Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Quality of Recovery (QoR-15) following the administration of intravenous lignocaine and fentanyl in patients undergoing septoplasty under general anaesthesia: A double-blinded, randomised, controlled trial

Background and Aims: Recovery from surgery and anaesthesia is usually observed through conventional indicators. The Quality of Recovery (QoR-15) score was specially designed to measure psychometric and functional recovery from the patient's perspective. This study aimed to evaluate QoR-15 following the administration of intravenous (IV) lignocaine or IV fentanyl in patients undergoing septoplasty surgery. Methods: This randomised, controlled trial was conducted on 64 patients of American Society of Anesthesiologists (ASA) physical status I and II, of either sex, of ages between 18 and 60 years, and who were scheduled for septoplasty. The primary end point was to compare the quality of recovery following the administration of IV lignocaine(group L) and IV fentanyl (group F) using the QoR-15 score in patients undergoing septoplasty. Secondary end points were to compare postoperative analgesia, recovery characteristics, and adverse effects in both groups. Statistical analysis was done using the Shapiro–Wilk test, paired t test/ Wilcoxon signed-rank test, and unpaired t test/Mann–Whitney U test. A P-value <0.05 was considered statistically significant. Results: There was a significant improvement in the postoperative QoR-15 score than in the preoperative score in both groups (P < 0.000). However, the postoperative QoR-15 score was significantly higher in group L compared to group F (P < 0.001). Total consumption of analgesic doses were reduced in group L (P=0.000). Time taken to achieve an Aldrete score >9 and gastrointestinal recovery was shorter in group L compared to group F. Conclusion: Both IV lignocaine and IV fentanyl improved postoperative QoR-15 score; however, lignocaine had a higher postoperative QoR-15 score than fentanyl, in addition to showing early discharge readiness, better analgesia, and better recovery profile in patients following septoplasty surgery.
Spatial connectivity of tumor and associated cells (SpaCell): a novel computational pathology biomarker

The tumor microenvironment (TME) is comprised of multiple cell types, with their spatial organization having been previously studied to identify associations with disease progression and response to therapy. These works, however, have focused on spatial interactions of a single cell type, ignoring spatial interplay between the remaining cells. Here, we introduce a framework to quantify complex spatial interactions on H&E-stained image between multiple cell families simultaneously within the TME, called spatial connectivity of tumor and associated cells (SpaCell). First, nuclei are segmented and classified into different families (e.g., cancerous cells and lymphocytes) using a combination of image processing and machine learning techniques. Local clusters of proximal nuclei are then built for each family. Next, quantitative metrics are extracted from these clusters to capture inter- and intra-family relationships, namely: density of clusters, area intersected between clusters, diversity of clusters surrounding a cluster, architecture of clusters, among others. When evaluated for predicting risk of recurrence in HPV-associated oropharyngeal squamous cell carcinoma (n=233, 107 vs 126 patients for training vs testing) and non-small cell lung cancer (n=186, 70 vs 116 patients for training vs for testing), SpaCell was able to differentiate between patients at high and low risk of recurrence (p=0.03 and p=0.02, respectively). SpaCell was compared against a deep learning and a state-of-the-art approach that uses single-family cell cluster graphs (CGG). CCG extracted metrics were not prognostic of disease-free survival (DFS) for oropharyngeal (p=0.98) nor lung (p=0.15) cancer, and deep learning was prognostic of DFS for lung (p=0.03) but not for oropharyngeal cancer (p=0.26). SpaCell was not only prognostic for both cancer types but also provides more explainability in terms of tumor biology.
Boundedness of solutions to Ginzburg–Landau fractional Laplacian equation

In this paper, we give the boundedness of solutions to Ginzburg–Landau fractional Laplacian equation, which extends the Herve–Herve theorem into the nonlinear fractional Laplacian equation. We follow Brezis’ idea to use the Kato inequality. A related linear fractional Schrodinger equation is also studied.
PENGENALAN CITRA WAJAH MENGGUNAKAN METODE TWO-DIMENSIONAL LINEAR DISCRIMINANT ANALYSIS DAN SUPPORT VECTOR MACHINE

