Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-series analysis of three years of groundwater level data (Seoul, South Korea) to characterize urban groundwater recharge

Abstract A time-series analysis was performed on 3 years of daily groundwater level data from 34 monitoring wells in the bedrock aquifer of Seoul to understand the characteristics of groundwater recharge in an urban setting and thereby provide a policy background for sustainable groundwater management. The cross-correlation function was used to estimate the lag time of groundwater level change after a rainfall event. Based on the lag time, monitored wells are classified into two groups. The delay was very short (1–7 days) in Group I wells (n = 12), whereas Group II wells (n = 22) showed a longer delay (17–58 days). Such variable response was considered to reflect differences in the transmission of hydraulic pressure induced by recharging rainwater through the unsaturated zone. The estimated delay time showed significant correlation with the elevation, depth to groundwater level, and thickness of soil (or alluvium). The shorter delay Group I wells generally had shallower groundwater levels and were preferentially located toward mountainous areas. Group I wells also tended to be located in areas with thinner soil (or alluvium) cover. However, ground cover characteristics (e.g. pavement) around wells had no correlation with the delay time. The characteristics of groundwater recharge in this urban setting therefore appear to be controlled by natural conditions such as topography and soil thickness, rather than by land use. The study suggests that natural conditions should be more carefully considered in the designation of Groundwater Preservation Areas.
Multiobjective Optimization Design of a Brushless Excitation Synchronous Motor Based on the Taguchi Method

Considering the issue of significant total harmonic distortion in the no-load back electromotive force (EMF) and torque ripple of a brushless excitation synchronous motor (BESM) used in a compressor, a multiobjective optimization design method of a BESM based on the Taguchi method was proposed. By selecting the polar arc coefficient, stator slot width, damping winding pitch, and air gap length as the optimization parameters, the maximum no-load back EMF, minimum back EMF total harmonic distortion rate, and minimum torque ripple are selected as the optimization objectives. Then, the Taguchi method was employed to construct an orthogonal table to enhance the performance of the optimization objectives. This approach facilitated the determination of the optimal parameter combination for optimization. Finally, the simulation results indicate significant enhancements in each optimized aspect of the motor’s performance following the optimization process. This outcome serves as validation for the efficacy of the Taguchi method in achieving an optimal design for the BESM.
The evaluation of superoxide dismutase 1 gene insertion/deletion variant in athletes

SUMMARY OBJECTIVE: Regular exercise benefits health by increasing the body's antioxidant defenses. However, excessive exercise can produce excessive reactive oxygen species, which can lead to oxidative stress. Superoxide dismutase is the primary enzyme involved in the elimination of reactive oxygen species. This study aimed to determine the relationship between the SOD1 gene insertion/deletion variant and elite athletes. METHODS: A total of 305 subjects, including 165 elite athletes from different branches and 140 sedentary individuals, participated in this study. The SOD1 insertion/deletion variant was genotyped using polymerase chain reaction. The results were evaluated statistically. RESULTS: There was no statistical significance between the athletes and control groups in terms of SOD1 insertion/deletion genotype distribution and allele frequency. Then, we evaluated the groups as females and males. There were no female athletes carrying the D/D genotype. The SOD1 I/I genotype and the I allele were more prevalent in female athletes than in the control group. There was a significant difference in terms of SOD1 I/I: I/D+D/D in females (p=0.028). SOD1 genotype and allele distribution did not differ between male athletes and male controls. CONCLUSION: As far as we know, this is the first study to evaluate the SOD1 insertion/deletion variant in athletes in Turkey. Our results showed that the SOD1 I allele was more common in female athletes, but not in male athletes.
Three Cases of Carney-Stratakis Syndrome: A Genetically Heterogeneous Disease

Abstract Carney-Stratakis syndrome (CSS) is an autosomal dominant rare syndrome, with incomplete penetrance, characterized by the association of paragangliomas and/or pheochromocytomas and gastrointestinal stromal tumors (GISTs). CSS is caused by germline heterozygous loss-of-function pathogenic variants (PVs) in the succinate dehydrogenase subunit genes (SDHB, SDHC, SDHD), with SDHB and SDHD being the most frequent. To date, only 2 germline SDHC PVs (c.43 C > T; c.405 + 1G > A) have been described in 3 patients with CSS. Three patients with CSS and very distinct clinical presentations are reported here: 1 caused by a germline SDHC large deletion and the others with metastatic GIST and negative genetic investigation for SDHx defects. Two cases (1 and 2) presented with pheochromocytoma (case 1 also with abdominal paraganglioma) and metastatic GIST. Although these 2 cases fulfilled the diagnostic criteria for CSS, the genetic investigation for SDHx PVs by next-generation sequencing and multiplex ligation-dependent probe amplification was negative. Case 3 had a large abdominal paraganglioma and a small low-grade GIST not associated with recurrence or metastasis. This case harbored a germline SDHC exon 3 deletion, not previously reported. In conclusion, CSS is a rare and morbid disease with distinct clinical presentations and genetic heterogeneity, which can contribute to underdiagnosis.
Literature Overview of the Relation Between Psoriasis and Alzheimer

