Sampling Rate Research Articles

OGC SO34 - Textbook Outcome in Gastric Cancer Elective Surgery: A multicentre analysis

Abstract Background Textbook outcome (TO) is a composite measure of surgical outcomes that, when collectively achieved, depict the optimal result for the patient. Multidimensional parameters, like TO offer a in depth picture of hospital performance, by combining multiple indicators. While an increasing number of studies are evaluating TO in cancer, its application for gastric cancer is still a recent advance. Since surgical reality differs worldwide, a common language is essential when setting outcome goals. This study aims to assess TO accomplishment in several hospital typologies that offer curative-intent resections for gastric cancer. Method A prospective observational multi-institutional audit and 90 day follow-up included data from 138 institutions, in 47 countries. This is a preliminary analysis with focus on TO. Adult patients who underwent elective gastric cancer resection with curative intent, between April 1st and September 30th, 2022, were included. Textbook Outcome (TO) was achieved when all the following seven criteria were met: complete resection (R0), more than 15 lymph nodes sampled, discharge before 21 days, no mortality, no severe morbidity (Clavien–Dindo over 2), no reintervention and no readmission within 90 days post-operative. Results A total of 380 collaborators submitted data on 1538 patients. The mean age was 64.2 years and 58.5% were males. A total of 734 (48%) from the included 1538 patients, achieved TO. “No reintervention in 90 days” was registered in 92% of patients. “No morbidity in 90 days” was observed in 71%. “More than 15 lymph nodes sampled" was achieved in only 79%. The 90-day morbidity and mortality rates were 38.2% (n=587) and 2.9% (n=45), respectively. Conclusion This study demonstrates that achieving Textbook Outcome in gastric cancer resection is seldom attainable. A significant variability across the different outcome parameters within the participating centres was expected. The high rates of mortality-free and reintervention-free outcomes express an effective postoperative care. The lower rates of adequate lymph node sampling and morbidity-free outcomes indicate areas for improvement. Further research should focus on standardizing protocols and improving specific surgical practices to enhance overall TO rates in gastric cancer patients, among the hospital typologies and geographic locations, included in this study.

Development of an internet-of-things-based controlled-source ultrasonic audio frequency electromagnetic receiver

Abstract. Electromagnetic exploration, characterized by its low cost, wide applicability, and high operational efficiency, finds extensive applications in fields such as oil and gas exploration, mineral prospecting, and engineering geology. Traditional controlled-source electromagnetic detection methods are typically confined to operating frequencies below 250 kHz, resulting in insufficient detection accuracy for applications such as shallow- and intermediate-depth exploration, thereby constraining their performance in high-resolution imaging. To address these challenges, we propose a controlled-source ultrasonic audio frequency electromagnetic receive system based on the internet of things (IoT). We investigate cascaded digital filtering and sampling techniques to extend the receiver's sampling rate range, thereby elevating the operating frequency of controlled-source electromagnetic acquisition from the conventional maximum of 250 kHz to 1 MHz. The receiver achieves a sampling rate of up to 2.5 MHz, comprising three magnetic field measurement channels and two electric field measurement channels. The instrument is compact, lightweight, and capable of real-time data storage locally and real-time data transmission to an upper computer. Additionally, IoT technology is introduced, leading to the design of a cloud-based real-time remote control and data acquisition scheme. Experimental results demonstrate the stability of the instrument, meeting the requirements of field exploration.

Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning

Background/Objectives: Breast cancer is the most common cancer in women worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a predictive classification model for breast cancer mortality using real-world data, including various clinical features. Methods: A total of 11,286 patients with breast cancer from the National Cancer Center were included in this study. The mortality rate of the total sample was approximately 6.2%. Propensity score matching was used to reduce bias. Several machine learning models, including extreme gradient boosting, were applied to 31 clinical features. To enhance model interpretability, we used the SHapley Additive exPlanations method. ML analyses were also performed on the samples, excluding patients who developed other cancers after breast cancer. Results: Among the ML models, the XGB model exhibited the highest discriminatory power, with an area under the curve of 0.8722 and a specificity of 0.9472. Key predictors of the mortality classification model included occurrence in other organs, age at diagnosis, N stage, T stage, curative radiation treatment, and Ki-67(%). Even after excluding patients who developed other cancers after breast cancer, the XGB model remained the best-performing, with an AUC of 0.8518 and a specificity of 0.9766. Additionally, the top predictors from SHAP were similar to the results for the overall sample. Conclusions: Our models provided excellent predictions of breast cancer mortality using real-world data from South Korea. Explainable artificial intelligence, such as SHAP, validated the clinical applicability and interpretability of these models.

