bluuebunny/crossref_metadata_2025 huggingface dataset:
How Does Fiscal Vertical Imbalance Affect Regional Green Technology Innovation in China—The Moderating Role of Financial Decentralization and Fiscal Transparency
Authors: Quan Feiguo , Liu Liu
Published: February 2025

Green technology innovation (GTI) is crucial for sustainable economic development and achieving “peak carbon” and “carbon neutrality” goals. While fiscal vertical imbalance (FVI) may exert an inhibiting effect on regional GTI, the existing literature has paid insufficient attention to investigating the underlying mechanisms and potential mitigation strategies for such impacts. Using provincial data from China (2005–2019), this study explores the impact of FVI on GTI through theoretical analysis and empirical testing. The results indicate that FVI significantly inhibits GTI, as validated by the dynamic system Generalized Method of Moments (GMM) and spatial Durbin model analyses. Mechanistically, FVI hinders GTI by altering government innovation preferences and reducing investments in environmental pollution control. Moreover, financial decentralization and fiscal transparency positively moderate this relationship, with nonlinear moderating effects. These findings suggest that enhancing regional financial decentralization and fiscal transparency can mitigate the negative effects of FVI on GTI, offering practical insights for harmonizing fiscal policies and green economic transitions.
Coal Mine Accident Risk Analysis with Large Language Models and Bayesian Networks
Authors: Du Gu , Chen An
Published: February 2025

Coal mining, characterized by its complex operational environment and significant management challenges, is a prototypical high-risk industry with frequent accidents. Accurate identification of the key risk factors influencing coal mine safety is critical for reducing accident rates and enhancing operational safety. Comprehensive analyses of coal mine accident investigation reports provide invaluable insights into latent risk factors and the underlying mechanisms of accidents. In this study, we construct an integrated research framework that synthesizes large language models, association rule mining, and Bayesian networks to systematically analyze 700 coal mine accident investigation reports. First, a large language model is employed to extract risk factors, identifying multiple layers of risks, including 14 direct, 38 composite, and 75 specific factors. Next, the Apriori algorithm is applied to mine 281 strong association rules, which serve as the foundation for constructing a Bayesian network model comprising 127 nodes. Finally, sensitivity analysis and critical path analysis are conducted on the Bayesian network to reveal seven primary risk factors primarily related to on-site safety management, the execution of operational procedures, and insufficient safety supervision. The novelty of our framework lies in its efficient processing of unstructured text data via large language models, which significantly enhances the accuracy and comprehensiveness of risk factor identification compared to traditional methods. The findings provide robust theoretical and practical support for coal mine safety risk management and offer valuable insights for risk management practices in other high-risk industries. From a policy perspective, we recommend that the government strengthen legislation and supervision of coal mine safety with a particular focus on the enforcement of operational procedures and on-site safety management, promote comprehensive safety education and training to enhance frontline personnel’s awareness and emergency response capabilities, and leverage data-driven technologies to develop intelligent risk early-warning systems. These measures will improve the precision and efficiency of safety management and provide a scientific basis for accident prevention and control.
The Hidden Threat of Microplastics in Desert Environments: Environmental Impact, Challenges, and Response Measures
Authors: Li Huiqi , Liu Huanpeng , Lin Qizhuan , Chen Ting , Peng Renyi
Published: February 2025

Microplastic (MP) pollution has garnered widespread attention because of its negative effects, even in the most remote areas of Earth. However, research on MP deposition in deserts, which account for 45% of Earth’s total land area, remains limited. Desert environments, characterized by large temperature fluctuations, high ultraviolet radiation, and strong winds, accelerate the degradation, aging, wind erosion, deposition, and migration of microplastics (MPs). In desert regions, MPs originate primarily from human activities, such as tourism waste, agricultural mulch residues, and artificial water storage systems. Additionally, wind transport, water entrainment, atmospheric deposition, and the migration of wildlife further influence the abundance of MPs in these areas. As MP pollution in desert soils intensifies, it negatively affects local microbial activity, crop yields, the reproduction of rare wildlife, and climate. In response, mitigation strategies, such as biodegradation, organic alternatives, and wasteland transformation, have been proposed. However, challenges remain, including a lack of specific research data and limited economic incentives for environmental protection measures. Drawing on existing research, this paper provides a comprehensive summary of the main sources of MPs in desert areas; the influence of environmental factors on their fate; their detrimental effects on ecosystems (including microorganisms, animals, plants, and climate); and current response measures. Valuable insights are extracted from the available data, highlighting the status and challenges of MP pollution in desert regions, and offering a useful reference for future research in this area.
Pumped Hydro Storage in the Brazilian Power Industry: A Sustainable Approach to Expanding Renewable Energy
Authors: da Silva Luciano José , Parente Virginia , de Jesus José Oduque Nascimento , Oliveira Esquerre Karla Patricia , Sahin Oz , de Araujo Wanderbeg Correia
Published: February 2025

