Search Results (433)

A new desmid microalga species, Cosmarium yassinii A.A. Saber, El-Sheekh, Kouwets et Cantonati sp. nov., was isolated from two hyper-arid mountain valleys, so-called “wadis”, in the Eastern Desert of Egypt. The distinctive morphological features of this new species were established using light and scanning electron microscopy observations, and also by documenting its life-cycle stages. Taxonomically, C. yassinii is characterized by a cell wall sculpture consisting of isolated granules or small warts arranged circularly in the swollen mid-region of each semicell, never forming parallel vertical ridges or costae as in morphologically similar species, and the interesting shape of the marginal granules appears as small emarginate “combs” or crenae, including its knobby zygospores. Similarities and differences with the morphologically most closely related species are discussed in detail. Ecologically, C. yassinii seems to prefer alkaline freshwater environments with lower nutrient concentrations and a NaCl/HCO₃ water type. The detailed assessment and documentation of the biodiversity of these peculiar freshwater ecosystems are a fundamental prerequisite to adequately inform their protection strategies. Full article

Background: Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder, and its exact causes are currently unknown. Neuroimaging research suggests that its clinical features are closely linked to alterations in brain functional network connectivity, yet the specific patterns and mechanisms underlying these abnormalities require further clarification. Methods: We recruited 36 children with ASD and 36 age- and sex-matched typically developing (TD) controls. Resting-state EEG data were used to construct static and dynamic low- and high-order functional networks across four frequency bands (δ, θ, α, β). Graph-theoretical metrics (clustering coefficient, characteristic path length, global efficiency, local efficiency) and state entropy were applied to characterize network topology and dynamic transitions between integration and segregation. Additionally, between-frequency networks were built for six band pairs (δ-θ, δ-α, δ-β, θ-α, θ-β, α-β), and network global measures quantified cross-frequency interactions. Results: Low-order networks in ASD showed increased δ and β connectivity but decreased θ and α connectivity. High-order networks demonstrated increased δ connectivity, reduced α connectivity, and mixed alterations in θ and β. Graph-theoretical analysis revealed pronounced α-band topological disruptions in ASD, reflected by a lower clustering coefficient and efficiency and higher characteristic path length in both low- and high-order networks. Dynamic analysis showed no significant entropy changes in low-order networks, while high-order networks exhibited time- and frequency-specific abnormalities, particularly in δ and α (0.5 s window) and δ (6 s window). Between-frequency analysis showed enhanced β-related coupling in low-order networks but widespread reductions across all band pairs in high-order networks. Conclusions: Young children with ASD exhibit coexisting hypo- and hyper-connectivity, disrupted network topology, and abnormal temporal dynamics. Integrating hierarchical, dynamic, and cross-frequency analyses offers new insights into ASD neurophysiology and potential biomarkers. Full article

The ultimate objective of research on hypernetwork robustness is to enhance its capability to withstand external attacks and natural disasters. For hypernetworks such as telecommunication networks, public safety networks, and military networks—where security requirements are extremely high—achieving higher communication robustness is essential. This study integrates the structural characteristics of hypernetworks with an optimization method for communication robustness by combining four key indicators: hyper-betweenness centrality, hyper-centrality of feature subgraph, hyper-centrality of Fiedler, and hyperdistance entropy. Using the best improvement performance ($BIP\_T$) as the evaluation metric, simulation experiments were conducted to comparatively analyze the effectiveness of these four indicators in enhancing the communication robustness of Barabási–Albert (BA), Erdos–Renyi (ER), and Newman–Watts (NW) hypernetworks, and theoretically derives the hyperedge addition threshold $\theta$. The results show that all four indicators effectively improve the communication robustness of hypernetworks, although with varying degrees of optimization. Among them, hyper-betweenness centrality demonstrates the most significant optimization effect, followed by hyper-centrality of feature subgraph and hyper-centrality of Fiedler, while hyperdistance entropy exhibits a relatively weaker effect. Furthermore, these four indicators and the proposed communication robustness optimization method exhibit strong generalizability and have been effectively applied to the WIKI-VOTE social hypernetwork. Full article

