Miracle-dz/UniFAR-FTUs huggingface dataset:
Split Ring Resonator Topology Based Microwave Induced Thermoacoustic Imaging (SRR-MTAI)

Microwave-induced thermoacoustic imaging (MTAI) using low-energy and long-wavelength microwave photons has great potential in detecting deep-seated diseases due to its unique ability of visualizing intrinsic electric properties of tissue in high resolution. However, the low contrast in conductivity between a target (e.g., a tumor) and the surroundings sets a fundamental limit for achieving a high imaging sensitivity, which significantly hinders its biomedical applications. To overcome this limit, we develop a split ring resonator (SRR) topology based MTAI (SRR-MTAI) approach to achieve highly sensitive detection by precise manipulation and efficient delivery of microwave energy. The in vitro experiments show that SRR-MTAI demonstrates an ultrahigh sensitivity of distinguishing a 0.4% difference in saline concentrations and a 2.5-fold enhancement of detecting a tissue target which mimicks a tumor embedded at a depth of 2 cm. The in vivo animal experiments conducted indicate that the imaging sensitivity between a tumor and the surrounding tissue is increased by 3.3-fold using SRR-MTAI. The dramatic enhancement in imaging sensitivity suggests that SRR-MTAI has the potential to open new avenues for MTAI to tackle a variety of biomedical problems that were impossible previously.
Comprehensive Custom-Pak® in Cataract and Vitreoretinal Surgery in Canadian Hospitals: Time and Budget Impact Analysis

Purpose Surgical procedure packs are commonly used in ophthalmological surgeries, but quantitative evidence demonstrating the time efficiency and economic impact of their use is limited. Evaluating the time and cost of surgical pack use is particularly important for publicly funded healthcare systems with tight budgets and/or an emphasis on value-based care. This study sought to estimate the economic impact of comprehensive surgical pack use in cataract and vitreoretinal (retina) surgeries across operating room (OR), materials management, and accounting departments in Canada. Methods A budget impact model developed for the United States (US) from a self-reported cross-sectional study was adapted for Canada. The US study obtained data via an online survey and timing exercises of surgical procedures. The model was adapted using relevant Canadian-specific labor and cost inputs. Use of generic commodity packs (no proprietary equipment-specific supplies) was compared to full utilization of Custom-Pak®, a comprehensive pack (disposables plus equipment-specific supplies) in cataract and retina surgeries at the facility and aggregate group (provincewide) levels. Results Switching from generic to comprehensive pack use in all 2500 cataract procedures at a community hospital saves 287 labor hours per year, primarily in the materials management department. Surgery preparation (OR) hours saved allow for an additional 196 potential procedures annually. Most of the Canadian Dollar (CAD) $39,815 annual cost savings are realized for the OR. Aggregating across 50,000 cataract surgeries at the provincial level yields savings of 5608 hours and 3916 additional procedures, translating to hidden cost reductions of CAD$790,632 annually. Implementing full Custom-Pak use at the facility level in 1000 retina cases saves $10,650 annually; provincewide, 127 potential additional procedures can be gained. Conclusion Comprehensive Custom-Pak use improves efficiency in cataract and retina surgeries saving substantial time and cost in Canadian hospitals, potentially allowing more patients to have these procedures, and reducing wait times.
Optimal fusion of sensor data for Kalman filtering

In this paper we consider the question of optimal fusion of sensor data for Kalman filtering. The basic problem is to design a linear filter whose output provides an unbiased minimum variance estimate of a signal process whose noisy measurements from multiple sensors are available for input to the filter. The problem is to assign weights to each of the sources (sensor data) dynamically so as to minimize estimation errors. We formulate the problem as an optimal control problem where the weight given to each of the sensor data is considered as one of the control variables satisfying certain constraints. There are as many controls as there are sensors. Using the control parametrization enhancing transform technique (CPET), we develop an efficient method for determining the optimal fusion strategy. Some numerical results are presented for illustration.
tRANSAC: Dynamic feature accumulation across time for stable online RANSAC model estimation in automotive applications

