Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Micro/Nano Fluidics for Mass-spectrometry Protein Analysis

Over the past decade there has been increasing interest in the development of miniaturized chemical analysis systems using MEMS technology to handle a small amount of liquid from mL down to nL, or even pL. However, due to the diversified process of chemical/biochemical analysis and a wide range of samples involved, a self-contained mass-produced lab-on-a-chip with the same economy of scale as electrical components has yet to be realized. Here, we report an effort to produce such a chip that can couple to a mass spectrometer for biomolecule analysis.
Penyuluhan Dasar Memulai Bisnis Rintisan dari Aspek Sumber Daya Manusia untuk Komunitas Silver Interfor

Tujuan dilaksanakannya pengabdian kepada masyarakat ini adalah untuk memberikan pelatihan singkat tentang pengenalan bisnis start-up, ditinjau dari dua hal yaitu servicescape dan manajemen sumber daya manusia. Pendekatan pelaksanaan pengabdian kepada masyarakat adalah melalui metode implisit, dimana diharapkan peserta pengabdian masyarakat mendapatkan gambaran tentang start-up usaha. Silver Interfor Community merupakan salah satu peserta pengabdian masyarakat yang didirikan di Universitas Bina Nusantara ini bertujuan sebagai wadah berkumpulnya komunitas yang ingin menimba ilmu praktis. Hasil dari pengabdian masyarakat ini adalah transfer ilmu dari pemberi layanan kepada peserta, dalam hal ini masyarakat.
Gain spectrum of the Ho-doped fiber amplifier

We demonstrate the efficient amplification of small signals in a spectral range of 2020–2120 nm by an all-fiber Ho-doped amplifier pumped at 1125 nm. A maximum gain of 35.8 dB was achieved at a wavelength of 2050 nm. A gain greater than 30 dB was obtained over the whole range.
Instability of the Distorted Polar Night Vortex: A Theory of Stratospheric Warmings

Abstract The properties and stability of forced planetary waves in a continuous baroclinc shear are investigated gated using a weakly nonlinear analysis. A steady solution exists which under certain conditions may be unstable via interaction with a free traveling wave of the same wavenumber. This wave then grows at the expense of the zonal mean available potential energy. The parameter range in which instability occurs is extended by finite-amplitude perturbations. Theoretical predictions are compared with results from a highly truncated model of the winter atmosphere. Agreement is good, even after assumptions of the theory have broken down. The theory does become inapplicable, however, following a mean wind reversal (stratosphere warming); in an cases studied, such warming were found to occur subsequent to the instability. The theory appears to explain many features of observed and model warmings. Results of the study suggest that warmings arise from the intrinsic instability of the winter atmosphere. Th.
Serial Weakening of Human-Based Attributes Regarding Their Effect on Content-Based Speech Recognition

Numerous studies have investigated automatic speech recognition tasks, such as content-based speech recognition, using machine learning techniques, such as deep learning. In general, each speech sample contains four main human-based attributes: i.e., content, emotion, gender, and speaker identity. Among them, the content has the lowest correlation with the other three attributes. However, to classify speech samples concerning each attribute, the model ignores the existence of unrelated attributes. This study shows that information on these non-content attributes is not always useful and can cause a content-based speech classifier to significantly underperform. Moreover, weakening the effects of one, two, or three attributes is possible, and weakening these attributes in a specific order is crucial. For this purpose, two-input, two-output autoencoders are proposed as a feature extraction method. These networks are specifically designed to reduce the level of information (in this case, one, two, or three attributes). The level of change in the performance of classifiers caused by using these pre-trained autoencoders helps rank the negative effect of selected human-based attributes. Based on the results obtained, gender has the most negative effect on the performance of content-based speech recognition models, and serial weakening gives the best results when considering the attributes in the following order: gender, speaker identity, and emotion.
Flow and heat transfer in pipe caused by localised cold spot

