Set Of Experiments Research Articles

Non-local modelling of freezing and thawing of unsaturated soils

A large part of the earth’s surface is covered by seasonally or permanently frozen soils. Considering the negative impact of climate change, future development of such regions can be underpinned by mathematical methods for accurate analysis of heat and moisture transport in freezing and thawing soils. Reported in this paper is a novel non-local formulation of water and heat transport in unsaturated soils. The formulation uses bond-based peridynamics (PD) and consists of a set of integral–difference formulations of energy and mass conservation. Specific features of freezing/thawing soils are incorporated by a combination of van Genuthcen and Clausius–Clapeyron relations. Computational results are compared with four sets of laboratory experiments to demonstrate the efficiency of the developed approach. The model can be used to analyse the effect of water flow on heat transfer in soils during thawing of permafrost soils. Further, it can be applied in modelling climate change effects, and can be used for construction of coupled physically justified models of frost heave.

On the exact and population bi-dimensional reproduction numbers in a stochastic SVIR model with imperfect vaccine

We aim to quantify the spread of a direct contact infectious disease that confers permanent immunity after recovery, within a non-isolated finite and homogeneous population. Prior to the onset of the infection and to prevent the spread of this disease, a proportion of individuals was vaccinated. But the administered vaccine is imperfect and can fail, which implies that some vaccinated individuals get the infection when being in contact with infectious individuals. We study the evolution of the epidemic process over time in terms of a continuous-time Markov chain, which represents a general SIR model with an additional compartment for vaccinated individuals. In our stochastic framework, we study two bi-dimensional variables recording infection events, produced by a single infectious individual or by the whole infected group, taking into account if the newly infected individual was previously vaccinated or not. Theoretical schemes and recursive algorithms are derived in order to compute joint probability mass functions and factorial moments for these random variables. We illustrate the applicability of our techniques by means of a set of numerical experiments.

Addressing the inherent stress non-uniformity in dynamic hollow cylinder testing

The Dynamic Hollow Cylinder Apparatus (DHCA) is renowned for its ability to subject soil samples to various cyclic stress paths, allowing for the investigation of the dynamic behavior of soils under complex cyclic loading conditions. This note explores the errors arising from the stress non-uniformity along the height of DHCA samples and examines their impact on the measured soil dynamic properties. After discussing the two globally used DHCA types, this study presents the stiffness and damping of sand derived from a set of Dynamic Hollow Cylinder experiments covering a wide range of dynamic stress paths and shear strain amplitudes. It is highlighted that the deviation of the results from established degradation models is primarily attributed to the errors associated with the stress non-uniformity, leading to up to a tenfold underestimation within the medium strain range. A simple correction to the shear stress amplitude calculation is proposed to minimize the impact of stress non-uniformity and improve the accuracy of the test results.

An evaluation model for in-situ bioremediation technology of petroleum hydrocarbon contaminated soil

Considering the interference of the complexity of underground environment to the bioremediation scheme, an evaluation model for bioremediation technology in the soil source area of oil contaminated sites was established. On the basis of traditional CDE model, a compartment model was coupled to express the adsorption and degradation process, and the spatial expression of biodegradation was enriched through environment-dependent factors. The visualization of the model was achieved based on COMSOL Multiphysics software platform. Two sets of indoor sandbox experiments on natural attenuation and bioaugmentation were carried out for 120 days to verify the prediction function of the model. The results showed that bioaugmentation greatly improved the remediation effect. Petroleum hydrocarbons with different occurrence states exhibited different spatial distributions under the influence of environmental factors. The prediction accuracy evaluation results of total petroleum hydrocarbons, bio available hydrocarbons and non extractable hydrocarbons showed excellent fitting degree, and the model had a good prediction function for petroleum hydrocarbon in soil under different bioremediation scenarios. This model can be used to screen bioremediation technical schemes, prevent pollution and assess risk of petroleum hydrocarbon contaminated sites.

