Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint interpretation of P‐wave time and amplitude anomalies in terms of lithospheric heterogeneities

Summary. In recent years several successful studies on inversion and interpretation of P-wave time anomalies recorded across various types of seismograph networks and arrays have been reported. Similar attempts on handling intrinsic amplitude anomalies appear for various reasons to have been less successful. In this study a joint investigation of NORSAR P-wave time and amplitude observations is described, starting with construction of a thin lens model at depths around 150-200 km on the basis of more than 4000 time residuals. Using Claerbout’s wave equation approach, the amplitude response has been calculated and compared to a composite amplitude surface constructed from many thousands of amplitude observations using a specific smoothing procedure. The agreement between observed and calculated amplitudes is satisfactory ; that is, the broad amplitude pattern across NORSAR and its migration as a function of wave incidence are very satisfactorily reproduced, though sharp, localized peaks are less accurately predicted. The latter deficiencies probably stem from the relatively large sensitivity of wave amplitude on velocity gradient perturbations which are not well sampled, even with NORSAR, using the relatively slowly varying time anomalies. Reversing this process, i.e. by calculating time anomalies from the composite amplitude surface via a modified Poisson equation, gave very satisfactory results - a correlation of 0.90 between observed and calculated values. On the basis of this study it is concluded that time and amplitude anomalies originate from the same lithospheric structures which, as a good first approximation, may be represented in terms of a 2-D heterogeneous layer at depths around 150-200 km or the bottom of the lithosphere. Furthermore, the velocity perturbations required for accounting for the anomalous P-wave amplitude observations are of the order of a few per cent and are in good agreement with similar results obtained by scattering analysis of precursor and coda waves.
Preliminary evaluation of human hair follicle-derived dermal papilla cell characteristics

Dermal Papilla Cells (DPCs) are mesenchymal cells residing at the base of the hair follicles. They play an important role in the hair cycle remodeling and hair follicle regeneration. In this study, DPCs were isolated, and cultured from human hair follicles and evaluated the expression of -SMA, versican by immunocytochemistry, and alkaline phosphatase activity. The human fibroblasts were used as a control to confirm the non-fibroblast DPCs. The results showed that the cells grew from the hair bulb and had fibroblast morphology. These bulb cells strongly expressed alkaline phosphatase activity -SMA, and versican while the human fibroblast lacked ALP activity and less expression of -SMA and versican. In conclusion, the protocol is established to isolate and culture DPCs from hair follicle bulbs, and ALP activity is suggested as the major marker for distinguishing DPCs and human fibroblasts.
On precisely modelling surface deformation due to interacting magma chambers and dykes

Combined data sets of InSAR and GPS allow us to observe surface deformation in volcanic settings. However, at the vast majority of volcanoes, a detailed 3-D structure that could guide the modelling of deformation sources is not available, due to the lack of tomography studies, for example. Therefore, volcano ground deformation due to magma movement in the subsurface is commonly modelled using simple point (Mogi) or dislocation (Okada) sources, embedded in a homogeneous, isotropic and elastic half-space. When data sets are too complex to be explained by a single deformation source, the magmatic system is often represented by a combination of these sources and their displacements fields are simply summed. By doing so, the assumption of homogeneity in the half-space is violated and the resulting interaction between sources is neglected. We have quantified the errors of such a simplification and investigated the limits in which the combination of analytical sources is justified. We have calculated the vertical and horizontal displacements for analytical models with adjacent deformation sources and have tested them against the solutions of corresponding 3-D finite element models, which account for the interaction between sources. We have tested various double-source configurations with either two spherical sources representing magma chambers, or a magma chamber and an adjacent dyke, modelled by a rectangular tensile dislocation or pressurized crack. For a tensile Okada source (representing an opening dyke) aligned or superposed to a Mogi source (magma chamber), we find the discrepancies with the numerical models to be insignificant (<'5 per cent) independently of the source separation. However, if aMogi source is placed side by side to an Okada source (in the strike-perpendicular direction), we find the discrepancies to become significant for a source separation less than four times the radius of the magma chamber. For horizontally or vertically aligned pressurized sources, the discrepancies are up to 20 per cent, which translates into surprisingly large errors when inverting deformation data for source parameters such as depth and volume change. Beyond 8 radii however, we demonstrate that the summation of analytical sources represents adjacent magma chambers correctly.
Investigating Pre-University Students’ Motivation to Learn and Their Use of Self-Regulated Learning Strategies