RANdom SAmple Consensus (RANSAC) is widely used in computer vision and automotive related applications. It is an iterative method to estimate parameters of mathematical model from a set of observed data that contains outliers. In computer vision, such observed data is usually a set of features (such as feature points, line segments) extracted from images. In automotive re-lated applications, RANSAC can be used to estimate lane vanishing point, camera view angles, ground plane etc. In such applications, changing content of road scene makes stable online model estimation difficult. In this paper, we propose a framework called tRANSAC to dynamically accumulate features across time so that online RANSAC model estimation can be stably performed. Feature accumulation across time is done in such a dynamic way that when RANSAC tends to perform robustly and stably, accumulated features are discarded fast so that fewer redundant features are used for RANSAC estimation; when RANSAC tends to perform poorly, accumulated features are discarded slowly so that more features can be used for better RANSAC estimation. Experimental results on road scene dataset for vanishing point and camera angle estimation show that the proposed tRANSAC method gives more stable and accurate estimates compared to baseline RANSAC method.
Anomalous behavior of the electrical resistivity in the giant magnetocaloric compound Gd5(Si0.1Ge0.9)4

High-resolution electrical resistance R and dR/dT measurements have been done in the (giant magnetocaloric) orthorhombic Gd 5 (Si 0 . 1 Ge 0 . 9 ) 4 compound to study their evolution through more than 50 thermal cycles between 300 K and 10 K. A detailed characterization of R and dR/dT behavior was made near the first-order magnetostructural transition at T S ∼80 K and the second-order magnetic transition at T N ∼128 K. Important precursor effects of the magnetostructural transition exist for 0.85≤T/T S ≤1, is causing an oscillatory regime in dR/dT below T S . The resistance of a Cd 5 (Si 0 . 1 Ge 0 . 9 ) 4 sample initially exhibits a sharp peak at T S followed by an abrupt decrease towards a minimum slightly above T S , but after a certain number of thermal cycles, a new R(T) regime sets in with no peak at T S . However, no significant changes occur in R(T) near T N . Thermal hysteresis associated with the first-order transition at T S shows a slow decrease upon thermal cycling, approaching virtual extinction after ∼50 thermal cycles. The change of the residual resistance with the number of thermal cycles was also studied and similarly displayed a saturation regime. A simple phenomenological approach is adopted in relation with the R(T) behavior in different thermal cycles, since a rigorous microscopic description is very problematic at the stage due to the complexity of the problems at hand.
Rate-Distortion Optimized Progressive Geometry Compression

During progressive transmission of 3D geometry models, the transmission order of details at different region has great effects on the quality of reconstructed models at low bit-rate. This work presents a rate-distortion (R-D) optimized progressive geometry compression scheme to improve the quality of reconstructed models by adjusting the transmission order of details. In this scheme, the input mesh is partitioned into parts, then each part is encoded into bit-stream independently, and the encoded bit-streams are truncated into segments while getting the R-D characteristics of every segment, at last all segments are assembled into a codestream based on R-D optimization, which ensure the region with rich detail are transmitted early and make the reconstructed mesh achieve better quality as soon as possible. Experimental results show that, as compared with the well-known PGC method, the proposed one provides better R-D performance. Moreover, it provides a novel way to realize the region of interest (ROI) coding of 3D meshes
Forced to go online: A case study of learning consecutive and simultaneous interpreting under Covid-19 in Iraq

This paper explores the perspectives of Iraqi undergraduates studying consecutive and simultaneous interpreting (CI, SI) in the Department of Translation at Mustansiriyah University in Iraq following the sudden transition to the online environment due to Covid-19. The online mode of teaching and learning has never been adopted in Iraq before. This study, therefore, aims to investigate the challenges and opportunities that have emerged from this unprecedented experience of online learning. It does so by adopting a mixed-method approach combining an online survey and two online focus group sessions. We also conducted two online pilot experiments to test the feasibility of the Zoom app to teach CI and SI synchronously. The main findings of this study suggest that while a synchronous online learning environment would potentially contribute to enhancing interpreting skills, the online mode cannot totally replace actual classes in the Iraqi higher education context unless a rigorous infrastructure is first established.
Surface plasmon resonance sensing using index-matched metallic nano-hole array structures

