Miracle-dz/UniFAR-FTUs huggingface dataset:
THERMALLY-OPTICALLY-THERMALLY STIMULATED LUMINESCENCE

A three-stage energy band model was studied. The model consists of electrons thermally stimulated from the ground state to the first excited state, after which they were optically stimulated into the second excited state and they were finally stimulated thermally into the conduction band. A set of simultaneous differential equations was generated from the models and three assumed conditions were applied to this model, which they were solved analytically and analytical expressions were obtained. The same set of simultaneous equations were solved numerically using ode 15s MATLAB solver. When considering first-order peaks, the kinetic parameters obtained were found to be in good agreement with the analytical expressions. But when considering non first-order peaks, the kinetic parameters obtained numerically were not in good agreement with the analytical expressions and explanations had been given. Second-order peaks could not be obtained despite careful selection of the kinetic parameters because the traps were quickly saturated and the quasi- equilibrium conditions assumed could no longer be satisfied. The stability of the excited TA-OSL signals produced by the model was also studied. The real stability of the excited TA-OSL signals produced by this model was found to be about 46 million years.
A Review on the Full Chain Application of 3D Printing Technology in Precision Medicine

Personalized precision medicine is a new direction for medical development, and advanced manufacturing technology can provide effective support for the development of personalized precision medicine. Based on the layered accumulation manufacturing principle, 3D printing technology has unique advantages in personalized rapid manufacturing, and can form complex geometric shape parts at low cost and high efficiency. This article introduces the application progress of 3D printing technology in medical models, surgical navigation templates, invisible aligners, and human implants, analyzes their advantages and limitations, and provides an outlook for the development trend of 3D printing technology in precision medicine.
Embrace the Dark Side: Advancing the Dark Energy Survey

The Dark Energy Survey (DES) is an ongoing cosmological survey intended to study the properties of the accelerated expansion of the Universe. In this dissertation, I present work of mine that has advanced the progress of DES. First is an introduction, which explores the physics of the cosmos, as well as how DES intends to probe it. Attention is given to developing the theoretical framework cosmologists use to describe the Universe, and to explaining observational evidence which has furnished our current conception of the cosmos. Emphasis is placed on the dark sector – dark matter and dark energy – the content of the Universe not explained by the Standard Model of particle physics. As its name suggests, the Dark Energy Survey has been specially designed to measure the properties of dark energy. DES will use a combination of galaxy cluster, weak gravitational lensing, angular clustering, and supernovae measurements to derive its state of the art constraints, each of which is discussed in the text. The work described in this dissertation includes science measurements directly related to the first three of these probes. The dissertation presents my contributions to the readout and control system of the Dark Energy Camera (DECam); the name of this software is SISPI. SISPI uses client-server and publish-subscribe communication patterns to coordinate and command actions among the many hardware components of DECam – the survey instrument for DES, a 570 megapixel CCD camera, mounted at prime focus of the Blanco 4-m Telescope. The SISPI work I discuss includes coding applications for DECam’s filter changer mechanism and hexapod,
Topological Materials Discovery Through the Lens of Electron Filling Constraints

Nodal semimetals, materials systems with nodal-point or -line Fermi surfaces, are much sought after for their novel transport and topological properties. Identification of experimental materials candidates, however, has mainly relied on exhaustive database searches. Here we show how recent studies on the interplay between electron filling and nonsymmorphic space-group symmetries can guide the search for nodal semimetals which are `filling-enforced'. We recast the previously-derived constraints on the allowed electron fillings for band insulators in the 230 space groups into a new form, which enables effective screening of materials candidates based solely on their (1) space group, (2) electron count in the formula unit, and (3) multiplicity of the formula unit. This criterion greatly reduces the number of candidate materials in a database of previously-synthesized compounds, and helps us to efficiently identify a handful of candidates, including the monoclinic crystals Ca$_2$GaPt$_2$, AgF$_2$, Ca$_2$InOsO$_6$ and Ru(PO$_3$)$_3$, and the orthorhombic crystal CsHg$_2$, from sampling only a few nonsymmorphic space groups. Based on ab initio calculations, we show that these materials have filling-enforced Dirac nodes near the Fermi energy. In addition, we also identify CaGaPt as a promising filling-enforced Dirac-ring semimetal candidate.
Lossless seeds for approximate string matching