Monitoring of Non-Lame Horses and Horses with Unilateral Hindlimb Lameness at Rest with the Aid of Accelerometers

The aim of this study was to determine whether horses exhibiting unilateral hindlimb lameness unload (rest) the lame limb more than the contralateral limb. The resting/unloading of the hindlimbs and the time spent lying down were measured using accelerometers. Ten non-lame horses and 20 lame horses were recruited for participation and monitored for 11 h overnight with accelerometers (MSR145, sampling rate: 1 Hz, and measuring range: ±15 g) attached to the lateral metatarsal and metacarpal regions of each limb. Metatarsal and metacarpal orientation were used to determine whether the limb was unloaded (rested) or loaded, respectively, or whether the horses were lying down. The relation of resting time between non-lame and lame limbs (non-lame/lame: 0.85 ± 1.2) of the lame horses differed significantly (p = 0.035) from that of the non-lame horses (right/left: 1.08 ± 0.47). Non-lame horses rested their hindlimbs evenly (left: 15 ± 10%; right: 17 ± 16%). Horses with unilateral hindlimb lameness unloaded the lame limb longer (lame limb: 61.8 ± 25.3%, non-lame limb: 38.2 ± 25.3%) than their contralateral limb. The lame horses (13 ± 11%) lay down longer (p = 0.012) than the non-lame horses (3 ± 6%). The degree of lameness determined by the participating veterinarians (Vet Score) (r = −0.691, p < 0.01) and the asymmetry evaluated by the lameness locator (ALL) (r = −0.426, p = 0.019) correlated with the resting ratio (rest time ratio). Both factors were also correlated with the time spent lying down (Vet Score (r = 0.364, p = 0.048) and the ALL (r = 0.398, p = 0.03)). The ALL and VET Score were significantly correlated (r = 0.557, p = 0.01). The results of this study provide a good baseline for future research into how individual resting patterns may help to detect pain.

Optical Characterization of Parasitic Motion in a Long-Stroke Shaker

Abstract We present an optical scheme to simultaneously characterize the cross-axis and rota- tional parasitic motions of a long-stroke shaker. By leveraging the geometric properties of a corner cube retroreflector mounted on the shaker load table, we independently sample pitch, yaw, and horizontal and vertical displacements with sampling rates above 10 kHz. We have applied our optical apparatus to a 400-mm-stroke shaker operated from 0.1 Hz to 100 Hz and present two forms of analysis: (A) Frequency-domain data for the four measured parasitic degrees of freedom, with the resulting implications for accelerometer calibration uncertainties, and (B) extracted trajectories of the shaker table, allowing visualization of the bowed linear guide below 0.5 Hz and higher harmonics and hysteresis above 10 Hz. Our findings demonstrate our optical measurement scheme to be an effective tool for the characterization of parasitic motion for long-stroke shakers. As an example, we determine the "gravity error" in accelerometer calibration with our shaker to be (1.3 ± 0.1) % times the acceleration amplitude at 0.1 Hz, providing a correction to reduce uncertainty from this effect by an order of magnitude.