Sistem biometrik saat ini telah menjadi primadona dan banyak diaplikasikan dalam berbagai bidang. Salah satu aplikasi biometrik yang telah sering digunakan adalah sistem pengenalan wajah (Face Recognition). Dua hal yang menjadi masalah utama pada pengenalan wajah adalah proses ekstraksi fitur dari sampel wajah yang ada dan juga teknik klasifikasi yang digunakan untuk mengklasifikasikan wajah yang akan dikenali berdasarkan fitur-fitur yang telah terpilih. Banyak metode ekstraksi fitur yang memakai model representasi data berdasarkan vektor. Hal ini mengakibatkan singular problem, sehingga banyak fitur-fitur wajah yang tidak dapat terekstraksi dengan baik. Pada penelitian ini digunakan metode Two Dimensional Linear Discriminant Analysis (TDLDA) untuk ekstraksi fitur, yang menilai secara langsung matrik within-class scatter dari matriks citra tanpa transformasi citra ke vektor, dan hal ini mengatasi singular problem dalam matrik within-class scatter. Untuk mendapatkan hasil pengenalan yang optimal, metode klasifikasi yang digunakan adalah pengklasifikasi Support Vector Machine (SVM). Pengklasifkasi SVM akan berusaha mencari hyperplane yang optimal dimana dua kelas pola dapat dipisahkan dengan maksimal. Penelitian ini mengintegrasikan metode TDLDA dan SVM untuk pengenalan wajah. Dengan kombinasi kedua metode tersebut terbukti dapat memberikan hasil yang optimal dengan tingkat akurasi pengenalan antara 84,18% sampai 100% dengan uji coba menggunakan basis data ORL, YALE, dan BERN.
DOA Estimation for Multiple Speech Sources Based on Flexible Single-Source Zones and Concentration Weighting

Direction-of-arrival (DOA) estimation is the key to many audio applications. Recently, sparse component analysis (SCA)-based methods have attracted much attention, in which single-source points (SSPs) and single-source zones (SSZs) where one source is dominant over the others in time–frequency (T–F) domain are usually detected to construct the pooled histogram containing multisource DOA information. Nonetheless, the SSZ size in existing methods is fixed and empirically predetermined, which cannot accommodate to the varying spectrotemporal property of speech sources. Furthermore, higher SSP concentration in an SSZ implies a locally stronger dominant source as well as more reliable DOA information extracted therein, which, however, is also not considered yet. To address these problems, a DOA estimation, algorithm for multiple speech sources based on flexible SSZs and concentration weighting, is presented in this article. First, in each frame, correlation coefficients of time delay vectors across adjacent frequency bins are calculated to identify SSPs, followed by flexible SSZs construction using a varying number of SSPs located at the consecutive frequency bins. Next, the number of SSPs in each flexible SSZ is considered as a proxy of corresponding concentration degree and employed as a weighting factor to form the pooled histogram. Finally, a matching pursuit (MP)-based approach is utilized to obtain multisource DOA estimates. Simulation results reveal that the proposed method significantly outperforms the existing approaches in terms of noise floor in pooled histogram, angular resolution, and performance under various signal-to-noise ratio (SNR) and reverberant conditions. Real-world experiments also verify its effectiveness and meanwhile demonstrate considerably reduced computational complexity.
Evaluating the Effectiveness of Planar and Waveguide 3D-Printed Antennas Manufactured Using Dielectric and Conductive Filaments

