Miracle-dz/UniFAR-FTUs huggingface dataset:
High-resolution digital beamforming of UWB signals based on Caratheodory representation for delay compensation and array extrapolation

: To realize high-resolution digital beamforming (DBF) of ultra-wideband (UWB) signals, we propose a DBF method based on Carath´eodory representation for delay compensation and array extrapolation. Delay compensation by Carath´eodory representation could achieve high interpolation accuracy while using the single channel sampling technique. Array extrapolation by Carath´eodory representation reformulates and extends each snapshot, consequently extends the aperture of the original uniform linear array (ULA) by several times and provides a better real-time performance than the existing aperture extrapolation utilizing vector extrapolation based on the two dimensional autoregressive (2-D AR) model. The UWB linear frequency modulated (LFM) signal is used for simulation analysis. Simulation results demonstrate that the proposed method is featured by a much higher spatial resolution than traditional DBF methods and lower sidelobes than using Lagrange fractional ﬁ lters.
Nonlinear Periodic and Solitary Water Waves on Currents in Shallow Water

A variable‐coefficient Korteweg–de Vries equation is used to model the deformation of nonlinear periodic and solitary water waves propagating on a unidirectional background current, which is either flowing in the same direction as the waves, or is opposing them. As well as the usual form of the Korteweg–de Vries equation, an additional term is needed when the background current has vertical shear. This term, which has hitherto been often neglected in the literature, is linear in the wave amplitude and represents possible nonconservation of wave action. An additional feature is that horizontal shear in the background current is inevitably accompanied by a change in total fluid depth, to conserve mass, and this change in depth is a major factor in the deformation of the waves. Using a combination of asymptotic analyses and numerical simulations, it is found that waves grow on both advancing and opposing currents, but the growth is greater when the current is opposing.
Construction of chronic glomerulonephritis‑related lncRNA‑mRNA regulatory network and lncRNA‑miRNA‑mRNA ceRNA network by bioinformatics analysis

Long non-coding RNAs (lncRNAs) are ncRNA transcripts >200 nucleotides that are important genetic regulators. LncRNAs can directly regulate mRNA through a lncRNA-mRNA regulatory mode and can also regulate mRNA through competitive binding to micro (mi)RNA, which is generally known as the competitive endogenous RNA (ceRNA) network. The present study evaluated the functional roles and regulatory networks of lncRNAs in chronic glomerulonephritis (CGN). The proliferative ability of mouse glomerular mesangial cells (GMCs) induced by different concentrations of lipopolysaccharide (LPS) was assessed using the Cell Counting Kit-8 assay, and RNA sequencing (RNA-seq) was performed to identify differentially expressed lncRNAs in LPS-induced GMCs. Based on the sequencing results, six lncRNAs were selected for validation using reverse transcription-quantitative PCR (RT-qPCR). Furthermore, the lncRNA-mRNA regulatory network and the lncRNA-miRNA-mRNA ceRNA network were constructed to assess the role and mechanism of CGN-related lncRNAs. To elucidate the biological functions of lncRNAs, Gene Ontology (GO) biological process term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on all mRNAs involved in the lncRNA-mRNA regulatory network and in the ceRNA network. A total of 1,532 differentially expressed lncRNAs, including 594 upregulated lncRNAs and 938 downregulated lncRNAs, were identified using RNA-seq. The results of RT-qPCR validation were consistent with RNA-seq results. An lncRNA-mRNA regulatory network, including 236 lncRNAs and 556 mRNAs, and a ceRNA network, including 6 lncRNAs, 18 miRNAs and 419 mRNAs, were successfully constructed. The GO biological process term enrichment and KEGG pathway enrichment analyses demonstrated that those lncRNAs were often related to inflammatory response and substance metabolism. The present study identified key CGN-related lncRNAs in LPS-induced GMCs, and further demonstrated a global view of the lncRNA-mRNA regulatory network and ceRNA network involved in CGN. These results offered novel insights into the roles of lncRNAs in the pathogenesis of CGN and identified potential diagnostic biomarkers.
No size fits all – a qualitative study of factors that enable adaptive capacity in diverse hospital teams

