Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstruction of past bottom water conditions of the Peruvian Oxygen Minimum Zone (OMZ) for the last 22,000 years and the benthic foraminiferal response to (de)oxygenation

The Peruvian margin is characterised by a strong OMZ as result of enhanced surface primary productivity and sluggish ventilation of the subsurface waters. This present study investigated the potential changes in the structure and shape of the OMZ since the Last Glacial Maximum using downcore distributions of the benthic foraminiferal assemblages. The sediment cores considered in this study were recovered from the lower oxic-suboxic OMZ boundary. In addition, stratigraphic information from 31 sediment cores was compiled with a focus on the time intervals since the LGM. Erosional features together with gaps depict a prograding feature on the continental slope from south to north during the deglaciation. The recent oceanographic and sedimentological observations showed that tide-topography interactions result in non-linear internal waves which shape the continental slope by erosion and remobilization of the sediments. The compilation of downcore records and the northward expanding feature of the hiatus revealed that the tide-topography interactions have progressively evolved since the LGM. The oxygen quantification approach was validated using multiple regression analysis on three data sets of living calcareous benthic foraminiferal distributions and measured oxygen concentrations from 1 to 18°S. It was later followed by application of a transfer function to the four sediment cores. Estimated bottom water oxygen concentrations were low compared to modern values however the trend of decreasing oxygen levels during the Termination I and slight increase in the Holocene was consistent across all cores. The overall change in the bottom water oxygen levels from the LGM and the Holocene was reckoned to be 25 μmol/kg. Deoxygenation was observed as a gradual expansion of the northern OMZ boundary during the last deglaciation. Comparison of the bottom water oxygen estimates with other proxies from the region showed that the deoxygenation did not always co-occur with enhanced surface productivity.
Optimization of Surface Acoustic Wave Resonators on 42°Y-X LiTaO3/SiO2/Poly-Si/Si Substrate for Improved Performance and Transverse Mode Suppression

SAW devices with a multi-layered piezoelectric substrate have excellent performance due to advantages such as a high quality factor, Q, low loss insertion, large bandwidth, etc. Prior to manufacturing, a comprehensive analysis and proper design are essential to evaluating the device’s key performance indicators, including the Bode Q value, bandwidth, and transverse mode suppression. This study explored the performance of SAW resonators employing a 42°Y-X LiTaO3 (LT) thin-plate-based multi-layered piezoelectric substrate. The thicknesses for each layer of the 42°Y-X LT/SiO2/poly-Si/Si substrate were optimized according to the index of phase velocity, Bode Q value, and bandwidth. The effect of the device structure parameters on the dispersion curve and slowness curve was studied, and a flat slowness curve was found to be favorable for transverse mode suppression. In addition, the design of the dummy configuration was also optimized for the suppression of spurious waves. Based on the optimized design, a one-port resonator on the 42°Y-X LT/SiO2/poly-Si/Si substrate was fabricated. The simulation results and measurements are presented and compared, which provides guidelines for the design of new types of SAW devices configured with complex structures.
ADMM for 1-bit DACs Quantization Precoding in Antenna Selection-Assisted Massive MU-MIMO

In massive multi-user multiple-input multiple-output (MU-MIMO) systems, to reduce the system power consumption, we propose an 1-bit digital-to-analog converters (DACs) quantization antenna selection-assisted massive MU-MIMO system and formulate a 1-bit precoding non-convex optimization problem. To solve the problem, we introduce the sparsity parameter and ℓ1-norm to relax the constraint of the antenna selection and use the alternating direction method of multipliers (ADMM) as the core method. Finally, simulation results demonstrate that, compared with existing algorithms, our algorithm significantly reduces the power consumption while ensuring the stability of system.
Environmental Controls on Calving in Grounded Tidewater Glaciers