Intraoperative Peripheral Frozen Margin Assessment in Soft Tissue Sarcoma

ABSTRACTBackground/ObjectivesIntraoperative peripheral margin sampling in soft tissue sarcoma (STS) is a routine practice among musculoskeletal oncologists. Practice patterns are variable, and evidence to support it is lacking. Rates of peripheral margin sampling at our institution were analyzed in addition to its clinical utility and cost‐effectiveness.MethodsPeripheral margin sampling patterns at a tertiary sarcoma center were retrospectively evaluated. Concordance between peripheral margins and final pathology was assessed using McNemar's test and κ Coefficient. Clinical outcomes were compared, and a cost‐utility analysis was performed.ResultsA total of 179 patients were included. 66% had peripheral margins sampled of which 23% had frozen margins analyzed. Ten patients had positive margins (5.5% of all patients; 8.4% in those with margins sampled) and R1 margins on the final tumor specimen were identified in 15 patients (8.4%). There were no R2 resections. Three patients underwent repeat surgical resection (20%). Three patients with R1 resections had negative peripheral margins sampled, suggesting falsely reassuring peripheral margins. Peripheral margin sampling averaged $5000/patient.ConclusionsRoutine peripheral margin sampling in STS resection is of questionable utility with added cost. Prospective studies are warranted to determine the optimal approach to surgical margin assessment.

Nondispersive ultraviolet monitoring of pulsed H2S gas delivery

This article describes time-resolved optical measurements of H2S partial pressure and mass flow in a pulsed gas delivery system approximating injection conditions encountered during atomic layer deposition. A high-speed nondispersive ultraviolet (NDUV) gas analyzer design is employed for in-line H2S detection in a gas delivery line with flowing carrier gas. An in-place analyzer calibration performed in a reference cell yields an H2S detection limit of ≈1.4 Pa (at 22 °C) at a sampling rate of 1 kHz. Flow measurements performed on the delivery line are used to evaluate the effects of adjustable delivery parameters on the time-dependent injection system output. Short pulse widths exhibit partial pressure transients attributed to flow development within the different volumes of the delivery system. After ≈1.0 s of injection, steady-state flow is established across flow elements. A partial pressure of H2S in the delivery line is found to vary linearly with upstream H2S pressure, consistent with choked flow. A stronger scaling of partial pressure is evident when the flow coefficient of the downstream metering valve is adjusted. Estimated steady-state H2S flow rates in the range of 0.05–0.21 mg/s are observed within a limited range of valve flow coefficients. However, further increases in the flow coefficient do not result in increased flow, likely due to conductance limitations in downstream flow system components. The utility of NDUV absorption measurements for high-pressure pulsed gas delivery systems is discussed.

Computationally Efficient Inference via Time-Aware Modular Control Systems

Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed meta control)—an architecture for learning optimal control by employing a physics-informed neural network when the state space is too large for reward-based learning. Second, we offer a way to leverage impulse response as a tool for system modeling and control. We propose IMPULSTM, a novel approach for incorporating time awareness into recurrent neural networks designed to accommodate irregular sampling rates in the signal. Third, we propose DIMAS, a modular approach to increasing computational efficiency in distributed control systems via domain-knowledge integration. We analyze the performance of the first two contributions on a set of corresponding benchmarks and then showcase their combined performance as a domain-informed distributed control system. The proposed approaches show satisfactory performance both individually in their respective applications and as a connected system.

IMMU-55. TUMOR-WIDE RNA SPLICING ABERRATIONS GENERATE IMMUNOGENIC PUBLIC NEOANTIGENS IN GLIOBLASTOMAS AND OTHER CANCER TYPES

