Search Results (46)

Background/Objectives: The regulatory SNPs (rSNPs) that disturb the binding of transcription factors (TFs) and alter the transcription levels of genes play a paramount role in the formation of different traits and are associated with many pathologies. The search for allele-specific events in RNA-seq and ChIP-seq data is a powerful genome-wide approach to detect rSNPs. Using this approach, we have identified the T → A rs2072580 substitution in the bidirectional SART3/ISCU promoter as a potential rSNP and demonstrated its association with colorectal cancer, relying on International Cancer Genome Consortium data. The goal of this work was to identify the TF binding site that is affected by the T → A substitution and to study the effect of this substitution on reporter gene expression in different plasmid constructs. Methods: Electrophoretic mobility shift assay (EMSA), cross-competition analysis and supershift assay, plasmid construction, and dual luciferase reporter assay. Results: The T → A rs2072580 substitution is shown to damage the binding site for ubiquitous TF CREB1 and to significantly decrease the activity of the heterologous promoter carrying the cassettes of two or three repeated CREB binding sites inserted upstream of it. However, the substitution disturbing the CREB1 binding site within the bidirectional promoter shared by SART3 and ISCU inhibits the promoter activity of only the SART3 gene but has no effect on the activity of the ISCU promoter. Conclusions: The performed comprehensive functional analysis of the T → A rs2072580 in the bidirectional SART3/ISCU promoter unambiguously implies it is an rSNP. These results form the background for further studies of this rSNP and its potential significance for various pathologies. Full article

The leakage signal of the hydraulic valve is a weak, nonlinear, and non-periodic signal that is easily overpowered by background noise from the surroundings. To address this issue, the Search and Rescue Team (SaRT) algorithm was introduced to adaptive coupled stochastic resonance, and a new signal-enhancement method based on SaRT for coupled multi-stable stochastic resonance (CMSR) was proposed for enhancing valve-leakage vibration signals. Initially, the method employs the rescaling technique to preprocess the signal, thereby transforming the fault signal into a small-parameter signal. Subsequently, the mutual correlation gain is utilized as an adaptive measure function of the SaRT algorithm to optimize the parameters of the coupled multi-stable stochastic resonance system. Ultimately, the output signal is solved by the fourth-order Runge–Kutta method. This study validated the method using sinusoidal signals and leakage signals of the check valve. The results demonstrate that all CMSR parameters require optimization. Furthermore, the noise reduction was effective for three different leakage signals of faulty check valves, in which the highest in the number of interrelationships increased by 6.9569 times and the highest amplitude ratio of the peak frequency increased by 11.7004 times. The data quality was significantly improved. Full article

In recent years, the rapid development of remote tower technology has made it crucial to accurately assess the situational awareness (SA) levels of remote tower controllers. Such an assessment is significant for controller training and remote tower system design. This study employed the SART scale to compare controllers’ SA scores in traditional and remote tower environments. Results revealed significant differences, especially in attention demand and situational understanding. Subsequently, a quantitative analysis of controllers’ perception, understanding, and decision-making abilities was conducted, integrating subjective and objective data. Eye-tracking, heart rate, working memory scales, and communication-coordination scales showed significant results. Experienced controllers had better psychological safety skills, while trainees were more likely to increase vigilance. Moreover, a series of sensitive SA indicators were identified. An evaluation index system was established using the entropy weight method. By calculating the Euclidean distance, Gray relational degree, and comprehensive proximity coefficient, the SA levels of controllers were comprehensively evaluated. The top five important indicators were average blink rate, scan length, average fixation duration, fixation duration, and average pupil diameter. These findings support enhancing air traffic control safety and refining SA assessment for remote tower controllers. Full article

In this article, the authors present contemporary problems in search and rescue operations at sea. The research focuses on the detection of the SART (Search and Rescue Transponder) device. This device is used to call for help and assist the rescuing vessel in tracking. Issues with their functionality may reduce the likelihood of finding a survivor. The authors designed an experiment to assess the effectiveness of using the device. The research conducted is a real-world experiment that involved a ship radar, a liferaft, a SART device, and a radar reflector. The experiment consisted of multiple trials to detect, locate, and track the device, as well as to assess the radar image features. Four scenarios were developed, considering different distances and radar settings. Performance evaluation indicators were also developed. The results are presented both graphically and numerically. A brief discussion of the obtained results and concise conclusions are provided. Along with the research findings, recommendations for the use of SART and radar on ships are also presented, as well as recommendations for improving training. The results are applicable to improving the effectiveness of SAR operations. Full article

