bluuebunny/crossref_metadata_2025_split huggingface dataset:
Support vector machine analysis to predict the value of tin exports as an effort to prevent environmental damage in the Bangka Belitung islands province
Authors: Dalimunthe D Y , Simarmata A C , Qisai A Z 
Published: December 2024

Abstract
The Bangka Belitung Islands Province is one of the largest tin producers in Indonesia, so tin mining and environmental damage continue to be hot topics among the people of Bangka Belitung. Therefore, research is needed to predict the value of tin exports as part of efforts to prevent environmental damage. The analysis method used in this research is a Support Vector Machine (SVM). The SVM method is included in the supervised learning category with a machine learning approach in creating algorithms to predict results accurately. The data used is data on the tin export value of the Bangka Belitung Islands Province for the period January 2017 to December 2023. Based on the results of the data analysis carried out, it was found that the SVM method that is suitable for predicting export value is the Radial Basis Function (RBF) kernel. Based on this method, the highest value obtained in the Bangka Belitung tin export data was $219,028,197 in May 2025 and the lowest was $2,752,377 in June 2026. It can be concluded that the predicted results of the Bangka Belitung tin export data fluctuated slightly downward in line with the decline in the commodity value of tin in this province. The hope is that the decrease in tin export predictions must also be anticipated by local government policies in dealing with the availability and type of damage caused by this non-renewable natural resource commodity.
Economical and technical analysis of Pangkalpinang waste power plant incinerator technology
Authors: Saragih W J , Asmar  , Puriza M Y , Rachmani A A , Anzari Y 
Published: December 2024

Abstract
Increased waste production has always been a problem for every city in Indonesia, including in Pangkalpinang. Population growth, inadequate processing, and utilization of waste are the primary causes of this issue. An alternative to reduce waste piles is to convert waste to fuel for waste power plants. To find out how much electrical energy potential in landfills and the feasibility of a waste power plant, an analysis is needed by calculating using existing formulas, forecasting with geometric methods, and investment assessment with NPV, IRR, and also the payback period. The calculation showed that the potential for electrical energy for 2013 was 6,661,924 kWh, while during the 2022 period was 13,161,362 kWh per year. The results of the forecasted waste potential for 2023 until 2033 increased the generated electricity. In 2023, the produced energy was 16,553,678 kWh/year. While in 2033, the amount of energy produced will be 21,190,137 kWh/year. Purchasing electricity from a waste power after being traded shows a sizable income. The construction of this waste power plant meets the feasibility considering the investment assessment made with a purchase price of electricity of more than IDR 1,600 per kWh.
Save food movement as a food waste mitigation scheme in the context of achieving SDGs and energy conservation in Riau province
Authors: Nizmi Y E , Hasibuan A I R S , Wahyudi H , Yulia R , Takwa T K 
Published: December 2024

Abstract
This study analyzes the critical role of the Save Food Movement in preventing food waste and promoting energy conservation in Riau Province, Indonesia. Food waste is a significant global issue that directly impacts the Sustainable Development Goals (SDGs), particularly those focused on responsible production and consumption. These practices are essential for energy conservation and ensuring food availability for future generations. The increasing attention to food waste in Indonesia, especially in Riau—one of fifteen provinces participating in the Save Food Movement initiated by the National Development Planning Agency (Bappenas)—highlights this commitment. This research employs qualitative methods, including interviews and document analysis of journals, reports, media coverage, official websites, and other sources. The findings reveal that the Save Food Movement in Riau Province fosters collaboration among various entities, including the National Amil Zakat Agency (Baznas), which established a Food Bank, and the Provincial Food and Horticulture Agency, appointed by Bappenas as the movement’s primary driver. Additionally, universities engage with local communities, acting as educational partners to mitigate food waste and promote energy conservation.
3D printed PCL-nHAp composite implants for the treatment of segmental bone defects: in vivo application in a rabbit model
Authors: Basoz D , Karaman M I , Buyuksungur S , Yucel D , Hasirci N , Kocaoglu B , Hasirci V 
Published: December 2024

