bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short-term power grid load forecasting based on optimized VMD and GA-BP
Authors: Wang Xiaojun , Li Xiaoming , Wang Jian , Gao Jun , Xin Lingling 
Published: April 2024

Abstract
The present article proposes an enhanced hybrid neural network model that combines variational mode decomposition (VMD) and genetic algorithm-backpropagation (GA-BP) to tackle the accurate prediction task of nonstationary and nonlinear power demand data. In comparison to existing methods, this study employs the North Gallic Hawk Optimization (NGO) algorithm to preliminarily ensure the optimization of the number of VMD modes, K and the penalty factor, α. Furthermore, it utilizes the envelope entropy criterion to determine the minimum optimal VMD components for predicting low-order spectral features vectors. Building upon this, the GA-BP optimization method establishes prediction models for the NGO-VMD model’s individual components. When compared with traditional methods, the GA-BP model exhibits significant advantages in terms of rapid convergence, fewer iterations and high prediction accuracy. Empirical results demonstrate that this approach holds promising prospects for practical application in power demand prediction. In comparison to traditional model predictive performance, the root mean square error is reduced by 60.36% and the mean absolute percentage error by 44.34%, providing robust support to enhance prediction accuracy and promote sustainable development of power systems.
Short-term electricity load forecasting based on CEEMDAN-FE-BiGRU-Attention model
Authors: Hu Haoxiang , Zheng Bingyang 
Published: April 2024

AbstractIntending to address the volatility and intricacy of power load data, a novel method for short-term power load forecasting is presented, utilizing the CEEMDAN-FE-BiGRU-Attention model. This paper introduces the innovative adaptive noise fully integrated Empirical Mode decomposition (CEEMDAN) algorithm, which effectively decomposes the sequence into modal components of various frequencies and residual components. This decomposition serves to reduce the complexity inherent in the original time series. Subsequently, the fuzzy entropy (FE) algorithm is employed to calculate the time complexity of each component, allowing for the reconstruction of different scale sequences, thereby enhancing computational efficiency. Finally, the sequences of varying scales are input into the bidirectional recurrent neural network (BiGRU) model, incorporating the attention mechanism for prediction. Notably, the prediction accuracy of the BiGRU model with attention mechanism surpasses that of the baseline GRU model for multifeature time series, resulting in a significant improvement in prediction accuracy. Experimental findings demonstrate that the proposed model outperforms traditional approaches, enabling better capture of the variation trends in power load data while reducing time series complexity. Moreover, the proposed model exhibits a remarkable reduction in mean absolute percentage error and root mean square error values by 90.24% and 85.05%, respectively, when compared to the single BiGRU-Attention model. This enhancement enhances the accuracy of power load prediction. These innovative methodologies endow the power load forecasting method proposed in this paper with greater potential and viability for real-world applications.
The material selection method for functional packaging of electromechanical products based on low carbon and cost constraint
Authors: Chenjie Gong , Feng Xu , Senyi Wang , Kaiwang Xia 
Published: April 2024

Abstract
Packaging is an important but easily overlooked part of the study of low-carbon lifecycle of electromechanical products, but with the significant growth of the express delivery business, the carbon emissions caused by packaging are increasingly noteworthy. In order to explore the carbon emissions of packaging lifecycle, the electromechanical products are used as the packaging object to discuss the connotation of functional packaging, low-carbon design boundary and research methods of packaging lifecycle and establish the calculation models of carbon footprint and cost of packaging lifecycle and the evaluation method of material selection. The case study of gasoline engines shows that material selection plays a decisive role in the packaging of electromechanical products based on low carbon and cost constraints.
Seasonal performance research of heat-source tower systems using different work materials
Authors: Mao Yifan , Li Yongcun , Wen Xiantai , Yuan Xiaolei , Wu Zhaofan 
Published: April 2024

