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This work demonstrates the use of an erbium-doped fiber amplifier (EDFA), a tunable bandpass filter (TBF), and a tunable delay line interferometer (TDLI) to form a ring laser that produces multi-format, multi-wavelength laser beams. The TDLI serves as the core of the proposed laser generation system. TDLI harnesses the weak Fabry–Pérot (FP) interferences generated by its built-in 50/50 beamsplitter (BS) with unalterable filtering characteristics and the interferences with free spectral range (FSR) adjustable from each of its two outputs with nearly complementary phases to superpose and generate a variable interference standing wave. The interferometric standing wave and weak FP interferences are used to form a spatial-hole burning to promote the excitation of multi-format and multi-wavelength lasers. The proposed system enables dual-wavelength spacing ranging from 0.3 nm to 3.35 nm, with a switchable wavelength position at approximately 1527 nm to 1535 nm, providing flexible tunability.

Fiber-optic sensors, such as fiber Bragg grating (FBG) sensors and fiber-optic interferometers, have excellent sensing capabilities for industrial, chemical, and biomedical engineering applications. This paper used machine learning to enhance the number of fiber-optic sensing placement points and promote the cost-effectiveness and diversity of fiber-optic sensing applications. In this paper, the framework adopted is the FBG cascading an interferometer, and a deep belief network (DBN) is used to demodulate the wavelength of the sampled complex spectrum. As the capacity of the fiber-optic sensor arrangement is optimized, the peak spectra from FBGs undergoing strain or temperature changes may overlap. In addition, overlapping FBG spectra with interferometer spectra results in periodic modulation of the spectral intensity, making the spectral intensity variation more complex as a function of different strains or temperature levels. Therefore, it may not be possible to analyze the sensed results of FBGs with the naked eye, and it would be ideal to use machine learning to demodulate the sensed results of FBGs and the interferometer. Experimental results show that DBN can successfully interpret the wavelengths of individual FBG peaks, and peaks of the interferometer spectrum, from the overlapping spectrum of peak-overlapping FBGs and the interferometer.

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.

Deploying distributed fiber-optic sensor (DFOS) technology to gather environmental parameters over expansive areas is an essential monitoring strategy in the context of comprehensive searches for anomalous places. This study utilizes a single temperature measurement channel within a commercial Raman-based distributed temperature sensing (RDTS) interrogator and divides it into two separate, uncorrelated paths to enable spatial duplex temperature measurements. The distinction between temperature events corresponding to each path in the dual separate path (DSP) in RDTS can be achieved when temperature events are concurrently occurring in the DSP. Additionally, the RDTS–DSP solution may integrate free space optics (FSO) into its fiber path, which serves to enhance the user-friendliness, scalability, and cost-effectiveness of DFOS technology. An RDTS measurement channel can effectively function as a DSP, thus doubling the RDTS measurement pathway, and can be combined with FSO to significantly improve RDTS performance.

This paper proposes a novel approach to enhance the multichannel fiber optic sensing systems by integrating an Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) to accurately predict sensor responses despite signals overlapping and crosstalk between sensors. The IFFT-DNN leverages both frequency and time domain information, enabling a comprehensive feature extraction which enhances the prediction accuracy and reliability performance. To investigate the IFFT-DNN’s performance, we propose a multichannel water level sensing system based on Free Space Optics (FSO) to measure the water level at multiple points in remote areas. The experimental results demonstrate the system’s high precision, with a Mean Absolute Error (MAE) of 0.07 cm, even in complex conditions. Hence, this system provides a cost-effective and reliable remote water level sensing solution, highlighting its practical applicability in various industrial settings.