The calving of icebergs is an important mass loss process in many glaciers worldwide, including the Antarctic and Greenland Ice Sheets. Despite its importance, calving is still relatively poorly reproduced in ice sheet models, largely due to the complexity of the processes involved. In this thesis a new grounded tidewater glacier model is presented using a physically realistic calving criterion, based on the penetration of crevasses in the ice, applied in a two-dimensional, vertically-resolved ice flow model using the finite element software Elmer. This is the only tidewater glacier model so far developed which can been used to model individual calving events, allowing a detailed analysis of calving at the terminus of the glacier. The model is tested with four environmental variables which have been thought to a↵ect calving rates: water depth in crevasses, basal water pressure, undercutting of the calving face by subaqueous melt and backstress from ice melange. Of the four variables, only crevasse water depth and basal water pressure were found to have a significant e↵ect on terminus behaviour when applied at a realistic magnitude. This is in contrast to previous modelling and observational studies, which had suggested that ocean temperatures could strongly influence the calving front. The apparent contradiction in results is likely to be caused by either the feedback processes linking air and ocean temperatures, or by the fact that floating ice is likely to respond more strongly to subaqueous melt and backstress than grounded ice. The lack of basal elevation data makes it di cult to distinguish grounded and floating ice in many regions. The results presented highlight the importance of good basal elevation data in interpreting glacier behaviour. They also raise the possibility that Greenland outlet glaciers could respond more strongly than previously thought to the recent trend of increased surface melt observed in Greenland, as surface ablation can strongly a↵ect calving dynamics through both the pooling of water in crevasses and changing water pressure at the glacier bed.
System Design of Insulator Inspection Software for HV Distribution Lines

This paper describes upgraded design and architecture, a need of new user interface of insulator inspection software (IIS) for Mongolian National Power Transmission Grid (NPTG) company. The IIS is developed by researchers from Chemnitz University of Technology (CUT) since 2017 and partly used by engineers and stakeholders from MNPT. Based on stakeholders' feedback developers need to upgrade the IIS user interface. Actual paper focused to show new aspects of IIS. The upgraded IIS will be possible to store video and photo data collected in individual branches in the server, and to process and analyze data from the server. Main aim of the high-level user interface gives opportunity to stakeholders get depth technical understanding about IIS and its functions in short time. The technical introduction of IIS to key stakeholders from company and ministry is essential important of future development of main system. Newly upgrades IIS will be test by the structure-oriented evaluation model. The web-based server method is applied data transmission between IIS and high-level user interface module.
Fire Detection and Notification System in Real-Time Using a Lightweight YOLOv8-Based Detector

In this paper, the FireDetection system is presented, which is designed to provide a robust and easily deployable solution for early fire detection. The system proposed uses the YOLOv8n detection model trained on an extended dataset, which includes detailed annotations for the presence of fire in each frame of the videos. The primary objective of this system is to furnish a resilient and readily deployable solution for early fire identification while simultaneously minimizing false positives and ensuring high frame rates to enable real-time applications. Through extensive experimentation, the results reveal significant enhancements in the model's performance, as evidenced by its analysis dataset accomplishments. With its robust architecture, advanced training, and reliable results, the FireDetection system emerges as a promising and valuable tool in the realm of fire prevention and early intervention, with potential applications in various scenarios that require swift and accurate fire detection. Finally, experimental results of this system demonstrate an improvement in state-of-the-art models.
Analytical study of social networking sites usage and social anxiety among physical education students

