Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence and Divergence in Two Historical Varieties of English

In the description of his divergence/convergence model of language change from IndoEuropean to Present-Day English, James Milroy states that the history of English is traditionally depicted as a continuous path leading from divergence in the earlier stages of English to convergence towards the emerging English standard variety in the 16th century (Milroy 1992, 50-51). This pathway is largely borne out by research on spelling, showing a gradual reduction of divergent orthographic variants in different earlier Middle English dialects in favour of "colourless" supraregional spellings in the late Middle English period (Smith 2000, 136; cf. also Schaefer 2012, 520-521; 524526). Even if one casts the net wider to include the relationship between the standardising Middle Scots1 variety, "the only dialect that compares in development and uses to the standard dialect which was developing in England at the same time" (Agutter 1990, 1), and the Southern English Standard,2 the directions of change remain the same. In the 16th century, Middle Scots and the developing Southern English Standard were perceived as two distinct varieties of English by contemporary observers (e.g. Horsbroch 1999, 5; McClure 1994, 37). Scots was not always clearly distinguished from English dialects, though. It has its origins in northern dialects of Old English3 and at the time of the first written attestations of Early Scots at the end of the 14th century, it is part of a "common speech area" with Northern Middle English varieties (Williamson 2002, 253), evincing many shared linguistic features with these (Agutter 1988, 1; Meurman-Solin 1997, 7). In the mid-15th century Middle Scots begins to diverge from Northern English, at a time when texts written in Northern Middle English dialects manifest the use of more and more supraregional features of the emerging Southern English Standard (Meurman-Solin 1997, 7; Williamson 2002, 253). Subsequently, in the middle of the 16th century, when Scots is maximally distinct from English, a gradual process of convergence of written Scots with the Southern English Standard begins, which is nearing completion by 1700 (Devitt 1989; Meurman-Solin 1993, 154-160; 1997, 16-19). The case for the divergence of Scots from Northern English and the following convergence with the Southern English Standard, which
A fast modeling of chemical reactions in industrial‐scale olefin polymerization fluidized beds using recurrence
 CFD

We present a novel approach for the fast modeling of exothermic chemical reactions in industrial-scale fluidized bed reactors. It implicates a fast olefin polymerization process, accounting for the catalyst activity, the solubility of the reaction gases in polymer, the particles crystallinity, and the reaction masses and heat transfer. We principally apply the transport-based recurrence computational fluid dynamics (rCFD) model upon the base of a short-term non-reactive simulation performed by a coarse-grained two-fluid model (cgTFM). Following the captured recurrent flows, the methodology propagates rapidly passive scalars far beyond the recorded simulation. The reaction kinetics of production/consumption rates due to polymerization are locally embedded into the individual solid/gas species concentrations. These in turn are considered in transporting the enthalpy and the generated heat by reaction. By doing so, the significant computational effort required to couple the thermodynamic effects of polymerization with the cgTFM (hybrid model), is drastically reduced using rCFD with very reliable agreement.
XRD study of phase transformations in lithiated graphite anodes by Rietveld method

Commercial anodes with different state of charge are investigated by X-ray diffraction technique using Rietveld method for data collected with standard laboratory equipment. It is shown that full profile refinement gives good approximation for quantitative description of the charge/discharge process and may be used for estimation of real state of charge (SoC). Careful analysis of the diffraction profile with Rietveld method allows us to quantitatively distinguish the contribution of different Li x C6 phases and estimate the real SoC.
A New Adaptive Broadband Thyristor-Controlled Impedance Matching Network

In this article, we present an adaptive broadband thyristor-controlled impedance matching network (TCIMN) for increasing power utilization from a power amplifier to the loads. Different from the conventional passive impedance matching methods, the proposed one can achieve load impedance matching under a wide frequency range of power amplifiers. First, the operation principle of TCIMN is introduced mathematically. Based on that, the parameter design method is proposed based on the required frequency range. Moreover, the operating range and harmonic analyses are provided to give the TCIMN firing angle operating ranges under different frequencies and alleviate the potential harmonic problem caused by thyristors. To control the proposed TCIMN, the dynamic impedance identification is applied to match the load reactive power adaptively. Finally, the simulation and experimental results of a prototype are given to demonstrate the validity of the proposed TCIMN, analysis, and control method. The results clearly show that the proposed TCIMN can adaptively match the load impedance for the different loads and broadband frequency range. More importantly, the power amplifier power utilization factor is significantly improved without causing harmonic pollutions.
Cercospora Identification in Spinach Leaves Through Resnet-50 Based Image Processing

