bluuebunny/crossref_metadata_2025 huggingface dataset:
Co-Pyrolysis of Mushroom Residue Blended with Pine Sawdust/Wheat Straw for Sustainable Utilization of Biomass Wastes: Thermal Characteristics, Kinetic/Thermodynamic Analysis, and Structure Evolution of Co-Pyrolytic Char
Authors: Meng Haiyu , Yang Heng , Wu Zhiqiang , Li Danting , Wang Zhe , Wang Dongqi , Wang Hui , Li Huaien , Li Jiake
Published: August 2024

Co-pyrolysis technology is considered to be one of the most promising methods for the sustainable utilization of biomass wastes, as it can realize waste reduction and convert wastes into high-value-added products with little impact on the environment. The evaluation of thermal characteristics and product properties is necessary for understanding this technique. In this paper, thermal characteristics and kinetic and thermodynamic analysis during the co-pyrolysis of mushroom residue (MR) with pine sawdust (PS) or wheat straw (WS) were investigated in a TGA. The carbon structure and surface textures of co-pyrolytic char were explored using Raman spectroscopy and a scanning electron microscope. As the PS or WS mass ratio increased, the devolatilization index increased obviously, indicating that volatile release was promoted and concentrated. Weak interactions were observed between 250 and 400 °C during the co-pyrolysis process, which primarily affected the mass transfer, resulting in a change in the thermal decomposition temperatures and rates. The interactions had no prominent influence on the volatiles’ yields. The non-additive performance of average activation energies for the blends was observed due to the interactions, and the lowest average activation energy was obtained when the PS or WS mass ratio was 50%. The lower average pre-exponential factor of the blends indicated the reduced complicacy of the pyrolysis reaction. The relatively small deviation between the activation energy and enthalpy change (4.94–5.18 kJ·mol−1) signified the energy sensitivity of product formation. PS promoted the formation of small aromatic rings (<6 fused rings) in co-pyrolytic chars, whereas WS favored the production of larger rings (≥6 fused rings). The surface textures of the co-pyrolytic chars became porous, and the greater fractal dimensions of the surface morphology for the co-pyrolytic chars indicated that the char surface became irregular and rough.
Under the Goal of Sustainable Development, Do Regions with Higher Energy Intensity Generate More Green Innovation? Evidence from Chinese Cities
Authors: Xing Yikang
Published: August 2024

Sustainable development is a global focal point. This study aims to analyze the relationship between energy intensity and green innovation using panel data from 285 Chinese cities spanning from 2003 to 2019. The novel element of this research is the first-time exploration of green innovation through the lens of local industry attributes. It explains the impact of energy intensity on the development of green innovation. By employing a two-way fixed-effect model, the regression analysis reveals that higher energy intensity in cities negatively influences green innovation. Robustness and mechanism tests indicate that in regions with higher energy intensity, governments issue more policies to encourage enterprises and research institutions to pursue green innovation. However, these regions have developed significant path dependence, and local enterprises are reluctant to increase green investment even when facing stringent regulations. Heterogeneity analysis proves that cities with higher levels of digitalization, R&D investment, urbanization, and per capita GDP exhibit an increase in green innovation. The research and findings of this study are of significant importance for policy and strategy formulation, allowing governments and enterprises to develop targeted economic strategies to improve energy efficiency and promote green innovation, thereby achieving sustainable development goals. Potential limitations include data quality, and some key information is difficult to obtain from public sources due to confidentiality, which may result in limitations and biases in the results.
Comparative Analysis of Face Mask Usage and Environmental Impact in Asian Cities during and after the COVID-19 Pandemic
Authors: Liu Chang , Liu Chen , Hotta Yasuhiko , Appleby Dwayne
Published: August 2024

