Browse Articles

Fractional order effects on solitary waves and chaotic regimes in the mKdV Burgers equation

Abstract This paper examines the space–time fractional modified Korteweg-de Vries Burgers (mKdV-Burgers) equation to address nonlinear wave dynamics of the equation through the improved F-expansion representation with the Riccati equation. The given strategy offers a methodical system of obtaining a wide category of precise analytical solutions, solitary wave solutions, kink-type solutions, periodic solutions, and rational solutions. The resulting results show the existence of dissipative and shock-like solitons, which add to the knowledge of nonlinear propagation phenomena in complicated media. Moreover, a dynamical study is performed in terms of bifurcation structures, phase portraits, Lyapunov exponents, and sensitivity analysis of changes between stable and chaotic states. These studies show that parameters of fractional order affect the stability and complexity of the system. This dynamical and analytical set of methods does not only confirm the efficiency of the improved F-expansion method but also contributes to new physical understanding of the fractional nonlinear evolution equations (FNLEEs). Findings may be generalized to fluid dynamics, plasma physics, and nonlinear optics, and the framework can be generalized to higher-dimensional or coupled fractional systems to control and predict multi-stable and chaotic behavior.

Graphene oxide and cannabidiol-based hybrid coatings on PMMA for biomedical applications

A multi-dimensional evaluation model for power enterprise procurement performance based on fuzzy analytic hierarchy process and TOPSIS integration

Power−efficient ramped stimulation in a fully implantable cochlear implant

Abstract Cochlear implants (CIs) are among the most established neuromodulation devices, providing auditory perception through electrical stimulation of the auditory nerve. While conventional stimulation strategies rely on symmetric biphasic rectangular pulses, alternative pulse shapes may offer improvements in neural activation and energy efficiency-particularly for fully implantable CI systems where power consumption is a key limitation. In this study, we investigate the efficacy of anodic-first ramped biphasic pulse shapes compared to conventional anodic-first rectangular pulses, using a custom-designed fully implantable cochlear implant (FICI) system in an in vivo guinea pig model. Electrically evoked auditory brainstem responses (eABRs) were recorded in response to four charge-balanced waveform types: Rectangular, RampUp, RampDown, and RampLong. In this single-subject feasibility study, ramped waveforms elicited significantly larger eABR amplitudes, steeper input-output functions, and shorter latencies compared to rectangular pulses. Additionally, we characterized transmission efficiency across the electrode–tissue interface by analyzing waveform spectra and their attenuation through a frequency-dependent medium model. After correcting for these medium-specific losses, in the anodic-first biphasic configuration, RampUp and RampLong pulses demonstrated up to 19–22% improvement in power efficiency relative to rectangular pulses at subthreshold response levels. These findings highlight the potential of ramped stimulation to reduce energy consumption without compromising-and in some cases enhancing-neural activation. Such improvements are especially valuable for fully implantable devices, supporting longer battery life and more sustainable stimulation strategies in next-generation CIs.

OCRNet a robust deep learning framework for alphanumeric character recognition to assist the visually impaired

Revealing sassanid dyeing practices through synchrotron FTIR

Graph-based federated learning approach for intrusion detection in IoT networks

Normative surface electromyography values for the orbicularis oculi muscle in healthy control subjects

Analysis of the clinical features of neurocristopathy-related hearing loss and how these relate to outcomes after cochlear implantation

Alteration of metabolic activity regulates mitochondrial temperature in diagnosis in HepG2 hepatocellular carcinoma cells

Rapid screening for acute rheumatic fever using machine learning analysis of host tissue reactive antibodies

One-class edge classification through heterogeneous hypergraph for causal discovery

Identifying key clinical and biochemical predictors of treatment outcomes in inflammatory bowel disease: a real-world evidence study

The dynamics of single stranded, positive sense RNA bacteriophages in the rumen virome

Comparison of preferred double guidewire technique and transpancreatic sphincterotomy for difficult biliary cannulation

Evaluating the reliability and clinical utility of artificial intelligence in first trimester prenatal screening and noninvasive prenatal testing

Kinetic analysis and characterization of the simultaneous reduction–sintering of NiO–Fe2O3 nanoprecursors for ferronickel alloy production

Fatigue in Post-COVID-Condition is accompanied by hypoperfusion of right-occipital areas

Background and purpose A proportion of individuals recovering from COVID-19 continue to experience persistent symptoms, including fatigue and cognitive difficulties — a syndrome commonly referred to as Post-COVID condition (PCC), which affects an estimated 2–10% of cases. In this study, we evaluated cerebral blood flow (CBF) to better understand the pathophysiological mechanisms underlying PCC. Materials and methods In this prospective, monocentric study, we analyzed clinical and cerebral blood flow (CBF) data from a cohort of 55 patients who met the WHO diagnostic criteria for Post-COVID condition (PCC) and underwent MRI approximately 11 months after a positive PCR test for SARS-CoV-2. These PCC patients were compared to a matched control group of 36 individuals who had contracted COVID-19 but did not develop PCC. CBF was assessed using arterial spin labeling (ASL), a promising non-invasive technique that provides high spatial resolution for quantifying cerebral blood flow. Additionally, we examined changes in gray matter volume and atrophy using FreeSurfer-based cortical morphometry. We further explored the relationship between regional CBF alterations and clinical symptoms, including cognitive and olfactory function, as well as fatigue. Results In our cohort, 59% of PCC patients could not return to their previous level of independence or employment due to symptoms, and 81% reported fatigue on the WEIMuS questionnaire. Conventional MRI showed no evidence of cortical atrophy. While no significant differences in regional CBF emerged after FDR correction, a more explorative threshold (p < 0.005) revealed reduced CBF in the right angular and middle occipital gyri in PCC patients. Fatigue, as assessed by the WEIMuS, was significantly correlated with reduced CBF in the right occipital regions, particularly for physical fatigue, but no associations were found with cognitive or olfactory performance. Conclusion In PCC patients, fatigue was associated with reduced perfusion in right-sided occipital regions, suggesting a potential pathophysiological basis for this symptom. These findings may also provide an imaging biomarker to aid in the diagnosis of PCC.

Design and development of a modified nanobubble-assisted advanced oxidation process (M-AOP) for high-efficiency wastewater treatment

Pomegranate disease diagnosis with severity estimation and treatment remedies using deep learning and RAG-based LLM

Abstract Pomegranate cultivation faces significant challenges due to fruit diseases that significantly impact crop yield and farmer income. Traditional methods for disease detection are often slow and prone to errors, delaying timely intervention. This paper proposes a deep learning-based system for automatic, multi-class disease classification in pomegranates using transfer learning. A dataset comprising 5099 annotated images was used to train and evaluate several CNN models, including DenseNet121, EfficientNetB0V2, MobileNetV2, ResNet50, VGG16, and InceptionV3. DenseNet121 emerged as the top performer, achieving an accuracy of 99.35%. To enhance practical value, a novel Healthy-Based Deviation Scoring (HBDS) method was developed to estimate disease severity using Grad-CAM ++ for lesion localization and Mahalanobis distance-based scoring, followed by Gaussian Mixture Model clustering. The severity predictions of the system were verified against manually labeled images, and the system has shown superior accuracy compared to pixel-based methods. Also, a recommendation module was integrated using a retrieval-augmented language model, which provides disease-specific treatment suggestions based on the predicted severity. The complete pipeline is implemented as a user-friendly web application that delivers real-time diagnosis, severity estimation, and actionable treatment plans, which offer a practical and scalable solution for modern precision agriculture.