Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind wave and water level dataset for Hornsund, Svalbard (2013–2021)

Abstract. Underwater pressure sensors were deployed near-continuously at various locations of the nearshore (8–23 m depth) Hornsund fjord, Svalbard, between July 2013 and February 2021. Raw pressure measurements at 1 Hz were used to derive mean water levels, wave spectra and bulk wave parameters for 1024 s bursts at hourly intervals. The procedure included subtracting atmospheric pressure, depth calculation, fast Fourier transform, correction for the decrease of the wave orbital motion with depth and adding a high-frequency tail. The dataset adds to the sparse in situ measurements of wind waves and water levels in the Arctic, and it can be used, for example, for analysing seasonal wind wave conditions and inter-annual trends and calibrating/validating wave models. The dataset is stored in the PANGAEA repository (https://doi.org/10.1594/PANGAEA.954020; Swirad et al., 2023).
Land use and land cover change in Manokwari, West Papua Province

The purpose of this study was to determine the extent of forest land change due to deforestation and forest degradation in Manokwari Regency as a development activity through land cover dynamics using satellite imagery data and make a land change prediction based on land change data analysis. This study used a descriptive method with an analysis of land cover changes using an overlay technique to determine land cover change. The results showed the change in land cover that occurred during the period 2009-2017 is dominated by changing of forested land to become settlement and plantation areas. The land cover has experienced a significant change in primary dryland forest, where nearly four thousand hectares of forest land have experienced degradation and deforestation. The land use prediction in 2025 was a reduction of the dry land forested area by about 9.1% while plantation and settlement were increased by about 4.5% and 2.0% respectively. Based on the comparison results, it was found that the predicted land use of Manokwari Regency in 2025 has land use suitability with the Regional Spatial Planning of 85.87%, not suitable at 6.8 %, and land use that allows for change at 7.33%.
Photon statistics and quantum phase distribution for coherent optical links in the presence of phase noise from both the laser source and the RF oscillator, and dispersion

A model of photon statistics and quantum phase distribution is analyzed for coherent optical communication links in the presence of quantum phase noise, dispersion and phase noise of the radio-frequency (RF) oscillator using an in-line optical amplifier. The quantum analysis of a dual drive Mach–Zehnder modulator and the quantum transfer function for an optical field mode across an in-line amplifier is performed by taking into account both the quantum and the classical phase noises. The resulting photon statistics are compared with the semiclassical model. We analyze the quantum phase distribution by considering the Wigner quasiprobability distribution for a coherent optical link. Here, we investigate the broadening of the quantum phase distribution due to quantum phase noise of the laser, the dispersive optical fiber, the phase noise of the RF oscillator and the inevitable noise coefficient of the optical amplifier for arbitrary amplified spontaneous emission (ASE) field modes. We analytically investigate the quantum analog to the bit error rate (BER) as a function of the laser’s linewidth, the linewidth of the RF oscillator and chromatic dispersion. The results of quantum models are compared with the semiclassical model. Quantum treatment of both the BER and variance is found to agree with prior results for a single-mode ASE field in the absence of phase noise but deviate when the effect of phase noise is considered. Including phase noise effects in the amplitude in the semiclassical treatment of the BER yields a new semiclassical expression that agrees with quantum expressions of machine precision for single-mode ASE fields.
Climate change is enforcing physiological changes in Arctic Ecosystems

