Complex Features Research Articles

Enhancing Semi-Supervised Semantic Segmentation of Remote Sensing Images via Feature Perturbation-Based Consistency Regularization Methods

In the field of remote sensing technology, the semantic segmentation of remote sensing images carries substantial importance. The creation of high-quality models for this task calls for an extensive collection of image data. However, the manual annotation of these images can be both time-consuming and labor-intensive. This has catalyzed the advent of semi-supervised semantic segmentation methodologies. Yet, the complexities inherent within the foreground categories of these remote sensing images present challenges in preserving prediction consistency. Moreover, remote sensing images possess more complex features, and different categories are confused within the feature space, making optimization based on the feature space challenging. To enhance model consistency and to optimize feature-based class categorization, this paper introduces a novel semi-supervised semantic segmentation framework based on Mean Teacher (MT). Unlike the conventional Mean Teacher that only introduces perturbations at the image level, we incorporate perturbations at the feature level. Simultaneously, to maintain consistency after feature perturbation, we employ contrastive learning for feature-level learning. In response to the complex feature space of remote sensing images, we utilize entropy threshold to assist contrastive learning, selecting feature key-values more precisely, thereby enhancing the accuracy of segmentation. Extensive experimental results on the ISPRS Potsdam dataset and the challenging iSAID dataset substantiate the superior performance of our proposed methodology.

Deviance Detection to Natural Stimuli in Population Responses of the Brainstem of Bats.

Deviance detection describes an increase of neural response strength caused by a stimulus with a low probability of occurrence. This ubiquitous phenomenon has been reported for humans and multiple other species, from subthalamic areas to the auditory cortex. Cortical deviance detection has been well characterized by a range of studies using a variety of different stimuli, from artificial to natural, with and without a behavioral relevance. This allowed the identification of a broad variety of regularity deviations that are detected by the cortex. Moreover, subcortical deviance detection has been studied with simple stimuli that are not meaningful to the subject. Here, we aim to bridge this gap by using noninvasively recorded auditory brainstem responses (ABRs) to investigate deviance detection at population level in the lower stations of the auditory system of a highly vocal species: the bat Carollia perspicillata (of either sex). Our present approach uses behaviorally relevant vocalization stimuli that are similar to the animals' natural soundscape. We show that deviance detection in ABRs is significantly stronger for echolocation pulses than for social communication calls or artificial sounds, indicating that subthalamic deviance detection depends on the behavioral meaning of a stimulus. Additionally, complex physical sound features like frequency- and amplitude modulation affected the strength of deviance detection in the ABR. In summary, our results suggest that the brain can detect different types of deviants already in the brainstem, showing that subthalamic brain structures exhibit more advanced forms of deviance detection than previously known.

Formation of the Lawn Hill Impact Structure

The Ordovician-aged Lawn Hill Impact Structure is perhaps unique among documented craters, in that it is largely filled with slumped, allochthonous Cambrian carbonates partly fluidised by the impact shock. It is a complex geological feature, with a thin horizon of suevite beneath carbonate sheets. The bulk of the impact ejecta material was either deposited on top of the mobilised carbonates, and not preserved owing to erosion, or is present beneath the base of current drilling. The geometry of the crater in its final form has been modelled in three dimensions using gravity data and dipole–dipole-induced polarisation geophysics, with control from an extensive drill-hole library. Post-impact modification, including terrace formation and carbonate fill, has resulted in an asymmetric ring syncline, which extends to depths >600 m. In the data-rich area of the Century zinc–lead–silver deposit, on the southwest margin of the crater, both intact and remnants of the crater rim and impact ejecta blanket were preserved above the deposit. The Proterozoic ore deposit is located on a detached terrace, which slumped into the crater and was buried by allochthonous carbonates from the Georgina basin. The sequence of events can be shown to comprise: impact with crater formation and injection of carbonate dykes and inter-thrust wedges in the crater walls with concurrent suevite deposits inside and surge deposits outside the crater; this was rapidly followed by slumping of carbonate mega-blocks transported by a fluidised carbonate slurry into the crater, concomitant with collapse of Proterozoic terraces; finally, collapse of the central crater uplift generated imbricate slices of Cambrian carbonate among mainly Proterozoic rocks inside the ring syncline.