Background: Endocrine-disrupting chemicals (EDCs) are pervasive in everyday environments. The impacts of these chemicals, along with EDC-related lifestyle and dietary habits on neurocognitive function, are not well understood. Methods: The Chang Gung Community Medicine Research Center conducted a cross-sectional study involving 887 participants. From this initial cohort, 120 individuals were selected based on their EDC exposure scores for detailed analysis. Among these, 67 participants aged 55 years or older were further chosen to undergo cognitive impairment assessments using the Ascertain Dementia-8 (AD-8) questionnaire. Results: These 67 older participants did not significantly differ in age, albuminuria, or estimated glomerular filtration rate compared to those with lower impairment scores. This study revealed that mono-(2-ethylhexyl) phthalate (MEHP) levels (8.511 vs. 6.432 µg/g creatinine, p = 0.038) were associated with greater risk of cognitive impairment (AD-8 ≥ 2). Statistical models adjusting for age, gender, and diabetes indicated that MEHP levels positively correlated with AD-8 scores, achieving statistical significance in more comprehensive models (β ± SE: 0.160 ± 0.076, p = 0.042). Logistic regression analysis underscored a significant positive association between high MEHP levels and higher AD-8 scores (odds ratio: 1.217, p = 0.006). Receiver operating characteristic curves highlighted the association of high MEHP levels and EDC exposure scores for significant cognitive impairment, with areas under the curve of 66.3% and 66.6%, respectively. Conclusion: Exposure to EDCs, specifically di-(2-ethylhexyl) phthalate, the precursor to MEHP, may be associated with neurocognitive impairment in middle-aged and older adults.

Background and Objectives: Although statins are recommended for secondary prevention of acute ischemic stroke, some population-based studies and clinical evidence suggest that they might be used with an increased risk of intracranial hemorrhage. In this nested case–control study, we used Taiwan’s nationwide universal health insurance database to investigate the possible association between statin therapy prescribed to acute ischemic stroke patients and their risk of subsequent intracerebral hemorrhage and all-cause mortality in Taiwan. Materials and Methods: All data were retrospectively obtained from Taiwan’s National Health Insurance Research Database. Acute ischemic stroke patients were divided into a cohort receiving statin pharmacotherapy and a control cohort not receiving statin pharmacotherapy. A 1:1 matching for age, gender, and index day, and propensity score matching was conducted, producing 39,366 cases and 39,366 controls. The primary outcomes were long-term subsequent intracerebral hemorrhage and all-cause mortality. The competing risk between subsequent intracerebral hemorrhage and all-cause mortality was estimated using the Fine and Gray regression hazards model. Results: Patients receiving statin pharmacotherapy after an acute ischemic stroke had a significantly lower risk of subsequent intracerebral hemorrhage (p < 0.0001) and lower all-cause mortality rates (p < 0.0001). Low, moderate, and high dosages of statin were associated with significantly decreased risks for subsequent intracerebral hemorrhage (adjusted sHRs 0.82, 0.74, 0.53) and all-cause mortality (adjusted sHRs 0.75, 0.74, 0.74), respectively. Conclusions: Statin pharmacotherapy was found to safely and effectively reduce the risk of subsequent intracerebral hemorrhage and all-cause mortality in acute ischemic stroke patients in Taiwan.

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product’s normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.

This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques.

Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

This paper introduces a new bidirectional integration approach that combines fiber sensor/free space optics (FSO) communication using an intensity and wavelength division multiplexer (IWDM) techniques-based long-distance fiber Bragg grating (FBG) sensor strain-sensing system. By implementing coarse wavelength division multiplexing (CWDM), the system achieves the simultaneous transmission of optical communication and fiber optical sensor (FOS) sensing signals, resulting in a highly capable, flexible, and cost-effective solution. The proposed FSO transmission technique addresses complex fiber cable installation concerns with topographical limitations. This bidirectional structure ensures the reliability and stability of the long-distance FBG sensor system, supported by extensive research and experimentation. A hybrid stacked gated recurrent units and long short-term memory (SGRU-LSTM) model is proposed to enhance strain measurement accuracy by predicting and measuring the central wavelength of overlapped strain-sensing FBG sensor signals. The results demonstrate the superiority of the proposed model in peak wavelength detection accuracy. The primary benefit of integrating communication and sensing is the significant reduction in construction costs by eliminating the requirement for two individual fiber optic systems, as the integration allows for a single system to fulfill both functions, resulting in more efficient and cost-effective implementation. Overall, this paper contributes to advancing long-distance FBG sensor systems by integrating fiber sensor/FSO communication and deep learning techniques, improving transmission distance, multiplexing capacity, measurement accuracy, system survivability, and cost-effectiveness.

