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The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette
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The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system’s performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.
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In this study, a two-stage cascade extraction process utilizing pulsed electric fields (PEF) (3 kV/cm, 10 kJ/kg) for initial extraction, followed by ultrasound (US) (200 W, 20 min)-assisted extraction (UAE) in a 50% (v/v) ethanol-water mixture (T = 50
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In this study, a two-stage cascade extraction process utilizing pulsed electric fields (PEF) (3 kV/cm, 10 kJ/kg) for initial extraction, followed by ultrasound (US) (200 W, 20 min)-assisted extraction (UAE) in a 50% (v/v) ethanol-water mixture (T = 50 °C, t = 60 min), was designed for the efficient release of valuable intracellular compounds from industrial cherry pomace. The extracted compounds were evaluated for total phenolic content (TPC), flavonoid content (FC), total anthocyanin content (TAC), and antioxidant activity (FRAP), and were compared with conventional solid-liquid extraction (SLE). Results showed that the highest release of bioactive compounds occurred in the first stage, which was attributed to the impact of PEF pre-treatment, resulting in significant increases in TPC (79%), FC (79%), TAC (83%), and FRAP values (80%) of the total content observed in the post-cascade PEF-UAE process. The integration of UAE into the cascade process further augmented the extraction efficiency, yielding 21%, 49%, 56%, and 26% increases for TPC, FC, TAC, and FRAP, respectively, as compared to extracts obtained through a second-stage conventional SLE. HPLC analysis identified neochlorogenic acid, 4-p-coumaroylquinic, and cyanidin-3-O-rutinoside as the predominant phenolic compounds in both untreated and cascade-treated cherry pomace extracts, and no degradation of the specific compounds occurred upon PEF and US application. SEM analysis revealed microstructural changes in cherry pomace induced by PEF and UAE treatments, enhancing the porosity and facilitating the extraction process. The study suggests the efficiency of the proposed cascade PEF-UAE extraction approach for phenolic compounds from industrial cherry pomace with potential applications to other plant-based biomasses.
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Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep camera–radar fusion neural networks offer a promising solution for reliable
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Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep camera–radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient camera–radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion’s superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving.
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This study explores the relationship between Environmental, Social, and Governance (ESG) practices and the market value of companies, with a focus on Brazil’s largest corporations. Recognizing the limitations of existing research tools for analyzing the impact of ESG factors, we introduce an innovative,
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This study explores the relationship between Environmental, Social, and Governance (ESG) practices and the market value of companies, with a focus on Brazil’s largest corporations. Recognizing the limitations of existing research tools for analyzing the impact of ESG factors, we introduce an innovative, open-source Dictionary of ESG Terms. This tool is designed to classify news content into the detailed categories established by the Sustainability Accounting Standards Board (SASB), thereby facilitating a nuanced analysis of ESG-related news and its subsequent effects on stock prices. Our analysis reveals that stock prices exhibit significant positive reactions to favorable ESG news and negative reactions to adverse ESG developments. Crucially, our findings underscore the discernment of investors, who appear to prioritize financially material ESG information over news bearing solely reputational or non-pecuniary significance. This distinction highlights the critical role of financial materiality in shaping market responses to ESG news. By providing empirical evidence from the Brazilian market, this study contributes to the broader discourse on ESG factors in corporate valuation. It offers practical tools and insights for investors, companies, and regulators aiming to better understand the complexities of ESG investment strategies. Through the application of our comprehensive ESG Dictionary, we shed light on the diverse dimensions of ESG impact, suggesting an approach to evaluate how ESG practices influence corporate market value in emerging economies.
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Climate change, especially in the form of temperature increase and sea acidification, poses a serious challenge to the sustainability of aquaculture and shellfish farming. In this context, lactic acid bacteria (LAB) of marine origin have attracted attention due to their ability to improve
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Climate change, especially in the form of temperature increase and sea acidification, poses a serious challenge to the sustainability of aquaculture and shellfish farming. In this context, lactic acid bacteria (LAB) of marine origin have attracted attention due to their ability to improve water quality, stimulate the growth and immunity of organisms, and reduce the impact of stress caused by environmental changes. Through a review of relevant research, this paper summarizes previous knowledge on this group of bacteria, their application as protective probiotic cultures in mollusks, and also highlights their potential in reducing the negative impacts of climate change during shellfish farming. Furthermore, opportunities for further research and implementation of LAB as a sustainable and effective solution for adapting mariculture to changing climate conditions were identified.
