Miracle-dz/UniFAR-FTUs huggingface dataset:
X-ray Ptychographic Imaging and Spectroscopic Studies of Plasma-Treated Plastic Films

Polyethylene terephthalate (PET) is a thermoplastic polyester with numerous applications in industry. However, it requires surface modification on an industrial scale for printing and coating processes and plasma treatment is one of the most commonly used techniques to increase the hydrophilicity of the PET films. Systematic improvement of the surface modification by adaption of the plasma process can be aided by a comprehensive understanding of the surface morphology and chemistry. However, imaging large surface areas (tens of microns) with a resolution that allows understanding the surface quality and modification is challenging. As a proof-of-principle, plasma-treated PET films were used to demonstrate the capabilities of X-ray ptychography, currently under development at the soft X-ray free-electron laser FLASH at DESY, for imaging macroscopic samples. In combination with scanning electron microscopy (SEM), this new technique was used to study the effects of different plasma treatment processes on PET plastic films. The studies on the surface morphology were complemented by investigations of the surface chemistry using X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FT-IR). While both imaging techniques consistently showed an increase in roughness and change in morphology of the PET films after plasma treatment, X-ray ptychography can provide additional information on the three-dimensional morphology of the surface. At the same time, the chemical analysis shows an increase in the oxygen content and polarity of the surface without significant damage to the polymer, which is important for printing and coating processes.
Image Security Retrieval Based on Chaotic Algorithm and Deep Learning

At present, most image retrieval methods are based on plain-text images which poses a threat to some professional fields, such as medicine, military, and finance. In order to achieve greater security for the network transmission security of the image, we establish a deep artificial neural network model to extract features by sample training. Then an image-encryption algorithm that matches and secures image retrieval is designed and integrated into an image-retrieval process based on deep learning. Experiments on multiple authoritative datasets show that the proposed algorithm can not only achieve secure retrieval of ciphertext images, but also improve retrieval efficiency obviously. Specifically, the experimental results on five data sets show that compared with the average performance of 16 comparison algorithms, each evaluation indicator has been significantly improved in our research, with Pre increased by 12.54% - 88.20%, Rec increased by 1.46% - 10.95%, F1 increased by 2.86% - 13.55% and mAP increased by 16.64% - 82.47%. Futhermore, the successful realization of the ciphertext retrieval provides some reference for the information security retrieval research.
A 0.092-mm2 2–12-GHz Noise-Cancelling Low-Noise Amplifier With Gain Improvement and Noise Reduction

This brief proposes a broadband noise-cancelling (NC) common-gate (CG)-common-source (CS) low-noise amplifier (LNA) with a passive network to improve the gain and noise performance simultaneously. In the CG-CS LNA with active feedforward, a current-bleeding (CBLD) circuit is required to provide the required DC current of the auxiliary amplifier, which introduces additional noise. In the proposed work, by adding a passive network at the output, the noise of the CBLD circuit can be made negligible. Moreover, the passive network also provides the shunt peaking to facilitate the gain and noise performance over the desired band. Fabricated in TSMC 40 nm CMOS technology, the measured results show the proposed LNA exhibits a gain of 16.5 – 19.5 dB, a noise figure (NF) of 3.2 – 5.2 dB from 2 to 12 GHz and an IIP3 of −3.5 dBm at 6 GHz. It consumes 9 mW from a 1.2 V supply and occupies a very compact die area of 0.092 mm2.
An 8.9–71.3 TOPS/W Deep Learning Accelerator for Arbitrarily Quantized Neural Networks

This brief presents the first general-purpose deep learning accelerator for arbitrarily quantized neural networks. A pre-processing and corresponding multiply-and-accumulate operator are proposed to perform the vector products between arbitrarily quantized two vectors. As a result, the accelerator can support precision scalability of quantization up to 4b for one of input activation and weight, and up to 8b for the other, respectively. The proposed approach shows a reduction of <inline-formula> <tex-math notation="LaTeX">$0.57\times $ </tex-math></inline-formula> area and <inline-formula> <tex-math notation="LaTeX">$0.78\times $ </tex-math></inline-formula> power consumption, respectively, compared to those with the conventional 8b linear precision which is required to perform an equal level of task accuracy. The implemented accelerator in 28nm CMOS operates at an operating frequency range of 100-to-250MHz over a supply voltage range of 0.75-to-1.2V. Depending on configurations, it shows an energy efficiency of 8.9-to-71.3TOPS/W.
Effect of electropolishing on mechanical property enhancement of Ti6Al4V porous materials fabricated by selective laser melting