3D printing is a technology suitable for creating electronics and electromagnetic devices. However, the manufacturing of both dielectric and conductive parts in the same process still remain a challenging task. This study explores the combination of 3D printing with traditional manufacturing techniques for antenna design and fabrication, giving the designer the advantage of using the additive manufacturing technology only to implement the most critical parts of a certain structure, ensuring a satisfying electromagnetic performance, but limiting the production cost and complexity. In the former part of the study, the focus is on three proximity-coupled patch antennas. It demonstrates how hybrid devices made of metal, dielectric, and 3D-printed (using Fused Filament Fabrication) conductive polymers can be successfully simulated and created for different operating frequency bands. In the latter part, the study compares three prototypes of a 5G-NR, high gain, and wideband waveguide antenna: respectively a fully 3D printed one made of electrifi (which is the most conductive commercial 3D-printable filament), an all-metal one, and a hybrid (3D-printed electrifi & metal) one. The results show a 15% reduction in efficiency when using the all-Electrifi configuration compared to all-metal one, and a 4-5% reduction when using the hybrid version.
Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population

Simple Summary The genomic estimated breeding value (GEBV) using data from Brangus heifers were obtained from genomic selection (GS) methods associating the single nucleotide polymorphisms (SNP) marker genotypes with phenotypic data for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, and percent intramuscular fat and longissimus muscle area) traits using the linkage disequilibrium (LD) between SNP markers and quantitative trait loci (QTL) and/or the genomic relationship between animals. The heritability estimates were found similar across genomic best linear unbiased prediction (the GBLUP), and the Bayesian (BayesA, BayesB, BayesC and Lasso) GS methods for k-means and random cluster. The Bayesian methods resulted in underestimates of heritabilities and overestimates of accuracy of GEBV. However, the GBLUP method resulted in more reasonable estimates of heritabilities and accuracies of GEBV for growth and carcass traits of heifers from a composite population. Abstract The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian (BayesA, BayesB, BayesC and Lasso) genomic selection (GS) methods for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, apercent intramuscular fat and longissimus muscle area) traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.06 and 0.21 for growth and carcass traits compared with those between 0.21 and 0.35 from the GBLUP methodologies. Although the prediction ability of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of heritability of growth and carcass traits. However, GBLUP resulted in accuracy of GEBV for growth and carcass traits that parallels previous reports.
Superpixel Consistency Saliency Map Generation for Weakly Supervised Semantic Segmentation of Remote Sensing Images

The weakly supervised semantic segmentation (WSSS) method aims to assign semantic labels to each image pixel from weak (image-level) instead of strong (pixel-level) labels, which can greatly reduce human labor costs. However, there are some problems in WSSS of remote sensing images, such as how to locate labels accurately and how to get precise segmentation edges. To address these issues, we propose a novel framework directly transferring the scene classification model to perform semantic segmentation. We first train a multilabel scene classification network as the encoder to obtain the pretrained model, and then, the feature learned by the model is transferred to the decoder. Different from other methods, we propose a saliency map generator (SMG) instead of the class activation map (CAM) for more accurate location information by making pixels belonging to the same class lie close together while different classes are separated in feature space. Meanwhile, we take the superpixel patch as processing unit to provide precise boundary inhibition for the saliency map. To assign semantic labels for each patch, combined with extracted salient region, we propose a module responsible for exploiting the consistency of spatial and semantic similarity between different patches. Finally, we incorporate the above two modules to supervise the training process of the decoder without generating pseudolabels as most methods do, thus simplifying the training process. Experimental results show that our method outperforms other weakly supervised approaches on dense labeling remote sensing dataset (DLRSD) and Wuhan dense labeling dataset (WHDLD) with at least a 3% improvement on mean intersection over union (mIoU).
Contact Tracing With District-Based Trajectories

Identifying the places an infected person has visited during a virus incubation time in order to conduct contact tracing is currently done using manual interviews since proximity-based contact tracing methods do not store geolocation information due to privacy concerns. During the incubation time, an infected person might visit several locations and either forget where they went or are reluctant to disclose their trip details. To minimize manual location tracing while preserving the user's privacy, the authors propose a mesh block sequence method where the trajectories are transformed into a mesh block sequence before being shared with health authorities. These simulations show that this a useful method by which to protect user privacy by concealing specific details related to a trajectory. While this simulation uses an Australian administrative region structure, this method is applicable in countries which implement similar administrative hierarchical building blocks.
Inference: An Elementary, General Approach

