Miracle-dz/UniFAR-FTUs huggingface dataset:
Lidar SLAM Based on Particle Filter and Graph Optimization for Substation Inspection

Simultaneous Localization and Mapping (SLAM) is the core technology of intelligent substation inspection robot. Because of lightweight computation, Rao-Blackwellized Particle Filter (RBPF) is widely used in two-dimensional SLAM. However, it suffers from poor positioning accuracy, low robustness and rapid cumulative errors despite recent improvement. This paper presents a lidar SLAM system based on RBPF and graph optimization that can adapt to unstructured operating environment of substation. Firstly, the diversity of particles is increased by rebuilding the resample algorithm to improve the robustness of the system, and high-quality poses are estimated in submaps. Secondly, the multi-submap system is established to construct odometry constraints (one pose corresponds to two submaps). Furthermore, loop detector is an important part of optimization algorithm, and the branch-bound method is used to reduce computation burden and accelerate the loop detection. Finally, global poses of robot are optimized by the whole odometry and loop constraints in real time. Experiment results show that the proposed method is more accurate than other methods, and can maintain and produce high-precision positioning and mapping in complex substation operation and maintenance environment. It provides a new idea for intelligent substation inspection and positioning method.
AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance

Abstract Aims One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training. Methods and results A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (n = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann–Whitney U test and Bland–Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF [2.77 mL/min/g (1.08)] and predicted with the AI-AIF (2.79 mL/min/g (1.08), P = 0.33. Bland–Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of −0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95%) of myocardial segments. Conclusion Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.
The Effectiveness and Safety of Metformin Compared to Sulfonylureas in Diabetic Nephropathy: A Systematic Review

Metformin and sulphonylureas are the most commonly used first-line anti-diabetic agents. However, medical practice guidelines and clinical experience caution against using these drugs in severe diabetic kidney disease. Consequently, the choice of anti-diabetic medicine in various stages of diabetic nephropathy should balance the benefits and risks to the patient. We aim to synthesize available evidence on the effectiveness and safety of metformin concerning sulfonylureas in patients with diabetic renal disease. The COSMOS-E (Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology) and MOOSE (Meta-Analyses and Systematic Reviews of Observational Studies in Epidemiology) guidelines were followed when designing the systematic review. The present study assessed the effectiveness of metformin and sulphonylurea monotherapy regarding renal function. Studies published from 2001 to 2022 were included. We have identified 570 records from PubMed, BioMed Central, LILACS (Literatura Latino-Americana e do Caribe em Ciências da Saúde), ScienceDirect, and PLoS (The Public Library of Science) Medicine databases. Eight cohort studies met the inclusion criteria. All studies reported adjusted hazard ratios with confidence limits. Metformin was found to be more effective in the following events: all-cause mortality, GFR (glomerular filtration rate), ESRD (end-stage renal disease) or death events, one-year risk of death or end-stage renal disease, cardiovascular events, heart failure hospitalization, and hypoglycemic episodes. However, metformin was less effective in acute renal replacement therapy, end-stage renal disease, and/or death, with a one-year risk of acute dialysis. Lactic acidosis was not significant with metformin. The present study recommends that metformin therapy is safe compared to sulfonylurea therapy in diabetic nephropathy patients, provided that the contraindications given in the guidelines are strictly adhered to.
Post-Stroke Experiences and Rehabilitation Needs of Community-Dwelling Chinese Stroke Survivors: A Qualitative Study

Stroke survivors encounter significant limitations in daily life activities and face increased risk of health complications such as stroke recurrence. Considering the escalating demand for personalised community rehabilitation services, this qualitative study was conducted to understand the current recovery experiences, needs, and expectations of community-dwelling stroke survivors. Fifty stroke survivors were recruited from two tertiary teaching hospitals and community centres in two provinces in mainland China. Semi-structured interviews were carried out, and participants were asked to describe their experiences of stroke, current lifestyles, exercise habits, and rehabilitation needs and expectations. Resulting data were thematically analysed. The majority of participants were first-time stroke survivors (80%) and lived with their family or caregivers (92%). Four main themes and twelve sub-themes emerged from the data: (1) shifts in social life, (2) shaken sense of self and perceived helplessness, (3) complex rehabilitation needs, and (4) perceptions and patterns of physical activity. Findings suggest that though survivors recognised their need for further rehabilitation, their demands remained unmet due to a combination of personal and external factors such as limited mobility and the absence of supportive companions and accessible facilities. The enhancement and diversification of home rehabilitation strategies are therefore necessary to make community rehabilitation more accessible and equitable.
Phenotypic and genomic characterization of Tylodelphys sp. metacercaria (Diesing 1850) (Trematoda: Diplostomidae) recovered from Lates niloticus (Linna