The COVID-19 pandemic has led to a surge in face mask demand, resulting in increased face mask waste and environmental impacts. This study investigates mask usage patterns and the environmental impacts of single-use and cloth masks across three phases: pre-COVID-19, COVID-19, and the new normal. A comprehensive survey conducted in five cities across four Asian countries reveals a surge in mask usage during COVID-19 (6.81 pieces/week), followed by a decline in the new normal (3.73 pieces/week), though usage remained higher than pre-COVID-19 levels (1.46 pieces/week). For single-use masks, age significantly impacts usage in all cities, while gender and education level affect usage in Shanghai, Harbin, and Depok. Household income influences mask use in Shanghai and Harbin. For cloth masks, education level significantly correlates with usage in most cities. The study highlights the significant environmental impact of mask use, particularly in densely populated urban areas. Switching to cloth masks for one year could reduce carbon footprints by 44.27–81.9 million kgCO2eq, decrease solid waste by 34.81–52.41 million kg, and reduce microplastic emissions by 6.50 to 15.56 trillion particles in the first 24 h after disposal. However, this transition may increase water usage by 1.73–1.86 billion m3H2Oeq. The study also offers policy recommendations on mask usage and disposal.
Changes in Wuhan’s Carbon Stocks and Their Spatial Distributions in 2050 under Multiple Projection Scenarios
Authors: Zhang Yujie , Wang Xiaoyu , Zhang Lei , Xu Hongbin , Jung Taeyeol , Xiao Lei
Published: August 2024

Urbanization in the 21st century has reshaped carbon stock distributions through the expansion of cities. By using the PLUS and InVEST models, this study predicts land use and carbon stocks in Wuhan in 2050 using three future scenarios. Employing local Moran’s I, we analyze carbon stock clustering under these scenarios, and the Getis–Ord Gi* statistic identifies regions with significantly higher and lower carbon-stock changes between 2020 and 2050. The results reveal a 2.5 Tg decline in Wuhan’s carbon stock from 2000 to 2020, concentrated from the central to the outer city areas along the Yangtze River. By 2050, the ecological conservation scenario produced the highest carbon stock prediction, 77.48 Tg, while the economic development scenario produced the lowest, 76.4 Tg. High-carbon stock-change areas cluster in the north and south, contrasting with low-change area concentrations in the center. This research provides practical insights that support Wuhan’s sustainable development and carbon neutrality goals.
The Effects of a Digital Marketing Orientation on Business Performance
Authors: Pașcalău Simona-Valentina , Popescu Felix-Angel , Bîrlădeanu Gheorghina-Liliana , Gigauri Iza
Published: August 2024

Customer relationship management (CRM) has become increasingly important as a result of the pressure organizations are under to remain competitive. CRM has been and is widely promoted as essential to a company’s ability to survive. According to this study, CRM is more than just a computer program or software package. We believe that for organizations to use CRM effectively, it must be viewed from a strategic point of view. Therefore, this study focuses on the consequences of digital marketing on business performance, specifically, the consequences of customer relationship orientation and the use of CRM technologies as a support for analysis, data integration, and access on business performance. This study addresses a contemporary and relevant research problem that has national and international relevance. The research is based on quantitative methods to test hypotheses. Data were gathered from 73 organizations. The findings show the relationships between CRM and customer satisfaction, market effectiveness, and profitability. Customer relationship orientation positively affects customer satisfaction, market effectiveness, and market profitability. CRM technologies significantly improve business performance. This research contributes to the existing knowledge by shedding light on the complex relationships among CRM, customer relationship orientation, market effectiveness, market profitability, and customer satisfaction. Based on the research results, we provide practical recommendations for managers and decision makers.
Water and Wastewater Management in Production Processes of PGE Energia Ciepła SA Branch 1 in Krakow in Light of Company Modernization
Authors: Kowalczyk Zbigniew , Winiarski Wojciech
Published: August 2024

Electricity production requires a very high water consumption. One way to reduce water demand in power plants might be effective wastewater recycling within the power plant, which can lead to almost zero wastewater discharge. The study aims to characterize the functioning of the water and wastewater management system before and after the modernization of a wastewater treatment installation owned by an electricity and heat producer using biomass and other solid fuels. The scope of work covered one of the largest energy companies in Poland: PGE Energia Ciepła SA Branch 1 in Krakow. Water management and the effectiveness of wastewater treatment installation modernization were assessed in terms of the BAT (Best Available Techniques) conclusions. Particular attention was paid to the analysis of changes in the quality parameters of wastewater after the modernization of the wet flue gas desulfurization installation. The research results prove that the modernization of the company’s water and wastewater system significantly reduced the emission of harmful substances into the environment and water consumption. After modernization, an effective reduction in the content of heavy metals and other pollutants in the wastewater was observed. A decrease in the content of cadmium was observed by 99%, nickel—96%, mercury—95%, and copper—83%.
The Improvement of Logistics Management in China: A Study of the Risk Perspective
Authors: Su Qing , Shi Yangyan , Gao Yangfei , Arthanari Tiru , Wang Mengmeng
Published: August 2024