We present an expository, general analysis of valid post-selection or post-regularization inference about a low-dimensional target parameter in the presence of a very high-dimensional nuisance parameter that is estimated using selection or regularization methods. Our analysis provides a set of high-level conditions under which inference for the low-dimensional parameter based on testing or point estimation methodswillberegulardespiteselectionorregularizationbiasesoccurringin the estimation of the high-dimensional nuisance parameter. A key element is the use of so-called immunized or orthogonal estimating equations that are locally insensitive to small mistakes in the estimation of the high-dimensional nuisance parameter. As an illustration, we analyze affine-quadratic models and specialize these results to a linear instrumentalvariablesmodelwithmanyregressorsandmanyinstruments. We conclude with a review of other developments in post-selection inference and note that many can be viewed as special cases of the general encompassing framework of orthogonal estimating equations provided in this article.
Simulation study of a novel ZnO nanowire cold cathode flat-panel x-ray source using EGSnrc for Talbot-Lau type grating interferometry

Talbot-Lau grating interferometry (GI) can conduct X-ray phase contrast imaging outside synchrotron facilities and simultaneously provide attenuation, differential phase contrast, and small angle scattering information about imaging samples, where periodically distributed X-ray line sources are required. In this study, we proposed a novel cold-cathode flat-panel X-ray source with micro-array anode target to generate such line sources without using grating G0. Its cathode was composed of densely arranged zinc oxide (ZnO) nanowires, which can generate electrons by field electron emission effect, whereas micro periodic distributed Al-Mo-Al strips were utilized as anode target. X-ray spatial distribution and spectrum of the source with different anode target period (p0) were studied via using EGSnrc. Structured X-ray illumination required for GI was obtained under Mo strips. Mean contrast of the X-ray spatial distribution under the Mo strips of flat panel sources with p0 of 3, 15, 30, 126 m were 33.36%, 81.98%, 91.55%, and 98.63%, respectively. The source can eliminate the use of G0 and thus related limitations of G0 on Talbot-Lau GI.
A Framework for Integrating The Product-Consumer Adoption Model of Electric Motorcycle: Preliminary Research

The transportation sector is the main source of air pollution in Indonesian cities. Pollution can reduce air quality and increase carbon emissions that impact climate change. The Indonesian Government has issued regulations to accelerate the migration from internal combustion engine vehicles to electric vehicles to overcome this problem. This research is focused on electric motorcycles (EM) because motorcycles dominate more than 80% of the total vehicles in Indonesia. This paper aims to investigate consumer purchase intention in EM by considering consumer and product views in the Indonesian context. This research develops a conceptual framework by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) and the discrete choice model. This research contributes to considering brand trust factors into UTAUT and integrating consumer and product aspects in developing a hybrid choice model. This study will be conducted on motorcycle owners in Indonesia. The findings from the research will provide the EM purchase intention of Indonesian consumers by considering the analysis of consumer behavior for various EM attributes.
Impact of the COVID-19 pandemic on the consumption patterns of psychotropic drugs and predictors of limited access to medication

