Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and control of fuel cell systems and fuel processors

MODELING AND CONTROL OF FUEL CELL SYSTEMS AND FUEL PROCESSORS by Jay Tawee Pukrushpan Co-Chairs: Anna Stefanopoulou and Huei Peng Fuel cell systems offer clean and efficient energy production and are currently under intensive development by several manufacturers for both stationary and mobile applications. The viability, efficiency, and robustness of this technology depend on understanding, predicting, and controlling the unique transient behavior of the fuel cell system. In this thesis, we employ phenomenological modeling and multivariable control techniques to provide fast and consistent system dynamic behavior. Moreover, a framework for analyzing and evaluating different control architectures and sensor sets is provided. Two fuel cell related control problems are investigated in this study, namely, the control of the cathode oxygen supply for a high-pressure direct hydrogen Fuel Cell System (FCS) and control of the anode hydrogen supply from a natural gas Fuel Processor System (FPS). System dynamic analysis and control design is carried out using model-based linear control approaches. A system level dynamic model suitable for each control problem is developed from physics-based component models. The transient behavior captured in the model includes flow characteristics, inertia dynamics, lumpedvolume manifold filling dynamics, time evolving spatially-homogeneous reactant pressure or mole fraction, membrane humidity, and the Catalytic Partial Oxidation (CPOX) reactor temperature. The goal of the FCS control problem is to effectively regulate the oxygen concentration in the cathode by quickly and accurately replenishing oxygen depleted during power generation. The features and limitations of different control configurations and the effect of various measurement on the control performance are examined. For example, an observability analysis suggests using the stack voltage measurement as feedback to the observer-based controller to improve the closed loop performance. The objective of the FPS control system is to regulate both the CPOX temperature and anode hydrogen concentration. Linear multivariable system analysis is used to identify the limitation of a decentralized controller and to design a model-based multivariable controller with significantly improved performance in CPOX temperature regulation. Further analysis unveils the critical controller cross-coupling term that contributes to the superior performance of the multivariable controller. c © Jay Tawee Pukrushpan 2003 All Rights Reserved
Finite Morse index solutions and asymptotics of weighted nonlinear elliptic equations

By introducing a suitable setting, we study the behavior of finite Morse index solutions of the equation \[ -\{div} (|x|^\theta \nabla v)=|x|^l |v|^{p-1}v \;\;\; \{in $\Omega \subset \R^N \; (N \geq 2)$}, \leqno(1) \] where $p>1$, $\theta, l\in\R^1$ with $N+\theta>2$, $l-\theta>-2$, and $\Omega$ is a bounded or unbounded domain. Through a suitable transformation of the form $v(x)=|x|^\sigma u(x)$, equation (1) can be rewritten as a nonlinear Schr\"odinger equation with Hardy potential $$-\Delta u=|x|^\alpha |u|^{p-1}u+\frac{\ell}{|x|^2} u \;\; \{in $\Omega \subset \R^N \;\; (N \geq 2)$}, \leqno{(2)}$$ where $p>1$, $\alpha \in (-\infty, \infty)$ and $\ell \in (-\infty,(N-2)^2/4)$. We show that under our chosen setting for the finite Morse index theory of (1), the stability of a solution to (1) is unchanged under various natural transformations. This enables us to reveal two critical values of the exponent $p$ in (1) that divide the behavior of finite Morse index solutions of (1), which in turn yields two critical powers for (2) through the transformation. The latter appear difficult to obtain by working directly with (2).
A comparison of transient heat pump cycle models using alternative flow descriptions

This article compares the effects of two different refrigerant flow modeling assumptions on the transient performance of vapor-compression heat pump cycles. This comparison is done on a dynamic system-level model of a flash tank vapor injection cycle that includes finite-volume heat exchanger models. The effect of the flow assumptions and specific slip ratio correlations on both the equilibrium operating point and the transient behavior of the cycle are demonstrated through both simulations and experiments. It is shown that equivalent simulations with different slip ratio correlations each have different equilibrium mass inventories and that some aspects of the dynamic system behavior, such as the suction superheat or pressure transients, exhibit significant differences both in simulations and in comparison to experimental data.
Deep Learning Embedded into Smart Traps for Fruit Insect Pests Detection