ABSTRACT Metal additive manufacturing technologies such as laser powder bed fusion (so-called selective laser melting) have been widely applied in the field of aerospace or medical device. Moreover, porous materials have been extensively studied due to the maturity of metal additive manufacturing technology. The stress shielding effects can be reduced and the bone integration between the human bones and medical devices can be improved by introducing porous materials. Thus, the medical device would be more stable after implantation. However, in our previous study, the mechanical properties of porous materials are not only affected by the porous structure and porosity but also affected by the surface roughness and the ratio between surface roughness and strut diameter. In this study, to investigate and explore such issues, two major topics will be researched, which are (1) the formulation development of the electrolyte with a surfactant to increase the surface finish effectiveness during the electropolishing process of 3D printed Ti-6Al-4V porous materials; (2) the exploration of the relationship between the mechanical properties and the surface roughness/other factors. Also, the effect of surface roughness on mechanical properties was evaluated in terms of work of fracture and specific energy absorption.
Simulation of Binodal and Spinodal Curves of Phase State Diagrams for Binary Polymer Systems

A new approach is proposed for simulating binodal and spinodal curves of phase diagrams for binary polymer systems. It is shown that the Flory–Huggins theory makes it possible to predict phase behavior in a wide range of temperatures and concentrations based on limited data on the components’ solubility. The approbation data of the technique are presented in the example of PS–PB and PS–PMMA systems, for which generalized phase diagrams are constructed.
Complete mitochondrial genome and phylogenetic analysis of Ixodes acutitarsus (Acari: Ixodidae)

Abstract Ixodes acutitarsus is regarded as the largest Ixodes tick around the world. I. acutitarsus is capable to transmit a wide range of animal and human pathogens. This research pioneered sequencing of the complete mitochondrial genome of I. acutitarsus. With a length of 14,475 bp, the complete mitochondrial genome encodes 13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), and one replication-initiating region. The phylogenetic relationship was established using the Maximum-likelihood method to indicate that I. acutitarsus and the others of the genus Ixodes fit into the same branch, which confirms the inclusion of I. acutitarsus in the genus Ixodes. The complete mitogenome of sequenced I. acutitarsus provides molecular evidence for the taxonomic status and phylogenetic position of several Ixodes species.
On Lightning-Induced Voltages in Overhead Lines Over High-Resistivity Ground

This paper presents an improved analytical expression for the calculation of the lightning horizontal electric field considering high values of ground resistivity, which is validated by comparing its results with published results obtained with other methods. The expression is used to calculate lightning-induced voltages in overhead power distribution lines and its results showed that conducted currents injected into the ground by the lightning flash should be considered in the assessment of power line outages, whenever the ground resistivity is higher than 1000 Ω·m. The results also show that, for very high values of ground resistivity, the line length and the ground permittivity affect the lightning-induced voltage peak value, whereas the line height, the return stroke velocity, and the stroke current front-time have negligible effects.
Infection Rate and Clinical Symptoms of Trichomoniasis among Women Referring to the Hospital in Mahshahr City in Khuzestan Province, Southwest of Iran

the 18s rRNA gene were finally sequenced. Findings: The prevalence rate of this parasite was determined to be 1.54%. The highest prevalence rate of infection and clinical symptoms were observed in women aged 31-40 years. Totally, clinical symptoms were observed in 64.70% of infected women, including vaginal itching and irritation (64.70%) and abnormal discharge (26.47%). Conclusion: The prevalence rate of Trichomonas infection was relatively low in women living in Mahshahr. In addition, about 35.29% of infected women were found to be clinically asymptomatic.
Stochastic simulation assessment of an automated vibration-based condition monitoring framework for wind turbine gearbox faults

Effectively monitoring the health of a wind turbine gearbox is a complex and often multidisciplinary endeavor. Recently, condition monitoring practices increasingly combine knowledge from fields like signal processing, machine learning, and mechanics. Such a diverse approach becomes necessary when dealing with the vast amount of data that is generated by the multitude of sensors that are typically placed on a wind turbine gearbox. Ideally, this approach needs to be automated and scalable as well, since it is unfeasible to perform all the necessary processing work manually in a continuous manner. This paper focuses on assessing the performance of such an automated processing framework for the case of gearbox fault detection using vibration measurements. A year of vibration measurements on a gearbox is simulated by stochastic variation of the operating conditions and the system behavior. A bearing fault is progressively introduced as to track the detection capabilities of the framework in such stochastic circumstances. The used signal model is based on previously obtained experience with experimental data sets originating from wind turbine gearboxes. The framework itself consists of multiple pre-processing steps where each step tries to deal with compensating for the external or unwanted influences such as speed variation or noise. Finally, multiple features are calculated on the pre-processed signals and trended as to see whether the processing scheme can provide any benefit compared to basic traditional statistical indicators. It is shown that the multi-step pre-processing approach is beneficial and robust for the advanced feature calculation and thus the early fault detection.
A multi points ultrasonic detection method for material flow of belt conveyor

