Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate search with constraints on indels with application in SPAM filtering

Finding distorted occurrences of search pattern(s) in the search string by applying constraints on elementary edit operations (indels (insertions/deletions) and substitutions) is a new category of the approximate string search problem that we introduce in this paper. The constraint on the total number of indels can improve the search efficiency when one knows the probabilities of these edit operations for the distorted pattern/text. Two approximate search algorithms with such a constraint, CRBP-Indels and Sankoff-Indels, are presented here and their performances are evaluated for different probabilities of insertions, deletions, and substitutions. The experimental results show that CRBP-Indels has better performance over Sankoff-Indels when the number of indels is greater than the number of substitutions. However, the Sankoff- Indels algorithm is better if the number of substitutions is greater than the number of indels. Possible application of these algorithms is in SPAM filtering for detection of deliberately distorted SPAM-words. In such a scenario, the number of indels applied on the original SPAM-words must be limited in order to maintain their intelligibility.
Apoplastic and Symplasmic Markers of Somatic Embryogenesis

Somatic embryogenesis (SE) is a process that scientists have been trying to understand for many years because, on the one hand, it is a manifestation of the totipotency of plant cells, so it enables the study of the mechanisms regulating this process, and, on the other hand, it is an important method of plant propagation. Using SE in basic research and in practice is invaluable. This article describes the latest, but also historical, information on changes in the chemical composition of the cell wall during the transition of cells from the somatic to embryogenic state, and the importance of symplasmic communication during SE. Among wall chemical components, different pectic, AGP, extensin epitopes, and lipid transfer proteins have been discussed as potential apoplastic markers of explant cells during the acquisition of embryogenic competence. The role of symplasmic communication/isolation during SE has also been discussed, paying particular attention to the formation of symplasmic domains within and between cells that carry out different developmental processes. Information about the number and functionality of plasmodesmata (PD) and callose deposition as the main player in symplasmic isolation has also been presented.
Ketamine Reduces the Surface Density of the Astroglial Kir4.1 Channel and Inhibits Voltage-Activated Currents in a Manner Similar to the Action of Ba2+ on K+ Currents

A single sub-anesthetic dose of ketamine evokes rapid and long-lasting beneficial effects in patients with a major depressive disorder. However, the mechanisms underlying this effect are unknown. It has been proposed that astrocyte dysregulation of extracellular K+ concentration ([K+]o) alters neuronal excitability, thus contributing to depression. We examined how ketamine affects inwardly rectifying K+ channel Kir4.1, the principal regulator of K+ buffering and neuronal excitability in the brain. Cultured rat cortical astrocytes were transfected with plasmid-encoding fluorescently tagged Kir4.1 (Kir4.1-EGFP) to monitor the mobility of Kir4.1-EGFP vesicles at rest and after ketamine treatment (2.5 or 25 µM). Short-term (30 min) ketamine treatment reduced the mobility of Kir4.1-EGFP vesicles compared with the vehicle-treated controls (p < 0.05). Astrocyte treatment (24 h) with dbcAMP (dibutyryl cyclic adenosine 5′-monophosphate, 1 mM) or [K+]o (15 mM), which increases intracellular cAMP, mimicked the ketamine-evoked reduction of mobility. Live cell immunolabelling and patch-clamp measurements in cultured mouse astrocytes revealed that short-term ketamine treatment reduced the surface density of Kir4.1 and inhibited voltage-activated currents similar to Ba2+ (300 µM), a Kir4.1 blocker. Thus, ketamine attenuates Kir4.1 vesicle mobility, likely via a cAMP-dependent mechanism, reduces Kir4.1 surface density, and inhibits voltage-activated currents similar to Ba2+, known to block Kir4.1 channels.
Higher serum β2-microglobulin is a predictive biomarker for cognitive impairment in spinal cord injury

