Related Areas Research Articles

Didactic model for the development of mathematical thinking in naval engineering students in Colombia

The learning-teaching process in math is fundamental for navy engineering students, where specific subjects have a strong theoretical foundation in basic sciences, more concise in mathematics related areas. This research has the objective to construct a didactical model based in context problem solving, modeling and mathematical visualization for the learning-teaching process of contents from the Ship Stability subject for navy engineering students in the Colombian navy University “Escuela Naval de Cadetes Almirante Padilla (ENAP)”. The work lies in the research-action approach of the qualitative research. Results show the model structure composed of four parts; the first one contains the model principles: the epistemological and didactical foundations and the objectives and goals of the constructed model. The second one is a characterization of the model is described, and the necessity for students. The third one, named, resolutive, has three phases (exploration, resolution, and action), that are subdivided in inter-related components one to another. Finally, the fourth part is a proposal for the practical implementation of the model. The model obtained generates a robust construction of mathematical context for problems related to ship stability in the naval context.

Gynecological Cancers and Microbiota Dynamics: Insights into Pathogenesis and Therapy.

In recent years, the relationship between the microbiota and various aspects of health has become a focal point of scientific investigation. Although the most studied microbiota concern the gastrointestinal tract, recently, the interest has also been extended to other body districts. Female genital tract dysbiosis and its possible impact on pathologies such as endometriosis, polycystic ovary syndrome (PCOS), pelvic inflammatory disease (PID), and gynecological cancers have been unveiled. The incursion of pathogenic microbes alters the ecological equilibrium of the vagina, triggering inflammation and compromising immune defense, potentially fostering an environment conducive to cancer development. The most common types of gynecological cancer include cervical, endometrial, and ovarian cancer, which occur in women of any age but especially in postmenopausal women. Several studies highlighted that a low presence of lactobacilli at the vaginal level, and consequently, in related areas (such as the endometrium and ovary), correlates with a higher risk of gynecological pathology and likely contributes to increased incidence and worse prognosis of gynecological cancers. The complex interplay between microbial communities and the development, progression, and treatment of gynecologic malignancies is a burgeoning field not yet fully understood. The intricate crosstalk between the gut microbiota and systemic inflammation introduces a new dimension to our understanding of gynecologic cancers. The objective of this review is to focus attention on the association between vaginal microbiota and gynecological malignancies and provide detailed knowledge for future diagnostic and therapeutic strategies.

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three image collections wherein rodent neuronal cell nuclei and cytoplasm are stained with diverse markers to highlight their anatomical or functional characteristics. Specifically, we release 1874 high-resolution images alongside 750 corresponding ground-truth annotations for several learning tasks, including semantic segmentation, object detection and counting. The contribution is two-fold. First, thanks to the variety of annotations and their accessible formats, we anticipate our work will facilitate methodological advancements in computer vision approaches for segmentation, detection, feature extraction, unsupervised and self-supervised learning, transfer learning, and related areas. Second, by enabling extensive exploration and benchmarking, we hope Fluorescent Neuronal Cells v2 will catalyze breakthroughs in fluorescence microscopy analysis and promote cutting-edge discoveries in life sciences.

Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics

Microplastics are an emerging contaminant that can persist in the environment for extended periods， posing risks to ecological systems. Recently， microplastic pollution has emerged as a major global environmental problem. In order to ensure accurate and scientific evaluation of the ecological risks associated with microplastic pollution， it is of paramount importance to improve the simplicity and reliability of microplastic identification， systematically analyze the pollution characteristics of microplastics in various environmental media， and clarify their environmental impacts. Machine learning technology has gained widespread attention in microplastic research by learning and analyzing large volumes of data to establish result evaluation or prediction models. The use of machine learning can enhance the automation and identification efficiency of visual and spectral identification of microplastics， provide scientific support for tracing the sources of microplastic pollution， and help reveal the complex environmental effects of microplastics. This review provides a summary of the application characteristics and limitations of machine learning in the aforementioned areas by reviewing the progress made in research that employs machine learning technology in microplastic identification and environmental risk assessment. Furthermore， the findings of the review will provide suggestions and prospects for the development and application of machine learning in related areas.