Ultra-thin vapour chamber based heat dissipation technology for lithium-ion battery

A powerful thermal management scheme is the key to realizing the extremely fast charging of battery electric vehicles. In this scheme, a water-cooled plate is set at the bottom of the battery modules, which has a remarkable heat dissipation ability but increases the temperature difference between the top and bottom of the battery. This temperature difference problem is more obvious during the high-rate charge/discharge process; in particular, in the area of battery tabs, which are usually located far from the cooling plate, heat accumulates, risking thermal runaway. To address this problem, a thermal management method based on an ultrathin vapour chamber (UTVC) is proposed in this paper to improve temperature uniformity and reduce the highest temperature of batteries. According to the number of applied UTVCs, a unilateral UTVC (U-UTVC) and a bilateral UTVC (B-UTVC) cooling approach were presented, the effectiveness of which was verified by a set of thermal performance experiments. The test data show that the temperature differences in the batteries under the U-UTVC and B-UTVC schemes are 39.77% and 73.54% lower than those under the traditional method at a 25 °C ambient temperature and a 1.39C charge rate, respectively. The temperature difference on the battery surface with the B-UTVC scheme is approximately 3 °C at 25 °C and 40 °C ambient temperature.

Autophagy machinery plays an essential role in traumatic brain injury-induced apoptosis and its related behavioral abnormalities in mice: focus on Boswellia Sacra gum resin.

Traumatic brain injury (TBI) is described as a structural damage or physiological disturbance of brain function that occurs after trauma and causes disability or death in people of all ages. New treatment targets for TBI are being explored because current medicines are frequently ineffectual and poorly tolerated. There is increasing evidence that following TBI, there are widespread changes in autophagy-related proteins in both experimental and clinical settings. The current study investigated if Boswellia Sacra Gum Resin (BSR) treatment (500mg/kg) could modulate post-TBI neuronal autophagy and protein expression, as well as whether BSR could markedly improve functional recovery in a mouse model of TBI. Taken together our results shows for the first time that BSR limits histological alteration, lipid peroxidation, antioxidant, cytokines release and autophagic flux alteration induced by TBI.

Changes in light use efficiency explains why diversity effect on biomass production is lower at high planting density in mixed-species plantations of Eucalyptus grandis and Acacia mangium

Understanding the effect of planting densities and species proportions on light absorption and light use efficiency can help to improve the management of mixed-species forest plantations. Our study aimed to disentangle the role of light interception and light use efficiency (LUE) on the biomass production of Eucalyptus grandis (E), a highly productive species in tropical conditions, and Acacia mangium (A), a N2-fixing species, in monocultures and mixed-species plantations for contrasting planting densities. A randomized block experiment set up over 4 ha in southern Brazil was intensively monitored for 14 months at mid rotation. The absorbed photosynthetically active radiation (APAR) was simulated for each tree of the experiment using the tri-dimensional MAESPA model parameterized with detailed in situ measurements of tree and foliage. LUE for stem wood production was estimated as the ratio of measured stem biomass production (SBP) and simulated APAR. The APAR of Eucalyptus trees did not significantly differ between monocultures and mixed plantations, the reduction of Eucalyptus density being compensated by an increase in light absorption of Eucalyptus individuals. The LUE of Eucalyptus trees in monoculture and mixed-species stands was found to be comparable only at low planting densities. The replacement of Eucalyptus trees with Acacia trees resulted in a reduction in Eucalyptus LUE only at high planting density. The SBP of Eucalyptus trees was mainly explained by differences in APAR, while both APAR and LUE explained the SBP of Acacia trees. The maximum stand production was obtained with monoculture of Eucalyptus at high density and no mixture reached this productivity. Reducing the proportion of Eucalyptus in mixture lead to a substantial decrease in stand production at high planting density due to a decrease in LUE, while this stand production reduction was offset at low planting density, underlying a higher diversity effect at low planting density. In the perspective of increasing diversity in forest plantations to foster multifunctionality, mixed plantations of Acacia and Eucalyptus at low planting density can be an interesting option to maintain a relatively high productivity, which is similar to Eucalyptus monocultures at the same low planting density.

