The ocean represents the cradle of life on Earth, making it essential to comprehend the complex interactions between marine animal behaviors and the physical microstructure of their environments in order to study their behavioral ecology. Due to the vastness of the ocean, traditional observational techniques are constrained by distance, which poses significant challenges for conducting extended and continuous research on marine animal behavior and ecology. To overcome these challenges, this paper introduces a behavior recording tag system incorporating temperature, pressure, and miniature inertial measurement unit (MIMU) sensors as data collection modules. These sensors are integrated with a main control module and a data storage module to gather and archive behavioral and environmental information. A combined positioning recovery method is proposed, developed, and validated to address the issue of retrieving the tag system post data collection. The behavior recording tag system’s performance was assessed through laboratory and pool tests. The findings show that the accuracy of temperature sensor is about 0.01 °C, the accuracy of pressure sensor is approximately 0.5% of full scale, the continuous data collection duration can extend to 3 days, and the recovery window time after surfacing exceeds 7 days, underscoring its viability as a marine animal behavior recorder.

The popup data communication beacon (PDCB) can send data to the shore and ships through the BeiDou navigation satellite system (BDS) when it surfaces. The data can be collected by a deep-sea landing vehicle (DSLV) and transmitted using a magnetic induction coil. PDCBs can reduce the cost of DSLV recovery and redeployment. Whether the data can be successfully sent mainly depends on the outlet height and roll angle of the PDCB. Thus, accurately assessing the effect of the roll angle on data transmission is crucial. In this study, first, the differential equation of roll motion was preliminarily established using the small-amplitude wave theory along with the shape characteristics of the PDCB. Next, the nonlinear term of the recovery moment was processed using the Linz Ted Poincaré method. Then, the wave current force was analyzed using the Morrison theoretical formula along with an additional inertia moment calculation formula that is suitable for slender cylindrical small buoys. Finally, the theoretical calculation results were verified using the computational fluid dynamics (CFD) method and pool test. The roll angle error of the theoretical calculation was within 5%. Thus, the heave and roll response of PDCBs can be evaluated using theoretical calculation methods. The proposed calculation formula of additional inertia moment has guiding significance for the further optimization of the structure.

Marine submerged buoys can effectively obtain various parameters of seawater, which plays an important role in the research of marine physical phenomena, marine environmental changes, and climate change. However, traditional self-contained submerged buoys usually work underwater at a depth of about 100 m, and the observation data cannot be obtained before their recovery, which cannot satisfy the needs of real-time data acquisition for marine scientific research. To solve this problem, this paper proposes a real-time communication subsurface mooring system that consists of a satellite communication buoy (SCB), conductivity–temperature–depth sensors (CTD), and an inductive coupling mooring cable. The underwater inductive coupling link collects the data from the underwater sensors and transmit it to the SCB. Then, the data will be transmitted to the station receiver via satellite communication module integrated into the SCB. In order to ensure a high success rate of data recovery, the stress analysis and hydrodynamic simulation of the SCB were carried out in this paper. The results show that the SCB maintained a relatively stable attitude in the 3–4 sea state. The attitude data obtained from the subsequent sea trial was consistent with the simulation results, and the success rate of satellite communication during this period was more than 95%. In this paper, a modular embedded hardware circuit was designed to meet the functional requirements of the subsurface mooring system. An efficient data recovery strategy was also developed, which ensured that the average power consumption of the system was low and the success rate of data recovery is not less than 90% when operating in the severe sea state for a long time. The system underwent sea trials in the South China Sea for more than 3 months from the end of 2021 to the beginning of 2022. It transmitted more than 2034 sets of seawater profile temperature, salinity, and depth data in real-time, with a success rate of over 91% of the total sample data. The CTD data returned in real-time from our system is consistent with the data of the HYCOM and World Ocean Atlas (WOA), and a cyclonic mesoscale eddy was detected in the operation area.

The current research used human-coded Reddit posts categorized by already established food parenting concepts (coercive control, structure, autonomy support, recipes) as a basis for machine learning models, with the objective of providing insight into topics related to feeding children discussed on social media and to provide a way for future research to use our trained machine-learned model. Reddit posts from specific, parenting-related subreddits were collected and labeled as they related to aspects of child-feeding behavior. Posts were then put through text pre-processing, converted into TF-IDF vectors, and used to train support vector machine binary and multiclass classification models. Other classifiers and text-preprocessing steps were also tested. After training, the binary model was able to classify posts with 86.1% accuracy as being about child feeding or not, up from a baseline accuracy of 57.6%. The multiclass model yielded a 79.1% accuracy to classify posts related to four categories of child feeding concepts (coercive control, autonomy support, structure, recipes), up from a baseline of 51.9%. The comparison models were found to perform less favorably. The best performing binary model is publicly available for use via the Social Media Macroscope and we provide details on how to use this model. Information is presented such that other researchers and professionals interested in examining issues related to feeding children posted on social media could effectively utilize the same approach.

