bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Large Language Models to Understand Suicidality in a Social Media–Based Taxonomy of Mental Health Disorders: Linguistic Analysis of Reddit Posts
Authors: Bauer Brian , Norel Raquel , Leow Alex , Rached Zad Abi , Wen Bo , Cecchi Guillermo 
Published: March 2024

Abstract

Background
Rates of suicide have increased by over 35% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years.

Objective
The aim of this study was to use large language models to understand natural language use during public web-based discussions (on Reddit) around topics related to suicidality.

Methods
We used large language model–based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health–related subreddits, with a focus on suicidality. We then applied dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower-dimensional Euclidean space for further downstream analyses. We analyzed 2.9 million posts extracted from 30 subreddits, including r/SuicideWatch, between October 1 and December 31, 2022, and the same period in 2010.

Results
Our results showed that, in line with existing theories of suicide, posters in the suicidality community (r/SuicideWatch) predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and many of the resulting subreddit clusters were in line with a statistically driven diagnostic classification system—namely, the Hierarchical Taxonomy of Psychopathology (HiTOP)—by mapping onto the proposed superspectra.

Conclusions
Overall, our findings provide data-driven support for several language-based theories of suicide, as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared on the web and may aid in the validation and refutation of different mental health theories.
Regression From Early GDM to Normal Glucose Tolerance and Adverse Pregnancy Outcomes in the Treatment of Booking Gestational Diabetes Mellitus Study
Authors: Simmons David , Immanuel Jincy , Hague William M. , Teede Helena , Nolan Christopher J. , Peek Michael J. , Flack Jeff R. , McLean Mark , Wong Vincent W. , Hibbert Emily J. , Kautzky-Willer Alexandra , Harreiter Jürgen , Backman Helena , Gianatti Emily , Sweeting Arianne , Mohan Viswanathan , Cheung N. Wah ,   TOBOGM Research Group, Simmons David , Cheung N. Wah , Immanuel Jincy , Hague William M. , Teede Helena , Nolan Christopher J. , Peek Michael J. , Flack Jeff R. , McLean Mark , Wong Vincent , Hibbert Emily , Gianatti Emily , Sweeting Arianne , Coat Suzette , Dalal Raiyomand , Soldatos Georgia , Padmanabhan Suja , Rajagopal Rohit , Rudland Victoria , Harreiter Jürgen , Kautzky-Willer Alexandra , Kiss Herbert , Backman Helena , Schwarcz Erik , Ross Glynis , Mohan Viswanathan , Mohan Anjana Ranjit , Ram Uma 
Published: March 2024

OBJECTIVE
To compare pregnancy outcomes among women with a normal oral glucose tolerance test (OGTT) before 20 weeks’ gestation (early) and at 24–28 weeks’ gestation (late) (no gestational diabetes mellitus, or No-GDM), those with early GDM randomized to observation with a subsequent normal OGTT (GDM-Regression), and those with GDM on both occasions (GDM-Maintained).

RESEARCH DESIGN AND METHODS
Women at &lt;20 weeks’ gestation with GDM risk factors who were recruited for a randomized controlled early GDM treatment trial were included. Women with treated early GDM and late GDM (according to the World Health Organization’s 2013 criteria) were excluded from this analysis. Logistic regression compared pregnancy outcomes.

RESULTS
GDM-Regression (n = 121) group risk factor profiles and OGTT results generally fell between the No-GDM (n = 2,218) and GDM-Maintained (n = 254) groups, with adjusted incidences of pregnancy complications similar between the GDM-Regression and No-GDM groups.

CONCLUSIONS
Women with early GDM but normal OGTT at 24–28 weeks’ gestation had pregnancy outcomes that were similar to those of individuals without GDM. Identifying early GDM likely to regress would allow treatment to be avoided.
Prediction of Rheological Parameters of Polymers by Machine Learning Methods
Authors: Kondratieva T. N. , Chepurnenko A. S. 
Published: March 2024