For big detection error of single point ultrasonic ranging technology used in material flow detection of belt conveyor when coal distributes unevenly or is large, a material flow detection method of belt conveyor is designed based on multi points ultrasonic counter ranging technology. The method can calculate approximate sectional area of material by locating multi points on surfaces of material and belt, in order to get material flow according to running speed of belt conveyor. The test results show that the method has smaller detection error than single point ultrasonic ranging technology under the condition of big coal with uneven distribution.
Research on SDG Fault Diagnosis of Ocean Shipping Boiler System Based on Fuzzy Granular Computing Under Data Fusion

Abstract The research work in this paper belongs to the application of granular computing, graph theory and its application in fault detection and diagnosis. It is a cross cutting and frontier research field in computer science, information science and graph theory. The results of this paper are of great significance to the application of the fault detection and diagnosis of the ocean boilers system. This research combines granular computing theory and signed directed graph, and proposes a new method of fault diagnosis, and applies it to the fault diagnosis of ocean ship boiler system.
Securing Voice Communications Using Audio Steganography

Although authentication of users of digital voice-based systems has been addressed by much research and many commercially available products, there are very few that perform well in terms of both usability and security in the audio domain. In addition, the use of voice biometrics has been shown to have limitations and relatively poor performance when compared to other authentication methods. We propose using audio steganography as a method of placing authentication key material into sound, such that an authentication factor can be achieved within an audio channel to supplement other methods, thus providing a multi factor authentication opportunity that retains the usability associated with voice channels. In this research we outline the challenges and threats to audio and voice-based systems in the form of an original threat model focusing on audio and voice-based systems, we outline a novel architectural model that utilises audio steganography to mitigate the threats in various authentication scenarios and finally, we conduct experimentation into hiding authentication materials into an audible sound. The experimentation focused on creating and testing a new steganographic technique which is robust to noise, resilient to steganalysis and has sufficient capacity to hold cryptographic material such as a 2048 bit RSA key in a short audio music clip of just a few seconds achieving a signal to noise ratio of over 70 dB in some scenarios. The method developed was seen to be very robust using digital transmission which has applications beyond this research. With acoustic transmission, despite the progress demonstrated in this research some challenges remain to ensure the approach achieves its full potential in noisy real-world applications and therefore the future research direction required is outlined and discussed.
ST8SIA6-AS1 Promotes the Epithelial-to-Mesenchymal Transition and Angiogenesis of Pituitary Adenoma

To investigate the effect of long noncoding RNA ST8SIA6-AS1 on the epithelial-to-mesenchymal transition (EMT) and angiogenesis of pituitary adenoma and its possible mechanism. The expression levels of ST8SIA6-AS1 and HOXA9 in noninvasive pituitary adenoma and invasive pituitary adenoma were detected using qRT-PCR. sh-ST8SIA6-AS1 transfection silenced the expression of ST8SIA6-AS1 in GH3 and GTI-1 cells. The effects of ST8SIA6-AS1 on the proliferation, invasion, angiogenesis, and EMT of GH3 and GTI-1 pituitary adenoma cells were detected. The migration ability of cells was detected through scratch assay. Dual luciferase analysis verified the targeting relationship between ST8SIA6-AS1 and miR-5195-3p. ST8SIA6-AS1 and HOXA9 were highly expressed in invasive pituitary adenoma. In pituitary adenomas, miR-5195-3p directly targeted HOXA9. miR-5195-3p is the target gene of ST8SIA6-AS1. ST8SIA6-AS1 knockdown inhibited the proliferation, invasion, angiogenesis, and EMT of pituitary adenoma. HOXA9 expression mediates the biological effect of ST8SIA6-AS1. ST8SIA6-AS1 targets miR-5195-3p to regulate the expression of HOXA9 and promote the EMT of pituitary adenomas.
Rapid and Non-Destructive Analysis of Corky Off-Flavors in Natural Cork Stoppers by a Wireless and Portable Electronic Nose

This article discusses the use of a handheld electronic nose to obtain information on the presence of some aromatic defects in natural cork stoppers, such as haloanisoles, alkylmethoxypyrazines, and ketones. Typical concentrations of these compounds (from 5 to 120 ng in the cork samples) have been measured. Two electronic nose prototypes have been developed as an instrumentation system comprise of eight commercial gas sensors to perform two sets of experiments. In the first experiment, a quantitative approach was used whist in the second experiment a qualitative one was used. Machine learning algorithms such as k-nearest neighbors and artificial neural networks have been used in order to test the performance of the system to detect cork defects. The use of this system tries to improve the current aromatic defect detection process in the cork stopper industry, which is done by gas chromatography or human test panels. We found this electronic nose to have near 100 % accuracy in the detection of these defects.
Opinion Mining Summarization and Automation Process: A Survey