Modern trends in the design of tourist and recreational complexes

The world experience of designing and building modern tourist and recreational zones is analyzed. The most famous examples of tourist and recreational facilities of the turn of the XX - XXI centuries are given . Similar temporal, stylistic, functional and planning features of tourist complexes are classified. Based on the performed analysis, conclusions are drawn about the prevailing trends in the design of recreational areas. It is revealed that an important design concept is to minimize the negative impact on the environment, preserve the surrounding nature and its rational use. Also important and necessary is the design of tourist and recreational areas of multifunctional purpose for a wide audience of tourists and their families, the all-season operation of facilities, the creation of a holistic artistic appearance in tourist complexes.

Mechanical advantage of subsidiary cells depends on the stomatal complex structure

Structural features of stomatal complexes influencing the mechanical advantage of subsidiary cells were studied. Light, scanning and transmission electron microscopy were used to research the morphology of Fagraea ceilanica stomatal complexes. Their guard cells are located on subsidiary cells. The walls of the subsidiary cells are thickened unevenly. The inner tangential walls are thin and most extensive as compared to other walls of these cells. The data obtained allowed a 3D model of F. ceilanica stomatal complex to be constructed. To elucidate the interaction between the guard and subsidiary cells during stomatal movements, we have applied modelling using the finite-element method. According to the modelling results, the mechanical advantage of the subsidiary cells depends on mutual location of the guard and subsidiary cells, uneven thickening, area and rigidity of the subsidiary cell walls and presence of substomatal chambers, into which the inner tangential walls of the subsidiary cells can bulge when turgor pressure in these cells increases. The modelling results were confirmed by the observed deformations in actual stomatal complex cells of F. ceilanica. The decrease in mechanical advantage of the subsidiary cells ensures stoma opening in the conditions of excessive saturation of the epidermis with water, for example, in the conditions of tropical rainforests.

Anatomical Dimensions and Types of the Anterior Clinoid Process in Libyan population by Using Computed Tomography: A cross Sectional Study

The anterior clinoid process (ACP) has a complex anatomical feature and surrounded by important structures which may complicate the surgical procedures during the anterior clinoidectomy. Our objective was to measure dimensions of anterior clinoid process, and to determine variations of its types. Forty-nine of cranial computed tomography scans were performed from adult Libyan patients on routine examination of paranasal sinus at Benghazi Medical Center between February 1, 2023, and April 14, 2023. The right and left ACP length and the base width were measured on the axial CT images. Data analysis was done using SPSS version 23. The means and standard deviations were calculated. The paired Student’s t test was used to determine the statistically significant differences in means between the right and left ACP. (P ≤ 0.05) was considered statistically significant at (95%) confidence interval. The length of the right and left ACPs were 11.2 ± 2.2 mm and 11.6 ± 2.4mm, respectively. The width of the right and left ACPs were 5.37± 1.22 mm and 5.42 ± 1.24mm, respectively. Type 2 anterior clinoid process was the commonest (75.5 %) while type 1 was the least common (4.1 %). There was no statistically significant difference between right and left sided measurements. Our study outcomes may be helpful for pre-operative planning. Presence of any variations may result in unnecessary injury to the complicated surgical procedures that involve removal of ACPs. Further research studies required with large sample size of population will give well understanding about the parameter of ACPs.

A unified pseudo-elastic model of continuous and discontinuous softening in the finite deformation of isotropic soft solids

Building on a recently devised approach of hyperelasticity with intrinsic softening, a new framework for capturing both continuous and discontinuous softening in the finite deformation of isotropic incompressible elastomers is considered here. The continuous and discontinuous softening effects of interest pertain to the loading and unloading paths, respectively, and the model is developed within the theory of pseudo-elasticity. The performance of the model in capturing these effects is then compared with the deformation behaviour of a variety of elastomers, including a range of hydrogels from truly independent double-network (t-DN) to tough and nanocomposite hydrogels, dielectric elastomers and carbon-black filled rubber specimens, under uniaxial and multiaxial deformations. The model is demonstrated to show a favourable simulation and prediction of the extant experimental data. The application of the model is then extended to capturing the softening behaviour under rate-dependent loading, based on a prior work by the authors incorporating the rate effects directly into the pseudo-hyperelastic model, by assuming that the model parameters evolve with the deformation rate. The capability of the model to capture the rate-dependent effects accurately will be shown by considering the rate-dependent behaviour of two hydrogel specimens. Given the simplicity of the functional form of the model, akin to a standard pseudo-elastic model, the low(er) number of model parameters, the ability to capturing the softening behaviour in the deformation of a wide range of elastomeric materials, and the easy extension for incorporating additional features such as the rate-effects, the proposed model provides another step towards the unification of various complex deformation features into a single modelling framework, for a more universal application to the finite deformation of soft solids.