Objective Recent studies have suggested that high levels of β2-microglobulin are linked to cognitive deterioration; however, it is unclear how this connects to spinal cord injury (SCI). This study sought to determine whether there was any association between cognitive decline and serum β2-microglobulin levels in patients with SCI. Methods A total of 96 patients with SCI and 56 healthy volunteers were enrolled as study participants. At the time of enrollment, specific baseline data including age, gender, triglycerides (TG), low-density lipoprotein (LDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), smoking, and alcohol use were recorded. Each participant was assessed by a qualified physician using the Montreal cognitive assessment (MoCA) scale. Serum β2-microglobulin levels were measured using an enzyme-linked immunosorbent assay (ELISA) reagent for β2-microglobulin. Results A total of 152 participants were enrolled, with 56 in the control group and 96 in the SCI group. There were no significant baseline data differences between the two groups (p > 0.05). The control group had a MoCA score of 27.4 ± 1.1 and the SCI group had a score of 24.3 ± 1.5, with the difference being significant (p < 0.05). The serum ELISA results revealed that the levels of β2-microglobulin in the SCI group were considerably higher (p < 0.05) than those in the control group (2.08 ± 0.17 g/mL compared to 1.57 ± 0.11 g/mL). The serum β2-microglobulin level was used to categorize the patients with SCI into four groups. As serum β2-microglobulin levels increased, the MoCA score reduced (p < 0.05). After adjustment of baseline data, further regression analysis showed that serum β2-microglobulin level remained an independent risk factor for post-SCI cognitive impairment. Conclusions Patients with SCI had higher serum levels of β2-microglobulin, which may be a biomarker for cognitive decline following SCI.
Climate Risk and the Cost of Information Opacity in Supply Chains

ABSTRACT: This paper examines to what extent information opacity exacerbates climate risks in supply chains. Understanding the information frictions in supply chain settings is important because disruptions originating from an opaque supplier can impose greater risk to the supply chain as a whole than those originating from a non-opaque supplier. Using proxies for the firm’s public information environments, we empirically examine the differential effects of information frictions during supply chain disruptions. Using natural disasters as firm-specific idiosyncratic climate shocks on suppliers, we find that customer firms suffer more when disrupted suppliers have opaque information environments. Moreover, these effects spill over further to other supply chain participants that are not directly linked to disrupted suppliers. These results are consistent with the disrupted firm’s information opacity exposing its trade partners to greater climate risks, resulting in greater economic losses. Additionally, we find that firms increasingly search their suppliers’ SEC filings when their suppliers are hit by climate-related shocks, suggesting trade partners find information in SEC filings useful despite extensive private information sharing. Overall, our paper speaks to the growing concerns over misinformation having a real impact during supply chain disruptions and highlights the role of public information environments in the context of supply chain climate risks.
Effect of Drug Encapsulation and Hydrothermal Exposure on the Structure and Molecular Dynamics of the Binary System Poly(3-hydroxybutyrate)-chitosan

In this work, film materials based on binary compositions of poly-(3-hydroxybutyrate) (PHB) and chitosan with different ratios of polymer components in the range from 0/100 to 100/0 wt. % were studied. Using a combination of thermal (DSC) and relaxation (EPR) measurements, the influence of the encapsulation temperature of the drug substance (DS) of dipyridamole (DPD) and moderately hot water (at 70 °C) on the characteristics of the PHB crystal structure and the diffusion rotational mobility of the stable TEMPO radical in the amorphous regions of the PHB/chitosan compositions is shown. The low-temperature extended maximum on the DSC endotherms made it possible to obtain additional information about the state of the chitosan hydrogen bond network. This allowed us to determine the enthalpies of thermal destruction of these bonds. In addition, it is shown that when PHB and chitosan are mixed, significant changes are observed in the degree of crystallinity of PHB, degree of destruction of hydrogen bonds in chitosan, segmental mobility, sorption capacity of the radical, and the activation energy of rotational diffusion in the amorphous regions of the PHB/chitosan composition. The characteristic point of polymer compositions was found to correspond to the ratio of the components of the mixture 50/50%, for which the inversion transition of PHB from dispersed material to dispersion medium is assumed. Encapsulation of DPD in the composition leads to higher crystallinity and to a decrease in the enthalpy of hydrogen bond breaking, and it also slows down segmental mobility. Exposure to an aqueous medium at 70 °C is also accompanied by sharp changes in the concentration of hydrogen bonds in chitosan, the degree of PHB crystallinity, and molecular dynamics. The conducted research made it possible for the first time to conduct a comprehensive analysis of the mechanism of action of a number of aggressive external factors (such as temperature, water, and the introduced additive in the form of a drug) on the structural and dynamic characteristics of the PHB/chitosan film material at the molecular level. These film materials have the potential to serve as a therapeutic system for controlled drug delivery.
Calculation of critical loading condition with nose curve using homotopy continuation method

A new method is presented for calculating the nose curves and critical loading conditions of power systems. The nose curve (PV curve), which donates the relationship between total load and system voltages, is calculated by a new approach based on the homotopy continuation method. The critical loading condition, which might be called the bifurcation point, is also calculated precisely as the final point of the nose curve. This method does not require an exhausting cut-and-try process or a rough-approximation process. It is based on the conventional Newton-Raphson load flow calculation, but it overcomes the numerical difficulties associated with the singularity of the Jacobian matrix. The results of applying the proposed method to the IEEE 118-bus system and to other large practical systems (e.g., a 496-bus system) verify its robustness and feasibility. >
Translation-based approaches to automated planning with incomplete information and sensing