Sensory processing differences, reported in up to 97% of individuals with autism spectrum disorder (ASD), are increasingly recognized as a defining feature of the condition, shaping perception, cognition, and adaptive behavior. Atypical sensory responsivity, ranging from hyper- to hypo-reactivity and sensory seeking, emerges early in development and contributes to the clinical and neurobiological heterogeneity of autism. Alterations in neural connectivity, the balance of excitation and inhibition, and multisensory integration are thought to underlie these sensory profiles, influencing emotional regulation, attention, and social interaction. Sensory features also interact with co-occurring conditions such as anxiety, attention deficit hyperactivity disorder, and sleep and feeding difficulties, thereby shaping developmental trajectories and influencing adaptive behavior. Clinically, these sensory dysfunctions have a significant impact on daily participation and quality of life, extending their effects to family functioning. Understanding individual sensory phenotypes is therefore essential for accurate assessment and personalized intervention. Current therapeutic approaches include Sensory Integration Therapy, Sensory-Based Interventions, Sequential Oral Sensory Approach, and structured physical activity programs, often complemented by behavioral and mindfulness-based techniques. Emerging neuroplasticity-oriented methods for targeted modulation of sensory processing networks include neurofeedback and non-invasive brain stimulation. Overall, current evidence highlights the central role of sensory processing in ASD and underscores the need for multidisciplinary, individualized approaches to optimize developmental trajectories and enhance adaptive functioning. This review provides an updated synthesis of sensory processing in ASD, integrating neurobiological, developmental, and clinical evidence to highlight established knowledge, unresolved questions, and priorities for future research. Full article

Neuroinflammation is a common pathophysiological feature of many disorders affecting the central nervous system, including migraine—one of the most prevalent neurological conditions, which significantly impairs quality of life, particularly when it progresses to the chronic form. The aim of the present study was to analyze oxidative changes following a single administration of nitroglycerin (NTG), as well as to investigate alterations in the glial microenvironment and inflammatory processes induced by chronic NTG administration. Registration of biosensor signals (HyPer7 and SypHer3s) in vivo did not reveal changes in hydrogen peroxide levels or pH following single NTG administration in striatum and cortex. In contrast, analysis of chronic NTG administration indicates neuroinflammatory processes occurring in the thalamus and the dentate gyrus of the hippocampus, but not in the somatosensory cortex without disruption of the BBB and decreased degranulation of meningeal mast cells. We observed a decrease in the mRNA expression in the thalamic tissue of the neuroprotective transforming growth factor beta 1 gene and an increase in the expression of the pro-inflammatory interferon gamma. The regional specificity of neuroinflammation supports the suggestion that maladaptive changes in these structures could play a critical role in the transition from episodic to chronic migraine. Full article

This study introduces a controlled laboratory setup for hyperspectral longwave infrared (LWIR) imaging of soils, designed to bridge the gap between laboratory measurements and remote sensing observations. A Fourier-transform hyperspectral LWIR imaging spectrometer (Telops Hyper-Cam LW) was employed, together with a specialized heating plate, rigorous calibration protocols, and a Spatial Averaging Before Blackbody Fitting (SABBF) method to enable accurate LWIR indoor measurements. Unlike established laboratory techniques that measure reflectance and calculate emissivity indirectly, this setup enables direct passive measurement of soil emissivity, replicating airborne and spaceborne LWIR measurements of the surface. The emissivity spectra of 12 variable soil samples obtained with the lab setup were compared and validated based on LWIR Hyper-Cam LW spectra acquired under outdoor conditions, then were subsequently analyzed to determine the mineral composition of each sample. Spectral features and indices were used to estimate the relative content of quartz, clay minerals, and carbonates, from the most to least abundant. The results demonstrate that the laboratory-based setup preserves spectral fidelity while offering improved repeatability, scheduling flexibility, and reduced dependence on weather. Beyond replicating outdoor measurements, this controlled setup is easy to install and provides a reproducible framework for LWIR soil spectroscopy that could be considered for standard laboratory protocols, enabling reliable mineral identification, calibration/validation of airborne and satellite LWIR data, and broader applications in soil monitoring and environmental remote sensing. Full article