Introduction. All polymer materials and composites based on them are characterized by pronounced rheological properties, the prediction of which is one of the most critical tasks of polymer mechanics. Machine learning methods open up great opportunities in predicting the rheological parameters of polymers. Previously, studies were conducted on the construction of predictive models using artificial neural networks and the CatBoost algorithm. Along with these methods, due to the capability to process data with highly nonlinear dependences between features, machine learning methods such as the k-nearest neighbor method, and the support vector machine (SVM) method, are widely used in related areas. However, these methods have not been applied to the problem discussed in this article before. The objective of the research was to develop a predictive model for evaluating the rheological parameters of polymers using artificial intelligence methods by the example of polyvinyl chloride.Materials and Methods. This paper used k-nearest neighbor method and the support vector machine to determine the rheological parameters of polymers based on stress relaxation curves. The models were trained on synthetic data generated from theoretical relaxation curves constructed using the nonlinear Maxwell-Gurevich equation. The input parameters of the models were the amount of deformation at which the experiment was performed, the initial stress, the stress at the end of the relaxation process, the relaxation time, and the conditional end time of the process. The output parameters included velocity modulus and initial relaxation viscosity coefficient. The models were developed in the Jupyter Notebook environment in Python.Results. New predictive models were built to determine the rheological parameters of polymers based on artificial intelligence methods. The proposed models provided high quality prediction. The model quality metrics in the SVR algorithm were: MAE – 1.67 and 0.72; MSE – 5.75 and 1.21; RMSE – 1.67 and 1.1; MAPE – 8.92 and 7.3 for the parameters of the initial relaxation viscosity and velocity modulus, respectively, with the coefficient of determination R2 – 0.98. The developed models showed an average absolute percentage error in the range of 5.9 – 8.9%. In addition to synthetic data, the developed models were also tested on real experimental data for polyvinyl chloride in the temperature range from 20° to 60°C.Discussion and Conclusion. The approbation of the developed models on real experimental curves showed a high quality of their approximation, comparable to other methods. Thus, the k-nearest neighbor algorithm and SVM can be used to predict the rheological parameters of polymers as an alternative to artificial neural networks and the CatBoost algorithm, requiring less effort to preset adjustment. At the same time, in this research, the SVM method turned out to be the most preferred method of machine learning, since it is more effective in processing a large number of features
Application of Special Calculation Techniques in the Design of All-Welded Gastight Structures of Boiler Units
Authors: Kurepin M. P. , Serbinovskiy M. Yu. 
Published: March 2024

Introduction. Improving the calculation methods of mechanical engineering facilities is an urgent and in-demand task. This fully applies to the techniques of calculating the strength of all-welded gastight boiler structures. Normative calculation techniques are based on simplified models that give limited possibilities for optimizing these structures. The low calculation accuracy inherent in such techniques is unacceptable under real design conditions, when an engineer is faced with the task of developing competitive structures in a short time, i.e., reducing metal consumption while providing the strength of these structures with limited development time. The use of simplified models was justified in the past, under conditions of insufficient development of computer technology. Application of the most advanced techniques based on computer modeling makes it possible to increase the accuracy of calculations, provide the optimization of such structures, and improve the quality of design. The objective of this study was to develop a new special procedure for calculating the strength of all-welded gastight structures based on computer modeling, using the most advanced methods of modeling the membrane wall and factors affecting it. The accompanying task was to verify the developed procedure based on comparing the results of calculations using the developed technique and the normative method.Materials and Methods. The developed technique is based on the replacement of the membrane wall with an orthotropic plate or shell. Computer modeling was used applying the finite element method of all-welded gastight structures, and the impacts to which they were subjected during operation, as well as an effective method for assessing the technical condition of these structures.Results. A new two-stage technique for calculating the strength of increased accuracy of all-welded gastight boiler structures was developed and patented. The calculation results were compared according to the proposed procedure and the normative method. It was shown that the proposed technique made it possible to increase the accuracy of modeling and calculation. The error in calculating all-welded gastight structures of a high-power boiler was reduced by more than 30% for the recommended steps between stiffeners. For specially reinforced membrane walls with steps exceeding the permissible values, the error reduction reached 70% or higher.Discussion and Conclusion. The developed technique is used in the modeling and calculation of all-welded gastight structures. Its application makes it possible to optimize the step between the stiffeners of the structure of the support and connecting nodes of gastight membrane walls. Based on the results of the application of the two-stage calculation procedure, new designs were developed and patented. The developed technique has been used in the real design of boilers since 2014.
Stress Martensite Nucleation in a State of Premartensitic Lattice Instability
Authors: Dolgachev Y. V. , Pustovoit V. N. , Vernigorov Y. M. 
Published: March 2024

