Miracle-dz/UniFAR-FTUs huggingface dataset:
A generalised projection technique for the synthesis of conformal arrays

A new technique for the synthesis of conformal arrays in the full three dimensional and vectorial case is presented. The considered approach aims to alleviate the most fundamental difficulty of power synthesis problems, i.e., the possibility of the synthesis algorithm being trapped by spurious solutions. The technique exploits a generalised projections algorithm, adopting a non-conventional choice for the variables occurring in the synthesis problem. This choice turns out to be more favourable from the point of view of the trapping problem.
The complete chloroplast genome sequence of a warm season ornamental grass, Cenchrus alopecuroides Thunb. 1794 (Poaceae)

Abstract Cenchrus alopecuroides Thunb. 1794 is a warm-season ornamental grass with glossy foliage and showy inflorescence, which is widely distributed from eastern Asia to Australia. Although this species is of great economic importance, little genomic sequence data are available. Here, we report the complete chloroplast genome sequence of C. alopecuroides, which provides valuable plastid genomic resources for further studies on this ornamental grass. The chloroplast genome of C. alopecuroides was 138,053 bp in length and exhibited a typical quadripartite structure, which comprised a pair of inverted repeat regions (22,331 bp) separated by a large (81,177 bp) and a small single copy (12,214 bp) region. In total, 110 unique genes were annotated in the chloroplast genome, containing 76 protein-coding genes, 30 transfer RNAs and four ribosomal RNAs. The overall GC content of the chloroplast genome was 38.6%. Phylogenetic analysis based on 11 whole-chloroplast genome sequences of Cenchrus species suggested that C. alopecuroides and C. compressus were sisters to each other and joint sisters to C. clandestinus.
THE EFFECT OF COLD CHAIN DISRUPTION ON THE MICROBIOLOGICAL PROFILE OF CHILLED FISH

Cold chain interruption during transport of samples to the laboratory may become a confounder in food hygiene practice. This study evaluates the influence of inadequate transport conditions on the microbiological quality of fish fillets from common carp (Cyprinus carpio), Atlantic salmon (Salmo salar), and Atlantic cod (Gadus morhua) packaged in plain, vacuum, or modified atmosphere packaging (MAP). The experiments simulated the temperature increase during sample transport to 3, 5, 8, 11, 14, 17, 20, and 25 °C with exposure times of 1, 2, 3, and 4 h. Aerobic plate count (APC), psychrotrophic microorganisms count (PMC), β-D-glucuronidase-positive Escherichia coli, and Salmonella spp. were evaluated immediately after the exposure to the elevated temperature (0 h), 3 h, and 24 h after the return to the normal temperature. Variability was observed even in the initial microbial contamination of fish fillets. Storage at higher temperatures led to an increase in microbial contamination of fish fillets Different types of packaging also played a role. In non-fatty fish (cod), CO2-containing MAP led to a decrease in pH and, therefore, to lower microbial growth. In fatty fish (salmon), the presence of CO2 did not lead to the reduction in pH and the degree of microbial contamination was, therefore, not affected by this intrinsic factor. If cold chain interruption occurs during the transport of samples to the laboratory, it is necessary to perform the analysis immediately as even storage in cold conditions until the next day can lead to increased levels of microbial contamination of such samples.
Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks

Geometrical assessments of human skulls have been conducted based on anatomical landmarks. If developed, the automatic detection of these landmarks will yield both medical and anthropological benefits. In this study, an automated system with multi-phased deep learning networks was developed to predict the three-dimensional coordinate values of craniofacial landmarks. Computed tomography images of the craniofacial area were obtained from a publicly available database. They were digitally reconstructed into three-dimensional objects. Sixteen anatomical landmarks were plotted on each of the objects, and their coordinate values were recorded. Three-phased regression deep learning networks were trained using ninety training datasets. For the evaluation, 30 testing datasets were employed. The 3D error for the first phase, which tested 30 data, was 11.60 px on average (1 px = 500/512 mm). For the second phase, it was significantly improved to 4.66 px. For the third phase, it was further significantly reduced to 2.88. This was comparable to the gaps between the landmarks, as plotted by two experienced practitioners. Our proposed method of multi-phased prediction, which conducts coarse detection first and narrows down the detection area, may be a possible solution to prediction problems, taking into account the physical limitations of memory and computation.
Asymptotic final size distribution of the multitype Reed–Frost process

The asymptotic final size distribution of a multitype Reed–Frost process, a chain-binomial model for the spread of an infectious disease in a finite, closed multitype population, is derived, as the total population size grows large. When all subgroups are of comparable size, the infection pattern irreducible and the epidemic started by a small number of initial infectives, the classical threshold behaviour is obtained, depending on the basic reproduction rate of the disease in the population, and the asymptotic distributions for small and large outbreaks can be found. The same techniques can then be used to study other asymptotic situations, e.g. small groups in an otherwise large population, large numbers of initial infectives and reducible infection patterns.
Avaliação química de medicamentos emagrecedores classificados como naturais