This study evaluates whether pumped hydro storage (PHS) systems are economically competitive compared to natural gas thermal power plants in meeting peak load demand in Brazil and identifies the barriers and challenges that hinder their widespread adoption. It also examines the strategies, market mechanisms, and policy implications necessary to improve the economic and operational viability of PHS, enabling greater integration of variable renewable energy sources into the Brazilian power system. Using the levelized cost of electricity (LCOE) method, PHS is compared with natural gas thermoelectric plants for peak demand scenarios in Brazil. The results of simulations indicate that PHS is economically viable for operations exceeding seven hours per day, offering lower costs. In contrast, natural gas technologies are more cost-effective for shorter operations. The results provide two key contributions: they characterise the basic conditions under which PHS systems are more competitive than thermal power plants in meeting electricity demand, and they propose a methodology for calculating the LCOE of the analysed technological options, tailored to the Brazilian energy market. The conclusions highlight the potential of PHS to contribute to Brazil’s sustainable energy transition, provided that appropriate policies are implemented. These policies are especially crucial in scenarios where PHS is not economically competitive, to ensure compensation mechanisms for the flexibility services provided and the implementation of carbon pricing. Additionally, retrofitting existing hydropower plants to incorporate PHS components may reduce costs and mitigate environmental impacts compared to constructing new PHS facilities.
Environmental Sustainability of Fashion Product Made from Post-Consumer Waste: Impact Across the Life Cycle
Authors: Islam Mazed , Shamsuzzaman Md , Hasan H M Rakib Ul , Atik Md Atiqur Rahman
Published: February 2025

The fashion industry has a detrimental environmental impact throughout its supply chain operations that needs immediate attention. Limited work focuses on measuring the environmental sustainability of clothing products made from post-consumer waste in the circular economy. This research aims to evaluate the environmental sustainability of fashion products made from post-consumer waste using the Higg Index tool developed by the Sustainable Apparel Coalition. Three t-shirt manufacturers—namely factory A (LEED certified), factory B (non-LEED certified), and factory C (Sub-contract) were considered as case studies. Data were collected through practice-based qualitative questions to manufacturing practitioners, which were supplemented by triangulations, and scores were obtained using the Higg Index product environmental sustainability tool. The findings highlight significant variations in most subsection analyses for product environment sustainability dimensions, scoring 369.5 (73.9%), 277.6 (55.5%), and 153.5 (30.7%) out of 500 by factories A, B, and C, respectively. Findings reveal significant differences in scores (from low to high) for the three t-shirt manufacturers. Various subsections revealed deficiencies in actual policies including product design, materials selection, manufacturing operations, and priorities regarding subcategories. Products made in a green factory and embracing the circular economy achieved the highest score, while the sub-contractor factory product obtained the lowest score. Findings highlight poor and emerging sustainable practices, identify challenges, and suggest improvement in the above-mentioned categories. The research urges stakeholders to embrace sustainable practices for fashion products to reduce environmental impact through life cycle stages and benefit the industry.
The Impact of Forest Management Success Rates on the Net Carbon Benefits of Using Timber in Construction
Authors: Gibson Andy , Pomponi Francesco
Published: February 2025

The climate emergency calls for carbon drawdown to be applied at scale to offset the ‘hard to abate’ emission reductions that threaten Net Zero. The use of biogenic materials in construction promises benefits in terms of low embodied carbon (EC), but timber harvested today only sequestered atmospheric carbon (AC) in the past. A reduction in future AC concentration is only possible from today, and harvesting timber harms a forest’s ability to sequester carbon in the future, unless a level of afforestation can be guaranteed. Current Whole Life Carbon (WLC) assessment methodologies confuse the perceived value of past sequestration, making it seem equivalent to EC, or implying a guarantee of future AC. This study seeks to connect these two opposing elements by finding a forest management ‘success’ value (FS) at which harvesting losses are outweighed by future sequestration, and a net benefit (in future AC terms) can be justifiably claimed. The research proposes a measure of forestry success (a standard established in terms of net sequestration per hectare) and cumulatively offsets losses through harvest against additional drawdown achieved in a well-managed forest. The results show that current boreal forest management regimes do not guarantee a net benefit, but that only modest improvements from a contemporary baseline would be required to see a net benefit by 2050. Recommendations are made to establish a carbon-focused standard for forestry management to replace current binary sustainability accreditations.
Empowering Breakthrough Innovations Through Digital Technology: The Effects of Digital Technology Depth and Breadth
Authors: Mu Renyan , Rao Jianhua , Zhu Junda
Published: February 2025

The advent of digital technology has empowered firms to make breakthrough innovations; however, how digital technology adoption (especially its depth and breadth) influences such innovations remains unknown. This study addresses this gap by analyzing panel data from 469 listed biomedical companies in China from 2010 to 2021 using a knowledge-based view (KBV). Results show the following: (1) both dimensions of digital technology adoption are positively correlated with firms’ breakthrough innovations; (2) firms’ knowledge recombination novelty mediates these relationships; (3) the mediating effect of knowledge recombination novelty diminishes as firms’ knowledge base increases. Additional analysis indicates that the positive impacts of digital technology depth and breadth on breakthrough innovations are stronger for enterprises located in cities with high digital infrastructure construction levels (high-DICs) than enterprises located in low (and medium)-DIC cities. These findings enhance existing research on the mechanisms and boundary conditions of digital technology-driven breakthrough innovation and offer new insights for enterprises to gain sustainable competitive advantage in the digital era.
Assessing the Technical Efficiency and Resilience of Rwandan Smallholder Farmers Participating in Small-Scale Irrigation Schemes
Authors: Benjamin Emmanuel Olatunbosun , Lotz Alexander , Ola Oreoluwa , Buchenrieder Gertrud Rosa
Published: February 2025