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We describe the nonabelian exterior square of a pro-p-group G (with p arbitrary prime) in terms of quotients of free pro-p-groups, providing a new method of construction of and new structural
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We describe the nonabelian exterior square of a pro-p-group G (with p arbitrary prime) in terms of quotients of free pro-p-groups, providing a new method of construction of and new structural results for . Then, we investigate a generalization of the probability that two randomly chosen elements of G commute: this notion is known as the “ complete exterior degree” of a pro-p-group and we will use it to characterize procyclic groups. Among other things, we present a new formula, which simplifies the numerical aspects which are connected with the evaluation of the complete exterior degree.
Full article
Inflammation is an essential contributor to various human diseases. Diosmetin (3′,5,7-trihydroxy-4′-methoxyflavone), a citrus flavonoid, can be used as an anti-inflammatory agent. All the information in this article was collected from various research papers from online scientific databases such as PubMed and Web of
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Inflammation is an essential contributor to various human diseases. Diosmetin (3′,5,7-trihydroxy-4′-methoxyflavone), a citrus flavonoid, can be used as an anti-inflammatory agent. All the information in this article was collected from various research papers from online scientific databases such as PubMed and Web of Science. These studies have demonstrated that diosmetin can slow down the progression of inflammation by inhibiting the production of inflammatory mediators through modulating related pathways, predominantly the nuclear factor-κB (NF-κB) signaling pathway. In this review, we discuss the anti-inflammatory properties of diosmetin in cellular and animal models of various inflammatory diseases for the first time. We have identified some deficiencies in current research and offer suggestions for further advancement. In conclusion, accumulating evidence so far suggests a very important role for diosmetin in the treatment of various inflammatory disorders and suggests it is a candidate worthy of in-depth investigation.
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Non-small-cell lung cancer (NSCLC) with comorbid interstitial pneumonia (IP) is a population with limited treatment options and a poor prognosis. Patients with comorbid IP are at high risk of developing fatal drug-induced pneumonitis, and data on the safety and efficacy of molecularly targeted
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Non-small-cell lung cancer (NSCLC) with comorbid interstitial pneumonia (IP) is a population with limited treatment options and a poor prognosis. Patients with comorbid IP are at high risk of developing fatal drug-induced pneumonitis, and data on the safety and efficacy of molecularly targeted therapies are lacking. KRAS mutations have been frequently detected in patients with NSCLC with comorbid IP. However, the low detection rate of common driver gene mutations, such as epidermal growth factor receptor and anaplastic lymphoma kinase, in patients with comorbid IP frequently results in inadequate screening for driver mutations, and KRAS mutations may be overlooked. Recently, sotorasib and adagrasib were approved as treatment options for advanced NSCLC with KRASG12C mutations. Although patients with comorbid IP were not excluded from clinical trials of these KRASG12C inhibitors, the incidence of drug-induced pneumonitis was low. Therefore, KRASG12C inhibitors may be a safe and effective treatment option for NSCLC with comorbid IP. This review article discusses the promise and prospects of molecular-targeted therapies, especially KRASG12C inhibitors, for NSCLC with comorbid IP, along with our own clinical experience.
Full article
Water, energy, food, and ecology are essential for achieving sustainable development in a region, and in order to achieve the Sustainable Development Goals, their security is also essential at a river basin scale. This study investigated the interrelationships among the water system, food
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Water, energy, food, and ecology are essential for achieving sustainable development in a region, and in order to achieve the Sustainable Development Goals, their security is also essential at a river basin scale. This study investigated the interrelationships among the water system, food system, energy system, and ecosystem in China’s Upper Han River, in alignment with Goals 2, 6, 7, and 15 of the United Nations’ Sustainable Development Goals (SDGs). To evaluate the achievement of the SDGs in the Upper Han River, this water–energy–food–ecology system was evaluated by a thorough evaluation index system according to Goals 2, 6, 7, and 15, and the weights of the indices were given using a combination of the CRITIC weighting method and entropy approach. The level of coupling coordination of the system from 2000 to 2021 was quantitatively evaluated by using a coupling coordination degree model. The autoregressive integrated moving average model was built to forecast the process of the indices from 2022 to 2041, and the predicted processes of the system were evaluated by the coupling coordination degree model. The degree of coupling coordination improved from 0.396 to 0.845, and the comprehensive assessment development index increased by 113% from 2000 to 2021, demonstrating that it was a stable development period in general. The fragile support capacity of the water system for the energy system, food system, and ecosystem had a great impact on the overall comprehensive evaluation index. SDG2 (food system), SDG6 (water system), SDG7 (energy system), and SDG15 (ecosystem) all have higher levels of internal conflict. These bi-directional dynamics tended to converge in the sufficiency development mode in the future period as well as the historical period. The analysis of the relationship showed that there were inherent connections and interactions between the four goals, as presented by the high level of coupling that persisted between SDG2, SDG6, SDG7, and SDG15. In the process of promoting the achievement of these goals, the coupling degree also tends to be coordinated from 2022 to 2041. The results offer a view for the river basin’s sustainable development and management.