We present a novel three-dimensional nano-hole array structure in an optically thick gold film, which benefits from Surface Plasmon (SP) energy matching between the top and bottom of the gold film. We have experimentally evaluated both a conventional and an SP energy-matched nano-hole array structure for Surface Plasmon Resonance (SPR) sensing. Also, detection of various bulk refractive index liquids has been tested for both structures based on the transmission intensity differences in a narrow detection band. We observed a 2-fold higher sensitivity during SPR sensing with the new structure compared to a conventional nano-hole array.
Three-dimensional particle-in-cell simulations of laser wakefield experiments

Plasma accelerator methods offer the potential to reduce the size of moderate and high energy accelerators by factors of 1000. In the past few years great advances have been made in the production of low emittance, high quality (i.e., monoenergetic) electron beams with energies between . 1 and 1 GeV using ultra-fast (<50 femtoseconds), high power (>10TW) lasers. The most noticeable of these advances were the experimental results presented in the ‘Dream Beam’ issue of Nature and in a recent issues of Physical Review Letters, Nature, and Nature Physics. The experimental progress have been made due to advances in lasers, diagnostics, plasma sources, and the knowledge of how to control of this highly nonlinear acceleration process. And this experimental progress has occurred simultaneously with and been in part due to advances in modeling capabilities. Using a hierarchy of particle-in-cell (PIC) codes OSIRIS, VORPAL, and QuickPIC, we have performed numerous full scale 3D simulations using parameters quoted from the Nature and Nature Physics articles. Our simulations have predicted results, provided agreement between simulations and experiments (within the shot-to-shot variations of the experiments), and provided insight into the complicated physics of the experiments. Most importantly, as our confidence in the fidelity of our methods increases we can now guide the planning of new experiments, and probe parameters that are not yet available. Thereby providing a ‘road map’ for generating high quality, high-charge 10 to 100 GeV electron beams for use in high-energy physics and light sources.
Partial pole placement by LQ regulators: an inverse problem approach

This paper gives a necessary and sufficient condition under which a state feedback control law places part of the closed-loop poles exactly at specified points and, at the same time, is linear quadratic optimal for some quadratic weightings. This is made possible by means of a solution to the inverse problem of optimal control. A design example is given to illustrate the result.
Using Process Mining to Reduce Fraud in Digital Onboarding

In the context of online banking, new users have to register their information to become clients through mobile applications; this process is called digital onboarding. Fraudsters often commit identity fraud by impersonating other people to obtain access to banking services by using personal data obtained illegally and causing damage to the organisation’s reputation and resources. Detecting fraudulent users by their onboarding process is not a trivial task, as it is difficult to identify possible vulnerabilities in the process to be exploited. Furthermore, the modus operandi for differentiating the behaviour of fraudulent actors and legitimate users is unclear. In this work, we propose the usage of a process mining (PM) approach to detect identity fraud in digital onboarding using a real fintech event log. The proposed PM approach is capable of modelling the behaviour of users as they go through a digital onboarding process, while also providing insight into the process itself. The results of PM techniques and the machine learning classifiers showed a promising 80% accuracy rate in classifying users as fraudulent or legitimate. Furthermore, the application of process discovery in the event log dataset produced an insightful visual model of the onboarding process.
S26.4: Reducing false positives rates by rotating microarray‐based genomotyping data