Artificial intelligence has revolutionised smart medicine, resulting in enhanced medical care. This study presents an automated detector chip for age-related macular degeneration (AMD) using a support vector machine (SVM) and three-dimensional (3D) optical coherence tomography (OCT) volume. The aim is to assist ophthalmologists by reducing the time-consuming AMD medical examination. Using the property of 3D OCT volume, a modified feature vector connected method called slice-sum is proposed, reducing computational complexity while maintaining high detection accuracy. Compared to previous methods, this method significantly reduces computational complexity by at least a hundredfold. Image adjustment and noise removal steps are excluded for classification accuracy, and the feature extraction algorithm of local binary patterns is determined based on hardware consumption considerations. Through optimisation of the feature vector connection method after feature extraction, the computational complexity of SVM detection is significantly reduced, making it applicable to similar 3D datasets. Additionally, the design supports model replacement, allowing users to train and update classification models as needed. Using TSMC 40 nm CMOS technology, the proposed detector achieves a core area of 0.12 mm2 while demonstrating a classification throughput of 8.87 decisions/s at a maximum operating frequency of 454.54 MHz. The detector achieves a final testing classification accuracy of 92.31%.

Diabetic kidney disease is the most common primary disease of end-stage kidney disease globally; however, a sensitive and accurate biomarker to predict this disease remains awaited. microRNAs are endogenous single-stranded noncoding RNAs that have intervened in different post-transcriptional regulations of various cellular biological functions. Previous literatures have reported its potential role in the pathophysiology of diabetic kidney disease, including regulation of Transforming Growth Factor-β1-mediated fibrosis, extracellular matrix and cell adhesion proteins, cellular hypertrophy, growth factor, cytokine production, and redox system activation. Urinary microRNAs have emerged as a novel, non-invasive liquid biopsy for disease diagnosis. In this review, we describe the available experimental and clinical evidence of urinary microRNA in the context of diabetic kidney disease and discuss the future application of microRNA in routine practice.

This paper proposes a novel sensing system based on a tunable delay line interferometer. The tunable delay line interferometer has been used to interpret strain, bringing us high accuracy as well as tunability. The shifted wavelength of the fiber Bragg grating (FBG) sensor caused by the applied strain can be visualized by an optical power meter (OPM) instead of an optical spectrum analyzer (OSA) by converting it to a power change using a tunable delay line interferometer (TDI). Different free spectral ranges (FSRs) are assigned to the TDI to investigate the accuracy and operation range of the proposed system. Thus, we achieve high accuracy and sensitivity by adjusting the FSR to 0.47 nm. Experimental results show that the maximum output power variation corresponding to a strain of 10 με is about 0.9 dB when the FSR is set to 0.47 nm. The proposed system is also cost-effective regarding the equipment utilized for interrogation: a tunable delay line interferometer and an optical power meter.

Background: Animal studies have demonstrated that an oral absorbent AST-120 modulates gut environment. However, this phenomenon remains unclear in humans. This study aimed to assess the effects of AST-120 on the gut microbiota, related functional capability and metabolomic profiling in advanced chronic kidney diseases (CKD) patients. Methods: Eight advanced CKD patients with AST-120 (CKD+AST), 24 CKD patients (CKD), and 24 non-CKD controls were enrolled. We analyzed 16S rRNA pyrosequencing of feces and serum metabolomics profiling. Results: The CKD+AST group exhibited dispersed microbial community structure (β-diversity, p < 0.001) compared to other groups. The relative abundances of at least 16 genera were significantly different amongst the three groups. Increases of fatty acids-producing bacteria (Clostridium_sensu_stricto_1, Ruminococcus_2, Eubacterium_nodatum and Phascolarctobacterium) associated with elevated serum acetic acid and octanoic acid levels were found in CKD+AST group. Analysis of microbial gene function indicated that pathway modules relevant to metabolisms of lipids, amino acids and carbohydrates were differentially enriched between CKD+AST and CKD groups. Specifically, enrichments of gene markers of the biosynthesis of fatty acids were noted in the CKD+AST group. Conclusion: Advanced CKD patients exhibited significant gut dysbiosis. AST-120 can partially restore the gut microbiota and intervenes in a possible signature of short- and medium-chain fatty acids metabolism.

