Miracle-dz/UniFAR-FTUs huggingface dataset:
Failure Characterization and Analysis of Sub-module of Press-Packed Devices in High Temperature Reverse Bias Test

The failure of sub-module of press-packed devices in course of high temperature reverse bias (HTRB) test was investigated in this paper. A high temperature reverse bias test platform for press-packed devices was built, which was capable of monitoring and collecting continuous leak current data of devices during the test. Through the analysis of the static characteristics of the chip before and after the high temperature reverse bias test, a method that uses the combination of IV and CV characteristic curves to characterize high temperature reverse bias failure was proposed. By means of finite element analysis to locate the reasons for the change of static characteristics, the accumulated charges around the terminal of chip were considered to be the primary source for the failure of sub-module under the HTRB test.
Suggested Video Steganography Algorithm Based on Power Low Transform (PLT) Using IoTs

Datum embedding or information hiding is a method that accepted us to secret embedded and extracted data from any file, such as picture file, a video file, and sound file. The base algorithm in steganography is the embedded and extraction algorithms. The objective of this paper is hiding a secret message in video frame to prevent an attacker from accessing the message. This paper suggested new algorithm to hide secret message in video, after convert video to number of frames, it takes each video frame applied the power low transform (PLT) in 3 or 3.5 or 4. In this paper takes power low transform only 3.5 to hide secret message in each video frame uses the secret key, the secret key is curve of cosh-exponential, each point in curve selected the location in frame to hide one bit from frame video. It obtains stego-frame video to Transmitted the for IoTs, and save in database in IoTs, and any authors can be access to this stego-frame video in database to known secret key. The outcome of this algorithm is very good in steganography video, it uses power low transform to obtain the high security, transparency, high capacity in secret message and powerful. These results are obtaining many tests PSNR, MSE, Entropy, Correlation coefficient, Histogram, and Capacity.
MIMO to Enhance the Performance of VLC Systems

In general, visible light communication (VLC) systems run at low data rates. By introducing multiple input and multiple output (MIMO) techniques, service at enhanced data rates can be viable for VLC systems. This work proposes MIMO technique to attain VLC system with high data rates. To attain MIMO method this system includes a number of photodiodes with a narrow field of view (FOV), which highlights the benefit of using MIMO technology in improving the data rate. The results show that using MIMO technique leads to enhance the VLC system when it is compared with the traditional system (without using MIMO). The results show that an improvement in the signal-to-noise ratio (SNR) level, which is the most attempt to determine communication quality, increasing the channel bandwidth and reducing the delay spread are achieved when MIMO technique is used.
Internet of Things enabled Parking System In Smart Cities

Internet of Things (IOT) performs a important function in connecting the encompassing environmental matters to the community and made clean to get entry to the ones un-net matters from any far off location. It’s inevitable for the humans to replace with the developing technology. And typically human beings are dealing with issues on parking automobiles in parking slots in a city. In this have a look at we layout a Smart Parking System (SPS) which permits the person to discover the closest parking vicinity and offers availability of parking slots in that respective parking place. And it specially awareness on lowering the time in locating the parking masses and additionally it avoids the needless journeying thru stuffed parking plenty in a parking place. Thus it reduces the gas intake which in flip reduces carbon footprints in an atmosphere.
Incorporating LSTM Method on Modified Deep Learning Technique for Sensor Based Human Activity Recognition System

In today’s scenario, mobile phones or smartphones are rapidly becoming the essential communication device in people’s lives. Human Activity Recognition has become the popular field of research in different fields such as medical care, monitoring and education. The sensors in smartphones enable us to use them for diverse applications. Healthcare is a major domain where human activity recognition is used. In this paper, a human activity recognition system has been developed that can detect six activities of daily living (ADL) along with human fall. Human fall occurs due to an accident that can cause serious injuries which may lead to major medical issues when the issue is not addressed properly. The proposed system uses a variant of deep learning technique to detect human activities and human fall. The accuracy is significantly increased by nearly 4% when compared with previous results.
NIR molecule induced self-assembled nanoparticles for synergistic in vivo chemo-photothermal therapy of bladder cancer