Heat losses caused by cold spots and natural convection flow is important in many industrial applications. In this paper we present both experimental work and modelling work. Experimentally, the cool-down in an instrumented pipe is investigated for different pipe inclinations. The experimental temperature field is discussed and interpreted by means of Computational Fluid Dynamics (CFD). For prediction of the evolvement of the local temperature and heat flow, selection of an appropriate turbulence model is critical. The predicted temperature profiles and heat extraction rates are compared with the experiments for selected turbulence models. Finally, industrial implications of the findings are discussed.
F-model-type phase transition in the 2D Flory model of polymer melting

The author performs a transfer matrix study of the F model and the Flory model of polymer melting. For the F model, which can be reformulated in terms of polymers, he calculates numerically the exponent eta (T) of the massless phase and the results are in good agreement with recent conjectures. For the Flory model the author finds a very similar behaviour suggesting that these two models have the same kind of transition. The discrepancy with Monte Carlo calculations where a first-order phase transition has been found is discussed.
Spinocerebellar Ataxia Type 35 Caused by a New TGM6 Variant: Video Documentation of a German Family

The Spinocerebellar ataxias represent a heterogeneous group of autosomal dominant transmitted neurodegenerative disorders. The number of causative genes is growing and up to now, more than 40 different SCAs have been described. 1 Variants in the transglutaminase six gene ( TGM6 ) — now known to cause ATX-TGM6 (SCA35) — have been ﬁ rst described in 2010, 2 but the clinical phenomenology has been only sparsely documented. A late onset slowly progressive cerebellar ataxia with gait problems, dysarthria, hand tremor, and mild abnormal eye movements has been reported to present the most common phenotype. 2,3 Less frequently, dystonia, myoclonus, and parkinsonism have been described. An episodic ataxia-like phenotype has also been reported. 4
Three New Antioxidative Phenolics From Phyllanthus emblica L. Fruit

Objective: The present study aimed to investigate the potential bioactive compounds from Phyllanthus emblica L. fruit. Methods: Acetone extracts (60%) of P emblica fruit were dissolved in water and fractionated sequentially with light petroleum and ethyl acetate (EtOAc). The water fraction was separated using Sephadex LH-20 and the isolated compounds were purified by preparative high-performance liquid chromatography. The structures of the isolated compounds were determined through HR-ESI-MS and spectroscopic methods, including 1D- and 2D-NMR, and the antioxidant abilities of the isolates were evaluated by 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and ferric reducing antioxidant power (FRAP) assays. Results: Three new phenolic compounds, mucic acid 1-ethyl ester 3-O-gallate (1), mucic acid 6-methyl ester 3-O-gallate (2), and mucic acid 6-ethyl ester 2-O-gallate (3), together with 4 known compounds (4-7), were isolated from P emblica fruit. All phenolics exhibited potent antioxidant abilities (DPPH: IC50 7.5-13.2 µM; ABTS: 1.12-2.84 µM Trolox/µM; FRAP: 1.11-2.26 µM Fe2+/µM). Conclusions: Our research contributes to the growing body of evidence demonstrating the benefits of P emblica for health and supports the functional food and nutraceutical applications of the fruit.
Optimization of STAR-RIS-Assisted Hybrid NOMA mmWave Communication

Simultaneously reflecting and transmitting reconfigurable intelligent surfaces (STAR-RIS) has recently emerged out as prominent technology that exploits the transmissive property of RIS to mitigate the half-space coverage limitation of conventional RIS operating on millimeter-wave (mmWave). In this paper, we study a downlink STAR-RIS-based multi-user multiple-input single-output (MU-MISO) mmWave hybrid non-orthogonal multiple access (H-NOMA) wireless network, where a sum-rate maximization problem has been formulated. The design of active and passive beamforming vectors, time and power allocation for H-NOMA is a highly coupled non-convex problem. To handle the problem, we propose an optimization framework based on alternating optimization (AO) that iteratively solves active and passive beamforming sub-problems. Channel correlations and channel strength-based techniques have been proposed for a specific case of two-user optimal clustering and decoding order assignment, respectively, for which analytical solutions to joint power and time allocation for H-NOMA have also been derived. Simulation results show that: 1) the proposed framework leveraging H-NOMA outperforms conventional OMA and NOMA to maximize the achievable sum-rate; 2) using the proposed framework, the supported number of clusters for the given design constraints can be increased considerably; 3) through STAR-RIS, the number of elements can be significantly reduced as compared to conventional RIS to ensure a similar quality-of-service (QoS).
Time optimal trajectory planning for a robot system under torque and impulse constraints