This article presents a novel approach to identify two species of fruit insect pests as part of a network of intelligent traps designed to monitor the population of these insects in a plantation. The proposed approach uses a simple Digital Image Processing technique to detect regions in the image that are likely the monitored pests and an Artificial Neural Network to classify the regions into the right class given their characteristics. This identification is done essentially by a Convolutional Neural Network (CNN), which learns the characteristics of the insects based on their images made from the adhesive floor inside a trap. We have trained several CNN architectures, with different configurations, through a data set of images collected in the field. We aimed to find the model with the highest precision and the lowest time needed for the classification. The best performance in classification was achieved by ResNet18, with a precision of 93.55% and 91.28% for the classification of the pests focused on this study, named Ceratitis capitata and Grapholita molesta, respectively, and a 90.72%overall accuracy. Yet, the classification must be embedded on a resource-constrained system inside the trap, then we exploited SqueezeNet, MobileNet, and MNASNet architectures to achieve a model with lesser inference time and small losses in accuracy when compared to the models we assessed. We also attempted to quantize our highest precision model to reduce even more inference time in embedded systems, which achieved a precision of 88.76% and 89.73% for C. capitata and G. molesta, respectively; notwithstanding, a decrease of roughly 2% on the overall accuracy was endured. According to the expertise of our partner company, our results are worthwhile for a real-world application, since general human laborers have a precision of about 85%.
Structural plasticity in leaves of Schinus terebinthifolius (Anacardiaceae) populations from three contrasting tropical ecosystems

Abstract. Schinus terebinthifolius Raddi (Anacardiaceae) is a pioneer tropical species that has been largely used in Brazil for land restoration. The species occurrence encompasses inland ecosystems such as the neotropical savanna (cerrado sensu stricto; Cerrado domain) through coastal ecosystems such as mangroves and the restinga (Atlantic Forest domain), all of which are characterized by environmental factors that highly contrast with one another. We analyzed the leaf anatomy of S. terebinthifolius populations from those three ecosystems aiming to verify whether any particular trait would provide the species with foliar anatomical plasticity, thereby enabling it to adapt to such environmentally disparate areas. We found that thickness of the leaf blade, adaxial palisade parenchyma, and spongy parenchyma differed significantly among the three ecosystems. The population from the restinga, an open, coastal ecosystem subjected to high light incidence, showed the highest means for these variables, followed by the populations from mangrove and cerrado. Leaf blade thickness in the restinga population was higher due mainly to increased palisade parenchyma thickness. The adaxial palisade parenchyma thickness in mangrove individuals was more than double the one in the cerrado population. The abaxial palisade parenchyma thickness showed no difference between the mangrove and restinga populations, yet in those two it was higher than in the cerrado population. Schinus terebinthifolius was revealed to have high potential of acclimating to different ecosystems, owing to a high capacity of developing anatomical responses to distinct stress conditions imposed by the surrounding environment, which confirms the species high phenotypic plasticity. Such high capacity of acclimating to contrasting environmental conditions by means of modulating the thickness of different leaf tissues could provide the species with an adaptive advantage in a climate-change scenario.
Design and Application of Anti-Liquid Sweat Suit Based on Polyvinyl Butyral SiO2 Nanocomposite

Abstract Since the changes in the content of potassium chloride and urea in sweat are related to the normal working conditions of the internal organs, and people have low sensitivity to daily food, therefore, the research on the content of potassium chloride and urea in sweat can help determine the physiological condition of the human body. This article aims to explore the design and application of anti-liquid sweat suits based on polyvinyl butyral SiO2 nanocomposites. This article first analyzes the performance of polyethylene and introduces the advantages of polyethylene. At the same time, it proposes the infrared spectroscopy analysis method of sweat multi-component, introduces the structure of BP neural network, and the flow chart of sweat single-component sample analysis. Then a comprehensive analysis of the SiO2 nanocomposite was carried out, and the properties of the polyvinyl butyral (PVB) material were explored. It provides a theoretical basis and reference value for the design and application of anti-liquid sweat suits based on polyvinyl butyral SiO2 nanocomposites. The experimental results in this paper show that the use of polyvinyl butyral (PVB) materials and SiO2 nanocomposite materials to prepare spinning sweat suits can increase the performance of anti-sweat and sweat by 60%, and at the same time, the firmness will also be increased.
Assessment of leopard translocations in South Africa