This paper identifies and empirically validates the potential risks in overseas third-party logistics services that support sustainable cross-border e-commerce logistics in China. This research addresses a significant gap in the current literature by conducting a comprehensive literature review and case study, primarily focusing on third-party logistics services’ benefits without adequately discussing the associated risks. Our study highlights the medium and high risks in forwarding and contracting processes, such as cost uncertainty, quality issues, and cultural and language differences. The findings contribute to academic research and practical applications by providing a theoretical foundation and practical guidance for enhancing logistics management in China’s sustainable cross-border e-commerce. This research promotes the resilience and sustainability of cross-border e-commerce supply chains and offers valuable insights for overseas 3PL service providers looking to expand their operations in China.
Development and Sensitivity Analysis of an Improved Harmony Search Algorithm with a Multiple Memory Structure for Large-Scale Optimization Problems in Water Distribution Networks
Authors: Lee Ho-Min , Sadollah Ali , Choi Young-Hwan , Joo Jin-Gul , Yoo Do-Guen
Published: August 2024

The continuous supply of drinking water for human life is essential to ensure the sustainability of cities, society, and the environment. At a time when water scarcity is worsening due to climate change, the construction of an optimized water supply infrastructure is necessary. In this study, an improved version of the Harmony Search Algorithm (HSA), named the Maisonette-type Harmony Search Algorithm (MTHSA), was developed. Unlike the HSA, the MTHSA has a two-floor structure, which increases the optimizing efficiency by employing multiple explorations on the first floor and additional exploitations of excellent solutions. Parallel explorations enhance the ability in terms of exploration (global search), which is the tendency to uniformly explore the entire search space. Additional exploitations among excellent solutions also enhance the ability of local searches (effective exploitation), which is the intensive exploration of solutions that seem to have high possibilities. Following the development of the improved algorithm, it was applied to water distribution networks in order to verify its efficiency, and the numerical results were analyzed. Through the considered applications, the improved algorithm is shown to be highly efficient when applied to large-scale optimization problems with large numbers of decision variables, as shown in comparison with the considered optimizers.
Strategic Planning and Organizational Performance: An Empirical Study on the Manufacturing Sector
Authors: Mousa Kawar Mohammed , Ali Khairi Ali Auso , Gurler Sabahat
Published: August 2024

In this research, the primary goal was to investigate the relationship between strategic planning and organizational performance in Iraq’s manufacturing context. This study’s primary data sources were 360 manager respondents. A structured questionnaire was used to collect primary data from manufacturing firms located throughout Iraq. To analyze the results, the researchers used descriptive statistics, correlation, and multiple regression analysis. SPSS version 16 software was used to conduct data analysis. The results reveal that the process of strategic planning has a beneficial effect on financial performance. Environmental scanning has a statistically significant positive effect on a company’s nonfinancial performance. Management participation and planning formality positively and statistically significantly affect a business’s nonfinancial performance at the 10 percent level. The domain of strategy and technique does not impact a company’s nonfinancial performance.
A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems
Authors: Youn Seok Jin , Lee Yong-Jae , Han Ha-Eun , Lee Chang-Woo , Sohn Donggyun , Lee Chulung
Published: August 2024

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.
Altitude-Shifted Climate Variables Dominate the Drought Effects on Alpine Grasslands over the Qinghai–Tibetan Plateau
Authors: Wang Xiangtao , Hu Zhigang , Zhang Ziwei , Tang Jiwang , Niu Ben
Published: August 2024

Drought has broad and deep influences on ecosystem dynamics and functions, particularly considering the lagged and cumulative effects of drought. Yet the individual role of climate variables in mediating such drought effects on vegetation remains largely unknown. Based on the Normalized Difference Vegetation Index (NDVI) and the standard precipitation evapotranspiration index (SPEI), here, we investigated the patterns and mechanisms of drought effects on alpine grasslands in the Qinghai–Tibetan Plateau (QTP) from 1982 to 2015. Drought imposed widespread lagged and cumulative impacts on alpine grasslands with notable spatial heterogeneity, showing that the southwestern and northeastern parts of the plateau were more sensitive and responded quickly to drought. Further, drought effects showed an evident elevation dependence across different grassland types, which could be explained by altitudinal shifts in climatic factors, including temperature and precipitation. Precipitation was the dominant factor in drought effects on alpine meadows, while temperature dominated the drought impacts on the alpine steppes. Such a divergent dominant factor implied that there would be different vegetation responses to future climate change among diverse types of alpine grasslands. To maintain the sustainability of alpine grassland, more effort should be applied to alpine steppes regarding pasture management, particularly in response to extreme drought due to warmer climates in the future.
Robust-mv-M-LSTM-CI: Robust Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic
Authors: Dinh Tan Ngoc , Thirunavukkarasu Gokul Sidarth , Seyedmahmoudian Mehdi , Mekhilef Saad , Stojcevski Alex
Published: August 2024