Abstract BACKGROUND Immunotherapy in gliomas is limited by tumor heterogeneity and low mutational burden. Aberrant RNA-splicing (neojunctions) offers a new source for targets, and our neoantigen discovery platform (SNIPP) characterizes a novel class of clonally-expressed splicing-derived neoantigens that elicit a CD8+ T-cell-mediated tumor killing response. METHODS SNIPP identified public neojunctions expressed in TCGA RNA-seq (positive sample rate (PSR) > 10%) and not in GTEx normal tissue RNA-seq data (PSR < 1%) across 12 cancer types. To characterize intratumorally-conserved neojunctions, we performed maximally-distanced multi-site biopsies (n=535) within glioma patients (n=56) and obtained RNA-seq data from each intratumoral site. Two independent algorithms then predicted peptide processing likelihood and HLA-binding affinity of ASN candidates. Neoantigen-specific TCR sequences characterized from subsequent PBMC in vitro sensitization and 10x V(D)J scRNA-seq were transduced into CD8+ T-cells for immunogenicity and cytotoxicity assays against glioma cell lines. RESULTS Our pipeline identified 789 public neojunctions, with 32 neojunctions concurrently identified in transcriptomic and proteomic glioma data and predicted to be presented by HLA-A*02:01 with high confidence. IVS and subsequent 10x VDJ scRNA-seq identified TCR clonotypes reactive against neojunctions in RPL22 (n=7) and GNAS (n=1), the latter being highly intratumorally-conserved (detected in > 90% of spatially-mapped biopsies across 17/56 patients (26.78%)). TCR-transduced T-cells demonstrated recognition and immunogenic activation against endogenously processed and presented neoantigens in multiple GBM PDX cell lines, and co-culturing these T-cells against both glioblastoma and melanoma cell lines demonstrated tumor-specific cytotoxicity. Furthermore, IDH1-mutant oligodendroglioma samples demonstrated significantly elevated expression of neojunctions over IDH1-mutant astrocytoma and IDH1wt subtypes. Differential gene expression (DESeq2) identified decreased expression of splicing factors due to oligodendroglioma-specific co-deletion of Chromosomes 1p/19q. siRNA knockdown of these splicing factors (e.g. SF3A3, SNRPD2) in IDH1wt glioma cells resulted in significantly increased expression of corresponding neojunctions. CONCLUSION Our unique SNIPP neoantigen discovery platform identified novel public tumor-wide splice-derived neoantigens and reactive TCRs, and its pan-cancer application characterized candidates that are targetable across multiple diseases.

Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes

Generative models, particularly diffusion-based approaches, have gained significant attention recently due to their ability to create realistic outputs. Despite their potential, the application of these models in manufacturing remains largely unexplored. This work presents a framework that addresses this gap by generating machined surface images guided by multiple sensor inputs in manufacturing. The proposed model integrates information from multiple sensors with varying sampling rates using multimodal embedding and employs a latent diffusion model to translate the fused sensor embedding into an image embedding, which is then converted into a machined surface image. The effectiveness of the framework is validated using real-world time-series data, including force, torque, acceleration, and sound, collected from various industrial processes, such as a carbon-fiber-reinforced plastic drilling process. The results demonstrate the model’s ability to predict defects from the generated machined surface images. The proposed approach can potentially revolutionize prognostics and health management (PHM) in smart manufacturing by enabling sensor-guided visual inspection, defect detection, process monitoring, and predictive maintenance.

A hybrid local-global neural network for visual classification using raw EEG signals

EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features from raw signals still perform lower than traditional frequency-domain features, and the methods are typically evaluated on small-scale datasets at a low sample rate, which can hinder the capabilities of deep-learning models. To overcome these limitations, we propose a hybrid local-global neural network, which can be trained end-to-end from raw signals without handcrafted features. Specifically, we first propose a reweight module to learn channel weights adaptively. Then, a local feature extraction module is designed to capture basic EEG features. Next, a spatial integration module fuses information from each electrode, and a global feature extraction module integrates overall time-domain characteristics. Additionally, a feature fusion module is proposed to extract efficient features in high sampling rate settings. The proposed model achieves state-of-the-art results on two commonly used small-scale datasets and outperforms baseline methods on three under-studied large-scale datasets. Ablation experimental results demonstrate that the proposed modules have a stable performance improvement ability on multiple datasets across different sample rates, providing a robust end-to-end learning framework.