Introduction Resilient healthcare research studies how healthcare systems and stakeholders adapt and cope with challenges and changes to enable high quality care. By examining how performance emerges in everyday work in different healthcare settings, the research seeks to receive knowledge of the enablers for adaptive capacity. Hospitals are defined as complex organizations with a large number of actors collaborating on increasingly complexity tasks. Consequently, most of today’s work in hospitals is team based. The study aims to explore and describe what kind of team factors enable adaptive capacity in hospital teams. Methods The article reports from a multiple embedded case study in two Norwegian hospitals. A case was defined as one hospital containing four different types of teams in a hospital setting. Data collection used triangulation of observation (115 h) and interviews (30), followed by a combined deductive and inductive analysis of the material. Results The study identified four main themes of team related factors for enabling adaptive capacity; (1) technology and tools, (2) roles, procedures, and organization of work, (3) competence, experience, knowledge, and learning, (4) team culture and relations. Discussion Investigating adaptive capacity in four different types of teams allowed for consideration of a range of team types within healthcare and how the team factors vary within and across these teams. All of the four identified team factors are of importance in enabling adaptive capacity, the various attributes of the respective team types prompt differences in the significance of the different factors and indicates that different types of teams could need diverse types of training, structural and relational emphasis in team composition, leadership, and non-technical skills in order to optimize everyday functionality and adaptive capacity.
Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station

The primary purpose of green communication is to reduce energy use. The base station (BS) is a radio receiver/transmitter that acts as the wireless network’s hub. It serves as a link between a wired and wireless network. To receive and transmit messages, BS uses a lot of energy. The use of effective sleep and wake-up/setup activities with an acceptable delay helps reduce base station power consumption. In this paper, the BS’s service process is modelled as a finite buffer queue with close down, sleep, and setup. After a certain number of user requests (URs) have accumulated in the system, to awaken the BS from multiple sleeps (MS) the -Policy is implemented. To produce probability generating functions, the supplementary variable approach is applied. The UR’s mean delay and the BS’s mean power consumption are calculated using simulation. According to computational studies, multiple sleeps with -policy consume less power than multiple sleeps without -policy.
Transfer of spin angular momentum of an incident wave into orbital angular momentum of the bound states in the continuum in an array of dielectric spheres

We consider scattering of electromagnetic plane waves by a linear periodic array of dielectric spheres. For incident waves with circular polarization with frequency tuned to the bound states with orbital angular momentum in the radiation continuum, the spin angular momentum of the incident wave transfers into the orbital angular momentum. This, in turn, gives rise to giant vortical power currents rotating around the array. Incident wave with linear polarization with frequency tuned to the Bloch bound state in the continuum induces giant laminar power currents.
An Enriched Prediction Intervals Construction Method with Hybrid Intelligent Optimization

Prediction intervals (PIs), within which future observations of time series are expected to fall, are a powerful method for uncertainty modeling and forecasting. This paper presents the construction of optimal PIs using an enriched extreme learning machine (ELM)-based method. While quality evaluation indices for PIs on reliability and sharpness of prediction results have been defined in the literature, this paper proposes a new PIs evaluation index, robustness, which focuses on the forecasting error. Combined with the above three indices, a more comprehensive objective function is then formed for optimal PIs construction. The paper also proposes an efficient hybrid quantum-behaved particle swarm optimization method with bacterial foraging mechanism to optimize the parameters in the ELM model. The effectiveness of the additional robustness index and the proposed improved ELM approach in determining higher quality PIs is demonstrated by applying them to PIs constructions for the cases of prediction in different datasets.
Report on BIPM/CIPM key comparison CCAUV.U-K4: absolute calibration of medical hydrophones in the frequency range 0.5 MHz to 20 MHz

The key compariosn CCAUV.U-K4 involved measurement of end-of-cable loaded sensitivity in units of volts/pascal of two travelling standards, 1 mm element diamater medical hydrophones at medical ultrasound frequencies. This is a repetition of key comparison CCAUV.U-K2 but the scope has been extended upwards to 20 MHz and downwards to 0.5 MHz. The reduction in the lower frequency provided an overlap with the underwater acoustics key comparison CCAUV.W-K1 which covers the range 1 kHz to 0.5 MHz. The results are analysed and presented in terms of degrees of equivalence, suitable for entry in the BIPM key comparison database. Main text To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm. org/. The final report has been peer-reviewed and approved for publication by the CCAUV, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Effects of High Pharmaceutical Concentrations in Domestic Wastewater on Membrane Bioreactor Treatment Systems: Performance and Microbial Community

