Search Results (187)

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

Background/Objectives: Given the heterogeneous nature of long COVID, its treatment and management remain challenging. This study aimed to investigate whether poor pre-pandemic sleep quality, its deterioration during the peak of the pandemic, and circadian preference increase the risk of long COVID symptoms. Methods: An online survey was conducted between 9 October and 12 December 2022, with 384 participants who had recovered from COVID-19 at least three months prior to data collection. Participants were categorized based on the presence of at least one long COVID symptom. Logistic regression models assessed associations between sleep-related variables and long COVID symptoms. Results: Participants with long COVID symptoms reported significantly poorer sleep quality, higher perceived stress, greater somatic and cognitive pre-sleep arousal, and elevated levels of post-traumatic stress symptoms, anxiety, depression, and aggression. Fatigue (39.8%) and memory problems (37.0%) were the most common long COVID symptoms. Sleep deterioration during the pandemic peak was reported by 34.6% of respondents. Pre-pandemic poor sleep quality, its deterioration during the pandemic, and poor sleep at the time of the survey were all significantly associated with long COVID. An extreme morning chronotype consistently predicted long COVID symptoms across all models, while an extreme evening chronotype was predictive only when accounting for sleep quality changes during the pandemic. COVID-19 frequency, severity, financial impact, and somatic pre-sleep arousal were significant predictors in all models. Conclusions: Poor sleep quality before the pandemic and its worsening during the pandemic peak are associated with a higher likelihood of long COVID symptoms. These findings underscore the need to monitor sleep health during pandemics and similar global events to help identify at-risk individuals and mitigate long-term health consequences, with important clinical and societal implications. Full article

Space object detection, as the foundation for ensuring the long-term safe and stable operation of spacecraft, is widely applied in a variety of close-proximity tasks such as non-cooperative target monitoring, space debris avoidance, and spacecraft mission planning. To strengthen the detection capabilities for non-cooperative spacecraft and space debris, a method based on You Only Look Once Version 11 (YOLO11) is proposed in this paper. On the one hand, to tackle the issues of noise and low contrast in images captured by spacecraft, bilateral filtering is applied to remove noise while preserving edge and texture details effectively, and image contrast is enhanced using the contrast-limited adaptive histogram equalization (CLAHE) technique. On the other hand, to address the challenge of small object detection in spacecraft, loss-guided online data augmentation is proposed, along with improvements to the YOLO11 network architecture, to boost detection capabilities for small objects. The experimental results show that the proposed method achieved $99.0\%$ mAP50 (mean Average Precision with an Intersection over Union threshold of 0.50) and $92.6\%$ mAP50-95 on the SPARK-2022 dataset, significantly outperforming the YOLO11 baseline, thereby validating the effectiveness of the proposed method. Full article

With the rising demand for improved living standards and environmental protection, malodor pollution has emerged as a critical concern for both the public and regulatory authorities. Accurate prediction of malodor gas composition is essential for effective environmental monitoring and safety management. However, existing online malodor detection systems often suffer from short-term sensor drift, compromising their accuracy and long-term stability. To address these challenges, this study proposes an advanced electronic nose (e-nose) detection framework based on a time series data analysis. This study presents a novel approach utilizing a multi-channel sensor array for gas sampling, which establishes a robust mapping relationship between sensor response patterns and gas concentration distributions. To address the challenges of sensor drift and enhance system stability, we propose an innovative Encoder-Decoder architecture IED-CNN-LSTM incorporating external compensation mechanisms. Experimental results demonstrate that the proposed IED-CNN-LSTM model outperforms conventional methods significantly in both prediction accuracy and long-term stability. The framework achieves enhanced feature extraction from sensor time series data, enabling more precise and reliable detection of malodorous compounds. This research contributes an effective solution for real-time environmental monitoring applications while offering substantial improvements in both performance metrics and practical implementation for industrial and regulatory scenarios. Full article