As of 2022, most automatic deburring trajectories are still generated using offline programming methods. The trajectories generated using these methods are often suboptimal, which limits the precision of the robotic arms used to perform automatic deburring and, in turn, results in workpiece dimensional errors. Therefore, despite advances in automated deburring trajectory generation, deburring is still mostly performed manually. However, manual deburring is a time-consuming, labor-intensive, and expensive process that results in small profit margins for organizational equipment manufacturers (OEMs). To address these problems and the obstacles to the implementation of automated deburring in the robotics industry, the present study developed an online automated deburring trajectory generation method that uses 2D contouring information obtained from linear contour scanning sensors, a CAD model, and curve fitting to detect burrs and generate appropriate trajectories. The method overcomes many of the limitations of common deburring methods, especially by enabling real-time trajectory tracking. When the method was tested using bicycle forks, work that originally took three to four people 8–12-h to complete was completed by one person in 30 min, and the production cost was reduced by 70%.

Background: Immune response assessed by the quantification of neutralizing antibodies (nAbs) and predictors associated with immunogenicity after the prime-boost ChAdOx1 (Oxford–AstraZeneca) COVID-19 vaccine in hemodialysis (HD) patients remains unclear. Methods: This prospective study enrolled 174 HD patients and 67 healthy subjects to evaluate antibodies against the spike protein 1 and receptor-binding domain of severe acute respiratory syndrome coronavirus type 2 after prime-booster vaccination, by using enzyme-linked immunosorbent assay and applied spline-based generalized additive model regression analysis to predict 50% neutralization titer (NT50). The correlation between HD parameters and NT50 was analyzed. Results: NT50 was lower in HD patients compared with healthy controls after the prime-boost dose (p < 0.001). The geometric mean titer ratios were higher in first-dose seronegative than in the seropositive subgroup in HD patients and healthy controls (6.96 vs. 2.36, p = 0.002, and 9.28 vs. 1.26, p = 0.011, respectively). After two doses of ChAdOx1, one-way ANOVA showed that Ca × P was positively associated with NT50 (p trend = 0.043) and multiple linear regression showed the similar results (p = 0.021). Kt/V (a quantification of dialysis adequacy) (OR = 20.295, p = 0.005) could independently predict seroconversion (NT50 ≥ 35.13 IU/mL). Conclusion: Adequacy of hemodialysis could independently predict seroconversion in HD subjects vaccinated with prime-boost doses of ChAdOx1.

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual’s life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on undergraduate neurotic students’ emotion regulation. We acquired data of their psychological states, physiological changes, and electroencephalogram (EEG), before and after BMI, in resting states and tasks. Through behavioral analysis, we found the students’ anxiety and stress levels significantly reduced after BMI, with p-values of 0.013 and 0.027, respectively. Furthermore, a significant difference between students in emotion regulation strategy, that is, suppression, was also shown. The EEG analysis demonstrated significant differences between students before and after MI in resting states and tasks. Fp1 and O2 channels were identified as the most significant channels in evaluating the effect of BMI. The potential of these channels for classifying (single-channel-based) before and after BMI conditions during eyes-opened and eyes-closed baseline trials were displayed by a good performance in terms of accuracy (~77%), sensitivity (76–80%), specificity (73–77%), and area-under-the-curve (AUC) (0.66–0.8) obtained by k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. Mindfulness can thus improve the self-regulation of the emotional state of neurotic students based on the psychometric and electrophysiological analyses conducted in this study.

A weakly-coupled multicore fiber can generate supermodes when the multi-cores are closer to enter the evanescent power coupling region. The high sensitivity strain sensors using tapered four-core fibers (FCFs) were demonstrated. The fan-in and fan-out couplers were used to carry out light coupling between singlemode fibers and the individual core of the FCFs. A broadband lightsource from superlumminescent diodes (SLDs) was launched into one of the four cores arranged in a rectangular configuration. When the FCF was substantially tapered, the asymmetric supermodes were produced to generate interferences through this corner-core excitation scheme. During tapering, the supermodes were excited based on a tri-core structure initially and then transited to a rectangular quadruple-core structure gradually to reach the sensitivity of 185.18 pm/μԑ under a tapered diameter of 3 μm. The asymmetric evanescent wave distribution due to the corner-core excitation scheme is helpful to increase the optical path difference (OPD) between supermodes for improving the strain sensitivity.

Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.