Miracle-dz/UniFAR-FTUs huggingface dataset:
Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall‐to‐wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM‐X data, simplifications are necessary to estimate canopy height with semi‐empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR). We trained the semi‐empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM‐X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. Our results indicated that the empirical parameters substantially improved the estimation from a root‐mean‐square‐error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land‐use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals. We demonstrate the potential of the combined exploitation of the TanDEM‐X and GEDI missions for a wall‐to‐wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data.
The Secretary Problem with Competing Employers on Random Edge Arrivals

The classic secretary problem concerns the problem of an employer facing a random sequence of candidates and making online hiring decisions to try to hire the best candidate. In this paper, we study a game-theoretic generalization of the secretary problem where a set of employers compete with each other to hire the best candidate. Different from previous secretary market models, our model assumes that the sequence of candidates arriving at each employer is uniformly random but independent from other sequences. We consider two versions of this secretary game where employers can have adaptive or non-adaptive strategies, and provide characterizations of the best response and Nash equilibrium of each game.
Exponential Stability of Bidirectional Associative Memory Neural Networks with Variable Delays and Impulsive

In this paper, a model of impulsive general BAMs neural networks is first formulated.We investigate impulsive effects on the stability of BAMs neural networks with variable delays and obtain some sufficient conditions ensuring exponential stability of the impulsive variable delays system.The results extend and improve some recent works for impulsive neural networks as well as non-impulsive neural networks.
Stochastic Geometry Based Performance Study for Wireless-Powered Backscatter Communications

Wirelessly powered backscatter communication (WP-BackCom) enables battery-free Internet of Things (IoT) nodes to passively modulate and backscatter information on the energy signals transmitted by power beacons (PBs) and to harvest energy for realizing energy self-sustainability. It has been deemed a promising solution for the large-scale deployment of IoT nodes. In this paper, we exploit stochastic geometry (SG) to develop a tractable framework to evaluate the successful transmission probability in WP-BackCom while considering the imperfect successive interference cancellation (SIC) at each gateway (GW) and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU). The successful transmission probability is defined as the probability for the event that not only the BU can harvest sufficient power from PBs for sustaining its operation but also the backscattered information can be successfully decoded by the GW. We derive the active probability into a closed form and then obtain the successful transmission probability based on the random spectrum access (RSA) policy. The closed-form expression for the successful transmission probability is derived under three special cases. We also devise a closed-form expression for the sub-optimal reflection coefficient to obtain a sub-optimal successful transmission probability. The analysis reveals that the sub-optimal reflection coefficient increases with the SIC coefficient. Numerical simulations validate the correctness of the above theoretical derivations and examine the impacts of various parameters on the successful transmission probability.
Enhancing Power System Resilience With Controlled Islanding Strategies

Power systems are not designed to survive a large number of simultaneous contingencies and are thus vulnerable to extreme events that can cause considerable societal and economical losses. Power systems resilience against adverse weather events could be enhanced with the implementation of Intentional Controlled Islanding (ICI). This paper presents a strategy design of ICI to improve system survivability in case of severe windstorms. It includes the design of a predetermined network split applied with a preventive and an adaptive approach, and a decision-making process based on the forecast wind speed. The network response to storms is studied dynamically, in order to model cascading failures and protection scheme effects. Simulations are conducted on the IEEE 39-bus system, then the resilience enhancement is assessed with a stochastic approach. ICI is demonstrated to be effective, as the results show an significant decrease of blackout risk while limiting unnecessary load shedding.
Predicting Blocking Bugs with Machine Learning Techniques: A Systematic Review

— The application of machine learning (ML) techniques to predict blocking bugs have emerged for the early detection of Blocking Bugs (BBs) in software components to mitigate the adverse effect of BBs on software release and project cost. This study presents a systematic literature review of the trends in the application of ML techniques in BB prediction, existing research gaps, and possible research directions to serve as a reference for future research and an application insight for software engineers. We constructed search phrases from relevant terms and used them to extract peer-reviewed studies from the databases of five famous academic publishers, namely Scopus, SpringerLink, IEEE Xplore, ACM digital library, and ScienceDirect. We included primary studies published between January 2012 and February 2022 that applied ML techniques to building Blocking Bug Prediction models (BBPMs). Our result reveals a paucity of literature on BBPMs. Also, previous researchers employed ML techniques such as Decision Trees, Random Forest, Bayes Network, XGBoost, and DNN in building existing BB prediction models. However, the publicly available datasets for building BBPMs are significantly imbalanced. De-spite the poor performance of the Accuracy metric where imbalanced datasets are concerned, some primary studies still utilized the Accuracy metric to assess the performance of their proposed BBPM. Further research is required to validate existing and new BBPM on datasets of commercial software projects. Also, future researchers should mitigate the effect of class imbalance on the proposed BB prediction model before training a BBPM.
Structural Reconstruction of Signed Social Networks