Abstract
The management and treatment of long bone defects are challenging clinical problems. In this study, in order to address the need for load bearing implants for segmental defects, 3D printed cylindrical implants of poly(caprolactone) (PCL) and nanohydroxyapatite (nHAp) composites were prepared and applied as lateral segments to the femurs of New Zealand white rabbits. The results of PCL-nHAp implant group obtained 6 weeks after the procedure were compared with those of the autografts. There was no significant difference between the yield and ultimate loads of autograft group and the implant group. Histological studies demonstrated similar new bone formation in both groups. Also, a sizeable callus formation around the autografts and bone ingrowth to the 3D printed implants were observed, and x-ray studies confirmed the formation of the callus. An increase was detected in the bone density around the defect site for both test groups. SEM revealed close interaction between the newly formed bone tissue and the struts of the 3D printed implant. mRUST values, which is an indicator of tissue healing, increased continuously during 6 weeks. In conclusion, 3D printed, 1.5 cm long cylindrical nHAp-PCL implants exhibited excellent bone healing and biomechanical stability in the large lateral segmental bone defects of the rabbits even in a relatively short implantation time as 6 weeks. We believe that these implants could serve as an alternative to autografts in the treatment of long bone defects.
Stratospheric injection of solid particles reduces side effects on circulation and climate compared to SO2 injections
Authors: Stefanetti Fabrice , Vattioni Sandro , Dykema John A , Chiodo Gabriel , Sedlacek Jan , Keutsch Frank N , Sukhodolov Timofei 
Published: December 2024

Abstract
Most research of stratospheric aerosol injection (SAI) for solar radiation modification has focused on injection of SO2. However, the resulting sulfuric acid aerosols lead to considerable absorption of terrestrial infrared radiation, resulting in stratospheric warming and reduced cooling efficiency. Recent research suggests that solid particles, such as alumina, calcite or diamond, could minimize these side effects. Here we use, for the first time, the atmosphere–ocean–aerosol–chemistry–climate model SOCOLv4.0, incorporating a solid particle scheme, to assess the climatic impacts of SAI by these injection materials. For each substance, we model tropical SAI by means of constant yearly injection of solid particles, aimed to offset the warming induced by a high-GHG emission scenario over the 2020–2100 period by 1 K. We show that solid particles are more effective than sulfur at minimising stratospheric heating, and the resulting side-effects on the general atmospheric circulation, stratospheric moistening, and tropopause height change. As a result, solid particles also induce less residual warming over the arctic, resulting in greater reduction of GHG-induced polar amplification compared to sulfuric acid aerosols. Among the materials studied here, diamond is most efficient in reducing global warming per unit injection, while also minimizing side effects.
Introduction to Data-Acquisition Device Features
Authors: Essick John 
Published: December 2024

Abstract
This chapter assumes that readers have access to a multifunction data-acquisition (DAQ) device from either NI (formerly, National Instruments) or Measurement Computing Corporation (MCC). Both NI and MCC are manufacturers of popular, high-quality DAQ devices, where MCC devices are the lower priced option with (satisfactorily) scaled-down specifications. Using the interactive program appropriate for their device (MAX for NI, DAQami for MCC), readers familiarize themselves with the capabilities of a multifunction DAQ device, including analog-to-digital conversion, digital-to-analog conversion, digital input/output operations, and digital pulse counting. In the course of this work, explanations are given of the following important digitized data concepts: analog input modes, range, resolution, sampling frequency, aliasing, digital input/output levels, and digital triggering. The NI USB-6002 and MCC USB-202 DAQ devices are highlighted, but the programs developed apply to most other NI and MCC multifunction DAQ multifunction devices. In a Do It Yourself project, readers create a millisecond-resolution stopwatch and a Use It! example investigates how to synchronize the repeated digital acquisition of a periodic waveform via digital triggering.
Data Files and Character Strings
Authors: Essick John 
Published: December 2024

