Detected Research Articles

Validation of MLC leaf open time calculation methods for PSQA in adaptive radiotherapy with tomotherapy units.

Treatment delivery safety and accuracy are essential to control the disease and protect healthy tissues in radiation therapy. For usual treatment, a phantom-based patient specific quality assurance (PSQA) is performed to verify the delivery prior to the treatment. The emergence of adaptive radiation therapy (ART) adds new complexities to PSQA. In fact, organ at risks and target volume re-contouring as well as plan re-optimization and treatment delivery are performed with the patient immobilized on the treatment couch, making phantom-based pretreatment PSQA impractical. In this case, phantomless PSQA tools based on multileaf collimator (MLC) leaf open times (LOTs) verifications provide alternative approaches for the Radixact® treatment units. However, their validity is compromised by the lack of independent and reliable methods for calculating the LOT performed by the MLC during deliveries. To provide independent and reliable methods of LOT calculation for the Radixact® treatment units. Two methods for calculating the LOTs performed by the MLC during deliveries have been implemented. The first method uses the signal recorded by the build-in detector and the second method uses the signal recorded by optical sensors mounted on the MLC. To calibrate the methods to the ground truth, in-phantom ionization chamber LOT measurements have been conducted on a Radixact® treatment unit. The methods were validated by comparing LOT calculations with in-phantom ionization chamber LOT measurements performed on two Radixact® treatment units. The study shows a good agreement between the two LOT calculation methods and the in-phantom ionization chamber measurements. There are no notable differences between the two methods and the same results were observed on the different treatment units. The two implemented methods have the potential to be part of a PSQA solution for ART in tomotherapy.

Advancing Highway Work Zone Safety: A Comprehensive Review of Sensor Technologies for Intrusion and Proximity Hazards

Highway work zones are critical areas where accidents frequently occur, often because of the proximity of workers to heavy machinery and ongoing traffic. With technological advancements in sensor technologies and the Internet of Things, promising solutions are emerging to address these safety concerns. This paper provides a systematic review of existing studies on the application of sensor technologies in enhancing highway work zone safety, particularly in preventing intrusion and proximity hazards. Following the PRISMA (preferred reporting items for systematic reviews and meta-analyses) protocol, the review examines a broad spectrum of publications on various sensor technologies, including GPS (global positioning system), radar, laser, infrared, radio-frequency identification, Bluetooth, ultrasonic, and infrared sensors, detailing their application in reducing intrusion and proximity incidents. The review also assesses these technologies in relation to their accuracy, range, power consumption, cost, and user-friendliness, with a specific emphasis on their suitability for highway work zones. The findings highlighted the potential of sensor technologies to significantly enhance work zone safety. As there are a wide range of sensor technologies to choose from, the review also revealed that the selection of sensors for a particular application needs careful consideration of pertinent factors. Finally, although sensor technologies offer promising solutions for enhancing highway work zone safety, their effective implementation requires comprehensive consideration of various factors beyond their technological capabilities, including developing integrated, cost-effective, user-friendly, and secure systems, and creating regulatory frameworks to support the rapid development of these technologies.

Reconfigurable Optical Sensor for Metal-Ion-Mediated Label-Free Recognition of Different Biomolecular Targets.

Reconfiguration of chemical sensors, intended as the capacity of the sensor to adapt to novel operational scenarios, e.g., new target analytes, is potentially game changing and would enable rapid and cost-effective reaction to dynamic changes occurring at healthcare, environmental, and industrial levels. Yet, it is still a challenge, and rare examples of sensor reconfiguration have been reported to date. Here, we report on a reconfigurable label-free optical sensor leveraging the versatile immobilization of metal ions through a chelating agent on a nanostructured porous silica (PSiO2) optical transducer for the detection of different biomolecules. First, we show the reversible grafting of different metal ions on the PSiO2 surface, namely, Ni2+, Cu2+, Zn2+, and Fe3+, which can mediate the interaction with different biomolecules and be switched under mild conditions. Then, we demonstrate reconfiguration of the sensor at two levels: 1) switching of the metal ions on the PSiO2 surface from Cu2+ to Zn2+ and testing the ability of Cu2+-functionalized and Zn2+-reconfigured devices for the sensing of the dipeptide carnosine (CAR), leveraging the well-known chelating ability of CAR toward divalent metal ions; and 2) reconfiguration of the Cu2+-functionalized PSiO2 sensor for a different target analyte, namely, the nucleotide adenosine triphosphate (ATP), switching Cu2+ with Fe3+ ions to exploit the interaction with ATP through phosphate groups. The Cu2+-functionalized and Zn2+-reconfigured sensors show effective sensing performance in CAR detection, also evaluated in tissue samples from murine brain, and so does the Fe3+-reconfigured sensor toward ATP, thus demonstrating effective reconfiguration of the sensor with the proposed surface chemistry.