La Planificacion Automatica es la disciplina de Inteligencia Artificial que estudia los procesos de razonamiento necesarios para conseguir las acciones que logren un objetivo dado. En presencia de informacion incompleta, el problema de planificacion puede ser modelado como una busqueda en el espacio de estados de creencia, cada uno de ellos representando un conjunto de estados posibles. Este problema es costoso ya que el numero de estados de creencia puede ser exponencial en el numero de estados, lo cual es exponencial en el numero de variables del problema. El uso de buenas representaciones de los estados y de heuristicas informadas resultan cruciales para escalar en este espacio de busqueda. En esta tesis se presentan traducciones para planificacion con informacion incompleta, que transforman el problema de busqueda en el espacio de estados de creencia, en busqueda en espacio de estados, donde cada nodo representa un estado de creencia. Hemos desarrollado herramientas para la generacion de planes para el problema traducido, ya sea con percepcion parcial o nula. A su vez, demostramos formalmente bajo que circunstancias las traducciones son polinomicas, completas y correctas. La evaluacion empirica remarca el valor de dichas traducciones.
Evolution of N-body open clusters

On discute de simulations realistes a r corps de l'evolution dynamique d'amas ouverts et on les compare aux observations. La plupart comportent 1000 corps dont les masses initiales suivent une loi de puissance de pente α=−2,75 et dont la valeur moyenne est 0,5 MO
MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net

Correctly specifying the imputation model when conducting multiple imputation remains one of the most signiVcant challenges in missing data analysis. This dissertation introduces a robust multiple imputation technique, Multiple Imputation with the Bayesian Elastic Net (MIBEN), as a remedy for this diXculty. A Monte Carlo simulation study was conducted to assess the performance of the MIBEN technique and compare it to several state-of-the-art multiple imputation methods.
An O(n2) maximal planarization algorithm based on PQ-trees

In this paper we investigate the problem how to delete a number of edges from a nonplanar graph G such that the resulting graph G’ is maximal planar, i.e., such that we cannot add an edge e E G – G’ to G’ without destroying planarity. Actually, our algorithm is a corrected and more generalized version of the maximal planarization algorithm of Jayakumar et al., based on the planarity testing algorithm using PQ-trees of Booth & Lucker. Our algorithm can be implemented to run in O(n²) time.
Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset.
SDR-Implementation of a Support Vector Machine-Assisted Covariance-Based Spectrum Sensing Algorithm in the Presence of Correlated Noise

Cognitive radio (CR) is an emergent technology that is considered for more efficient spectrum utilization. Spectrum sensing (SS), i.e., detection of primary user (PU) in the frequency band, is one of the key tasks of cognitive radio systems. Recently, various works on the support vector machine (SVM)-assisted covariance-based spectrum sensing (CSS) in multireceiver systems were presented and evaluated with computer simulations. However, the real environment is different, especially since the noise is often assumed to be spatially uncorrelated, while it is correlated in reality. Therefore, we propose a four-receivers software-defined radio (SDR)-based test platform for experimental evaluation of SVM-assisted CSS techniques. Furthermore, we implement an SVM-assisted CSS technique in the proposed platform to assess its performance when two and four receivers are activated. The measurements showed that increasing the number of receiving channels does indeed enhance the detection probability. Moreover, we evaluated the algorithm in both white and correlated noise scenarios. The evaluation demonstrates how the presence of correlated noise deteriorated the performance of the SVM-assisted CSS compared with the case where only white noise is present. More specifically, the simulations showed that to reach 99% detection probability when noise correlation is present, the implemented algorithm requires an signal-to-noise ratio (SNR) value that is 10-dB greater than the one required when only white noise is present.
Genome-Wide Analysis and Functional Characterization of LACS Gene Family Associated with Lipid Synthesis in Cotton (Gossypium spp.)