Accurate prediction of tool wear plays a vital role in improving machining quality in intelligent manufacturing. However, traditional Gaussian Process Regression (GPR) models are constrained by linear assumptions, while conventional filtering algorithms struggle in noisy environments with low signal-to-noise ratios. To address these challenges, this paper presents an innovative tool wear prediction method that integrates a nonlinear mean function and a multi-kernel function-optimized GPR model combined with an LSTM-enhanced particle filter algorithm. The approach incorporates the LSTM network into the state transition model, utilizing its strong time-series feature extraction capabilities to dynamically adjust particle weight distributions, significantly enhancing the accuracy of state estimation. Experimental results demonstrate that the proposed method reduces the mean absolute error (MAE) by 47.6% and improves the signal-to-noise ratio by 15.4% compared to traditional filtering approaches. By incorporating a nonlinear mean function based on machining parameters, the method effectively models the coupling relationships between cutting depth, spindle speed, feed rate, and wear, leading to a 31.09% reduction in MAE and a 42.61% reduction in RMSE compared to traditional linear models. The kernel function design employs a composite strategy using a Gaussian kernel and a 5/2 Matern kernel, achieving a balanced approach that captures both data smoothness and abrupt changes. This results in a 58.7% reduction in MAE and a 64.5% reduction in RMSE. This study successfully tackles key challenges in tool wear monitoring, such as noise suppression, nonlinear modeling, and non-stationary data handling, providing an efficient and stable solution for tool condition monitoring in complex manufacturing environments. Full article

Cyberhate presents a multifaceted, context-sensitive challenge that existing detection methods often struggle to tackle effectively. Large language models (LLMs) exhibit considerable potential for improving cyberhate detection due to their advanced contextual understanding. However, detection alone is insufficient; it is crucial for software to also promote healthier user behaviors and empower individuals to actively confront the spread of cyberhate. This study investigates whether integrating large language models (LLMs) with persuasive technology (PT) can effectively detect cyberhate and encourage prosocial user behavior in digital spaces. Through an empirical study, we examine users’ perceptions of a self-monitoring persuasive strategy designed to reduce cyberhate. Specifically, the study introduces the Comment Analysis Feature to limit cyberhate spread, utilizing a prompt-based fine-tuning approach combined with LLMs. By framing users’ comments within the relevant context of cyberhate, the feature classifies input as either cyberhate or non-cyberhate and generates context-aware alternative statements when necessary to encourage more positive communication. A case study evaluated its real-world performance, examining user comments, detection accuracy, and the impact of alternative statements on user engagement and perception. The findings indicate that while most of the users (83%) found the suggestions clear and helpful, some resisted them, either because they felt the changes were irrelevant or misaligned with their intended expression (15%) or because they perceived them as a form of censorship (36%). However, a substantial number of users (40%) believed the interventions enhanced their language and overall commenting tone, with 68% suggesting they could have a positive long-term impact on reducing cyberhate. These insights highlight the potential of combining LLMs and PT to promote healthier online discourse while underscoring the need to address user concerns regarding relevance, intent, and freedom of expression. Full article

Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic model regression. An adaptive health index (HI) extraction method was also introduced, which leverages unsupervised deep learning and variational mode decomposition to effectively extract health indicators from multiple measurements of complex systems. The effectiveness of the proposed method was validated using both the C-MAPSS and FLEA datasets. The case study results demonstrate that the proposed prognostic method delivered an outstanding performance. Specifically, the feature extraction method effectively reduced the measurement noise and produced robust HIs, while the multi-output sparse probabilistic model achieved lower prediction errors and a higher accuracy. Compared to traditional single-step forward-prediction methods, the proposed approach significantly reduced the time required for long-term predictions in complex systems, thus improving support for online status monitoring. Full article

For scenarios where the type of structural break in a time series is unknown, this paper proposes a modified ratio-type test statistic to enable effective online monitoring of structural breaks, while circumventing the estimation of long-term variance. Under specific assumptions, we rigorously derive the asymptotic distribution of the test statistic under the null hypothesis and establish its consistency under the alternative hypothesis. In cases where both variance and mean breaks coexist, we introduce a refined mixed-break monitoring procedure based on the consistent estimation of breakpoints. The proposed method first provides consistent estimations of the mean change points and variance change points separately; then, mean and variance removal are performed on original data; finally, the previously removed trend is added back. Compared to traditional monitoring methods, which have to use two test statistics, this method requires only one to simultaneously monitor both types of change points, resulting in a significantly simplified monitoring process. This approach effectively reduces mutual interference between the two types of breaks, thereby enhancing the power of the test. Extensive numerical simulations confirm that this method can accurately detect the presence of structural breaks and reliably identify their types. Finally, case studies are provided to demonstrate the efficacy and practical applicability of the proposed method. Full article