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Recently, a signal sorting algorithm based on the congruence transform has been proposed, which is effective in dealing with the staggered Pulse Repetition Interval (PRI) signals. It can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking,
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Recently, a signal sorting algorithm based on the congruence transform has been proposed, which is effective in dealing with the staggered Pulse Repetition Interval (PRI) signals. It can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking, and it is less affected by interfered pulses and pulse loss. Nevertheless, we find that the algorithm causes pseudo-peaks in the remainder histogram when sorting signals such as sliding PRI, sinusoidal PRI, etc. (collectively referred to as periodic PRI signal in this paper) and pseudo-peaks will cause errors in signal sorting. To solve the issue of pseudo-peaks when sorting periodic PRI signals, an improved sorting algorithm based on congruence transform is proposed. According to the analysis of the congruence characteristics of the periodic PRI signal, a novel method is proposed to identify pseudo-peaks based on the histogram peak amplitude and symmetric difference set. The signal sorting algorithm based on congruence transform is improved to achieve a good sorting effect on periodic PRI signals. Simulation experiments demonstrate that the novel algorithm can effectively sort periodic PRI signals and improve Precall, Pd, and Pf by 6.9%, 5.1%, and 3.2%, respectively, compared to the typical similar algorithms.
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Eleni Papantoniou, Konstantinos Arvanitakis, Konstantinos Markakis, Stavros P. Papadakos, Olga Tsachouridou, Djordje S. Popovic, Georgios Germanidis, Theocharis Koufakis and Kalliopi Kotsa
Life2024, 14(4), 449; https://doi.org/10.3390/life14040449 (registering DOI) - 28 Mar 2024
Infections with human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) represent one of the greatest health burdens worldwide. The complex pathophysiological pathways that link highly active antiretroviral therapy (HAART) and HIV infection per se with dyslipidemia make the management of lipid
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Infections with human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) represent one of the greatest health burdens worldwide. The complex pathophysiological pathways that link highly active antiretroviral therapy (HAART) and HIV infection per se with dyslipidemia make the management of lipid disorders and the subsequent increase in cardiovascular risk essential for the treatment of people living with HIV (PLHIV). Amongst HAART regimens, darunavir and atazanavir, tenofovir disoproxil fumarate, nevirapine, rilpivirine, and especially integrase inhibitors have demonstrated the most favorable lipid profile, emerging as sustainable options in HAART substitution. To this day, statins remain the cornerstone pharmacotherapy for dyslipidemia in PLHIV, although important drug–drug interactions with different HAART agents should be taken into account upon treatment initiation. For those intolerant or not meeting therapeutic goals, the addition of ezetimibe, PCSK9, bempedoic acid, fibrates, or fish oils should also be considered. This review summarizes the current literature on the multifactorial etiology and intricate pathophysiology of hyperlipidemia in PLHIV, with an emphasis on the role of different HAART agents, while also providing valuable insights into potential switching strategies and therapeutic options.