Introduction. The combined effect on the phase transformation process, involving a combination of heat treatment and external action, is a major technology solution for obtaining the required properties of steel products. When hardening steel in a constant magnetic field with a strength of 1–2 MA/m, martensite formation is observed at higher temperatures. In addition, when compared to conventional hardening, there are changes in structure and properties. Such effects cannot be explained only in terms of thermodynamics, since the expected shift in the equilibrium temperature between austenite and martensite in a magnetic field of such strength does not exceed 4–8°C. To explain the effects that occur during hardening in a magnetic field, it is proposed to consider the features of martensitic transformation in highspeed steel when exposed to an external magnetic field in the temperature range of austenite superplasticity. This research was aimed at identifying the features of martensitic transformation in the presence of a constant magnetic field in steel with account for the phenomena occurring in the premartensitic state.Materials and Methods. Samples made of steel R6M5 were used. Characteristics of the martensitic transformation were studied using the potentiometric method of electrical resistance. The data were recorded using an L-CARD E14-440 analog-to-digital converter with the LGraph2 software package. The sample was heated by passing current. The sample was placed in the interpolar space of an open-type laboratory electromagnet FL-1, which provided the creation of a magnetic field with a strength of 1.2 MA/m.Results. The obtained differentiated dependences were characterized by electrical resistance anomalies (low-temperature peaks) at a temperature corresponding to the appearance of a ferromagnetic phase as a result of martensitic transformation. In a magnetic field, the development of martensitic transformation began at a higher temperature, which could not be explained in terms of thermodynamics. Thus, the formation of stress martensite was observed in microvolumes of austenite with ferromagnetic ordering, which perceived the energy of the external field through magnetostrictive stresses. Under conditions of superplastic austenite, such stresses were sufficient to initiate shear transformation. The minimum possible size of lattice instability fluctuations (1.372 nm) was determined.Discussion and Conclusion. Exposure to a magnetic field under hardening intensified the processes of some magnetic decomposition of austenite. At temperatures close to the beginning of the martensitic transformation, the existing areas of magnetic inhomogeneity were superimposed on the effects of the phenomenon of instability of the crystal lattice. In the temperature range Md-Mn, when austenite exhibited superplasticity, the formation of stress martensite in microvolumes of austenite with ferromagnetic ordering was significantly facilitated
Helminth Fauna of Bats of the Rostov Region: Review. 2. Trematoda
Authors: Evsyukov A. P. , Tsygankova M. G. 
Published: March 2024

Introduction. The class of Trematoda includes two subclasses — Aspidogastrea and Digenea. Representatives of the former do not parasitize in bats. The definitive hosts for digenetic trematodes (Digenea) are vertebrates, including bats, in which the sexual generation (maritae) parasitizes. Gastropods or bivalves are the intermediate hosts. Supplementary hosts for the trematodes of bats are larvae and imagos of flying insects. Since in most cases the development of parthenites and cercariae of trematodes is related to water environment, the supplementary hosts are the groups of insects whose larvae live in the water. The list of bat species living in the Rostov region has been compiled in the previous research. In the present article the authors investigate the trematode fauna parasitizing in bats of the region under study. The aim of this review is to analyse the current state of knowledge on the species composition of the helminth parasitizing in chiropterans of the Rostov region.Materials and Methods. The research materials have been obtained from the libraries and open access resources: PubMed (pubmed.ncbi.nlm.nih.gov), CyberLeninka (cyberleninka.ru), Google Scholar (scholar.google.com), BHL (www.biodiversitylibrary.org), JSTOR (www.jstor.org). Some data has been provided by the colleagues.Results. 42 trematode species from 10 genera, 5 families and 1 order can parasitize in 15 bat species living in the Rostov region. The Lecithodendriidae family (24 species) and the genus Paralecithodendrium (9 species) are represented by a large number of species. Of the listed trematodes, 33 species used to be recorded on the territory of Russia.Discussion and Conclusion. The results of the conducted systematic review have revealed that the largest number of trematode species parasitizes in two bat species living in the Rostov region: the common noctule (27 species) and the serotine bat (24 species). The smallest number of species (4 per each) is found in the giant noctule and the soprano pipistrelle bat. Two species (Plagiorchis elegans and Plagiorchis vespertilionis) have been found in the companion cats and dogs includingly, wheras a case of the latter parazitising in humans has also been recorded. The presence of bats in the urban environment enables the full life cycle of these trematodes. There exists possibility of the companion animals and people to get infected through incidental ingestion of the infested insects.
An applied study of a technique incorporating machine learning algorithms to optimize water demand prediction
Authors: Wang Ruiyi , Zou Xiangling , Song Haojing 
Published: March 2024