Introdução: Atualmente, o consumo de medicamentos emagrecedores naturais ocupa lugar de destaque nas vendas de medicamentos, principalmente em decorrência da facilidade de acesso, tendo em vista que são amplamente ofertados na internet, bem como no comércio especializado ou não. Tal situação aumenta o potencial de adulteração desses medicamentos, pois a fiscalização pelos órgãos de controle é difícil de ser aplicada de forma satisfatória. Desse modo, o presente trabalho tem por objetivo avaliar a possível presença de substâncias anorexígenas sintéticas em medicamentos emagrecedores naturais vendidos informalmente no mercado local, na região da Tríplice Fronteira de Foz do Iguaçu-PR. Métodos: A abordagem experimental foi baseada inicialmente em um estudo preliminar por meio do perfil cromatográfico de 3 diferentes medicamentos emagrecedores naturais em experimentos de Cromatografia de Camada Delgada (CCD), com dois sistemas eluentes denominados TA e TS2. Após os estudos preliminares realizados por CCD, os medicamentos foram examinados por Cromatografia Gasosa acoplada à Espectrometria de Massas (CG/EM) utilizando o cromatógrafo Agilent Technologies 7890A. Resultados: As bandas majoritárias dos medicamentos MT e BT apresentaram a mesma migração que o padrão de sibutramina nos sistemas eluentes TA (Rf~0,75) e TS2 (Rf~0,92), indicando a adulteração por tal substância. A sensibilidade maior da técnica CG/EM, no entanto, permitiu constatar que os três medicamentos examinados continham sibutramina na composição. Conclusões: A presença de sibutramina nos três medicamentos examinados evidencia a necessidade de uma melhor fiscalização pelos órgãos competentes, no que se refere aos medicamentos vendidos no mercado informal.
Endobronchial Ultrasound is Useful in the Assessment of Bronchial Wall Changes Related to Bronchial Thermoplasty

Background Bronchial thermoplasty (BT) is an interventional endoscopic treatment for severe asthma leading to the clinical improvement, but morphologic changes of bronchial wall related to the procedure and predictors of a favorable response to BT remain uncertain. The aim of the study was to validate an endobronchial ultrasound (EBUS) in assessing the effectiveness of BT treatment. Methods Patients with severe asthma who met the clinical criteria for BT were included. In all patients clinical data, ACT and AQLQ questionnaires, laboratory tests, pulmonary function tests and bronchoscopy with radial probe EBUS and bronchial biopsies were collected. BT was performed in patients with the thickest bronchial wall L2 layer representing ASM. These patients were evaluated before and after 12 months of follow-up. The relationship between baseline parameters and clinical response was explored. Results Forty patients with severe asthma were enrolled to the study. All 11 patients qualified to BT successfully completed the 3 sessions of bronchoscopy. BT improved asthma control (P=0.006), quality of life (P=0.028) and decreased exacerbation rate (P=0.005). Eight of the 11 patients (72.7%) showed a clinically meaningful improvement. BT also led to a significant decrease in the thicknesses of bronchial wall layers in EBUS (L1 decreased from 0.183 to 0.173 mm, P=0.003; L2 from 0.207 to 0.185 mm, P = 0.003; and L3–5 from 0.969 to 0.886 mm, P=0.003). Median ASM mass decreased by 61.8% (P=0.002). However, there was no association between baseline patient characteristics and the magnitude of clinical improvement after BT. Conclusion BT was associated with a significant decrease in the thickness of the bronchial wall layers measured by EBUS including L2 layer representing ASM and ASM mass reduction in bronchial biopsy. EBUS can assess bronchial structural changes related to BT; however, it did not predict the favorable clinical response to therapy.
A Lightweight Dual-Branch Swin Transformer for Remote Sensing Scene Classification

The main challenge of scene classification is to understand the semantic context information of high-resolution remote sensing images. Although vision transformer (ViT)-based methods have been explored to boost the long-range dependencies of high-resolution remote sensing images, the connectivity between neighboring windows is still limited. Meanwhile, ViT-based methods commonly contain a large number of parameters, resulting in a huge computational consumption. In this paper, a novel lightweight dual-branch swin transformer (LDBST) method for remote sensing scene classification is proposed, and the discriminative ability of scene features is increased through combining a ViT branch and convolutional neural network (CNN) branch. First, based on the hierarchical swin transformer model, LDBST divides the input features of each stage into two parts, which are then separately fed into the two branches. For the ViT branch, a dual multilayer perceptron structure with a depthwise convolutional layer, termed Conv-MLP, is integrated into the branch to boost the connections with neighboring windows. Then, a simple-structured CNN branch with maximum pooling preserves the strong features of the scene feature map. Specifically, the CNN branch lightens the LDBST, by avoiding complex multi-head attention and multilayer perceptron computations. To obtain better feature representation, LDBST was pretrained on the large-scale remote scene classification images of the MLRSN and RSD46-WHU datasets. These two pretrained weights were fine-tuned on target scene classification datasets. The experimental results showed that the proposed LDBST method was more effective than some other advanced remote sensing scene classification methods.
The antioxidant activity, preliminary phytochemical screening of Zingiber zerumbet and antimicrobial efficacy against selective endodontic bacteria