A Review of the Evidence on the Role of Floors and Shoes in the Dissemination of Pathogens in a Healthcare Setting.

Background: It is generally accepted that shoes and floors are contaminated with pathogens including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), and Clostridium difficile, yet correlation to clinical infection is not well established. Because floors and shoes are low-touch surfaces, these are considered non-critical surfaces for cleaning and disinfection. The purpose of this review is to assess peer-reviewed literature inclusive of floors and shoe soles as contributors to the dissemination of infectious pathogens within healthcare settings. Methods: Using the Preferred Reporting Items for Systematic Reviews (PRISMA) methodology, PubMed and Medline were searched for articles assessing the presence of pathogens on or the transmission of pathogens between or from floors or shoe soles/shoe covers. Inclusion criteria are the human population within healthcare or controlled experimental settings after 1999 and available in English. Results: Four hundred eighteen articles were screened, and 18 articles documented recovery of bacterial and viral pathogens from both floors and shoes. Seventy-two percent (13/18) of these were published after 2015, showing increased consideration of the transfer of pathogens to high-touch surfaces from shoe soles or floors during patient care. Conclusions: There is evidence that floors and shoes in healthcare settings are contaminated with several different species of health-care-associated pathogens including MRSA, VRE, and Clostridium difficile.

Mutations associated with autism lead to similar synaptic and behavioral alterations in both sexes of male and female mouse brain

Autism spectrum disorder (ASD) is a neurodevelopmental disorder based on synaptic abnormalities. The estimated prevalence rate of male individuals diagnosed with ASD prevails over females is in a proportion of 4:1. Consequently, males remain the main focus in ASD studies in clinical and experimental settings. Meanwhile, some studies point to an underestimation of this disorder in females. In this work, we studied the sex differences of the synaptic and behavioral phenotypes of ASD mouse models. Juvenile male and female Shank3Δ4–22 and Cntnap2−/− mutant mice and their WT littermates were used in the experiments. The animals were subjected to a Three-Chamber Sociability Test, then euthanized, and the whole cortex was used for the evaluation of the synaptic phenotype. Protein levels of glutamatergic (NR1) and GABAergic (GAD1 and VGAT) neuronal markers were measured. Protein level of synaptophysin (Syp) was also measured. Dendritic spine density in somatosensory neurons was analyzed by Golgi staining methods. Spine Density and GAD1, NR1, VGAT, and Syp levels were significantly reduced in Shank3Δ4–22 and Cntnap2−/− mice compared to the control group irrespective of sex, indicating impaired synaptic development in the mutant mice. These results were consistent with the lack of differences in the three-chamber sociability test between male and female mice. In conclusion, female ASD mice of both mutations undergo similar synaptic aberrations as their male counterparts and need to be studied along with the male animals. Finally, this work urges the psychiatry scientific community to use both sexes in their investigations.

A Sketch-texture Retrieval Framework using Perceptual Similarity

Sketch-based image retrieval is an important research topic in the field of image processing. Hand-drawn sketches consist only of contour lines, and lack detailed information such as color and textons. As a result, they differ significantly from color images in terms of image feature distribution, making sketch-based image retrieval a typical cross-domain retrieval problem. To solve this problem, we constructed a perceptual space consistent with both textures and sketches, and using perceptual similarity for sketch-based texture retrieval. To implement this approach, we first conduct a set of psychological experiments to analyze the similarity of visual perception of the textures, then we create a dataset of over a thousand hand-drawn sketches according to the textures. We proposed a layer-wise perceptual similarity learning method that integrates perceptual similarity, with which we trained a similarity prediction network to learn the perceptual similarity between hand-drawn sketches and natural texture images. The trained network can be used for perceptual similarity prediction and efficient retrieval. Our experimental results demonstrate the effectiveness of sketch-based texture retrieval using perceptual similarity.

Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)

Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

To alert or alleviate? A natural experiment on the effect of anti-phishing laws on corporate IT and security investments

In the United States, between 2005 and 2017, 23 states enacted anti-phishing laws to prosecute those suspected of phishing. As the primary targets of phishing attacks, firms' interpretations and reactions toward these laws are worth investigating. Utilizing a unique dataset in a natural experimental setting, this study employed the difference-in-differences method to contrast firms' investment decisions related to IT and cybersecurity in states in which such laws had been enacted and those in states without such laws, both before and after their enactment. We found that firms with different operational experiences react to the enactment of the anti-phishing laws in different ways. We further demonstrate the moderating roles of the industry risk landscape and IT capability. Specifically, firms with high-IT increased investments in both IT and cybersecurity while the risk landscape stimulated investments in cybersecurity only. This suggests that the risk landscape facilitates sensitivity to the immediate risk signaled by enactment of the laws, and IT capability further enables the alignment between IT investments and security objectives. This study also discusses the policy implications of our findings.

Plant and yeast consortium for efficient remediation of dyes and effluents: a biochemical and toxicological study.

Textile effluent carries a range of dyes that may be recalcitrant and resistant to biodegradation. A unique consortium of the Fimbristylis dichotoma and Saccharomyces cerevisiae is exploited for the biodegradation of an azo dye Rubine GFL and actual textile effluent. This consortium enhances the rate of biodegradation of Rubine GFL and actual textile effluent with an excellent rate of biodegradation of 92% for Rubine GFL and 68% for actual textile effluent when compared to the individual one within 96 h. Speedy decolorization of Rubine GFL and actual textile effluent was observed due to the induction of oxido-reductive enzymes of the FD-SC consortium. Along with the significant reduction in the values of COD, BOD, ADMI, TSS, and TDS with 70, 64, 65, 41, and 52%, respectively, in experimental sets treated with FD-SC consortium. The biodegradation of Rubine GFL was confirmed with UV-Vis spectroscopy at the preliminary level, and then, metabolites formed after degradation were detected and identified by FTIR, HPLC, and GC-MS techniques. Also, decolorization of the dye was observed in the sections of the root cortex of Fimbristylis dichotoma. The toxicity of dye and metabolites formed after degradation was assessed by seed germination and bacterial count assay, where increased germination % and bacterial count from31×107CFUsto 92 × 107 CFUs reflect the nontoxic nature of metabolites. Furthermore, the nontoxic nature of metabolites was confirmed by fish toxicity on Cirrhinus mrigala showed normal structures of fish gills and liver in the groups treated with FD-SC consortium proving the better tactic for biodegradation of dyes and textile effluent.

Detecting Thyroid Disease Using Optimized Machine Learning Model Based on Differential Evolution

Thyroid disease has been on the rise during the past few years. Owing to its importance in metabolism, early detection of thyroid disease is a task of critical importance. Despite several existing works on thyroid disease detection, the problem of class imbalance is not investigated very well. In addition, existing studies predominantly focus on the binary-class problem. This study aims to solve these issues by the proposed approach where ten types of thyroid diseases are considered. The proposed approach uses a differential evolution (DE)-based optimization algorithm to fine-tune the parameters of machine learning models. Moreover, conditional generative adversarial networks are used for data augmentation. Several sets of experiments are carried out to analyze the performance of the proposed approach with and without model optimization. Results suggest that a 0.998 accuracy score can be obtained using AdaBoost with DE optimization which is better than existing state-of-the-art models.