Microarray-based comparative genomic hybridization is leading to an increased understanding of bacterial evolution and patho-genesis by efficiently comparing whole genomes. Here, a sample genome is compared to a reference genome via comparative hybridization using two different fluorescent dyes. The logratio of the fluorescence intensities for sample and reference genome for each gene on the array is then usually compared to a cutoff classifying the belonging gene as absent or present with respect to the sample genome. The resulting list of candidate absent genes then undergoes confirmational analyses, i.e. PCR or sequencing, which are decisive with respect to both time and costs of the experiment. Thus, there is vital interest to reduce the rate of false positives in the list of candidate absent genes from the comparative genomic hybridization step. Our approach to accomplish this task uses the expected relationship between the logratio and the mean log intensities for absent genes to linear transform the inten-sity data before comparing to a cutoff. For validated data from a series of comparative genomic hybridizations for two Bartonella species we assessed significance and efficacy of the new approach. We show that we are able to halve the rate of false positives in the list of candidate absent genes for a typical comparative genomic hybridization experiment, thus significantly reducing time and costs of necessary confirmational analyses.
Fasting Glucose Level on the Oral Glucose Tolerance Test Is Associated with the Need for Pharmacotherapy in Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) has a rapidly increasing prevalence, which poses challenges to obstetric care and service provision, with known serious long-term impacts on the metabolic health of the mother and the affected offspring. The aim of this study was to evaluate the association between glucose levels on the 75 g oral glucose tolerance test and GDM treatment and outcomes. We performed a retrospective cohort study of women with GDM attending a tertiary Australian hospital obstetric clinic between 2013 and 2017, investigating the relationship between the 75 g oral glucose tolerance test (OGTT) glucose values, and obstetric (timing of delivery, caesarean section, preterm birth, preeclampsia), and neonatal (hypoglycaemia, jaundice, respiratory distress and NICU admission) outcomes. This time frame encompassed a change in diagnostic criteria for gestational diabetes, due to changes in international consensus guidelines. Our results showed that, based on the diagnostic 75 g OGTT, fasting hyperglycaemia, either alone or in combination with elevated 1 or 2 h glucose levels, was associated with the need for pharmacotherapy with either metformin and/or insulin (p < 0.0001; HR 4.02, 95% CI 2.88–5.61), as compared to women with isolated hyperglycaemia at the 1 or 2 h post-glucose load timepoints. Fasting hyperglycaemia on the OGTT was more likely in women with higher BMI (p < 0.0001). There was an increased risk of early term birth in women with mixed fasting and post-glucose hyperglycaemia (adjusted HR 1.72, 95% CI 1.09–2.71). There were no significant differences in rates of neonatal complications such as macrosomia or NICU admission. Fasting hyperglycaemia, either alone or in combination with post-glucose elevations on the OGTT, is a strong indicator of the need for pharmacotherapy in pregnant women with GDM, with significant ramifications for obstetric interventions and their timing.
A computational model for assessing experts’ trustworthiness

The algorithmic detection of disinformation online is currently based on two strategies: on the one hand, research focuses on automated fact-checking; on the other hand, models are being developed to assess the trustworthiness of information sources, including both empirical and theoretical research on credibility and content quality. For debates among experts, in particular, it might be hard to discern (less) reliable information, as all actors by definition are qualified. In these cases, the use of trustworthiness metrics on sources is a useful proxy for establishing the truthfulness of contents. We introduce an algorithmic model for automatically generating a dynamic trustworthiness hierarchy among information sources based on several parameters, including fact-checking. The method is novel and significant, especially in two respects: first, the generated hierarchy represents a helpful tool for laypeople to navigate experts’ debates; second, it also allows to identify and overcome biases generated by intuitive rankings held by agents at the beginning of the debates. We provide an experimental analysis of our algorithmic model applied to the debate on the SARS-CoV-2 virus, which took place among Italian medical specialists between 2020 and 2021.
Experimental and numerical investigation of edge tones

We study both, by experimental and numerical means the fluid dynamical phenomenon of edge tones. Of particular interest is the verification of scaling laws relating the frequency f to given quantities, namely d, the height of the jet, w, the stand–off distance and the velocity of the jet. We conclude that the Strouhal number Sd is related to the geometrical quantities through Sd = C ⋅ (d / w)n with n ≈ 1, in contrast to some analytical treatments of the problem. The constant C of the experiment agrees within 13–15% with the result of the numerical treatment. Only a weak dependence on the Reynolds number with respect to d is observed. In general, a very good agreement of the experimental and the numerical simulations is found.
Statistical relationship discovery in SNP data using Bayesian networks