Bladder cancer (BC) is one of the commonest malignancies in the urinary system. Bladder cancer is divided into non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC) according to the depth of invasion. Besides, the prognosis of MIBC remains poor. Surgical resection combined with radiotherapy or chemotherapy is the standard treatment for MIBC. However, the major obstacle that hinders successful chemotherapy is its lack of tumor targeting. Here, we fabricated nanoparticles that respond to near-infrared laser irradiation in order to increase the drug accumulation at the tumor sites and combine chemotherapy with photothermal therapy to overcome challenges of bladder cancer treatment. IR780 and Doxorubicin (DOX) were loaded into albumin nanoparticles (IR780-DOX@Albumin NPs). In the process of IR780-DOX@Albumin NPs synthesis, the near-infrared molecule IR780 was used as the assembly molecular bridge. Under irradiation, the nanoparticles were decomposed due to the degradation of IR780 while the release of DOX increased. Nanoparticles can be ingested by tumor cells in a short time. The IR780-DOX@Albumin NPs were sensitive to near-infrared laser irradiation. Near-infrared laser irradiation can promote the release of the drugs from the nanoparticles and induce a photothermal effect, thus destroying the tumor cells. Given the excellent tumor-targeting ability and negligible toxicity to normal tissue, IR780-DOX@Albumin NPs can greatly increase the concentration of chemotherapeutic drugs in tumor cells. This study combines photothermal therapy and chemotherapy to treat MIBC, so as to avoid chemotherapy resistance, reduce the toxicity to normal cells, and achieve the purpose of improving the treatment of MIBC.
An acoustic exploration of event construals in Bengali language

The world is full of events, and events are construed. One of the major research questions, therefore, seeks to understand the way events are construed through language. Event construals involve the syntacto-semantic properties of certain linguistic categories such as Verb, Tense, Aspect, Modality, etc. Serial Verb Constructions (SVC) and Complex Predicate Constructions (CPC) are no exception to this very fact. In this work, we look forward to compare both linear and non linear acoustical features generated from SVC and CPC events found in sentences of Bengali language. For this, we recorded 60 common utterances of Bengali language containing SVC and CPC events individually (around 36 of them belong to SV category while the rest belong to CP category), from 1 male and 1 female native Bengali speakers. The serial verb construction in these utterances may contain two (2) serial verbs in either simultaneous or sequential order, while few sentences are such that they can be interpreted both as SV or CP events. The main objective is to look for robust acoustic features which lead to perceptual categorization of events as SV or CP with a particular linguistic background. Various linear features like MFCC (Mel frequency Cepstral Coefficients), spectral skewness/energy, pause duration, pitch profile and nonlinear features like Fractal Dimension (FD) have been employed for the classification purpose. This work is a pilot study of an ongoing project which looks to explore the concept integrating capacity of human brain in terms of Syntactic Compositionality or Semantic Combinatorics in a complex sentence. This preliminary acoustic study reveals interesting new results in terms of perceptual linguistic representation of the event construals.
Students Performance Prediction by Mining Behavioural Pattern

Analysing and monitoring students’ progress and performance is an active research area in educational data mining. Some research work uses direct construct relating to academic achievements such as GPA, SAT in predicting performance while others make use of psychometric construct in measuring skill and knowledge, ability and educational achievement from data gotten from questionnaires. In this paper, we propose the use of psychometric construct as a method of extracting non-cognitive features from campus check-ins data in predicting academic performance of college students. A P-FRAME framework was developed and non-cognitive attributes extracted through psychological theories were used as the predicting variables. The extracted variable was applied to various penalized regression algorithm and the results were compared. The result of our experiments showed a high correlation between the actual score and the predicted score which is within 2:0 of reported score.
Comparison of methods for automatic classification of Russian-language texts

In this paper, a comparative analysis of methods for automatic classification of text information using various machine learning algorithms based on artificial neural networks is carried out. The paper presents results of the text classification considering various ways of a vector space formation, such as bag-of-words, n-gram, word embedding, and various architectures of neural networks. The research is based on the text corpus in Russian.
Parameter estimation for high dimensional classification model on colon cancer microarray dataset

In classification problems, logistic regression is among powerful techniques for discrimination. It provides directive probabilities of sample classification and interpretable coefficients. When it comes to model high dimensional dataset, however, logistic regression with its Newton-Raphson method of parameter estimation is no longer applicable, especially on low sample size and extremely high dimension. By applying cross-entropy algorithm on regularized logistic regression, it was able to well performing parameter estimation and highly accurate classification result.
Refractive index sensing with hollow metal–insulator–metal metasurfaces