Social networking sites usages (SNSu) are online platforms used for communication, sharing, and connecting with others. This paper aims to investigate the current status of SNSu among students in physical education college, as well as the differences in SNSu based on gender. Additionally, we aimed to assess the level of social anxiety among the study participants and examine whether it varied by gender and SNSu. We conducted a descriptive survey among 1,109 physical education students (688 females and 421 males) enrolled in public universities. We administered the social anxiety disorder-adult and SNSu questionnaire using Google Forms. We analyzed the data using means, standard deviations, frequencies, percentages, One-way ANOVA, and chi-square tests with a confidence level of 95% (p<0.05). The results of the study showed that both male and female students used social networking sites (SNS) more than 10 times a day, with percentages of 46.3% and 46.1%, respectively. Additionally, 43.7% of male and 49.1% of female students reported using SNS for more than three hours per day. The mean score for social anxiety was 20.20, indicating a severe level of social anxiety among the study participants. Based on the responses of the sample study, the results showed an increase in negative indicators associated with SNSu and these unfavorable indicators were accompanied by an increase in the level of social anxiety.
Unsupervised Pre-Training of Deep Neural Classifiers

The paper is devoted to studying the issues of pre-training of deep neural network models. A generalized approach for pre-training deep models is proposed, which allows achieving better performance in final accuracy and can be used when training on small datasets. The effectiveness of the proposed approach is demonstrated on examples of training deep neural classifiers models using the MNIST and CIFAR-10 datasets. The obtained results can be used to train fully connected and convolutional types of layers.
AI-Based Risk Monitoring in COVID Cases After Lockdown

The methods of machine learning give opportunities to enhance pandemic reaction and risk mitigation by adding mechanistic predictive model to pandemic preparation and reaction based on several sources of data at each and every stage, from the micro to the global level. Nevertheless, these approaches depend on the accessibility of relevant data and the transmission and presentation of that information in order to provide tools and recommendations for judgement. Because of the absence of uniformity in the recording and availability of differentiated, extensive statistics on COVID-19 in India, the use of algorithms powered by AI and their efficacy for forecasting the development and consequent impact of this condition in populations have indeed been limited. If these limitations are disregarded, they increase the risk of utilising AI technologies poorly and represent lost opportunities for Methodologies to make a good contribution. Governmental agencies should work with AI researchers and industry experts to develop data collection and sharing standards in order to streamline future readiness for outbreaks and other calamities.
An Enhanced Detector for Vulnerable Road Users Using Infrastructure-Sensors-Enabled Device

The precise and real-time detection of vulnerable road users (VRUs) using infrastructure-sensors-enabled devices is crucial for the advancement of intelligent traffic monitoring systems. To overcome the prevalent inefficiencies in VRU detection, this paper introduces an enhanced detector that utilizes a lightweight backbone network integrated with a parameterless attention mechanism. This integration significantly enhances the feature extraction capability for small targets within high-resolution images. Additionally, the design features a streamlined ‘neck’ and a dynamic detection head, both augmented with a pruning algorithm to reduce the model’s parameter count and ensure a compact architecture. In collaboration with the specialized engineering dataset De_VRU, the model was deployed on the Hisilicon_Hi3516DV300 platform, specifically designed for infrastructure units. Rigorous ablation studies, employing YOLOv7-tiny as the baseline, confirm the detector’s efficacy on the BDD100K and LLVIP datasets. The model not only achieved an improvement of over 12% in the mAP@50 metric but also realized a reduction in parameter count by more than 40%, and a 50% decrease in inference time. Visualization outcomes and a case study illustrate the detector’s proficiency in conducting real-time detection with high-resolution imagery, underscoring its practical applicability.
Modeling Heat Transfer in Coal Combustion Systems for Enhanced Thermal Eﬀiciency

: This research paper delves into the intricate domain of modeling heat transfer in coal combustion systems with the primary objective of achieving enhanced thermal efficiency in power generation processes. As the global demand for energy continues to rise, optimizing the performance of coal-fired power plants becomes imperative for sustainable and efficient energy production. The research begins by establishing a comprehensive numerical model that incorporates the complexities of coal combustion, accounting for factors such as combustion kinetics, particle characteristics, and fluid dynamics. Through a meticulous integration of computational fluid dynamics (CFD) and heat transfer models, the study aims to unravel the intricate interactions governing heat transfer processes in coal-fired environments. The outcomes of this research are anticipated to contribute significantly to the development of more efficient and environmentally sustainable coal-fired power generation methods.
Fusion estimation for state-saturated multi-rate systems with integral measurement and sensor resolution