Abstract
The heat-source tower system has been evaluated in an enthalpy difference laboratory. This study analyzes the seasonal performance under various operating situations and fluid quality. In the summer and fall seasons, water is used as the circulating fluid in the tower. In the spring and winter seasons, glycol solution is used as the circulating fluid. Some parameters of the heat-source tower (e.g. the temperature of the solution inlet and outlet, the flow rate of the solution inlet and outlet, the temperature of the air inlet and outlet, the inlet air volume and the moisture content of the inlet air) are considered and measured to obtain its heat transfer characteristics. The simulation model of heat-source tower is constructed based on the mechanism of heat and mass transfer. This model is validated by the experimental results. The heat exchange and latent heat exchange of the system are analyzed under different parameters, as well as the variation law of inlet and outlet temperature and moisture content differences. The results show that the system has solution moisture absorption during winter operation. However, for every 9000 m3/h increase in air volume, the rate of solution dilution decreases between 9 and 43%. The rate of solution dilution is reduced by 11–31% for every 1°C in addition to the inlet solution temperature. Meanwhile, the heat dissipation in summer is about 2.8 times of the heat dissipation in winter.
The Changing Face of Nuclear Proliferation
Authors: Kaplow Jeffrey M 
Published: April 2024

AbstractA rich literature has identified a number of important drivers of nuclear proliferation. Most of this work, however, treats the determinants of proliferation as constant over the entire nuclear age—the factors leading to proliferation are assumed to be the same in 2010 as they were in 1945. But there are reasons to suspect that the drivers of proliferation have changed over this time: nuclear technology is easier to come by, the global strategic environment has shifted, and the nuclear nonproliferation regime has come into being. To examine changes in the dynamics of nuclear proliferation, I adapt a cross-validation technique frequently used in the machine learning literature. I create a rolling window of training data with which statistical models of proliferation are built, and I then test the predictive power of these models against data from other time periods. The result of this analysis is a temporal map of how the determinants of proliferation have changed over time. My findings suggest that the underlying dynamics of nuclear proliferation have indeed shifted, with important implications both for the literature on nuclear proliferation and for policymakers interested in limiting the future spread of nuclear weapons.
Comrades in Arms? Rebel Leadership Roles and Coups in Rebel Regimes
Authors: Ashley Sean Paul 
Published: April 2024

Abstract
Why do some winning rebel groups build loyal postwar militaries, while others do not? Surprisingly, though victorious rebel political and military elites cooperate successfully to seize sovereign power, they often struggle to share it. In roughly one-third of rebel regimes, military elites attempt to depose their leaders through military coups. I contend that postwar coup risk is primarily shaped by the wartime roles of rebel leaders. Rebel leaders that occupy a frontline role—exposing themselves to the hazards of the conflict theater—establish regimes less likely to suffer postwar coups. Frontline leaders win greater prestige and stronger legitimacy among military audiences. Further, frontline leaders’ greater proximity to their military commanders allows them to develop institutional structures that more effectively reduce military autonomy. However, remote leaders, who are absent from the conflict theater, exercise weaker postwar control over the armed forces, resulting in greater coup risk. I find support for this theory using original cross-national data on all rebel regimes and the wartime roles of victorious rebel leaders between 1945 and 2010. A controlled case comparison of civil–military relations in two rebel regimes—Guinea-Bissau and Uganda—further illustrates the mechanisms tying wartime roles to postwar coups.
Performance evaluation of aged asphalt rejuvenated with waste cooking oil on rheological and chemical characteristics
Authors: Li Yan , He Yinzhang , Hu Qinshi , Li Zhe , Zhang Jiupeng , Zhao Xiaokang 
Published: April 2024