Medium and Long-term Carbon Emission Projections and Emission Reduction Potential Analysis of the Lingang Special Area Based on the LEAP Model

Based on the existing statistical data of the Lingang Special Area in Shanghai and considering its future socio-economic development， industrial structure， and technological development， a LEAP-Lingang model was developed to analyze the evolution trends of energy demand and carbon emissions under the baseline scenario， low-carbon scenario， and enhanced low-carbon scenario. To enhance the prediction accuracy of the model， the Logistic population growth model was used to predict future population data， and the learning curve model was used to simulate the cost evolution trend of related carbon reduction technologies. In addition， an economic evaluation model for carbon reduction technologies was developed， and the economic costs and emission reduction potential of typical carbon reduction technologies were evaluated by drawing a marginal emission reduction cost curve. The results showed that under the enhanced low-carbon scenario， the renewable energy accounted for 69% of the primary energy consumption， and the electric energy accounted for 91% of the terminal energy demand in 2060. The Lingang Special Area could achieve carbon peak by 2030， and the carbon emissions in 2060 were predicted to decrease by 94% compared to that in the baseline scenario. In terms of contribution to emission reduction， clean energy substitution， industrial structure optimization， and terminal energy efficiency improvement played a key role in reducing carbon emissions near the port. In the medium term （until 2035）， they were predicted to contribute 35.1%， 27.3%， and 16.2% of carbon emissions， respectively， and in the long term （until 2060）， they should contribute 50.6%， 8.75%， and 7.7% of carbon emissions， respectively. Regarding specific carbon reduction technologies， hydrogen power generation； water electrolysis for hydrogen； and carbon capture， utilization， and storage （CCUS） technology were of great significance for achieving net-zero emissions， but the costs of emission reduction were relatively high. The research results can provide ideas and references for the low-carbon and green development of the Lingang Special Area and related areas.

Acute gastrointestinal permeability after traumatic brain injury in mice precedes a bloom in Akkermansia muciniphila supported by intestinal hypoxia

Traumatic brain injury (TBI) increases gastrointestinal morbidity and associated mortality. Clinical and preclinical studies implicate gut dysbiosis as a consequence of TBI and an amplifier of brain damage. However, little is known about the association of gut dysbiosis with structural and functional changes of the gastrointestinal tract after an isolated TBI. To assess gastrointestinal dysfunction, mice received a controlled cortical impact or sham brain injury and intestinal permeability was assessed at 4 h, 8 h, 1 d, and 3 d after injury by oral administration of 4 kDa FITC Dextran prior to euthanasia. Quantification of serum fluorescence revealed an acute, short-lived increase in permeability 4 h after TBI. Despite transient intestinal dysfunction, no overt morphological changes were evident in the ileum or colon across timepoints from 4 h to 4 wks post-injury. To elucidate the timeline of microbiome changes after TBI, 16 s gene sequencing was performed on DNA extracted from fecal samples collected prior to and over the first month after TBI. Differential abundance analysis revealed that the phylum Verrucomicrobiota was increased at 1, 2, and 3 d after TBI. The Verrucomicrobiota species was identified by qPCR as Akkermansia muciniphila, an obligate anaerobe that resides in the intestinal mucus bilayer and produces short chain fatty acids (e.g. butyrate) utilized by intestinal epithelial cells. We postulated that TBI promotes intestinal changes favorable for the bloom of A. muciniphila. Consistent with this premise, the relative area of mucus-producing goblet cells in the medial colon was significantly increased at 1 d after injury, while colon hypoxia was significantly increased at 3 d. Our findings reveal acute gastrointestinal functional changes coupled with an increase of beneficial bacteria suggesting a potential compensatory response to systemic stress after TBI.

Watershed, lake, and food web factors influence diazotrophic cyanobacteria in mountain lakes

AbstractCyanobacterial blooms can occur in freshwater ecosystems largely isolated from development and not experiencing extensive cultural eutrophication. For example, remote mountain lakes can experience intense blooms of diazotrophic (nitrogen‐fixing) cyanobacteria caused by factors acting at different spatial and temporal scales. In this study, we examined how cross‐scale interactions among watershed, lake, and food web characteristics influence diazotrophic cyanobacteria biovolume in mountain lakes. We quantified diazotrophic cyanobacteria biovolume, zooplankton abundance, and physico‐chemical variables for 29 lakes in the Cascade Mountains of Oregon, USA, in summer 2019. Watershed characteristics were compiled from historical datasets available for the region. Diazotrophic cyanobacteria biovolume ranged across the lakes from 0 to 1,930,000 μm3 mL−1; Dolichospermum was the most common genus. Random forest models showed that 11 watershed, lake, and food web characteristics explained 76% of the variance in diazotrophic cyanobacteria biovolume among the sampled lakes. Structural equation models suggested that the drainage ratio (i.e., the relative area of the lake to the watershed) was positively related to phosphorus concentrations and, in turn, to diazotroph biovolume. Among lakes, hypolimnetic dissolved oxygen was negatively correlated with diazotroph biovolume, possibly due to the release of nutrients, like phosphate and iron, bound to sediments. In addition, zooplankton grazers were negatively related to diazotrophic cyanobacteria biovolume, potentially reflecting the influence of stocked fish. Thus, lake management must account for bottom‐up factors, such as nutrient loading, which is influenced by lake morphometry and watershed size, as well as top‐down factors, such as fish stocking, to effectively mitigate diazotrophic cyanobacterial blooms.