Refractive index sensing with metal–insulator–metal (MIM) metasurfaces featuring a continuous dielectric film between two metallic layers suffers from a low spatial overlap between high field enhancement regions and the analyte placed above. Recent studies have thus turned toward hollow MIM metasurfaces, particularly suited for fluid analytes which can infiltrate the hollow cavities. Here we describe a general procedure for reaching the optimal design in three most relevant configurations: mushroom-type structures with narrow dielectric pedestals carrying the top metallic ribbon array, hollow structures with the metallic ribbon array resting on a distant lateral support, and hollow structures in which the metallic ribbons are carried by an encapsulating layer from top. We contend that since a majority of the resonant eigenmode energy is contained within the analyte, very high refractive index sensitivities are possible for three different measurement methods: spectral, reflectance and phase interrogation. This is confirmed by numerical simulations demonstrating terahertz spectral sensitivities of above 700 GHz RIU−1 with a normalized sensitivity of around 0.6 RIU−1 (RIU stands for refractive index unit). Detection limits and dynamic ranges are estimated for both bulk refractive index sensing and thin film detection. Refractive index sensitivities and corresponding figure-of-merit factors are shown to reach maxima in the critical coupling regime characterized by equal radiative and non-radiative decay rates of the resonant mode which is controlled by cavity height. Since this regime is associated with a zero reflectance which prevents measurements of any signal, metasurfaces should operate close to the critical coupling point where reflected beam is still measurable. The final optimization is done by decay rate engineering in order to achieve narrower resonances and improve sensing performance.
Pattern transferring of Prolift-100 polymer sacrificial layers with controlled sidewall profile

While wet pattern transferring of polymers is faster than dry processing, it can lead to excessive undercut that will erode and distort features in patterned sacrificial regions during micro electro mechanical systems (MEMSs) fabrication. Dry etching processes based on O2, CF4 and N2 chemistry reduce undercutting of features to around 2 µm, but the resulting sidewall profiles of the patterned polymer islands are generally unsuitable for subsequent conformal deposition of thin films for surface micromachined MEMS fabrication. This paper presents a dry etching process for Prolift-100-16 polymer sacrificial layers, that employs a combination of a hard mask and a subsidiary sacrificial layer, that overcomes the limitations of undercut control and conformal deposition of physical vapour deposition techniques. The dry etching process is optimized to reduce the feature undercut to as low as 1 µm, while also producing sidewall profiles suitable for subsequent conformal deposition of thin films.
Effects of Mesoscopic Structure on Crack Resistance of Asphalt Mastic and Calibration of Contact Parameters

This paper investigated the effects of mesoscopic structure on crack resistance of asphalt mastic basing on the bending failure simulation with discrete element method. A Dynamic Link Library wrote by C++ is proposed for the modelling process of particle filling. Response surface and variance analysis are used to calibrate mesoscopic mechanical parameters of contact model, determine the influence of different parameters on deformation in bending failure test and the relationship between the parameters is fitted by mathematical formula. The effects of air void content and fine aggregate surface texture on crack resistance of asphalt mastic at -10 °C were studied then. It was proved that the adhesive strength and bonding area between fine aggregates co-influence the ultimate bending failure load, and the contact stiffness influences the ultimate bending deflection. Mathematical correlation formula between the radius of binding area and the critical stress that satisfies the ultimate load of asphalt mastic beam can be fitted in same form. The flexural strain energy density of asphalt mastic is increased with the decrease of air void content. The results are of positive significance for improving the mechanical properties of asphalt road in cold regions.
Utilization of the maros karst landscape based on the morphology (case study in bantimurung subdistrict, maros district, sulawesi selatan)

Karst region of Maros Regency is valuable due to the natural resources available for the local economy. This study aims to map the karst geomorphology units using GIS techniques and to illustrate the use of the landscape by surrounding communities. Information on karst distribution is needed because the karst potential has scientific, economic, and humanitarian values, which are a wealth of resources. The variables used in this study are geological, altitude, drainage pattern, and slope maps. We determine the distribution of karst geomorphological units using the overlay method. Primary data processing and descriptive analysis are used to determine the utilization of each karst geomorphology unit. The result is that the karst has an area of 33.58 km² or 20% of the total area, which covers the 10.17 km² plain karst, 15.84 km² plateau karst, 6.07 km² cone karst, and 1.49 km² tower karst. The plain karst is used for agricultural land, while the plateau karst and cone karst for tourism services, with the cone karst in the form of guano fertilizer sources. Meanwhile, tower karsts are utilized for limestone mining.
Different pH levels medium effects in IAA production of phylloplane bacterium Serratia plymuthica strain UBCF_13

Indole-3-acetic acid [IAA] is the main source of auxin in plants that plays a major role in controlling various physiological processes. The ability of bacterium in IAA biosynthesis depends on several environmental factors, usually triggered by acidic stresses. This study was aimed to optimize the IAA production by phylloplane bacterium Serratiaplymuthica strain UBCF_13 with various pH levels. Optimization was carried out by using 0.2% L-Tryptophan in Luria Bertani[LB] media with various pH levels namely pH 4.0; 4.5; 5.0; 5.5; 6.0; 6.5; 7.0 and 9.0. UBCF_13 showed the best IAA production at pH 6.0 and 0.2% L-Tryptophan exhibited IAA concentration in the amount of 24.13 ppm. The UBCF_13 extracellular protein profile analysis showed that at pH 6.0 induced L-Tryptophan 0.2% produced protein bands of 60.8 kDa and 45 kDa in size. These protein bands are assumed to be ipdC and nitrilase proteins which are involved in the IAA biosynthesis. The application of IAA was carried out using chili peppers [Capsicum annuum L. ] germination of the Lotanbar genotype. The application was using supernatant produced from media of pH 6.0 and 0.2% L-Tryptophan. The results show that root and hypocotyl lengths were 4.10 cm and 2.90 cm, respectively. Meanwhile, root and hypocotyl lengths in the control sample only 2.78 cm and 1.88 cm. The results indicated that the IAA production of UBCF_13 can promote the growth of chili seedling. A further detailed study on the visualization of protein profiles at the optimum treatment needs to be done. Two-DimensionalElectrophoresis is recommended to be applied so that protein bands can be accurately separated to identify detailed proteins involved in the IAA biosynthesis.
Incremental Learning Based on Angle Constraints