In a number of developing countries, low productivity and technical inefficiency, with climate change looming in the background, remain a severe challenge for the agricultural sector, especially smallholder farmers. To enhance smallholder farmers’ livelihoods in terms of agricultural productivity while mitigating the adverse effects of climate change, improving technical efficiency in a sustainable manner is a promising option. One possible alternative is the use of solar-powered small-scale irrigation systems in areas vulnerable to climate change to ensure sufficient access to water. This study uses stochastic frontier analysis to analyze technical efficiency and its determinants among smallholder farmers who benefit from a solar-powered small-scale irrigation scheme in Gitaraga, Bugesera District, Rwanda. Similar smallholders from a neighboring village, who were not participating in the irrigation scheme, represent the control group. The results suggest that inputs such as land, water, and labor are positively correlated to agricultural productivity. Farmers participating in the irrigation scheme are 31.2 percentage points more technically efficient compared to non-participants, despite similar climatic conditions. Thus, relaxing the water constraint on arable land will increase agricultural productivity. Explanatory inefficiency determinants include years of farming experience and market access. Subsequently, policy makers should continue to support programs that improve smallholder access to sustainable irrigation schemes, other infrastructure, extension services, and upstream value chains, as well as markets.
The Impact of Carbon Emissions Trading and Market Participants on Green Innovation: A Synergistic Effect
Authors: Hu Yucai , Liu Jiancheng , Hao Ruotong , Shen Jiaxin , Fan Shanshan
Published: February 2025

How to incentivize green innovation is an important issue of great concern to scholars. Drawing on the theories of evolutionary analysis and neoclassical economic analysis, this study incorporates environmental policies and market participants into a unified analytical framework to explore the synergistic impacts of carbon emissions trading (CET) and market participants on green innovation. Using a sample of Chinese listed companies from 2006 to 2018, the empirical results based on the difference in difference (DID) model show the following: first, the CET pilot policy significantly promotes pilot firms’ green innovation; second, economic and environmental legitimacy are the mechanisms through which CET promotes corporate green innovation; and third, further analysis shows that CET and market participants have a synergistic effect on firms’ green innovation. This study provides new evidence as to how market-oriented CET affects green innovation through market participants, which provides a theoretical reference for policymakers to use market-based environmental regulation to promote green transformation.
Online Review-Assisted Product Improvement Attribute Extraction and Prioritization Method for Small- and Medium-Sized Enterprises
Authors: Wang Keqin , Lei Angqi , Huang Zhihong , Gao Zhijiao , Ma Qingyu , Zheng Chen , Li Jing , Eynard Benoît , Xiao Jinhua
Published: February 2025

Small- and medium-sized enterprises (SMEs) play a vital role in the global economy, driving innovation and economic growth, despite constraints on their financial and operational resources. In the competitive landscape of modern markets, continuous product design improvement has become essential for SMEs to meet dynamic user requirements, enhance satisfaction, and maintain competitiveness. Online reviews have emerged as valuable sources of user feedback, offering real-time, large-scale insights into user preferences. However, existing methods for leveraging online reviews in product design improvement have significant limitations, including insufficient attention paid to the hierarchical structure of different attributes when extracting product improvement attributes and a lack of quantitative attribute prioritization strategies. These shortcomings often result in suboptimal improvement and inefficient resource allocation, particularly for SMEs with limited resources. To address these challenges, this study proposed a novel online review-assisted method for product design improvement tailored to the needs of SMEs. The proposed method incorporates a hierarchical latent Dirichlet allocation model to extract and organize product attributes hierarchically, thereby enabling a comprehensive understanding of user requirements. Furthermore, a marginal utility-based approach is employed to prioritize product improvement attributes quantitatively, ensuring that the most impactful attributes are addressed efficiently. The effectiveness of the proposed method was demonstrated through a case study on the design improvement of a robotic vacuum cleaner developed using a typical SME in robotic cleaning solutions.
A Novel BoHV-1-Vectored Subunit RVFV Vaccine Induces a Robust Humoral and Cell-Mediated Immune Response Against Rift Valley Fever in Sheep
Authors: Pavulraj Selvaraj , Stout Rhett W. , Chowdhury Shafiqul I.
Published: February 2025