Abstract The most common type of dementia disease is Alzheimer, which placing a heavy burden on the healthcare system all over the world. At the same time, psoriasis is also one of the most common health problems, as a skin disease. Alzheimer's disease (AD) is more often in patients with psoriasis than in the general people. Several evidence has proved the relation between AD and psoriasis through immune-mediated pathophysiologic processes. This review aims to summary the potential relation between AD and psoriasis, and provide suggestions based on the relationship at the same time. Neurologists, dermatologists should pay attention to the relationship between Alzheimer’s disease and psoriasis. Dermatology and neurology need referral each other when it is necessary.
Efectos regionales de la politica monetaria: implicaciones para los paises de la zona euro

Following the Optimum Currency Areas approach, asymmetric shocks play a fundamental role to determine the benefits and the costs derived from the establishment of a monetary area. In this sense, some recent studies show that an important reduction in the level of the asymmetries of the shocks in real terms in the last years between the European countries has been produced as a result of the major coordination of the economic policies and the approximation of the productive structures. However, an important question in the new economic scene, and not enough considered in the recent literature, is the possible destabilization role of the common monetary policy derived from differences in monetary policy transmission mechanisms in the different Euro Zone countries and regions. In this paper, we consider this question from a theoretical point of view and we analyse empirically the existence of regional differences in the effects of the monetary policy for the Spanish case with the aim of identifying the most important factors of this asymmetry at the European level.
Educación y puEblos autóctonos. línEas dE trabajo con El puEblo pigmEo mbuti En la rEpública dEmocrática dEl congo Education and indigenous peoples. Lines of work with the Mbuti Pygmy people in the Democratic Republic of Congo

rEsumEn: El desafio educativo con los pueblos autoctonos de Africa pasa por la aceptacion de estos como tales y por ende por el reconocimiento de toda una serie de elementos dirigidos a garantizar su supervivencia. La complejidad de este desafio esta en que no es posible abordar la cuestion educativa de forma aislada sino que es necesario hacerlo a la vez teniendo en cuenta otros multiples factores sociales, culturales y politicos. Se apuesta de todas formas por una etnoeducacion que consiga dar valor a los elementos sociales propios del pueblo pigmeo aunque sea desde un ambito educativo circunscrito en la informalidad. El objetivo en si es rescatar el derecho de un pueblo a su supervivencia a traves de una etnoeducacion que fortalezca los elementos culturales internos que se estan viendo confrontados con los provenientes de otras culturas. Palabras clave: Educacion Diferenciada, Tierras Ancestrales, Dialogo Intercultural, Supervivencia, Etnoeducacion.
Analysis of the commercial grades for beef carcasses

Based on a statistical analysis of data acquired in the process of instrument testing (video image analysis, bioelectrical impedance analysis) for objective classification, the objective character of the criteria for classification of conformation and fatness was also identified. During the tests, inspectors subjectively classified 118 young bulls and carried out their detailed dissection. The analysis identified significant correlations between conformation and weight of beef carcass (correlation coefficient r = 0.82) and its cuts. In addition, the analysis revealed almost a zero correlation between conformation and the relative (percentage) proportion of meat in beef carcass. On the contrary, the estimated fatness proved a close dependence on the proportion of fat in the whole carcass and in its separate cuts. During the analysis of a group of animals with similar fatness values, significant correlations were identified between conformation and the relative proportion of saleable meat and meat I - large cuts (r = 0.758). It is evident from the data ascertained in this way that an estimate of fatness is very important for the actual quality of the carcass and proportion of its lean meat. The analysis also revealed that the fatness as an autonomous criterion offers a relatively precise estimate of the fat content in carcass.
Framework humanization optimizes potency of anti-CD72 nanobody CAR-T cells for B-cell malignancies