Background: Despite the efforts of the health system to improve access to medications during the coronavirus disease of 2019 (COVID-19) pandemic, such as online consultations, encouraging generic prescriptions, and other measures to limit the storage of medication, psychotropic patients faced significant challenges in accessing their medications. Objectives: This study aimed (1) to compare the consumption of psychotropics before and during the pandemic, (2) to assess the association between having difficulties finding the medications and the general characteristics of the patients, and (3) to assess the predictors of these difficulties. Design: A case–control study was performed in which 128 patients (cases) were recruited during the pandemic (July–October 2021), and 256 patients (controls) using psychotropics before the pandemic were matched for age and sex. Methods: Data were collected using a uniform survey given to patients using psychotropics and filled out at their time and place preferences. Results: More patients used antipsychotics and anti-anxiety medications before the pandemic, while antidepressants were used more during the pandemic. Almost half of the patients reported facing difficulties finding their medications in both time frames. Before the pandemic, these difficulties were noted per increase in age and being employed and were less faced if patients had medical assistance or lived in the north of Lebanon. However, more patients reported having difficulties accessing their medication during the pandemic, with no significant differences. Conclusion: The consumption of psychotropic drugs was higher among cases. All patients faced challenges in accessing their medication throughout the pandemic, while older and employed patients had more limitations before the pandemic. Further investigations exploring viable solutions are recommended in order to maintain sustainable access to treatment.
Factors affecting the livelihood diversification of Vietnam households

Sustainable livelihood is not only the livelihood objective but also households’ expectations. Livelihood diversification is aimed at ensuring the farmer's resilience to shocks and improving livelihood sustainability. Research on livelihood diversification in rural areas contributes to the development of agricultural development policies, social security policies for all farm households in Vietnam. The authors applied statistic and regression methods to analyze VARHS’s database which is surveyed 12 provinces in Vietnam over years. This paper is carried out to analyze and evaluate factors affecting the probability of choosing to diversify the livelihoods of Vietnamese farmers by developing a diversification index (SID) and SID’s determinants based on the DFID framework. The results show that the decision to diversify livelihood is influenced by many factors, including age, planted areas, asset value, saving money. Since then, the authors propose some implications for promoting the livelihood diversification of Vietnamese farmers.
Prevalence and Molecular Characterisation of Blastocystis sp. Infecting Free-Ranging Primates in Colombia

Infection with Blastocystis sp. has been reported in free-living and captive non-human primates (NHPs); however, surveys on Blastocystis sp. from north-western South America are scarce. This study aimed to identify Blastocystis sp. in free-ranging NHPs living in Colombia. A total of 212 faecal samples were collected from Ateles hybridus, Cebus versicolor, Alouatta seniculus, Aotus griseimembra, Sapajus apella, and Saimiri cassiquiarensis. Smears and flotation were used for morphological identification. For samples microscopically classified as positive for Blastocystis sp. , we used conventional PCR to amplify and sequence two regions of the SSU rRNA gene and used Maximum Likelihood methods and Median Joining Network analyses for phylogenetic analyses. Via microscopy, 64 samples were Blastocystis sp. positive. Through molecular analyses, 18 sequences of Blastocystis sp. subtype 8 (ST8) were obtained. Strain and allele assignment together with a comparative phylogenetic approach confirmed that the sequences were ST8. Alleles 21, 156, and 157 were detected. Median Joining network analyses showed one highly frequent haplotype shared by specimens from Colombia and Peru and close relationships between haplotypes circulating in NHPs from Colombia, Ecuador, Brazil, and Mexico. This survey could support the elaboration of a more accurate epidemiological picture of the Blastocystis sp. infecting NHPs.
Synchronous Acute Appendicitis and Acute Cholecystitis

The occurrence of synchronous acute cholecystitis and appendicitis is rare. There are few cases reported and small series in the literature. We report the case of a 77-year-old male who presented to the emergency department with right-sided abdominal pain. He was diagnosed preoperatively with acute calculous cholecystitis. During the initial laparoscopy, a complicated appendiceal phlegmon was found and was treated with a one-step laparoscopic approach and subsequent antibiotics. The patient had an uneventful recovery and was discharged on postoperative day (POD) 2. The pathology report confirmed both diagnoses and an incidental low-grade appendiceal mucinous neoplasm. Although uncommon, it is important to be aware of the possibility of both simultaneous pathologies in a patient who presents to the emergency department with abdominal pain.