Rift Valley fever (RVF) is a vector-borne zoonotic viral disease that causes abortion storms, fetal malformations, and neonatal mortality in livestock ruminants. In humans, RVF can lead to hemorrhagic fever, encephalitis, retinitis, or blindness, and about 1% of patients die. Since there are no registered vaccines for human use, developing RVF vaccines for use in animals is crucial to protect animals and prevent the spread of the virus from infecting humans. We recently developed a live bovine herpesvirus type 1 quadruple gene-mutant vector (BoHV-1qmv) that lacks virulence and immunosuppressive properties. Further, we engineered a BoHV-1qmv-vectored subunit Rift Valley fever virus (RVFV) vaccine (BoHV-1qmv Sub-RVFV) for cattle, in which a chimeric polyprotein coding for the RVFV Gc, Gn, and bovine granulocyte–macrophage colony-stimulating factor (GMCSF) proteins is fused but cleaved proteolytically in infected cells into individual membrane-anchored Gc and secreted Gn-GMCSF proteins. Calves vaccinated with the BoHV-1qmv Sub-RVFV vaccine generated moderate levels of RVFV-specific serum-neutralizing (SN) antibodies and cellular immune responses. In the current study, we repurposed the BoHV-1qmv Sub-RVFV for sheep by replacing the RVFV Gc and Gn ORF sequences codon-optimized for bovines with the corresponding ovine-codon-optimized sequences and by fusing the sheep GM-CSF ORF sequences with the Gn ORF sequence. A combined primary intranasal-plus-subcutaneous primary immunization induced a moderate level of BoHV-1 (vector)- and vaccine strain MP12-specific SN antibodies and MP-12-specific cellular immune responses. Notably, an intranasal booster vaccination after 29 days triggered a rapid (within 7 days) rise in MP-12-specific SN antibody titers. Therefore, the BoHV-1qmv-vectored subunit RVFV vaccine is safe and highly immunogenic in sheep and can potentially be an efficient subunit vaccine for sheep against RVFV.
Monkeypox Virus Occurrence in Wastewater Environment and Its Correlation with Incidence Cases of Mpox: A Systematic Review and Meta-Analytic Study
Authors: Omatola Cornelius A. , Ogunsakin Ropo E. , Olaniran Ademola O. , Kumari Sheena
Published: February 2025

The COVID-19 pandemic has increased the interest in the use of wastewater-based surveillance (WBS) strategy for infectious disease monitoring, especially when clinical cases are underreported. The excretion of monkey virus (MPXV) in the feces of both symptomatic and preclinical individuals has further driven the interest in WBS applicability to MPXV monitoring in wastewater to support its mitigation efforts. We performed a systematic review with meta-analysis, using six databases to assess MPXV detection in wastewater. We performed a random-effects model meta-analysis to calculate the pooled prevalence at a 95% confidence interval (95% CI). Also, we carried out a subgroup analysis according to the country regions and a sensitivity analysis excluding studies classified as having a high risk of bias. The overall MPXV positivity rate in wastewater was estimated at 22% (95% CI: 14−30%; I2 = 94.8%), with more detection rate in North America (26%, 95% CI: 8–43%) compared to Europe and Asia (22%, 95% CI: 12–31%). The MPXV detection rate was significantly higher in 2022 studies (22%, 95% CI: 13–31%) compared to 2023 (19%, 95% CI: 14–25%). The real-time PCR platform significantly detected more MPXV (24%, 95% CI: 14–34%) than the digital droplet PCR-based studies (17%, 95% CI: 4–31%), which was used less frequently. Viral concentration with centrifugation procedure indicated higher detection rates (21%, 95% CI: 10–33%) than other known sample concentration protocols. Generally, MPXV detection rates in wastewater samples strongly correlate with incidence cases of mpox (range of R = 0.78–0.94; p < 0.05). Findings from this study suggest that WBS of MPXV could be employed as an epidemiological early warning tool for disease monitoring and mpox outbreak prediction similar to the clinical case-based surveillance strategies.
Sustainable Water Production: Solar Energy Integration in Multi-Effect Desalination Plants
Authors: Muftah Ali K. , Zili-Ghedira Leila , Abugderah Mabruk M. , Hassen Walid , Becheikh Nidhal , Alshammari Badr M. , Kolsi Lioua
Published: February 2025

This study addresses the increasing demand for sustainable water production by investigating the integration of solar energy into a multi-effect desalination (MED) plant located in Zuara, Libya. It is acknowledged that regions with high water demand frequently experience elevated levels of solar radiation; thus, this approach seeks to reduce dependence on fossil fuels and mitigate harmful emissions. Three operational modes were analyzed and compared: (1) daytime-only solar operation, (2) hybrid solar–boiler operation, and (3) solar operation with thermal energy storage. A validated mathematical model, informed by real empirical data, was used to assess energy and exergy efficiencies as well as production costs per cubic meter of desalinated water. The results indicate a trade-off between cost-effectiveness and environmental impact. The fossil fuel-based mode demonstrated the highest energy efficiency but also produced the greatest pollution. Conversely, the solar-only daytime mode, while free of emissions, was the costliest (USD 1.4/m3) due to its limited operational hours. The hybrid mode offered a compromise, presenting lower costs (USD 0.79/m3) and moderate land use, yet it still relied on fossil fuels. The thermal storage mode facilitated continuous, clean desalination at a higher cost (USD 0.97/m3) and required the largest solar field area. This study demonstrates the potential of solar-powered MED plants for sustainable water production, particularly in regions characterized by abundant sunlight and water scarcity. Furthermore, this research provides a comprehensive framework for selecting the optimal operational mode based on specific priorities, such as minimizing environmental impact, reducing costs, or maximizing operational flexibility.
Untangling the Characteristics and Ecological Processes of Microbial Community Assembly in the Source Area of the East Route of the South-to-North Water Diversion Project in China Under Different Water Periods
Authors: Cai Wei , Wen Xin , Zhao Yueru , Wu Xiusen , Zheng Haoran , Chen Jiangtao , Hu Zhengyang , Zhong Qin , Wu Jun
Published: February 2025

