JoVE Neuroscience is a multidisciplinary section devoted to investigations of the structure, function, physiology, and pathophysiology of the brain and nervous system. Included methodologies range from molecular and cellular level studies to full central and peripheral neural systems. Potential treatment platforms and surgical techniques for neurological diseases and disorders are also presented in this section.

Neuroscience

JoVE Medicine serves as a connection between basic research and clinical applications. Articles may present medical procedures, case studies or clinical trial methodologies. Included studies are often modeled towards predictive, preventative, personalized, and practical approaches to treatment of disease. The goal of research methodologies in this section is to improve overall patient care and prognosis.

Medicine

JoVE Immunology and Infection includes content from a variety of fields working toward a shared initiative of improving global health. Techniques evaluating the biological response to pathogens from the molecular to organismal level are presented as well as therapeutic agents and their efficacy in treating disease.

Immunology and Infection

JoVE Genetics publishes video articles demonstrating experimental approaches to investigate gene function and regulation. This includes methods to study gene expression, mutagenesis, epigenetics, genetic determinants of development, genetic disorders, population genetics and evolution.

Genetics

JoVE Environment is dedicated to research methodologies that address environmental concerns and seek to better understand Earth's ecosystem.  Special consideration is given to experimental methodologies that assess society’s impact on the environment, suggest solutions for protecting Earth’s resources, and develop sustainable fuel sources. Moving towards an environmentally conscious planet is a collaborative effort with contributions from both life and physical scientists.

Environment

JoVE Engineering encompasses a broad range of experimental and instrumental techniques utilized in physics research. Investigations in this area strive to address and answer a broad range of scientific questions, such as device mechanisms and efficiencies, using physical tools. This approach often requires a combination of specialties, and research in this area tends to be interdisciplinary with contributions from mechanical, electrical, and chemical engineers.

Engineering

JoVE Developmental Biology publishes research methodologies in the field of developmental biology. This includes methods that are used to study biological development in vitro and in vivo at the molecular, cellular, tissue, organ and whole organism levels.

Developmental Biology

JoVE Chemistry surveys a broad scope of chemical disciplines. This collection includes fundamental and applied research that investigates molecular interactions, properties, and structure spanning in the following core divisions: analytical, organic, organometallic, inorganic, bioinorganic, and biochemistry. This section also includes the design and preparation of materials and small molecules for advanced or unique applications.

Chemistry

JoVE Cancer Research illustrates experimental approaches in biomedical research and clinical practice aimed at understanding, detecting, treating and preventing cancer. The video articles in this section demonstrate methods to study the cellular and molecular mechanisms of cancer, animal models of cancer research, development of new therapies and diagnostics, tumor imaging, chemical and radiation treatments, surgical procedures and improvements in therapy. These video articles offer researchers and clinical practitioners an efficient way to learn the technologies and experimental methods in cancer research.

Cancer Research

JoVE Biology welcomes all general biology research methodologies. Content in this section canvases all fields of cell, molecular, and organismal biology, ranging from new applications of standard techniques to novel approaches aimed at understanding the functions of life and living organisms. This diverse section includes, but is not limited to, techniques in physical biology, cellular biochemistry, genetics, physiology, systems biology and a combination of eukaryotic and prokaryotic model systems.

Biology

JoVE Bioengineering merges both physical and life sciences to understand and predict biological processes. Applying physical science tools to life science questions allow for the discovery of better technologies to measure, diagnose, and clinically treat disease.

Bioengineering

JoVE Biochemistry publishes experiments that investigate the structure, function, and interactions of biological molecules. The video articles in this section demonstrate methods to study nucleic acids, proteins, carbohydrates and lipids as well as enzyme function, protein folding, molecular interactions and biochemical reactions.

Biochemistry

JoVE Behavior documents observational and experimental techniques that seek to understand human and animal behavior. Work in this field includes analysis of genetic causes and physiological responses to internal or external stimuli as well as the implicated brain regions.