The fusion estimation problem for a class of multi-rate systems (MRS) subject to state saturation is examined in this paper. varying sensors have varying sensor sampling rates (SSRs), and in MRS, the system’s status update rate (SUR) and the SSR can diﬀer from one another. In addition, to incorporate the requirements of actual engineering practice, the measurement output of sensors is inﬂuenced by the sensor resolution and the phenomenon of integral measurements. The aim of this paper is to design an appropriate set of local estimators for each sensor node, taking into account the eﬀects of integral measurement, sensor resolution and multi-rate sampling mechanism. Then, Design of the upper bound on the error covariance of the estimator can be obtained by solution of the matrix diﬀerence equation, and the relevant ﬁlter parameter is determined by minimizing the obtained upper bound. For local state estimation, an acceptable fusion estimating approach based on the covariance intersection (CI) method is given, and its consistency is demonstrated. Lastly, the eﬀectiveness and appropriateness of the suggested multi-rate fusion estimation
Contribution of physical processes to variability of dissolved silicate in the Labrador Sea between 1980 and 2015

We use output of an eddy-permitting biogeochemical ocean general circulation model to investigate the drivers of the observed decline in the pre-bloom inventory of dissolved silicate (DSi) and its InterAnnual Variability (IAV) over the period 1980-2015. Specifically, the relative impacts of an abrupt decrease in Deep Winter Convection (DWC) and changes in Arctic inflow on DSi concentrations at the regional scale are examined. The IAV of the upper layer DSi inventory 15 covaries with both the Arctic inflow and DWC, however, the pre-bloom decline seems driven primarily by the DWC and associated winter vertical mixing, while the contribution of Arctic inflow is negligible. Our study suggests that the inventory responds to natural decadal variability which is influenced by two major climate modes, the North Atlantic and the Arctic Oscillations, with the former appearing to be the main control.
Child Penalty Estimation and Mothers’ Age at First Birth

Motherhood continues to pose signiﬁcant challenges to women’s careers, and a correct assessment of its eﬀects is crucial for understanding the persistent gender inequality in the labor market. We show that the prevalent approach to estimate post-birth earnings losses – so called “child penalties” – is prone to yield substantially biased results. We demonstrate that the biases stem from conventional event studies pooling together ﬁrst-time mothers of all ages, without considering their distinct characteristics and the varying impact of motherhood. To address the biases, we propose a novel approach that accounts for the heterogeneity by building upon recent advancements in the econo-metric literature on diﬀerence-in-diﬀerences models. Applying it to administrative data from Germany, we demonstrate that considering heterogeneity by maternal age at birth is crucial for both methodological correctness and a deeper understanding of gender inequality. Our approach yields substantially larger estimates of earnings losses after childbirth (by 20 percent), indicating that the costs of motherhood and related gender gaps in Germany are even larger than previously thought. Moreover, we demonstrate that eﬀects and their interpretation diﬀer signiﬁcantly depending on maternal age at birth. We show that younger ﬁrst-time mothers experience larger career costs of motherhood, as they miss out on the phase of the most rapid career progression.
Q-learning and Neural Networks in Automated Driving Systems

With the development of artificial intelligence, machine learning, and other disciplines, automation in various fields is becoming popular. Therefore, autonomous driving systems in the automotive field represent a major trend in the future of intelligent transportation systems. This paper focuses on the application of Q-learning and deep neural networks in autonomous driving systems. The simulation in Python is used to simulate the condition of the car driving alone on the road. Firstly, the normal car driving situation was analyzed and the actions and states in Q-learning were set. The action set-up ensures the distance sensors’ number and direction. Then, this paper draws the neural network and simulates it to get the data. Secondly, it analyzes the resulting reward scores and iteration times and identifies uncertainties through line graphs. In addition, it observes the simulation video and derives the reasons why uncertainty appears. In the simulation, the Best Score, Average generation time and Genetic distance were obtained from the simulation against the number of Gens. The data for genetic distance was found to increase with the number of times it was generated, indicating that the Q-learning component was successfully run in the simulation. And it was found that the uncertainty was due to the birth point of the vehicle. According to the uncertainty, further development of the simulation could include more road environments and different obstacles.
Transgenic rodents as dynamic models for the study of respiratory rhythm generation and modulation: a scoping review and a bibliometric analysis