Translocations are commonly employed to mitigate human–carnivore conflict but rarely evaluated, resulting in conflicting reports of success, particularly for leopards (Panthera pardus). We evaluate the status of available leopard translocation data, the factors driving the intentional removal of leopards, and the potential causal factors associated with successful and failed translocation events. We obtained data on 60 leopard translocation events across five provinces in South Africa between 1994 and 2021. We considered a successful translocation outcome when (1) the animal was moved outside of its original home range, (2) the animal established a new home range away from the capture site, (3) no substantive livestock losses were linked to the translocated animal in the post-release monitoring period, and (4) the animal survived at least 6 months post-translocation. If mortality occurred due to factors that were equally likely to impact resident individuals and were unrelated to the translocation event (e.g., poaching), the event was not considered a failed effort. Most translocations were the result of human–carnivore conflict (HCC; 82%, n = 49), stressing the high prevalence of HCC and the importance of advocating preventative conflict mitigation efforts to conserve leopards. The leopards were moved distances from 2.5 to 196.3 km (63.3 ± 51.7km). Forty (67%) translocation events had unknown outcomes, indicating the limited data available on translocation outcomes. This also indicates the disparity in the objectives of translocations by various entities involved with translocations and suggests that monitoring be a prerequisite for future translocations. Twenty events offered reliable outcomes by means of post-event monitoring, with seven (12%) considered successful, with three (5%) as failures, and with four (7%) not moved beyond their original home ranges, while six (8%) ended in unrelated deaths. The failed events were attributed to inter/intra-specific competition, and one animal returned to its original home range after a translocation distance of 68 km. Translocation success was strongly explained by translocation distance. We found that damage-causing leopards were successfully translocated under specific conditions, and longer translocation distances increase success. Translocations are commonly employed but are still poorly monitored. We discuss basic standardized protocols to improve future leopard translocations (including pre- and post-monitoring) while advocating alternative non-lethal practices to reduce the prevalence of human–carnivore conflict.
A trio of biological rhythms and their relevance in rhythmic mechanical stimulation of cell cultures

The primary aim of this article is to provide a biological rhythm model based on previous theoretical and experimental findings to promote more comprehensive studies of rhythmic mechanical stimulation of cell cultures, which relates to tissue engineering and regenerative medicine fields. Through an interdisciplinary approach where different standpoints from biology and musicology are combined, we explore some of the core rhythmic features of biological and cellular rhythmic processes and present them as a trio model that aims to afford a basic but fundamental understanding of the connections between various biological rhythms. It is vital to highlight such links since rhythmic mechanical stimulation and its effect on cell cultures are vastly underexplored even though the cellular response to mechanical stimuli (mechanotransduction) has been studied widely and relevant experimental evidence suggests mechanotransduction processes are rhythmic.
PHYTOREMEDIATION OF REACTIVE TURQUOISE BLUE H5G USING IN VITRO CULTURES OF SOLANUM VIRGINIANUM (L.)

Solanum virginianum (L.) belonging to family Solanaceae selected for decolourisation of Reactive Turquoise Blue H5G dye. In vitro grown cultures of S. virginianum were able to remove more than 50% dye concentration up to 110mg/l. comparative analysis of biochemical and antioxidant study showed more activity in treated plants as compared to untreated plants. The phytotoxicity study demonstrated the non-toxic nature of degraded metabolites. Use of such non-edible yet medicinal plant for phytoremediation is discussed.
Leptospira interrogans prevents macrophage cell death and pyroptotic IL1β release through its atypical lipopolysaccharide

Leptospira interrogans are bacteria that can infect all vertebrates and are responsible for leptospirosis, a neglected zoonosis. Some hosts are susceptible to leptospirosis whereas mice are resistant and get chronically colonized. Although leptospires escape recognition by some immune receptors, they activate NLRP3-inflammasome and trigger IL1β secretion. Classically, IL1β secretion is associated with lytic inflammatory cell death called pyroptosis, resulting from cytosolic LPS binding to inflammatory caspases. Interestingly, we showed that L. interrogans do not trigger cell death in either murine, human, hamster, or bovine macrophages, escaping both pyroptosis and apoptosis. Strikingly, we also revealed in murine cells, a potent antagonistic effect of leptospires and their atypical LPS on spontaneous and E. coli LPS-induced cell death. The leptospiral LPS efficiently prevents caspase 11 dimerization and subsequent gasdermin D cleavage. Finally, we showed that pyroptosis escape by leptospires prevents massive IL1 β release, and we consistently found no major role of IL1-Receptor in controlling experimental leptospirosis in vivo. Overall, our findings described a novel mechanism by which leptospires dampen inflammation, thus potentially contributing to their stealthiness. Graphical abstract
Seasonality of host-seeking Ixodes ricinus nymph abundance in relation to climate