Behavior

Artificial neural network-based computer image processing technology has rapidly developed in recent years and found widespread applications across multiple fields. In medical image processing, UNet and its variants have shown great success in lesion detection, cell segmentation, and polyp segmentation tasks. This research presents a modified U-shaped network with reduced network parameters achieved by decreasing the network depth and increasing the network channels to enhance the model's learning ability. To counteract the reduction in learning capacity caused by depth compression, a hybrid-channel convolutional module is introduced to replace the original network's convolutional module. The model introduces a channel attention mechanism between the layer and the point convolution layer to improve the practical channel feature extraction ability. The article also concludes that using mixed-depth convolution can effectively solve the size span of the segmentation target, making it difficult for one model to be fit for multiple data sets. The proposed model achieves state-of-the-art results on two popular public datasets, MoNuseg and GlaS, with a mean dice increase of 1.0% and 1.37%, respectively. The total parameters of the modified model are reduced to 1.71M, representing a 38.6x reduction compared to UCtransNet, with 65.6M parameters.

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MixKNet: A Modified U-shaped Network with Hybrid Channel Convolution for Medical Image Segmentation

Artificial neural network-based computer image processing technology has rapidly developed in recent years and found widespread applications across ...

Microneedles have recently gained recognition as a promising method in transdermal drug delivery owing to their minimally invasive, painless nature, and they can be flexibly engineered through geometric modifications to meet specific therapeutic requirements. Hence, this study aims to develop microneedle master molds utilizing Computer Numerical Control (CNC) milling technology to create polymeric microneedles with diverse degrees of sharpness to enhance therapeutic efficacy. Previously, our team successfully optimized two critical machining parameters, feed rate, and ramp angle, while analyzing their influence on the obtained needles. Hence, this article presents a comprehensive set of experimental protocols for fabricating and evaluating the performance of the polymeric-based model needles. Furthermore, this work also provides representative results to demonstrate the reproducibility and therapeutic efficacy of the sample needles. Our research team anticipates that this straightforward protocol will enable researchers to create cost-effective, high-precision microneedles, thereby advancing microneedle technology for further biomedical applications, particularly during the pandemic.

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Fabrication of Ultra-sharp Polymeric Microneedles via Computer Numerical Control (CNC) Micromachining and Micromolding

Microneedles have recently gained recognition as a promising method in transdermal drug delivery owing to their minimally invasive, painless nature, and ...

With the rise of high-performance electric vehicles (EVs) which require advanced suspension systems capable of delivering precision, high force output, and rapid adaptability, magnetorheological (MR) dampers are critical for achieving rapid-response suspension control. However, their temperature sensitivity limits reliability under extreme conditions. This protocol presents a systematic approach to address this challenge. A high-performance MR fluid is synthesized using carbonyl iron particles dispersed in a thermally stable carrier fluid with anti-wear agents and antioxidants. We propose an Exponential Linear Mixed Analysis (ELMA) model and its parameter identification method, which can be considered as a superior alternative to the bi-plastic Bingham model. The ELMA framework is extended to MR dampers, with temperature compensation algorithms improving current tracking accuracy by 3.98% and force tracking accuracy by 7.75% (peak: 19.92%). Joint CarSim/Simulink simulations demonstrate that temperature-compensated Sky-hook and Mixed SH-ADD algorithms reduce vertical acceleration variance by 11.97% and peak pitch rate by 41.78% on Class D roads. This protocol bridges MR fluid physics to adaptive damper control, offering a replicable workflow for enhancing EV suspension systems in extreme thermal environments.

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Smart Mobility Boosting Using High-Fidelity Magnetorheological Fluid Modeling for Adaptive Damper Control Development

With the rise of high-performance electric vehicles (EVs) which require advanced suspension systems capable of delivering precision, high force output, ...

As a major cause of death worldwide, cardiovascular diseases-especially arrhythmias-require the creation of precise and automated technologies for early diagnosis and detection. To identify arrhythmias from electrocardiogram (ECG) signals, this paper introduces a deep learning-based classification model that focuses on five main heartbeat types: Normal (N), Left Bundle Branch Block (L), Right Bundle Branch Block (R), Atrial Premature Beat (A), and Premature Ventricular Contraction (V). We leverage Lead I signals from several sources, such as the INCART 12-lead, Sudden Cardiac Death Holter, Supraventricular, and MIT-BIH Arrhythmia databases, yielding more than 3.9 million training and 112,575 testing segments.
Examples of data preparation include 180 sample, fixed-window segmentation, Min-Max normalization, and class balancing with the Synthetic Minority Over-sampling Technique (SMOTE). The hybrid architecture uses Transformer layers to model temporal dependencies and 1D Convolutional Neural Networks (CNNs) to extract spatial features. The Adam optimizer with dropout and batch normalization for regularization trains the model.
The proposed system outperforms the TN4 model and other cutting-edge benchmarks, achieving 99.99% accuracy, precision, and F1-score across all classes. Feature robustness is further improved by applying deep hybrid architectures and convolutional neural networks, which were motivated by earlier studies. The suggested paradigm advances artificial intelligence-driven, individualized digital healthcare and has great promise for scalable, real-time arrhythmia identification.