Abstract
Readers learn how to use the NumPy library’s file I/O functions to store data in, and read data from, computer files. The commonly used comma-separated values (CSV) spreadsheet format is described and then readers create NumPy-based programs that store and read data in one- and two-dimensional CSV-formatted files. Numeric formatting, escape sequences, and the Unicode character coding scheme are covered. In the Do It Yourself project, readers learn the NumPy-based method for storing and reading data in binary-formatted files, while the Use It! example explores how to store streaming data as it is acquired during runtime in order to prevent data loss in the event of a program crash.
Constructing an HTML Tree
Authors: Panchekha Pavel , Harrelson Chris 
Published: December 2024

Abstract
So far, our browser sees web pages as a stream of open tags, close tags, and text. But HTML is actually a tree, and though the tree structure hasn’t been important yet, it will be central to later features like CSS, JavaScript, and visual effects. So this chapter adds a proper HTML parser and converts the layout engine to use it.
The Predicting Value of Existing Models for Idiopathic Hyperaldosteronism in Chinese Population
Authors: Bai Ying , Shen Hang , Song Ying , Feng Zhengping , Du Zhipeng , Hu Jingbo , Yang Shumin , Li Qifu 
Published: December 2024

Abstract

OBJECTIVE
To evaluate the predicting value of existing models for idiopathic hyperaldosteronism (IHA) in the Chinese population.

METHODS
In this cross-sectional study, 387 patients with primary aldosteronism (PA) in the First Affiliated Hospital of Chongqing Medical University from January 2013 to March 2021 were evaluated. Briefly, 275 patients with unilateral PA and 112 with IHA were included. Patients with PA were subtyped according to adrenal venous sampling or postoperative remission situation. Based on the subtype, the diagnostic value of 7 IHA prediction models was evaluated using the receiver operating characteristic (ROC) curve and sensitivity and specificity analysis.

RESULTS
Among the 7 IHA prediction models, the Kobayashi2018 score and Xiao model were verified to have high diagnostic efficiency, with an area under the ROC curve (AUC) of 0.88 and 0.92, respectively. The AUC of the other 5 models ranged from 0.51 to 0.80. The recommended cutoffs of the model were further verified. Kobayashi2018 score (≥8 points) and Umakoshi model (normal serum K+ with negative adrenal computed tomography) were found to have relatively high sensitivity (38% and 36%, respectively) when high specificity (≥95%) was achieved.

CONCLUSION
Considering simplicity, feasibility, generalizability, and accuracy, the Kobayashi2018 score or Umakoshi model is recommended to predict IHA in the Chinese population.
An efficient joint model for high dimensional longitudinal and survival data via generic association features
Authors: Nguyen Van Tuan , Fermanian Adeline , Barbieri Antoine , Zohar Sarah , Jannot Anne-Sophie , Bussy Simon , Guilloux Agathe 
Published: December 2024

ABSTRACT
This paper introduces a prognostic method called FLASH that addresses the problem of joint modeling of longitudinal data and censored durations when a large number of both longitudinal and time-independent features are available. In the literature, standard joint models are either of the shared random effect or joint latent class type. Combining ideas from both worlds and using appropriate regularization techniques, we define a new model with the ability to automatically identify significant prognostic longitudinal features in a high-dimensional context, which is of increasing importance in many areas such as personalized medicine or churn prediction. We develop an estimation methodology based on the expectation–maximization algorithm and provide an efficient implementation. The statistical performance of the method is demonstrated both in extensive Monte Carlo simulation studies and on publicly available medical datasets. Our method significantly outperforms the state-of-the-art joint models in terms of C-index in a so-called “real-time” prediction setting, with a computational speed that is orders of magnitude faster than competing methods. In addition, our model automatically identifies significant features that are relevant from a practical point of view, making it interpretable, which is of the greatest importance for a prognostic algorithm in healthcare.
Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling
Authors: Norwood Peter , Davidian Marie , Laber Eric 
Published: December 2024