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Conventional beamforming methods for reconfigurable reflector antennas assume full control over the amplitude and phase of the reflected field. Here, we develop a novel beamforming methodology for reflecting Programmable Metasurfaces (PMS) with capacitive memory. Although utilizing such fully reactive PMS simplifies antenna design
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Conventional beamforming methods for reconfigurable reflector antennas assume full control over the amplitude and phase of the reflected field. Here, we develop a novel beamforming methodology for reflecting Programmable Metasurfaces (PMS) with capacitive memory. Although utilizing such fully reactive PMS simplifies antenna design and reduces energy consumption, the PMS reflection magnitude is unity and thus a global optimization of the reflection phases over the PMS unit cells is required in each beamforming scenario. We propose an implementation of such an optimization method rooted in the traditional Fourier transform-based beamforming and evaluate its performance. Additionally, we show that a pair of trained feed-forward neural networks (FFNN) with one input, one hidden, and one output layer can replace time-consuming global optimizations in the case of a PMS comprising unit cells. We train the FFNNs on a dataset obtained for typical single- and dual-beam beamforming scenarios. After training, the FFNNs perform requested beamforming tasks within a fraction of second and with about the same accuracy as the original optimization algorithm. The proposed methodology may find applications in future mobile telecommunication systems that require real-time beamforming on low-end hardware. The same beamforming methodology can be also employed in short-range wireless power transfer systems.
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Rebeca Costa Vitor, Joana Thaisa Santos Oliveira, Adan William de Melo Navarro, Ana Carolina Ribeiro Lima, Gabriela Mota Sena de Oliveira, Alexandre Dias Munhoz, Anaiá da Paixão Sevá, Paula Elisa Brandão Guedes and Renata Santiago Alberto Carlos
Background: Feline obesity is the most common nutritional disease in cats. This study aimed to investigate the differences between systolic blood pressure (SBP) and circulating concentrations of glucose, fructosamine, and serum amyloid-A (SAA) in ideal-weight, overweight, and obese cats. Methods: The animals were
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Background: Feline obesity is the most common nutritional disease in cats. This study aimed to investigate the differences between systolic blood pressure (SBP) and circulating concentrations of glucose, fructosamine, and serum amyloid-A (SAA) in ideal-weight, overweight, and obese cats. Methods: The animals were divided into three groups: ideal-weight (BCS 5, N = 20), overweight (BCS 6, N = 20), and obese cats (BCS ≥ 7, N = 20). SBP, circulating concentrations of glucose, fructosamine, and SAA were evaluated. Results: The SBP values of the ideal-weight, overweight, and obese cats were 140.0 mmHg, 160.0 mmHg, and 160.0 mmHg, respectively. The blood glucose and fructosamine levels for the ideal, overweight, and obese cats were 104.0 mg/dL and 245.0 µmol/L, 123.0 mg/dL and 289.0 µmol/L, and 133.0 mg/dL and 275.0 µmol/L, respectively, for each group. The SAA values were <5ug/mL in all the groups. The SBP values of the cats with ideal BCS were significantly lower compared to overweight (p = 0.019) and obese (p = 0.001) cats. The blood glucose values of obese cats were higher than those of ideal-weight cats (p = 0.029). There was no statistical difference between the groups for fructosamine and SAA. Conclusions: Obese cats had significantly higher SBP and blood glucose concentrations than ideal-weight cats, showing the effect of BSC on these parameters.
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This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the
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This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detection panel is devised, and a deep reconstruction-based contribution diagram is illustrated for locating the faulty variables. Finally, the comparative results from two case studies highlight the superiority in both detection accuracy and diagnostic performance.
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Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. Given that the neuronal structure of HTM is ill-equipped
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Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. Given that the neuronal structure of HTM is ill-equipped for the complexity of long-term marine time series applications, this study proposes a new, improved HTM model, incorporating Gated Recurrent Units (GRUs) neurons into the temporal memory algorithm to overcome this limitation. The capacities and advantages of the proposed model were tested and evaluated on time series data collected from the Xiaoqushan Seafloor Observatory in the East China Sea. The improved HTM model both outperforms the original one in short-term and long-term predictions and presents results with lower errors and better model stability than the GRU model, which is proficient in long-term predictions. The findings allow for the conclusion that the mechanism of online learning has certain advantages in predicting ocean observation data.
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Central nervous system (CNS) damage leads to severe neurological dysfunction as a result of neuronal cell death and axonal degeneration. As, in the mature CNS, neurons have little ability to regenerate their axons and reconstruct neural loss, demyelination is one of the hallmarks
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Central nervous system (CNS) damage leads to severe neurological dysfunction as a result of neuronal cell death and axonal degeneration. As, in the mature CNS, neurons have little ability to regenerate their axons and reconstruct neural loss, demyelination is one of the hallmarks of neurological disorders such as multiple sclerosis (MS). Unfortunately, remyelination, as a regenerative process, is often insufficient to prevent axonal loss and improve neurological deficits after demyelination. Currently, there are still no effective therapeutic tools to restore neurological function, but interestingly, emerging studies prove the beneficial effects of lipid supplementation in a wide variety of pathological processes in the human body. In the future, available lipids with a proven beneficial effect on CNS regeneration could be included in supportive therapy, but this topic still requires further studies. Based on our and others’ research, we review the role of exogenous lipids, pointing to substrates that are crucial in the remyelination process but are omitted in available studies, justifying the properly profiled supply of lipids in the human diet as a supportive therapy during CNS regeneration.