Cotton (Gossypium spp.) is the fifth largest oil crop in the world, and cottonseed provides abundant vegetable oil resources and industrial bioenergy fuels for people; therefore, it is of practical significance to increase the oil content of cotton seeds for improving the oil yield and economic benefits of planting cotton. Long-chain acyl-coenzyme A (CoA) synthetase (LACS) capable of catalyzing the formation of acyl-CoAs from free fatty acids has been proven to significantly participate in lipid metabolism, of which whole-genome identification and functional characterization of the gene family have not yet been comprehensively analyzed in cotton. In this study, a total of sixty-five LACS genes were confirmed in two diploid and two tetraploid Gossypium species, which were divided into six subgroups based on phylogenetic relationships with twenty-one other plants. An analysis of protein motif and genomic organizations displayed structural and functional conservation within the same group but diverged among the different group. Gene duplication relationship analysis illustrates the LACS gene family in large scale expansion through WGDs/segmental duplications. The overall Ka/Ks ratio indicated the intense purifying selection of LACS genes in four cotton species during evolution. The LACS genes promoter elements contain numerous light response cis-elements associated with fatty acids synthesis and catabolism. In addition, the expression of almost all GhLACS genes in high seed oil were higher compared to those in low seed oil. We proposed LACS gene models and shed light on their functional roles in lipid metabolism, demonstrating their engineering potential for modulating TAG synthesis in cotton, and the genetic engineering of cottonseed oil provides a theoretical basis.
Probabilistic methods in massive graphs and internet computing

To study the power law graph, we propose a new random graph model with given expected degree sequences. In this model, each vertex is assigned to a weight (or the expected degree) and edges are assigned to each pairs independently with probability proportional to the product of their end-vertex-weights. The classical random graph G(n,p) of Erdohs and Renyi can be viewed as a special case with the equal weights. We show that the distribution of the connected components depending primarily on the average degree d and the second-order average degree d˜. Here d˜ denotes the ratio of the sum of squares of the expected degree and the sum of the expected degrees of vertices. For example, we prove that the giant component exists if the expected average degree d is greater than 1, and there is no giant component if the expected second-order average degree d˜ is at most 1. Examples are given to illustrate that both bounds are best possible. We also obtain the tight upper bound on the size of the second largest connected components when d > 1. The existence of such a bound regardless the weight distribution is a quite amazing fact. We further identify a family of the random graphs, whose average distance can be determined. These graphs are called “admissible”. The admissible graphs include G(n,p) and the power law graph with the power β > 3, and much more. For the admissible graph, we proved that almost surely the average distance is (1 + o(1)) lognlogdd . Similarly we examine the “strongly admissible” graph. We show that almost surely that the diameter of a strongly admissible graph is Θ( lognlogdd ). Again, this result applies to G(n,p) and the power law graph with the power β > 3. The random power law graph is defined as a special random graph with power-law distribution weights. As we point out, the random power law graphs with the power β > 3 are both admissible and strongly admissible. In the last chapter, we will focus on how the power law graphs are generated. We examine three important aspects of power law graphs, (1) the evolution of power law graphs, (2) the asymmetry of in-degrees and out-degrees, (3) the “scale invariance” of power law graphs. (Abstract shortened by UMI.)
A Unifying Semantics for Causal Reasoning about Action

One of the principal concerns in the research area of Reasoning about Action and Change is determining the ramifications of actions in changing environments. A particular tendency emerging in recent literature endorses the explicit incorporation of causal knowledge in logic-based action theories. It is argued that causal extensions not only enhance the expressive power of theories of action, but may also provide more concise and intuitive representations. This dissertation investigates semantics for causal reasoning about action and change. It does so by exploring the role of several fundamental underlying principles, such as the Principle of Minimal Change and the Principle of Causal Change. The development of this work culminates in a general unifying semantics for a class of action theories represented by a number of recent and influential approaches. We focus on three of the most prominent causal frameworks in the Reasoning about Actions literature: the causal systems with fixed-points suggested by McCain and Turner, the causal relationship approach of Thielscher, and Sandewall’s causal propagation semantics (also known as the transition cascade semantics). Each is studied via a semantics which includes a preferential component augmented with a causal relation. The foregoing results are used to develop a general augmented preferential-style semantics that subsumes the causal systems with fixed-points and the causal relationship approach. The causal propagation semantics of Sandewall is shown to be a special case as well, characterised under certain uniformity assumptions. The unifying general augmented preferential semantics, emerging as a result of this study, captures both Principles of Change and shows their clear and distinct roles — they are not inter-reducible but go hand-in-hand in causal action theories. Furthermore, the general semantics emphasises the role of contextual information affecting both minimality and causality, and provides a means for balancing different contributing factors. It is argued that hidden or less immediate forces shaping our motivating approaches become transparent with the help of the general semantics. In addition, it is hoped that the unifying semantics would provide further insights into views on causation and minimality, shared by these and other approaches.