ABSTRACT
Response-adaptive randomization (RAR) has been studied extensively in conventional, single-stage clinical trials, where it has been shown to yield ethical and statistical benefits, especially in trials with many treatment arms. However, RAR and its potential benefits are understudied in sequential multiple assignment randomized trials (SMARTs), which are the gold-standard trial design for evaluation of multi-stage treatment regimes. We propose a suite of RAR algorithms for SMARTs based on Thompson Sampling (TS), a widely used RAR method in single-stage trials in which treatment randomization probabilities are aligned with the estimated probability that the treatment is optimal. We focus on two common objectives in SMARTs: (1) comparison of the regimes embedded in the trial and (2) estimation of an optimal embedded regime. We develop valid post-study inferential procedures for treatment regimes under the proposed algorithms. This is nontrivial, as even in single-stage settings standard estimators of an average treatment effect can have nonnormal asymptotic behavior under RAR. Our algorithms are the first for RAR in multi-stage trials that account for non-standard limiting behavior due to RAR. Empirical studies based on real-world SMARTs show that TS can improve in-trial subject outcomes without sacrificing efficiency for post-trial comparisons.
BioElectra-BiLSTM-Dual Attention classifier for optimizing multilabel scientific literature classification
Authors: Inaam ul haq Muhammad , Li Qianmu , Mahmood Khalid , Shafique Ayesha , Ullah Rizwan 
Published: December 2024

Abstract
Scientific literature is growing in volume with time. The number of papers published each year by 28 100 journals is 2.5 million. The citation indexes and search engines are used extensively to find these publications. An individual receives many documents in response to a query, but only a few are relevant. The final documents lack structure due to inadequate indexing. Many systems index research papers using keywords instead of subject hierarchies. In the scientific literature classification paradigm, various multilabel classification methods have been proposed based on metadata features. The existing metadata-driven statistical measures use bag of words and traditional embedding techniques, like Word2Vec and BERT, which cannot quantify textual properties effectively. In this paper, we try to solve the limitations of existing classification techniques by unveiling the semantic context of the words using an advanced transformer-based recurrent neural networks (RNN) approach incorporating Dual Attention and layer-wise learning rate to enhance the classification performance. We propose a novel model, BioElectra-BiLSTM-Dual Attention that extracts the semantic features from the titles and abstracts of the research articles using BioElectra-encoder and then BILSTM layer along with Dual Attention label embeddings their correlation matrix and layer-wise learning rate strategy employed for performance enhancement. We evaluated the performance of the proposed model on the multilabel scientific literature LitCovid dataset and the results suggest that it significantly improves the macro-F1 and micro-F1 score as compared to the state-of-the-art baselines (ML-Net, Binary Bert, and LitMCBert).
Image sentiment analysis based on distillation and sentiment region localization network
Authors: Zhang Hongbin , Feng Ya , Yuan Meng , Hou Jingyi , Zhang Jin , Li Guangli 
Published: December 2024

Abstract
Accurately identifying the emotions in images is crucial for sentiment content analysis. To detect local sentiment regions and acquire discriminative sentiment features, we propose a novel model named Distillation-guided and Contrastive-enhanced Sentiment Region Localization Network (DC-SRLN) to effectively complete image sentiment analysis. Two smart but heterogeneous SRLNs are designed first to pursue local sentiment regions. Then an innovative contrastive learning mode is implemented between global and local features to further enhance the discriminative ability of the sentiment features. Third, the enhanced global and local sentiment features are seamlessly integrated to guide each SRLN accurately capture local sentiment regions. Finally, an adaptive feature fusion module is created to fuse the heterogeneous features from the two SRLNs and generate a new multi-view multi-granularity sentiment semantics with more discriminative ability for image sentiment analysis. Extensive experimental results on three prevailing datasets, namely Twitter I, FI, and ArtPhoto, exhibit that DC-SRLN achieves satisfactory accuracies of 93.2%, 80.6%, and 78.7%, respectively, outperforming recent state-of-the-art baselines. Moreover, DC-SRLN needs less training time, demonstrating its high practicality. The code of DC-SRLN is freely available at https://github.com/Riley6868/DC-SRLN.
Surgery for blood culture-negative infective endocarditis: outcomes and the role of molecular biological imaging as diagnostic approach
Authors: Grubitzsch Herko , Puritz Johannes Erik , Greve Dustin , Biefer Hector Rodriguez Cetina , von Schöning Dinah , Kikhney Judith , Moter Annette , Sima Stefanie-Teodora , Schneider-Reigbert Matthias , Stegemann Miriam Songa , Pfäfflin Frieder , Falk Volkmar 
Published: December 2024

Abstract

OBJECTIVES
The study aimed to analyse outcomes of surgery for blood culture-negative infective endocarditis (BCNIE) and to evaluate the role of molecular biological imaging.