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder, characterised by the build-up of amyloid beta (Aβ) plaques and neurofibrillary tangles comprising hyper-phosphorylated tau. Increasing evidence indicates that in the early stages of AD, elevated levels of oligomeric forms of Aβ and phosphorylated tau (p-tau) gives rise to impaired synaptic function which ultimately drives AD-associated cognitive abnormalities. Thus, developing drugs that can limit the synaptic impairments that occur early in AD may have therapeutic benefits. Clinical evidence increasingly supports a link between lifestyle choices and AD risk. Indeed, there is an association between the circulating levels of the metabolic hormone leptin, mid-life obesity and disease risk, which has in turn stimulated interest in targeting the leptin system to treat AD. It is well-established that leptin readily accesses the brain, with the hippocampus, a key region that degenerates in AD, identified as a prime target for this hormone. Within the hippocampus, leptin has cognitive enhancing properties as it markedly influences the cellular events underlying hippocampal-dependent learning and memory, with significant impact on synaptic plasticity and trafficking of glutamate receptors at hippocampal excitatory CA1 synapses. Moreover, studies using a range of cell-based systems and animal models of disease indicate not only that leptin has powerful pro-cognitive effects, but also that leptin protects against the unwanted synapto-toxic effects of Aβ and tau, as well as enhancing neuronal cell viability. Moreover, recent studies have demonstrated that smaller leptin-based molecules replicate the full repertoire of protective features of whole leptin. Here we review the evidence that the leptin system is a potential novel avenue for drug discovery in AD. Full article

With the large-scale integration of wind power into the grid, the accuracy of wind farm cluster power prediction has become a key factor for the sustainability of modern power systems. Reliable ultra-short-term forecasts support the secure dispatch of high-penetration renewable energy, reduce wind curtailment, and improve the low-carbon and economical operation of power systems. Aiming at the problem of significant differences in wind turbine characteristics, this paper proposes a prediction method based on an improved density-based spatial clustering of applications with noise (DBSCAN) and a hybrid deep learning model. First, the wind speed signal is decomposed at multiple scales using successive variational modal decomposition (SVMD) to reduce non-stationarity. Subsequently, the DBSCAN parameters are optimized by the fruit fly optimization algorithm (FOA), and dimensionality reduction is performed by principal component analysis (PCA) to achieve efficient clustering of wind turbines. Next, the representative turbines with the highest correlation are selected in each cluster to reduce computational complexity. Finally, the SVMD-TCN-BiGRU-MSA-GJO hybrid model is constructed, and long-term dependence is extracted using a temporal convolutional network (TCN); the temporal features are captured by bidirectional gated recurrent units (BiGRUs); the feature weights are optimized by a multi-head self-attention mechanism (MSA), and the hyper-parameters are, in turn, optimized by golden jackal optimization (GJO). The experimental results show that this method reduces the MAE, RMSE, and MAPE by 14.02%, 12.9%, and 13.84%, respectively, and improves R² by 3.9% on average compared with the traditional model, which significantly improves prediction accuracy and stability. These improvements enable more accurate scheduling of wind power, lower reserve requirements, and enhanced stability and sustainability of power system operation under high renewable penetration. Full article

This study addresses the critical issue of high casualty rates in frontal collisions by proposing structural optimization methods for the energy-absorbing box of lightweight electric vehicles. A small pure electric car was selected as the research object. A finite element model for frontal collision was established in HyperMesh and solved using the LS-DYNA explicit dynamics solver. The parameters such as the acceleration of the B-pillar of the vehicle, the compression distance of the energy absorption box and the energy absorption are analyzed in this study. Energy absorption was used as the primary crashworthiness indicator while ensuring that the peak collision force, compression distance of the energy-absorbing box, and acceleration of the B-pillar complied with safety standards. Results demonstrate that Scheme 2 (featuring reduced wall thickness and a single induced groove) outperformed other designs, increasing energy absorption by 3% and reducing mass by 17% compared to the baseline model. This conclusion can provide a reference basis for the subsequent vehicle collision analysis. Full article