Background Approximately 50% of patients who receive anti-CD19 CAR-T cells relapse, and new immunotherapeutic targets are urgently needed. We recently described CD72 as a promising target in B-cell malignancies and developed nanobody-based CAR-T cells (nanoCARs) against it. This cellular therapy design is understudied compared with scFv-based CAR-T cells, but has recently become of significant interest given the first regulatory approval of a nanoCAR in multiple myeloma. Methods We humanized our previous nanobody framework regions, derived from llama, to generate a series of humanized anti-CD72 nanobodies. These nanobody binders were inserted into second-generation CD72 CAR-T cells and were evaluated against preclinical models of B cell acute lymphoblastic leukemia and B cell non-Hodgkin’s lymphoma in vitro and in vivo. Humanized CD72 nanoCARs were compared with parental (“NbD4”) CD72 nanoCARs and the clinically approved CD19-directed CAR-T construct tisangenlecleucel. RNA-sequencing, flow cytometry, and cytokine secretion profiling were used to determine differences between the different CAR constructs. We then used affinity maturation on the parental NbD4 construct to generate high affinity binders against CD72 to test if higher affinity to CD72 improved antitumor potency. Results Toward clinical translation, here we humanize our previous nanobody framework regions, derived from llama, and surprisingly discover a clone (“H24”) with enhanced potency against B-cell tumors, including patient-derived samples after CD19 CAR-T relapse. Potentially underpinning improved potency, H24 has moderately higher binding affinity to CD72 compared with a fully llama framework. However, further affinity maturation (KD<1 nM) did not lead to improvement in cytotoxicity. After treatment with H24 nanoCARs, in vivo relapse was accompanied by CD72 antigen downregulation which was partially reversible. The H24 nanobody clone was found to have no off-target binding and is therefore designated as a true clinical candidate. Conclusion This work supports translation of H24 CD72 nanoCARs for refractory B-cell malignancies, reveals potential mechanisms of resistance, and unexpectedly demonstrates that nanoCAR potency can be improved by framework alterations alone. These findings may have implications for future engineering of nanobody-based cellular therapies.
Spatial modelling of stream water quality along an urban–rural gradient

Abstract It is widely known that intensive land use generally decreases stream water quality, but the influence of watershed physiography is relatively poorly understood. Since management planning has to take into account the protection of water quality, the current status of stream water must be identified. The potential effects of land use and watershed physiography variables on water quality were studied in an extensive set of 83 watersheds in the Helsinki region, Finland, covering wide land‐use intensity gradient. The aims of this study were to test if the geographical information of watershed land‐use data can be used to model the stream water quality, and to examine whether the spatial water quality models are improved after including predictors of watershed physiography to the land‐use model. Water quality variables were related to watershed predictors by utilizing generalized additive models and linear mixed models, and the independent effect of the variables was investigated using a hierarchical partitioning approach. While land use turned out to be the most influential factor explaining water quality, all models improved significantly after incorporating the watershed characteristics, such as topography and soil. These results were consistent across three modelling techniques. This study, with its novel approach to examine the impacts of several watershed physiographic characteristics on urban stream water quality in northern Europe, demonstrates that spatial land use and watershed physiography data can be used as cost‐efficient predictors in stream water quality models.
Magnetic penetration depth of Aluminum thin films

We present a study of the superconducting penetration depth $\lambda$ in aluminum thin films of varying thickness. The range of thicknesses chosen spans from the thin-film regime to the regime approaching bulk behavior. The penetration depths observed range from $\lambda = 163.3\pm0.4~\rm{nm}$ for the thinnest $20~\rm{nm}$ samples down to $\lambda = 53.6\pm0.4~\rm{nm}$ for the $200~\rm{nm}$-thick ones. In order to accurately determine $\lambda$, we performed complementary measurements using the frequency of superconducting $LC$ resonators as well as the resistance of normal-state meanders. Both methods yield comparable results, providing a well-characterized set of values of $\lambda$ in aluminum in the relevant range for applications in fields such as quantum computing and microwave radiation detector technologies.
Contagion dynamics in time-varying metapopulation networks with node's activity and attractiveness

The metapopulation network model is effectively used to study the spatial spread of epidemics with individuals mobility. Considering the time-varying nature of individual activity and the preferences for attractive destinations in population mobility, this paper develops a time-varying network model in which activity of a population is correlated with its attractiveness. Based on the model, the spreading processes of the SIR disease on different correlated networks are studied, and global migration thresholds are derived. It is observed that increasing the correlation between activity and attractiveness results in a reduced outbreak threshold but suppresses the disease outbreak size and introduces greater heterogeneity in the spatial distribution of infected individuals. We also investigate the impact of non-pharmacological interventions (self-isolation and self-protection) on the spread of epidemics in different correlation networks. The results show that the simultaneous implementation of these measures is more effective in negatively correlated networks than in positively correlated or non-correlated networks, and the prevalence is reduced significantly. In addition, both self-isolation and self-protection strategies increase the migration threshold of the spreading and thus slow the spread of the epidemic. However, the effectiveness of each strategy in reducing the density of infected populations varies depending on different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. These findings contribute to a better understanding of epidemic spreading in large-scale time-varying metapopulation networks and provide insights for epidemic prevention and control.
Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception

Active recognition enables robots to intelligently explore novel observations, thereby acquiring more information while circumventing undesired viewing conditions. Recent approaches favor learning policies from simulated or collected data, wherein appropriate actions are more frequently selected when the recognition is accurate. However, most recognition modules are developed under the closed-world assumption, which makes them ill-equipped to handle unexpected inputs, such as the absence of the target object in the current observation. To address this issue, we propose treating active recognition as a sequential evidence-gathering process, providing by-step uncertainty quantification and reliable prediction under the evidence combination theory. Additionally, the reward function developed in this paper effectively characterizes the merit of actions when operating in open-world environments. To evaluate the performance, we collect a dataset from an indoor simulator, encompassing various recognition challenges such as distance, occlusion levels, and visibility. Through a series of experiments on recognition and robustness analysis, we demonstrate the necessity of introducing uncertainties to active recognition and the superior performance of the proposed method.
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal

The rich spectral information of the graph Laplacian has been instrumental in graph theory, machine learning, and graph signal processing for applications such as graph classification, clustering, or eigenmode analysis. Recently, the Hodge Laplacian has come into focus as a generalisation of the ordinary Laplacian for higher-order graph models such as simplicial and cellular complexes. Akin to the traditional analysis of graph Laplacians, many authors analyse the smallest eigenvalues of the Hodge Laplacian, which are connected to important topological properties such as homology. However, small eigenvalues of the Hodge Laplacian can carry different information depending on whether they are related to curl or gradient eigenmodes, and thus may not be comparable. We therefore introduce the notion of persistent eigenvector similarity and provide a method to track individual harmonic, curl, and gradient eigenvectors/-values through the so-called persistence filtration, leveraging the full information contained in the Hodge-Laplacian spectrum across all possible scales of a point cloud. Finally, we use our insights (a) to introduce a novel form of Hodge spectral clustering and (b) to classify edges and higher-order simplices based on their relationship to the smallest harmonic, curl, and gradient eigenvectors.
GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence

We present GigaPose, a fast, robust, and accurate method for CAD-based novel object pose estimation in RGB images. GigaPose first leverages discriminative"templates", rendered images of the CAD models, to recover the out-of-plane rotation and then uses patch correspondences to estimate the four remaining parameters. Our approach samples templates in only a two-degrees-of-freedom space instead of the usual three and matches the input image to the templates using fast nearest-neighbor search in feature space, results in a speedup factor of 35x compared to the state of the art. Moreover, GigaPose is significantly more robust to segmentation errors. Our extensive evaluation on the seven core datasets of the BOP challenge demonstrates that it achieves state-of-the-art accuracy and can be seamlessly integrated with existing refinement methods. Additionally, we show the potential of GigaPose with 3D models predicted by recent work on 3D reconstruction from a single image, relaxing the need for CAD models and making 6D pose object estimation much more convenient. Our source code and trained models are publicly available at https://github.com/nv-nguyen/gigaPose
A General Framework for User-Guided Bayesian Optimization

The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient method to solve these problems with minimal knowledge of the underlying function dynamics. However, the ability of Bayesian optimization to incorporate prior knowledge or beliefs about the function at hand in order to accelerate the optimization is limited, which reduces its appeal for knowledgeable practitioners with tight budgets. To allow domain experts to customize the optimization routine, we propose ColaBO, the first Bayesian-principled framework for incorporating prior beliefs beyond the typical kernel structure, such as the likely location of the optimizer or the optimal value. The generality of ColaBO makes it applicable across different Monte Carlo acquisition functions and types of user beliefs. We empirically demonstrate ColaBO's ability to substantially accelerate optimization when the prior information is accurate, and to retain approximately default performance when it is misleading.
Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning

This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks. To set the stage for this union, we explore two different flavors of model space problems that have been studied in the AI planning literature and explore the effect of an LLM on those tasks. We empirically demonstrate how the performance of an LLM contrasts with combinatorial search (CS) – an approach that has been traditionally used to solve model space tasks in planning, both with the LLM in the role of a standalone model space reasoner as well as in the role of a statistical signal in concert with the CS approach as part of a two-stage process. Our experiments show promising results suggesting further forays of LLMs into the exciting world of model space reasoning for planning tasks in the future.