Miracle-dz/UniFAR-FTUs huggingface dataset:
Steady state and time resolved laser-induced fluorescence of garlic plants treated with titanium dioxide nanoparticles

ABSTRACT The study involves investigation of the effect of the interaction of titanium dioxide nanoparticles with garlic plant by spectroscopy techniques. For this, garlic plants have been grown in the laboratory under controlled conditions of light flux, temperature, humidity, and nutrient media. The growth and biomass parameters in terms of shoot length, fresh, and dry mass are found to increase upon the treatment of titanium dioxide nanoparticles while a reduction is observed in the root length of the garlic plants. The steady state laser-induced fluorescence, time resolved laser-induced fluorescence, and ultraviolet visible spectra of the control and titanium dioxide nanoparticles-treated plants have been acquired. The curve fitting data reveal that titanium dioxide nanoparticles decrease the intensity and fluorescence intensity ratio of red and far red chlorophyll fluorescence bands indicating increase in the photosynthetic activity and chlorophyll content. The evaluation of life time of the excited chlorophyll molecule shows that life time is effected by the treatment of the titanium dioxide nanoparticles. The results pertaining to ultraviolet visible measurement indicate increase in the concentration of chlorophyll a, chlorophyll b, total chlorophyll, carotenoid, and quercetin in the leaves of garlic plants treated with titanium dioxide nanoparticles.
Optimization of Coconut Butter Prepared by Incorporating Peanut Butter and Dairy Butter

The coconut butter was formulated by incorporating the peanut butter and dairy butter to bring out the acceptable consistency and studied is about the physicochemical, sensory parameters of the developed product. Three different ratio of coconut butter: Peanut butter: Dairy butter (3:1:1, 2:1:0.5, 1:1:0.3) were optimized. The optimized butter was stabilized by adding hydrogenated palm oil, gelatin, and guar gum in 1, 1.5, 2 ratios, respectively for controlling the oil migration from the developed butter. Two sweetener were used in the mixed butter namely sugar and the combination of sugar honey in two different ratio whereas 9% and 13%, and salt was used two different ratio that is 0.9% and 1.1%. The suitable kind of sweetener, stabilizer and the level of sweetener, salt, stabilizer and ratio of mixed butter were identified using sensory evaluation (9 point hedonic scale) to yield a mixed butter which resembles as a commercial peanut butter, the self life is estimated both in room temperature (25C) and refrigerator temperature (4C) through peroxide value and free fatty acid value by the level of rancidity of the mixed butter.
Elaboración de quesito antioqueño reducido en sodio y adicionado con bifidobacterium lactis

The quesito antioqueno (QA), fresh, soft, milled and salty cheese, without added bacteria. This cheese contains 2,1% of sodium chloride. Were supplemented with Bifidobacterium lactis (bb12) and It was salty with NaCl (Q1) and some mixtures of NaCl/KCl (3:1 (Q2)) and 1:1 (Q3), w/w), to reduce the sodium content and give probiotic characteristics. There were no significant differences between treatments (Q1, Q2 and Q3) (P>0,05) in some compositional (moisture, MG/MS, total protein, ash, acidity), chemical (pH) and physical (hardness, adhesiveness, springiness, cohesiveness, gumminess, chewiness, resilience) characteristics. A significant difference was observed by storage time in moisture, pH, protein content and acidity (P<0,05). With respect to Na and K content, there was a significant differences between treatments (P<0,05) but was not in the Ca content. In Q2 the Na level was decreased 24,2% and K increased 143% in average; in Q3 the Na level was decreased 48,3% and K increased 311%. The processed QA with 50% of Na substitution maintains the traditional compositional a physicochemical characteristics, therefore, according to this investigation results, can be performed this substitution and it is an excellent matrix to include probiotics in the people diet.
Nanocomposite with Promoted Electrocatalytic Behavior Based on Bimetallic Pd–Ni Nanoparticles, Manganese Dioxide, and Reduced Graphene Oxide for Efficient Electrooxidation of Ethanol