With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external failure, and, last, avoid any undesirable consequences. However, achieving accurate prediction of RUL is complicated for EV applications due to various reasons such as the complex operational characteristics, dynamic changes in the model parameters during the aging process, extraction of battery parameters, data preparation, and hyper-parameter tuning of the predictive model. This research proposes a novel approach that integrates Particle Swarm Optimization (PSO) with a multi-model technique for RUL prediction. The framework integrates many machine learning (ML) models and deep learning (DL) models. Combining domain knowledge, advanced optimization techniques, and learning models to make high-accuracy RUL predictions reduces maintenance costs and improves battery management systems. This study uses domain-driven feature engineering to extract battery-specific indicators, including voltage drops, charging time, and temperature fluctuations, to increase model accuracy. Among the evaluated models, LSTM demonstrates superior performance, achieving a mean absolute error (MAE) of 0.34, a root mean square error (RMSE) of 0.76, and an R² of 0.93, providing the best results in RUL prediction. The proposed research uniquely integrates PSO-based optimization with domain-driven feature engineering across multiple machine learning and deep learning models, demonstrating a unified and novel approach that significantly improves the prediction accuracy of RUL in LIBs. Full article

Background: Hypospadias is often associated with abnormal prepuce development. Investigating the differences between the inner prepuce of hypospadias patients and normal controls at the transcriptomic level and histological characteristics helps to reveal the causes of its developmental abnormalities or implement targeted treatments. Materials and Methods: Dorsal and ventral inner preputial tissues were collected from 31 hypospadias patients and 21 phimosis children (controls). Differences in gene expression between the two groups were studied via transcriptomic sequencing and enrichment analysis. Corresponding histological features were further validated by histological staining. Results: Transcriptomic sequencing results showed that, compared to the control group, the dorsal inner prepuce of the hypospadias group had 97 upregulated and 10 downregulated genes; the ventral prepuce had 140 upregulated and 99 downregulated genes. Among all upregulated genes, 44 were closely related to fibrosis. Other significantly enriched terms included cornified envelope formation, efferocytosis, C-type lectin receptor signaling pathway, and complement and coagulation cascades. Histological validation revealed that the dorsal inner prepuce of hypospadias children contained more collagen fibers, a higher ratio of type I/III collagen, and lower microvessel density, showing some correlation with the severity of hypospadias. Conclusions: This study demonstrated a hyper-fibrotic state in the inner prepuce of hypospadias, which may significantly impact post-operative wound healing and complications. Full article

Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and fast Fourier transform (FFT) for extracting comprehensive temporal–spectral characteristics from vibration data. The methodology employs a hybrid deep learning architecture integrating convolutional neural networks (CNNs) with Transformers, where CNNs identify local features while Transformers capture extended dependencies. Meta-learning-enhanced Bayesian optimization and HyperBand (Meta-BOHB) is utilized for efficient hyperparameter selection. Evaluation on the Case Western Reserve University (CWRU) dataset using 5-fold cross-validation demonstrates a mean classification accuracy of 99.91% with exceptional stability (±0.08%). Comparative analysis reveals superior performance regarding precision, convergence rate, and loss metrics compared to existing approaches. Cross-dataset validation using Mechanical Fault Prevention Technology (MFPT) and Paderborn University (PU) datasets confirms robust generalization capabilities, achieving 100% and 98.75% accuracy within 5 and 7 iterations, respectively. Ablation studies validate the contribution of each component. Results demonstrate consistent performance across diverse experimental conditions, indicating significant potential for enhancing reliability and reducing operational costs in industrial fault diagnosis applications. The proposed method effectively addresses key challenges in bearing fault detection through advanced signal processing and optimized deep learning techniques. Full article