In recent years, the explosion of social media platforms and the public collection of social data has brought forth a growing desire and need for research capabilities in the realm of social media and social data analytics. Research on this scale, however, requires a high level of computational and data-science expertise, limiting the researchers who are capable of undertaking social media data-driven research to those with significant computational expertise or those who have access to such experts as part of their research team. The Social Media Macroscope (SMM) is a science gateway with the goal of removing that limitation and making social media data, analytics, and visualization tools accessible to researchers and students of all levels of expertise. The SMM provides a single point of access to a suite of intuitive web interfaces for performing social media data collection, analysis, and visualization via for open-source and commercial tools. Within the SMM social scientists are able to process and store large datasets and collaborate with other researchers by sharing ideas, data, and methods. This document functions as a brief primer on the initial build of the SMM and we end this paper discussing future directions for the SMM.

Social Media Intelligence and Learning Environment (SMILE) is an open source framework bringing cutting-edge computational models on social media data to social science researchers and students with any level of programming and computation expertise. Many existing social media analysis tools require programming knowledge, a fee, or are closed source, making it challenging for social science researchers to apply existing and new methods to social media data. SMILE provides a user-friendly web interface, through which researchers can perform a wide spectrum of research tasks, ranging from social media data collection, natural language processing, text classification, social network analysis, and generating human readable outputs and visualizations. SMILE has adopted several technologies to support its needs. The data service of SMILE leverages the GraphQL language to provide an efficient and succinct API for client to communicate with a heterogeneous collection of social media APIs, including Twitter and Reddit. SMILE implements a microservices design and utilizes Amazon AWS services, such as Lambda and Batch for computation, S3 for data storage, and Elasticsearch for a Twitter streaming database, which makes it more portable, economic, and resilient. Analysis outputs can be shared with the larger community using Clowder, an open source data management system to support data curation of long tail data and metadata. SMILE is one of the main applications deployed as a standalone tool within the Social Media Macroscope (SMM), a science gateway based on the HUBzero platform. Over 200 users have used SMILE since its first release in 2018.

The convective atmospheric boundary layer was modeled in the water tank. In the entrainment zone (EZ), which is at the top of the convective boundary layer (CBL), the turbulence is anisotropic. An anisotropy coefficient was introduced in the presented anisotropic turbulence model. A laser beam was set to horizontally go through the EZ modeled in the water tank. The image of two-dimensional (2D) light intensity fluctuation was formed on the receiving plate perpendicular to the light path and was recorded by the CCD. The spatial spectra of both horizontal and vertical light intensity fluctuations were analyzed. Results indicate that the light intensity fluctuation in the EZ exhibits strong anisotropic characteristics. Numerical simulation shows there is a linear relationship between the anisotropy coefficients and the ratio of horizontal to vertical fluctuation spectra peak wavelength. By using the measured temperature fluctuations along the light path at different heights, together with the relationship between temperature and refractive index, the one-dimensional (1D) refractive index fluctuation spectra were derived. The anisotropy coefficients were estimated from the 2D light intensity fluctuation spectra modeled by the water tank. Then the turbulence parameters can be obtained using the 1D refractive index fluctuation spectra and the corresponding anisotropy coefficients. These parameters were used in numerical simulation of light propagation. The results of numerical simulations show this approach can reproduce the anisotropic features of light intensity fluctuations in the EZ modeled by the water tank experiment.

Water tank experiments and numerical simulations are employed to investigate the characteristics of light propagation in the convective boundary layer (CBL). The CBL, namely the mixed layer (ML), was simulated in the water tank. A laser beam was set to horizontally go through the water tank, and the image of two-dimensional (2D) light intensity fluctuation formed on the receiving plate perpendicular to the light path was recorded by CCD. The spatial spectra of both horizontal and vertical light intensity fluctuations were analyzed, and the vertical distribution profile of the scintillation index (SI) in the ML was obtained. The experimental results indicate that 2D light intensity fluctuation was isotropically distributed in the cross section perpendicular to the light beam in the ML. Based on the measured temperature fluctuations along the light path at different heights, together with the relationship between temperature and refractive index, the refractive index fluctuation spectra and the corresponding turbulence parameters were derived. The obtained parameters were applied in a numerical model to simulate light propagation in the isotropic turbulence field. The calculated results successfully reproduce the characteristics of light intensity fluctuation observed in the experiments.

In the summer of 2005 and the spring of 2006,flux measurements were twice taken in Nanjing Municipal Party School and Pukou area.Heat flux,latent heat flux,carbon dioxide flux as well as friction velocity were obtained applying the eddy-covariance(EC) technique.In order to eliminate the impact of complex terrain,a planar-fit(PF) method for tilt correction was adopted.A thorough analysis of the PF method indicated that PF coefficients are closely related to wind direction.Thus,wind directions must be taken into consideration when processing data.To be specific,winds from all directions were divided into several sectors and PF method was applied to each of them in order to generate a fitted plane for each sector.This method was named sector planar fit(SPF) as distinguished from the general planar fit(GPF) which doesn't consider wind sectors.The differences of corrected fluxes by the two methods(GPF/SPF) for the two seasons and two locations were mainly considered.It was clearly revealed that both urban and suburban flux results share a consistent trend in spring and summer;geographically,in urban areas,the corrected fluxes using SPF and GPF show obvious differences,differences are much smaller in suburban areas.Moreover,the vertical velocity w was corrected using the two methods and it was found that w also exhibits significant differences.Finally,according to the probability distribution of corrected vertical wind velocity by the two methods,it was concluded that the distribution of corrected vertical velocities by SPF is closer to normal than GPF.