This study presented a comprehensive analysis of the microbial ecology in water diversion rivers (WDRs) in the source area of the East Route of the South-to-North Water Diversion Project (ER-SNWDP) in China across various water periods. Proteobacteria, Chloroflexi, Acidobacteriota, and Bacteroidota were identified as the dominant microbial phyla in river sediment. During the wet period, microbial communities exhibited the highest richness, biodiversity, and the most intense antagonistic relationships compared to those in the dry and normal water periods. Generally, the microbial network predominantly existed in symbiotic models characterized by mutual benefit and symbiosis throughout all periods. During the dry period, the microbial co-occurrence network was found to be the most complex, with microbial OTUs showing the closest interconnections. The dominant mechanisms governing community diversity, succession, and biogeography were spatial turnover of species and stochastic processes. A more pronounced impact of stochastic processes on microbial community assemblages was observed during normal or wet periods than the dry period. Functional prediction of metabolic pathways indicated that the main ecological functions of microbial communities encompassed carbohydrate metabolism, amino acid metabolism, energy metabolism, etc. This study could provide essential scientific data for ecological regulation, ecological protection, and water resources management in WDRs.
Spatial and Temporal Scales of Variability of Mollusks in a Strongly Threatened Mediterranean Coastal Lagoon (Mar Menor, Murcia, Spain)
Authors: Sánchez-Fernández Olga , Marcos Concepción , Puerta Patricia , Sala-Mirete Antonio , Pérez-Ruzafa Angel
Published: February 2025

Coastal lagoons are dynamic and highly productive systems that offer a remarkable number of ecological services and benefits for humans. However, our understanding of them is still far from adequate. The Mar Menor lagoon is an ecosystem subject to anthropogenic pressures that have worsened in recent years. These pressures include coastal works, such as dredging and sand dumping, as well as changes in agricultural regimes that have induced a process of eutrophication that set off alarms after the eutrophic crisis that occurred in 2016. Benthic organisms, and in particular mollusks, are very sensitive to environmental variations, often serving as indicators of these changes. This work analyzes the malacofauna of the Mar Menor from 1981 to 2019 in the context of the environmental changes that have occurred in it during these years. Eighty-six species have been recorded throughout our study period, and species richness, abundances, local assemblage structures, along with changes in the main environmental parameters of the water column (salinity, temperature, and chlorophyll a concentration) have been used to explain the composition of the communities of the main lagoon habitats and to detect their spatial and temporal variations. With the information provided, the complete inventory of mollusks reported in the lagoon has been updated to 126 species. The results indicate that, during these almost 40 years, the total number of species has remained relatively constant, but with a high percentage of occasional and very rare species, along with a high rate of change from one species to another over time, accompanied by variations in the abundance and dominance of some species compared to others depending on the environmental conditions and pressures that the lagoon has undergone. The high spatial and temporal heterogeneity detected is determined by the restricted connectivity with the open sea, the diversity of environments and habitats, and the changes in environmental conditions due to human actions.
Modeling the Nexus of Climate Change and Deforestation: Implications for the Blue Water Resources of the Jari River, Amazonia
Authors: Rufino Paulo Ricardo , Gücker Björn , Volk Martin , Strauch Michael , Cardozo Francielle da Silva , Boëchat Iola Gonçalves , Faramarzi Monireh , Pereira Gabriel
Published: February 2025

Deforestation and agricultural practices, such as livestock farming, disrupt biogeochemical cycles, contribute to climate change, and can lead to serious environmental problems. Understanding the water cycle and changes in discharge patterns at the watershed scale is essential to tracking how deforestation affects the flow to downstream water bodies and the ocean. The Amazon basin, which provides about 15–20% of the freshwater flowing into the oceans, is one of the most important river systems in the world. Despite this, it is increasingly suffering from anthropogenic pressure, mainly from converting rainforests to agricultural and livestock areas, which can drive global warming and ecosystem instability. In this study, we applied a calibrated Soil and Water Assessment Tool (SWAT) model to the Jari River Watershed, a part of the Brazilian Amazon, to assess the combined effects of deforestation and climate change on water resources between 2020 and 2050. The model was calibrated and validated using observed streamflow. The results show an NS of 0.85 and 0.89, PBIAS of −9.5 and −0.6, p-factor of 0.84 and 0.93, and r-factor of 0.84 and 0.78, for periods of calibration and validation, respectively, indicating a strong model performance. We analyzed four scenarios that examined different levels of deforestation and climate change. Our results suggest that deforestation and climate change could increase surface runoff by 18 mm, while groundwater recharge could vary between declines of −20 mm and increases of 120 mm. These changes could amplify streamflow variability, affect its dynamics, intensify flood risks, and reduce water availability during dry periods, leading to significant risks for the hydrology of Amazonian watersheds and human water supply. This, in turn, could profoundly impact the region’s megadiverse flora and fauna, which directly depend on balanced streamflow in the watersheds.
Application of Environmental DNA in Aquatic Ecosystem Monitoring: Opportunities, Challenges and Prospects
Authors: Chang Huihui , Ye Tao , Xie Zhaohui , Liu Xinhu
Published: February 2025