Despite pharmaceuticals being widely detected in water-bodies worldwide, what remain unclear are the effects of high pharmaceutical concentrations on the treatment efficiency of biological wastewater treatment processes, such as membrane bioreactor (MBR) systems. This study investigated the efficiency of MBR technology in the treatment of synthetic wastewater containing a mixture of five typical pharmaceuticals (ofloxacin, sulfamethoxazole, sulfamethylthiadiazole, carbamazepine and naproxen) with a total concentration of 500 µg/L. Both the control MBR (MBRc) without pharmaceutical dosing and the MBR operated with high influent pharmaceutical concentrations (MBRe) were operated under room temperature with the same hydraulic retention time of 11 h and the same sludge retention time of 30 d. The removal efficiency rates of total nitrogen and total phosphorus were 83.2% vs. 90.1% and 72.6% vs. 57.8% in the MBRc vs. MBRe systems, and both MBRs achieved >98% removal of organics for a 180-day period. The floc size decreased, and membrane fouling became more severe in the MBRe system. Microbial diversity increased in the MBRe system and the relative abundances of functional microbe differed between the two MBRs. Furthermore, the total relative abundances of genes involved in glycolysis, assimilating nitrate reduction and nitrification processes increased in the MBRe system, which could account for the higher organics and nitrogen removal performance. This work provides insights for MBR operation in wastewater treatment with high pharmaceutical concentrations.
A latent scale model to minimize subjectivity in the analysis of visual rating data for the National Turfgrass Evaluation Program

Introduction Traditional evaluation procedure in National Turfgrass Evaluation Program (NTEP) relies on visually assessing replicated turf plots at multiple testing locations. This process yields ordinal data; however, statistical models that falsely assume these to be interval or ratio data have almost exclusively been applied in the subsequent analysis. This practice raises concerns about procedural subjectivity, preventing objective comparisons of cultivars across different test locations. It may also lead to serious errors, such as increased false alarms, failures to detect effects, and even inversions of differences among groups. Methods We reviewed this problem, identified sources of subjectivity, and presented a model-based approach to minimize subjectivity, allowing objective comparisons of cultivars across different locations and better monitoring of the evaluation procedure. We demonstrate how to fit the described model in a Bayesian framework with Stan, using datasets on overall turf quality ratings from the 2017 NTEP Kentucky bluegrass trials at seven testing locations. Results Compared with the existing method, ours allows the estimation of additional parameters, i.e., category thresholds, rating severity, and within-field spatial variations, and provides better separation of cultivar means and more realistic standard deviations. Discussion To implement the proposed model, additional information on rater identification, trial layout, rating date is needed. Given the model assumptions, we recommend small trials to reduce rater fatigue. For large trials, ratings can be conducted for each replication on multiple occasions instead of all at once. To minimize subjectivity, multiple raters are required. We also proposed new ideas on temporal analysis, incorporating existing knowledge of turfgrass.
Cyclicity in homotopy algebras and rational homotopy theory

Abstract Kadeishvili proposes a minimal C ∞ {C_{\infty}} -algebra as a rational homotopy model of a space. We discuss a cyclic version of this Kadeishvili C ∞ {C_{\infty}} -model and apply it to classifying rational Poincaré duality spaces. We classify 1-connected minimal cyclic C ∞ {C_{\infty}} -algebras whose cohomology algebras are those of ( S p × S p + 2 ⁢ q - 1 ) ⁢ ♯ ⁢ ( S q × S 2 ⁢ p + q - 1 ) {(S^{p}\times S^{p+2q-1})\sharp(S^{q}\times S^{2p+q-1})} , where 2 ≤ p ≤ q {2\leq p\leq q} . We also include a proof of the decomposition theorem for cyclic A ∞ {A_{\infty}} and C ∞ {C_{\infty}} -algebras.
An Investigation of the Potential of Improving an R1234yf Parallel Flow Condenser Performance Using Modeling and Hybrid Procedure of the Modified NSGA-II and TOPSIS