In this work, a nanocomposite containing manganese dioxide (MnO2) modified reduced graphene oxide (rGO) supported bimetallic palladium–nickel (Pd–Ni) catalyst is prepared by electrodeposition method. The nanocomposite modifier film is prepared by forming a thin layer of graphene oxide (GO) via drop-casting of GO nanosheet dispersion on glassy carbon electrode (GCE), followed by electrochemical reduction of the film to provide rGO/GCE. Then, a two-step potential procedure is applied to deposit MnO2 nanoparticles on rGO/GCE. At the optimum deposition conditions, MnO2 nanoparticles with a thickness of 30–50 nm homogeneously covered the rGO surface (MnO2/rGO/GCE). Finally, the bimetallic Pd–Ni nanoparticles are electrodeposited on MnO2/rGO/GCE at a fixed potential to form a uniform dispersion with an average particle size of about 50 nm (Pd–Ni–MnO2/rGO/GCE). The morphology and crystalline structure of the prepared nanocomposites are characterized using XRD, SEM, EDX, FTIR, AFM, and Raman spectroscopy. The cat.
BIOFILTRATION TREATMENT FOR IRON- AND MANGANESE-RICH GROUNDWATER AT LOW ON-SITE TEMPERATURES

iii ACKNOWLEDGEMENTS . v DEDICATION . vi TABLE OF CONTENTS . vii LIST OF TABLES . xi LIST OF FIGURES . xii ABBREVIATIONS & SYMBOLS . xiii 1. GENERAL INTRODUCTION . 1 1.1. Rationales and objectives of the research . 1 1.2. Significance of the research . 3 1.3. Thesis organization . 4 2. BACKGROUND REVIEW AND KNOWLEDGE GAPS . 5 2.1. Manganese in water supplies . 5 2.2. Water treatment technologies for manganese removal . 6 2.2.1. Physical and chemical treatment . 6 2.2.2. Biofiltration technology . 7 2.3. Manganese-oxidizing bacteria . 10 2.4. Manganese biofiltration in Canada . 13 2.5. Problems with biofilter functionalization for manganese removal . 15
Epithelial-stromal interaction in pancreatic cancer: The role of collagen type V

The main focus of this work was to unravel the functional role of collagen type V, which is mainly produced by pancreatic stellate cells, in the epithelial-stromal interaction in pancreatic cancer. To this aim, different in vitro and in vivo approaches were used to investigate the influence of collagen type V and of its downstream signaling pathway on pancreatic cancer cells as well as on endothelial and stromal cells. It could be shown that collagen type V promotes the aggressive phenotype of pancreatic cancer, starting from the early phases of pancreatic carcinogenesis until the metastatic disease.
Modeling degradation of Lithium-ion batteries for second-life applications: preliminary results

This paper presents a novel battery modeling framework based on the enhanced single particle model (ESPM) to account for degradation mechanisms of retired electric vehicle batteries. While accounting for the transport and electrochemical phenomena in the battery solid and electrolyte phases, the dominant anode-related aging mechanisms, namely, solid electrolyte interphase (SEI) layer growth and lithium plating, are modeled. For the first time, the loss of active material (LAM), which describes the tendency of anode and cathode, over time, to reduce the electrode material available for intercalation and deintercalation, is introduced in the ESPM. Moreover, the coupling of the aging mechanisms with the LAM dynamics provides a comprehensive means for the prediction of both linear and non-linear capacity fade trajectories, crucial to assess the health of batteries that are considered for second-life applications. Relying on data borrowed from [13], a model parameter identification and a comprehensive sensitivity analysis are performed to prove the effectiveness of the modeling approach.
Wildfire smoke affects detection of birds in Washington State