Recognition of intraglomerular histological features with deep learning in protocol transplant biopsies and its association with kidney function and prognosis

Abstract Background and hypothesis The Banff classification may not adequately address protocol transplant biopsies categorized as normal in patients experiencing unexplained graft function deterioration. This study seeks to employ convolutional neural networks to automate the segmentation of glomerular cells and capillaries and assess their correlation with transplant function. Methods 215 patients were categorized into three groups. In the Training cohort, glomerular cells, and capillaries from 37 patients were manually annotated to train the networks. The Test cohort (24 patients) compared manual annotations to automated predictions, while the Application cohort (154 protocol transplant biopsies) examined predicted factors in relation to kidney function and prognosis. Results In the test cohort, the networks recognized histological structures with Precision, Recall, F-score, and Intersection Over Union exceeding 0.92, 0.85, 0.89, and 0.74, respectively. Univariate analysis revealed associations between the estimated Glomerular Filtration Rate at biopsy and relative endothelial area (r=0.19, P=.027), endothelial cell density (r=0.20, P=.017), mean parietal epithelial cell area (r=-0.38, P<.001), parietal epithelial cell density (r=0.29, P<.001), and mesangial cell density (r=0.22, P=.010). Multivariate analysis retained only endothelial cell density as associated with estimated Glomerular Filtration Rate (Beta=0.13, P=.040). Endothelial cell density (r=-0.22, P=.010) and mean podocyte area (r=0.21, P=.016) were linked to proteinuria at biopsy. Over 44±29 months, 25 patients (16%) reached the primary composite endpoint (dialysis initiation, or 30% estimated Glomerular Filtration Rate sustained decline), with relative endothelial area, mean endothelial cell area, parietal epithelial cell density below medians linked to this endpoint (Hazard Ratios respectively of 2.63, P=.048, 2.60, P=.039, and 3.23, P=.019). Conclusion This study automated the measurement of intraglomerular cells and capillaries. Our results suggest that the precise segmentation of endothelial and epithelial cells may serve as a potential future marker for the risk of graft loss.

METABOLIC PROFILE OF 4F-MDMB-BICA IN HUMAN URINE

The purpose of the study is to study the metabolic profile of the cannabimimetic 4F-MDMB-BICA in the urine of consumers and to identify markers of 4F-MDMB-BICA use using gas chromatography with a mass spectrometric detector (GC-MS) and liquid chromatography with tandem mass spec-trometry (LC-MS/ MS) in the urine of consumers of psychoactive substances, for chemical-toxicological and forensic chemical analysis. Material and methods. The study of urine samples of 4F-MDMB-BICA cannabimimetic users using enzymatic hydrolysis in combination with solid-phase extraction and gas chromatography with mass spectrometric detection showed that the 4F-MDMB-BICA marker and, in some cases, its nor-metabolite are detected in the urine of consumers. The 4F-MDMB-BICA nor-metabolite is a common biotransformation product for the cannabimimetics 4F-MDMB-BICA and 5F-MDMB-PICA, resulting from the hydrolysis of the ester bond and N-dealkylation of the heterocyclic core. Results. 35 compounds were identified by liquid chromatography with tandem mass spectrometry, including the original parent 4F-MDMB-BICA, 31 metabolites of biotransformation phases I and II, and 3 metabolite artifacts; it was found that the latter are the product of intramolecular cyclization of 4F-MDMB-BICA metabolites. 16 previously undescribed compounds, including 15 metabolites and one artifact were identified. The putative structures, mass spectra and some characteristics of the identified compounds are given, including retention time, MRM transitions, and relative peak area of the substance. Conclusion. From the presented data, it can be seen that the main pathways of 4F-MDMB-BICA metabolism in the human body are hydrolysis of the ester group, hydroxylation of alkyl radicals and the heterocyclic core, oxidative defluorination, and N-dealkylation. The hydrolysis products of the ester bond and hydroxyl derivatives are subsequently conjugated with glucuronic acid.