METHODS
Patients undergoing surgery for native or prosthetic valve endocarditis from 2013 to 2022 were analysed regarding blood culture-positive infective endocarditis (BCPIE) and BCNIE. For laboratory diagnostics in BCNIE, excised valves or prostheses underwent conventional microbiological culture and fluorescence in situ hybridization combined with 16S rRNA-gene polymerase chain reaction and sequencing (FISHseq).

RESULTS
Of 521 patients overall, we identified 473 patients (342 males, mean age 63 ± 14.4 years) with preoperative blood cultures: 396 with BCPIE (83.7%) and 77 with BCNIE (16.3%). Preoperative characteristics and operative procedures were comparable between groups and the calculated perioperative risk (EuroSCORE II) was identical (BCNIE: 12.2 ± 8.8%, BCPIE: 12.9 ± 11.9%, P = 0.788). At surgery, signs of infective endocarditis were present in 71%, missing in 18% and inconclusive in 10% of patients with BCNIE. While valve cultures alone identified pathogens in 32% of BCNIE patients, the combination with FISHseq confirmed the infective aetiology in 98% and identified causative pathogens in 52%. Overall, early mortality was similar in BCNIE (16.9%) and BCPIE (18.2%, P = 0.620), but increased in 37 BCNIE patients without pathogen identification compared to 40 patients with pathogen identification (27.0% vs 7.5%, P = 0.032).

CONCLUSIONS
Integrating FISHseq as molecular biological imaging technique into valve analysis algorithms in patients undergoing surgery for BCNIE increases diagnostic gain and potentially improves outcome.
A previously reported bottleneck in human ancestry 900 kya is likely a statistical artifact
Authors: Deng Yun , Nielsen Rasmus , Song Yun S 
Published: December 2024

Abstract
It was recently reported that a severe ancient bottleneck occurred around 900 thousand years ago in the ancestry of African populations, while this signal is absent in non-African populations. Here, we present evidence to show that this finding is likely a statistical artifact.
Life Course Patterns of Work History and Cognitive Trajectories Among Community-Dwelling Older Adults
Authors: Wang Yi , Wang Xingzhi , Fu Peipei , Jiang Hantao , Wang Xueqing , Zhou Chengchao 
Published: December 2024

Abstract

Objectives
Prior research indicated that diverse work experiences in early and middle life stages are associated with cognitive function in later life. However, whether life course patterns of work history are associated with later life cognitive function in China remains unknown.

Methods
Data were derived from the China Health and Retirement Longitudinal Study, and 5,800 participants aged 60 years or older were included. We used sequence analysis to identify the work history patterns between ages 18 and 60. Growth curve modeling was performed to evaluate how the work-history patterns are associated with global and domain-specific (i.e., mental intactness and episodic memory) cognitive function.

Results
We identified 8 work patterns, distinguished by duration, transitions, timing, and sequence of work history. Compared with individuals exposed to lifelong agricultural work, those engaged in lifelong nonagricultural employed work had better cognitive function in later life and experienced slower rates of cognitive decline in both global cognitive function and the mental intactness domain. In addition, individuals who shifted from agricultural to nonagricultural employed work early (around age 30) had better global cognitive function in later life, whereas the association was not significant for such a transition later (around age 50).

Discussion
Our study highlights the importance of the duration, sequence, and timing of transitions in work history for cognitive health among older Chinese. Future interventions and policies aimed at improving cognitive function should fully consider the cumulative and dynamic nature of work from a life course perspective.