ABSTRACT Wildfire smoke is likely to have direct health effects on birds as well as influence movement, vocalization, and other avian behaviors. These behavioral changes may affect if and how birds are observed in the wild, although research on the effects of wildfire smoke on bird behavior is limited. To evaluate how wildfire smoke affects detection of birds, we combined data from eBird, an online community science program, with data from an extensive network of air quality monitors in the state of Washington over a 4-year period. We assessed how PM2.5, a marker of smoke pollution, affected the probability of observing 71 bird species during the wildfire seasons of 2015–2018 using bird observations from 62,908 eBird checklists. After accounting for habitat, weather conditions, seasonality, and survey effort, we found that PM2.5 affected the probability of observing 37% of study species. The ambient concentration of PM2.5 was negatively associated with the probability of observing 16 species and positively associated with the probability of observing 10 species, indicating that birds exhibit species-specific behavioral changes during wildfire smoke events that influence how they are observed. Our results suggest that wildfire smoke impacts the presence, availability, and/or perceptibility of birds. Impacts of smoke pollution on human observers, such as impaired visibility, may also influence detection of birds. These results provide a foundation for developing mechanistic hypotheses to explain how birds, and our studies of them, are impacted by wildfire smoke. Given the projected increase in large-scale wildfire smoke events under future climate change scenarios, understanding how birds are affected by wildfire smoke—and how air pollution may influence our ability to detect them—are important next steps to inform wildlife research and avian conservation. LAY SUMMARY Wildfire smoke events threaten public health and are likely to impact the health and behavior of birds, influencing their perceptibility. In this study, we characterized how wildfire smoke affects detection of birds. We assessed how PM2.5, a marker of wildfire smoke, affected the probability of observing 71 bird species in Washington State. We found that outdoor concentrations of PM2.5 affected the probability of observing 37% of study species. PM2.5 was negatively related to the detection of 16 species and positively related to the detection of 10 species. Our results suggest that wildfire smoke impacts the presence, availability, and/or perceptibility of birds. Given the projected increase in the frequency of large-scale wildfire smoke events, understanding how birds are affected by smoke—and how air pollution may influence our ability to detect birds—are critical next steps in avian conservation.
Quality Management in Respiratory Care

The word “quality” refers to the features of a product or service to which a certain value is ascribed. When it comes to hospital-based practices, quality has often been considered to be specific to the care provided. However, this specific perspective is transitioning toward a broader concept after the evolution of quality-improvement projects and quality frameworks at the organizational level. Respiratory therapy departments have been identified as an essential part of any hospital because the key nature of discipline for respiratory therapists is widely understood. Due to their professional accountability and professional values, respiratory therapists often have administrative roles in infection control practices and quality-improvement projects. Therefore, it would be ideal to have a core team of respiratory therapists trained in quality management and to initiate quality-improvement processes at the departmental level. Every respiratory therapy department should have its own quality-improvement team to assist with the process of training, implementation, and analysis. Thus, this article aimed to discuss the role of respiratory therapists and respiratory therapy departments in quality-improvement processes and projects to set benchmarks and enhance outcomes.
Planned missing data designs and methods: Options for strengthening inference, increasing research efficiency and improving animal welfare in ecological and evolutionary research

Ecological and evolutionary research questions are increasingly requiring the integration of research fields along with larger data sets to address fundamental local‐ and global‐scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns. Planned missing data design (PMDD), where data are randomly and deliberately missed during data collection, combined with missing data procedures, can be useful tools when working under greater research constraints. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and demonstrate with simulated data sets how missing data procedures work with incomplete data. PMDDs can provide researchers with a unique toolkit that can be applied during the experimental design stage. Planning and thinking about missing data early can (1) reduce research costs by allowing for the collection of less expensive measurement variables; (2) provide opportunities to distinguish predictions from alternative hypotheses by allowing more measurement variables to be collected; and (3) minimize distress caused by experimentation by reducing the reliance on invasive procedures or allowing data to be collected on fewer subjects (or less often on a given subject). PMDDs and missing data methods can even provide statistical benefits under certain situations by improving statistical power relative to a complete case design. The impacts of unplanned missing data, which can cause biases in parameter estimates and their uncertainty, can also be ameliorated using missing data procedures. PMDDs are still in their infancy. We discuss some of the difficulties in their implementation and provide tentative solutions. While PMDDs may not always be the best option, missing data procedures are becoming more sophisticated and more easily implemented and it is likely that PMDDs will be effective tools for a wide range of experimental designs, data types and problems in ecology and evolution.
Ca2+ signal contributing to jasmonic acid‐induced direct and indirect defense against the whitefly Bemisia tabaci in tomato plants