How Does Artificial Intelligence Impact Green Development? Evidence from China

Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model and mediating effect model are constructed to test the spatial spillover effect and transmission mechanism between AI and green development. This study shows that (1) there is a significant inverted U shape in the impact of AI on green development; (2) the heterogeneity analysis finds that the structural dividend of AI is more obvious in capital-intensive and technology-intensive areas, which can more fully release its empowering effect on green development; (3) AI can not only directly affect green development but also indirectly affect green development by promoting green technology innovation and optimizing industrial structures, etc.; (4) AI has a significant inverted U-shaped spatial spillover effect on green development, and the development of local AI has a radiation-driven effect on the green development performance of its spatially related areas. The research methodology of this paper can be used for future research, and the results could provide support for the formulation of regional AI applications and green development policies.

Season-dependent physiological behavior of Miscanthus x giganteus growing on heavy-metal contaminated areas in relation to soil properties

Miscanthus x giganteus is often considered as a suitable plant species for phytomanagement of heavy metal polluted sites. Nevertheless, its physiological behavior in response to the level of metal toxicity throughout the growing season remains poorly documented. Miscanthus x giganteus was cultivated on three sites in Belgium (BSJ: non-polluted control; CAR: slightly contaminated; VM strongly polluted by Cd, Pb, Cu, Zn, Ni and As). The presence of Miscanthus improved soil biological parameters assessed by measurement of enzyme activity and basal soil respiration on the three considered sites, although to a lower level on VM site. Heavy metal accumulation in the shoot was already recorded in spring. It displayed a contrasting distribution in the summer leaves since heavy metals and As metalloid accumulated mainly in the older leaves of CAR plants while showing a uniform distribution among leaves of different ages in VM plants. Comparatively to plants growing on BSJ, net photosynthesis decreased in plants growing on CAR and VM sites. The recorded decrease was mainly related to stomatal factors in CAR plants (decrease in stomatal conductance and in Ci) but to non-stomatal factors such as decrease in carboxylation efficiency and non-photochemical quenching in VM plants. Stomata remained open in VM plants which presented lower instantaneous and intrinsic water use efficiencies than CAR and BSJ plants. High proportions of heavy metals accumulated in CAR plants were bound to the cell wall fraction while the soluble and organelle-rich fractions were proportionally higher in VM plants, leading to a decrease in cell viability and cell membrane damages. It is concluded that not only the intensity but also the nature of physiological responses in Miscanthus x giganteus may drastically differ depending on the pollution level.

Public access to documents in EU external relations

As the EU's competences and activities in the area of external relations expand, it becomes increasingly relevant for citizens and civil society to be able to access documents related to EU external action in order to effectively participate in the democratic life of the EU. Yet many of the documents held by EU institutions on external affairs potentially fall within the scope of the ‘international relations’ and ‘defence and military matters’ exceptions contained in the EU's Access to Documents Regulation 1049/2001. This contribution critically assesses the application of these exceptions by the Court of Justice of the EU, the Ombudsman and the EU institutions themselves, and identifies which rationales might warrant withholding access to specific types of documents that concern EU external relations. In light of the expansion of the EU's external competences, this article argues that EU institutions should properly substantiate any refusal to disclose a document in this field, and the CJEU should apply stronger judicial scrutiny of the use of these exceptions, in order to safeguard the fundamental right of access to documents, and safeguard the democratic legitimacy of decision-making in the EU.

The effect of fog on atmospheric electric fields

Naturally occurring electric fields exist in the atmosphere as part of the global electric circuit. These fields are produced by global phenomena, but they are strongly modified by local conditions. One such local effect is fog, which is the reduction in visibility by droplets suspended in the air. For over a century, it has been known that fog can often result in larger values of electric field compared to that in clear air conditions. However, the physical processes controlling the electric field and its variability in fog are still not fully understood. More detailed information on this is required to assess whether electric field measurements can be used as a fog prediction method, which was suggested many decades ago but is still an open question. Furthermore, improving the understanding of electrical processes in fog will also help in related areas of research such as cloud electrification processes and the impact of charge on the behaviour of aerosol and droplets.This study aims to fully characterise the electric field in fog using an extensive dataset of over 17 years, measured from the University of Reading Atmospheric Observatory, UK. This encompasses electric field, visibility, and meteorological data from over a hundred fog events, which allows us to examine the behaviour of electric fields in fog conditions statistically, rather than only focusing on individual case studies as is often the case in the literature. The long series of data from the site allows the variability in the electrical data during fog events to be more fully characterised than previously.