Remote sensing and site-level observations have shown unprecedented changes in Arctic ecosystems owing to climate warming. These observations include greening and browning trends in Arctic vegetation as well as changes in species composition both in the tundra and the boreal forests. Here, we leveraged solar induced chlorophyll fluorescence (SIF) to study changes in ecosystem phenology across the pan-Arctic domain from 2000 to 2020. Ecoregions, as a proxy for plants’ functional traits, were the single most important variable to explain the spatial and phenological heterogeneity in observed SIF trends. We observed unique regional trends in ecosystems responses to climate change affecting the timing of spring photosynthesis onset, magnitude of peak productivity during the growing season (GS) and fall senescence. While, Photosynthetic activity in the early GS showed increasing trends across the vast majority of the pan-Arctic, it tends to decline at the end of the season for nearly half of the land area, including parts of North America but more significantly in central Siberia. The observed changes in phenology highlight the role of biodiversity in regional climate sensitivity and emphasizes the need for better representations of sub-biomes to community level information in Arctic ecosystem process models and projections. These results also highlight the importance of phenology information in ecosystem models for better understanding of the timing and magnitude of carbon uptake in the Arctic domain with accelerated changes in climate.
Revolutionizing Treatment for Chronic Subdural Hematoma: Promising Outcomes With Middle Meningeal Artery Embolization

Chronic subdural hematoma (cSDH) is a common condition that typically requires surgical intervention. Middle meningeal artery embolization (MMAE) has emerged as a potential alternative treatment option, although the choice of embolization material remains a topic of debate. In this case series, we report on the outcomes of 10 patients with cSDH who underwent MMAE. Most patients experienced symptom relief and a significant reduction in cSDH size post-procedure. Despite the presence of comorbidities and risk factors, most patients had positive outcomes following MMAE treatment. Only one patient required surgical intervention after the MMAE procedure due to the progression of symptoms, while MMAE successfully prevented recurrence in most patients. Our findings suggest that MMAE can be a promising treatment option for selected patients with cSDH. However, further studies are needed to compare the efficacy and safety of different embolization materials in MMAE procedures for cSDHs.
Towards Incorporating Toeplitz Covariance into Asymptotic Maximum Likelihood for Estimating Nominal Direction of Spatially Distributed Source

Abstract – This paper addresses the covariance matrix estimation for improving the problem of estimating the nominal direction of a spatially distributed source in Rayleigh channel by employing a sensor array at the receiver. Three associated contributions are affordable herein. We first propose a large-sample approximation of maximum likelihood (ML) estimator for estimating a covariance matrix with linearly affine structure. Later, a connection of the proposed estimator to an existed criterion is secondly provided. This is to emphasize that it yields the same solution as the weighted covariance-matching criterion. The application area of such a notification stems from the fact that when invoking the uniform linear array with phase reference at the first element, the array covariance matrix is not only Hermitian but also Toeplitz. Based on such a priori knowledge, it is advantageous to make use of structured covariance matrix in parameter estimation. Towards this end, the last contribution for decreasing the directional estimate error is to incorporate the imposed Toeplitz-Hermitian matrix into another large-sample approximation of the ML estimator. Numerical simulation of the proposition is verified and then indicated that, particularly in non-asymptotic region, the asymptotically efficient estimator with Toeplitz constraint always outperforms the conventional approach without the additional information.
Phenotypic Interindividual Differences in the Dynamic Structure of Sleep in Healthy Young Adults

Introduction Evaluating the dynamic structure of sleep may yield new insights into the mechanisms underlying human sleep physiology. Methods We analyzed data from a 12-day, 11-night, strictly controlled laboratory study with an adaptation night, 3 iterations of a baseline night followed by a recovery night after 36 h of total sleep deprivation, and a final recovery night. All sleep opportunities were 12 h in duration (22:00–10:00) and recorded with polysomnography (PSG). The PSG records were scored for the sleep stages: rapid eye movement (REM) sleep; non-REM (NREM) stage 1 sleep (S1), stage 2 sleep (S2), and slow wave sleep (SWS); and wake (W). Phenotypic interindividual differences were assessed using indices of dynamic sleep structure – specifically sleep stage transitions and sleep cycle characteristics – and intraclass correlation coefficients across nights. Results NREM/REM sleep cycles and sleep stage transitions exhibited substantial and stable interindividual differences that were robust across baseline and recovery nights, suggesting that mechanisms underlying the dynamic structure of sleep are phenotypic. In addition, the dynamics of sleep stage transitions were found to be associated with sleep cycle characteristics, with a significant relationship between the length of sleep cycles and the degree to which S2-to-W/S1 and S2-to-SWS transitions were in equilibrium. Discussion Our findings are consistent with a model for the underlying mechanisms that involves three subsystems – characterized by S2-to-W/S1, S2-to-SWS, and S2-to-REM transitions – with S2 playing a hub-like role. Furthermore, the balance between the two subsystems within NREM sleep (S2-to-W/S1 and S2-to-SWS) may serve as a basis for the dynamic regulation of sleep structure and may represent a novel target for interventions aiming to improve sleep.
Contribution of material properties of cellular components on the viscoelastic, stress-relaxation response of a cell during AFM indentation