In the high-attention-demanding environment of underground coal mines, distraction is a major cause of unsafe behavior and decreased safety performance. Research on the cognitive neural mechanisms and monitoring of distraction in miners is limited. This study used an electroencephalogram (EEG) to examine the correlation between distraction and brain activity in coal miners, aiming to provide an objective method for monitoring distraction in coal miners. Thirty participants completed a simulated hazard recognition task, using the Sustained Attention to Response Task (SART) and noise to induce distraction. Brain activity was recorded and labeled as focused or distracted based on the correctness of the hazard recognition task. EEG features were extracted and selected, and a Random Forest model for distraction identification was constructed based on the selected features. In the focused state, delta power in the temporal region and theta power in the frontal region increased significantly. In the distracted state, alpha power in the temporal and occipital regions and beta power in the occipital and parietal regions increased. The selected EEG features could be used to identify distraction with 84% accuracy. This method can objectively identify distraction in coal miners, highlighting the potential of using EEG for real-time distraction monitoring. Full article

Non-destructive testing (NDT) enables the determination of internal defects and flaws in concrete structures without damaging them, making it a common application in current bridge concrete inspections. However, due to the complexity of the internal structure of this type of concrete, limitations regarding measurement point placement, and the extensive detection area, accurate defect detection cannot be guaranteed. This paper proposes a method that combines the Simultaneous Algebraic Reconstruction Technique with Group Sparsity Regularization (SART-GSR) to achieve tomographic imaging of bridge concrete under sparse measurement conditions. Firstly, a mathematical model is established based on the principles of the tomographic imaging of bridge concrete; secondly, the SART algorithm is used to solve for its velocity values; thirdly, on the basis of the SART results, GSR is applied for optimized solution processing; finally, simulation experiments are conducted to verify the reconstruction effects of the SART-GSR algorithm compared with those of the SART and ART algorithms. The results show that the SART-GSR algorithm reduced the relative error to 1.5% and the root mean square error to 89.76 m/s compared to the SART and ART algorithms. This improvement in accuracy makes it valuable for the tomographic imaging of bridge concrete and provides a reference for defect detection in bridge concrete. Full article

Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creativity, termed freely moving mind wandering (FMMW). In this study, we examined the behavioral and neural associations between FMMW and creativity. Methods: We initially validated a questionnaire measuring FMMW by comparing its results with those from the Sustained Attention to Response Task (SART). Data were collected from 1316 participants who completed resting-state fMRI scans, the FMMW questionnaire, and creative tasks. Correlation analysis and Bayes factors indicated that FMMW was associated with creative thinking (AUT). To elucidate the neural mechanisms underlying the relationship between FMMW and creativity, Hidden Markov Models (HMM) were employed to analyze the temporal dynamics of the resting-state fMRI data. Results: Our findings indicated that brain dynamics associated with FMMW involve integration within multiple networks and between networks (r = −0.11, p_FDR < 0.05). The links between brain dynamics associated with FMMW and creativity were mediated by FMMW (c’ = 0.01, [−0.0181, −0.0029]). Conclusions: These findings demonstrate the relationship between FMMW and creativity, offering insights into the neural mechanisms underpinning this relationship. Full article

Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the “Liwan 3-1” offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy. Full article

In the field of armored vehicles, up to 70% of accidents are associated with low levels of situational awareness among the occupants, highlighting the importance of situational awareness in improving task performance. In this study, we explored the mechanisms influencing situational awareness by simulating an armored vehicle driving platform with 14 levels of experimentation in terms of five factors: experience, expectations, attention, the cueing channel, and automation. The experimental data included SART and SAGAT questionnaire scores, eye movement indicators, and electrocardiographic and electrodermal signals. Data processing and analysis revealed the following conclusions: (1) Experienced operators have higher levels of situational awareness. (2) Operators with certain expectations have lower levels of situational awareness. (3) Situational awareness levels are negatively correlated with information importance affiliations and the frequency of anomalous information in non-primary tasks. (4) Dual-channel cues lead to higher levels of situational awareness than single-channel cues. (5) Operators’ situational awareness is lower at high automation levels. Full article