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Understanding visitors’ spatial choice behavior is important in developing effective policies to counteract overcrowdedness in attractive urban heritage areas. This research presents a comprehensive analysis of visitor location choice behavior, aiming to address two primary objectives. First, this paper investigates the relationship between
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Understanding visitors’ spatial choice behavior is important in developing effective policies to counteract overcrowdedness in attractive urban heritage areas. This research presents a comprehensive analysis of visitor location choice behavior, aiming to address two primary objectives. First, this paper investigates the relationship between visitor segments and the choice of particular Points of Interest (POIs). Second, this paper explores the impacts of visitors’ experiences and visitor segments on their revisit intentions. We used a sample of 320 visitors who had been to Amsterdam within the last five years to collect data about their location choice behavior and intention to revisit after a recent visit to the city. Combining the revealed choices and intentions of pre-defined visitor segments obtained from a stated choice experiment, association rules are extracted to reveal differences in the patterns of behaviors related to the segment. The findings identify associations between various POIs, including museums such as the Rijksmuseum and Madame Tussauds, and visitor classes, which include “cultural attraction seekers”, “selective sightseers”, and “city-life lovers”. Furthermore, binary logistic regression analysis reveals that affective experiences, such as feelings of comfort, happiness, and annoyance, have a significant influence on visitors’ intentions to revisit the destination in the future. This research found that “cultural attraction seekers” and “selective sightseers” display a higher likelihood of considering a return visit to the city.
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The current article focuses on the examination of nonlinear instability and dynamic transitions in a double-diffusive rotating couple-stress fluid layer. The analysis was based on the newly developed dynamic transition theory by T. Ma and S. Wang. Through a comprehensive linear spectrum analysis
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The current article focuses on the examination of nonlinear instability and dynamic transitions in a double-diffusive rotating couple-stress fluid layer. The analysis was based on the newly developed dynamic transition theory by T. Ma and S. Wang. Through a comprehensive linear spectrum analysis and investigation of the principle of exchange of stability (PES) as the thermal Rayleigh number crosses a threshold, the nonlinear orbital changes during the transition were rigorously elucidated utilizing reduction methods. For both single real and complex eigenvalue crossings, local pitch-fork and Hopf bifurcations were discovered, and directions of these bifurcations were identified along with transition types. Furthermore, nondimensional transition numbers that signify crucial factors during the transition were calculated and the orbital structures were illustrated. Numerical studies were performed to validate the theoretical results, revealing the relations between key parameters in the system and the types of transition. The findings indicated that the presence of couple stress and a slow diffusion rate of solvent and temperature led to smoother nonlinear transitions during convection.
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Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation
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Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages.
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Accretionary prisms are composed mainly of ancient marine sediment scraped from the subducting oceanic plate at convergent plate boundaries. Anoxic groundwater is stored in deep aquifers associated with accretionary prisms and can be collected via deep wells. We investigated how such groundwater pumping
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Accretionary prisms are composed mainly of ancient marine sediment scraped from the subducting oceanic plate at convergent plate boundaries. Anoxic groundwater is stored in deep aquifers associated with accretionary prisms and can be collected via deep wells. We investigated how such groundwater pumping affects the microbial community in a deep aquifer. Groundwater samples were collected from a deep well drilled down to 1500 m every six months (five times in total) after completion of deep well construction and the start of groundwater pumping. Next-generation sequencing and clone-library analyses of 16S rRNA genes were used to describe the subterranean microbial communities in the samples. The archaea: the prokaryote ratio in groundwater increased significantly from 1 to 7% (0 and 7 months after initiating groundwater pumping) to 59 to 72% (13, 19, and 26 months after initiating groundwater pumping), and dominant prokaryotes changed from fermentative bacteria to sulfate-reducing archaea. The optimal growth temperature of the sulfate-reducing archaea, estimated based on the guanine-plus-cytosine contents of their 16S rRNA genes, was 48–52 °C, which agreed well with the groundwater temperature at the deep-well outflow. Our results indicated that, in deep aquifers, groundwater pumping enhances groundwater flow, and the supply of sulfate-containing seawater activates the metabolism of thermophilic sulfate-reducing archaea.
