bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simultaneous detection of carbendazim and pendimethalin residues using a compact Raman spectrometer
Authors: Zhang Peng , Feng Zheng , Du Liangguang , Pan Weifan , Younus Junaid , Guo Xiaowei , Yang Xue 
Published: October 2024

Abstract
Surface-enhanced Raman spectroscopy (SERS) exhibits significant latent capacity in the prompt and accurate detection of minute concentrations of pesticides. In this study, carbendazim and pendimethalin were simultaneously detected within tobacco foliage by utilizing a PS@Ag composite substrate coupled with a compact Raman spectrometer. The detection limits for carbendazim is 0.1 mg/kg, and for pendimethalin, it is 5 mg/kg. The recovery rates for carbendazim within tobacco foliage ranged from 85.2% to 112.12%. And the recovery rates for pendimethalin ranged from 90.38% to 113.42%. The combination of the PS@Ag composite substrate in conjunction with a Raman spectrometer provides a highly effective means for the concurrent identification of pesticides in tobacco foliage.
Research on residual stress control in polycarbonate molding based on orthogonal analysis
Authors: Deng Zhaohu , Huang Jian 
Published: October 2024

Abstract
Residual internal stress is a primary factor affecting the performance and quality of transparent food containers formed by polycarbonate injection molding. Adjusting the injection molding process parameters stands out as the most effective means to control residual internal stress. This study employs numerical simulation to analyze the influence of injection molding process parameters on material internal stress during the polymer injection molding process, providing a reference for optimizing molding process parameters. Initially, through an analysis of the mechanism of internal stress formation, it is determined that the internal stress of beverage containers is mainly composed of cooling internal stress. Consequently, a mathematical model based on thermo-viscoelastic constitutive relations is selected to numerically simulate and calculate the formation of cooling internal stress during the injection molding process of PC materials. Numerical simulation results are obtained accordingly. Building upon this, orthogonal experiments are conducted based on the analysis data from numerical simulation, and through experimental comparison, the consistency between numerical simulation results and experimental results is validated. Finally, combining the results of experiments with numerical simulation, it is concluded that melt temperature is the primary factor influencing molding internal stress, followed by mold temperature and holding pressure, with injection speed being the least influential factor on residual stress.
Analysis of green energy-saving retrofit for air-conditioning system in UHVDC converter stations
Authors: Wang Hui , Li Chenxi , MA Shiwen , Yin Xiwei , Yu Mingduo , Lu Mengxue 
Published: October 2024

Abstract
The air-conditioning system in ultra-high-voltage direct current (UHVDC) converter stations uses hydrofluorocarbons (HFCs) as refrigerants currently, which pose a certain environmental hazard. Moreover, the cooling system of the converter valves contains a great deal of low-grade thermal energy, with waste heat being directly discharged into the air, resulting in significant energy wastage and air pollution. In light of the functional characteristics and energy wastage situation of current converter station buildings, a green energy-saving retrofit scheme for the air-conditioning system in UHVDC converter stations is proposed. It adopts a transcritical CO2 cycle system for building cooling and introduces an ejector into the system to improve its performance. Simultaneously, the heat pump technology is used to elevate the grade of waste heat from the converter valves, which is then utilized for heating in the control building. Simulation results demonstrate that this scheme is effective in enhancing ecological and environmental benefits while promoting green development of the converter station.
IEC104 protocol-based substation data upload IoT cloud platform implementation
Authors: Yang Chao , Chen Deji , Hu Hongyuan 
Published: October 2024

Abstract
The Industrial Internet of Things (IoT) has been developing rapidly under the background of Internet technology, and the most crucial part of its realization is the unified collection and access of heterogeneous data from multiple sources across networks and regions. For the access of substation system data, this paper designs a scheme to read data from IEC 60870-5-104 protocol and upload the data to IOT cloud platform by converting the data to data format supported by Kafka protocol, solving the problem of incompatibility between communication protocols, obtaining data from 104 secondary stations by connecting to them, parsing and processing the data in different formats, and converting the data to JSON format by processing and sending it to the IOT cloud platform. The data is converted into JSON format through processing and sent to the Kafka server, and the cloud platform obtains its data by subscribing to the corresponding topics. This approach makes substation data access more convenient, and efficiently and effectively saves the cost of hardware gateway, solves the incompatibility and interoperability between the underlying equipment access protocol and the cloud platform communication protocol, and builds a bridge for the remote transmission of the underlying equipment data of the Industrial Internet of Things.
Efficiency evaluation of electricity demand prediction models in lower-income countries: a case study of panama
Authors: Dong Youliang , Yan Changshun 
Published: October 2024