The Schlieren technique is widely adopted for visualizing supersonic jets owing to its non-invasiveness to the flow field. However, extending the classical Schlieren method for quantitative refractive index measurements is cumbersome, especially for three-dimensional supersonic flows. Background-oriented Schlieren has gained increasing popularity owing to its ease of implementation and calibration. This study utilizes multi-view-based tomographic background-oriented Schlieren (TBOS) to reconstruct axisymmetric and weakly non-axisymmetric supersonic jets, highlighting the impact of flow axisymmetry breaking on TBOS reconstructions. Several classical TBOS reconstruction algorithms, including FDK, SART, SIRT, and CGLS, are compared quantitatively regarding reconstruction quality. View spareness is identified to be the main cause of degraded reconstruction quality when the flow experiences axisymmetry breaking. The classic visual hull approach is explored to improve reconstruction quality. Together with the CGLS tomographic algorithm, we successfully reconstruct the weakly non-axisymmetric supersonic jet structures and confirm that increasing the nozzle bevel angle leads to wider jet spreads. Full article

Infestations of pests are perhaps an anthropogenic catastrophe for trees. Aeolesthes sarta (Sart longhorned beetle—SLB) is one of the most severe pests that cause serious damage to a number of hardwood tree species, i.e., Populus, Salix, Acer, Juglans, and Malus. To investigate people’s attitudes towards pest damage cost and future control extent of SLB, a door-to-door method was adopted to survey two major cities (Quetta—QU and Peshawar—PE) of the northwestern region of Pakistan where this pest has caused severe damage. Respondents were asked about SLB pest knowledge, pest damage costs, preferences for control choices, and program extent. According to respondents, more trees (181 ± 1.20 trees/ha/annum) were damaged in QU compared to PE. Populus spp. was the dominant tree genre that attacked and damaged the most. Around 85% of respondents from both cities stated the pest damage cost was calculated as high for QU (480,840.80 ± 4716.94$/annum) compared to PE. Respondents in both locations strongly supported (more than 82%) biological control of future SLB outbreaks. They all agreed that protecting ecologically vulnerable places and wildlife habitats should be the primary priority in a future SLB outbreak. Respondents from both cities who preferred to protect more land area in future SLB outbreaks were calculated to be high for QU (61%) compared to PE (58%). However, city variations in opinions regarding forest-type priority that should be protected and control options were observed. Socio-demographic characteristics were discovered to impact pest damage cost positively, as well as preferred SLB control extent. The findings of this study can help policymakers and forest managers develop publicly permissible pest control plans and make more accurate predictions about future pest outbreaks. Full article

Observing the vertical diffusion distribution of methane fugitive emissions from oil/gas facilities is significant for predicting the pollutant’s spatiotemporal transport and quantifying the random emission sources. A method is proposed for methane’s vertical distribution mapping by combining the laser path-integral sensing in non-non-cooperative open paths and the computer-assisted tomography (CAT) techniques. It uses a vertical-plume-mapping optical path configuration and adapts the developed dynamic relaxation and simultaneous algebraic reconstruction technique (DR-SART) into methane-emission-distribution reconstruction. A self-made miniaturized TDLAS telemetry sensor provides a reliable path to integral concentration information in non-non-cooperative open paths, with Allan variance analysis yielding a 3.59 ppm·m sensitivity. We employed a six-indexes system for the reconstruction performance analysis of four potential optical path-projection configurations and conducted the corresponding validation experiment. The results have shown that that of multiple fan-beams combined with parallel-beam modes (MFPM) is better than the other optical path-projection configurations, and its reconstruction similarity coefficient (ε) is at least 22.4% higher. For the different methane gas bag-layout schemes, the reconstruction errors of maximum concentration (γ_m) are consistently around 0.05, with the positional errors of maximum concentration (δ) falling within the range of 0.01 to 0.025. Moreover, considering the trade-off between scanning duration and reconstruction accuracy, it is recommended to appropriately extend the sensor measurement time on a single optical path to mitigate the impact of mechanical vibrations induced by scanning motion. Full article