The phytophagous whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) causes serious crop yield losses around the world especially by vectoring viruses. Exogenous jasmonic acid (JA) induces direct plant resistance to B. tabaci, but the underlying mechanism of JA‐induced exogenous defenses is not well understood. Here, we demonstrate that exogenous JA not only increases the cytosolic Ca2+ concentration ([Ca2+]cyt) in tomato mesophyll cells but also induces the expression of Ca2+‐sensor genes and plant defense genes. Pre‐treatment with Ca2+ inhibitor (ruthenium red) significantly repressed the elevation of [Ca2+]cyt, expression of JA‐induced genes, and emission of plant volatiles induced by JA, thus reducing the JA‐induced direct (feeding choice and fitness of B. tabaci) and indirect [olfactory choice and parasitism by the parasitoid Encarsia formosa Gahan (Hymenoptera: Aphelinidae)] plant resistance to B. tabaci. These results indicate that the Ca2+ signal induced by JA mediates and directs the plants defenses against whitefly. Present results will not only give us a better understanding of how exogenous JA influences the crop community, but also deliver some clues for the potential application of exogenous JA, as a plant elicitor, in whitefly management.
Characterization of resistance to rice leaf folder, Cnaphalocrocis medinalis, in mutant Samba Mahsuri rice lines

The rice leaf folder, Cnaphalocrocis medinalis Guenée (Lepidoptera: Crambidae), an important foliage feeding insect in rice, Oryza sativa L. (Poaceae), is widely distributed throughout Asia, causing frequent outbreaks and considerable yield losses. Mutants of the rice strain Samba Mahsuri (MSM) represent unique plant material and are explored here for the first time for resistance to the rice leaf folder. Stringent rapid field screening for two consecutive seasons led to the identification of 10 lines as moderately resistant, with low damage area as well as low damage score. Upon assessment of various morphological traits potentially contributing to the resistance, damage area was negatively correlated with leaf length and positively with leaf width. Among the biochemical traits, leaf nitrogen content was positively correlated with leaf folder damage whereas silica was negatively correlated. Oviposition preference by leaf folder adult moths and larval settling preference were low on MSM 139 and MSM 127. We also observed that leaf folder larvae took more time for leaf selection (3.1 min) and making a primary fold (41.5 min), with more head swings (846) in moderately resistant MSM lines. Similarly, in these lines, leaf folders showed a low intrinsic rate of increase (rm = 0.15 females per female per day), low weekly multiplication rate (2.85), and low net reproductive rate (R0 = 137.5 females per female per generation) with increased generation time (T = 32.8 days) and doubling time (4.5 days), indicating their relatively poor population growth potential. Overall, the moderately resistant mutant lines we identified showed reduced ovipositional preference and larval settling preference, extended larval developmental period with low survival, reduced primary fold length, and an increased number of binds and head swings with a low intrinsic rate of increase resulting in reduced population build‐up. These lines possessing a good genetic makeup have a great potential to be used in breeding programs for the development of leaf folder resistant varieties.
SmartOS: towards automated learning and user-adaptive resource allocation in operating systems