The Application and Challenge of Non-Human Primate Experimental Animals in the Field of Neuroscience

Non-human primates (NHPs) are highly valued in the field of neuroscience due to their similarity to humans in terms of brain structure and function. They exhibit complex social behaviors and cognitive abilities, and their use in research has led to significant breakthroughs in the understanding of brain development, neurodegenerative diseases, and psychiatric disorders. In recent years, the breeding and use of NHPs has expanded, making them increasingly important as experimental animals. However, with this expansion comes the need for researchers to address important biosafety and animal ethics concerns. It is critical that animal welfare is ensured and that scientific objectives are achieved while observing ethical guidelines. This paper aims to provide an overview of NHPs in neuroscience, their applications in research, and the ethical considerations that must be taken into account. It highlights the various breeding and handling practices that are employed to ensure the health and welfare of NHPs in experimental settings. Additionally, the paper summarizes the challenges associated with the use of NHPs in research and the benefits that have been realized from their use. Overall, this paper underscores the importance of recognizing the role of NHPs in neuroscience, and the responsibility that researchers have to ensure their welfare and to address ethical considerations. By doing so, researchers can continue to advance our understanding of the brain and develop new treatments for neurological and psychiatric disorders.

Multiformity of extracellular microelectrode recordings from Aδ neurons in the dorsal root ganglia: a computational modeling study.

Microelectrodes serve as a fundamental tool in electrophysiology research throughout the nervous system, providing a means of exploring neural function with a high resolution of neural firing information. We constructed a hybrid computational model using the finite element method and multicompartment cable models to explore factors that contribute to extracellular voltage waveforms that are produced by sensory pseudounipolar neurons, specifically smaller A-type neurons, and that are recorded by microelectrodes in dorsal root ganglia. The finite element method model included a dorsal root ganglion, surrounding tissues, and a planar microelectrode array. We built a multicompartment neuron model with multiple trajectories of the glomerular initial segment found in many A-type sensory neurons. Our model replicated both the somatic intracellular voltage profile of Aδ low-threshold mechanoreceptor neurons and the unique extracellular voltage waveform shapes that are observed in experimental settings. Results from this model indicated that tortuous glomerular initial segment geometries can introduce distinct multiphasic properties into a neuron's recorded waveform. Our model also demonstrated how recording location relative to specific microanatomical components of these neurons, and recording distance from these components, can contribute to additional changes in the multiphasic characteristics and peak-to-peak voltage amplitude of the waveform. This knowledge may provide context for research employing microelectrode recordings of pseudounipolar neurons in sensory ganglia, including functional mapping and closed-loop neuromodulation. Furthermore, our simulations gave insight into the neurophysiology of pseudounipolar neurons by demonstrating how the glomerular initial segment aids in increasing the resistance of the stem axon and mitigating rebounding somatic action potentials.NEW & NOTEWORTHY We built a computational model of sensory neurons in the dorsal root ganglia to investigate factors that influence the extracellular waveforms recorded by microelectrodes. Our model demonstrates how the unique structure of these neurons can lead to diverse and often multiphasic waveform profiles depending on the location of the recording contact relative to microanatomical neural components. Our model also provides insight into the neurophysiological function of axon glomeruli that are often present in these neurons.

Incoherent noise-induced distortions of Rayleigh wave ellipticity measurements obtained with three-component beamforming

SUMMARY For site characterization, the elliptic particle motion of Rayleigh waves and its frequency dependence is a well-known property that aroused less interest than the frequency dependence of the phase velocity. More than 50 yr ago, ellipticity was already recognized as providing information independent from phase velocity, despite the difficulties inherent to its accurate and precise measurement. Several techniques were developed during the last two decades to extract the ellipticity curve from ambient vibration recordings, with a single three-component (3C) station, with pairs of 3C stations and more recently with 3C arrays. The latter has the advantage over the other approaches that the sign of the ellipticity can be retrieved. Moreover, higher order mode separation is possible under certain conditions. Nevertheless, Rayleigh Three-component BeamForming (RTBF) proposed by Wathelet et al. encounters difficulties in the presence of significant levels of incoherent noise when the true ellipticity is vanishing or when it has a high absolute value. In this work, the analytical expressions of the beam power for a single source wavefield are revised under more realistic assumptions for the incoherent noise azimuthal distribution. The proposed model also includes an asymmetric distribution of the incoherent noise between vertical and horizontal components, which was not the case in the original publication. Switching from ellipticity to angular ellipticity drastically simplifies the formalism. Moreover, it naturally leads to a new steering matrix (all-component ellipticity steering) which solves the limitation around zero and infinity observed for RTBF. Interestingly, the accuracy of the ellipticity is no longer influenced by the absolute level of incoherent noise but by the difference between the incoherent noise ratio on vertical and horizontal components. A method based on the second derivative of the beam power versus the radial wavenumber is finally proposed to experimentally measure the noise ratio difference, which allows experimental values to be corrected. The methodology is compared with classical vertical beamforming and RTBF for a synthetic case and three experimental data sets.