In this modern age, the internet is a powerful source of information. Roughly, one-third of the world population spends a significant amount of their time and money on surfing the internet. In every field of life, people are gaining vast information from it such as learning, amusement, communication, shopping, etc. For this purpose, users tend to exploit websites and provide their remarks or views on any product, service, event, etc. based on their experience that might be useful for other users. In this manner, a huge amount of feedback in the form of textual data is composed of those webs, and this data can be explored, evaluated and controlled for the decision-making process. Opinion Mining (OM) is a type of Natural Language Processing (NLP) and extraction of the theme or idea from the user's opinions in the form of positive, negative and neutral comments. Therefore, researchers try to present information in the form of a summary that would be useful for different users. Hence, the research community has generated automatic summaries from the 1950s until now, and these automation processes are divided into two categories, which is abstractive and extractive methods. This paper presents an overview of the useful methods in OM and explains the idea about OM regarding summarization and its automation process.
Review of Voltage and Frequency Grid Code Specifications for Electrical Energy Storage Applications

To ensure the stability and reliability of the power network operation, a number of Grid Codes have been used to specify the technical boundary requirements for different countries and areas. With the fast propagation of the usage of Electrical Energy Storage (EES), it is quite important to study how the EES technology with its development can help the Grid Code realization. The paper provides a comprehensive study of Great Britain (GB) Grid Code mainly on its voltage and frequency relevant specifications, with a comparison of other countries’ grid operation regulations. The different types of EES technologies with their technical characteristics in relation to meeting Grid Codes have been analysed. From the study, apart from direct grid-connection to provide grid services on meeting Grid Codes, EES devices with different technologies can be used as auxiliary units in fossil-fuelled power plants and renewable generation to support the whole systems’ operation. The paper also evaluates the potentials of different types of EES technologies for implementing the relevant applications based on the Grid Codes. Some recommendations are given at the end, for the EES technology development to help the Grid Code realization and to support the relevant applications.
Performance Evaluation of 198 Village Governments using Fuzzy TOPSIS and Intuitionistic Fuzzy TOPSIS

Currently, volatility, uncertainty, complexity, and ambiguity (VUCA) have become unavoidable problems. In addition, knowledge or information that is not managed properly can result in inappropriate decision-making processes within an organization. Business Intelligence conception is then becoming an essential view for converting unstructured data and information into a more actionable strategic plan that allows organizations to make competitive decisions. Village Government (VG) is the smallest organization in the Indonesian government system because VG implemented regulation and development programs in all areas of a national government. VG executes a series of tasks every year starting from planning, budgeting, administrating, executing, and reporting. However, the important role of VG in the development of a country brings also some drawbacks such as corruption and other domino effects. Several factors have been identified that cause those problems such as lack of capabilities in managing village organization and human resources quality. Monitoring and evaluation regarding those VG performances normally have been done each year. However, measurable evaluation standard for VG performance until recently has not been determined nationally. This study is intended to make a comprehensive standard of village government performance assessment through a Good Governance Framework approach. This study involved 198 villages from Madiun Regency as a case study. Seventy-four measured parameters were proposed to evaluate VG performance mapping. Fuzzy TOPSIS is implemented to rank those 198 villages into 4 groups of VG performance levels. The fuzzy TOPSIS classification result has been validated by using manual scoring and the accuracy reached 86,4%.
A Lean Systems Engineering Approach for the Development of Safety-critical Avionic Systems

The strong cost pressure of the market and rigorous safety regulations affect the development of avionic systems. Safety standards like SAE ARP4754A and RTCA DO-178C require high efforts for assuring compliance with applicable airworthiness requirements. Hence, industry is forced to continuously optimize their lifecycle processes and tool environments to facilitate the development of safety-critical systems. In this paper, we report on our experience of adopting lean enablers to systems engineering. The approach covers requirements quality analysis, model-based systems engineering, model-based testing, product family engineering and safety analysis. The experiences are gained from an industrial case study in the aerospace domain.
A unified finite volume framework for phase‐field simulations of an arbitrary number of fluid phases

While the phase-field methodology is widely adopted for simulating two-phase flows, the simulation of an arbitrary number ( N ≥ 2) of fluid phases at physical fidelity is non-trivial and requires special attention concerning mathematical modelling, numerical discretization, and solution algorithm. We present our most recent work with a focus on validation for multiple immiscible, incompressible, and isothermal phases, enhancing further our library for diffuse interface phase-field interface capturing methods in OpenFOAM (FOAM-extend 4.0/4.1). The phase-field method is an energetic variational formulation based on the work of Cahn and Hilliard where the interface is composed of a physical diffuse layer resembling realistic interfaces. The evolution of the phases is then governed by the minimization of the free energy of the system. The accuracy of the method is demonstrated for a number of test problems, including a floating liquid lens, bubble rise in two stratified layers, and drop impact onto thin