Today's operating systems typically apply a one-size-fits-all approach to resource management, such as applying a scheduler that treats all processes of equal importance. The goal of this paper is to explore a learning-based approach to resource management in modern operating systems in which the OS automatically learns what tasks the user deems to be most important at that time and seamlessly adjusts allocation of CPU, memory, I/O, and network bandwidth to prioritize user preferences on demand. We demonstrate an implementation of such a learning-based OS in Linux and present evaluation results showing that a reinforcement learning-based approach can rapidly learn and adjust system resources to meet user demands.
Learning-based extraction of first-order logic representations of API directives

Developers often rely on API documentation to learn API directives, i.e., constraints and guidelines related to API usage. Failing to follow API directives may cause defects or improper implementations. Since there are no industry-wide standards on how to document API directives, they take many forms and are often hard to understand by developers or challenging to parse with tools. In this paper, we propose a learning based approach for extracting first-order logic representations of API directives (FOL directives for short). The approach, called LEADFOL, uses a joint learning method to extract atomic formulas by identifying the predicates and arguments involved in directive sentences, and recognizes the logical relations between atomic formulas, by parsing the sentence structures. It then parses the arguments and uses a learning based method to link API references to their corresponding API elements. Finally, it groups the formulas of the same class or method together and transforms them into conjunctive normal form. Our evaluation shows that LEADFOL can accurately extract more FOL directives than a state-of-the-art approach and that the extracted FOL directives are useful in supporting code reviews.
Environmental factors involved in genesis of retromolar - oropharynx junction cancer

The retromolar-oropharynx junction cancer is a rare malignancy situated at the border between the oral cavity and the oropharynx. There is an important number of factors involved in the ethiology, pathogenesis and evolution of patients with this kind of lesions. We performed a prospective study involving 50 patients with this type of cancer, hospitalized between 20132015 in the Otorhinolaryngology and Maxillofacial Departments of the Sf. Spiridon Emergency Hospital, Iasi, Romania, to identify the environmental and biologic factors that may be involved in the damaged tissues. The purpose of our study is to appraise and validate the significance of the risk factors involved in the occurrence of deglutition and swallowing troubles, to achieve a treatment protocol for this kind of patients.
Overview of Node Power Management System for Self-Powered Wireless Sensor Network

For wireless sensor network (WSN) nodes, research on power management systems with self-powered functions is of great significance. This article first introduces a typical self-powered IoT node system, focusing on the energy consumption of self-powered WSN nodes. Secondly, it introduces the environmental energy harvesting technology of WSN systems, focusing on the analysis of light energy, mechanical energy, thermal energy and radio frequency the development status of energy. Thirdly analysis of energy harvesting-oriented power management control methods, focusing on the development of MPPT control algorithms. Finally, a comprehensive introduction to the power management system. The technical difficulties are summarized and the development trend of node power management system for self-powered WSN based on environmental energy harvesting is prospected.
SPRIG: A Learned Spatial Index for Range and kNN Queries

A corpus of recent work has revealed that the learned index can improve query performance while reducing the storage overhead. It potentially offers an opportunity to address the spatial query processing challenges caused by the surge in location-based services. Although several learned indexes have been proposed to process spatial data, the main idea behind these approaches is to utilize the existing one-dimensional learned models, which requires either converting the spatial data into one-dimensional data or applying the learned model on individual dimensions separately. As a result, these approaches cannot fully utilize or take advantage of the information regarding the spatial distribution of the original spatial data. To this end, in this paper, we exploit it by using the spatial (multi-dimensional) interpolation function as the learned model, which can be directly employed on the spatial data. Specifically, we design an efficient SPatial inteRpolation functIon based Grid index (SPRIG) to process the range and kNN queries. Detailed experiments are conducted on real-world datasets. The results indicate that, compared to the traditional spatial indexes, our proposed learned index can significantly improve the index building and query processing performance with less storage overhead. Moreover, in the best case, our index achieves up to an order of magnitude better performance than ZM-index in range queries and is about 2.7 × , 3 × , and 9 × faster than the multi-dimensional learned index Flood in terms of index building, range queries, and kNN queries, respectively.