A systematic review on the early detection of oral cancer using artificial intelligence and electronic tongue technology

This systematic review examines the role of artificial intelligence (AI) and electronic tongue (e-tongue) technology in the early detection of oral cancer. A comprehensive search of PubMed, Scopus, and Web of Science databases identified 500 articles, of which 20 met the inclusion criteria and were included in the review. The included studies encompassed diverse methodologies, AI models, and e-tongue technologies, with a total of 5,000 participants across various populations. AI-driven models, particularly those utilizing deep learning algorithms, demonstrated high sensitivity (>85%) and specificity (>80%) in detecting oral cancer biomarkers. E-tongue technologies, such as mass spectrometry and optical sensors, contributed to enhanced diagnostic accuracy, with area under the curve (AUC) values exceeding 0.85 in several studies. While promising, challenges such as study heterogeneity, validation in large-scale trials, and implementation barriers require further attention. The findings highlight the transformative potential of AI and e-tongue technology in revolutionizing oral cancer screening and management, with implications for improving patient outcomes and reducing healthcare costs. Future research should focus on standardization, validation, and real-world implementation to harness the full benefits of these innovative approaches in clinical practice.

Lightweight Single Image Super-Resolution via Efficient Mixture of Transformers and Convolutional Networks

In this paper, we propose a Local Global Union Network (LGUN), which effectively combines the strengths of Transformers and Convolutional Networks to develop a lightweight and high-performance network suitable for Single Image Super-Resolution (SISR). Specifically, we make use of the advantages of Transformers to provide input-adaptation weighting and global context interaction. We also make use of the advantages of Convolutional Networks to include spatial inductive biases and local connectivity. In the shallow layer, the local spatial information is encoded by Multi-order Local Hierarchical Attention (MLHA). In the deeper layer, we utilize Dynamic Global Sparse Attention (DGSA), which is based on the Multi-stage Token Selection (MTS) strategy to model global context dependencies. Moreover, we also conduct extensive experiments on both natural and satellite datasets, acquired through optical and satellite sensors, respectively, demonstrating that LGUN outperforms existing methods.

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Abstract Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.

Pelatihan ChatGBT kepada Guru di Majelis Pendidikan Muhammadiah kota semarang untuk Peningkatan literasi digital

The development of artificial intelligence (AI)--based learning models has made significant progress alongside the abundance of data. This enables the creation of complex deep-learning models to tackle increasingly intricate tasks. Evolving machine learning algorithms become a key factor in enhancing AI model capabilities. The demand for smart and efficient solutions from the business sector drives the adoption of AI technology, supported by advances in sensor technology, the Internet of Things (IoT), natural language processing (NLP), and image recognition. This article highlights the potential impact of AI model development on the learning experience, especially at the Elementary (SD), Junior High (SMP), and Senior High School (SMA) levels. Implementing AI models in elementary and secondary schools can support student progress assessment, provide material recommendations based on student understanding, and develop skills. The study discusses a teacher training initiative using ChatGPT to understand and utilize artificial intelligence in education. Training results show that teachers can effectively create varied and engaging learning materials using ChatGPT. Despite AI's benefits, cultural and social values remain irreplaceable, such as ethics towards teachers and social interactions among students. In conclusion, digital literacy training for teachers is essential to enhance their ability to develop modern and effective learning models, with AI as a valuable tool in creating dynamic and interactive learning environments.

Surface roughness analysis of side-polished fiber based on the importance of texture features

In order to explore the use of side-polished fibre (SPF) for microprobe-type "lab-on-fibre", this study presents an analysis of the surface roughness in side-polished fiber (SPF) using the gray level co-occurrence matrix (GLCM) texture feature analysis method. Experimental results show that the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy, and low mean values in the angular second moment (ASM), homogeneity, and correlation. Employing the random forest (RF) feature importance ranking method based on the Gini coefficient and out-of-bag (OOB) error estimation, this study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces. A feature subset comprising variance, ASM, entropy, and contrast is identified as optimal. Utilizing this subset, the paper conducts an RF classification validation experiment on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95.65%.This research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity. It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.

