Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing The Use of AI for Analysis and Prediction in the Fashion Industry

The COVID-19 pandemic has made all aspects of human life assisted by technology and big data. It starts from the education sector, economy, communication, health, and manufacturing to fashion. As we all know fast fashion has become one of the most significant contributors of waste. During the flow of developing a collection, for example; the production and distribution process can cause ethical issues and contradict sustainability matters. Several studies from 2010 to date have initiated AI (Artificial Intelligent) technology, a computer vision that alleviates the use of carbon footprints in the fashion industry. AI presents robust evidence to the audience, since it is visual and statically calculated, furthermore it is less costly and energy saving. AI abstracts the similarities or differences across all clothing and collections from the dataset. Its implementation can be used in many fashion careers with different purposes. By reviewing across the computer vision journals complemented with fashion management literatures, this article eventually provides insights of the implementation of AI for analysis and prediction from fashion photos or dataset.
Active Surveillance of Root Caries in Vivo with CP-OCT

The active surveillance of root caries lesions to monitor potential remineralization or decay progression is challenging for the clinician, due to unreliable diagnostic information. The conventional visual and tactile methods for assessing the lesion activity are not reliable, and the clinician is often unable to determine if the lesion is progressing or has been arrested. An important marker of an arrested lesion is a highly mineralized transparent surface zone (TSL) that forms when the mineral is deposited in the outer layer of the lesion. The purpose of this study was to determine if cross-polarization optical coherence tomography (CP-OCT) could be used to detect changes in the lesion severity and activity during active monitoring. In total, 18 subjects with 22 suspected active root caries lesions were evaluated using CP-OCT at the baseline, 3 months, and 6 months. All subjects were instructed to use a high fluoride dentifrice at the baseline. The results showed that CP-OCT was able to discriminate the active from the arrested lesions by identifying the presence of a TSL on arrested lesions. The results also indicated that the mean TSL thickness increased significantly (p < 0.05) for the nine lesion areas. In addition, CP-OCT was able to show the progression of demineralization, erosion, and changes in gingival contours in scanned areas. CP-OCT was valuable for monitoring the activity and severity of root caries lesions in vivo. CP-OCT can be used to assess the activity of root caries lesions at a single time point by detecting the presence of a TSL at the lesion surface indicative of the lesion arrest.
Design, Synthesis and Biological Evaluation of Quinazolinamine Derivatives as Breast Cancer Resistance Protein and P-Glycoprotein Inhibitors with Improved Metabolic Stability

A series of twenty-two quinazolinamine derivatives showing potent inhibitory activities on breast cancer resistance protein (BCRP) and p-glycoprotein (P-gp) were synthesized. A cyclopropyl-containing quinazolinamine 22 was identified as a dual BCRP and P-gp inhibitor, while azide-containing quinazolinamine 33 showed BCRP inhibitory activity. These lead compounds were further investigated in a battery of mechanistic experiments. Compound 22 changed the localization of BCRP and P-gp in cells, thus inhibiting the efflux of anticancer drugs by the two ATP-binding cassette (ABC) transporters. In addition, both 22 and 33 significantly stimulated the ATP hydrolysis of the BCRP transporter, indicating that they can be competitive substrates of the BCRP transporter, and thereby increase the accumulation of mitoxantrone in BCRP-overexpressing H460/MX20 cells. Azide derivative 33, exhibited a greater inhibitory effect on BCRP after UV activation and can serve as a valuable probe for investigating the interactions of quinazolinamine derivatives with BCRP. Notably, the dual BCRP and P-gp inhibitors 4–5, 22–24, 27, and BCRP inhibitor 33 showed improved metabolic stability compared to Ko143.
Getting Ready for all Types of Users: A Virtual Reality Training Center for Automated Vehicles

The development of highly automated driving (HAD) is in rapid progress. One of the main remaining challenges, however, is to validate highly automated driving in interaction with humans in the various roles in which they will participate in general traffic situations. This includes not only the role from the potential safety driver's, passenger's or pedestrian's perspective, but also - related to the step from SAE autonomy level 4 to level 5 - the consideration of the safety driver located in a remote control room. In this work, we present a classification of the different human roles in automated driving and outline their requirements. Afterwards, we address the conceptualization of a training room and present our realization of the overall system as well as the underlying software and hardware architecture and finally demonstrate the applicability in different use cases. The suitability of the described training center was exemplary evaluated on one use case with questionnaires. The presented system will further serve for data generation, for data analysis and as a training environment for improving interactions between AI-based highly automated driving systems and humans.
Homogenization Methods and Macro-Strength of Composites