ABSTRACT The close relationship between the mechanical properties of biological cells, namely, elasticity, viscosity, and the state of its disease condition has been widely investigated using atomic force microscopy (AFM). In this study, computational simulation of the AFM indentation is carried out using a finite element (FE) model of an adherent cell. A parametric evaluation of the material properties of the cellular components on the viscoelastic, stress-relaxation response during AFM indentation is performed. In addition, the loading rate, the size of the nucleus, and the geometry of the cell are varied. From the present study, it is found that when comparing the material properties derived from experimental force-deflection curves, the influence of loading rates should be accommodated. It also provides a framework that can quantify the variation of the mechanical property with various stages of malignancy of the cancer cell, a potential procedure for cancer diagnosis.
A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information

Purpose This study aimed to develop a deep learning model based on chest radiography (CXR) images and clinical data to accurately classify gram-positive and gram-negative bacterial pneumonia in children to guide the use of antibiotics. Methods We retrospectively collected CXR images along with clinical information for gram-positive (n=447) and gram-negative (n=395) bacterial pneumonia in children from January 1, 2016, to June 30, 2021. Four types of machine learning models based on clinical data and six types of deep learning algorithm models based on image data were constructed, and multi-modal decision fusion was performed. Results In the machine learning models, CatBoost, which only used clinical data, had the best performance; its area under the receiver operating characteristic curve (AUC) was significantly higher than that of the other models (P<0.05). The incorporation of clinical information improved the performance of deep learning models that relied solely on image-based classification. Consequently, AUC and F1 increased by 5.6% and 10.2% on average, respectively. The best quality was achieved with ResNet101 (model accuracy: 0.75, recall rate: 0.84, AUC: 0.803, F1: 0.782). Conclusion Our study established a pediatric bacterial pneumonia model that utilizes CXR and clinical data to accurately classify cases of gram-negative and gram-positive bacterial pneumonia. The results confirmed that the addition of image data to the convolutional neural network model significantly improved its performance. While the CatBoost-based classifier had greater advantages owing to a smaller dataset, the quality of the Resnet101 model trained using multi-modal data was comparable to that of the CatBoost model, even with a limited number of samples.
Bluetooth-Based Healthcare Information and Medical Resource Management System

This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker’s burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.
Semantic Integration and Retrieval of Multimedia Metadata

A complete and automatic mapping of the whole MPEG-7 standard to OWL has been set up based on XML2OWL mapping and XML2RDF mapping to move multimedia metadata to semantic web.The generated explicit semantic metadata and MPEG-7 OWL ontology as an "upper-ontology" for multimedia metadata has made semantic integration and retrieval of multimedia metadata from instances of all the metadata sources possible.
Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and deploying personalized medicine and targeted clinical trials. Recent advances in ML have enabled the integration of wider ranges of data including both medical records and imaging (radiomics). However, the development of prognostic models is complex as no modeling strategy is universally superior to others and validation of developed models requires large and diverse datasets to demonstrate that prognostic models developed (regardless of method) from one dataset are applicable to other datasets both internally and externally. Using a retrospective dataset of 2,552 patients from a single institution and a strict evaluation framework that included external validation on three external patient cohorts (873 patients), we crowdsourced the development of ML models to predict overall survival in head and neck cancer (HNC) using electronic medical records (EMR) and pretreatment radiological images. To assess the relative contributions of radiomics in predicting HNC prognosis, we compared 12 different models using imaging and/or EMR data. The model with the highest accuracy used multitask learning on clinical data and tumor volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction, outperforming models relying on clinical data only, engineered radiomics, or complex deep neural network architecture. However, when we attempted to extend the best performing models from this large training dataset to other institutions, we observed significant reductions in the performance of the model in those datasets, highlighting the importance of detailed population-based reporting for AI/ML model utility and stronger validation frameworks. We have developed highly prognostic models for overall survival in HNC using EMRs and pretreatment radiological images based on a large, retrospective dataset of 2,552 patients from our institution. Diverse ML approaches were used by independent investigators. The model with the highest accuracy used multitask learning on clinical data and tumor volume. External validation of the top three performing models on three datasets (873 patients) with significant differences in the distributions of clinical and demographic variables demonstrated significant decreases in model performance. Significance: ML combined with simple prognostic factors outperformed multiple advanced CT radiomics and deep learning methods. ML models provided diverse solutions for prognosis of patients with HNC but their prognostic value is affected by differences in patient populations and require extensive validation.
Traffic Vibration Signal Analysis of DAS Fiber Optic Cables with Different Coupling Based on an Improved Wavelet Thresholding Method