In any context, especially in the academic and professional contexts, having the motivation to succeed is essential. This study aimed to investigate the relationship between motivation to learn and the use of self-regulated learning (SRL) strategies employed by pre-university students at a selected university. A quantitative method was used to retrieve data from 110 participants who were selected through a purposive sampling method from a Centre of Foundation Studies at a selected public university in Malaysia to discover whether the 3 aspects of motivation, namely, self-efficacy, intrinsic value, and test anxiety impact students’ self-regulated learning. An online 5-Likert scale questionnaire consisting of 3 parts, which are the demographic profile, motivational beliefs, and self-regulated learning, and a total of 47 questions was used for data collection. The instruments of the survey were adapted from Pintrich and De Groot (1990). Findings suggest a significantly high correlation between students’ motivati on to learn and self-regulated learning. Meanwhile, correlation analysis
Experimental study of thermal modeling for stratospheric platform airships

R&D work on the Japanese stratospheric platform airship is discussed from the viewpoint of the thermal modeling. The work includes two experiments on the thermal characteristics of the 25m-long airship and the 35m-long PV-equipped airship model. The validity of the current thermal model was confirmed by comparing the calculated results with the measured data. The other important work is the Ground-to-Stratosphere (GTS) flight test. A 47mlong airship-shaped balloon was flown to the stratosphere. The flight simulation program into which the thermal model was integrated, was developed and applied successfully to predict the ascent behavior and the flight trajectory.
Management of Caching Policies and Redundancy over Unreliable Channels

—Caching plays a central role in networked systems, reducing the load on servers and the delay experienced by users. Despite their relevance, networked caching systems still pose a number of challenges pertaining their long term behavior. In this paper, we formally show and experimentally evidence conditions under which networked caches tend to synchronize over time. Such synchronization, in turn, leads to performance degradation and aging, motivating the monitoring of caching systems for eventual rejuvenation, as well as the deployment of diverse cache replacement policies across caches to promote diversity and preclude synchronization and its aging effects. Based on trace-driven simulations with real workloads, we show how hit probability is sensitive to varying channel reliability, cache sizes, and cache separation, indicating that the mix of simple policies, such as Least Recently Used (LRU) and Least Frequently Used (LFU), provide competitive performance against state-of-art policies. Indeed, our results suggest that diversity in cache replacement policies, rejuvenation and intentional dropping of requests are strategies that build diversity across caches, preventing or mitigating performance degradation due to caching aging.
Plant–soil feedbacks among boreal forest species

Plant–microbial interactions in soils are considered to play a central role in regulating biodiversity in many global ecosystems. However, studies on plant–soil feedbacks (PSFs) and how these affect forest stand patterns in boreal regions are rare. We conducted a fully reciprocal PSF glasshouse experiment using four boreal tree species. Alnus glutinosa, Betula pendula, Picea abies and Pinus sylvestris seedlings were grown under controlled conditions in sterilised soil with or without soil inoculum collected under mature trees of each of the four species. Bacterial, fungal and oomycete communities in the rhizosphere were investigated using metabarcoding and correlated with differences in plant biomass. Alder grew best in conspecific soil, whereas birch grew equally well in all soil types. Pine and spruce grew best in heterospecific soil, particularly in soil from their successional predecessor. Ectomycorrhizal fungi (EMF) enhanced the growth of most seedlings, and Actinomycetota supported alder and birch growth and fungal plant pathogens hampered pine growth. Increased growth was linked to the ability of trees to recruit specific EMF and root‐associated fungi in heterospecific soils. Synthesis. This study experimentally examines the influence of root‐associated microbiota on the growth of boreal tree species. The observed plant–soil feedbacks mirror the successional patterns found in boreal forests, suggesting a possible contribution of soil microbiota to the successional progression. Species‐specific ectomycorrhizal fungi and a few bacteria rather than fungal plant pathogens or oomycetes seem to drive the feedbacks by promoting seedling growth in heterospecific soils.
In Silico Identification of Natural Food Compounds as Potential Quorum-Sensing Inhibitors Targeting the LasR Receptor of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a major cause of nosocomial infections and is often associated with biofilm-mediated antibiotic resistance. The LasR protein is a key component of the quorum system in P. aeruginosa, allowing it to regulate its biofilm-induced pathogenicity. When the bacterial population reaches a sufficient density, the accumulation of N-(3-oxododecanoyl) acyl homoserine lactone (3O-C12-HSL) leads to the activation of the LasR receptor, which then acts as a transcriptional activator of target genes involved in biofilm formation and virulence, thereby increasing the bacteria’s antibiotic resistance and enhancing its virulence. In this study, we performed a structure-based virtual screening of a natural food database of 10 997 compounds against the crystal structure of the ligand-binding domain of the LasR receptor (PDB ID: 3IX4). This allowed us to identify four molecules, namely ZINC000001580795, ZINC000014819517, ZINC000014708292, and ZINC000004098719, that exhibited a favorable binding mode and docking scores greater than −13 kcal/mol. Furthermore, the molecular dynamics simulation showed that these four molecules formed stable complexes with LasR during the 150-ns molecular dynamics (MD) simulation, indicating their potential for use as inhibitors of the LasR receptor in P. aeruginosa. However, further experimental validation is needed to confirm their activity.
On Detecting and Defending AdvDrop Adversarial Attacks by Image Blurring and Adaptive Noising

AdvDrop, a recently emerged adversarial attack method, generates adversarial samples by dropping information from the clean images. Compared with the traditional adversarial attack based on adding perturbation, AdvDrop is more robust against current de-noising based defense techniques, which poses a great threat to the security of DNNs. To defend AdvDrop, this paper proposes an image blurring detection method to detect AdvDrop adversarial samples and also proposes a defense method based on adaptive noising combined with fast and flexible denoising convolution neural network (FFDNet) filtering to restore the correct class of adversarial samples. Image blurring is first conducted to add a mask to the spectrum of the input image in the frequency domain. Thanks to the robustness of the DNN, the blurring operation has no significant impact on the accuracy of the clean samples but prevents the attackers from extracting useful image features. As a result, the adversarial samples can be detected by analyzing the consistency of the predicted classification output by DNN. In the defense method, we introduce adaptive noising to enhance the high-frequency information in each image. Combined with FFDNet filtering, this strategy increases the diversity of local changes in the image while preserving the overall image structure. As a result, DNN can focus more on essential image features rather than those created by the attacker. We evaluated our detection and defense methods on Cifar-10 and ImageNet datasets for some DNN models, and the results surpassed the current state-of-the-art methods.
Gait Recognition Scheme Focusing on Operating Characteristics at Feature Points Detected by OpenPose

The gait recognition system uses the characteristics of a person’s gait to authenticate the individual. Generally, it applies a walking model to extract features from a captured video. In the conventional works, two types of methods have been studied: model-based methods, which extract features by fitting a person model on an image, and appearance-based methods, which extract features based on time-series silhouette images. In this study, we introduce a prediction operation to extract features in the model-based method. The proposed method consists of two machine learning models. One is a keypoint prediction model that uses OpenPose to estimate the skeleton and predict skeletal movements of a typical person. The other is an identification model that identifies individuals based on the difference between the keypoints and predictions of the target person. Our computer simulations show that the proposed method performs better in terms of identification accuracy than the method that directly uses skeletal coordinate data.
A Full-Scale Connected CNN-Transformer Network for Remote Sensing Image Change Detection

Recent studies have introduced transformer modules into convolutional neural networks (CNNs) to solve the inherent limitations of CNNs in global modeling and have achieved impressive performance. However, some challenges have yet to be addressed: first, networks with simple connections between the CNN and transformer perform poorly in small change areas; second, networks that only use transformer structures are prone to attaining coarse detection results and excessively generalizing feature boundaries. In addition, the methods of fusing the CNN and transformer have the issue of a unilateral flow of feature information and inter-scale communication, leading to a loss of change information across different scales. To mitigate these problems, this study proposes a full-scale connected CNN–Transformer network, which incorporates the Siamese structure, Unet3+, and transformer structure, used for change detection in remote sensing images, namely SUT. A progressive attention module (PAM) is adopted in SUT to deeply integrate the features extracted from both the CNN and the transformer, resulting in improved global modeling, small target detection capacities, and clearer feature boundaries. Furthermore, SUT adopts a full-scale skip connection to realize multi-directional information flow from the encoder to decoder, enhancing the ability to extract multi-scale features. Experimental results demonstrate that the method we designed performs best on the CDD, LEVIR-CD, and WHU-CD datasets with its concise structure. In particular, based on the WHU-CD dataset, SUT upgrades the F1-score by more than 4% and the intersection over union (IOU) by more than 7% compared with the second-best method.
Metabolome and Transcriptome Combinatory Profiling Reveals Fluconazole Resistance Mechanisms of Trichosporon asahii and the Role of Farnesol in Fluconazole Tolerance

Trichosporon asahii is a basidiomycete yeast that is pathogenic to humans and animals, and fluconazole-resistant strains have recently increased. Farnesol secreted by fungi is a factor that causes variations in fluconazole resistance; however, few studies have explored the underlying mechanisms. Therefore, this study aims to delineate the fluconazole resistance mechanisms of T. asahii and explore farnesol’s effects on these processes. A comparative metabolome–transcriptome analysis of untreated fluconazole-sensitive (YAN), fluconazole-resistant (PB) T. asahii strains, and 25 μM farnesol-treated strains (YAN-25 and PB-25, respectively) was performed. The membrane lipid-related genes and metabolites were upregulated in the PB vs. YAN and PB-25 vs. PB comparisons. Farnesol demonstrated strain-dependent mechanisms underlying fluconazole tolerance between the YAN and PB strains, and upregulated and downregulated efflux pumps in PB-25 and YAN-25 strains, respectively. Membrane lipid-related metabolites were highly correlated with transporter-coding genes. Fluconazole resistance in T. asahii was induced by membrane lipid bio-synthesis activation. Farnesol inhibited fluconazole resistance in the sensitive strain, but enhanced resistance in the resistant strain by upregulating efflux pump genes and membrane lipids. This study offers valuable insights into the mechanisms underlying fungal drug resistance and provides guidance for future research aimed at developing more potent antifungal drugs for clinical use.
Ketone bodies mediate alterations in brain energy metabolism and biomarkers of Alzheimer’s disease

Alzheimer’s disease (AD) is the most common form of dementia. AD is a progressive neurodegenerative disorder characterized by cognitive dysfunction, including learning and memory deficits, and behavioral changes. Neuropathology hallmarks of AD such as amyloid beta (Aβ) plaques and neurofibrillary tangles containing the neuron-specific protein tau is associated with changes in fluid biomarkers including Aβ, phosphorylated tau (p-tau)-181, p-tau 231, p-tau 217, glial fibrillary acidic protein (GFAP), and neurofilament light (NFL). Another pathological feature of AD is neural damage and hyperactivation of astrocytes, that can cause increased pro-inflammatory mediators and oxidative stress. In addition, reduced brain glucose metabolism and mitochondrial dysfunction appears up to 15 years before the onset of clinical AD symptoms. As glucose utilization is compromised in the brain of patients with AD, ketone bodies (KBs) may serve as an alternative source of energy. KBs are generated from the β-oxidation of fatty acids, which are enhanced following consumption of ketogenic diets with high fat, moderate protein, and low carbohydrate. KBs have been shown to cross the blood brain barrier to improve brain energy metabolism. This review comprehensively summarizes the current literature on how increasing KBs support brain energy metabolism. In addition, for the first time, this review discusses the effects of ketogenic diet on the putative AD biomarkers such as Aβ, tau (mainly p-tau 181), GFAP, and NFL, and discusses the role of KBs on neuroinflammation, oxidative stress, and mitochondrial metabolism.
The Study of Exposure to Isophorone and the Risk of Getting Cancer: A Systematic Review and Meta-analysis Protocol

Background: This protocol recommends ways to conduct a systematic review and meta-analysis on the association between isophorone exposure and cancer risk. Methods: This study will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible studies will include observational research examining the association between isophorone exposure and cancer outcomes. General search terms will be used to search databases such as PubMed, Web of Science, and Scopus. Two independent reviewers will screen articles, extract data, and assess study quality and risk of bias. Data synthesis and analysis will include meta-analysis, subgroup analyses, sensitivity analyses, and assessment of publication bias. Results: The systematic review and meta-analysis will provide a comprehensive assessment of the association between isophorone exposure and cancer risk. Data from multiple studies will be pooled to achieve an overall estimate of the association, considering sources of heterogeneity and evaluating the quality of included studies. Publication bias will also be assessed. The findings will contribute to our understanding of the potential health effects of isophorone exposure and inform future research and decision-making. Conclusion: This protocol outlines a systematic and transparent approach to assessing the association between isophorone exposure and the risk of cancer. This study will provide insights into the potential health implications of isophorone exposure, preventive measures, and regulatory decisions. By following established guidelines and employing methodology, this study will provide an evidence-based assessment of the available literature, advancing our understanding of the association between isophorone exposure and cancer risk.
Safe operating envelope based on a single-track model for yaw instability avoidance of articulated heavy vehicles

The electrification of commercial vehicles has led to new wheel torque allocation options for propulsion and braking of articulated heavy vehicles. Each unit or axle can be individually braked or propelled whilst the other units or axles are excluded from such action. This may achieve the best energy efficiency. However, this can also lead to potential yaw stability problems such as jackknifing and trailer swing, especially under bad loading and weather conditions. This paper describes the above instabilities and introduces a nonlinear single-track model to study the vehicle dynamics of the tractor-semitrailer combination. The effects of different vehicle and environment parameters are analysed with this vehicle model. A safe operating envelope for limiting the wheel forces is obtained using this vehicle model. Since the vehicle model introduced in this paper is computationally effective, it can be run online in real vehicles, with an instantaneous safe operating envelope obtained for the momentary conditions. Thus, yaw instabilities such as jackknifing and trailer swing may be avoided.
Active vs. Passive, The Case of Sector Equity Funds

This paper examines performance of 95 actively managed U.S. sector equity mutual funds from 29 fund families relative to their peer exchange-traded funds, SPDR sector ETFs, in the period of 2008 to 2017. Our results do not show considerable evidence that actively managed sector mutual funds outperform their passive counterparties. None of the mutual fund portfolios produces a signifi- cant positive alpha through factor models or delivers a significant positive alpha against their peer ETFs. When focusing on the nine oldest actively managed Fidelity sector mutual funds, outperform- ance in the period of 1999–2010, which is reported in literature, appears to fade away during the pe- riod of 2011-2017. Alpha analyses of a larger sample of 60 sector mutual funds show similar performance deterioration in the same 19-year period. The results indicate that U.S. sector equity market has become more efficient in the past decade.