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Convolutional Neural Network-Transformer Model to Predict and Classify Early Arrhythmia Using Electrocardiogram Signal

As a major cause of death worldwide, cardiovascular diseases-especially arrhythmias-require the creation of precise and automated technologies for early ...

Cyber-Physical System (CPS) blends computational intelligence with physical processes, which enables instant monitoring, decision-making capability, and automation services throughout various vital domains. Moreover, Generative Artificial Intelligence (AI) faces considerable barriers to deployment within CPS because distributed environments with sensitive data present serious privacy and security maintenance challenges. Current techniques, such as Federated Learning (FL), encounter difficulties both in their model diversity and the risk that privacy may be compromised. The Generative Proxy Learning Framework (GPLF) serves as our innovative solution that utilizes Proxy-based Federated Learning (ProxyFL) specifically adapted for Generative AI applications within Cyber-Physical Systems (CPS). In GPLF, each participant maintains two models: Participants operate a private model dedicated to local data analysis together with a shared proxy model that enables protected node collaboration. As the essential foundation of generative AI mechanisms, advanced Diffusion Models deliver high-fidelity synthetic data together with key data feature preservation. The models generate synthetic sensor data, which enables improved anomaly detection and supports predictive modeling through authentic CPS behavior representations under various scenarios. The system achieves advanced privacy protection with differential privacy mechanisms in proxy data updates, while direct peer communication in the network benefits from advanced encryption protections. GPLF serves CPS platforms by connecting to real-time sensors and IoT devices that support secure generative processes, including anomaly detection, synthetic data creation, and predictive modeling. Test results from benchmark CPS datasets show considerable performance improvements with 25% less privacy leakage and 25% better data exchange capabilities, together with an 18% improvement in generative task accuracy to support its transformative potential for secure, intelligent CPS operations.

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Diffusion-Driven Proxy Learning Strategy with Secure Peer Interactions for Generative Intelligence in Cyber-Physical System

Cyber-Physical System (CPS) blends computational intelligence with physical processes, which enables instant monitoring, decision-making capability, and ...

Network traffic prediction, a critical application of time series analysis, is essential for network management and optimization. Traditional statistical models and machine learning methods have been employed for network traffic prediction, where recent advancements in multi-layer perception mixer architectures, particularly TSMixer, have achieved state-of-the-art performance. However, TSMixer faces challenges with periodic signals and long-term predictions. To address these limitations, we propose a Quantum TSMixer (QTSMixer) model, a hybrid quantum-classical approach that leverages quantum neural networks for enhanced feature extraction related to periodic signals and long-term dependencies. By introducing trainable parameters to control the strength of the quantum components, the hybrid structure of multi-layer perception and quantum neural network is generated. QTSMixer's cross-domain application potential and practical application capability are demonstrated through empirical analysis on real-world datasets, in which QTSMixer outperforms TSMixer by 6.72% in the long-term network traffic prediction dataset. In the future, the development of QTSMixer can be further explored in other fields, such as financial market analysis and weather prediction.

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A Quantum-Classical Hybrid Model for Long-term Network Traffic Prediction

Network traffic prediction, a critical application of time series analysis, is essential for network management and optimization. Traditional statistical ...

The ability to create photorealistic 3D reconstructions of real-world environments from standard color images has numerous applications in fields such as healthcare, education, and industry. This paper presents a novel system that integrates advanced techniques such as structure from motion, incremental scene registration, and Gaussian Splatting to reconstruct detailed 3D models. These models are seamlessly integrated into virtual reality environments to enable immersive interaction. The pipeline begins with the capture of 360-degree images using a matrix-based pattern for comprehensive coverage. The images are processed using COLMAP to estimate camera poses and generate a sparse point cloud. Gaussian Splatting refines the reconstruction by optimizing the positions, shapes, and appearance of the points to produce highly realistic 3D environments. These models are then rendered in Unity, enabling interaction by VR headsets and supporting additional features such as avatars driven by large language models. One use case demonstrates the versatility of the system. In healthcare, the approach creates controlled, familiar virtual spaces for therapy, particularly beneficial for people with autism spectrum disorders. The immersive environments and interactive avatars provide a safe and comfortable environment for personalized therapy. This system offers significant advantages, including high visual fidelity, ease of image acquisition, and immersive user experiences. However, challenges remain, such as the computational cost of Gaussian Splatting and its reliance on accurate camera pose estimation. By overcoming these limitations, this method has the potential to transform applications in various domains, ranging from therapeutic interventions to industrial design and cultural preservation.