Cercospora is a contagious disease that occurs in plant leaves. Spinach is one of the healthiest food that are preferred by the people nowadays. Thus cercospora is the disease that also occurs in the spinach leaves, it also affects the humans. It causes the serious effect in both the spinach plants and also human and animals consuming it. Therefore the usage of image processing and deep learning is done in order to find out the cercospora affected plant and preventing it from the spreading from one plant to the other. To acquire the process of segmentation the plant image feed is given. This process produces an accurate result and as a outcome the cercospora spread can be controlled at the initial stage. This helps farmers to proceeds the decision faster for providing a needed treatment. In proposed system by using the convolutional neural network (CNN) and the Resnet-50 architecture, through which cercospora is identified from the different classes for the spinach leaf disease were detected and classified from the healthy leaves. The result obtained has a greater accuracy in training and testing from a few datasets and thus using this process the identification and the measures can be taken.
Fast and Efficient Estimation of Frequencies

Parameter estimation of multiple sinusoids is one of the fundamental problems of signal processing having numerous application areas, including wireless communications and array signal processing. This article proposes an iterative, fast, and efficient algorithm to estimate the frequencies and amplitudes of multiple sinusoids in noisy environments. The proposed approach interpolates on the <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-shifted DFT coefficients of multiple sinusoids. We theoretically demonstrate that the performance of the proposed algorithm depends on DFT-shift, number of iterations and minimum DFT frequency separation parameters. Further, mean square error of the proposed algorithm achieves the Cramér-Rao lower bound when these parameters are selected appropriately. We also propose solid bounds regarding how these parameters can be selected for optimum overall performance. The total computational cost of the proposed algorithm is in the order of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(K N\log N)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> is the number of sinusoidal components and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the signal length. We provide comprehensive numerical simulations that confirm the correctness of our theoretical deductions.
Time‐varying reliability method based on linearized Nataf transform

In actual engineering, material properties, load effects, and other factors of mechanical structures change due to long‐term use. In order to understand the operation of a mechanical structure in real time, it is crucial to obtain the dynamic trajectory of its reliability. Considering the time variability of a mechanical structure over time, uncertain random variables are introduced to express the uncertainty of various parameters of structures, and the Wiener process is used to describe the strength degradation process of structures so as to solve the calculation problem of time‐varying reliability of mechanical structures. Based on the advanced first‐order and second moment method (AFOSM), the proposed linearized Nataf change is used to complete the transformation from related nonnormal variables to independent standard normal variables in order to simplify the calculation process of reliability solution and solve the reliability calculation problem of random parameters subjected to arbitrary distribution. The deduced random variable sensitivity factor indicates the degree of influence of different random variables on the reliability of the mechanical structure, providing a theoretical basis for the optimal design and maintenance of a mechanical structure. The proposed method is analyzed using the cantilever beam and compared with the nonlinear Nataf transform and verified by the Monte Carlo simulation results. The results show that the proposed method can effectively solve the reliability sensitivity problem of structural system strength degradation.
Design and analysis of a novel topology for slotless brushless DC (BLDC) motors with enhanced torque and efficiency

Seyed Reza Mousavi‐aghdam, Third floor, Department of Electrical Engineering, University of Mohaghegh Ardabili 56131, Ardabil province, Iran. Email: r.mousaviaghdam@uma.ac. ir Abstract A novel high‐torque slotless brushless DC (BLDC) motor is proposed. The proposed topology has a unique concentrated winding structure and accordingly the motor design is effectively modified with its dual‐rotor structure. The rotor structure of the proposed motor may be modified itself and form three different topologies. First, the basic structures of the new slotless BLDC motor and its operation principle are described. Then, design methodology of the proposed motor is explained. Initial analysis of the motor is subsequently conducted using 2D finite element (FE) simulations. Next, design parameters of the proposed motor are further optimized using Salp algorithm. The proposed topologies are compared with the conventional single and dual‐rotor structures. Finally, experimental prototype is built for verification. The simulation and experimental results exhibit that the proposed slotless BLDC motor has high torque and efficiency in comparison with the conventional structures and may be a good candidate for small‐sized BLDC motors. Moreover, the results show that the proposed BLDC motor has nearly ideal back‐EMF which makes sensorless methods more efficient for the proposed motor.
Safety, efficiency, and treatment satisfaction in children with primary immunodeficiency receiving subcutaneous immunoglobulin treatment