Modeling real-world signed networks is a challenging task due to the highly dynamic microlevel growth processes involved in it. The network reconstruction is a problem in which we define a model that not only captures the patterns followed by different structural and spectral properties of real-world networks but also minimizes numerical error. In this article, we define a simple yet an effective network generation mechanism that can learn model parameters efficiently and produces simulated networks having structural properties close to a given input real-world signed social network. In the proposed model, corresponding to each node, a characteristic function is defined that controls its dynamics and its link formation process. A family of exponential functions is suited well to include aging and local growth, including triangle formation of different types of balanced and unbalanced triangles, and produce a wide range of degree distributions. In the proposed model, two layers are modeled independently, and further superimposition of layers is applied to get the final simulated signed network. Apart from that, the experimental results manifest that our proposed model, signed network structural reconstruction model (SNSRM), is able to replicate the characteristic properties of the various real-world signed networks more closely compared to the state-of-the-art models.
Sodium Chloride Enhances Recombinant Adeno-Associated Virus Production in a Serum-Free Suspension Manufacturing Platform Using the Herpes Simplex Virus System

The increase in effective treatments using recombinant adeno-associated viral (rAAV) vectors has underscored the importance of scalable, high-yield manufacturing methods. Previous work from this group reported the use of recombinant herpes simplex virus type 1 (rHSV) vectors to produce rAAV in adherent HEK293 cells, demonstrating the capacity of this system and quality of the product generated. Here we report production and optimization of rAAV using the rHSV system in suspension HEK293 cells (Expi293F) grown in serum and animal component-free medium. Through adjustment of salt concentration in the medium and optimization of infection conditions, titers greater than 1 × 1014 vector genomes per liter (VG/liter) were observed in purified rAAV stocks produced in Expi293F cells. Furthermore, this system allowed for high-titer production of multiple rAAV serotypes (2, 5, and 9) as well as multiple transgenes (green fluorescent protein and acid α-glucosidase). A proportional increase in vector production was ob.
Body Language Analysis in Healthcare: An Overview

Given the current COVID-19 pandemic, medical research today focuses on epidemic diseases. Innovative technology is incorporated in most medical applications, emphasizing the automatic recognition of physical and emotional states. Most research is concerned with the automatic identification of symptoms displayed by patients through analyzing their body language. The development of technologies for recognizing and interpreting arm and leg gestures, facial features, and body postures is still in its early stage. More extensive research is needed using artificial intelligence (AI) techniques in disease detection. This paper presents a comprehensive survey of the research performed on body language processing. Upon defining and explaining the different types of body language, we justify the use of automatic recognition and its application in healthcare. We briefly describe the automatic recognition framework using AI to recognize various body language elements and discuss automatic gesture recognition approaches that help better identify the external symptoms of epidemic and pandemic diseases. From this study, we found that since there are studies that have proven that the body has a language called body language, it has proven that language can be analyzed and understood by machine learning (ML). Since diseases also show clear and different symptoms in the body, the body language here will be affected and have special features related to a particular disease. From this examination, we discovered that it is possible to specialize the features and language changes of each disease in the body. Hence, ML can understand and detect diseases such as pandemic and epidemic diseases and others.
Local and Systemic Delivery of the BimS Gene Nano-Complex for Efficient Oral Squamous Cell Carcinoma Therapy