Abstract Antibiotic resistance is rising across the world. For a very long time, bitter ginger (Zingiber zerumbet) has been used as one of the most popular herbal remedies to treat a wide range of common diseases. Ginger has been shown to have antioxidant and antibacterial activity. It has various bioactive chemicals that might be utilized as an alternative treatment option for many infectious diseases. The present study aimed to examine the biochemical profile of ginger, antioxidant, and antibacterial activity against selective endodontic microbes. Antioxidant was measured using DPPH and antibacterial activity was performed using disk diffusion tests. Streptococcus mutants, Enterococcus faecalis, Staphylococcus spp. , and Lactobacillus spp. were tested for antibacterial activity. Before evaluating the dried extracts, all solvents were eliminated using rotary evaporation. The obtained IC50 value revealed that ethanol extract had the greatest antioxidant activity. Concerning each bacterium, the plant extracts demonstrated considerable antibacterial activity (p = .001). Ethanol extracts showed the strongest antibacterial activity against the studied microorganisms. This study highlights that the Zingiber zerumbet (Z. zerumbet) is a strong antibacterial herb against multidrug‐resistant (MDR) gram‐positive bacteria. It may also be employed as a possible natural antioxidant source.
On the Problems of Topology

In December 1946, as part of the Bicentennial Celebration of Princeton University, a conference was held on "The Problems of Mathematics". The participants of the Topology Session of this Conference have submitted a large number of unsolved problems, and it is the purpose of this article to present these problems to the mathematical public. In order to make the presentation more understandable and palatable it is necessary to include a birds-eye view of some of the trends of present day Topology. An effort is made to include opinions expressed during the Session and in the subsequent informal discussions. Very little effort towards objectivity has been made in writing this report; in a field that moves as rapidly and in as many directions as Topology today, it is impossible to remain an impartial observer. The classical unsolved problems in Topology (e.g. the "Hauptvermutung", Poincares hypothesis, triangulability of manifolds, Dehn's lemma) have not been restated.
STRATEGIC MANAGEMENT AND PERFORMANCE OF MANUFACTURING FIRMS IN DELTA STATE

This study examined the relationship that exist between strategic management and the performance of manufacturing firms in Delta State Nigeria. During the course of the study, four research questions and four null hypotheses were formulated and the hypotheses were tested. The population of the study was 136 drawn out of some selected manufacturing firms in Delta State, from which a sample of 101 subjects were derived using the Taro-Yamani Formula. The instrument used for data collection was the questionnaire. Face and content validity were jointly used to establish the validity of the research instrument. Relevant statistical tools were deployed for the purpose of data analysis and the formulated hypotheses were tested by means of the regression technique. The findings of the study showed that there is significant relationship between strategic management process and performance of manufacturing firms in Delta State, Nigeria. This study thus recommended that manufacturing firms should always adopt the right and accurate strategies in other to achieve better and impressive performance. Keywords: Strategic Management, Manufacturing Firms, Business Model, Nigeria, Strategy Formulation.
Birkhoff theorems for R+R/sup 2/ gravity theories with torsion

A Birkhoff theorem recently proved by Ramaswamy and Yasskin for one specific R+R/sup 2/ gravity theory with torsion is extended to include most R+R/sup 2/ theories. Out of the five-parameter family of R+R/sup 2/ theories, two four-parameter families are shown to obey a Birkhoff theorem. In addition, the entire five-parameter family is shown to obey a weakened Birkhoff theorem: If the assumption of asymptotic flatness is added to the assumption of spherical symmetry, then the only solution is the Schwarzschild metric with zero torsion.
The Impacts of Remote Learning and In-Person Instruction Transitions on the Mental Health of Children and their Caregivers