ABSTRACT In recent years, compact heat exchangers have been used widely to increase the performance of automotive air conditioning systems. In the present work, a R1234yf parallel flow condenser is optimized to investigate the potential of performance improvement without changing the condenser dimensions including length, width, and depth. To achieve this aim, a one-dimensional finite element model is developed to predict the parallel flow condenser performance. The developed model is then used for optimization procedure after validating by the experimental data. The modified NSGA-II approach is applied to maximize heat transfer rate and minimize entropy generation number, refrigerant pressure drop, and air pressure drop as the objective functions. The non-dominated optimum design points are then plotted and trade-off optimum points are obtained using the technique for order of preference by similarity to ideal solution. Hydraulic diameter and flat tube arrangement are the most important design parameters in an independent two-objective optimization of heat transfer rate and refrigerant pressure drop and the optimized design variables result in heat transfer rate increase of about 1.4% and refrigerant pressure drop reduction of about 80.6%. However, the outcome of considering heat transfer rate as the only objective function is 5.2% increment in heat transfer rate. Such design points unveil significant optimum design principles that can be obtained only by using a multi-objective optimization approach.
Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.
Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network

Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice.
Edge Product Cordial Labeling of Some Cycle Related Graphs

For a graph having no isolated vertex, a function is called an edge product cordial labeling of graph G, if the induced vertex labeling function defined by the product of labels of incident edges to each vertex is such that the number of edges with label 0 and the number of edges with label 1 differ by at most 1 and the number of vertices with label 0 and the number of vertices with label 1 also differ by at most 1. In this paper, we discuss edge product cordial labeling for some cycle related graphs.
STANDARADIZATION OF MEAT ANALOGUES INCORPORATED WITH TENDER JACKFRUIT

The study was carried out for three months during 2021 at Department of Community Science, College of Agriculture, Kerala Agricultural University, Vellanikkara and standardization was done formulating varied treatments of meat analogues followed by organoleptic evaluation in which sensory attributes such as appearance, colour, flavour, taste, texture and overall acceptability of tender jackfruit incorporated cowpea and chickpea meat analogues were evaluated. The results revealed that among different treatments, the treatments T5 (40% CWP+25% TJ +25% WG +5% DSF +5% OMF) and T10 (50% CP+20% TJ+20% WG +5% DSF +5% OMF) were observed to have highest scores for all the sensory attributes including appearance, colour, flavour, texture, taste and overall acceptability of tender jackfruit incorporated meat analogues, respectively. Statistical analysis by applying Kendall’s (W) test showed that Kendall’s (W) value among different treatments of each set of both tender jackfruit incorporated meat analogues was highly significant with regards to all quality attributes. This study revealed that incorporation of wheat gluten along with tender jackfruit both at 20-25 percent showed improved sensory qualities such as texture, flavour, taste and overall acceptability with higher consumer preference.
Outcomes of Endoscopic Resection in Pediatric Skull Base Chordoma: A Systematic Review

The endoscopic approach has been recommended as a primary option for treating chordomas, and it is associated with better resection rates and fewer surgical complications than transcranial surgery. This review aimed to assess the long-term consequences and evidence in the current literature regarding the endoscopic approach's efficacy in treating skull-base chordoma in children. A systematic review was conducted based on the PubMed, Web of Science, and EMBASE databases to examine the clinical outcomes of endoscopic endonasal surgery for pediatric skull base chordoma tumors. The review included studies published in English that employed specific research designs and reported on pediatric patients with skull base chordoma. Of the 268 studies initially considered, 25 met our eligibility criteria and were included in the final analysis. The average age of the patients was 11.5 years, with approximately equal number of males and females. The endoscopic endonasal approach (EEA) was the most commonly used modality. Gross total resection (GTR) was achieved in 62.7% of patients, while 18.09% had a subtotal resection (STR), and 13.83% had near-total resection only. Most patients showed significant to moderate improvement from their baseline condition and had no recurrence during their follow-up. Our findings further endorse that the endoscopic approach is a viable primary treatment option for pediatric skull base chordoma.
Software Implementation of Binary BCH Decoder Using Microcontroller

In this paper a decoder of binary BCH code is implemented using a PIC microcontroller for code length n=127 bits with multiple error correction capability, the results are presented for correcting errors up to 13 errors. The Berkelam-Massey decoding algorithm was chosen for its efficiency. The microcontroller PIC18f45k22 was chosen for the implementation and programmed using assembly language to achieve highest performance. This makes the BCH decoder implementable as a low cost module that can be used as a part of larger systems. The performance evaluation is presented in terms of total number of instructions and the bit rate.