Environmental DNA (eDNA) technology is a method for identifying specific biological species by monitoring the presence of DNA fragments in the environment. This technology has the capacity to detect a wide range of species, including elusive and cryptic organisms, by analyzing the genetic material in the environment. The advantages of high sensitivity, wide spatial coverage and non-invasiveness provide many opportunities for its application in identifying and monitoring aquatic organisms, improving our ability to detect and quantify biodiversity. Furthermore, eDNA technology can provide an accurate, convenient and standardizable solution for regularly monitoring aquatic ecosystems. The utilization of eDNA in ecology and conservation has witnessed substantial growth in recent years. However, eDNA still faces numerous challenges, including DNA degradation, risk of contamination and the absence of standardized protocols. Nonetheless, the application of eDNA in aquatic ecosystem monitoring holds considerable promise, particularly in light of technological advancements. As technology evolves, the accuracy, scalability and applicability of eDNA in diverse ecosystems are steadily improving. This paper aims to provide a comprehensive review of the application of eDNA technology in aquatic ecosystem monitoring, addressing its technical limitations and potential future developments.
The Potential of Thailand in Advancing the Classic Car EV Conversion Industry: A Transition Strategy
Authors: Thammasiriroj Warit , Poompipatpong Chedthawut , Khumpunja Puchong
Published: February 2025

The adoption of electric vehicles (EVs) in Thailand has been growing steadily. This research study investigated the Thai automotive industry cluster and completed an analysis of the EV transition to create a domestic industry, its social and environmental impacts, the laws supporting EVs, and consumer acceptance. The results of the study found that Thailand had 1.53 million EVs, accounting for 8.12% of all vehicles in 2023. This growing industry has a significant impact on Thailand’s GDP, highlighting the need for a strategic transition to support the development of the EV conversion industry. A particular focus is on the conversion of internal combustion engine vehicles that are over 10 years old. These debt-free vehicles, having simpler technology, present an ideal opportunity for modification as part of a broader strategy to prepare the domestic automotive sector for EV production and usage. A strong legal framework supports Thailand’s efforts by promoting the EV sector and EV conversion. The country is already a major automotive manufacturing hub, ranked 11th globally, with a robust automotive parts industry. Additionally, Thailand has a significant stock of older and classic cars, with 18.26% of its vehicles in 2023 being over 20 years old. The absence of laws restricting vehicle lifespan further facilitates the EV conversion process. Thailand’s legal measures also promote economic growth by supporting industries related to classic cars, with tax incentives designed to stimulate these sectors. The EV conversion business in Thailand attracted over USD 300 million in investment in 2023, with a survival rate of 97.33%, reflecting continuous year-on-year growth. These developments indicate that Thailand is well positioned to become a leading hub for EV conversion in Southeast Asia. If Thailand wants the EV conversion industry to grow without affecting stakeholders, those involved must consider the following issues or develop a transition strategy: (1) investment in the EV conversion industry; (2) environmental aspects of EV conversion; (3) laws related to EVs; and (4) consumers who are considering EV conversion. Countries with automotive industries similar to Thailand can consider using this model in their countries as well.
Recognition of State of Health Based on Discharge Curve of Battery by Signal Temporal Logic
Authors: Ning Jing , Xiao Bing , Zhong Wenhui
Published: February 2025

In order to study an algorithm that recognizes the state of health (SOH) of a battery rapidly and can be easily integrated into the micro-controller unit (MCU), it is proposed that signal temporal logic (STL) language is employed to describe the discharge curves, because the STL language is a formal language with strict mathematical definitions and the syntax is composed of simple logic, “and”, “or”, and “not”, under the constraints of time and parameter variation ranges, which is realizable and interpretable. Firstly, the drop voltage amplitude, drop time, voltage rebound amplitude, voltage rebound time, starting voltage, and ending voltage of the discharge curve are selected as the features of the STL formula, so the first-level and second-level primitive formulas are constructed to express the voltage of a battery in good health and poor health clearly. Secondly, the impurity measures of the information gain, misclassification gain, Gini gain, and robust extended gain are presented as the objective functions. Thirdly, the interpreter embedded in the MCU can interpret and execute each STL sentence. The voltage of a battery in good health rises slowly and falls slowly, while the voltage of a battery in poor health rises quickly and falls quickly. When the STL describes the discharge curve as “slow down slow up”, the battery is in good health. When the STL describes the discharge curve as “fast down, fast up”, the battery is in poor health. Among the different objective functions, the highest mean accuracy of the STL reaches 87.5%. In terms of the mean runtime, the extended misclassification gain and the extended Gini gain of the first-level primitives are 00851s and 0.0993, respectively. Under the same mean accuracy of 87%, the information gain and Gini gain of the second-level primitives are 0.2593 s and 0.2341 s. Compared with the existing machine learning algorithms, in terms of the mean runtime, the STL algorithm is superior to the CNN-BiLSTM-MHA model, RNN-LSTM-GRU model, and EC-MKRVM model. In terms of the mean accuracy, compared with the highest correct rate of the CNN-BiLSTM-MHA model, that is, 91.7%, the difference is 4%. As a means of quickly detecting whether the battery is in a healthy state, the accuracy difference is negligible, so the STL algorithm is apparently superior in terms of performance and realizability.
Perbandingan Metode Soxhletasi dan Perebusan untuk Isolasi Kafein dari Daun Teh
Authors: Huda Muhammad Badrul , Prayogo Fitra Adi
Published: February 2025