While the COVID-19 pandemic was initially rooted in social unity and acceptance of lockdown procedures, long-standing social distancing measures have kept students out of the physical classroom on and off for nearly two years. The instability of transitions and outcomes of remote learning has gained attention, mainly as frustrations over increased transmission rates and subsequent quarantines occur. Evidence has shown that the pandemic has had negative mental health outcomes on the caregivers of school-aged children. Still, there have been no known studies regarding the effects of shifting transitions between virtual and traditional learning modalities. The present study examined caregivers’ and children’s mental health experiences with remote and return to in-person learning and the correlation between caregiver and child mental health throughout the transition of learning platforms through survey data. A series of ANCOVA models showed that 74% of participants indicated that they and their children showed an increase in at least one mental health concern during remote learning. Post-hoc analysis showed a significant decrease in children’s mental health concerns after their return to in-school learning. These results suggest that more support for caregivers and students during shifting learning modalities is necessary to reduce negative mental health symptoms.
A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

MEMS gyroscopes are one of the core components of inertial navigation systems. The maintenance of high reliability is critical for ensuring the stable operation of the gyroscope. Considering the production cost of gyroscopes and the inconvenience of obtaining a fault dataset, in this study, a self-feedback development framework is proposed, in which a dualmass MEMS gyroscope fault diagnosis platform is designed based on MATLAB/Simulink simulation, data feature extraction, and classification prediction algorithm and real data feedback verification. The platform integrates the dualmass MEMS gyroscope Simulink structure model and the measurement and control system, and reserves various algorithm interfaces for users to independently program, which can effectively identify and classify seven kinds of signals of the gyroscope: normal, bias, blocking, drift, multiplicity, cycle and internal fault. After feature extraction, six algorithms, ELM, SVM, KNN, NB, NN, and DTA, were respectively used for classification prediction. The ELM and SVM algorithms had the best effect, and the accuracy of the test set was up to 92.86%. Finally, the ELM algorithm is used to verify the actual drift fault dataset, and all of them are successfully identified.
Develop and Validate a Survey to Assess Adult’s Perspectives on Autonomous Ridesharing and Ridehailing Services

Autonomous vehicles (AVs) have generated excitement for the future of transportation. Public transit agencies and companies (i.e., Uber) have begun developing shared autonomous transportation services. Most AV surveys focus on public opinion of perceived benefits and concerns of AVs but are not directly tied to field implementation of AVs. Experience and exposure to new technology affect adults’ perceptions and level of technology acceptance. As such, the Autonomous RideShare Services Survey (ARSSS) was developed to assess adults’ perceptions of AVs before and after being exposed to AVs. Face validity and content validity were established via focus groups and subject-matter experts (CVI = 0.95). Adults in the U.S. (N = 553) completed the ARSSS, and a subsample (N = 100) completed the survey again after two weeks. An exploratory and confirmatory factor analysis demonstrated that the ARSSS consists of three factors that can be used to reliably quantify users’ perceptions of AVs: (a) Intention to Use, Trust, and Safety (r = 0.85, p < 0.001, ICC = 0.99); (b) Potential Benefits (r = 0.70, p < 0.001, ICC = 0.97); and (c) Accessibility (r = 0.78, p < 0.001, ICC = 0.96) of AVs. These are key factors in predicting intention to use and acceptance of AVs. Results from the ARSSS may inform the acceptance among users of these AV technologies.
Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China’s regional carbon price.
Temperature dependent operation of the vertical cavity surface emitting laser

The temperature dependence of the vertical cavity surface emitting laser is evaluated using a laser model based on the no-k selection rule. The predictions indicate that the temperature for the minimum threshold current has a substantial dependence on the loss in the laser, represented by the photon lifetime, which is in agreement with experimental findings. Using the Fermi energies as parameters to connect the carrier densities at threshold to the lasing frequency, a family of design curves, which are dependent only on the laser cavity dimension and a universal loss parameter K, is produced. Since K encompasses all significant design variables, the design tool enables a compromise to be chosen between the magnitude of the threshold current and its required temperature sensitivity. >
A Mobile Hybrid Robot and Its Accuracy Issue in Machining of Large-Scale Structures

Accurate and efficient manufacturing of large-scale structures is an important and challenging issue. To this end, a mobile hybrid robot named CraftsRobot is designed and developed for in-situ machining the local features of large-scale structures. The mobile hybrid robot comprises a mobile vehicle, a guide rail, a two degree-of-freedom (DoF) mechanical arm, and a five-DoF light-weight parallel module. The interaction mechanism between the parallel module and the mechanical arm is disclosed to build the error prediction model. On this basis, a model predictive compensation method is proposed. In each servo cycle, the command pose of the parallel module is modified and under the effect of the interaction-induced error superimposed on the modified command pose, the parallel module will reach the target pose. The results of simulation and experiment both verify the effectiveness of the compensation model. After compensation, the mobile hybrid robot is applied to machine the features on a typical large-scale structure and the machining errors are within 0.04 mm. The experimental results show that the designed robot can machine local features of large-scale structures in situ and meet the precision requirements. Together, this study provides a novel approach to achieve accurate and efficient manufacturing of large-scale structures.