OBJECTIVE Patients with Inborn Errors of Immunity, also known as Primary Immunodeficiency (PID), are prone to recurrent bacterial infections and these patients often require lifelong IgG replacement therapy. The aim of this presentation is to evaluate the efficacy, safety, and patient satisfaction in PID patients receiving subcutaneous immunoglobulin (SCIG) treatment and to share our expe-riences. METHODS Twenty-one patients who were followed up with the diagnosis of PID by our Pediatric Allergy and Immunology Clinic and received regular intravenous immunoglobulin therapy (IVIG) befo-re starting SCIG treatment were included in the study. RESULTS A total of 21 patients were included in the study. 10 of the patients (47.6%) were female, 11 (52.4%) were male, and the mean age was 8.8±4.42 years. Five of the patients were Syrian patients living in the refugee camp. Threshold IgG levels of the patients were evaluated every 3 months. IgG levels were significantly higher than baseline IVIG levels at weeks 3, 6, and 12 of SCIG treatment, respectively. There was no significant difference between 3rd, 6th and 12th months of SCIG treatment. A statistically significant decrease was observed in the frequency of infections in patients who received SCIG treatment (p=0.003). During SCIG treatment, the total infection rate was 4.1/person/year. According to the TSQM-9 satisfaction questionnaire, the annual hospitalization rate was 0.9/patient/year for IVIG and 0.4/patient/year for SCIG (p>0.005), and 61.9% of patients were moderately satisfied, 14.2%. 19% were very satisfied and 4.7% were not satisfied with the treatment. When the satisfaction criteria were evaluated, it was observed that the patients mostly (71%) were satisfied with the absence of vascular access prob-lems and the comfort of self-application at home. CONCLUSION SCIG therapy causes high serum IgG levels and a reduced frequency of infections and can be a safe, effective, and well-tolerated treatment alternative in patients with PID with high patient satisfaction.
ASSESSMENT OF SOCIO-ECONOMIC IMPACT OF COVID-19 PANDEMIC AMONG THE RESIDENTS OF GWAGWALADA, FCT ABUJA, NIGERIA

Purpose: The emergence of COVID-19 and the measures put in place to curtail the spread of the virus and the perceived hardship imposed on people prompted this study with the view to assess the level of awareness of COVID-19 pandemic among the residents of Gwagwalada, examine the level of their access to basic health, social facilities and identify needs and level of support they received during COVID-19 lockdown. Methodology: A cross section survey research was employed and a stratified random sampling was employed in the selection of the respondents. Krejcie & Morgan, (1970) model was use in determining the sample size, a sample of 373 respondents out of 22308 population was selected and used for this study. The first respondent was selected at random and the subsequent ones systematically. The selected people were served with the questionnaire. The information collected were analyzed and the results were presented in tables and percentages. Findings: The results showed that majority of the respondents are within the ages of 30-39yrs which constituted 38.6%. It showed that 55.4% of the respondents are married, 61% had tertiary education, 53.7% was farmers, and 18.3% was civil servants. The average monthly income of the respondents was between N18.000-N35, 000, results showed that 99% was aware of the COVID19 pandemic. About 39% of them knew the symptoms of COVID-19 as cough, fever, tiredness, sore throat, and difficulty in breathing. Their sources of information include rumors (79%), social networks (70%), and TV/Radio jingles (75%). The lockdown affected the social and economic activities of the people such as education, family interaction, their leisure, security among others. Economic effects such as poverty, loss of job, closure of small and medium scale businesses among others. About 87% of the respondents indicated that they never received any assistance/support during the lockdown. As a coping strategies, the respondents sold their assets, reduce their rate of consumption, play games, accepted low wages etcetera and the youths went into protest which lead to looting COVID-19 warehouses in the study area in search of palliatives. The study recommends among others, prompt economy recovery plans in terms of expanded palliatives, social security safety nets and stimulus to residents and Small Medium Enterprises (SMEs).
Task-oriented Battlefield Situation Information Hybrid Recommendation Model

In the process of interaction between users and battlefield situation information, combat tasks are the key factors that affect users’ information selection. In order to solve the problems of battlefield situation information recommendation (BSIR) for combat tasks, we propose a task-oriented battlefield situation information hybrid recommendation model (TBSI-HRM) based on tensor factorization and deep learning. In the model, in order to achieve high-precision personalized recommendations, we use Tensor Factorization (TF) to extract correlation relations and features from historical interaction data, and use Deep Neural Network (DNN) to learn hidden feature vectors of users, battlefield situation information and combat tasks from auxiliary information. The results are predicted through logistic regression. To solve the multi-source heterogeneity of battlefield situation information, we design a hybrid learning and presentation model that integrates multiple deep learning models such as Doc2Vec, fully connected network and convolutional neural network (CNN), to integrate the rich and diverse data information in situational awareness system effectively. We perform experiments with the maneuvers dataset to test and evaluate the model through scenario simulation.
Privacy-Preserving Streaming Truth Discovery in Crowdsourcing With Differential Privacy