Photoluminescence features of nickel-nitrogen complexes in Ib HPHT diamond matrix

The multiple color centers based on nickel-nitrogen complexes in synthetic Ib HPHT diamond matrices were optically characterized with an accent on a comparative analysis of temperature dependencies of photoluminescence spectra of two different Ni-N complexes with the phononless electronic transitions at 1.56 eV and 1.66 eV. The comparison shows that the intensities, widths, and energies of the bands of electronic and vibronic optical transitions, as well as temperature dependences of these parameters, measured in temperature range of 77–300 K, are essentially differ due to distinction in electron-vibrational interaction in the complexes.

Effect of localization–delocalization transition on thermoelectric properties of Bi2Te2Se topological insulator

The thermal transport properties of Bi2Te2Se topological insulators show a range of complex features. Large bulk resistivities coexisting with prominent Shubnikov–de Haas quantum oscillations and proximity to metallic states mark this p-band system as an unconventional topological quantum material. Here, using the density functional plus dynamical mean-field theory method, we show how localization–delocalization transition underpins the T-dependence of thermoelectric responses from room down to low temperatures. We present the implications of our many-particle analysis to resistivity, Seebeck coefficient, thermal conductivity, and Lorenz number and propose that related broadband systems close to electronic transitions could be of use in thermoelectrics.

A role of frontal association cortex in long-term object recognition memory of objects with complex features in rats.

Perirhinal cortex is a brain area that has been considered crucial for the object recognition memory (ORM). However, with the use of an ORM enhancer named RGS14414 as gain-in-function tool, we show here that frontal association cortex and not the Perirhinal cortex is essential for the ORM of objects with complex features that consisted of detailed drawing on the object surface (complex ORM). An expression of RGS14414, in rat brain frontal association cortex, induced the formation of long-term complex ORM, whereas the expression of the same memory enhancer in Perirhinal cortex failed to produce this effect. Instead, RGS14414 expression in Perirhinal cortex caused the formation of ORM of objects with simple features that consisted of the shape of object (simple ORM). Further, a selective elimination of frontal association cortex neurons by treatment with an immunotoxin Ox7-SAP completely abrogated the formation of complex ORM. Thus, our results suggest that frontal association cortex plays a key role in processing of a high-order recognition memory information in brain.

Analysis of E2F1 single-nucleotide polymorphisms reveals deleterious non-synonymous substitutions that disrupt E2F1-RB protein interaction in cancer

Cancer is a medical condition that is caused by the abnormal growth and division of cells, leading to the formation of tumors. The E2F1 and RB pathways are critical in regulating cell cycle, and their dysregulation can contribute to the development of cancer. In this study, we analyzed experimentally reported SNPs in E2F1 and assessed their effects on the binding affinity with RB. Out of 46, nine mutations were predicted as deleterious, and further analysis revealed four highly destabilizing mutations (L206W, R232C, I254T, A267T) that significantly altered the protein structure. Molecular docking of wild-type and mutant E2F1 with RB revealed a docking score of −242 kcal/mol for wild-type, while the mutant complexes had scores ranging from −217 to −220 kcal/mol. Molecular simulation analysis revealed variations in the dynamics features of both mutant and wild-type complexes due to the acquired mutations. Furthermore, the total binding free energy for the wild-type E2F1-RB complex was −64.89 kcal/mol, while those of the L206W, R232C, I254T, and A267T E2F1-RB mutants were −45.90 kcal/mol, −53.52 kcal/mol, −55.67 kcal/mol, and −61.22 kcal/mol, respectively. Our study is the first to extensively analyze E2F1 gene mutations and identifies candidate mutations for further validation and potential targeting for cancer therapeutics.

Induction motor bearing fault classification using deep neural network with particle swarm optimization‐extreme gradient boosting

AbstractIntelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, while complex feature extraction techniques can pose challenges for industry practitioners. Conversely, advanced feature extraction may not ensure that the model effectively learns these features for classification. A feature fusion approach that combines statistical and deep learning features to address these challenges is proposed. Since statistical features form the foundation for general feature extraction, statistical and deep learning features are combined using Extreme Gradient Boosting (XGBoost) algorithm with Particle Swarm Optimization (PSO). The PSO algorithm automates parameter tuning for XGBoost. A deep neural network (DNN) adaptively extracts hidden features, improving bearing fault classification precision using t‐SNE representation. Results successfully prove the DNN's ability to classify diverse motor faults using deep learning features. Thus, integrating statistical features with XGBoost further enhances DNN's performance. To ensure robustness, the proposed method has been compared with different motor fault classification methods and validated across different motor fault datasets, showcasing improved classification accuracy and robust performance, even amidst varying noise levels. This approach represents a promising advancement in intelligent fault diagnosis within industrial contexts.