Research on the design and application of Analog-to-Digital converters

Abstract. In the modern digital era, the conversion of analog signals to digital data is a crucial process for a wide range of applications in industries such as consumer electronics, medical devices, and telecommunications. The accelerated evolution of digital technology is driving a surge in demand for high-performance analog-to-digital converters (ADCs). The paper presents a comprehensive analysis of analog-to-digital converters, emphasizing their types, applications, performance measurements, and most recent developments. In the paper, emphasis is placed on the mechanisms of different ADC architectures, such as Successive Approximation Register (SAR), Sigma-Delta (-), and Flash ADCs, and their respective strengths and weaknesses are evaluated. In terms of technique, it synthesizes and summarizes research results from multiple academic sources and technical papers. In addition to underscoring the pivotal concerns of ADC design, including power consumption, resolution, and sample rate, the research delves into novel approaches that leverage the use of advanced materials and techniques. The research results demonstrate the continued development of ADC technology and its critical function in improving the effectiveness and performance of digital systems.

Signal Processing for Novel Noise Radar Based on de-chirp and Delay Matching

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments.

Exploring the value of hybrid capture-based next-generation sequencing technology in the suspected diagnosis of bloodstream infections

Background Determining the source of infection is significant for the treatment of bloodstream infections (BSI). The gold standard of blood infection detection, blood cultures, have low positive rates to meet clinical needs. In this study, we investigated the ability of hybrid capture-based next generation sequencing technology to detect pathogens in peripheral blood samples collected from patients with suspected BSI. Blood cultures and capture sequencing assays were also analyzed against the final clinical diagnoses. Methods In this study, peripheral blood samples were collected from patients with fever, chills, and suspected BSI at Jinshazhou Hospital of Guangzhou University of Chinese Medicine from March 2023 to January 2024. All samples were tested by three different technologies: plasma capture sequencing technology, white blood cell capture sequencing technology, and blood culture. Relevant clinical diagnostic information was also collected. The performances of the blood cultures were then compared to those of both plasma capture sequencing technology and white blood cell capture sequencing technology. Results A total of 98 patients were included in this study. The positive rates of probe capture next generation sequencing (NGS technology) in plasma and white blood cells were 81.63% and 65.31%, respectively, which were both significantly higher than that of the blood culture, which was 21.43% (p < 0.001). Taking blood culture as the standard control, the sensitivity and specificity of plasma capture sequencing were 85.71% and 71.43%, respectively, while the sensitivity and specificity of white blood cell sequencing were 76.19% and 81.82%, respectively. Upon final clinical diagnosis, the clinical agreement rates of the blood cultures, plasma capture sequencing, and white blood cell capture sequencing were 39.80%, 83.67%, and 73.47%, respectively. Conclusion Our study demonstrates the high accuracy of probe capture sequencing technology compared to blood cultures in the identification of pathogenic microorganisms in BSI upon final clinical diagnosis. Among the different sample types, white blood cell samples had a lower clinical compliance rate compared to plasma samples, possibly due to the higher host rate in cell samples, which impairs the sensitivity of pathogen detection.

Trade-Off Between Wear/Corrosion Performance and Mechanical Properties in D-AlNiCo Poly-Quasicrystals Through CNT Addition to the Microstructure

An ultrafine-grained Al71Ni14.5Co14.5/CNT poly-quasicrystal (QC/CNT) composite was synthesized using spark plasma sintering of powder components developed through electroless Ni-P/CNT plating of Co particles and mechanical alloying. The performance of the synthesized samples was studied using various testing methods, such as room temperature/hot compression, wear, and corrosion tests. The results were compared to the properties of alloy samples fabricated from raw and coated powders (without CNTs). The wear rate and friction coefficient of the quasicrystalline samples improved significantly due to the contribution of the CNTs. The wear rate of the CNT-containing specimens was 0.992 × 10−4 mm3/N/m, which is 47.1% lower than that of the QC sample. The positive impact of the CNTs on the corrosion potential and current density was further validated by the potentiodynamic polarization tests in a saline solution. However, these improvements in surface properties came at the cost of a 21.5% reduction in compressive strength, although the compressive strength still remained above 1.1 GPa at 600 °C. The results highlight an interesting trade-off between surface properties and mechanical strength, pointing toward the development of materials suitable for extreme conditions.

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning

Objective.Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.Approach.We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.Main results.LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.Significance.Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.