Moving a fragile object from an initial point to a goal location in minimum time without damage is pursued in this paper. In order to achieve the goal, first of all, the range of maximum acceleration and velocity are specified, which the manipulator can generate dynamically on the path that is planned a priori considering the geometrical constraints. Later, considering the impulsive force constraint of the object, the range of maximum acceleration and velocity are going to be obtained to keep the object safe while the manipulator is earning it along the curved path. Finally, a time-optimal trajectory is planned within the maximum allowable range of the acceleration and velocity. This time optimal trajectory planning can be applied for real applications and is suitable for not only a continuous path but also a discrete path.
Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter

Hydrological models are widely used for flood forecasting, and proper initialization of hydrological models is essential. Although the unscented Kalman filter (UKF) has shown promise in the context of operational forecasting, it is limited by its intrinsic instability caused by the loss of positive semidefiniteness of the covariance matrix. Effective methods for tackling this problem have not been seen in the literature. The cubature Kalman filter (CKF) is a powerful nonlinear filter for state estimation, which has received little attention in the hydrologic context. In this paper, to overcome the instability problem of the UKF, a new modified UKF (UKF‐M) is proposed by modifying the default UKF tuning strategy, and a novel nonlinear filter (CKF) is introduced to update the states of hydrological models. The methods are tested using a lumped hydrological model. The results of the stability experiment suggest that both the UKF‐M and CKF can overcome the instability problem, while the original UKF can fail due to the loss of positive semidefiniteness of the covariance matrix. The results of a reforecast experiment in an operational context show that both filters can improve the forecast performance by updating the model state based on the observed discharge, with the UKF‐M achieving better forecast performances. The UKF‐M and CKF can be used to overcome the instability problem of the original UKF and are valuable methods for improving forecast performances of hydrological models by state updating.
Prevention of Gallstones After Bariatric Surgery using Ursodeoxycholic Acid: A Narrative Review of Literatures

Obesity by itself is a factor in the development of gallstone disease, and periods of weight loss after bariatric surgery further increase the risk of gallstone formation. In patients with obesity, hypersecretion of cholesterol may increase the risk of gallstone formation, which is approximately five-fold higher than that in the general population. The incidence of gallstone formation after bariatric surgery is 10–38% and often associated with a proportional increase in the risk of developing biliary complications. Routine postoperative administration of ursodeoxycholic acid (UDCA) is recommended to prevent gallstone formation. Several randomized trials have indicated that UDCA can effectively prevent gallstones and reduce the risk of cholecystectomy after bariatric procedures. The effective daily dose of UDCA in each study ranged from 500 to 1,200 mg, and it may be considered at least during the period of rapid weight loss (first 3–6 months postoperatively) to decrease the incidence of symptomatic gallstones.
Defect levels of semi-insulating CdMnTe:In crystals

Using photoluminescence (PL) and current deep-level transient spectroscopy (I-DLTS), we investigated the electronic defects of indium-doped detector-grade CdMnTe:In (CMT:In) crystals grown by the vertical Bridgman method. We similarly analyzed CdZnTe:In (CZT:In) and undoped CdMnTe (CMT) crystals grown under the amount of same level of excess Te and/or indium doping level to detail the fundamental properties of the electronic defect structure more readily. Extended defects, existing in all the samples, were revealed by synchrotron white beam x-ray diffraction topography and scanning electron microscopy. The electronic structure of CMT is very similar to that of CZT, with shallow traps, A-centers, Cd vacancies, deep levels, and Te antisites. The 1.1-eV deep level, revealed by PL in earlier studies of CZT and CdTe, were attributed to dislocation-induced defects. In our I-DLTS measurements, the 1.1-eV traps showed different activation energies with applied bias voltage and an exponential dependence on the trap-filling time, which are typical characteristics of dislocation-induced defects. We propose a new defect-trap model for indium-doped CMT crystals.
Changing Rhizosphere Microbial Community and Metabolites with Developmental Stages of Coleus barbatus