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Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and
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Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction.
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Marienid Flores-Colón, Mariela Rivera-Serrano, Víctor G. Reyes-Burgos, José G. Rolón, Josué Pérez-Santiago, María J. Marcos-Martínez, Fatima Valiyeva and Pablo E. Vivas-Mejía
Int. J. Mol. Sci.2024, 25(7), 3793; https://doi.org/10.3390/ijms25073793 (registering DOI) - 28 Mar 2024
Metastasis and drug resistance are major contributors to cancer-related fatalities worldwide. In ovarian cancer (OC), a staggering 70% develop resistance to the front-line therapy, cisplatin. Despite proposed mechanisms, the molecular events driving cisplatin resistance remain unclear. Dysregulated microRNAs (miRNAs) play a role in
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Metastasis and drug resistance are major contributors to cancer-related fatalities worldwide. In ovarian cancer (OC), a staggering 70% develop resistance to the front-line therapy, cisplatin. Despite proposed mechanisms, the molecular events driving cisplatin resistance remain unclear. Dysregulated microRNAs (miRNAs) play a role in OC initiation, progression, and chemoresistance, yet few studies have compared miRNA expression in OC samples and cell lines. This study aimed to identify key miRNAs involved in the cisplatin resistance of high-grade-serous-ovarian-cancer (HGSOC), the most common gynecological malignancy. MiRNA expression profiles were conducted on RNA isolated from formalin-fixed-paraffin-embedded human ovarian tumor samples and HGSOC cell lines. Nine miRNAs were identified in both sample types. Targeting these with oligonucleotide miRNA inhibitors (OMIs) reduced proliferation by more than 50% for miR-203a, miR-96-5p, miR-10a-5p, miR-141-3p, miR-200c-3p, miR-182-5p, miR-183-5p, and miR-1206. OMIs significantly reduced migration for miR-183-5p, miR-203a, miR-296-5p, and miR-1206. Molecular pathway analysis revealed that the nine miRNAs regulate pathways associated with proliferation, invasion, and chemoresistance through PTEN, ZEB1, FOXO1, and SNAI2. High expression of miR-1206, miR-10a-5p, miR-141-3p, and miR-96-5p correlated with poor prognosis in OC patients according to the KM plotter database. These nine miRNAs could be used as targets for therapy and as markers of cisplatin response.
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A flying ad hoc network (FANET) is formed from a swarm of drones also known as unmanned aerial vehicles (UAVs) and is currently a popular research subject because of its ability to carry out complicated missions. However, the specific features of UAVs such
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A flying ad hoc network (FANET) is formed from a swarm of drones also known as unmanned aerial vehicles (UAVs) and is currently a popular research subject because of its ability to carry out complicated missions. However, the specific features of UAVs such as mobility, restricted energy, and dynamic topology have led to vital challenges for making reliable communications between drones, especially when designing routing methods. In this paper, a novel optimized link-state routing scheme with a greedy and perimeter forwarding capability called OLSR+GPSR is proposed in flying ad hoc networks. In OLSR+GPSR, optimized link-state routing (OLSR) and greedy perimeter stateless routing (GPSR) are merged together. The proposed method employs a fuzzy system to regulate the broadcast period of hello messages based on two inputs, namely the velocity of UAVs and position prediction error so that high-speed UAVs have a shorter hello broadcast period than low-speed UAVs. In OLSR+GPSR, unlike OLSR, MPR nodes are determined based on several metrics, especially neighbor degree, node stability (based on velocity, direction, and distance), the occupied buffer capacity, and residual energy. In the last step, the proposed method deletes two phases in OLSR, i.e., the TC message dissemination and the calculation of all routing paths to reduce routing overhead. Finally, OLSR+GPSR is run on an NS3 simulator, and its performance is evaluated in terms of delay, packet delivery ratio, throughput, and overhead in comparison with Gangopadhyay et al., P-OLSR, and OLSR-ETX. This evaluation shows the superiority of OLSR+GPSR.
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