Despite the superior performance of existing anomaly detection methods, they are often limited to single-class detection tasks, requiring separate models for each class. This constraint hinders their detection performance and deployment efficiency when applied to real-world multi-class data. In this paper, we propose a dynamic visual adaptation framework for multi-class anomaly detection, enabling the dynamic and adaptive capture of features based on multi-class data, thereby enhancing detection performance. Specifically, our method introduces a network plug-in, the Hyper AD Plug-in, which dynamically adjusts model parameters according to the input data to extract dynamic features. By leveraging the collaboration between the Mamba block, the CNN block, and the proposed Hyper AD Plug-in, we extract global, local, and dynamic features simultaneously. Furthermore, we incorporate the Mixture-of-Experts (MoE) module, which achieves a dynamic balance across different features through its dynamic routing mechanism and multi-expert collaboration. As a result, the proposed method achieves leading accuracy on the MVTec AD and VisA datasets, with image-level mAU-ROC scores of 98.8% and 95.1%, respectively. Full article

The rapid evolution of 5G and emerging 6G networks has increased system complexity, data volume, and security risks, making anomaly detection vital for ensuring reliability and resilience. However, existing machine learning (ML)-based approaches still face challenges related to poor generalization, weak temporal modeling, and degraded accuracy under heterogeneous and imbalanced real-world conditions. To overcome these limitations, a hybrid time series transformer–deep belief network (HTST-DBN) is introduced, integrating the sequential modeling strength of TST with the hierarchical feature representation of DBN, while an improved orchard algorithm (IOA) performs adaptive hyper-parameter optimization. The framework also embodies the concept of symmetry and asymmetry. The IOA introduces controlled symmetry-breaking between exploration and exploitation, while the TST captures symmetric temporal patterns in network traffic whose asymmetric deviations often indicate anomalies. The proposed method is evaluated across four benchmark datasets (ToN-IoT, 5G-NIDD, CICDDoS2019, and Edge-IoTset) that capture diverse network environments, including 5G core traffic, IoT telemetry, mobile edge computing, and DDoS attacks. Experimental evaluation is conducted by benchmarking HTST-DBN against several state-of-the-art models, including TST, bidirectional encoder representations from transformers (BERT), DBN, deep reinforcement learning (DRL), convolutional neural network (CNN), and random forest (RF) classifiers. The proposed HTST-DBN achieves outstanding performance, with the highest accuracy reaching 99.61%, alongside strong recall and area under the curve (AUC) scores. The HTST-DBN framework presents a scalable and reliable solution for anomaly detection in next-generation mobile networks. Its hybrid architecture, reinforced by hyper-parameter optimization, enables effective learning in complex, dynamic, and heterogeneous environments, making it suitable for real-world deployment in future 5G/6G infrastructures. Full article

The flotation stage is a critical segment of mineral processing production. In copper concentrate flotation, predicting the concentrate grade is essential for maintaining a stable flotation process, ensuring concentrate quality, and enhancing profits. To improve the prediction accuracy for the concentrate grade, we propose a prediction method based on an improved eXtreme Gradient Boosting (XGBoost) model using real copper concentrate flotation data in the paper. To address the issues of outliers and missing values in the collected dataset, we firstly present an outlier detection and imputation method using the Inter-Quartile Range (IQR) method and the MissForest (MF) algorithm. An XGBoost-based model is developed for predicting the copper concentrate grade. The model is trained using some key indicators, including feed grade, ore throughput, reagent concentration, pulp flow rate, air flow rate, level, and pH value, as the input features. Moreover, hyper-parameter tuning is optimized based on a Tree-Structured Parzen Estimator (TPE). Combining the IQR/MissForest with TPE-optimized XGBoost can enable an end-to-end prediction pipeline for the copper concentrate grade in the flotation process to address the issues of data anomalies and missing values in the flotation process, as well as the low efficiency of multi-parameter tuning, ensuring the accuracy of data processing and the effectiveness of model training. The experimental results demonstrate that compared with some traditional prediction methods, such as support vector machines, the proposed method achieves about a 25.3% reduction in the Root Mean Square Error (RMSE), indicating our method’s superior performance. Full article