Abstract
This study investigates the influence of varying aging processes on the rheological behavior and microscopic features of bio-based recycled asphalt, focusing on the utilization of waste cooking oil (WCO) as a rejuvenator. The research evaluates the rheological and fatigue characteristics of the modified binder, alongside a detailed microscopic analysis to determine the molecular composition changes in both aged and recycled asphalt. The incorporation of WCO was found to compromise rutting performance slightly, yet significantly enhance fatigue resistance and reduce susceptibility to low-temperature cracking. With the progression of aging, an increased dosage of WCO effectively mitigated the negative impacts, bringing the molecular composition and functional groups of the aged asphalt closer to that of the original material. Despite these improvements, a full restoration to the asphalt’s original properties remained elusive. These findings underscore the potential of waste bio-oil as a sustainable resource for enhancing the durability and environmental friendliness of asphalt pavements.
PMC-LLaMA: toward building open-source language models for medicine
Authors: Wu Chaoyi , Lin Weixiong , Zhang Xiaoman , Zhang Ya , Xie Weidi , Wang Yanfeng 
Published: April 2024

Abstract

Objective
Recently, large language models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering (QA) situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this article, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.

Materials and methods
We adapt a general-purpose LLM toward the medical domain, involving data-centric knowledge injection through the integration of 4.8M biomedical academic papers and 30K medical textbooks, as well as comprehensive domain-specific instruction fine-tuning, encompassing medical QA, rationale for reasoning, and conversational dialogues with 202M tokens.

Results
While evaluating various public medical QA benchmarks and manual rating, our lightweight PMC-LLaMA, which consists of only 13B parameters, exhibits superior performance, even surpassing ChatGPT. All models, codes, and datasets for instruction tuning will be released to the research community.

Discussion
Our contributions are 3-fold: (1) we build up an open-source LLM toward the medical domain. We believe the proposed PMC-LLaMA model can promote further development of foundation models in medicine, serving as a medical trainable basic generative language backbone; (2) we conduct thorough ablation studies to demonstrate the effectiveness of each proposed component, demonstrating how different training data and model scales affect medical LLMs; (3) we contribute a large-scale, comprehensive dataset for instruction tuning.

Conclusion
In this article, we systematically investigate the process of building up an open-source medical-specific LLM, PMC-LLaMA.
Large language models facilitate the generation of electronic health record phenotyping algorithms
Authors: Yan Chao , Ong Henry H , Grabowska Monika E , Krantz Matthew S , Su Wu-Chen , Dickson Alyson L , Peterson Josh F , Feng QiPing , Roden Dan M , Stein C Michael , Kerchberger V Eric , Malin Bradley A , Wei Wei-Qi 
Published: April 2024

Abstract

Objectives
Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts.

Materials and Methods
We prompted four LLMs—GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard—in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network.

Results
GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values).

Conclusion
GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
Determination of β-Galactooligosaccharides (GOS) in Infant Formula: Collaborative Study, Final Action 2021.01
Authors: Cuany Denis , Austin Sean 
Published: April 2024

Abstract

Background
We previously published a method for the determination of β-galactooligosaccharides (GOS) in infant formula and adult nutritionals, which is currently First Action AOAC Method 2021.01. In this study, reproducibility data were collected to support the promotion of the method to Final Action.

Methods
A collaborative study was organized in which 14 laboratories from eight different countries participated. Initially, laboratories were requested to analyze two practice samples and request guidance from the study director in case of issues. Successful laboratories proceeded to analyze seven samples (six infant formula and one adult nutritional) received as blind duplicates.

Results
Thirteen laboratories reported acceptable results for practice sample 1. Practice sample 2 could only be delivered to eight of the laboratories due to restrictions at customs. The 13 laboratories successfully analyzing practice sample 1 were requested to continue with the analysis of the multilaboratory trial (MLT) samples. Laboratory 14 was unable to solve some technical difficulties, so their data could not be used. Out of the seven samples tested, results for six infant formulas met the requirements of the AOAC Standard Method Performance Requirements (SMPR®) 2014.003, with repeatability (RSDr) ranging from 1.4 to 4.7% and reproducibility (RSDR) ranging from 8.1 to 11.6%. The adult nutritional sample returned results outside the range of the SMPR, having an RSDr of 9.9%, higher than the SMPR target of ≤6%, and an RSDR of 12.1%, just above the SMPR target of ≤12%.