There is growing concern about climate change and its impact on human health. Specifically, global warming could increase the probability of emerging infectious diseases, notably because of changes in the geographical and seasonal distributions of disease vectors such as mosquitoes and ticks. For example, the range of Ixodes ricinus, the most common and widespread tick species in Europe, is currently expanding northward and at higher altitudes. However, little is known about the seasonal variation in tick abundance in different climates. Seasonality of I. ricinus is often based on expert opinions while field surveys are usually limited in time. Our objective was to describe seasonal variations in I. ricinus abundance under different climates. To this end, a seven-year longitudinal study, with monthly collections of I. ricinus host-seeking nymphs, was carried out in France, in six locations corresponding to different climates. Tick data were log-transformed and grouped between years so as to obtain seasonal variations for a typical year. Daily average temperature was measured during the study period. Seasonal patterns of nymph abundance were established for the six different locations using linear harmonic regression. Model parameters were estimated separately for each location. Seasonal patterns appeared different depending on the climate considered. Western temperate sites showed an early spring peak, a summer minimum and a moderate autumn and winter abundance. More continental sites showed a later peak in spring, and a minimum in winter. The peak occurred in summer for the mountainous site, with an absence of ticks in winter. In all cases except the mountainous site, the timing of the spring peak could be related to the sum of degree days since the beginning of the year. Winter abundance was positively correlated to the corresponding temperature. Our results highlight clear patterns in the different sites corresponding to different climates, which allow further forecast of tick seasonality under changing climate conditions.
Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock

Biological age, distinct from an individual’s chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Deep learning approaches on imaging datasets of the eye have proven powerful for a variety of quantitative phenotype inference tasks and provide an opportunity to explore organismal aging and tissue health. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals’ chronological age. These predictions led to the concept of a retinal aging clock, “eyeAge”, which we employed for a series of downstream longitudinal analyses. eyeAge was used to predict chronological age on timescales under a year using longitudinal fundus imaging data from a subset of patients. To further validate the model, it was applied to a separate cohort from the UK Biobank. The difference between individuals’ eyeAge and their chronological age, hereafter “eyeAgeAccel”, was computed and used for genome-wide association analysis (GWAS). EyeAge predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UKBiobank, respectively). Additionally, eyeAgeAccel was highly independent of blood marker-based measures of biological age (e.g. “phenotypic age”), maintaining an all-cause mortality hazard ratio of 1.026 even in the presence of phenotypic age. Longitudinal studies showed that the resulting models were able to predict individuals’ aging, in time-scales less than a year, with 71% accuracy. The individual-specific component to this prediction was confirmed with the identification of multiple GWAS hits in the independent UK Biobank cohort. The knockdown of the fly homolog to the top hit, ALKAL2, which was previously shown to extend lifespan in flies, also slowed age-related decline in vision in flies. In conclusion, predicted age from retinal images can be used as a biomarker of biological aging that is independent from assessment based on blood markers. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.
Brainstem fMRI signaling of surprise across different types of deviant stimuli

The detection of deviant stimuli is crucial to orient and adapt our behavior. Previous work showed that infrequent (hence deviant) stimuli elicit phasic activation of the brainstem locus coeruleus (LC), which releases noradrenaline and controls central arousal. However, it is unclear whether the detection of behaviorally-relevant deviant events selectively trigger LC responses, or also other neuromodulatory systems related to dopamine, acetylcholine, and serotonin. Here, we combined human fMRI recordings optimized for brainstem imaging with pupillometry (a peripheral marker of central arousal) to perform a mapping of deviant-related responses in subcortical structures. Participants had to detect deviant items in a “local-global” paradigm that distinguishes between deviance based on the stimulus probability and the sequence structure. fMRI responses to deviant stimuli were quite distributed, detected in the LC but also other subcortical nuclei and many cortical areas. Both types of deviance elicited responses in the pupil, LC and other neuromodulatory systems. Our results reveal that the detection of task-relevant deviant items recruits the same multiple subcortical systems across computationally different types of deviance.
REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data

In the recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aim is to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.