Abstract
Electricity plays a crucial role in the development of countries, serving as the foundation for the functioning of modern societies and as a key indicator of a nation’s level of modernization and overall strength. Predicting a country’s electricity demand provides valuable insights into the evolving patterns of electrical energy provision and usage and helps to improve the flexibility of the energy distribution network. In addition, it provides early warnings and decision support for governments, businesses, and individuals about future electricity consumption. This plays a pivotal function in optimizing power distribution, adjusting the proportion of power generation sources, and ensuring a continuous and secure electricity supply. This paper uses the example of electricity demand in Panama and uses the LSTM model and Prophet model to compare their predictions for electricity demand in lower-income countries, with the aim of improving accuracy. Furthermore, it introduces a data set representing the electricity demand of higher-income countries, using the United Kingdom as a representative, to compare the forecast results between the two countries. The results indicate that within the Panama data set, the Prophet model exhibits better accuracy than the LSTM model. On the contrary, in the data set representing higher-income countries with the United Kingdom as a representative, the LSTM model outperforms the Prophet model. This leads to the conclusion that the selection of a predictive model is specific to each country, influenced by factors such as development status, geographic location, policies, energy markets, and more. A specific model may not be universally applicable due to these unique conditions, requiring tailored-modeling approaches for precise and efficient allocation and scheduling of electricity resources. This approach ensures the improvement of residents’ living standards and quality of life while meeting the typical demands of a country’s economic and social development.
Evaluation of power system instability probability considering output randomness of grid-connected converter
Authors: Wang Liqiang , Liu Hongqing , Wang Qi , Cong Yu , Zheng Xiuxian 
Published: October 2024

Abstract
In recent years, there has been a notable increase in the installed capacity of new energy sources. Nevertheless, the random nature of the output power of G-CCs may potentially result in power system instability. This paper presents a closed-loop interconnection model comprising a G-CC as a feedback subsystem and a VSC-HVDC system as a feedforward subsystem. The instability condition of the closed-loop system is derived from the open-loop mode resonance theory. Subsequently, a mathematical model is developed to represent the random output of the G-CC. This approach allows the probability of open-loop mode resonant instability of the power system to be evaluated, as well as the risk of sub-synchronous resonant instability caused by the output randomness of the G-CC. Finally, validation is performed by building a simulation example system and a time-domain simulation system in MATLAB.
Large-signal stability analysis of an islanded DC microgrid with constant power loads based on mixed potential function
Authors: Zhang Shaojie , Wen Sufang , Wang Shunli 
Published: October 2024

Abstract
In DC microgrids, power electronic converters are widely used to connect loads and renewable energy sources to generate different voltages. These loads operate as constant power loads (CPL) within a closed control loop. When voltage fluctuates, these loads display negative impedance characteristics, potentially leading to system instability. Therefore, the analysis of the impact of CPL on DC microgrids during large disturbances is crucial. This paper employs mixed potential theory to study the large disturbance stability of DC microgrids under CPL. Deriving the stability criterion for the system under significant perturbations reveals the impact of load types and control parameters on system stability and provides a detailed analysis of how specific system parameters affect stability. Consequently, the stability criterion can ensure the system’s stability under large disturbances without the need for repeated calculations and simulations. Simulation results verify the correctness of this stability criterion.
Numerical investigation of the effective mechanical properties of architected structures: a comparative study of flexural stiffness, homogenization, and elastic anisotropy
Authors: García-Gutiérrez Magali E , Giraldo Betancur Astrid L , Cuan-Urquizo Enrique , Alvarado-Orozco Juan M , González-Hernández Jesús 
Published: October 2024