Penelitian ini bertujuan untuk mengisolasi dan memurnikan kafein dari daun teh (Camellia sinensis) menggunakan dua metode ekstraksi: soxhletasi dan perebusan. Prinsip "like dissolves like" diterapkan dalam pemilihan pelarut untuk ekstraksi kafein. Kedua metode diikuti dengan kristalisasi dan rekristalisasi untuk meningkatkan kemurnian kafein yang diisolasi. Hasil penelitian menunjukkan perbedaan signifikan dalam rendemen dan kemurnian kafein yang diperoleh. Metode perebusan menghasilkan rendemen kafein sebesar 0,38% dengan titik leleh 235°C, mendekati nilai literatur untuk kafein murni (235-238°C). Sebaliknya, metode soxhletasi menghasilkan rendemen lebih rendah sebesar 0,008% dengan titik leleh 210°C, mengindikasikan kemurnian yang lebih rendah. Analisis visual kristal kafein juga menunjukkan perbedaan, dengan metode perebusan menghasilkan kristal putih-hijau dan metode soxhletasi menghasilkan kristal oranye-coklat. Penelitian ini menyoroti pentingnya pemilihan metode ekstraksi yang tepat untuk optimalisasi hasil dan kemurnian kafein dari daun teh, dengan implikasi signifikan untuk aplikasi dalam industri makanan, minuman, dan farmasi.
Kata kunci: Caffeine, Tea Leaves, Soxhlet Extraction, Boiling Method, Crystallization
 
ABSTRACT
This study aimed to isolate and purify caffeine from tea leaves (Camellia sinensis) using two extraction methods: soxhletation and boiling. The principle of "like dissolves like" was applied in selecting solvents for the extraction of caffeine, a nonpolar compound that is more soluble in organic solvents. Both methods were followed by crystallization and recrystallization to improve the purity of the isolated caffeine. The results showed significant differences in the yield and purity of the caffeine obtained. The boiling method produced a caffeine yield of 0.38% with a melting point of 235°C, close to the literature value for pure caffeine (235-238°C). In contrast, the soxhletation method produced a lower yield of 0.008% with a melting point of 210°C, indicating lower purity. Visual analysis of the caffeine crystals also showed differences, with the boiling method producing white-green crystals and the soxhletation method producing orange-brown crystals. This study highlights the importance of selecting an appropriate extraction method to optimize the yield and purity of caffeine from tea leaves, with significant implications for applications in the food, beverage and pharmaceutical industries.
Keywords: Caffeine, Tea Leaves, Soxhlet Extraction, Boiling Method, Crystallization
Tesis y tesistas 2024
Authors:   Facultad de Informática
Published: February 2025

En un trabajo conjunto de la Secretaría de Postgrado, la Dirección de Postgrado y la Secretaría Administrativa de Postgrado se presenta el Libro de Tesis y Tesistas correspondiente al año 2024, en la Facultad de Informática de la UNLP. En sus páginas se pueden recorrer la síntesis de las Tesis de Doctorado y Maestría, así como los Trabajos Finales de Especialización aprobados a lo largo del año 2024. Acompañamos estos resúmenes de los datos de los autores, de sus directores y también un listado de los distinguidos profesores e investigadores de Argentina y de Universidades del exterior que han sido Jurados en las Tesis. En los 20 trabajos que se sintetizan y cuyos contenidos completos se pueden obtener del repositorio institucional de la UNLP (SEDICI) se refleja el intenso trabajo que desarrolla el área de Postgrado de la Facultad de Informática, trabajo iniciado en 1995 en la Facultad de Ciencias Exactas y consolidado a partir de 1999 en la Facultad de Informática. Es interesante resaltar que de estas 20 Tesis, 1 corresponde a docentes-investigadores con cargos en la Facultad de Informática de la UNLP y 19 no tienen relación directa con nuestra Facultad. En todos los casos hay participación de docentes-investigadores de la Facultad en la dirección/codirección de las Tesis y TFIs. Con este libro esperamos contribuir a visibilizar los resultados de la actividad académica del Postgrado, así como facilitar la trasmisión de conocimientos generados en la Facultad de Informática de la UNLP.
Wolfgang Marx (ed.), “I Don't Belong Anywhere”: György Ligeti at 100, (Turnhout: Brepols, 2022). ISBN 9780367339555, 300 pp, £110 (hardback).
Authors: Favali Federico
Published: February 2025

A Review of Wolfgang Marx (ed.), “I Don't Belong Anywhere”: György Ligeti at 100, (Turnhout: Brepols, 2022). ISBN 9780367339555, 300 pp, £110 (hardback).
“Um governo azul como o cosmo”
Authors: Souza Carla Dameane Pereira de
Published: February 2025