: Metacercaria of Tylodelphys sp. a member of the Diplostomidae, was isolated naturally from the gas bladder of Lates niloticus (Nile Perch), found in Lake Nasser (southern Egypt) . The morphology, ultrastructure, and genomic characterization were investigated and described. The main characteristics were shown by the light microscope and these were confirmed by the electron microscope. The examined fish were found to be infected with Tylodelphys sp. , with prevalence of 86%, with the highest prevalence shown to be in summer (26%) and the lowest in winter (16%). Morphologically, by light and scanning electron microscopy, the parasite was found to be linguiform, where the fore body comprised most of the worm and the hind body was reduced to a small, conical, pointed end. The tegument was unarmed and smooth. A small oral and a large ventral sucker were detected. The body contained two types of papillae; a fine anterior and a coarse posterior one. The genetic analysis of Tylodephys sp. showed it was similar to those of the species of fish origin found in GenBank, and a description of a new Egyptian genotype (accession number MH367017) was provided. The morphotype detected was found to have about 99% genetic similarity in the 12S rRNA region with other helminthes. The obtained results support an evolutionary addition of the Egyptian species of Tylodelphys sp. aiding in its taxonomy and diagnosis.
Controlling the False Discovery Rate: A New Application to Account for Multiple and Dependent Tests in Local Statistics of Spatial Association

Assessing the significance of multiple and dependent comparisons is an important, and often ignored, issue that becomes more critical as the size of data sets increases. If not accounted for, false-positive differences are very likely to be identified. The need to address this issue has led to the development of a myriad of procedures to account for multiple testing. The simplest and most widely used technique is the Bonferroni method, which controls the probability that a true null hypothesis is incorrectly rejected. However, it is a very conservative procedure. As a result, the larger the data set the greater the chances that truly significant differences will be missed. In 1995, a new criterion, the false discovery rate (FDR), was proposed to control the proportion of false declarations of significance among those individual deviations from null hypotheses considered to be significant. It is more powerful than all previously proposed methods. Multiple and dependent comparisons are also fundamental in spatial analysis. As the number of locations increases, assessing the significance of local statistics of spatial association becomes a complex matter. In this article we use empirical and simulated data to evaluate the use of the FDR approach in appraising the occurrence of clusters detected by local indicators of spatial association. Results show a significant gain in identification of meaningful clusters when controlling the FDR, in comparison to more conservative approaches. When no control is adopted, false clusters are likely to be identified. If a conservative approach is used, clusters are only partially identified and true clusters are largely missed. In contrast, when the FDR approach is adopted, clusters are fully identified. Incorporating a correction for spatial dependence to conservative methods improves the results, but not enough to match those obtained by the FDR approach.
A kernel density smoothing method for determining an optimal number of clusters in continuous data

While data clustering algorithms are becoming increasingly popular across scientific, industrial and social data mining applications, model complexity remains a major challenge. Most clustering algorithms do not incorporate a mechanism for finding an optimal scale parameter that corresponds to an appropriate number of clusters. We propose , a kernel-density smoothing-based approach to data clustering. Its main ideas derive from two unsupervised clustering approaches – kernel density estimation (KDE) and scale-spacing clustering (SSC). The novel method determines the optimal number of clusters by first finding dense regions in data before separating them based on data-dependent parameter estimates. The optimal number of clusters is determined from different levels of smoothing after the inherent number of arbitrary shape clusters has been detected without a priori information. We demonstrate the applicability of the proposed method under both nested and non-nested hierarchical clustering methodologies. Simulated and real data results are presented to validate the performance of the method, with repeated runs showing high accuracy and reliability.
Dynamics of Lipid Profile in Antiretroviral-Naïve HIV-Infected Patients, Treated with TAF-Based Regimens: A Multicenter Observational Study