The evolution of in vitro models of lung fibrosis: promising prospects for drug discovery.

Lung fibrosis is a complex process, with unknown underlying mechanisms, involving various triggers, diseases and stimuli. Different cell types (epithelial cells, endothelial cells, fibroblasts and macrophages) interact dynamically through multiple signalling pathways, including biochemical/molecular and mechanical signals, such as stiffness, affecting cell function and differentiation. Idiopathic pulmonary fibrosis (IPF) is the most common fibrosing interstitial lung disease (fILD), characterised by a notably high mortality. Unfortunately, effective treatments for advanced fILD, and especially IPF and non-IPF progressive fibrosing phenotype ILD, are still lacking. The development of pharmacological therapies faces challenges due to limited knowledge of fibrosis pathogenesis and the absence of pre-clinical models accurately representing the complex features of the disease. To address these challenges, new model systems have been developed to enhance the translatability of preclinical drug testing and bridge the gap to human clinical trials. The use of two- and three-dimensional in vitro cultures derived from healthy or diseased individuals allows for a better understanding of the underlying mechanisms responsible for lung fibrosis. Additionally, microfluidics systems, which replicate the respiratory system's physiology ex vivo, offer promising opportunities for the development of effective therapies, especially for IPF.

Predicting the Intention to Use Mobile Health Applications Among Young Adults in Malaysia

Today’s healthcare system faces several issues, including inefficiency, inequality, and inefficiency. Mobile devices with complex features and user-friendly interfaces have emerged as one of the most important ways to address concerns, opening up new mHealth in the mobile applications industry. Therefore, this study aims to investigate the variables affecting intention to use mobile health applications among young adults in Malaysia. This study used a purposive sampling technique with a correlational cross-sectional research design. The data utilised to empirically assess the suggested model, which is an extension of UTAUT, was collected through an online survey among mobile users. To analyse 312 data sets, smart partial least square was used. The findings revealed that the intention to use mobile health applications is favourably influenced by performance expectancy, facilitating conditions, and health consciousness. However, effort expectancy and social influence do not influence the intention to use mobile health applications. To boost the application of mHealth, it might be necessary for relevant authorities to recognise the obstacles to their use.

Rare Earth 2‐Methyl‐3‐furoate Complexes: Effect of Steric Hindrance on Corrosion Inhibitor Properties

AbstractThe preparation and characterization of six rare earth 2‐methyl‐3‐furoate (2m3fur) complexes are detailed in this study. Analysis through single‐crystal X‐ray diffraction, as well as powder XRD, reveals that all six compounds belong to one of two structural groups: {[RE2(2m3fur)6(EtOH)] ⋅ H2O}n (RE=La, Ce, Pr) and [RE3(2m3fur)9]n (RE=Y, Er, Yb). These structural groups feature carboxylate coordinated linear polymers. The former complexes have two distinct metal centers, one ten coordinate and one nine, with lattice water participating in a hydrogen bond with coordinated ethanol. The latter structures, involving erbium, ytterbium, and yttrium, have three unique metal centers: two eight coordinate, and one seven‐coordinated. In corrosion inhibition tests on mild steel in 0.01 M NaCl solution, the maximum efficiency was observed with [Y3(2m3fur)9]n at 59 %, which is less effective than yttrium 3‐furoate (90 % efficiency). The results suggest that the steric effect of the methyl group adversely affects corrosion inhibition.

Fourier feature decorrelation based sample attention for dense crowd localization

Crowd localization, which prevails to extract the independent individual features, plays an significant role in critical analysis for crowd scene. Dense trivial features of individual targets are frequently susceptible to interference from complex background features, which makes it difficult to obtain satisfactory predictions for individual targets. Aiming at this issue, a Fourier feature decorrelation based sample attention is proposed for dense crowd localization. The correlation between features are decoupled in the Fourier transform domain, which induces the model to focus more on the true correlation between individual target features and labels. From the perspective of Fourier feature correlation between samples, independence test statistic optimization with cross-covariance operator is developed for feature decorrelation within the sample attention framework. The sample attention with global weight learning is iteratively optimized through matching the prediction loss, which can induce model partial out the spurious correlation between target-irrelevant features and labels. Experimental results show that the method proposed in this paper outperforms the current advanced crowd location methods on public dense crowd datasets.