Measuring the speed of gravity at short distances: Sensitivity estimate

AbstractAn experimental set up was proposed to determine the speed of gravitational signals traveling in air or in some other medium. It involves two vibrating masses—the emitters, which will be the sources of periodic tidal gravitational signals—and one sapphire‐made mass that will act as a detector, positioned between the two emitters. The detector is planned to be suspended in vacuum and cooled down to 4.2 K, and its vibrational amplitude should be measured by a microwave signal (with ultra‐low phase‐noise) that is expected to resonate with the whispering gallery modes inside the detector. The mechanical and electrical quality factors of sapphire are quite high, yielding a very narrow detection band that reduces the detector sensitivity while amplifying the phase difference of the emitters' signals. The frequencies of the normal modes of the detector were previously determined using a finite element program. In this work, these frequencies are applied to the calculation of a first estimate of the sensitivity of the experiment.

Vitamin B12 Deficiency and the Nervous System: Beyond Metabolic Decompensation-Comparing Biological Models and Gaining New Insights into Molecular and Cellular Mechanisms.

Vitamin B12 (VitB12) is a micronutrient and acts as a cofactor for fundamental biochemical reactions: the synthesis of succinyl-CoA from methylmalonyl-CoA and biotin, and the synthesis of methionine from folic acid and homocysteine. VitB12 deficiency can determine a wide range of diseases, including nervous system impairments. Although clinical evidence shows a direct role of VitB12 in neuronal homeostasis, the molecular mechanisms are yet to be characterized in depth. Earlier investigations focused on exploring the biochemical shifts resulting from a deficiency in the function of VitB12 as a coenzyme, while more recent studies propose a broader mechanism, encompassing changes at the molecular/cellular levels. Here, we explore existing study models employed to investigate the role of VitB12 in the nervous system, including the challenges inherent in replicating deficiency/supplementation in experimental settings. Moreover, we discuss the potential biochemical alterations and ensuing mechanisms that might be modified at the molecular/cellular level (such as epigenetic modifications or changes in lysosomal activity). We also address the role of VitB12 deficiency in initiating processes that contribute to nervous system deterioration, including ROS accumulation, inflammation, and demyelination. Consequently, a complex biological landscape emerges, requiring further investigative efforts to grasp the intricacies involved and identify potential therapeutic targets.

Temporally-aware node embeddings for evolving networks topologies

Static node embedding algorithms applied to snapshots of real-world applications graphs are unable to capture their evolving process. As a result, the absence of information about the dynamics in these node representations can harm the accuracy and increase processing time of machine learning tasks related to these applications. Aiming at fill the gap regarding the inability of static methods to capture evolving processes on dynamic networks, we propose a biased random walk method named Evolving Node Embedding (EVNE). EVNE leverages the sequential relationship of graph snapshots by incorporating historic information when generating embeddings for the next snapshot. It learns node representations through a neural network, but differs from existing methods as it: (i) incorporates previously run walks at each step; (ii) starts the optimization of the current embedding from the parameters obtained in the previous iteration; and (iii) uses two time-varying parameters to regulate the behavior of the biased random walks over the process of graph exploration. Through a wide set of experiments we show that our approach generates better embeddings, outperforming baselines by up to 20% in a downstream node classification task. EVNE’s embeddings achieve better performance than others, based on experiments with four classifiers and five datasets. In addition, we present seven variations of our model to show the impact of each of EVNE’s mechanisms.