In order to better understand the damage phenomenon and failure mechanism of planar lightwave circuit (PLC) optical splitters under force cycling, this paper established an online test experimental platform to study their optical and mechanical performance response under the action of force cycling. The research results show that under the action of force cyclic loading, the weakest area in the PLC optical splitter is the eight-channels output fiber array–PLC chip adhesively bonded joint; the moments of force cycle loading and unloading cause the insertion loss (IL) of the PLC optical splitter to fluctuate suddenly, especially at the moment of unloading. In addition, the research results show that under the action of force cyclic loading, the local deformation and damage behavior of the weak area can be reflected by the optical performance parameter indicators monitored in real time. This study helps to identify the location of weak areas of PLC optical splitters and understand their response behavior under force cyclic loads, which can provide a useful reference for subsequent measures to improve their long-term reliability. Full article

This narrative review aims to evaluate the current evidence on the use of cardiac telerehabilitation (CTR) in patients after myocardial infarction, focusing on the effectiveness of e-learning platforms and remote monitoring for addressing cardiovascular risk factors, improving physical fitness, and enhancing patient adherence. The review also explores the limitations and gaps in the literature, highlighting the need for future research to optimize CTR approaches. A comprehensive literature search was conducted using PubMed and Scopus, focusing on specific keywords. The search yielded fifteen randomized controlled trials. Data from these studies were analyzed to evaluate the methodology, interventions, patient characteristics, and outcomes related to the use of CTR in managing cardiovascular risk factors and improving physical fitness. The included studies demonstrated that CTR interventions, delivered via online platforms, phone calls, and smart devices, were effective in improving cardiovascular risk factors, physical activity levels, and overall patient satisfaction. CTR appears to be associated with improvements in exercise tolerance, VO₂ max, body composition, and adherence. While the outcomes were promising, there is still limited evidence regarding the long-term impact of CTR on cardiovascular risk factors and lifestyle interventions, particularly in non-exercise components like dietary management and psychological support. Cardiac telerehabilitation presents a feasible and effective alternative to traditional in-hospital rehabilitation programs for patients recovering from myocardial infarction. The integration of e-learning platforms and smart devices enhances patient adherence, improves cardiovascular risk factors, and increases access to rehabilitation services, particularly for those who face barriers to traditional care. However, further large-scale studies are needed to establish standardized protocols and best practices for CTR. Additionally, future research should address disparities in access to digital health technologies, especially among rural and underserved populations, to ensure equitable access to these innovative approaches. Full article

This review paper aims to present a comprehensive overview of the evaluation, simulation, and control of heating, ventilation, and air conditioning (HVAC) systems in nuclear power plants (NPPs), specifically highlighting their importance in maintaining operational safety, thermal performance, and energy efficiency. The study’s authors summarize recent developments in HVAC technologies, such as passive cooling systems, data-driven energy management frameworks, and intelligent control strategies, to cope with the specific challenges of NPPs. Various passive cooling systems, including heat pipes, thermosyphons, and loop heat pipes, have proven themselves by their ability to remove residual heat from spent fuel pools and reactors power plants with high efficiency. Through experimental studies, they have shown their ability to eliminate operational vulnerability to accidents or guarantee any desired long-term cooling. Intelligent sensor networks allow a more data-driven approach to HVAC control, enabling online energy management frameworks and advanced intelligent control systems. These exhibit considerable promise for optimizing HVAC performance, decreasing energy consumption, and improving operational flexibility in multi-zone systems. Such capabilities are ideal for addressing the dynamic and safety-critical nature of NPPs. They are first enabled by the use of these technologies for real-time monitoring, predictive maintenance, and adaptive control. When applied with advanced HVAC control systems, passive cooling techniques provide an exciting route to improve safety and energy efficiency. An overview of the key findings is that robust thermal management solutions combined with intelligent control and intelligent adaptation are essential when addressing the rapidly evolving demands of nuclear energy systems. This work highlights the priorities in the next generation of nuclear power plants, which should actively pursue seamless integration of out-of-system technologies into existing NPP infrastructures, enabling scalable, cost-effective, and resilient solutions. Full article