Conclusion
The method described is suitable for the determination of GOS in infant formula.

Highlight
A method is described which is suitable for the determination of GOS in infant formula.
Trace mineral source and chromium propionate supplementation affect performance and carcass characteristics in feedlot steers
Authors: Spears Jerry W , Loh Huey Yi , Lloyd Karen E , Heldt Jeff S , Engle Terry E 
Published: April 2024

Abstract
Angus-crossbred steers (n = 400; 369.7 ± 7.6 kg) were used to determine the influence of trace mineral (TM) source and chromium propionate (Cr Prop) supplementation on performance, carcass characteristics, and ruminal and plasma variables in finishing steers. Steers were blocked by body weight (BW) and randomly assigned within block to treatments in a 2 × 2 factorial arrangement, with factors being: 1) TM source (STM or HTM) and 2) Cr supplementation (0 or 0.25 mg Cr/kg DM, −Cr or + Cr, respectively). Treatments consisted of the addition of: 1) sulfate TM (STM; 90, 40, and 18 mg/kg DM of Zn, Mn, and Cu, respectively), 2) STM and 0.25 mg Cr/kg DM from Cr Prop, 3) hydroxychloride TM (HTM; 90, 40, and 18 mg/kg DM of Zn, Mn, and Cu, respectively), and 4) HTM and 0.25 mg Cr/kg DM from Cr Prop. Each treatment consisted of 10 replicate pens with 10 steers per pen. Body weights were obtained on consecutive days at the initiation and termination of the 154-d study. Steers were fed a steam-flaked corn-based finishing diet. Ractopamine hydrochloride was fed for the last 31 d of the study. Ruminal fluid and blood samples were obtained from one steer per pen on days 28 and 84 for ruminal volatile fatty acids (VFA) and plasma TM and glucose analysis. Steers were slaughtered at the end of the study and individual carcass data were collected. No Cr × TM source interactions (P = 0.48) were detected. Steers supplemented with HTM had greater (P = 0.04) hot carcass weight (HCW), dressing percentage (DP), longissimus muscle (LM) area, and USDA yield grade (YG), and tended (P = 0.12) to have greater average daily gain (ADG) than those receiving STM. Average daily gain, gain:feed, dressing percentage, and longissimus muscle area were greater (P = 0.04) for + Cr steers compared to—Cr steers. Hot carcass weight tended (P = 0.06) to be greater for + Cr steers. Ruminal acetate concentrations at 28 d were lesser (P = 0.01) for HTM vs. STM steers, and greater (P = 0.04) for + Cr steers compared to—Cr steers. Plasma concentrations of Zn, Cu, and Mn were not affected by TM source or Cr supplementation. Steers supplemented with Cr had greater (P = 0.05) plasma glucose concentrations than—Cr steers at 28 but not at 84 d. Results of this study indicate replacing STM with HTM improved carcass characteristics in finishing steers, and Cr Prop supplementation improved steer performance and carcass characteristics.
The inflammatory injury of porcine small intestinal epithelial cells induced by deoxynivalenol is related to the decrease in glucose transport
Authors: Tang Xiaopeng , Zeng Yan , Xiong Kangning , Li Meijun 
Published: April 2024