With the rapid growth of the Internet, it has become easy to obtain new data for many application domains. However, when adding new data to the current system of artificial neural networks (ANNs) to learn, it can cause the network to completely forget what it has learned before, which is called catastrophic forgetting. The main reason for these problems is the inability of ANNs to balance new classes with old ones. Therefore, to address the challenge of learning new knowledge while not suffering from catastrophic forgetting, some incremental learning algorithms have been proposed to alleviate. This paper proposes features that balance new classes with old classes by using angular distillation. And some exemplars from the old classes are retained to improve the performance on the old data. The effectiveness of our algorithm is demonstrated on CIFAR-100 dataset.
Moses, Noah and Joseph effects in Lévy walks

We study a method for detecting the origins of anomalous diffusion, when it is observed in an ensemble of times-series, generated experimentally or numerically, without having knowledge about the exact underlying dynamics. The reasons for anomalous diffusive scaling of the mean-squared displacement are decomposed into three root causes: increment correlations are expressed by the ‘Joseph effect’ (Mandelbrot and Wallis 1968 Water Resour. Res. 4 909), fat-tails of the increment probability density lead to a ‘Noah effect’ (Mandelbrot and Wallis 1968 Water Resour. Res. 4 909), and non-stationarity, to the ‘Moses effect’ (Chen et al 2017 Phys. Rev. E 95 042141). After appropriate rescaling, based on the quantification of these effects, the increment distribution converges at increasing times to a time-invariant asymptotic shape. For different processes, this asymptotic limit can be an equilibrium state, an infinite-invariant, or an infinite-covariant density. We use numerical methods of time-series analysis to quantify the three effects in a model of a non-linearly coupled Lévy walk, compare our results to theoretical predictions, and discuss the generality of the method.
MSIGEN: Multi-Scale Illumination-Guided Low-Light Image Enhancement Network

Images lack of light often show the characteristics of low visibility, low contrast and high noise, due to limited imaging equipment. To address the problem, inspired by Retinex theory, we propose a two-stage method called Multi-Scale Illumination-Guided Low-Light Image Enhancement Network (denoted as MSIGEN). In the first stage, we employ an enhancement module to achieve low-light image enhancement, including a Decom-Net for decomposing into illumination and reflectance, an Adjust-Net for illumination adjustment, a Restore-Net for the illumination fusion map guided reflectance restoration. Secondly, the enhancement module is introduced to refine the initial images to effectively remove visual defects amplified and ensure the naturalness of the image. In addition, extensive experiment results demonstrate the advantages of our method and the effect of this method has reached the state-of-the-art.
Effect of audio pre-processing technique for neural network on lung sound classification

In this paper, we intended to study the effect of two audio pre-processing methods to multilayer feedforward network performance on lung sound classification problem. The first one is Mel Frequency Cepstral Coefficients (MFCC) and the second is the MFCC supplement with Linear Discriminant Analysis (LDA). Datasets applied in this study came from Kaggle Respiratory Sound Database, which is the largest resource for machine learning. As a result, MFCC supplement with LDA has both good performance and significant improvement than conventional MFCC.
Addition of fermented catfish waste into ration for increasing performance of Drake in Growth Period

Utilization of organic waste is wasted by fermentation into quality feed ingredients in an effort to improve drake performance. Good farming zero waste. This study aims to study the effect of FCW (fermented catfish waste) to improve drake performance and obtain levels of use of fermented catfish waste in rations that produce the best drake performance. The research method uses a completely randomized experimental design (CRD) 4 treatment rations (0, 1.67, 3.33, and 5% FCW) each treatment repeats 5 times. One hundred drake aged 1 day were kept until 12 weeks old, put in 20 units of the cage. The results showed that the addition of FCW in the ration had a significant influence on drake performance. The ration given 10% FCW produced the best drake performance. This is supported by the following data the average consumption of drake rations that is 8.240 gram, the average weight gain 2.280 gram, and the average conversion rations 3.61.