Distributed Acoustic Sensing (DAS) is a novel technology that uses fiber optics to sense and monitor vibrations. It has demonstrated immense potential for various applications, including seismology research, traffic vibration detection, structural health inspection, and lifeline engineering. DAS technology transforms long sections of fiber optic cables into a high-density array of vibration sensors, providing exceptional spatial and temporal resolution for real-time monitoring of vibrations. Obtaining high-quality vibration data using DAS requires a robust coupling between the fiber optic cable and the ground layer. The study utilized the DAS system to detect vibration signals generated by vehicles operating on the campus road of Beijing Jiaotong University. Three distinct deployment methods were employed: the uncoupled fiber on the road, the underground communication fiber optic cable ducts, and the cement-bonded fixed fiber optic cable on the road shoulder, and compared for their outcomes. Vehicle vibration signals under the three deployment methods were analyzed using an improved wavelet threshold algorithm, which was verified to be effective. The results indicate that for practical applications, the most effective deployment method is the cement-bonded fixed fiber optic cable on the road shoulder, followed by the uncoupled fiber on the road, and the underground communication fiber optic cable ducts are the least effective. This has important implications for the future development of DAS as a tool for various fields.
Pharmaceutical company responses to Canadian opioid advertising restrictions: A framing analysis

The pharmaceutical industry’s promotion of opioids in North America has been well-documented. Yet despite the clear consequences of improperly classifying pharmaceutical company messaging and frequently permissive approaches that allow the pharmaceutical industry to self-regulate its own advertising, there has been scarce investigation to date of how pharmaceutical industry stakeholders interpret definitions of “advertising. ” This study explores how variations of “marketing” and “advertising” are strategically framed by the different actors involved in the manufacturing and distribution of pharmaceutical opioids. We employed a framing analysis of industry responses to Health Canada’s letter to Canadian manufacturers and distributors of opioids requesting their commitment to voluntarily cease all marketing and advertising of opioids to health care professionals. Our findings highlight companies’ continuing efforts to frame their messaging as “information” and “education” rather than “advertising” in ways that serve their interests. This study also calls attention to the industry’s continual efforts to promote self-regulation and internal codes of conduct within a highly permissive federal regulatory framework with little concern for violations or serious consequences. While this framing often occurring out of public sight, this study highlights the subtle means through which the industry attempts to frame their promotion strategies away from “marketing”. These framing strategies have significant consequences for the pharmaceutical industry’s capacity to influence healthcare professionals, patients, and the general public.
Enhancement and Optimization of Underwater Images and Videos Mapping

Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.
A new kind of definition evaluation function of image

It is proposed to use Energy Gradient as evaluation fuction of digital image,which is important to defecting image definition.The mathematical models for evaluation fuction,which characterize the preferable efficiency,have more high signal-to-noise,better reliability,powerful unimodality than other ones.It can be well applied to definition evaluation fuction of image.