Purpose Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, with more than 300,000 new cases annually. Despite advances in existing treatments, including surgery, radiation, chemotherapy, and immunotherapy, the overall survival and prognosis have remained poor. However, gene therapy based on non-viral vectors provides new ideas for the treatment of OSCC. Here, we aimed to prepare and describe the synthesis, biosafety, and preclinical efficacy of DOTAP-mPEG-PCL (DMP) in OSCC gene therapy. Methods We prepared a nano-sized hybrid cationic micelle DMP. DMP micelles were prepared by self-assembling cationic lipid DOTAP and mPEG-PCL polymer. We evaluated the characteristics of this cationic micelle in vitro. Combined with encoding the apoptosis-inducing BimS gene, we established the DMP/phBimS complex and evaluated its anti-tumor effect in vitro. We also established a mouse tongue xenograft model to evaluate the antitumor effect of the DMP/phBimS complex in vivo through local and systemic administration prospectively. Results The DMP cationic micelle is spherical in shape, with an average diameter of 28.32 ± 3.56 nm and an average zeta potential of 43.43 ± 0.82 mV. By activation of lipid raft-mediated endocytosis caveolin-mediated endocytosis, DMP could efficiently deliver plasmid into SCC15 cells (efficiency: 52.07% ± 1.63%), with an ideal biosecurity. When loaded by plasmid encoding the apoptosis-inducing BimS gene, the DMP/phBimS complex exhibited an obvious anti-proliferation effect of SCC15 in vitro through the apoptosis pathway (33.9% ± 2.62% apoptosis rate). By local administration, the DMP/phBimS complex showed ideal anti-tumor properties in the nude mouse tongue xenograft model, with an average tumor inhibition rate of 65.66%. Furthermore, through systematic administration, the DMP/phBimS complex obviously inhibited OSCC growth, with an average inhibition rate of 45.63% (DMP/phBimS) and an appropriate biocompatibility. Conclusion The DMP/phBimS complex is an optional effective option for suicide gene therapy for OSCC.
Signature Recognition and Detection of Skilled Forgeries Using Image Transformation and Multistream CNN

Person identification through their credentials such as biometrics or signature is very important for one’s privacy and has become the most integral part for recognition. Prevention of forgeries in handwritten signatures has gain prominence in recent times. To serve this target this paper carried out Image Transformation techniques and an Artificial Intelligence model to effectively notice the differences of genuine and forged signature. Grass-fire transformations and optical flow captures the disparity in signatures. Proposed system uses Deep learning framework with ResNet 50 along with Convolutional Neural network (CNN). Comparative studies have been done using SVC2004 and SUSIG benchmark with the existing literature.
Optimal Estate Taxation: More (about) Heterogeneity across Dynasties

Standard models on optimal estate taxation do not allow for intergenerational transmission of bequest motives. However, correlation in bequest motives may exist due to genetic and cultural transmission of preferences or indirect reciprocity. I introduce such intergenerational correlation to a simple model with heterogeneously altruistic parents. I derive two insights for optimal linear estate taxation under a Utilitarian welfare measure. First, this correlation implies a higher optimal estate tax rate. Second, estate tax rates should be higher for those parents who inherited themselves.
Explainable Metaphor Identification Inspired by Conceptual Metaphor Theory

Metaphor is not only a linguistic phenomenon but also reflects the concept projection between source and target domains in human cognition. Previous sequence tagging-based metaphor identification methods could not model the concept projection, resulting in a limitation that the outputs of these models are unexplainable in the predictions of the metaphoricity labels. In this work, we propose the first explainable metaphor identification model, inspired by Conceptual Metaphor Theory. The model is based on statistic learning, a lexical resource, and a novel reward mechanism. Our model can identify the metaphoricity on the word-pair level, and explain the predicted metaphoricity labels via learned concept mappings. The use of the reward mechanism allows the model to learn the optimal concept mappings without knowing their true labels. Our method is also applicable for the concepts that are out of training domains by using the lexical resource. The automatically generated concept mappings demonstrate the implicit human thoughts in metaphoric expressions. Our experiments show the effectiveness of the proposed model in metaphor identification, and concept mapping tasks, respectively.
Particulate suspension: a review of studies characterizing particulates and volatile organic compounds emissions during additive manufacturing processes

Abstract As additive manufacturing (AM) has become an evolving discipline in many industries, including manufacturing, medical, and aerospace, it becomes important to identify the risk coming from human exposure to particulates and Volatile Organic Compounds (VOCs) in AM which can lead to serious and chronic health issues. To address this issue, this article first provides a summary of previously reported particulate and VOCs characterization studies during AM processes, including equipment, environmental setups, variables studied, and instrumentation reported in the literature. We then reported a synopsis of the nature of the exposure, characteristics of the emitted particulates and VOCs, and associated health risks for different AM settings in a systematic manner. The key factors contributing to the harmful emissions include the use of toxic material compounds, high operating temperature, manual handling of hazardous materials, and limitations of the underlying printing technology among others. For that matter, we have outlined potential pathways to control hazardous exposure. Our recommendations include adopting safer operational practices, developing regulatory frameworks for facilities and equipment manufacturers, and seeking better technologies that minimize harmful emissions. Our aim is to benefit early-stage researchers, regulators, and industry practitioners in understanding and advancing knowledge of health hazards, safer practices, and technologies in AM.
The Effect of Subtractive Manufacturing Technologies on the UWB Antenna’s Scattering Parameter