The duplication of chromosome 21, as evidenced in Down Syndrome (DS), has been linked to contraindications to health, such as chronotropic and respiratory incompetence, neuromuscular conditions, and impaired cognitive functioning. The purpose of this study was to examine the effects of eight weeks of prescribed exercise and/or cognitive training on the physical and cognitive health of adults with DS. Eighty-three participants (age 27.1 ± 8.0 years) across five continents participated. Physical fitness was assessed using a modified version of the six-minute walk test (6MWT), while cognitive and executive functions were assessed using the Corsi block test, the Sustained-Attention-To-Response Task (SART), and the Stroop task (STROOP). All were completed pre- and post-intervention. Participants were assigned to eight weeks of either exercise (EXE), 3 × 30 min of walking/jogging per week, cognitive training (COG) 6 × ~20 min per week, a combined group (COM), and a control group (CON) engaging in no intervention. 6MWT distance increased by 11.4% for EXE and 9.9% for COM (p < 0.05). For SART, there were positive significant interactions between the number of correct and incorrect responses from pre- to post-intervention when participants were asked to refrain from a response (NO-GO-trials) across all experimental groups (p < 0.05). There were positive significant interactions in the number of correct, incorrect, and timeout incompatible responses for STROOP in EXE, COG, and COM (p < 0.05). Walking generated a cognitive load attributed to heightened levels of vigilance and decision-making, suggesting that exercise should be adopted within the DS community to promote physical and cognitive well-being. Full article

Rapid perception of the location of the fire point is crucial to building fire emergency response in the process of building fire emergency response, which can help firefighters direct fire-fighting operations, effectively control fire sources, and provide strong evidence for the analysis and investigation of fire causes. This paper uses acoustic CT temperature measurement technology to determine the fire source location of a building fire and verifies its validity and applicability as follows: we construct various fire point numerical models based on the fire dynamics simulator (FDS) and obtain temperature data at different times; neural network means were used to obtain the time-of-flight (TOF) of an acoustic wave traveling; the large ill-conditioned matrix equation of acoustic flight under different meshing schemes was constructed and solved based on the simultaneous algebraic reconstruction technique (SART) and least squares QR-decomposition (LSQR), and then reconstruction temperature data under each scheme were obtained. Through the error analysis, the reconstruction effect of each reconstruction scheme is evaluated, and then the applicability of the location coordinate determination of the fire point is analyzed. The results show that the determination of the fire location under the conditions of various fire points in the building space can be realized by acoustic CT temperature measurement technology. Full article

This study was carried out using a large cohort (N = 4265; 416 deceased) of older, community-dwelling adults from The Irish Longitudinal Study on Ageing (TILDA). The study compared the performance of a new 3-item health index (HI) with two existing measures, the 32-item frailty index (FI) and the frailty phenotype (FP), in predicting mortality risk. The HI was based on the objective measurement of resting-state systolic blood pressure sample entropy, sustained attention reaction time performance, and usual gait speed. Mortality data from a 12-year follow up period were analyzed using Cox proportional regression. All data processing was performed using MATLAB and statistical analysis using STATA 15.1. The HI showed good discriminatory power (AUC = 0.68) for all-cause mortality, similar to FI (AUC = 0.68) and superior to FP (AUC = 0.60). The HI classified participants into Low-Risk (84%), Medium-Risk (15%), and High-Risk (1%) groups, with the High-Risk group showing a significant hazard ratio (HR) of 5.91 in the unadjusted model and 2.06 in the fully adjusted model. The HI also exhibited superior predictive performance for cardiovascular and respiratory deaths (AUC = 0.74), compared with FI (AUC = 0.70) and FP (AUC = 0.64). The HI High-Risk group had the highest HR (15.10 in the unadjusted and 5.61 in the fully adjusted models) for cardiovascular and respiratory mortality. The HI remained a significant predictor of mortality even after comprehensively adjusting for confounding variables. These findings demonstrate the effectiveness of the 3-item HI in predicting 12-year mortality risk across different causes of death. The HI performed similarly to FI and FP for all-cause mortality but outperformed them in predicting cardiovascular and respiratory deaths. Its ability to classify individuals into risk groups offers a practical approach for clinicians and researchers. Additionally, the development of a user-friendly MATLAB App facilitates its implementation in clinical settings. Subject to external validation in clinical research settings, the HI can be more useful than existing frailty measures in the prediction of cardio-respiratory risk. Full article