Dense Network Research Articles

Information Support or Emotional Support? Social Support in Online Health Information Seeking among Chinese Older Adults.

Online Health Information Seeking (OHIS) serves as an alternative form of social capital that can help older adults alleviate offline medical-related stress. This study collected and analyzed user interaction data from Patient-to-Doctor and Patient-to-Peer platforms and compared the roles of social support between them. Significant differences were identified in the dimensions of social support (information, emotional, and companion) on the Patient-to-Peer platforms compared with Patient-to-Doctor platforms (p < 0.05). The overall and core-core network density values for social support on Patient-to-Peer platforms were higher than those on Patient-to-Doctor platforms. Patient-to-Doctor interactions focused on information support, displaying a more centralized and efficient network with structural holes pertaining to treatment effects. By contrast, Patient-to-Peer interactions provided more emotional support, with a dispersed and redundant network containing structural holes related to individual information. Companion support was found to be weaker on both platforms. Additionally, digital literacy, surrogate seeking, and altruistic information significantly explained the variances between the two platforms (p < 0.01), with surrogate seeking playing a crucial role. These findings enhance our understanding of OHIS disparities among older adults and their surrogates, offering valuable insights for developing effective support systems and regulatory frameworks for health information platforms.

Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks.

To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network's feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time-frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time-frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness.

How Local Pollution and Transboundary Air Pollution Impact Air Quality in Jakarta?

Jakarta’s rapid population growth and industrialization have severely impacted air quality, with limited monitoring stations and increasingly complex pollutant sources influenced by meteorological factors. Source apportionment studies are critical for addressing urban air quality issues because knowing the source of pollution is an important step in overcoming urban air quality problems. This study uses the Geographically Weighted Regression (GWR) and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to analyze the distribution and sources of NO2, SO2, and CO in Jakarta and surrounding areas. Data includes spatial information from road networks, industries, and the energy sector during the wet (December) and dry (June) months of 2023. GWR results show that network density significantly contributes to pollutant concentrations, particularly during the dry month, while industrial and energy sectors display seasonal variations. The HYSPLIT model indicates that in December, air masses carrying pollutants mainly originate from the northwest and north, and in June, from the southeast and southwest. These patterns underscore the regional influence of transboundary air pollution from industrial and energy plants. The combined GWR and HYSPLIT findings provide a comprehensive understanding of air pollution dynamics and the critical role of meteorological factors in pollutant distribution.

Leukocyte-and Platelet-Rich Fibrin for enhanced tissue repair: an in vitro study characterizing cellular composition, growth factor kinetics and transcriptomic insights

BackgroundLeukocyte- and platelet-rich fibrin (L-PRF) is an autologous platelet concentrate, prepared by centrifugation of blood and consisting of a dense fibrin network with incorporated leukocytes and platelets. This study aims to perform an in-depth analysis of the cells, growth factors, and transcriptome of L-PRF.Methods and resultsFresh, 1 week and 2 weeks cultured human L-PRF membranes and liquid L-PRF glue were characterized on cellular and transcriptional level using flow cytometry (n = 4), single-cell RNA sequencing (n = 5) and RT-qPCR. Growth factor kinetics were investigated using ELISA (EGF, VEGF, PDGF-AB, TGF-β1, bFGF). L-PRF contained a large number of viable cells (fresh 97.14 ± 1.09%, 1 week cultured 93.57 ± 1.68%), mainly granulocytes in fresh samples (53.9 ± 19.86%) and T cells in cultured samples (84.7 ± 6.1%), confirmed with scRNA-seq. Monocytes differentiate to macrophages during 1 week incubation. Specifically arterial L-PRF membranes were found to release significant amounts of VEGF, EGF, PDGF-AB and TGF-β1.ConclusionWe characterized L-PRF using in vitro experiments, to obtain an insight in the composition of the material including a possible mechanistic role for tissue healing. This was the first study characterizing L-PRF at a combined cellular, proteomic, and transcriptional level.

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission.