A Radar Echo Extrapolation Model Based on a Dual-Branch Encoder–Decoder and Spatiotemporal GRU

Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of existing methods to extract and characterize complex spatiotemporal features from radar echo images remains insufficient, resulting in suboptimal forecasting accuracy. This paper proposes a novel extrapolation algorithm based on a dual-branch encoder–decoder and spatiotemporal Gated Recurrent Unit. In this model, the dual-branch encoder–decoder structure independently encodes radar echo images in the temporal and spatial domains, thereby avoiding interference between spatiotemporal information. Additionally, we introduce a Multi-Scale Channel Attention Module (MSCAM) to learn global and local feature information from each encoder layer, thereby enhancing focus on radar image details. Furthermore, we propose a Spatiotemporal Attention Gated Recurrent Unit (STAGRU) that integrates attention mechanisms to handle temporal evolution and spatial relationships within radar data, enabling the extraction of spatiotemporal information from a broader receptive field. Experimental results demonstrate the model’s ability to accurately predict morphological changes and motion trajectories of radar images on real radar datasets, exhibiting superior performance compared to existing models in terms of various evaluation metrics. This study effectively improves the accuracy of precipitation forecasting in radar echo images, provides technical support for the short-range forecasting of precipitation, and has good application prospects.

Directed Test Generation for Hardware Validation: A Survey

The complexity of hardware designs has increased over the years due to the rapid advancement of technology coupled with the need to support diverse and complex features. The increasing design complexity directly translates to difficulty in verifying functional behaviors as well as non-functional requirements. Simulation is the most widely used form of validation using both random and constrained-random test patterns. The random nature of test sequences can cover a vast majority of scenarios, however, it can introduce unacceptable overhead to cover all possible functional and non-functional scenarios. Directed tests are promising to cover the remaining corner cases and hard-to-detect scenarios. Manual development of directed tests can be time-consuming and error-prone. A promising avenue is to perform automated generation of directed tests. In this article, we provide a comprehensive survey of directed test generation techniques for hardware validation. Specifically, we first introduce the complexity of hardware verification to highlight the need for directed test generation. Next, we describe directed test generation using various automated techniques, including formal methods, concolic testing, and machine learning. Finally, we discuss how to effectively utilize the generated test patterns in different validation scenarios, including pre-silicon functional validation, post-silicon debug, as well as validation of non-functional requirements.

Microsphere photolithography with dynamic angular spectra control for metasurface fabrication.

Microsphere photolithography (MPL) is a promising technique for cost-effective fabrication of large-scale metasurfaces. This approach generates an array of photonic jets by the collimated illumination of self-assembled microspheres. The photonic jets can be precisely steered within the unit cell defined by each microsphere by changing the angle of incidence. This allows for the creation of complex metasurface element geometries. Computer controlled articulation of the substrate relative to a static UV source allows the direct-write of different metasurface elements. However, this is time-consuming and requires registration between each exposure for complex features. This paper investigates a single exposure method with the dynamic continuous angle of incidence control provided by a Digital Micromirror Device (DMD) in the front Fourier plane of the projection system. The grayscale values of the DMD pixels can be adjusted to provide optical proximity correction. Larger patterns can be achieved by scanning the substrate relative to the exposure beam. This approach is demonstrated with the creation of hierarchical patterns. This work greatly simplifies the MPL exposure process for complex resonators and provides potential for full light field control.

Fe(III)-Based Phenylsilsesquioxane/Acetylacetonate Complexes: Synthesis, Cage-like Structure, and High Catalytic Activity.

Unprecedented iron-based silsesquioxane/acetylacetonate complexes were synthesized. The intriguing cage-like structure of compounds is alkaline metal-dependent: the Fe2Li2 complex includes condensed Si6-silsesquioxane and four acetylacetonate ligands; the Fe4Na4 complex exhibits two cyclic Si4-silsesquioxane and eight acetylacetonate ligands, while the Fe3K3 complex features two cyclic Si3-silsesquioxane and six acetylacetonate ligands. The latter case is the very first observation of small trimeric silsesquioxane ligands in the composition of cage-like metallasilsesquioxanes. The Fe4Na4-based complex exhibits a record high activity in the oxidation of inert alkanes with peroxides (55% yield of oxygenates in cyclohexane oxidation). It also acts as a catalyst in the cycloaddition of CO2 with epoxides, leading to cyclic carbonates in good yields (58-96%).