The paper investigates the effect of commonly used subtractive manufacturing technologies on the scattering parameter of an Ultra-WideBand (UWB) antenna that was designed, realized, and measured. As a result, the conventional copper layer etching method using two chemical solutions based on ferric chloride and hydrochloric acid, as well as the Computer Numerical Control (CNC) machining, are critically analyzed. As proven by the obtained results, the etchant, its temperature, and the etching time has a direct effect on the quality of the realized UWB antenna’s edges. Along with the scattering parameter of the antenna, the cross sections’ analysis of the quality of the edges is presented. The Printed Circuit Board (PCB) with the copper layer was used in three thicknesses (17.5, 35, and 70 $\mu$m) for the experiments described in the paper. The polyimide film with a thickness of 50 $\mu$m was used as the substrate material. Due to the thin polymeric film and the narrow width of the feeding element (330 $\mu$m), CNC machining is at the limit of its possibilities. Additionally, the paper analyzes the effect of the etching solution’s temperature on the edge quality in the high frequency area.
Reinforcement Learning Enabled Autonomous Manufacturing Using Transfer Learning and Probabilistic Reward Modeling

Here we propose a reinforcement learning enabled physical autonomous manufacturing system (AMS) that is capable of learning the manufacturing process parameters to autonomously fabricate a complex-geometry artifact with desired performance characteristics. The poor sample efficiency of traditional RL algorithms challenges real-world manufacturing decision making due to a high variable cost from raw material, machine utilization, and labor costs. To make decision making sample efficient, we propose to leverage a first-principles based source task for training, transfer effective representations from trained knowledge, and then use these representations to interact with the physical system to learn a probabilistic model of the target reward function. We deploy this idea to a novel dataset obtained from a custom physical AMS machine that can autonomously manufacture phononic crystals, a complex geometry artifact with spectral response as performance characteristic. We demonstrate that our method uses as low as 25 artifacts to model the interesting part of the target reward function and find an artifact with high reward. This task typically requires manual design of phononic crystals and extensive empirical iterations on the order of hundreds.
Molecular Mechanism of Equine Endometrosis: The NF-κB-Dependent Pathway Underlies the Ovarian Steroid Receptors’ Dysfunction

Endometrosis is a frequently occurring disease decreasing mares’ fertility. Thus, it is an important disease of the endometrium associated with epithelial and stromal cell alterations, endometrium gland degeneration and periglandular fibrosis. Multiple degenerative changes are found in uterine mucosa, the endometrium. However, their pathogenesis is not well known. It is thought that nuclear factor-κB (NF-κB), a cell metabolism regulator, and its activation pathways take part in it. The transcription of the profibrotic pathway genes of the NF-κB in fibrotic endometria differed between the follicular (FLP) and mid-luteal (MLP) phases of the estrous cycle, as well as with fibrosis progression. This study aimed to investigate the transcription of genes of estrogen (ESR1, ESR2) and progesterone receptors (PGR) in equine endometria to find relationships between the endocrine environment, NF-κB-pathway, and fibrosis. Endometrial samples (n = 100), collected in FLP or MLP, were classified histologically, and examined using quantitative PCR. The phase of the cycle was determined through the evaluation of ovarian structures and hormone levels (estradiol, progesterone) in serum. The transcription of ESR1, ESR2, and PGR decreased with the severity of endometrial fibrosis and degeneration of the endometrium. Moreover, differences in the transcription of ESR1, ESR2, and PGR were noted between FLP and MLP in the specific categories and histopathological type of equine endometrosis. In FLP and MLP, specific moderate and strong correlations between ESR1, ESR2, PGR and genes of the NF-κB pathway were evidenced. The transcription of endometrial steroid receptors can be subjected to dysregulation with the degree of equine endometrosis, especially in both destructive types of endometrosis, and mediated by the canonical NF-κB pathway depending on the estrous cycle phase.