Coleus barbatus is a medicinal herb belonging to Lamiaceae. It is the only living organism known to produce forskolin, which is a labdane diterpene and is reported to activate adenylate cyclase. Microbes associated with plants play an important role in maintaining plant health. Recently, the targeted application of beneficial plant-associated microbes and their combinations in abiotic and biotic stress tolerance has gained momentum. In this work, we carried out the rhizosphere metagenome sequencing of C. barbatus at different developmental stages to understand how rhizosphere microflora are affected by and affect the metabolite content in plants. We found that the Kaistobacter genus was abundantly present in the rhizosphere of C. barbatus and its accumulation pattern appears to correlate with the quantities of forskolin in the roots at different developmental stages. Members of the Phoma genus, known for several pathogenic species, were in lower numbers in the C. barbatus rhizosphere in comparison with C. blumei. To our knowledge, this is the first metagenomic study of the rhizospheric microbiome of C. barbatus, which may help to explore and exploit the culturable and non-culturable microbial diversity present in the rhizosphere.
Transformation and Classification of Ordinal Survey Data

Currently, Machine Learning is being significantly used in almost all of the research domains. However, its applicability in survey research is still in its infancy. We in this paper, attempt to highlight the applicability of Machine Learning in survey research while working on two different aspects in parallel. First, we introduce a pattern-based transformation method for ordinal survey data. Our purpose behind developing such a transformation method is twofold. Our transformation facilitates easy interpretation of ordinal survey data and provides convenience while applying standard Machine Learning approaches. Second, we demonstrate the application of various classification techniques over real and transformed ordinal survey data and interpret their results in terms of their suitability in survey research. Our experimental results suggest that Machine Learning coupled with the Pattern Recognition paradigm has a tremendous scope in survey research.
Average Gradient-Based Adversarial Attack

Deep neural networks (DNNs) are vulnerable to adversarial attacks which can fool the classifiers by adding small perturbations to the original example. The added perturbations in most existing attacks are mainly determined by the gradient of the loss function with respect to the current example. In this paper, a new average gradient-based adversarial attack is proposed. In our proposed method, via utilizing the gradient of each iteration in the past, a dynamic set of adversarial examples is constructed first in each iteration. Then, according to the gradient of the loss function with respect to all the examples in the constructed dynamic set and the current adversarial example, the average gradient can be calculated, which is used to determine the added perturbations. Different from the existing adversarial attacks, the proposed average gradient-based attack optimizes the added perturbations through a dynamic set of adversarial examples, where the size of the dynamic set increases with the number of iterations. Our proposed method possesses good extensibility and can be integrated into most existing gradient-based attacks. Extensive experiments demonstrate that, compared with the state-of-the-art gradient-based adversarial attacks, the proposed attack can achieve higher attack success rates and exhibit better transferability, which is helpful to evaluate the robustness of the network and the effectiveness of the defense method.
Diseño y ficción: el diseño especulativo en la creación de futuros posibles

El mundo en el que vivimos experimenta cambios que cada vez son más rápidos, nos encontramos en una era en la que problemáticas sociales, ambientales, políticas, económicas son cada vez más críticas. Sin embargo, al mismo tiempo, un maravilloso e interesante mundo está tomando forma a nuestro alrededor.
Codelivery of Phytochemicals with Conventional Anticancer Drugs in Form of Nanocarriers