In order to expose reflections related to the contemporary Chilean Mapuche drama, this article aims to establish a connection with the dramatic text Los pueblos te llaman Nahuelpan Presidente (People call you President Nahuelpan (2018)), by Roberto Cayuqueo. Based on the reflections presented, I intend to indicate how this dramaturgy is connected – according to my interpretation – with a deeper movement of historical and social consciousness of Chilean artists who, based on the recognition of their ancient roots, have taken up an important space in the Chilean and Latin American drama scenario. By reviewing and reformulating methods of theatrical construction already consolidated upon its ancient background, these collectives have enabled the visibility of themes related to Chilean native people, their ways of living and expressing their bodies, voices, testimonials, claims; their struggles, deaths, and grief.
Enhancing Natural Disaster Monitoring: A Deep Learning Approach to Social Media Analysis Using Indonesian BERT Variants
Authors: Fitriani Karlina Elreine , Faisal Mohammad Reza , Mazdadi Muhammad Itqan , Indriani Fatma , Nugrahadi Dodon Turianto , Prastya Septyan Eka
Published: February 2025

Social media has become a primary source of real-time information that can be leveraged by artificial intelligence to identify relevant messages, thereby enhancing disaster management. The rapid dissemination of disaster-related information through social media allows authorities to respond to emergencies more effectively. However, filtering and accurately categorizing these messages remains a challenge due to the vast amount of unstructured data that must be processed efficiently. This study compares the performance of IndoRoBERTa, IndoRoBERTa MLM, IndoDistilBERT, and IndoDistilBERT MLM in classifying social media messages about natural disasters into three categories: eyewitness, non-eyewitness, and don’t know. Additionally, this study analyzes the impact of batch size on model performance to determine the optimal batch size for each type of disaster dataset. The dataset used in this study consists of 1000 messages per category related to natural disasters in the Indonesian language, ensuring sufficient data diversity. The results show that IndoDistilBERT achieved the highest accuracy of 81.22%, followed by IndoDistilBERT MLM at 80.83%, IndoRoBERTa at 79.17%, and IndoRoBERTa MLM at 78.72%. Compared to previous studies, this study demonstrates a significant improvement in classification accuracy and model efficiency, making it more reliable for real-world disaster monitoring. Pre-training with MLM enhances IndoRoBERTa’s sensitivity and IndoDistilBERT’s specificity, allowing both models to better understand context and optimize classification results. Additionally, this study identifies the optimal batch sizes for each disaster dataset: 32 for floods, 128 for earthquakes, and 256 for forest fires, contributing to improved model performance. These findings confirm that this approach significantly improves classification accuracy, supporting the development of machine learning-based early warning systems for disaster management. This study highlights the potential for further model optimization to enhance real-time disaster response and improve public safety measures more effectively and efficiently.
PENDIDIKAN ISLAM DI PESANTREN
Authors: Husni Muhammad , Mudhofar
Published: February 2025

Until now, the fact is that these kyai are intellectuals who have high creativity. They have such brilliant thoughts, feelings, ideas and experiences in developing learning developments in the traditional Islamic education tradition in Islamic boarding schools. Education in Islamic boarding schools is a pioneer for the progress of society. Besides that, it has a broad scientific capacity. This is proven by the many forms and models of learning in the pesantren and there is even a special curriculum for learning in the pesantren itself. aims to understand (1) how to take advantage of the wealth of classical Islamic scientific repertoire that Islamic boarding schools have as a means of responding to current problems while remaining inclusive in accepting changes for the better (2) developing the methodological aspects that the pesantren will run as one Islamic educational institutions. This research is a literature review with the hope of being able to track and prove the dynamics of Islamic development in Islamic boarding schools through the scientific transformation of the Islamic boarding schools.
The Features of Children with Tuberculosis at Sidawangi Pulmonary Hospital in Indonesia: Interactions within the Home as a Potential Transmission Risk
Authors: Pratiwi Witri , Toipah Iip , Rachmawan Yogi Puji , Reki Winarto
Published: February 2025

Background: Tuberculosis (TB) is an infectious disease that can infect children. The prevalence of pediatric TB in Indonesia has increased in recent years, causing a major burden on children's health.
Aims: To determine the characteristics of pediatric TB patients at Sidawangi Pulmonary Hospital, West Java Province, Indonesia.
Methods: A cross-sectional study was conducted at Sidawangi Pulmonary Hospital, West Java Province, Indonesia. The samples were new cases of pediatric TB from January 2020 to July 2023. Data were collected and analyzed from outpatient and inpatient medical records. Logistic regression was used to analyze the association between age and household contact.
Results: There were 190 pediatric TB patients collected during the study period. Most of them were less than 5 years old (37.9%). The proportion of male and female was almost same. Most pediatric TB patients had household contacts (73.7%), especially those <1 year old. TB patients aged <1 year had an AOR of 31.94 (95%CI 3.8­­­–268.35) for household contact compared to those aged 10-17 years.
Conclusion: Pediatric TB patients at Sidawangi Pulmonary Hospital are mostly less than 5 years old. Compared to older age groups, pediatric TB patients aged <1 year had the highest proportion of household contacts. The government needs to pay attention to the risk of TB transmission to children through household contact, especially in younger children.
Received: 27 January 2025 | Reviewed: 11 February 2025 |
Revised: 22 February 2025 | Accepted: 23 February 2025.