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-importantly-the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Ecological Network Construction in High-Density Water Network Areas Based on a Three-Dimensional Perspective: The Case of Foshan City

The acceleration of urbanization has resulted in varying degrees of impact on the stability and health of high-density urban ecosystems. Building urban ecological networks is crucial for safeguarding biodiversity and sustaining ecosystem vitality. In this study, the city of Foshan was selected as the study area, which is a prime representative of a high-density water network city. Additionally, a morphological spatial pattern analysis was employed to identify the ecological source. We built an ecological resistance surface using geographic, natural, and behavioral elements, adjusting it based on the density of the water network and the building height. Following this, the circuit theoretical model was utilized to create an ecological network by identifying ecological corridors. There were three key findings. First, the ecological network consisted of 30 ecological source sites and 53 ecological corridors, and 103 ecological “pinch points” and 193 ecological barrier points were identified. Second, the ecological sources were predominantly situated in the southwestern and northern parts of Foshan City. Meanwhile, the suburbs of Foshan City contained the primary ecological barrier points, mainly stemming from new construction sites, while the key ecological “pinch points” were concentrated at river junctions. The third outcome was the recommendations to (a) boost the connectivity of the ecological network in the suburbs, (b) improve the connection of the water network in urban areas, and (c) focus on enhancing landscape connectivity. The objective was to develop approaches for optimizing urban ecological networks, leading to better connectivity and improved ecological network quality.

A Harmony-Based Approach for the Evaluation and Regulation of Water Security in the Yellow River Water-Receiving Area of Henan Province

Water security, as a crucial component of national security, plays a significant role in maintaining regional stability and ensuring the healthy and rapid development of the economy and society. The Yellow River water-receiving area of Henan Province (YRWAR-HN) is selected as the research area in this study. Firstly, a comprehensive evaluation index system is constructed based on the actual water security problems of the research area, and the single index quantification–multiple indices syntheses–poly-criteria integration method (SMI-P) is introduced to quantify the water security degree of 14 cities in the YRWAR-HN from 2010 to 2021. Then, the obstacle degree model is used to identify the key obstacle indexes that restrict the improvement of water security. Finally, the harmonious behavior set optimization method is adopted to carry out the regulation of water security, and the improvement path of water security in the YRWAR-HN is formulated. The results indicate the following: (1) the water security degree of the YRWAR-HN shows a fluctuating upward trend, increasing from 0.4348 (2010) to 0.6766 (2021), a significant rise of 55.61%. The water security level improves from the relatively unsafe level to the relatively safe level. Hebi City exhibits the fastest rate of water security improvement, while Xinxiang City shows the slowest rate. (2) The density of the river network (X1) and the proportion of investment in water conservancy and environmental protection in the total investment (X15) are the two indexes with the highest obstacle degree, with the average obstacle degrees being 15.09% and 10.79%, respectively. (3) The combination of the composite regulation scenario and improvement Path 2 is the optimal regulation strategy for water security in the YRWAR-HN. From the implementation process, Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu may prioritize improving their flood and drought disaster defense capabilities and emergency response capabilities, continuously enhancing the flood prevention and disaster reduction system. Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou may prioritize resolving the regional water supply and demand conflicts, balancing development and conservation, actively seeking transboundary and external water transfers, and strengthening the capacity for water conservation and intensive utilization. Puyang City may prioritize enhancing its comprehensive water environment management capabilities, increasing investment in water conservancy and the environment, improving production processes, reducing pollutant emissions, and mitigating agricultural non-point source pollution.

Enhanced diagnostic accuracy for multiple lung diseases using a fine-tuned MobileNetV2 model with advanced pre-processing techniques

Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping anatomical structures obscuring areas of interest, the presence of noise potentially masking abnormalities, and low contrast diminishing differentiation. In this research undertaking, we explored an enhanced MobileNetV2 approach to augmenting the accuracy of diagnosing multiple concomitant lung pathologies. We employed an inclusive methodology, incorporating several progressive steps. We leveraged contrast-limited adaptive histogram equalization to heighten the clarity of the dataset’s images. Bilateral filtering was applied to refine contrast and sharpness, along with utilizing a dense convolutional neural network. Additional techniques were utilized as well, such as image standardization, Gaussian blur, and histogram equalization, to further increase contrast and reduce noise. This was performed through rotations, scaling, horizontal flipping, brightness adjustment, elastic transformations, and random cropping and padding to generate more realistic exemplars. We defined and compiled a purpose-specific MobileNetV2 architecture for this research endeavor. The model was evaluated based on several metrics, including accuracy, precision, recall, F1-score, and specificity, after every epoch, to refine the training process. We used Bayesian optimization for more efficient fine-tuning of the model. Conditional random fields and ensemble averaging methods were employed for postprocessing. These enhancements led to superior results. After image enhancement, the model achieved an accuracy of 99.20 %, with a precision of 98.90 %, a recall of 99.10 %, and an F1-score of 99.00 %. The specificity of the predictions reached 99.40 %. These improvements assist medical workers in making more informed decisions. However, it is important to note that the current algorithm does not influence the effectiveness of treatment or recovery from the condition.

Effects of Flammulina velutipes powder on the emulsion properties of chicken myofibrillar protein under low-salt conditions.