The aim of this article is to present an application of Bayesian networks for discovery of affinity relationships based on genetic data. The presented solution uses a search and score algorithm to discover the Bayesian network structure which best fits the data i.e. the alleles of single nucleotide polymorphisms detected by DNA microarrays. The algorithm perceives structure learning as a combinatorial optimization problem. It is a randomized local search algorithm, which uses a Bayesian-Dirichlet scoring function. The algorithm's testing procedure encompasses tests on synthetic data, generated from given Bayesian networks by a forward sampling procedure as well as tests on real-world genetic data. The comparison of Bayesian networks generated by the application and the genetic evidence data confirms the usability of the presented methods.
Coupled design of test signals, sampling intervals, and filters for system identification

This paper discusses the problem of optimal design of experimental conditions for linear system identification. It is demonstrated that, in general, to achieve maximal return from an experiment, coupled design of all the experimental conditions, namely the test signal, sampling intervals and filters, should be carried out simultaneously. For the case of uniform sampling it is shown that joint design of the presampling filter, sampling rate and input can be carded out in the frequency domain. For the case of nonuniform sampling a sequential design procedure is developed which optimizes the information increment between samples.
Cytotoxic Lymphocyte-Related Gene Signature in Triple-Negative Breast Cancer

To curate the signature genes of cytotoxic lymphocytes (CLs) and explore the heterogeneity based on the CL-related (CLR) gene signature, we analyzed the gene expression of 592 patients with histologically diagnosed triple-negative breast cancer. Based on the 13-gene panel, CLR signatures were curated and associated with the stage of tumor size. Patients in the CLR-low group exhibited the worse overall survival (OS) (median OS, 75.23 months vs. 292.66 months, p < 0.0001) and were characterized by the upregulation of the NF-κB, Wnt, and p53 pathways, the positive regulation of angiogenesis, and a higher expression of immune checkpoints including CTLA4, LAG3, CD86, ICOS, ICOSLG, and TNFSF9. In cancer immunotherapy cohorts (GSE157284, GSE35640, IMvigor210), a higher CLR signature score was remarkably associated with greater tumor shrinkage and immune characteristics consisting of higher PD-L1 and neoantigen expression, as well as an inflamed tumor microenvironment. In the pan-cancer atlas, the CLR signature was notably associated with patient survival and revealed a profound heterogeneity across the malignancy types. In sum, the CLR signature is a promising indicator for immune characteristics, tumor shrinkage, and survival outcomes following cancer immunotherapy in addition to the prognostic heterogeneity in the pan-cancer atlas.
Analisis DNA Hasil Isolasi Pada Produk Pangan Olahan Ikan (Surimi Ikan) Menggunakan Nano Photometer

Analisis mutu DNA untuk melihat kualitas DNA hasil isolasi merupakan sebuah teknik analisis yang dapat digunakan untuk mendukung pengujian bidang biologi molekuler yang menggunakan instrumen PCR atau real-time PCR. Tujuan dari penelitian ini adalah untuk menganalisis kualitas DNA hasil isolasi pada sampel produk pangan olahan ikan. Manfaat penelitian ini diharapkan dapat menjadi sumber informasi yang mendukung penelitian di bidang molekuler pada ruang lingkup pengawasan mutu dan autentifikasi pada produk pangan olahan ikan. Analisis DNA hasil isolasi dianalisis menggunakan NanoPhotometer untuk mengukur nilai konsentrasi dan kemurnian dengan panjang gelombang A260/A280. Data yang diperoleh dari hasil pengukuran kemudian diinterpretasikan menggunakan nilai uji rata-rata untuk menganalisis mutu DNA hasil isolasi. Data hasil penelitian menunjukan nilai konsentrasi DNA berada pada kisaran 14,5 – 17,8 dengan rata-rata nilai konsentrasi berada pada 16,1. Untuk nilai kemurnian DNA hasil isolasi berada pada kisaran 1,89 – 2.07 dengan rata-rata nilai kemurnian berada pada 1,97. Dengan hasil tersebut maka dapat disimpulkan bahwa hasil isolasi DNA yang diukur menggunakan nano fotometer menunjukkan nilai kualitas DNA hasil isolasi yang baik.