Abstract
The mechanical behavior of architected structures is influenced by various parameters, including the topology of their unit cells. This anisotropic nature requires the determination of the mechanical properties under different loading scenarios. This study employs numerical investigation to characterize the influence of topology on the mechanical properties of eight architected structures, focusing on effective elastic properties and anisotropic elastic behavior. The analyzed topologies encompass four based on struts (lattices) and four based on triply periodic minimal surfaces (TPMS), comprising Sheet and Network phases. Initially, beams composed of architected structures are subjected to flexure, with Euler–Bernoulli and Tymoshenko’s theories utilized in a first numerical approach to determine their effective properties. Subsequently, a numerical homogenization method along with the Voigt-Reuss-Hill scheme is employed in a second approach. A more substantial influence of topology on the effective properties is observed in low relative densities. The study revealed that for a relative density of 10%, the appropriate selection of the topology increases the stiffness of a structure by up to ∼126%. The EBT approach underestimated the stiffness by up to ∼26% due to neglecting the impact of shear on beam deflection. The tensorial anisotropy index revealed up to ∼27% higher anisotropy compared to the Zener index. These findings provide a valuable numerical tool for the comparison and selection of architected structures suitable for diverse applications.
Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors
Authors: Sun Liang , Chen Mohan 
Published: October 2024

Abstract
The recently proposed machine learning-based physically-constrained nonlocal (MPN) kinetic energy density functional (KEDF) can be used for simple metals and their alloys (Sun and Chen 2024 Phys. Rev. B 109 115135). However, the MPN KEDF does not perform well for semiconductors. Here we propose a multi-channel MPN (CPN) KEDF, which extends the MPN KEDF to semiconductors by integrating information collected from multiple channels, with each channel featuring a specific length scale in real space. The CPN KEDF is systematically tested on silicon and binary semiconductors. We find that the multi-channel design for KEDF is beneficial for machine-learning-based models in capturing the characteristics of semiconductors, particularly in handling covalent bonds. In particular, the CPN5 KEDF, which utilizes five channels, demonstrates excellent accuracy across all tested systems. These results offer a new path for generating KEDFs for semiconductors.
The Armed Debtor
Authors: Jones Corredera Edward 
Published: October 2024

Abstract
This chapter studies the structuring role of debates over colonial and war debts in the construction of Argentina. The United Provinces, constituted as independent and sovereign states, drew on principles of the law of nations to undermine Buenos Aires’ pursuit of domination over the region. The failure to unite the two sides led to the fragmentation of Argentina into two federations, the Unitarian League and the Federalist League. This division prompted debates over who ought to pay the debts incurred during the revolution. When France blockaded the port of Buenos Aires over unpaid claims, exiled thinkers sought to harness foreign interest in the region to topple Juan Manuel de Rosas. This outlook on debt and intervention informed the mid-nineteenth-century constitutional and diplomatic efforts of Juan Bautista Alberdi. Alberdi sought to resolve dilemmas that had emerged from the lack of resolution regarding war debts and the foreign loans before Argentina’s internal divisions led to his demise. During some of the toughest years of his life, he received a letter from Carlos Calvo. This letter triggered an intellectual exchange between the two jurists which led Alberdi to condemn international law as a threat to Latin America.
China
Authors: Prontera Andrea 
Published: October 2024

AbstractThis chapter analyses the Chinese green foreign energy policy and green power strategy. First, the chapter explains the shift in China’s energy policy from traditional energy security concerns, which have not disappeared, to technological dominance. This process has unfolded in parallel with the unexpected rise of China in several sectors that are key to the current energy transition, from manufacturing to critical minerals. Next, the chapter focuses on the structuring of the Chinese green energy state and explains how it has influenced the country’s green foreign engagements. A special focus is placed on the green components of the Belt and Road Initiative and on recent innovations introduced under Xi Jinping. Finally, the chapter discusses the greening of the Chinese global strategy as renewables and low-carbon technology are increasingly embedded in Beijing’s wider approach to extending its influence within the current international system.
Clinical utility of EBV DNA testing of blood in immunocompetent and immunocompromised patients: A single-center experience
Authors: Gupta Simran , Turbett Sarah E , Klontz Erik , Kotton Camille N 
Published: October 2024

Abstract

Objectives
Epstein-Barr virus (EBV) causes different clinical presentations in immunocompetent and immunocompromised persons, and thus indications for testing vary between these populations. We reviewed our institution’s EBV DNA testing across these populations to understand its clinical utility and appropriateness.