Modeling of runoff formation processes with aufeis feed in mountain cryosphere of the north-east of Russia

Aufeis are a key element in the chain of water exchange processes in the permafrost zone. The hydrological role of aufeis in the formation of river flow can be comparable to that of glaciers. Observations of aufeis during the construction of the Baikal-Amur Mainline showed that the share of aufeis runoff in the annual river flow can reach 35% for watersheds with an area of up to 500 km2. Despite the long history of studying aufeis and the results achieved, there are no methods for predicting the development of aufeis processes, as well as hydrological models that take into account the share of aufeis feeding in river runoff. This is due to the lack of observational data on the dynamics and development factors of aufeis. In the last century, long-term studies were carried out on some aufeis in Siberia and the Far East. One of these unique objects is the giant Anmangynda aufeis, which forms in the upper reaches of the Kolyma River basin. The aim of the research is to study the hydrological regime of the Anmangynda river basin and develop a method for accounting of aufeis runoff (module «Aufeis») in the distributed hydrological model «Hydrograph». The module “Aufeis” takes into account two factors of aufeis destruction – under the influence of solar radiation and thermal erosion destruction. The input data is the area of aufeis at the beginning of the warm season and daily meteorological data. The calculation parameters are the coefficients of ice melting and evaporation from ice, as well as the coefficient of the relative area of aufeis depending on the period of destruction, calculated for the modern climatic period. The result of the calculation is the aufeis runoff and the characteristics of aufeis for a given interval. Based on historical data and materials obtained during own field research, the module «Aufeis» was tested. The deviation of the calculated and observed values was 2—10% and 1—9% for the maximum values of the area and volume of the Anmangynda aufeis, respectively. Results of numerical calculations for the period 1967–2022 revealed the dynamics of the contribution of the Anmangynda aufeis to the river flow in different seasons. The expanded functionality of the Hydrograph model has improved the quality of modeling for rivers where aufeis form. For the river basin Anmangynda average Nash-Sutcliffe coefficient and annual runoff error were 0,57 and 13,0% compared to 0,41 and 18,0% when the module «Aufeis» was not used.

Mapping individual structural covariance network in development brain with dynamic time warping.

A conspicuous property of brain development or maturity is coupled with coordinated or synchronized brain structural co-variation. However, there is still a lack of effective approach to map individual structural covariance network. Here, we developed a novel individual structural covariance network method using dynamic time warping algorithm and applied it to delineate developmental trajectories of topological organizations of structural covariance network from childhood to early adulthood with a large sample of 655 individuals from Human Connectome Project-Development dataset. We found that the individual structural covariance network exhibited small-worldness property and the network global topological characteristics including small-worldness, global efficiency, local efficiency, and modularity linearly increase with age while the shortest path length linearly decreases with age. The nodal topological properties including betweenness and degree increased with age in language and emotion regulation related brain areas, while it decreased with age mainly in visual cortex, sensorimotor area, and hippocampus. Moreover, the topological attributes of structural covariance network as features could predict the age of each individual. Taken together, our results demonstrate that dynamic time warping can effectively map individual structural covariance network to uncover the developmental trajectories of network topology, which may facilitate future investigations to establish the links of structural co-variations with respect to cognition and disease vulnerability.

Selecting Questionnaire Items for a specific Population Post COVID

This paper describes a study of retired independent women in participative co-living. The objective of the study is to create an inventory questionnaire to be used in the selection of prospective members of a co-living arrangement in Canada. Qualitative research methods were used comprising textual analysis of participants’ journals and related documents like the Charter of the Babayagas House in Paris and documents in the public domain as well as established wellbeing questionnaires found in a literature search. Based on the qualities and characteristics uncovered, several items were generated which were found to align with items in well-established wellbeing questionnaires. Apart from the questionnaire items, related areas of concern are discussed. Overall, interesting aspects that would be valuable to researchers examining similar contexts emerged and comments on the relative importance and relevance of questions for a number of items are included in the findings. Aspects of the participants’ activities which were deemed relevant are also included.