Abstract
The present study aimed to investigate the effects of deoxynivalenol (DON) stimulation on inflammatory injury and the expression of the glucose transporters sodium-dependent glucose transporter 1 (SGLT1) and glucose transporter protein 2 (GLU2) in porcine small intestinal epithelial cells (IPEC-J2). Additionally, the study aimed to provide initial insights into the connection between the expression of glucose transporters and the inflammatory injury of IPEC-J2 cells. DON concentration and DON treatment time were determined using the CCK‑8 assay. Accordingly, 1.0 µg/mL DON and treatment for 24 h were chosen for subsequent experiments. Then IPEC-J2 cells were treated without DON (CON, N = 6) or with 1 μg/mL DON (DON, N = 6). Lactate dehydrogenase (LDH) content, apoptosis rate, and proinflammatory cytokines including interleukin (IL)-1β, Il-6, and tumor necrosis factor α (TNF-α) were measured. Additionally, the expression of AMP-activated protein kinase α1 (AMPK-α1), the content of glucose, intestinal alkaline phosphatase (AKP), and sodium/potassium-transporting adenosine triphosphatase (Na+/K+-ATPase) activity, and the expression of SGLT1 and GLU2 of IPEC-J2 cells were also analyzed. The results showed that DON exposure significantly increased LDH release and apoptosis rate of IPEC-J2 cells. Stimulation with DON resulted in significant cellular inflammatory damage, as evidenced by a significant increase in proinflammatory cytokines (IL-1β, IL-6, and TNF-α). Additionally, DON caused damage to the glucose absorption capacity of IPEC-J2 cells, indicated by decreased levels of glucose content, AKP activity, Na+/K+-ATPase activity, AMPK-α1 protein expression, and SGLT1 expression. Correlation analysis revealed that glucose absorption capacity was negatively correlated with cell inflammatory cytokines. Based on the findings of this study, it can be preliminarily concluded that the cell inflammatory damage caused by DON may be associated with decreased glucose absorption.
Characterization of iso-LSD metabolism using human liver microsomes in comparison to LSD and its applicability as urinary biomarker for LSD consumption
Authors: Ng Xue Qin , Goh Evelyn Mei Ling , Hamzah Asimah , Yao Yi Ju , Moy Hooi Yan 
Published: April 2024

Abstract
Urinalysis of lysergic acid diethylamide (LSD) poses a challenge due to its rapid metabolism, resulting in little to no LSD detectable in urine. Instead, its primary metabolite, 2-oxo-3-hydroxy-LSD, is predominantly detected. In this study, we observed several urine profiles with iso-LSD detected together with 2-oxo-3-hydroxy-LSD. Iso-LSD is derived from illicit preparation of LSD as a major contaminant, and it was detected at higher abundance than LSD and 2-oxo-3-hydroxy-LSD in certain urine samples. Therefore, the metabolism of iso-LSD and its potential as a viable urinary biomarker for confirming LSD consumption is of interest. For metabolism studies, LSD and iso-LSD were incubated in human liver microsomes (HLMs) at 0 min, 60 min and 120 min to characterize their metabolites using LC–QTOF-MS. For urinary analysis, 500 µL of urine samples underwent enzymatic hydrolysis and clean-up using supported-liquid extraction (SLE) prior to analysis by LC–QTOF-MS. From HLM incubation study of LSD, the metabolites detected were dihydroxy-LSD, 2-oxo-LSD, N-desmethyl-LSD (nor-LSD) and 2-oxo-3-hydroxy-LSD with LSD levels decreasing significantly throughout all time points, consistent with the existing literatures. For HLM study of iso-LSD, metabolites eluting at retention times after the corresponding metabolites of LSD were detected, with iso-LSD levels showing only a slight decrease throughout all time points, due to a slower metabolism of iso-LSD compared to LSD. These findings corroborate with the urinalysis of 24 authentic urine samples, where iso-LSD with 2-oxo-3-hydroxy-LSD was detected in the absence of LSD. Based on our findings, iso-LSD is commonly detected in urine (18 out of 24 samples) sometimes with traces of possible 2-oxo-3-hydroxy-iso-LSD. The slower metabolism and high detection rate in urine make iso-LSD a viable urinary biomarker for confirming LSD consumption, especially in the absence of LSD and/or 2-oxo-3-hydroxy-LSD.