Anticancer drugs in monotherapy are ineffective to treat various kinds of cancer due to the heterogeneous nature of cancer. Moreover, available anticancer drugs possessed various hurdles, such as drug resistance, insensitivity of cancer cells to drugs, adverse effects and patient inconveniences. Hence, plant-based phytochemicals could be a better substitute for conventional chemotherapy for treatment of cancer due to various properties: lesser adverse effects, action via multiple pathways, economical, etc. Various preclinical studies have demonstrated that a combination of phytochemicals with conventional anticancer drugs is more efficacious than phytochemicals individually to treat cancer because plant-derived compounds have lower anticancer efficacy than conventional anticancer drugs. Moreover, phytochemicals suffer from poor aqueous solubility and reduced bioavailability, which must be resolved for efficacious treatment of cancer. Therefore, nanotechnology-based novel carriers are employed for codelivery of phytochemicals and conventional anticancer drugs for better treatment of cancer. These novel carriers include nanoemulsion, nanosuspension, nanostructured lipid carriers, solid lipid nanoparticles, polymeric nanoparticles, polymeric micelles, dendrimers, metallic nanoparticles, carbon nanotubes that provide various benefits of improved solubility, reduced adverse effects, higher efficacy, reduced dose, improved dosing frequency, reduced drug resistance, improved bioavailability and higher patient compliance. This review summarizes various phytochemicals employed in treatment of cancer, combination therapy of phytochemicals with anticancer drugs and various nanotechnology-based carriers to deliver the combination therapy in treatment of cancer.
Financial Well-Being and Financial Capability among Low-Income Entrepreneurs

Financial well-being is a key component of quality of life and overall well-being and is likely to affect other aspects of quality of life, such as health and health care. The COVID-19 pandemic presents an immense crisis of financial well-being among low-income entrepreneurs and has left many small-scale entrepreneurs financially fragile. We argue that promoting the financial capability of low-income entrepreneurs is effective in protecting their financial well-being from a crisis. To examine the association between financial capability and the financial well-being of low-income entrepreneurs, we use the 2016 National Financial Well-Being Survey, which provides the latest and comprehensive measurement of financial capability, including financial knowledge, financial skills, and access to financial products and services. Our analyses show that, compared to their higher-income counterparts, low-income entrepreneurs have statistically lower levels of financial well-being, financial knowledge, financial skills, and access to mainstream financial products; they also have a statistically higher risk of using high-fee alternative financial products. In addition, low-income entrepreneurs have larger barriers to accessing mainstream financial products than low-income non-entrepreneurs. The results indicate that financial capability plays a significant role in promoting the financial well-being of low-income entrepreneurs.
Confidence intervals for probability density functions under strong mixing samples

It is shown that the empirical likelihood (EL) ratio statistic for a probability density function (p.d.f.) is asymptotically -type distributed under a strong mixing sample, which is used to obtain an EL-based confidence interval (CI) for the p.d. f. Results of a simulation study on the finite sample performance of the CI are reported.
Thermal activation from the fluxoid and the voltage states of dc SQUIDs

The probability density of thermal fluctuations about different types of nonequilibrium steady states of a dc SQUID are evaluated by generalizing a technique used before for the fluctuations of a single Josephson junction. Probability densities obtained for both ‘‘running’’ and ‘‘beating’’ modes are used to calculate thermal activation rates as well as the various branches of the I‐V characteristic. The results are compared with the experiments of Voss et al. and good agreement is found.
A Novel Robust Digital Subarray Partition Algorithm With Binary Coding

In large-scale phased-array radar signal processing, the digital subarray partition technique would reduce the system size and computational complexity effectively. However, increasing intersubarray spacing will lead to the grating lobes, which degrades the beamforming performance. In this communication, by merging the concepts of encoding technology and subarray division, a novel digital subarray structure based on binary coding is proposed to suppress the grating lobes and lower the sidelobe level. The core of the algorithm is optimally accommodating the binary coding so that the array is divided into multiple layers of subarrays, including uniform and interleaved structures with various intersubarray spacing. Thus, the diversity of subarray phase centers is guaranteed, and the periodicity of the phase centers is broken. Simulation results are given to demonstrate the superiorities of the proposed algorithm in beamforming and efficiency performance.