This study aimed to investigate the influence of varying concentrations of Flammulina velutipes powder (FVP) (0, 4, 8, 12, 16, and 20 g L-1, based on FVP weight per liter of emulsion) on the stability, rheological properties, microstructure, and interfacial protein content of chicken myofibrillar protein (MP) emulsions under low-salt conditions. Visual assessment and the Turbiscan stability index revealed that the stability of MP emulsions improved with increasing FVP concentration. The greatest stability was achieved when the FVP concentration was 16 gL-1. The incorporation of FVP enhanced the elasticity and viscosity of the emulsions by forming a dense three-dimensional network structure. The droplet size of the emulsions initially decreased and then increased with increasing FVP concentration. The addition of FVP increased the amount of protein absorbed by the emulsion layer. Flammulina velutipes powder is promising as a stabilizer that could improve the emulsifying and functional properties of low-salt myofibrillar protein emulsions. © 2024 Society of Chemical Industry.

Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.

Face shape transfer via semantic warping

Face reshaping aims to adjust the shape of a face in a portrait image to make the face aesthetically beautiful, which has many potential applications. Existing methods 1) operate on the pre-defined facial landmarks, leading to artifacts and distortions due to the limited number of landmarks, 2) synthesize new faces based on segmentation masks or sketches, causing generated faces to look dissatisfied due to the losses of skin details and difficulties in dealing with hair and background blurring, and 3) project the positions of the deformed feature points from the 3D face model to the 2D image, making the results unrealistic because of the misalignment between feature points. In this paper, we propose a novel method named face shape transfer (FST) via semantic warping, which can transfer both the overall face and individual components (e.g., eyes, nose, and mouth) of a reference image to the source image. To achieve controllability at the component level, we introduce five encoding networks, which are designed to learn feature embedding specific to different face components. To effectively exploit the features obtained from semantic parsing maps at different scales, we employ a straightforward method of directly connecting all layers within the global dense network. This direct connection facilitates maximum information flow between layers, efficiently utilizing diverse scale semantic parsing information. To avoid deformation artifacts, we introduce a spatial transformer network, allowing the network to handle different types of semantic warping effectively. To facilitate extensive evaluation, we construct a large-scale high-resolution face dataset, which contains 14,000 images with a resolution of 1024 × 1024. Superior performance of our method is demonstrated by qualitative and quantitative experiments on the benchmark dataset.

Proteomic networks and related genetic variants associated with smoking and chronic obstructive pulmonary disease

BackgroundStudies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.MethodsLeveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.ResultsWe identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.ConclusionsIn this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Hysteretic response to different modes of ramping an external field in sparse and dense Ising spin glasses

We consider the hysteretic behavior of Ising spin glasses at T=0 for various modes of driving. Previous studies mostly focused on an infinitely slow speed Ḣ by which the external field H was ramped to trigger avalanches of spin flips by starting with destabilizing a single spin while few have focused on the effect of different driving methods. First, we show that this conventional protocol imposes a system size dependence. Then, we numerically analyze the response of Ising spin glasses at rates Ḣ that are fixed as well, to elucidate the differences in the response. Specifically, we compare three different modes of ramping (Ḣ=c/N, Ḣ=c/N, and Ḣ=c for constant c) for two types of spin glass systems of size N, representing dense networks by the Sherrington–Kirkpatrick model and sparse networks by the lattice spin glass in d=3 dimensions known as the Edwards Anderson model. Depending on the mode of ramping, we find that the response of each system, in form of spin-flip avalanches and other observables, can vary considerably. In particular, in the N-independent mode applied to the lattice spin glass, which is closest to experimental reality, we observe a percolation transition with a broad avalanche distribution between phases of localized and system-spanning responses. We explore implications for combinatorial optimization problems pertaining to sparse systems.

Social networks of threatened Chaco tortoises (Chelonoidis chilensis) in the wild

Abstract We studied encounters between Chaco tortoises (Chelonoidis chilensis) that are in a threatened state of conservation in Patagonia, Argentina, which constitutes the southern limit of their geographical distribution. We monitored six individuals during different behavioural periods of the year (mating, nesting, and brumation), recording 24 344 encounters after tagging the animals with GPS tags. Constructing bipartite networks of burrows and tortoises, in addition to their projections, we discovered a spatial clustering of the burrows. Moreover, our research revealed that as tortoises approach the brumation period they use fewer unique burrows, engage in less burrow sharing, and increasingly favour spending time in their preferred burrow. During the mating and nesting periods some burrows are shared, whereas this was not the case during the brumation period. We studied the daily interactions through proximity-based networks, accounting for the daytime encounters during the different annual behavioural periods. We found the highest network density during the mating period, decreasing to zero, as expected, for the brumation period, when tortoises remain in their burrows during both night and day. Male–male interactions were more probable during the post-brumation and mating periods. Moreover, the number of daily encounters after sharing a burrow for a given night was found to be significant, which could be an indication of social behaviour and memory. Performing numerical simulations, we found that the proximity-based network densities were much higher than expected by chance, which could be attributable to individual selection of preferred sites or could be indicative of social behaviour among these threatened reptiles.