Horan Valley Basin Geomorphological Aspects Assessment by Integrating Hypsometric Analysis with Remotely Sensed Morphometric Characteristics

The extraction, study, and accurate interpretation of the morphology database of a basin are the basic blocks for building a valid geomorphological understanding of this basin. In this work, a new approach is presented which is to use three different GIS based methods to extract databases with specific geographical information and then use the concept of information intersection to make a realistic geomorphological perspective for the study area. In the first method, data integration of remote sensing images from Google Map and SRTM DEM images were used to identify Horan basin borders. In the second method, the principle of data integration was represented by extracting the quantitative values of the morphometric characteristics that were affected by the geomorphological condition of the studied basin, such as the shape factor, circulation factor, and relief ratio, then eliciting an optimal conception of the geomorphological condition of the basin from the meanings and connotations of these combined transactions. The third method used the same principle by taking the optimal inferences from the integration of the interpretation of the values of the Hypsometry integration coefficients for each area in the basin separately with the integration value of the drawing curve for the relative heights of the basin areas with their relative areas. It was found, from the values of the coefficients, that the areas (A, B, C, D, and F) were still in the early stages of youth. Whereas the E region was in the maturity stage and the G region was in the monadnock stage of the geomorphological cycle. As for the integral value of the curve, it indicated 48. 559 % erosion from the surface of the basin only, and that its boundaries were subject to change and widening.

Two new methods for the estimation and interpretation of the range of feasible profiles in multivariate curve resolution and their implications to analytical chemistry

AbstractTwo new models have been recently introduced for studying the remaining rotational ambiguity in the bilinear decomposition of matrix data. One of the models is N‐BANDS, which yields two extreme profiles per sample component, corresponding to maximum or minimum signal contribution function or relative component area under its concentration profile. It is highly useful for computing the relative root mean square error due to rotational ambiguity in estimated analyte concentrations (RMSERA), which numerically quantifies the impact of the phenomenon in terms of prediction uncertainty. Since N‐BANDS successfully consider the presence of instrumental noise in the data, it is extremely useful for the analysis of real data sets. The other model is SW‐N‐BANDS, which is similar to N‐BANDS, but is applied in a sensor wise manner, that is, computing the maximum and minimum intensity value at each sensor. It provides the boundaries of the full set of feasible profiles, and helps to better understand the behavior of a given component under the application of several constraints. Both models are described in light of both simulations and experimental data, illustrating their main characteristics of importance to analytical chemistry studies.

Sensory gating and gaining in sleep: the balance between the protection of sleep and the safeness of life (a review).

Sleep is a brain state characterised by a low vigilance level and diminished consciousness. Reaction to and processing of external stimuli is attenuated in sleep. During sleep, the reticular thalamic nucleus reduces the flow of sensory activity to the cerebral cortex through inhibition of the thalamus. This sensory gating process facilitates sleep. After reaching the afferent layers of primary cortex, the reduced sensory flow is adjusted, gained, and processed within various cortical layers before being transferred by the corticofugal system back to appropriate subdivisions of the thalamus as feedback. Thalamic subdivisions then dispatch this sensory information to related areas of the cerebral cortex, where it is (sub)consciously perceived. When necessary, a sleeping individual can be awakened by a wake-up call, either by stimuli indicating danger, or by personally meaningful stimuli. It is safe for a sleeping individual that it can be aroused when necessary. Evidently, there are two processes by which the brain adjusts the response to sensory stimuli before entering (sub)consciousness. Firstly 'sensory gating', a process favourable to the maintenance of sleep by reducing the sensory input to the brain through the reticular thalamic nucleus and secondly 'sensory gaining', a process implying that the gained preserved sensory input is continuously analysed by the corticofugal system to detect dangerous and relevant environmental elements, indispensable for safeness and well-being of the sleeper.

High-speed target tracking control system based on short-time rotational reflection imaging.

High-speed tracking technology has wide applications in the military and aerospace industry. However, existing approaches, such as camera arrays or Doppler radar systems, suffer from high cost and inconvenience. This paper reports a high-speed target tracking control system based on short-time rotational reflection imaging, specifically aimed at overcoming certain limitations. In the system we designed, a high-speed camera coupled with a rotating reflector is used to achieve reliable high-speed target tracking. This paper first introduces the working principle and mathematical model of the system, then analyzes the key technologies, including motor response delay time and rotational speed curve fitting, and, finally, verifies the feasibility of the system and the correctness of the theory based on a series of experiments. Experimental results demonstrated that our work is efficient and accurate in target tracking and image clarity. The developed system demonstrates significant potential for widespread use across military and aerospace sectors. Furthermore, the insights gained from our investigation into key technologies could act as a reference point for fellow researchers in related scientific areas.