Development of an ultra-low-cost wireless data acquisition system for monitoring strain and deflection

Strain gauges are gaining popularity for monitoring stress and strain within structural elements and for detecting and identifying structural damage. Although they are relatively inexpensive, they are not as widely used as accelerometers in structural health monitoring. This is due to several factors, such as the high cost of data acquisition systems required to collect strain data and the need for a large number of gauges to monitor a structure. Therefore, this research aims to develop a low-cost wireless strain gauge data acquisition system with acceptable accuracy and a high sampling rate. This system is composed of commonly available electronic components and open-source software, with a cost of $20. The system’s precision in measuring displacement was assessed in a laboratory experiment, and further experiments were conducted on an aluminum beam to investigate its response under both static and dynamic conditions. Additionally, the accuracy of the system for measuring deflection and strain in reinforced concrete elements was assessed through a bending test on a reinforced concrete beam. The findings indicated that the developed data acquisition system can accurately measure displacement, with a root mean square error of 0.1 mm, as well as strain values within the range of a few micro-strains.

A study on the thermal behavior of coal gangue mountains under airflow influence based on MD and CFD simulations

The long-term accumulation of coal gangue can lead to an increase in internal temperature. If the temperature becomes excessive, it can cause spontaneous combustion, posing serious environmental risks. The process is significantly influenced by external airflow. This study analyzes the thermodynamic properties and oxidation heat release rate of coal gangue samples through laboratory experiments. Subsequently, molecular dynamics (MD) simulations are used to investigate the mechanism of oxidation heat release during the heating process of coal gangue under varying oxygen supply conditions. Based on these findings, a coupled three-field model for the heating process of coal gangue mountains is developed. Using computational fluid dynamics (CFD) methods, transient simulations are conducted to analyze the internal thermal behavior of coal gangue mountains under the influence of external airflow, clarifying the risks of spontaneous combustion. The results indicate that after reaching T2 (295.16 °C), coal gangue enters an accelerated oxidation phase, with an increased heat release rate, heightening the risk of spontaneous combustion. The oxidation heat release rate of coal gangue accelerates with rising temperature and oxygen concentration, entering a rapid reaction phase after 300 °C, with a sharp increase in reaction rate. Compared to N2, coal gangue surfaces exhibit stronger adsorption of O2, and temperature changes affect N2 diffusion more sensitively (O2 diffusion coefficient increases by 0.312 Å2/ps, N2 diffusion coefficient increases by 0.334 Å2/ps). The isosteric heat of O2 adsorption in coal gangue decreases significantly with increasing adsorption amount, and higher temperatures hinder competitive adsorption of O2. The temperature in the deep and foot areas of the coal gangue mountains is lower, while the middle and upper areas near the slope surface exhibit higher temperatures, indicating a higher risk of spontaneous combustion in these regions. When environmental wind speed is too high or too low, both the internal temperature of the coal gangue mountains and the area of spontaneous combustion risk zones decrease. At a wind speed of 3 m/s, the spontaneous combustion risk zone reaches its maximum area of 73.47 m2, but when the wind speed increases to 7 m/s, the risk area decreases to 65.72 m2.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Impedance measurement of rolling bearings using an unbalanced AC wheatstone bridge

Industry 4.0 drives the demand for cost-efficient and reliable process data and condition monitoring. Therefore, visualizing the state of tribological contacts becomes important, as they are regularly found in the center of many applications. Utilizing rolling element bearings as sensors and monitoring their health by the electrical impedance method are promising approaches as it allows, e.g., load sensing and detection of bearing failures. The impedance cannot be measured directly, but there are various methods available. This paper discusses advantages and disadvantages and suggests the AC Wheatstone bridge as a reliable way of measuring impedances with low phase angles at sampling rates in the kHz range. The corresponding equations are introduced, a simulation built, an uncertainty mode and effects analysis carried out and sample measurement results of real rolling elements shown. It can be demonstrated that the AC Wheatstone bridge meets the proposed requirements for sensory utilization and condition monitoring when the bearing is operated in the hydrodynamic regime.