Abstract
In water resource management, accurate water demand prediction is essential for developing effective water supply strategies and optimizing resource allocation. This study aims to investigate machine learning algorithms, particularly echo state network (ESN) models, to improve the accuracy and efficiency of water demand prediction. ESN models are selected for their excellent nonlinear time series processing capabilities, which address the challenges of traditional prediction methods when dealing with complex water resource systems. By optimizing the parameters of the ESN model, the study hopes to provide a more scientific and efficient method for residential domestic and agricultural water demand forecasting, thus supporting more refined water resources planning and management decisions. Residential water demand prediction and crop water demand prediction are the two parts of this study. In the prediction of residential water demand, based on the actual data of City Z, the optimized ESN model predicts the water demand in 2025, and the total water demand in the baseline scenario is 790.9 million m3, and the expected values of water demand in different scenarios combined with the economic growth rate and the change of water price range from 659.4708 million m3 to 730.448 million m3. The article’s accuracy analysis of crop water demand prediction indicates that the model’s relative errors in predicting the water demand of the three major crops are limited to 10%. The ESN model optimized using the machine learning algorithm in this paper has good potential for water demand prediction and is an efficient and accurate prediction tool for managing water resources.
Optimization of Enterprise Economic Management Model and Risk Control Based on Marginal Analysis Approach
Authors: Wu Xiaoyun 
Published: March 2024

Abstract
As an effective economic decision-making tool in enterprise economic management, Marginal Analysis is crucial to reflect the small change relationship between costs and benefits. This paper aims to develop a multi-objective marginal analysis optimization algorithm (MMAOA) that utilizes marginal analysis to optimize the enterprise economic management model and risk control. This algorithm's improvement of the population initialization method and constraint handling enhances the quality of understanding and search efficiency. In this study, an improved population initialization method is designed to ensure that the population is within the upper and lower limits of the output power. Then the constraints are reasonably handled by the penalty function method and the correction method to enhance the feasibility and robustness of the algorithm. Guided by the concept of Pareto optimality, MMAOA is successfully applied to multi-objective optimization problems, and the algorithm can efficiently generate a set of Pareto optimal solutions under the consideration of equilibrium constraints and inequality constraints, in which the objective function values of certain optimization instances are improved by more than 10%. Following marginal analysis, this study presents the disparity in ROI between the East and West and the three stages of agricultural economic efficiency. This article provides a new optimization tool for enterprise financial management and a quantitative analysis method for risk assessment.
A Study on Mobile Phone Interface Interaction Design for the Elderly Based on Digital Technology
Authors: Yuan Lei 
Published: March 2024

Abstract
The rapid development of digital technology significantly impacts the use of cell phone interfaces by the elderly. This paper aims to analyze the interaction design of cell phone interfaces for the elderly to enhance their user experience and ease of use. This paper investigates the evaluation system for evaluating cell phone interface interaction for the elderly, including interface size, Layout, and functionality. A group of elderly people aged 65-75 are invited to evaluate the effects of different design options by observing their performance under different interface sizes (large size 6.0 inches, medium size 5.5 inches, small size 4.5 inches) and layouts (nine-cell grid and combination type). The evaluation metrics are task completion time, number of operation errors, and eye movement data. The 6.0-inch interface experienced an average task completion time of 6.97 seconds, a decrease of approximately 6 seconds and 1 second from the 4.5-inch and 5.5-inch interfaces, respectively. The large-size interface had an average error rate of 1.11 per task, significantly less than the average error rate in the 4.5-inch and 5.5-inch interfaces. The more extensive interface significantly decreased user gaze points and gaze time, indicating more efficient information acquisition. More extensive cell phone interfaces and combined layouts can significantly improve interaction efficiency and reduce operation errors for elderly users, enhancing user experience. The findings of this study offer practical guidance for designing cell phone interfaces for the elderly and aid in promoting cell phone interface design for the elderly.
Implementation Strategy of Guangxi Urban Resilient Grid Based on Integrated Utilization of Distributed Renewable Energy and Buildings in the Internet Era
Authors: Zhang Juncheng , Li Min , Tan Jing , Tan Xiaohong , Tao Yigang 
Published: March 2024

Abstract
Facing global climate change and energy crises, urban power grids, particularly in Guangxi, need resilience enhancements to cope with these unprecedented challenges. This study proposes a resilience improvement strategy by integrating distributed renewable energy with building systems, tailored to Guangxi’s unique conditions. We demonstrate that optimizing the network structure and protection settings significantly enhances fault recovery capabilities through system analysis, model development, and simulation. Implementing a distributed multi-microgrid system increased the resilience power supply rate by an average of 15% across eight cities in Guangxi, with a 20% improvement in the system’s resilience emergence rate. This was achieved by optimizing power interactions using a dynamic consistency algorithm, showcasing a viable pathway to bolster urban grid resilience through renewable integration and offering insights applicable to similar urban settings.