In modern society with a highly developed Internet, online public opinions on swine epidemic diseases have become one of the important influencing factors for the fluctuation of pork prices. In this paper, the Baidu AI large model, Time-Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-VAR) and Latent Dirichlet allocation (LDA) approaches are employed to investigate the dynamic impact of online public opinion regarding live swine epidemic diseases on fluctuations in pork price. The results show that: (1) Online public attention and negative sentiment exert significant time-varying impacts on pork price fluctuations, with these impacts being most pronounced in the short term and gradually diminishing over the medium and long term. (2) During the outbreaks of swine epidemic diseases, the impulse impact of online public attention and negative sentiment on pork price fluctuations exhibits distinct stage-specific characteristics. Initially, the impact is negative and subsequently turns positive before eventually waning. (3) The online discourse surrounding swine epidemic diseases can be categorized into four topics including disease transmission, vaccine technology, industry development, and disease prevention and control. Online public attention towards these four topics associated with negative sentiments generally contributes to variations in pork prices. Based on findings, several policy recommendations are proposed, including the timely release of swine epidemic disease information, the establishment and enhancement of the online public opinion monitoring and early warning system, as well as adherence to routine prevention and control of pig epidemic diseases. Full article

Background: Parkinson’s disease has a significant impact on vocal characteristics and speech patterns, making them potential biomarkers for monitoring disease progression. To effectively utilise these biomarkers, it is essential to understand how they evolve over time as this degenerative disease progresses. Objectives: This review aims to identify the most used vocal features in Parkinson’s disease monitoring and to track the temporal changes observed in each feature. Methods: An online database search was conducted to identify studies on voice and speech changes associated with Parkinson’s disease progression. The analysis examined the features and their temporal changes to identify potential feature classes and trends. Results: Eighteen features were identified and categorised into three main aspects of speech: articulation, phonation and prosody. While twelve of these features exhibited measurable variations in Parkinsonian voices compared to those of healthy individuals, insights into long-term changes were limited. Conclusions: Vocal features can effectively discriminate Parkinsonian voices and may be used to monitor changes through disease progression. These changes remain underexplored and necessitate more evidence from long-term studies. The additional evidence could provide clinical insights into the disease and enhance the effectiveness of automated voice-based monitoring. Full article

The fermentation of oolong tea is a critical process that determines its quality and flavor. Current fermentation control relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. In this study, using Tieguanyin oolong tea as the research object, features including the tea water loss rate, aroma, image color, and texture were obtained using weight sensors, a tin oxide-type gas sensor, and a visual acquisition system. Support vector regression (SVR), random forest (RF) machine learning, and long short-term memory (LSTM) deep learning algorithms were employed to establish models for assessing the fermentation degree based on both single features and fused multi-source features, respectively. The results showed that in the test set of the fermentation degree models based on single features, the mean absolute error (MAE) ranged from 4.537 to 6.732, the root mean square error (RMSE) ranged from 5.980 to 9.416, and the coefficient of determination (R²) values varied between 0.898 and 0.959. In contrast, the data fusion models demonstrated superior performance, with the MAE reduced to 2.232–2.783, the RMSE reduced to 2.693–3.969, and R² increased to 0.982–0.991, confirming that feature fusion enhanced characterization accuracy. Finally, the Sparrow Search Algorithm (SSA) was applied to optimize the data fusion models. After optimization, the models exhibited a MAE ranging from 1.703 to 2.078, a RMSE from 2.258 to 3.230, and R² values between 0.988 and 0.994 on the test set. The application of the SSA further enhanced model accuracy, with the Fusion-SSA-LSTM model demonstrating the best performance. The research results enable online real-time monitoring of the fermentation degree of Tieguanyin oolong tea, which contributes to the automated production of Tieguanyin oolong tea. Full article

Achieving the ambitious economic, social, and environmental goals of the UN Sustainable Development Goals (SDGs) requires strategic digital governance improvements that promote long-term and equitable participation in emerging technologies. However, research lacks clear confirmation regarding how governments’ varied policy investments in spheres, like infrastructure expansion, skills programming, and cybersecurity, specifically contribute to holistic sustainable development progress monitored across international benchmarks. Addressing persistent uncertainties, this study statistically modeled if national expenditures directed toward information and communications technology (ICT) access, digital literacy initiatives, and online privacy protections predict higher performances meeting SDGs longitudinally. Analyzing recent country-level data across 27 European nations, structural equation modeling uncovered positive relationships between all three complementary digital governance priority areas and national SDG Index achievement over time. Beyond theoretically validating the instrumental role of availability, capabilities, and security advancements for balanced digitization, findings offer policymakers vital empirical guidance to amplify social returns on ICT investments. The results also demonstrate practical tools to track implementation impacts amidst unrelenting technological shifts. Ultimately, equitably mainstreaming technologies’ vast problem-solving potential necessitates evidence-based digital governance carefully expanding equitable participation. This work aims to equip leaders to purposefully craft enabling, empowering ICT policy ecosystems advancing urgent development aims. Full article