Analysis of vapor bubble diameter, departure frequency and dynamics in a single cavity

The ongoing quest for methods that enhance the efficiency of heat transfer processes, particularly those involving phase change, underscores the importance of comprehending the dynamics of vapor bubbles during boiling. This study investigates heat transfer mechanisms and the dynamics of vapor bubbles within the nucleate boiling regime. Experimental tests were conducted on a flat copper surface featuring a single cavity, focusing on analyzing vapor bubble growth and departure stages. The working fluid examined was HFE-7100 under saturated conditions. The formation and growth aspects of vapor bubbles, including their diameter (Dd) and departure frequency (f), were investigated through experimental data obtained by two different techniques: by an optical sensor of variable resistance capable of generating an analog signal from a voltage change and by a high-speed camera that captures images immediately after the instant that the bubble detached from the surface. Both techniques provide visual insights into the boiling phenomenon. An escalation in heat flux and, consequently, wall superheat resulted in an increased bubble departure frequency. Furthermore, the optical flow analysis successfully identified velocity and vorticity fields induced by micro convection, the prevailing heat transfer mode in nucleated boiling. A slight horizontal displacement trend over time was observed, which may be caused by the asymmetry in the velocity profile, confirmed by velocity peaks tending toward one direction. Vortex analysis reveals rotational motion concentrated in specific regions, with noticeable deformation near the bottom of the bubble and asymmetric movement contributing to vorticity. These findings provide insights into the vapor bubble dynamics, which is important for understanding the cavity rewetting phenomena.

Simulation analysis of a photonic crystal fiber refractive index sensor based on a double-layer film structure and surface plasmon resonance technology

A photonic crystal fiber surface plasmon resonance sensor based on a double-layer membrane structure is designed and analyzed. In the simple sensing structure with only one air hole size, TiO2 and Au layers with specific thicknesses are sequentially coated on the optical fiber to form a double-layer structure. The sensing characteristics of the double-layer membrane structure are studied by the finite element method. Compared to the single-layer membrane structure, the double-layer membrane sensor has significant sensing properties such as a better wavelength sensitivity and a smaller full width at half maximum in the loss spectrum. In the refractive index range between 1.37 and 1.43, the maximum wavelength sensitivity and average wavelength sensitivity of the sensor are 19,900 nm/RIU and 7417 nm/RIU, respectively, and the resolution can be up to 5.03×10−6RIU. The proposed photonic crystal fiber optic sensor achieves high performance with a simpler sensing structure than previous photonic crystal fiber optic sensors, and eliminates the step of polishing, which will greatly reduce the difficulty of actual fabrication and the error due to uneven polishing. The results show that the photonic crystal fiber optic sensor with a double-layer membrane structure has excellent performance. Due to its high sensitivity and resolution, it has great potential for applications in environmental monitoring, biosensing and chemical sensing.

Different fluorescent labels report distinct components of spHCN channel voltage sensor movement.

We used voltage clamp fluorometry to probe the movement of the S4 helix in the voltage-sensing domain of the sea urchin HCN channel (spHCN) expressed in Xenopus oocytes. We obtained markedly different fluorescence responses with either ALEXA-488 or MTS-TAMRA covalently linked to N-terminal Cys332 of the S4 helix. With hyperpolarizing steps, ALEXA-488 fluorescence increased rapidly, consistent with it reporting the initial inward movement of S4, as previously described. In contrast, MTS-TAMRA fluorescence increased more slowly and its early phase correlated with that of channel opening. Additionally, a slow fluorescence component that tracked the development of the mode shift, or channel hysteresis, could be resolved with both labels. We quantitated this component as an increased deactivation tail current delay with concomitantly longer activation periods and found it to depend strongly on the presence of K+ ions in the pore. Using collisional quenching experiments and structural predictions, we established that ALEXA-488 was more exposed to solvent than MTS-TAMRA. We propose that components of S4 movement during channel activation can be kinetically resolved using different fluorescent probes to reveal distinct biophysical properties. Our findings underscore the need to apply caution when interpreting voltage clamp fluorometry data and demonstrate the potential utility of different labels to interrogate distinct biophysical properties of voltage-gated membrane proteins.