Background: The introduction of tenofovir alafenamide (TAF) in antiretroviral therapy has deeply modified the choice of the backbone for different treatment regimens, allowing the prevention of the bone and renal toxicity that was related to the previous formulation of tenofovir disoproxil fumarate (TDF). At the same time, literature data show an onset of dyslipidemia after a switch from TDF to TAF. To better understand the possible role of TAF in dyslipidemia, antiretroviral-naïve HIV-infected patients were evaluated, comparing those treated with TAF/emtricitabine with those with abacavir/lamivudine. Methods: We enrolled 270 antiretroviral-naïve HIV-infected patients in an observational, retrospective, longitudinal, multicenter study; they started treatment from 2017 to 2019 and were followed up for at least 72 weeks. We divided patients into two groups, one treated with a TAF-based backbone in their antiretroviral regimens (TAF group) and one without TAF (NO TAF group), to evaluate possible differences in the dynamics of lipid profiles from baseline(T0) to week 24 (T24), 48 (T48) and 72 (T72). Results: No significant differences were observed at baseline between the 2 groups. In the TAF group we observed a significant development of hypercholesterolemia throughout the follow-up (p < 0.0001), not evident in the NO TAF group, that instead showed a significant increase in high-density lipoprotein (HDL). There were no significant differences between the two groups regarding triglycerides, low-density lipoprotein (LDL) and cardiovascular risk index (CRI). A cholesterol-lowering treatment with statin, finally, was prescribed in 6 patients in both groups during the study. At binary logistic regression analysis, no factor was independently associated with hypercholesterolemia, except for higher age at T0. Conclusions: This real-life study shows that in HIV-naïve patients, TAF was associated with hypercholesterolemia throughout the follow-up. The clinical significance of this hypercholesterolemia will have to be clarified in further studies.
A kriging method for the solution of nonlinear programs with black‐box functions

In this article, a new methodology is developed for the optimization of black-box systems lacking a closed-form mathematical description. To properly balance the computational cost of building the model against the probability of convergence to global optimum, a hybrid methodology is proposed. A kriging approach is first applied to provide information about the global behavior of the system considered, whereas a response surface method is considered close to the optimum to refine the set of candidate local optima and find the global optimum. The kriging predictor is a global model employing normally distributed basis functions, so both an expected sampling value and its variance are obtained for each test point. The presented work extends the capabilities of existing response surface techniques to address the refinement of optima located in regions described by convex asymmetrical feasible regions containing arbitrary linear and nonlinear constraints. The performance of the proposed algorithm is compared to previously developed stand-alone response surface techniques and its effectiveness is evaluated in terms of the number of function calls required, number of times the global optimum is found, and computational time. © 2007 American Institute of Chemical Engineers AIChE J, 2007
Single‐cell microliter‐droplet screening system (MISS Cell): An integrated platform for automated high‐throughput microbial monoclonal cultivation and picking

Solid plates have been used for microbial monoclonal isolation, cultivation, and colony picking since 1881. However, the process is labor‐ and resource‐intensive for high‐throughput requirements. Currently, several instruments have been integrated for automated and high‐throughput picking, but complicated and expensive. To address these issues, we report a novel integrated platform, the single‐cell microliter‐droplet screening system (MISS Cell), for automated, high‐throughput microbial monoclonal colony cultivation and picking. We verified the monoclonality of droplet cultures in the MISS Cell and characterized culture performance. Compared with solid plates, the MISS Cell generated a larger number of monoclonal colonies with higher initial growth rates using fewer resources. Finally, we established a workflow for automated high‐throughput screening of Corynebacterium glutamicum using the MISS Cell and identified high glutamate‐producing strains. The MISS Cell can serve as a universal platform to efficiently produce monoclonal colonies in high‐throughput applications, overcoming the limitations of solid plates to promote rapid development in biotechnology.
Histopathology and Ultrastructure in Liver and Gonads of the Nile Tilapia (Oreochromis niloticus) as a Bio-Monitor for Water Quality Assessment in Lake Manzalah

Despite being one of Egypt's most significant fish production resources, Lake Manzalah (Northeast Egypt) is subject to substantial inputs of pollution from domestic, agricultural and industrial sources. The liver and gonads of Oreochromis niloticus were evaluated for histological and ultrastructural alterations in the current study to monitor the possible effects
Robust Approximate Modeling from Noisy Point Evaluations