Analyzing the impact of conversation structure on predicting persuasive comments online

The topic of persuasion in online conversations has social, political and security implications; as a consequence, the problem of predicting persuasive comments in online discussions is receiving increasing attention in the literature. Following recent advancements in graph neural networks, we analyze the impact of conversation structure in predicting persuasive comments in online discussions. We evaluate the performance of artificial intelligence models receiving as input graphs constructed on the top of online conversations sourced from the “Change My View” Reddit channel. We experiment with different graph architectures and compare the performance on graph neural networks, as structure-based models, and dense neural networks as baseline models. Experiments are conducted on two tasks: (1) persuasive comment detection, aiming to predict which comments are persuasive, and (2) influence prediction, aiming to predict which users are persuasive. The experimental results show that the role of the conversation structure in predicting persuasiveness is strongly dependent on its graph representation given as input to the graph neural network. In particular, a graph structure linking only comments belonging to the same speaker in the conversation achieves the best performance in both tasks. This structure outperforms both the baseline model, which does not consider any structural information, and structures linking different speakers’ comments with each other. Specifically, the F1 score of the best performing model is 0.58, which represents an improvement of 5.45% over the baseline model (F1 score of 0.55) and 7.41% over the model linking different speakers’ comments (F1 score of 0.54).

Three-dimensional grid-free sound source localization method based on deep learning

Sound source localization (SSL) technology is a popular method for identifying the locations of noise sources, which serves as a prerequisite for noise control. Deep learning, as a data-driven tool, shows broad perspectives in the field of SSL with its powerful nonlinear fitting ability. The existing deep learning-based SSL methods only provide a two-dimensional (2D) representation of the sound source location and cannot obtain the specific coordinates of the sound source in three-dimensional (3D) space. Although traditional beamforming methods can be directly generalized to 3D scenes in principle, they suffer from the limitations of insufficient vertical resolution and high computational cost. Therefore, a 3D grid-free SSL method (3DGF) informed by deep learning is suggested in this study to enhance the accuracy and computational efficiency of 3D localization. First, the number of data channels is compressed to respect limited memory resources during the training process. Subsequently, a dense convolutional neural network (DenseNet) model is utilized to obtain the 3D spatial coordinates of the sound source using the processed 3D beamforming map as input. Since the coordinates are continuous and are not constrained by the grid of the beamforming map, the grid-free strategy presents more accurate localization results. Then, the effects of the volume of training data and the compression ratio are analyzed, respectively, in simulation, and the localization performance with different signal-to-noise ratios (SNRs) is also tested. Finally, by comparing 3DGF with DAMAS, both simulation and experimental results demonstrate that 3DGF improves the accuracy and efficacy of 3D localization. Meanwhile, its satisfactory generalization ability and robustness against noise highlight its potential for practical applications.

Effect of microstructure rafting on deformation behaviour and crack mechanism during high-temperature low-cycle fatigue of a Ni-based single crystal superalloy

The low cycle fatigue behaviours of a microstructure rafting Ni-based single crystal superalloy have been experimentally investigated at 980 ℃. Deformation of γ/γ’ phases and the corresponding dislocation configurations were investigated, highlighting rafting γ/γ’ morphology that contributes to crack initiation and propagation, as well as macro-scale accumulated plastic strain. Unlike the discrete slip lines of a virgin superalloy, intense slips developed along the parallel {111} slip plane result in crossed slip bands in the rafting superalloy. The decreased resistance of widened γ channels to dislocation movement, along with the prevention of dislocation cutting through γ’ precipitates in pre-existing dense dislocation networks, facilitates crack propagation in the γ channel in the slightly rafting superalloy. As the rafting state increases, the dislocation network loses its protective effect by reducing coherency stress and acting as a superdislocation source, which facilitates crack propagation along the γ/γ’ interface. Finally, a microstructure-based fatigue model is developed considering the reduction of deformation resistance induced by rafting. The fatigue loading control mode effect is introduced by a combination of resolved shear stress and tensile stress effects on crack initiation. The LCF life of rafting Ni-based superalloys significantly decreases under stress-controlled conditions compared to strain-controlled conditions due to the increase in cumulative plastic strain. However, the insignificant impact of the initial surface oxide layer on LCF life is revealed.

