurements are performed to estimate the relative dielectric permittivity (RDP) of the specimens before fire exposure (BFE) and after fire exposure (AFE). The compressive strengths of the cores extracted from the specimens are obtained also for BFE and AFE cases. Attained RDP values are in conformance with the previous research results and contribute additional evidence to the published literature. As the temperature levels are increased, RDP and strength-related values of concrete specimens decrease. The strength-related parameters are more sensitive to elevated temperatures, whereas the change in the RDP values is rather gradual. Through multiple correlations within 95% confidence intervals, core compressive strength loss, elastic modulus decrease, and tensile strength loss are, individually, estimated as a function of relative RDP change and applied temperature levels. Empirical formulas are obtained with high coefficients of correlation. The relative effects of RDP change and temperature are more pronounced in elastic modulus decrease and tensile strength loss compared to compressive strength loss. Overall, the outcomes of this paper confirm the viability of using the GPR technique for concrete dielectric characterisation in estimating the strength-related properties of concrete exposed to high temperatures, which might reduce the need for destructive testing.
Scalar curvature on compact complex manifolds

In this paper, we prove that, a compact complex manifold $X$ admits a smooth Hermitian metric with positive (resp. negative) scalar curvature if and only if $K_X$ (resp. $K_X^{-1}$) is not pseudo-effective. On the contrary, we also show that on an arbitrary compact complex manifold $X$ with complex dimension $\geq 2$, there exist smooth Hermitian metrics with positive total scalar curvature and one of the key ingredients in the proof relies on a recent solution to the Gauduchon conjecture by G. Szekelyhidi, V. Tosatti and B. Weinkove.
Compound Magnetic Detection Method for Receiver Positioning of Wide-area Dynamic Wireless Charging

This digest presents a compound magnetic position detection method for the wide-area dynamic wireless charging that can realize dynamic positioning of two-dimension. To achieve accurate detection, electromagnetic sensors are arranged on the transmitter (Tx) coils and a permanent magnet is installed on the receiver (Rx) to measure the horizontal distance between the sensors and the Rx. Meanwhile, the coupling law is investigated to recognize the rough position of the Rx, thereby enhancing the accuracy. The designed detection method coordinates the switchover between the Tx coils and optimizes the performance of the wide-area DWPT system in a cost-effective manner.
Intestinal Parasitic Infection and Nutritional Status in Children under Five Years Old: A Systematic Review

Intestinal parasitic infections are common infectious diseases causing many health problems and impaired growth and physical development. Children under five years old are the most vulnerable to infections, due to their immature immunity and feeding and exploratory behaviours. This systematic review aimed to assess the relationship between intestinal parasitic infections and undernutrition among children under 5 years old. Fifteen studies met the inclusion and exclusion criteria and were classified as high-quality studies. Twelve parasites were reported, including Ascaris lumbricoides, Cryptosporodium spp. , Entamoeba histolytica, Enterobius vermicularis, Giardia lamblia, hookworm, Hymenolepis nana, Strongyloides sterocalis, Taenia spp. and Trichuris trichuria. Ascariasis is the most reported infection, with a prevalence ranging from 10.77% in Ethiopia to 57.14% in Malaysia, and is correlated with stunting (OR 2.17 (95% CI 1.14, 4.13), p = 0.02). Giardiasis is the second most reported infection, with a prevalence ranging from 4.43% in Ethiopia to 66.33% in the Central African Republic, and is related to an increased risk of stunting (OR 2.34 (95% CI 1.07, 5.10), p = 0.03)), wasting (OR 2.90 (95% CI 1.12, 7.49, p = 0.03)), and being underweight (OR 1.53 (95% CI 1.02, 2.29, p = 0.04)). The third and fourth most prevalent infections are T. trichiura and hookworm infections. Intestinal parasitic infections can occur very early in life and cause significant growth retardation. It is important to understand the prevalence and effects of infection based on the parasite species in order to implement therapeutic interventions and prevention controls.
Intersection space cohomology of three-strata pseudomanifolds

The theory of intersection spaces assigns cell complexes to certain stratified topological pseudomanifolds depending on a perversity function in the sense of intersection homology. The main property of the intersection spaces is Poincaré duality over complementary perversities for the reduced singular (co)homology groups with rational coefficients. This (co)homology theory is not isomorphic to intersection homology, instead they are related by mirror symmetry. Using differential forms, Banagl extended the intersection space cohomology theory to 2-strata pseudomanifolds with a geometrically flat link bundle. In this paper, we use differential forms on manifolds with corners to generalize the intersection space cohomology theory to a class of 3-strata spaces with flatness assumptions for the link bundles. We prove Poincaré duality over complementary perversities for the cohomology groups. To do so, we investigate fiber bundles on manifolds with boundary. At the end, we give examples for the application of the theory.
Many-body quantum dynamics in the decay of bent dark solitons of Bose–Einstein condensates