Navigating the frontier: Additive Manufacturing’s role in synthesizing piezoelectric materials for flexible electronics

Additive manufacturing (AM) has significantly transformed the fabrication of functional materials, particularly in electronics and biomedical engineering. This study reviews stereolithography (SLA), selective laser sintering (SLS), fused deposition modeling (FDM), direct ink writing (DIW), and inkjet printing for flexible electronic applications. The review highlights SLA-based 3D printing’s better ability to optimize material compositions, printing procedures, and post-processing methods to improve material characteristics. Photosensitive materials and shrinkage-induced internal tensions seems to be its major constraint. Additionally, SLS 3D printing has improved composite materials' electrical, mechanical, and thermal properties. It has drawbacks including permeable structures and internal tensions. In FDM 3D printing, mechanical and electrical qualities are improved for piezoelectric sensor manufacture. Warping and nozzle blockage require additional study. DIW’s versatility in constructing complicated structures with increased features for energy harvesting and sensor development is also mentioned. We identify ink development and printer nozzle clogging issues. The review concludes that inkjet printing can provide a variety of materials for flexible electronics. Since it integrates the latest discoveries with technological developments, this study may help guide future research and promote innovation in the sector. Overall, additive manufacturing methods provide a new era of sensor technology by offering unrivalled flexibility and versatility.

Advanced Denoising and Meta-Learning Techniques for Enhancing Smart Health Monitoring Using Wearable Sensors

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems.

A Non-Source Optical Fiber Sensor for Multi-Point Methane Detection

Fast, accurate, real-time measurement of gas concentration is an important task for preventing coal mining disasters. In order to develop an accurate monitoring method for methane gas concentration at different locations in a mine environment, a non-source optical fiber sensor for multi-point methane detection has been developed in this paper. A 16-channel fiber splitter and a multi-channel time-sharing acquisition module are employed within the sensor, enabling simultaneous detection of methane gas at 16 points by a host. Furthermore, the methane sensors are connected to the monitoring host via an all-optical method, achieving non-source and long-range detection of methane. To assess the performance of the methane gas sensor, experiments were conducted to evaluate its detection range, response time, and stability. The results indicated that the average detection error was approximately 1.84% across the full range, and the response time did not exceed 10 s. The minimum detection limit of the sensor, as determined by the 1σ criteria, was obtained as 58.42 ppm. Additionally, the concentrations of methane gas measured at varying distances (1 km, 2 km, 5 km) were found to be essentially consistent over an extended period. These results suggest that the development of this non-source optical fiber sensor holds significant potential for providing a method for mine environment, multi-point online methane gas detection.

Affordable Highly Sensitive Fiber Optical Sensor Based on Coupled Fibers with Harmonically Varying Refractive Index

Optical fiber sensors consist of multiple Mach–Zehnder (MZ) interferometers and are common in the protection of different compounds. These sensors are very sensitive to any intrusion or threat. However, the spatial resolution is proportional to the number of MZ interferometers along the sensor. Consequently, a long sensor with a high resolution can be costly. In this paper, we suggest replacing the cascaded MZ interferometers with a couple of adjacent fibers, each of which have a harmonically varying refractive index. In this theoretical study, it is shown that two fibers with varying refractive indices demonstrate a sensitivity equivalent to that of multiple MZ interferometers. Furthermore, when the coupling coefficient between the fibers is weak, an analytical expression can be derived for the transmission between the fibers. This transmission reveals a quantization rule for which the light coupling between the two fibers vanishes.

Understanding Tree Mortality Patterns: A Comprehensive Review of Remote Sensing and Meteorological Ground-Based Studies

Land degradation, desertification and tree mortality related to global climate change have been in the spotlight of remote sensing research in recent decades since extreme climatic events could affect the composition, structure, and biogeography of forests. However, the complexity of tree mortality processes requires a holistic approach. Herein, we present the first global assessment and a historical perspective of forest tree mortality by reviewing both remote sensing and meteorological ground-based studies. We compiled 254 papers on tree mortality that make use of remotely sensed products, meteorological ground-based monitoring, and climatic drivers, focusing on their spatial and temporal patterns and the methods applied while highlighting research gaps. Our core results indicate that international publications on tree mortality are on the increase, with the main hotspots being North America (39%) and Europe (26%). Wetness indicators appear as the barometer in explaining tree mortality at a local scale, while vegetation indicators derived from multispectral optical sensors are promising for large-scale assessments. We observed that almost all of the studies we reviewed were based on less than 25 years of data and were at the local scale. Longer timeframes and regional scale investigations that will include multiple tree species analysis could have a significant impact on future research.