The thesis deals with lossless seeds, which were originally proposed for the purposes of the filtration phase in the DNA similarity search. The case of designing seeds for one error is studied first and sufficiently solved using the greedy algorithm. Obtained results are generalized for the case of two errors, nevertheless, it is shown that the same algorithm does not provide asymptotically optimal seeds in this case. Further on, an idea of seed design based on the so-called cyclic rulers is introduced. Possibilities of generalization for the case of more errors are mentioned. At the end the software created for the purposes of this thesis, which is given on the enclosed CD, is described.
Development of a COVID-19 Patients’ Fatality Prediction System Using Swarm Intelligent Convolution Neural Network

Aims: This work aims to develop a system that can be used to accurately and timely predict the fatality of a positively tested COVID-19 patient through the use of a deep learning technique – a swarm intelligent convolutional neural network. Methodology: The dataset used in this study was acquired from the Kaggle repository database. The dataset contains the Lung Chest X-Ray images of COVID-19 patients. The images were pre- processed to obtain the desired image quality for further processing. This was followed by segmenting the pre-processed images. An Enhanced Firefly Algorithm (EFA) was formulated by applying the roulette wheel selection procedure to model the movement process of the firefly as a deterministic process to assist the standard Firefly Algorithm (FA) and application of Chaotic Sinusoidal Map Function to model the attractive process of the firefly which establishes a balance between exploration and exploitation in FA. The EFA was applied to optimize Convolution Neural Network (CNN) hyper-parameters (number of layers, number of filters per layer, filter size and batch size). The segmented result was subsequently presented to EFA-CNN feature extraction and prediction of COVID-19 patient fatality cases. The formulated deep learning models (EFA-CNN and CNN) were implemented using Matrix Laboratory 2020a software. The implemented models were evaluated using specificity, sensitivity, false positive rate, accuracy, and recognition time/rate to determine the performance of the developed models. Results: The findings revealed that the EFA-CNN model performs better in the prediction of COVID-19 patients’ fatality compared to the CNN model. It was also discovered that the formulated EFA applied to select optimal values of the hyper-parameters for the CNN architecture accounted for improved recognition accuracy and reduced recognition time of the developed COVID-19 Patients’ Fatality Prediction System. Conclusion: The system developed will assist both the government and healthcare workers in providing the needed computational capability for the prediction of the fatality level of a positively tested COVID-19 patient.
Quantum spin nematic liquid in the low-dimensional anisotropic magnets -S=1/2 delta spin chain with the anisotropic ferromagnetic interaction in magnetic field-

The magnetization process of the S=1/2S=1/2 delta chain with the anisotropic ferromagnetic interaction is investigated using the numerical diagonalization of finite-size clusters. It is found that the spin nematic liquid phase appears in higher magnetization region, as well as the SDW liquid one in lower region.
Recent progress in analog memory-based accelerators for deep learning

We survey recent progress in the use of analog memory devices to build neuromorphic hardware accelerators for deep learning applications. After an overview of deep learning and the application opportunities for deep neural network (DNN) hardware accelerators, we briefly discuss the research area of customized digital accelerators for deep learning. We discuss how the strengths and weaknesses of analog memory-based accelerators match well to the weaknesses and strengths of digital accelerators, and attempt to identify where the future hardware opportunities might be found. We survey the extensive but rapidly developing literature on what would be needed from an analog memory device to enable such a DNN accelerator, and summarize progress with various analog memory candidates including non-volatile memory such as resistive RAM, phase change memory, Li-ion-based devices, capacitor-based and other CMOS devices, as well as photonics-based devices and systems. After surveying how recent circuits and systems work, we conclude with a description of the next research steps that will be needed in order to move closer to the commercialization of viable analog-memory-based DNN hardware accelerators.
Experimental evidence of trap level modulation in silicon nitride thin films by hydrogen annealing