Comparison of facial skin ageing in healthy Asian and Caucasian females quantified by in vivo line-field confocal optical coherence tomography 3D imaging.

Quantitative biomarkers of facial skin aging were investigated in 109 healthy Asian female volunteers, aged 20 to 70 years. In vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging, enhanced by Artificial Intelligence (AI)-based quantification algorithms, was utilized to compute various metrics, including stratum corneum thickness (SC), viable epidermal (VE) thickness, and Dermal-Epidermal Junction (DEJ) undulation along with cellular metrics for the temple, cheekbone, and mandible. Comparison with data from a cohort of healthy Caucasian volunteers revealed similarities in the variations of stratum corneum and viable epidermis layers, as well as cellular shape and size with age in both ethnic groups. However, specific findings emerged, such as larger, more heterogeneous nuclei in both layers, demonstrated by an increase in nuclei volume and their standard deviation, and increased network atypia, all showing significant age-related variations. Caucasian females exhibited a flatter and more homogeneous epidermis, evidenced by a decreased standard deviation of the number of layers, and a less dense cellular network with fewer cells per layer, indicated by a decrease in cell surface density. Ethnicity-wise comparisons highlighted distinct biological features specific to each population. Asian individuals showed significantly higher DEJ undulation, higher compactness, and lower cell network atypia compared to their Caucasian counterparts across age groups. Differences in stratum corneum and viable epidermal thickness on the cheekbone were also significant. LC-OCT 3D imaging provides valuable insights into the aging process in different populations and underscores inherent biological differences between Caucasian and Asian female volunteers.

A comparison of smartphone and infrasound microphone data from a fuel air explosive and a high explosive.

For prompt detection of large (>1 kt) above-ground explosions, infrasound microphone networks and arrays are deployed at surveyed locations across the world. Denser regional and local networks are deployed for smaller explosions, however, they are limited in number and are often deployed temporarily for experiments. With the expanded interest in smaller yield explosions targeted at vulnerable areas such as population centers and key infrastructures, the need for more dense microphone networks has increased. An "attritable" (affordable, reusable, and replaceable) and flexible alternative can be provided by smartphone networks. Explosion signals from a fuel air explosive (thermobaric bomb) and a high explosive with trinitrotoluene equivalent yields of 6.35 and 3.63 kg, respectively, were captured on both an infrasound microphone and a network of smartphones. The resulting waveforms were compared in time, frequency, and time-frequency domains. The acoustic waveforms collected on smartphones produced a filtered explosion pulse due to the smartphone's diminishing frequency response at infrasound frequencies (<20 Hz) and was found difficult to be used with explosion characterization methods utilizing waveform features (peak overpressure, impulse, etc.). However, the similarities in time frequency representations and additional sensor inputs are promising for other explosion signal identification and analysis. As an example, a method utilizing the relative acoustic amplitudes for source localization using the smartphone sensor network is presented.

Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification

ABSTRACTEnsuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. This study introduces an advanced approach for automated rice plant disease detection and classification by integrating deep learning and metaheuristic optimization techniques. Specifically, a deep dense neural network (DNN) is employed for its capacity to capture intricate patterns in images and extreme learning machine (ELM) for classification. To enhance the optimization process, an innovative variant of the Shuffled Shepherd Optimization (SSO) algorithm, known as Enhanced Artificial Shuffled Shepherd Optimization (EASSO), is introduced. EASSO incorporates adaptive strategies and enhanced exploration–exploitation mechanisms, enabling more efficient convergence during the training of the DNN. The proposed system operates by processing high‐resolution images of rice plants through the DNN, extracting nuanced features indicative of various diseases, including blast, bacterial blight, and brown spots. EASSO optimizes the DNN's parameters, maximizing its accuracy in disease classification. The synergy between DNN and EASSO ensures a robust and adaptive model capable of handling diverse and complex disease patterns. This automated approach significantly reduces the reliance on manual inspection, enabling timely intervention and improving overall agricultural productivity. Experimental results demonstrate the superiority of the DNN‐EASSO framework over traditional methods, showcasing higher accuracy rates and faster convergence. The incorporation of Enhanced Artificial Shuffled Shepherd Optimization enhances the precision and reliability of disease classification, making this integrated system a valuable tool for farmers and agricultural practitioners. This research represents a significant stride toward sustainable agriculture, showcasing the potential of advanced technologies in ensuring food security worldwide.