Enhanced Room-Temperature NO2 Sensing through Deep Functional Group Hybridization in Nitrogen-Doped Monolayer Ti3C2Tx.

Nitrogen dioxide (NO2) is a significant environmental and human health hazard. Current NO2 sensors often lack sensitivity and selectivity under ambient conditions. This study investigates ammonia pyrolysis modification of monolayer Ti3C2Tx MXene to enhance NO2 detection at room temperature. Nitrogen-doped Ti3C2Tx demonstrates a substantial improvement in sensitivity, with a response of 8.87% to 50 ppm of NO2 compared to 0.65% for the original sensor, representing a 13.8-fold increase. The nitrogen-doped sensor also exhibits superior selectivity and linearity for NO2 under ambient conditions. Theoretical analysis shows that nitrogen incorporation promotes enhanced interaction between Ti3C2Tx and its surface oxygen-containing functional groups through electronic hybridization, resulting in improved adsorption energy (1.80 |eV|) and electron transfer efficiency (0.67 |e|) for NO2, thereby enhancing its gas-sensing performance. This study highlights the potential of ammonia pyrolysis-treated Ti3C2Tx MXene for advancing NO2 sensor technologies with heightened performance at room temperature.

Fiber optic relative humidity and temperature sensor with the cascaded Vernier effect based on the C-shaped cavity structure

The water-absorbent sensing film, coated on the surface of traditional optical fiber humidity sensors, often suffers from detachment issues. In this paper, we present what we believe to be a new fiber-optic cascaded Fabry-Perot interferometer sensor for detecting relative humidity (RH) and temperature, without the need for sophisticated instrumentation. The sensing structure comprises two sections of single-mode optical fibers and a C-shaped cavity between them. The C-shaped cavity is created by grinding the side of a hollow-core fiber with fiber optic abrasive paper. The Vernier effect arises from the cascaded interaction between the C-shaped cavity filled with ultraviolet optical glue (NOA61) and the subsequent single-mode fiber pigtail. The sensor exhibits a high RH sensitivity of 0.248 nm/%RH (35-95%RH) and an RH resolution of up to 0.08%RH. It also has high-temperature sensitivities of -1.091 nm/°C (25 - 65°C). Furthermore, simultaneous measurement of RH and temperature is achieved by establishing a dual parameter matrix, and the sensor's response time and recovery time for RH and temperature are within 300s. Therefore, this work provides a simple and cost-effective manufacturing process and the proposed RH and temperature sensor features a compact size, strong environmental adaptability, and significant potential for practical applications.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Colorimetric IPN hydrogels embedded with colloidal photonic crystals: A novel approach for the detection of ethanol and Ba2+ ions in water

A critical bottleneck in sensor technology is the rapid and precise detection of specific analytes in complex matrices, hindering advancements in environmental monitoring, healthcare, and industrial process control. This study addresses this challenge by introducing a novel composite hydrogel sensor designed for rapid and selective detection of ethanol and barium ions (Ba2+) in aqueous environments. The sensor integrates interpenetrating network (IPN) hydrogels with embedded colloidal photonic crystals (CPCs), synthesized via a solution-based polymerization approach. This innovative configuration allows CPCs to dynamically adjust their photonic bandgap in response to environmental changes, manifesting as a visible, colorimetric shift. This response stems from the synergy between the mechanical properties of the IPN hydrogel and the optical sensitivity of CPCs. Upon exposure to analytes such as ethanol and Ba2+, the sensor exhibits a rapid and reversible color transition that is directly proportional to their concentration. Notably, ethanol (0 vol%–80 vol%) and Ba2+ (5–17.5 mM) induce a distinct blueshift in the photonic bandgap and trigger a color change from red–orange to green due to the alteration in the swelling behavior of the IPN hydrogel, affecting its lattice constant. The IPN hydrogel–CPC composite demonstrates exceptional operational stability and facilitates rapid detection, making it ideal for on-site applications without the need for complex equipment. These characteristics make the composite hydrogel sensor a promising candidate for environmental monitoring, industrial process control, and public health diagnostics, paving the way for the development of next-generation responsive sensor materials.