The energy level of electron traps in silicon nitride (SiNx) thin films was investigated by discharging current transient spectroscopy (DCTS). Results indicate that the trap level of the SiNx thin films becomes deeper with decreasing composition (N/Si) and shallower after hydrogen annealing. The dependence of the trap level on the SiNx composition and the modulation of the trap level by hydrogen annealing are possibly related to the change in the number of Si–H bonds in the SiNx thin films.
On the model checking of finite state transducers over semigroups

Sequential reactive systems represent programs that interact with the environment by receiving signals or requests and react to these requests by performing operations with data. Such systems simulate various software like computer drivers, real-time systems, control procedures, online protocols etc. In this paper, we study the verification problem for the programs of this kind. We use finite state transducers over semigroups as formal models of reactive systems. We introduce a new specification language LP-CTL* to describe the behavior of transducers. This language is based on the well-known temporal logic CTL* and has two distinguished features: 1) each temporal operator is parameterized with a regular expression over input alphabet of the transducer, and 2) each atomic proposition is specified by a regular expression over the output alphabet of the transducer. We develop a tabular algorithm for model checking of finite state transducers over semigroups against LP-CTL* formulae, prove its correctness, and estimate its complexity. We also consider a special fragment of LP-CTL* language, where temporal operators are parameterized with regular expressions over one-letter alphabet, and show that this fragment may be used to specify usual Kripke structures, while it is more expressive than usual CTL*.
Protein content and amino acid composition in seed of bread wheat (Triticum aestivum L.)

The aim of this study was determination of protein content and amino acid composition in seeds of bread wheat, with particular focus of evaluation essential amino acids (EAAs). For analysis used flour samples of grained seed of 10 wheat variety, which selected in different breeding center (in Novi Sad and Kragujevac, Serbia). Kjeldahl method was used for determination of nitrogen (N) contents which value multiplied with coefficient 5.7 for computing protein content (protein contents = 5.7 x % N contents). Amino acids analyses of wheat samples were performed by ion exchange chromatography, followed by the ninhydrin colour reaction and photometric detection at 570 nm and 440 nm (for proline). The results showed that the mean protein content for wheat varied from the lowest value 10.24% in Ljubicevka to the highest 14.21% in Fortuna variety. The mean contents (g 100 g-l protein) of nonessential amino acids (NEAAs) were aspartic acid 5.42%, serine 4.23%, glutamic acid 18.51%, proline 12.18%, glycine 4.17%, alanine 3.64%, tyrosine 2.52%, arginine 5.02%, while mean contents of essential amino acids (EAAs) were threonine 2.86%, valine 4.52%, methionine 1.28%, isoleucine 3.87%, leucine 5.87%, phenylalanine 4.62%, histidine 2.46%, lysine 2.91%. The wheat variety contained relatively high concentration of the most deficient EAAs. The variety Ljubicevka contained relatively high concentration of the most deficient EAAs (threonine, valine, leucine, lysine,). The highest value (29.31 g 100 g-l protein) of total essential amino acids (TEAA), 59.79 g 100 g-l protein of total nonessential amino acids (TNEAA) as well as the highest value (90.35 g 100 g-l protein) of total amino acids (TAA). Obtained results provide variability of wheat variety on the base of amino acid contents and indicate reliable variety for selecting desirable parents in breeding program for improving nutrient quality.
Uncertainty in the Management of Tropical Peatlands for Oil Palm Plantations due to Drainage Practices