We consider approximate modeling of stable linear shift-invariant systems in the H? sense from approximate point evaluations at approximately known frequencies. Two error structures for the point evaluations are studied: pointwise bounded error and a certain error averaging structure. A main motivation for the present work comes from currently active research problems concerning modeling for robust control design from experimental data. Several results are given on various aspects of approximation algorithm performance, and on robust convergence. A constrained least absolute deviations method based on minimizing the value of the error averaging prior subject to a model prior restricting the complexity of the behaviour of the model is proposed. This linear programming method is a strongly optimal algorithm within factor two with respect to the model and error priors used in its construction. Relationships between problems of identification of nominal models and uncertainty modeling are studied.
Hypothesis Testing with Efficient Method of Moments Estimation

Efficient method of moments estimation techniques include many commonly used techniques, including ordinary least squares, two- and three-stage least squares, quasi maximum likelihood, and versions of these for nonlinear environments. For models estimated by any efficient method of moments technique, the authors define analogues to the maximum likeliho od based Wald, likelihood ratio, Lagrange multiplier, and minimum chi-squared statistics. They prove the mutual asymptotic equivalence of the four in an environment that allows for disturbances that are auto correlated and heteroskedastic. They also describe a very convenient way to test a linear hypothesis in a linear model. Copyright 1987 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
Performance of Robust RA Estimator for Bidimensional Autoregressive Models

The additive AR-2D model has been successfully related to the modeling of satelital images both optic and of radar of synthetic opening. Having in mind the errors that are produced in the process of captation and quantification of the image, an interesting subject, is the robust estimation of the parameters in this model. Besides the robust methods in image models are also applied in some important image processing situations such as segmentation by texture and image restoration in the presence of outliers. This paper is concerned with the development and performance of the robust RA estimator proposed by Ojeda (1998) for the estimation of parameters in contaminated AR-2D models. Here, we implement this estimator and we show by simulation study that it has a better performance than the classic least square estimator and the robust M and GM estimators in an additive outlier contaminated image model.
Engaging Paulo Freire on deliberative democracy: Dialogical pedagogy, deliberation and inclusion in a transformative higher education online education space

An effective education system is an environment where students feel cared for, included and are able to deliver critical dialogical input in their learnings on Learning Management Systems (LMS) platforms. The article aims to epitomizes quality education where skills, values and equal distribution of resources can be accessed by all. This includes effectively trained lecturers who manages diversity and teach effectively, to foster success, and to provide a safe and friendly classroom environment for students. This article comes from a larger work done on how to administer clear dialogical and caring aims (policy) in higher online education spaces where students grow holistically and critically. The paper focuses on the kind of space lecturers need to create online in-order to provide students the opportunity to be part of a caring teaching and learning process in order to form part of an active citizenry beyond their immediate context. This article employed a qualitative research methodology, where a questionnaire was used for lecturers at the Faculty of Theology and Religion at the University of the Free State. These questionnaires covered 30 lecturers who made use of the University’s LMS’ platform. The data was analyzed through the interpretivist paradigm. The finding in this article reveal that the little to no cultivation of critical pedagogical action vis-à-vis the enactment of activism and justice in and through higher education in the context of real pedagogical action in online (LMS) higher education spaces are important. The study is significant because it emphasizes a topic that are helpful in understanding how critical pedagogical action through Freire’s dialogical theory in the online (LMS) higher education platforms ought to be engaged.Contribution: The contribution in this article is an amendment to Freire’s pedagogy frame work framed with the Faculty of Theology and Religion and extending his notion of dialogical engagement to deliberative action in the online (LMS) higher education space is critical tenant for student wholistic growth.
Spatial hole burning and self‐focusing in vertical‐cavity surface‐emitting laser diodes

We measure spatial hole burning in weakly index guided vertical‐cavity surface‐emitting laser diodes (VCSELs) by spatially and spectrally resolving the spontaneous emission near field above threshold and show that spatial holes produce a significant lensing effect. We demonstrate experimentally, and with a simple model, that in weakly index guided VCSELs, self‐focusing causes the fundamental mode width to decrease with increasing output power, exacerbating the spatial hole burning problem and inducing a transition to multimode operation at relatively low powers.