A multi-phase periodic composite subjected to inhomogeneous shrinkage or temperature deformation and prescribed mechanical loads is considered. The asymptotic homogenisation is applied for calculation of homogenized macrostresses. A non-local approximate macro-strength condition, defined on homogenised stress-field, is derived from the micro-strength conditions and their convergence to the approximate macro-strength condition, as the structure period tends to zero, is proved.
A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.
Effects of the α − ε phase transition on wave propagation and spallation in laser shock-loaded iron

Despite extensive research work on the $\ensuremath{\alpha}\text{\ensuremath{-}}\ensuremath{\epsilon}$ phase transition occurring in shock-loaded iron, the kinetics of this transformation remain largely unknown. Here, we present time-resolved free surface velocity measurements in iron foils of thicknesses ranging from 150 to $520\text{ }\ensuremath{\mu}\text{m}$ subjected to laser shocks of peak pressure of about 130 GPa and duration of about 3 ns. The records show an elastic precursor followed by a plastic front, but the double wave structure that is usually associated with the phase change does not appear over such short propagation distances. The measured profiles are compared to the predictions of one-dimensional simulations involving rate-dependent descriptions of both twinning and phase transitions. Such comparisons provide an estimate of a time constant governing the transformation kinetics, which is found to strongly condition the evolution and attenuation of the pressure pulse during its propagation. The spall stress evaluated from the records is shown to be significantly higher after the $\ensuremath{\alpha}\text{\ensuremath{-}}\ensuremath{\epsilon}\text{\ensuremath{-}}\ensuremath{\alpha}$ cycle. Metallurgical observations of the recovered samples confirm both the phase transition and the spall damage inferred from the velocity profiles. Finally, they show the very clear change in fracture surface morphology to the so-called smooth spall expected above the phase transformation.
Impact of Facebook on Social Support and Emotional Wellbeing in Perinatal Women during Three Waves of the COVID-19 Pandemic in Mexico: A Descriptive Qualitative Study

The COVID-19 pandemic affected the mental health of pregnant and postpartum women in unique, unprecedented ways. Given the impossibility of delivering face-to-face care, digital platforms emerged as a first-line solution to provide emotional support. This qualitative study sought to examine the role that a closed Facebook group (CFG) played in providing social support for Mexican perinatal women and to explore the concerns they shared during the COVID-19 pandemic. A thematic analysis of all the posts in the CFG yielded nine main categories: (1) COVID-19 infections in participants and their families; (2) fear of infection; (3) infection prevention; (4) health services; (5) vaccines; (6) concerns about non-COVID-19-related health care; (7) effects of social isolation; (8) probable mental health cases; and (9) work outside the home. Participants faced stressful situations and demands that caused intense fear and worry. In addition to household tasks and perinatal care, they were responsible for adopting COVID-19 preventive measures and caring for infected family members. The main coping mechanism was their religious faith. The CFG was found to be a useful forum for supporting perinatal women, where they could share concerns, resolve doubts, and obtain information in a warm, compassionate, and empathetic atmosphere. Health providers would be advised to seek new social media to improve the quality of their services.
Least Squares Fourier Series Solutions to Boundary Value Problems

Fourier series solutions are obtained for mixed boundary value problems for the Laplace equation in the plane and in the sphere. The nature of the boundary conditions presents difficulties in obtaining the Fourier coefficients, thus suggesting the use of variational procedures, e.g., least squares, to obtain the coefficients in the separated variable solutions. It is shown that for certain types of mixed boundary conditions numerically useful solutions can be obtained employing a hand held, programmable calculator, whereas other mixed boundary conditions require more computing power, say that needed to invert a $16 \times 16$ matrix. Modern applications of mixed boundary value problems are given, e,g. , cryogenic thermal insulation, nuclear power generation, nondestructive testing, transportation of liquified natural gas, etc. This material is suitable for presentation in the first (applied) course in partial differential equations.
Multimodal Embodied Conversational Agents: A discussion of architectures, frameworks and modules for commercial applications

With the recent advancements in automated communication technology, many traditional businesses that rely on face-to-face communication have shifted to online portals. However, these online platforms often lack the personal touch essential for customer service. Research has shown that face-to- face communication is essential for building trust and empathy with customers. A multimodal embodied conversation agent (ECA) can fill this void in commercial applications. Such a platform provides tools to understand the user’s mental state by analyzing their verbal and non-verbal behaviour and allows a human-like avatar to take necessary action based on the context of the conversation and as per social norms. However, the literature to understand the impact of ECA agents on commercial applications is limited because of the issues related to platform and scalability. In our work, we discuss some existing work that tries to solve the issues related to scalability and infrastructure. We also provide an overview of the components required for developing ECAs and their deployment in various applications.
The probabilities of rooted tree-shapes generated by random bifurcation

The set of rooted trees, generated by random bifurcation at the terminal nodes, is considered with the aims of enumerating it and of determining its probability distribution. The account of enumeration collates much previous work and attempts a complete perspective of the problems and their solutions. Asymptotic and numerical results are given, and some unsolved problems are pointed out. The problem of ascertaining the probability distribution is solved by obtaining its governing recurrence equation, and numerical results are given. The difficult problem of determining the most probable tree-shape of given size is considered, and for labelled trees a conjecture at its solution is offered. For unlabelled shapes the problem remains open. These mathematical problems arise in attempting to reconstruct evolutionary trees by the statistical approach of Cavalli-Sforza and Edwards.
ZGSO Spinel Nanoparticles with Dual Emission of NIR Persistent Luminescence for Anti-Counterfeiting Applications

The property of persistent luminescence shows great potential for anti-counterfeiting technology and imaging by taking advantage of a background-free signal. Current anti-counterfeiting technologies face the challenge of low security and the inconvenience of being limited to visible light emission, as emitters in the NIR optical windows are required for such applications. Here, we report the preparation of a series of Zn1+xGa2−2xSnxO4 nanoparticles (ZGSO NPs) with persistent luminescence in the first and second near-infrared window to overcome these challenges. ZGSO NPs, doped with transition-metal (Cr3+ and/or Ni2+) and in some cases co-doped with rare-earth (Er3+) ions, were successfully prepared using an improved solid-state method with a subsequent milling process to reach sub-200 nm size particles. X-ray diffraction and absorption spectroscopy were used for the analysis of the structure and local crystal field around the dopant ions at different Sn4+/Ga3+ ratios. The size of the NPs was ~150 nm, measured by DLS. Doped ZGSO NPs exhibited intense photoluminescence in the range from red, NIR-I to NIR-II, and even NIR-III, under UV radiation, and showed persistent luminescence at 700 nm (NIR-I) and 1300 nm (NIR-II) after excitation removal. Hence, these NPs were evaluated for multi-level anti-counterfeiting technology.
On the genesis of quasi-steady vortices in a rotating turbulent flow

Turbulent flows subjected to rotation display vortices parallel to the rotation axis and exhibiting a long timescale compared to the turbulent turnover time and the rotation period. A similar flow pattern is observed arising from the thermal instability in a rotating fluid. We demonstrate the analogy between turbulence and thermal convection in a rotating fluid. A basic quasi-geostrophic turbulent flow is considered which is forced at the bottom of the layer by a stochastic component of velocity parallel to the rotation axis. The turbulent basic state has no mean flow and the gradient along the rotation axis of the turbulent kinetic energy −∂z〈ω2〉 is analogous to the mean temperature profile in thermal convection. The linear perturbation equations of this basic turbulent state are given, where the thermal diffusion equation is replaced by the turbulent kinetic energy equation. Using a simple closure of this equation the model demonstrates the occurrence of an instability when the Reynolds number exceeds a critical value. Marginal stability curves are deduced by numerical integration of the perturbation equations. The results show order-of-magnitude agreement with laboratory experiments.
Performance of Multi-Antenna Proactive Eavesdropping in 5G Uplink Systems

This paper studies the performance of multi-antenna proactive eavesdropping in 5G uplink systems with spatially correlated Rayleigh fadings, where the base station (BS) serves an illegal user and a multi-antenna legitimate monitor eavesdrops the suspicious link with the help of jamming attack. Based on a practical assumption of channel state information (CSI) in 5G uplink, i.e., imperfect instantaneous CSI of the suspicious link at the BS and that of eavesdropping link at the monitor, and only jamming channel statistics at the monitor, we first give a statistical jamming beamforming design. Then, semi-closed form expressions of eavesdropping non-outage probability and relative average eavesdropping rate are, respectively, derived for delay-sensitive and delay-tolerant scenarios. Via reasonable approximations and asymptotic analysis, we gain many insights on the effect of key system parameters, e.g., location-dependent channel path loss and angular spread, jamming energy and the number of antennas. Further, we provide the optimal energy allocation between pilot and data jamming and the optimal antenna allocation between jamming and eavesdropping under total energy constraint and total antenna number, respectively, which are both explicit functions of system parameters. Finally, simulation results validate our analytical results.
Activities against Lung Cancer of Biosynthesized Silver Nanoparticles: A Review

Nanomedicine is an interdisciplinary field where nanostructured objects are applied to treat or diagnose disease. Nanoparticles (NPs) are a special class of materials at nanometric scale that can be prepared from lipids, polymers, or noble metals through bottom-up approaches. Biological synthesis is a reliable, sustainable, and non-toxic bottom-up method that uses phytochemicals, microorganisms, and enzymes to induce the reduction of metal ions into NPs. Silver (Ag) NPs exhibit potent therapeutic properties that can be exploited to overcome the limitations of current treatment modalities for human health issues such as lung cancer (LC). Here, we review the preparation of AgNPs using biological synthesis and their application against LC using in vitro and in vivo models. An overview of the staging, diagnosis, genetic mutations, and treatment of LC, as well as its main subtypes, is presented. A summary of the reaction mechanisms of AgNPs using microbial cell cultures, plant extracts, phytochemicals, and amino acids is included. The use of capping agents in the biosynthesis of AgNPs with anticancer activity is also detailed. The history and biological activities of metal-based nanostructures synthesized with gold, copper, palladium, and platinum are considered. The possible anticancer mechanisms of AgNPs against LC models are covered. Our perspective about the future of AgNPs in LC treatment and nanomedicine is added.
Recombinant Peptide Production Softens Escherichia coli Cells and Increases Their Size during C-Limited Fed-Batch Cultivation

Stress-associated changes in the mechanical properties at the single-cell level of Escherichia coli (E. coli) cultures in bioreactors are still poorly investigated. In our study, we compared peptide-producing and non-producing BL21(DE3) cells in a fed-batch cultivation with tightly controlled process parameters. The cell growth, peptide content, and cell lysis were analysed, and changes in the mechanical properties were investigated using atomic force microscopy. Recombinant-tagged somatostatin-28 was expressed as soluble up to 197 ± 11 mg g−1. The length of both cultivated strains increased throughout the cultivation by up to 17.6%, with nearly constant diameters. The peptide-producing cells were significantly softer than the non-producers throughout the cultivation, and respective Young’s moduli decreased by up to 57% over time. A minimum Young’s modulus of 1.6 MPa was observed after 23 h of the fed-batch. Furthermore, an analysis of the viscoelastic properties revealed that peptide-producing BL21(DE3) appeared more fluid-like and softer than the non-producing reference. For the first time, we provide evidence that the physical properties (i.e., the mechanical properties) on the single-cell level are significantly influenced by the metabolic burden imposed by the recombinant peptide expression and C-limitation in bioreactors.
Echo state neural network based ensemble deep learning for short-term load forecasting

Precise electricity load forecasts assist in planning, maintaining, and developing power systems. However, the electricity load's un-stationary and non-linear characteristics impose substantial challenges in anticipating future demand. Recently, a deep echo state network (DESN) with multi-scale features has been proposed for sequential tasks. Inspired by its structure, this paper offers a novel ensemble deep learning algorithm, the ensemble deep ESN (edESN), for load forecasting. First, hierarchical reservoirs are stacked to enforce the deep representation similar to the DESN. Then, instead of computing the readout weights based on the global states, the edESN trains a different readout layer for each scale. Finally, the network combines the outputs from each scale as the final prediction. The edESN is evaluated on twenty publicly available load datasets. This paper compares the edESN with eleven forecasting methods, and the comparative results demonstrate the proposed model's superiority in load forecasting.
Deep Learning Based Impostor Detection By Invariant Features From Nir Finger Vien Imaging

Biometrics mostly used as personal identification accordingly securing a client against the unapproved utilization of his or her identity. Acquiring biometric information is getting to be simpler. Smartphones and other advanced technologies exist from which biometric data can be collected easily without the knowledge of others. Finger vein authentication is a method for biometric verification that depends on a vein pattern, which is located beneath the human finger's skin. Veins are covered with skin that cannot be copied by others. In this research, our focus is on the invariant features of finger veins. We have extracted invariant features from various state of the art deep learning techniques and then classified with multiclass SVM. For this purpose, we have used publicly available finger vein image databases. The performance has been evaluated by various evaluation metrics and comparative analysis of various deep learning approaches has been presented to describe the performance of these models on the said data set.