The conversion of tropical peatlands to oil palm plantations has affected the long-term storage stability of water and carbon. The conversion requires a drainage system that results in land subsidence and, in turn, reduces the carrying capacity of water storage and carbon stocks. This study aims to analyze subsidence from long-term observations (2004-2020) to obtain an appropriate water management measure for three scenarios of drainage depths at the oil palm plantations in Jambi Province. It is found that the reduction is quite variable depending on the level of drainage depths. The subsidence was 55 cm, 49 cm, and 34.7 cm for deep, moderate, and shallow drainage conditions. The groundwater level was deeper than 100 cm, which is far below the threshold of 40 cm, as stated in the government regulations. However, the regulations are still debated since subsidence must occur in drained peatlands regardless of the water level. The observed large subsidence implies that better water management in a new site is crucial and necessary to reduce the impact of peatlands degradation relative to current conditions and that high rates of land subsidence should be accepted as an inevitable change from the conversion of tropical peatlands to oil palm plantations.
PENGARUH KUALITAS PELAYANAN PUBLIK AIR BERSIH TERHADAP KEPUASAN PELANGGAN PERUSAHAAN DAERAH AIR MINUM KOTA KUPANG

Air bersih merupakan sumber daya alam yang menjadi salah satu kebutuhan pokok hajat hidup masyarakat Indonesia. Dalam prakteknya, pelayanan air bersih dikelola oleh Perusahaan Daerah Air Minum atau PDAM. Namun pada kenyataannya, banyak masyarakat yang mengeluhkan bahwa kualitas pelayanan air bersih tidak sesuai dengan apa yang diharapkan, Khususnya di Kota Kupang. Pelayanan Publik Air Bersih yang dilakukan oleh Perusahaan Daerah Kota Kupang belum sesuai dengan standar kualitas, kuantitas dan kontinuitas pelayanan air bersih. Penelitian ini bertujuan untuk mengukur besaran pengaruh kualitas pelayanan publik ar bersih yang ditinjau dari aspek tangibles, reliability, responsiveness, assurance dan empathy terhadap kepuasan pelanggan. Metode dalam penelitian ini menggunakan survey dengan pendekatan kuantitatif. Hasil Penelitian diperoleh skor p-value dari aspek Tangibles sebesar 0,259 yang berarti tidak adanya pengaruh positif terhadap kepuasan pelanggan, kemudian pada aspek Reliability, Resposiveness, Assurance dan Empathy secara berurutan diperoleh skor p-value sebesar 0,045, 0,005, 0,048, dan 0,039. Keempat aspek tersebut mempunyai pengaruh yang positif terhadap kepuasan pelanggan.
An Expectation Maximization Method to Learn the Group Structure of Deep Neural Network

Analyzing multivariate time series data is important for many applications such as automated control, sensor fault diagnosis and financial data analysis. One of the key challenges is to learn latent features automatically from dynamically changing multivariate input. Convolutional neural networks (CNNs) have been successful to learn generalized feature extractors with shared parameters over the spatial domain in visual recognition tasks. For high-dimensional multivariate time series, designing an appropriate CNN model structure is challenging because the kernels may need to be extended through the full dimension of the input volume. To address this issue, we propose an Expectation Maximization (EM) method to learn the group structure of deep neural networks so that we can process the multiple high-dimensional kernels efficiently. This algorithm groups the kernels for each channel using the EM method and partition the kernel matrix into a block matrix. The EMmethod assumes the Gaussian Mixture Model (GMM) and the parameters of the GMM is updated together with the parameters of deep neural network by end-to-end backpropagation learning.
Study of the Variability of the Chemical Profile, and Biological Activity Approaches of Hedychium coronarium J. Koenig Essential Oil from Different Habitats of Uttarakhand, India