The pre-Bötzinger complex, situated in the ventrolateral medulla, serves as the central generator for the inspiratory phase of the respiratory rhythm. Evidence strongly supports its pivotal role in generating, and, in conjunction with the post-inspiratory complex and the lateral parafacial nucleus, in shaping the respiratory rhythm. While there remains an ongoing debate concerning the mechanisms underlying these nuclei’s ability to generate and modulate breathing, transgenic rodent models have significantly contributed to our understanding of these processes. However, there is a significant knowledge gap regarding the spectrum of transgenic rodent lines developed for studying respiratory rhythm, and the methodologies employed in these models. In this study, we conducted a scoping review to identify commonly used transgenic rodent lines and techniques for studying respiratory rhythm generation and modulation. Following PRISMA guidelines, we identified relevant papers in PubMed and EBSCO on 29 March 2023, and transgenic lines in Mouse Genome Informatics and the International Mouse Phenotyping Consortium. With strict inclusion and exclusion criteria, we identified 80 publications spanning 1997–2022 using 107 rodent lines. Our findings revealed 30 lines focusing on rhythm generation, 61 on modulation, and 16 on both. The primary in vivo method was whole-body plethysmography. The main in vitro method was hypoglossal/phrenic nerve recordings using the en bloc preparation. Additionally, we identified 119 transgenic lines with the potential for investigating the intricate mechanisms underlying respiratory rhythm. Through this review, we provide insights needed to design more effective experiments with transgenic animals to unravel the mechanisms governing respiratory rhythm. The identified transgenic rodent lines and methodological approaches compile current knowledge and guide future research towards filling knowledge gaps in respiratory rhythm generation and modulation.
Pre-Morbid Risk Factors for Amyotrophic Lateral Sclerosis: Prospective Cohort Study

Aim: In the absence of effective treatments for amyotrophic lateral sclerosis (ALS), a neurodegenerative disorder with high case fatality, there is a clear need to identify its primary risk factors. Methods: UK Biobank is a prospective cohort study in which baseline data were captured between 2006 and 2010 in 502,649 participants aged 37 to 73 years. Follow-up for ALS hospitalisations and death was made via national registries. Results: Eleven years of event surveillance gave rise to 301 hospitalisations and 279 deaths due to ALS. After adjustment for selected confounding factors, being older (hazard ratio per 10 year increase; 95% confidence interval: 1.92; 1.58, 2.33) and male (1.37; 1.00, 1.87) were associated with elevated rates of hospitalisation for ALS. Similar effects were apparent when death ascribed to the disorder was the outcome of interest. Of the remaining 23 social, biological, and behavioural risk indices, however, there was only a suggestion that taller people experienced an increased risk of hospitalisation (per SD increase: 1.31; 1.09, 1.59). Conclusion: In the present, large-scale study, other than well known associations, we did not find convincing evidence of links with ALS for other risk indices. adjustment for age, sex, ethnicity, smoking status, physical activity, diabetes, lung function, and deprivation score. ventative topics include: diagnosis, prognosis, treatment, screening, prevention, risk factor modification, systematic reviews, risk & safety of medical interventions, epidemiol- ogy & biostatistical methods, and evaluation of guidelines, translational medicine, health policies & economic evaluations. The manuscript management system is completely online and includes a very quick and fair peer-review system, which is all easy to use.