Differential privacy (DP) has gained popularity in truth discovery recently due to its strong privacy guarantee. However, existing DP mechanisms for streaming data publication are not suitable for truth discovery as they fail to consider the different reliabilities of individuals, while the DP-based approaches for truth discovery are not suitable for streaming data because they ignore the correlations between truths over time. Directly applying these existing methods to streaming crowdsourced data would lead to low accuracy of the discovered truth. To solve this problem, in this paper, we propose an edge computing based privacy-preserving truth discovery mechanism, named PrivSTD, for streaming crowdsourced data to realize high accuracy of discovered truth while protecting the privacy of workers. Specifically, edge servers are introduced between the untrusted cloud server and workers to securely calculate the local truths and workers’ reliabilities. A truth-dependent budget recycle mechanism is proposed for each edge server to adaptively determine the perturbed timestamp and allocate the privacy budget according to the changing pattern of local truths. Besides, a reliability-based perturbation mechanism is proposed to reduce the perturbation magnitude on the basis of worker's reliability. We theoretical analyze the data utility and computation cost of PrivSTD, and prove that PrivSTD can satisfy <inline-formula><tex-math notation="LaTeX">$w$</tex-math><alternatives><mml:math><mml:mi>w</mml:mi></mml:math><inline-graphic xlink:href="ren-ieq1-3062775.gif"/></alternatives></inline-formula>-event (<inline-formula><tex-math notation="LaTeX">$\epsilon,\delta$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>ε</mml:mi><mml:mo>,</mml:mo><mml:mi>δ</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="ren-ieq2-3062775.gif"/></alternatives></inline-formula>)-differential privacy. Extensive experimental results on synthetic and real-world datasets demonstrate that PrivSTD achieves better utility than the state-of-the-art approaches.
In good company: Engaging in the maker movement alongside campus partners

Abstract Makerspaces have become increasingly common in academic libraries, but libraries are not alone in their enthusiasm for the creative and innovative culture known as the maker movement. As more libraries develop makerspaces, so do other campus units and universities. This article presents a case study of the Indiana University Libraries’ makerspace initiatives and their role in a larger network of makerspaces on the IU Bloomington campus. The author synthesizes examples from several institutions to make recommendations for libraries looking to contribute to the maker culture at their institution.
Floral scent compounds and emission patterns of three Styrax species

Styrax is a gorgeous species combined with high medicinal and ornamental values, however, information about its floral scents is limited. This study aimed to reveal the floral scent compounds and the dynamic changes in the flowering process of Styrax japonicus, S. grandiflora and S. calvescens. Static headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was adopted in the present study. The results showed that 24, 22 and 22 volatile compounds were present at three flowering stages, among which linalool, ocimene, α-pinene and germacrene D dominated in different species. Terpenes were the main floral scent compounds in all species, whereas there was considerable relative content of ketones in S. japonicus. Among the major terpenes, α-pinene, ocimene and myrcene were the common volatiles in these species, while β-elemene and allo-ocimene were the specific volatiles in S. japonicus and S. calvescens, respectively. The highest content of terpenes occurred at initial flowering stage in three species. The differences in the type and content of principal compounds contributed to the fragrance diversity among these species. A solid foundation for understanding the complexity of volatile emission could be obtained from our findings, meanwhile, effective utilization of abundant terpenes in flowers of Styrax species should be applied.
Molecular mechanism of secreted amyloid-β precursor protein in binding and modulating GABABR1a†

A recent phenomenal study discovered that the extension domain of secreted amyloid-β precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABABR1a) and modulate its synaptic transmission. The work provided an important structural foundation for the modulation of GABABR1a; however, the detailed molecular interaction mechanism, crucial for future drug design, remains elusive. Here, we further investigated the dynamical interactions between sAPP peptides and the natively unstructured sushi 1 domain using all-atom molecular dynamics simulations, for both the 17-residue sAPP peptide (APP 17-mer) and its minimally active 9 residue segment (APP 9-mer). We then explored mutations of the APP 9-mer with rigorous free energy perturbation (FEP) calculations. Our in silico mutagenesis studies revealed key residues (D4, W6, and W7) responsible for the binding with the sushi 1 domain. More importantly, one double mutation based on different vertebrate APP sequences from evolution exhibited a stronger binding (ΔΔG = −1.91 ± 0.66 kcal mol−1), indicating a potentially enhanced GABABR1a modulator. These large-scale simulations may provide new insights into the binding mechanism between sAPP and the sushi 1 domain, which could open new avenues in the development of future GABABR1a-specific therapeutics.