The beyond mean-field (MF) dynamics of a bent dark soliton (BDS) embedded in a two-dimensional repulsively interacting Bose–Einstein condensate is explored. We examine the case of a single BDS comparing the MF dynamics to a correlated approach, the multi-configuration time-dependent Hartree method for bosons. Dynamical snaking of this bent structure is observed, signaling the onset of fragmentation which becomes significant during the vortex nucleation. In contrast to the MF approximation ‘filling’ of the vortex core is observed, leading in turn to the formation of filled-core vortices, instead of the MF vortex–antivortex pairs. The resulting smearing effect in the density is a rather generic feature, occurring when solitonic structures are exposed to quantum fluctuations. Here, we show that this filling owes its existence to the dynamical building of an antidark structure developed in the next-to-leading order orbital. We further demonstrate that the aforementioned beyond MF dynamics can be experimentally detected using the variance of single shot measurements. Additionally, a variety of excitations including vortices, oblique dark solitons, and open ring dark soliton-like structures building upon higher-lying orbitals is observed. We demonstrate that signatures of the higher-lying orbital excitations emerge in the total density, and can be clearly captured by inspecting the one-body coherence. In the latter context, the localization of one-body correlations exposes the existence of the multi-orbital vortex-antidark structure.
Polarized positron production at MeV electron accelerators

The Polarized Electrons for Polarized Positrons (PEPPo) experiment has demonstrated the efficient transfer of polarization from electrons to positrons produced by the bremsstrahlung radiation of a polarized electron beam in a high-$Z$ target. Positron polarization up to 82\% has been measured for an initial electron beam momentum of 8.19 MeV/$c$, limited only by the electron beam polarization. Combined with the high intensity and high polarization performances of polarized electron sources, this technique extends efficient polarized positron capabilities from GeV to MeV electron accelerators. This presentation reviews the PEPPo proof-of-principle experiment and addresses the perspectives for future applications.
Plumbing constructions and the domain of outer communication for 5-dimensional stationary black holes

The topology of the domain of outer communication for 5-dimensional stationary bi-axisymmetric black holes is classified in terms of disc bundles over the 2-sphere and plumbing constructions. In particular we find an algorithmic bijective correspondence between the plumbing of disc bundles and the rod structure formalism for such spacetimes. Furthermore, we describe a canonical fill-in for the black hole region and cap for the asymptotic region. The resulting compactified domain of outer communication is then shown to be homeomorphic to $S^4$, a connected sum of $S^2\times S^2$'s, or a connected sum of complex projective planes $\mathbb{CP}^2$. Combined with recent existence results, it is shown that all such topological types are realized by vacuum solutions. In addition, our methods treat all possible types of asymptotic ends, including spacetimes which are asymptotically flat, asymptotically Kaluza-Klein, or asymptotically locally Euclidean.
Academic Studies on the Turkish Education of Turks in Bulgaria

Language is an integral component of identity, and for minorities the ability to use and receive education in the mother tongue is an important right that allows the transmission of culture, creates kinship, and strengthens identity. This article examines Turkish education access for Turks in Bulgaria by reviewing Turkish academic studies about this topic, in order to identify gaps. This research uses the literature review method to investigate published articles, Master’s and Doctoral theses, books, and reports. Within the scope of this literature review the issue is discussed within the framework of the concepts of bilingualism, mother tongue, education policies, and language policies. This article presents and discusses specific research topics that are not present in the current literature available in Turkish and identifies areas of study to close gaps in the literature.
Drugs and Drug Delivery Systems Targeting Amyloid-\b{eta} in Alzheimers Disease

Alzheimer's disease (AD) is a devastating neurodegenerative disorder with no cure and limited treatment solutions that are unable to target any of the suspected causes. Increasing evidence suggests that one of the causes of neurodegeneration is the overproduction of amyloid beta (A\b{eta}) and the inability of A\b{eta} peptides to be cleared from the brain, resulting in self-aggregation to form toxic oligomers, fibrils and plaques. One of the potential treatment options is to target A\b{eta} and prevent self-aggregation to allow for a natural clearing of the brain. In this paper, we review the drugs and drug delivery systems that target A\b{eta} in relation to Alzheimer's disease. Many attempts have been made to use anti-A\b{eta} targeting molecules capable of targeting A\b{eta} (with much success in vitro and in vivo animal models), but the major obstacle to this technique is the challenge posed by the blood brain barrier (BBB). This highly selective barrier protects the brain from toxic molecules and pathogens and prevents the delivery of most drugs. Therefore novel A\b{eta} aggregation inhibitor drugs will require well thought-out drug delivery systems to deliver sufficient concentrations to the brain.
Class number and regulator computation in cubic function fields

We present computational results on the divisor class number and the regulator of a cubic function field over a large base field. The underlying method is based on approximations of the Euler product representation of the zeta function of such a field. We give details on the implementation for purely cubic function fields of signatures $(3,1)$ and $(1, 1; 1, 2)$, operating in the ideal class group and infrastructure of the function field, respectively. Our implementation provides numerical evidence of the computational effectiveness of this algorithm. With the exception of special cases, such as purely cubic function fields defined by superelliptic curves, the examples provided are the largest divisor class numbers and regulators ever computed for a cubic function field over a large prime field. The ideas underlying the optimization of the class number algorithm can in turn be used to analyze the distribution of the zeros of the function field's zeta function. We provide a variety of data on a certain distribution of the divisor class number that verify heuristics by Katz and Sarnak on the distribution of the zeroes of the zeta function.
Motor Bearing Fault Diagnosis based on Hilbert-Huang transform and Convolutional Neural Networks

Motor Bearing vibration signal contains its operating state information and can be used for bearing fault diagnosis. Facing the nonlinear and non-stationary signal of bearing vibration, the accuracy of existing methods still needs to be improved. In this paper, Hilbert-Huang transform is proposed to process these signals and obtain the time frequency spectrums. Then Convolutional neural network is applied to diagnose bearing faults for its perfect ability of image recognition. Comparing with other signal processing methods, this method achieves better accuracy.