The objective of the current study was to evaluate the altitudinal variability in chemical compounds, nematicidal, insecticidal, herbicidal, and antifungal activity of the essential oils of H. coronarium collected from four different habitats of Tarai and Kumaun region of Uttarakhand. Essential oils were hydro-distilled from rhizomes of H. coronarium, and their yield varied from 0.05 to 0.25% (v/w). Based on GC-MS analysis, the major compounds observed were 1, 8-cineole (12.21–18.27%), coronarin E (10.20–14%), α-terpineol (5.32–10.13%), terpinen-4-ol (2.20–4.67%), α-pinene (4.90–8.77%), Z-caryophyllene (4.67–12.29%), and linalool (1.96–4.62%). To visually observe the variation in the oil composition profile of essential oils, heat map clustering, Venn diagram, and principal component analysis (PCA) were performed. To evaluate the pesticidal properties, the essential oils were tested under laboratory conditions. Additionally, significant nematicidal activity was demonstrated by essential oils against Meloidogyne incognita, good insecticidal activity against Spodoptera litura, and moderate herbicidal activity on Raphanus raphanistrum subsp. sativus, and effective antifungal activity against Fusarium oxysporum and Curvularia lunata was observed in the current investigation.
Circularly polarized two‐layer conical DRA based on metamaterial

In this article, we propose a conical dielectric resonator antenna with metamaterial operating in the C band and excited by a circular polarization. The circular polarization of the antenna is produced by an inclination of 45° on the two environments with a different dielectric constant of the slot excitation. The metamaterial is superimposed on the antenna by λr and thus allows to increase the gain of 3 dB in all the bandwidth. Simulation results are carried out using Ansoft's HFSS software. The parametric study of the antenna has been performed by varying the permittivity ɛ2 and the radius a2 in a first step and by adding the metamaterial in a second step. The obtained bandwidths of the conical dielectric resonator antenna without and with metamaterial are 1.14 GHz and 1.07 GHz, respectively, with a corresponding increase in gain of about 3 dB.
Review of Simulation Methods for Design of Spin Logic

While the scaling of CMOS electronics continues and reaches the sub-10 nanometer range, active research is being conducted on logic devices beyond CMOS to find a path to a more energy efficient integrated circuit platform for computing. Among them, a prominent option is spintronic devices, remarkable for their non-volatility and low switching energy. Simulation is the key part of this research due to spintronics’ reliance on novel materials, device structures, and circuit architecture. We review recent publications which often traverse these hierarchical levels of the computing stack. Prevalent methods, comparison with experiments, use in proposing new logic concepts are surveyed.
‘Insiders eating home-grown food’, home gardens of Chinese smallholders, and hidden resistance in the food regime transition

ABSTRACT Scholarly interest in the counter-movement during the third food regime transition has mainly been focused on open resistance. However, that view omits the grand picture consisting of 230 million Chinese smallholders and their everyday practices. This paper analyzes home gardening by Chinese smallholders based on a 10-year follow-up investigation. It reveals that their goal is to achieve ‘insiders eating home-grown food’, constituting a hidden and unconfrontational resistance to commercialization and globalization. Moreover, such resistance has formed a powerful force with strategic importance in China, having the potential to enlighten the counter-movement and thereby reshape the food regime.
Analysis of Millimeter Wave Resonator

The direct and efficient Transverse Transmission Line (TTL) method is used to analyze the unilateral fin-line resonator. In this method the Maxwell's equations in Fourier transformed domain, are firstly used to obtain the electromagnetic fields in terms of the transversal electromagnetic fields. An inhomogeneous equation system is obtained, in which the current densities in the conductor fins are related to the electric fields in the slot. The TTL method in conjunction with the Galerkin’s procedure and Parseval’s relation are applied to obtaining a homogeneous equation system with two variables. The determinant of this system gives the complex resonance frequency.
Two homoclinic solutions of a nonperiodic fourth-order impulsive differential equation

Abstract In this paper, we are concerned with a kind of nonperiodic fourth-order impulsive differential equation. By applying the mountain pass theorem, we shall establish several new criteria for the existence of at least two nontrivial homoclinic solutions of the addressed equation. Some recent results in the literature are generalized and improved. In addition, a numerical example is provided to show the applicability and effectiveness of our main results.
Anomalous Situations Recognition in Surveillance Images Using Deep Learning

: Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terror-ism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers