Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing depth in diffuse optical spectroscopy and structured illumination imaging: a Monte Carlo study

Diffuse optical spectroscopy (DOS) and its modification employing structured illumination are widely used in monitoring biotissue oxygenation. In such measurements it is important to know the probing volume for definite source-detector configuration; however, it cannot be measured directly. Monte Carlo simulations allow to trace the probing depth of each individual photon contributing to the signal, which provides a numerical solution for this problem. In this study we investigate distributions of photons over maximal depth reached in turbid media (probing depth) with optical parameters typical for cutaneous tissues at the wavelength of 600 nm. Different configurations of probing illumination are considered, such as collimated point source, one-dimension sinusoidal and rectangular patterns. For collimated point source and zero source-detector separation the number of collected photons monotonously decreases with the probing depth while a pronounced maximum in the distribution is manifested with the increase of source-detector separation. The position of this maximum shifts to higher depths with the decrease of µ a . For one-direction sinusoidal and rectangular illumination patterns it is shown that when the photons are collected near the center of a bright stripe, the peak of the distribution remains close to the surface. When the photons are collected near the center of a dark stripe the peak shifts towards higher depths with the decrease in spatial duty cycle and spatial frequency of the illumination pattern. Employment of rectangular illumination pattern seems more efficient for DOS applications due to wider abilities for controlling probing depth.
Integer wavelet transforms using the lifting scheme

Abst ract: Due to its good decorrelating properties, the wavelet transform is a powerful tool for signal analysis. The lifting scheme is an efficient algorithm to calculate wavelet transforms and it allows for the construction of second-generation wavelets. Furthermore it can easily be converted to an integer transform, which is of great importance for multimedia applications. We show how the lifting scheme can be used for oneand twodimensional signals. In the 2D case, we consider a rectangular grid on which we construct second-generation wavelets based on a red-black blocking scheme. Compared to classical tensor product wavelets on the same grid, these wavelets show less anisotropy.
Spatial Adaptive Fusion Consistency Contrastive Constraint: Weakly Supervised Building Facade Point Cloud Semantic Segmentation

Semantic segmentation of building facade point clouds has diverse applications. The development of semantic segmentation methods is inextricably linked to datasets. The available building facade datasets suffer from a lack of abundant semantic categories and data completeness. To compensate for these shortcomings, we propose a new building facade dataset characterized by various categories and relatively complete 3-D building facades. In addition, most existing methods focus on fully supervised learning, which relies on manually labeling large-scale point cloud data and results in high time and labor costs. In this article, we propose an effective weakly supervised building facade segmentation approach, called spatial adaptive fusion consistency contrastive constraint (SAF-C3), to solve the above problem. We first design a multirandom point cloud augmentor as an auxiliary supervision branch to enhance the learning ability of the original network branch. Then, we present a spatial adaptive fusion (SAF) module to extract discriminative features for building facade point clouds. Finally, we propose a spatial consistency contrastive constraint to explore the contrastive property in feature space and to ensure the predictive consistency among the augmentation and original branches. The proposed method achieves a significant performance improvement against the state-of-the-art methods on two building facade point cloud datasets through extensive experiments. In particular, the performance of SAF-C3 with 1% labels significantly surpasses the baseline network with 100% labels.
Varietal Differences of Yield, Morphological, and Biochemical Parameters of Allium cepa L. under Precipitation Excess in Different Phenological Phases

Flooding is an important factor, decreasing Allium cepa bulb yield and quality. A comparison, in terms of biometrical and biochemical parameters, of five Allium cepa cultivars, grown at two different locations, characterized by contrasting conditions of water availability, i.e., precipitation excess at the end (A) or at the beginning (B) of plant growth, revealed a significant decrease in bulb weight, height, and diameter, and an increase in oxidative stress parameters, such as total antioxidant activity and polyphenol content, monosaccharides, proline, malonic dialdehyde in the condition of excessive soil humidity at the end of the vegetation period (A). Among the five cultivars studied (Zolotnichok, Zolotie cupola, Black prince, Globus, and Myachkovsky), the lowest variations of the above parameters under precipitation excess at cycle end or beginning were recorded in Zolotnichok and Zolotie cupola, which was in accordance with their high adaptability. Cultivar Myachkovsky showed the highest differences of the parameters examined between A and B conditions. Outer scale biochemical parameters demonstrated the highest stability in both regions. The participation of proline, monosaccharides, total polyphenols, and total antioxidant activity in plant defense against hypoxia, caused by waterlogging, was proved by high correlation coefficients between inner scale parameters (‘r’ from +0.714 to +0.920) and the latter with bulb yield (‘r’ from −0.745 to −0.924). High adaptability cultivars (Zolotie cupola, Zolotnichok, Black prince) showed significantly lower MDA inner/outer scales ratio and lipids outer/inner scales ratio compared to cultivars with moderate adaptability in (B) conditions. The results provide important information regarding biochemical peculiarities of Allium cepa in diverse soil humidity, which should be considered in future breeding activities of onion genotypes, characterized by high adaptability to different water excess conditions.
Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic–boreal regions

Abstract. Spaceborne microwave remote sensing (300 MHz–100 GHz) provides a valuable method for characterizing environmental changes, especially in Arctic–boreal regions (ABRs) where ground observations are generally spatially and temporally scarce. Although direct measurements of carbon fluxes are not feasible, spaceborne microwave radiometers and radar can monitor various important surface and near-surface variables that affect terrestrial carbon cycle processes such as respiratory carbon dioxide (CO2) fluxes; photosynthetic CO2 uptake; and processes related to net methane (CH4) exchange including CH4 production, transport and consumption. Examples of such controls include soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties and land cover. Microwave remote sensing also provides a means for independent aboveground biomass estimates that can be used to estimate aboveground carbon stocks. The microwave data record spans multiple decades going back to the 1970s with frequent (daily to weekly) global coverage independent of atmospheric conditions and solar illumination. Collectively, these advantages hold substantial untapped potential to monitor and better understand carbon cycle processes across ABRs. Given rapid climate warming across ABRs and the associated carbon cycle feedbacks to the global climate system, this review argues for the importance of rapid integration of microwave information into ABR terrestrial carbon cycle science.
Rendezvous Under Cyclic Pursuit With Heterogeneous Input and Communication Delays

The existing techniques solve the rendezvous problem and characterize the final convergence point in the absence or presence of homogeneous input/communication delays. In this brief, we analyze the rendezvous problem of double-integrator multi-agent system (MAS) in the presence of heterogeneous input and communication delays. We employ cyclic pursuit strategy to achieve rendezvous of the delayed MAS. It is established in the literature that accommodation of one negative position consensus gain will expand the reachable set beyond the convex hull of the initial position of the agents engaged in cyclic pursuit. Consonantly, we accommodate at most one negative position consensus gain in the system and derive sufficient conditions on the gains for a stable rendezvous. For double-integrator system, damping gains are upper bounded by the input delays while the lower bound of the negative position consensus gain does not depend on the input delays explicitly. The final rendezvous point varies with homogeneous/heterogeneous communication delays as well as with the heterogeneous input delays. The findings are illustrated by numerical simulations.
Impacts of land cover change and water management practices on the Tarim and Konqi river systems, Xinjiang, China

Abstract. The Tarim and Konqi Rivers in western China have experienced dramatic changes in streamflow and riparian vegetation due to climatic variability, land cover change, and water management including interbasin water transfers. To assess the extent and evolution of vegetation dynamics along these rivers, we use Landsat and MODIS images for land cover classification, spectral mixture analysis, and landscape phenology analysis. From 1998 to 2011, agriculture nearly tripled in extent, from 1376 to 3742 km2. Natural riparian vegetation persisted in aggregate but experienced losses (to agriculture) in some areas while expanding into barren land elsewhere. Spectral mixture analysis suggests that interbasin water transfers from the Konqi to the Tarim River increased near-channel riparian vegetation on the Tarim at the expense of vegetation on the Konqi. A time-series of MODIS images reveals a pattern of increasing and decreasing greenness across the region, including loss of vegetation in distal regions that were formerly subject to sporadic seasonal flooding but now are cut off from their water supply due to water management. These results suggest that satellite remote sensing may play a valuable role in monitoring the effects of changing land use and hydrology on riparian systems in Central Asia and other arid regions.
Kernel Based High Order "Explicit" A-Stable Scheme for Nonlinear Degenerate Advection-Diffusion Equations

In this paper, we present a novel numerical scheme for solving a class of nonlinear degenerate parabolic equations with non-smooth solutions. The proposed method relies on a special kernel based formulation of the solutions found in our early work on the method of lines transpose and successive convolution. In such a framework, a high order weighted essentially non-oscillatory (WENO) methodology and a nonlinear filter are further employed to avoid spurious oscillations. High order accuracy in time is realized by using the high order explicit strong-stability-preserving (SSP) Runge-Kutta method. Moreover, theoretical investigations of the kernel based formulation combined with an explicit SSP method indicates that the combined scheme is A-stable and up to third order accuracy. Evaluation of the kernel based approach is done with a fast $\mathcal{O}(N)$ summation algorithm. The new method allows for much larger time step evolution compared with other explicit schemes with the same order accuracy, leading to remarkable computational savings.
Multimodal piezoelectric energy harvesting on a thin plate integrated with SSHI circuit: an analytical and experimental study

Multimodal piezoelectric energy harvesting can be achieved by integrating piezo-patch harvesters into plate-like structures available in marine, aerospace, and automotive applications. A synchronized switch harvesting on inductor (SSHI) interface as the harvesting circuit has been well studied for cantilever beams, considering the single vibration mode of the structure. However, integrating a two-dimensional electromechanical structure with a SSHI circuit for multimodal energy harvesting is missing in the literature. This paper evaluates the performance of the SSHI interface integrated with a piezoelectric energy harvester (PEH) on a plate-like host structure. The analytical solution is developed based on an equivalent impedance approach to predict the steady-state electrical response of the harvester as a closed-form solution. The experiments are conducted to validate the analytical solution for the system’s first and second vibration modes. The experimental results reveal that integration of SSHI to a plate-like harvester introduces a multi-switching behavior rather than a standard single-switching behavior. Due to the multimodal vibrational characteristics of the plate, the circuit switch is triggered several times at each half period of the vibration, which increases the energy dissipation of the circuit and thus reduces the output voltage. On the other hand, single switching at each half period of the vibration happens for lower piezoelectric voltage levels. This is the desired behavior of the SSHI circuit where the analytical prediction matches with the experimental data. Finally, the energy harvesting performance of the SSHI circuit is compared against the standard rectifier, showing 183% and 134% power output enhancement for the first and second vibration modes, respectively.
Intraoral Drug Delivery: Highly Thiolated κ-Carrageenan as Mucoadhesive Excipient

Aim: This study aims to design a novel thiolated κ-carrageenan (κ-CA-SH) and evaluate its potential as an excipient for the design of mucoadhesive drug delivery systems. Methods: Native κ-carrageenan (κ-CA) was thiolated with phosphorous pentasulfide in sulfolane and characterized via 1H NMR, FTIR, as well as Ellman’s test. Cytotoxicity was assessed via resazurin assay. In vitro release of the model drug, benzydamine hydrochloride, was determined. Tensile and mucosal residence time studies were performed on buccal and small intestinal mucosa. Mucoadhesive features were investigated via rheological studies with freshly isolated porcine mucus. Results: Thiolated κ-CA (κ-CA-SH) with 1213.88 ± 52 µmol/g thiol groups showed no cytotoxicity at a concentration of 1% (m/v) and low cytotoxicity up to 2% (m/v). Benzydamine hydrochloride showed slow release in solution for both polymers. Tensile studies on buccal and intestinal mucosa showed an up to 2.7-fold and 7.7-fold enhancement in the maximum detachment force (MDF) and total work of adhesion (TWA) of κ-CA-SH vs. κ-CA, respectively. The κ-CA-SH exhibited an up to 4.4-fold improved dynamic viscosity with mucus and significantly prolonged residence time on mucosa compared to native κ-CA. Conclusion: Since highly thiolated κ-CA shows a slow release of positively charged active pharmaceutical ingredients and enhanced mucoadhesive properties, it might be a promising excipient for local drug delivery in the oral cavity.
Classifier PGN: Classification with High Confidence Rules

Associative classifiers use a set of class association rules, generated from a given training set, to classify new instances. Typically, these techniques set a minimal support to make a first selection of appropriate rules and discriminate subsequently between high and low quality rules by means of a quality measure such as confidence. As a result, the final set of class association rules have a support equal or greater than a predefined threshold, but many of them have confidence levels below 100%. PGN is a novel associative classifier which turns the traditional approach around and uses a confidence level of 100% as a first selection criterion, prior to maximizing the support. This article introduces PGN and evaluates the strength and limitations of PGN empirically. The results are promising and show that PGN is competitive with other well-known classifiers.ACM Computing Classification System (1998): H.2. 8, H.3. 3.
Di(2-ethylhexyl) phthalate mediates IL-33 production via aryl hydrocarbon receptor and is associated with childhood allergy development

Background Few studies assess cord blood biomarkers to predict prenatal exposure to di(2-ethylhexyl) phthalate (DEHP) on the development of allergic diseases later in childhood. IL-33 has been indicated to play an important role in allergic diseases. We evaluated the association of prenatal DEHP exposure and IL-33 in cord blood on the development of allergic diseases. We also investigated the mechanism of DEHP in human lung epithelial cells and asthma animal models. Methods 66 pregnant women were recruited, and their children followed when they were aged 3 years. Maternal urinary DEHP metabolites were determined using liquid chromatography-electrospray-ionization-tandem mass spectrometry. The effect of DEHP on IL-33 production was investigated in human lung epithelial cells and club cell-specific aryl hydrocarbon receptor (AhR) deficiency mice. ELISA and RT-PCR, respectively, measured the IL-33 cytokine concentration and mRNA expression. Results The concentrations of maternal urinary DEHP metabolites and serum IL-33 in cord blood with childhood allergy were significantly higher than those in the non-childhood allergy group. DEHP and MEHP could induce IL-33 production and reverse by AhR antagonist and flavonoids in vitro. Enhanced ovalbumin-induced IL-4 and IL-33 production in bronchoalveolar lavage fluid (BALF) by DEHP exposure and suppressed in club cell-specific AhR null mice. Kaempferol has significantly reversed the DEHP effect in the asthma animal model. Conclusions Cord blood IL-33 level was correlated to childhood allergy and associated with maternal DEHP exposure. IL-33 might be a potential target to assess the development of DEHP-related childhood allergic disease. Flavonoids might be the natural antidotes for DEHP.
Quadratic Residual Multiplicative Filter Neural Networks for Efficient Approximation of Complex Sensor Signals

In this research, we present an innovative Quadratic Residual Multiplicative Filter Neural Network (QRMFNN) to effectively learn extremely complex sensor signals as a low-dimensional regression problem. Based on this novel neural network model, we introduce two enhanced architectures, namely FourierQResNet and GaborQResNet. These networks integrate the benefits of quadratic residual neural networks, multiplicative filter neural networks, and several filters in signal processing to effectively capture complex signal patterns, thereby addressing issues associated with convergence speed, precision, and spectral bias. These architectures indicate effectiveness in reducing spectral bias, thereby improving the accuracy of signal approximation. After conducting comprehensive experiments on ten very complex test signals from diverse application domains, the proposed architectures have demonstrated superior ability in approximating intricate sensor signals and mitigating spectral bias effectively. The numerical results of the experiments reveal that FourierQResNet and GaborQResNet exhibit excellent performance compared to other existing neural network architectures and models in accurately estimating complicated sensor signals, with admiringly small errors. In addition, the findings emphasize the importance of mitigating spectral bias in order to achieve reliable learning from sensor data. The implications of these results are significant in various domains that require precise and reliable sensor data analysis, including healthcare, environmental monitoring, aviation, IoT applications, and industrial automation. This research significantly advances the field of sensor signal approximation and opens new avenues for enhancing data interpretation reliability and accuracy in complex signal environments.
The successive raising estimator and its relation with the ridge estimator

ABSTRACT The raised estimators are used to reduce collinearity in linear regression models by raising a column in the experimental data matrix which may be nearly linear with the other columns. The raising procedure has two components, namely stretching and rotating, which we can analyze separately. We give the relationship between the raised estimators and the classical ridge estimators. Using a case study, we show how to determine the perturbation parameter for the raised estimators by controlling the amount of precision to be retained in the original data.
The effect of shortening the quarantine period and lifting the indoor mask mandate on the spread of COVID-19: a mathematical modeling approach

In this paper, we present a mathematical model to assess the impact of reducing the quarantine period and lifting the indoor mask mandate on the spread of Coronavirus Disease 2019 (COVID-19) in Korea. The model incorporates important epidemiological parameters, such as transmission rates and mortality rates, to simulate the transmission of the virus under different scenarios. Our findings reveal that the impact of mask wearing fades in the long term, which highlights the crucial role of quarantine in controlling the spread of the disease. In addition, balancing the confirmed cases and costs, the lifting of mandatory indoor mask wearing is cost-effective; however, maintaining the quarantine period remains essential. A relationship between the disease transmission rate and vaccine efficiency was also apparent, with higher transmission rates leading to a greater impact of the vaccine efficiency. Moreover, our findings indicate that a higher disease transmission rate exacerbates the consequences of early quarantine release.
An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China

Landslides are common and highly destructive geological hazards that pose significant threats to both human lives and property on a global scale every year. In this study, a novel ensemble broad learning system (BLS) was proposed for evaluating landslide susceptibility in Taiyuan City, Northern China. Meanwhile, ensemble learning models based on the classification and regression tree (CART) and support vector machine (SVM) algorithms were applied for a comparison with the BLS-AdaBoost model. Firstly, in this study, a grand total of 114 landslide locations were identified, which were randomly divided into two parts, namely 70% for model training and the remaining 30% for model validation. Twelve landslide conditioning factors were selected for mapping landslide susceptibility. Subsequently, three models, namely CART-AdaBoost, SVM-AdaBoost and BLS-AdaBoost, were constructed and used to map landslide susceptibility. The frequency ratio (FR) was used to assess the relationship between landslides and different influencing factors. Finally, the three models were validated and compared on the basis of both statistical-based evaluations and ROC curve-based evaluations. The results showed that the integrated model with BLS as the base learner achieved the highest AUC value of 0.889, followed by the integrated models that used CART (AUC = 0.873) and SVM (AUC = 0.846) as the base learners. In general, the BLS-based integrated learning methods are effective for evaluating landslide susceptibility. Currently, the application of BLS and the integrated BLS model for evaluating landslide susceptibility is limited. This study is one of the first efforts to use BLS and the integrated BLS model for evaluating landslide susceptibility. BLS and its improvements have the potential to provide a more powerful approach to assess landslide susceptibility.