Methods
We conducted a retrospective chart review of adult patients with positive EBV nucleic acid amplification (NAAT) testing from November 2022 to 2023. We recorded demographics, indications for testing, EBV-related diagnosis, laboratory results, and whether testing influenced patient treatment.

Results
Of 3560 EBV NAAT tests, 187 (5.3%) were positive, representing 124 unique adult patients (51 immunocompetent and 73 immunocompromised). Reactivation of EBV was the most common diagnosis in both populations, followed by acute EBV infection in the immunocompetent and non-posttransplant lymphoproliferative disorder malignancies in the immunocompromised. In immunocompromised patients, positive tests led to treatment changes more frequently than in immunocompetent patients (27.4% vs 11.7%, P = .06). Testing affected clinical management in 0% to 2% of cases when sent for sepsis/shock and nonspecific viral syndromes compared to 37% to 49% of cases when sent for posttransplant and malignancy screening/monitoring.

Discussion
EBV NAAT testing infrequently influenced clinical management in immunocompetent individuals but had a notable impact in immunocompromised patients, particularly those undergoing posttransplant or malignancy screening. This underscores the need for targeted testing to optimize resource utilization and cost-effectiveness.
How does lexical coverage affect the processing of L2 texts?
Authors: Pellicer-Sánchez Ana , Webb Stuart , Wang Andi 
Published: October 2024

Abstract
Lexical coverage, i.e. the extent to which words in a text are known, is considered an important predictor of reading comprehension, with studies suggesting 98% lexical coverage leads to adequate comprehension. However, no studies to date have examined how the various lexical coverage percentages suggested in the literature are reflected by the cognitive effort involved in processing text and the attention that is devoted to the unknown vocabulary. This study used eye-tracking to examine how lexical coverage affects the processing of text (global measures) and unknown vocabulary (word-level measures), as well as the relationship between processing time on unknown vocabulary and learning. Advanced L2 learners of English read a text in one of four lexical coverage conditions (90%, 95%, 98%, 100%) while their eye movements were recorded. Knowledge of unknown pseudowords in the texts was assessed via an immediate, meaning recall post-test. Results showed that only one of the three global measures examined showed a processing advantage for the 98% condition, reflected by longer saccades and less effortful reading than the 90% and 95% conditions. Crucially, lexical coverage did not have a significant impact on the amount of attention spent on unknown vocabulary. Processing times were found to significantly predict vocabulary gains.
Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators
Authors: Guo Yang , Xiao Zhiqiang 
Published: October 2024

Abstract
The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.
Modified throughput ninhydrin method for the qualitative assessment of dietary protein absorption in pig plasma
Authors: Pierzynowska Kateryna , Zaworski Kamil , Wychowański Piotr , Donaldson Janine , Woliński Jarosław , Borowitz Drucy , Gallotto Robert , Pierzynowski Stefan 
Published: October 2024

Abstract
Protein maldigestion and malabsorption lead to malnutrition and are a feature of exocrine pancreatic insufficiency (EPI). Although it is the current standard, measurement of nitrogen in stool to assess protease activity is indirect. Up to 80% of hydrolysed proteins appear in blood in the form of peptides, so we developed a method to measure peptide-derived amino acids in plasma as a relevant measure of proteolysis, verified its accuracy, precision, and linearity, and validated it in a porcine model. We modified a ninhydrin method. Large proteins were eliminated from plasma with 10 kDa-cut-off centrifugal filters. Free and total amino acids were measured in permeate before and after its hydrolysis. Peptide-derived amino acids were quantified by subtracting free amino acids from total amino acids. We verified the method in vitro and by comparing results in healthy and EPI pigs. The accuracy of the analysis was close to 100%, with excellent precision (mean relative standard deviation for low, medium, and high amino acid levels = 0.88%) and with stringent linearity (r2 = 0.986, %RE = 5.23). The high-throughput ninhydrin method detected levels of peptide-derived amino acids in vivo with maximal changes seen approximately 2 hours postprandially in young pigs. The AUC and Cmax were significantly higher in healthy compared to EPI pigs (P = .0026 and P = .0037, respectively). The high-throughput ninhydrin method is a sensitive, reliable, and practical method for the estimation of dietary peptide-derived amino acids. This assay endpoint could serve as a direct biomarker of protein digestion and absorption.