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Photorealistic Learned Landscapes for Augmented Reality

The ability to create photorealistic 3D reconstructions of real-world environments from standard color images has numerous applications in fields such as ...

Moir&#233; superlattices constitute a versatile platform to investigate emergent phenomena arising from the interplay of strong correlations and topology, while offering flexible in situ tunability. However, the fabrication of such moir&#233; superlattices is challenging. It is difficult to achieve highly uniform devices with a precise twist angle because of the unintentional introduction of heterostrain, twist angle disorder, and angle/lattice relaxation during the nanofabrication process. This article introduces an optimized, experience-informed protocol for fabricating high-quality graphene-based moir&#233; superlattice devices, focusing on a modified dry transfer technique. The transfer process is performed in a highly tunable, custom-built transfer setup that enables precise position, angle, and temperature control. By combining rigorous flake selection criteria, pre-cleaned bubble-free bottom gates, and graphene laser ablation, the moir&#233; superlattice is constructed by deliberately overlaying twisted graphene flakes at a submicron speed at room temperature. Through precise control of the transfer process, the resulting graphene moir&#233; superlattice devices exhibit high uniformity and desired twist angles. This optimized protocol addresses existing challenges in the fabrication of graphene-based moir&#233; superlattice devices and paves the way for further advances in the rapidly evolving field of moir&#233; materials.

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Optimized Fabrication Procedure for High-Quality Graphene-based Moir&#233; Superlattice Devices

Moir&#233; superlattices constitute a versatile platform to investigate emergent phenomena arising from the interplay of strong correlations and topology, ...

Traditional electromagnetic flowmeters are inherently prone to external interference and uneven velocity distribution during measurement, which severely limits their accuracy. In this study, an improved method is proposed, which optimizes the excitation drive waveform, performs multiple filtering and amplification of the electrode input, and uses a Complex Programmable Logic Device to achieve rapid switching between positive and negative induction signals. This enables smooth rectification and, in combination with software filtering techniques, achieves highly precise performance. Additionally, empty pipe detection is realized by recognizing excitation waveform and input waveform patterns.
Experimental verification shows that the designed electromagnetic flowmeter achieves an accuracy of 0.1% within a flow velocity range of 0.1-15 m/s, with system repeatability errors of less than 1%. The results validate the effectiveness of the proposed method in high-precision flow measurement. The study demonstrates that high-precision detection can be achieved with minimal additional cost, which is important for industry applications.

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High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Traditional electromagnetic flowmeters are inherently prone to external interference and uneven velocity distribution during measurement, which severely ...

In situ photo-rheology is a powerful technique for characterizing the evolution of photo-responsive materials during irradiation. This technique enables time-resolved analysis of the mechanical properties of materials undergoing photo-induced molecular changes, such as during additive manufacturing, photo-polymerization, or photo-stimulation. We present a rheological method to monitor dynamic network formation by photo-responsive star polymers. This article describes procedures to configure a rheometer for simultaneous measurement and irradiation, prepare and load photo-responsive polymer samples, identify appropriate measurement parameters, and analyze time-resolved viscoelasticity. Mechanical and kinetic information provides insight into the dynamics of photo-responsive polymer networks. The goal of this protocol is to provide an example of experimental parameter selection, in situ photo-rheological measurements, and mechanical analysis of photo-responsive materials undergoing irradiation. Additionally, this protocol identifies potential modifications to customize the setup for many possible photo-responsive systems, thereby providing guidelines for future users across the materials science community. In situ&#160;photo-rheology improves the understanding of photo-responsive material properties, which is important for engineering materials with predictable processing by additive manufacturing, tunable mechanical performance, and stimuli-responsive functions.

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In situ Photo-rheology Monitors Viscoelastic Changes in Photo-responsive Polymer Networks

In situ photo-rheology is a powerful technique for characterizing the evolution of photo-responsive materials during irradiation. This technique enables ...

Journal - engineering