The digitalization of the global landscape of electricity consumption, combined with the impact of the pandemic and the implementation of lockdown measures, has required the development of a precise forecast of energy consumption to optimize the management of energy resources, particularly in pandemic contexts. To address this, this research introduces a novel forecasting model, the robust multivariate multilayered long- and short-term memory model with knowledge injection (Robust-mv-M-LSTM-CI), to improve the accuracy of forecasting models under uncertain conditions. This innovative model extends the capabilities of mv-M-LSTM-CI by incorporating an additional branch to extract energy consumption from adversarial noise. The experiment results show that Robust-mv-M-LSTM-CI demonstrates substantial improvements over mv-M-LSTM-CI and other models with adversarial training: multivariate multilayered long short-term memory (adv-M-LSTM), long short-term memory (adv-LSTM), bidirectional long short-term memory (adv-Bi-LSTM), and linear regression (adv-LR). The maximum noise level from the adversarial examples is 0.005. On average, across three datasets, the proposed model improves about 24.01% in mean percentage absolute error (MPAE), 18.43% in normalized root mean square error (NRMSE), and 8.53% in R2 over mv-M-LSTM-CI. In addition, the proposed model outperforms “adv-” models with MPAE improvements ranging from 35.74% to 89.80% across the datasets. In terms of NRMSE, improvements range from 36.76% to 80.00%. Furthermore, Robust-mv-M-LSTM-CI achieves remarkable improvements in the R2 score, ranging from 17.35% to 119.63%. The results indicate that the proposed model enhances overall accuracy while effectively mitigating the potential reduction in accuracy often associated with adversarial training models. By incorporating adversarial noise and COVID-19 case data, the proposed model demonstrates improved accuracy and robustness in forecasting energy consumption under uncertain conditions. This enhanced predictive capability will enable energy managers and policymakers to better anticipate and respond to fluctuations in energy demand during pandemics, ensuring more resilient and efficient energy systems.
Assessment of Economic Viability of Direct Current Fast Charging Infrastructure Investments for Electric Vehicles in the United States
Authors: Bernal Daniel , Raheem Adeeba A. , Inti Sundeep , Wang Hongjie
Published: August 2024

As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in the U.S. using two case studies and exploring different operational strategies including sole ownership and collaborative ventures with public and private entities. The results indicate that based on the current adoption and utilization rates in the U.S., the business model involving an owner-operator collaborating with a public partner ensures profitability and protects the investment in DCFC stations from financial losses. The study also highlights that demand charges and electricity retail prices are the factors that affect the profitability of a DCFC station.
Earthquake Engineering Technology and Its Application
Authors: Chen Su , Xu Chong , Li Shuang
Published: August 2024

Recent earthquakes, such as the Osaka–Kobe earthquake (1995) and the Wenchuan earthquake (2008), have challenged earthquake engineering and its application [...]
Integrating Perishables into Closed-Loop Supply Chains: A Comprehensive Review
Authors: Zarreh Mobin , Khandan Mohammad , Goli Alireza , Aazami Adel , Kummer Sebastian
Published: August 2024

In an era where sustainability and efficient resource utilization are paramount, the closed-loop supply chain (CLSC) emerges as a critical approach, particularly in the context of perishable goods. The perishability of products adds a layer of complexity to supply chain management, necessitating innovative strategies for maximizing product life and minimizing waste. This comprehensive review article delves into the integration of perishable products within the framework of CLSC. The study thoroughly examines existing research to identify gaps and outline future research directions. It emphasizes the unique challenges and complexities of managing perishable goods, a crucial but often overlooked component in sustainable supply chain practices. The review highlights the balance between efficiency and sustainability, underscoring the importance of reverse logistics and circular economy principles in enhancing supply chain resilience. By synthesizing various methodologies and findings, the article presents a holistic view of the current state of perishable product management in CLSCs, offering valuable insights for academia and industry practitioners. The study not only contributes to the theoretical understanding of CLSCs, but also proposes practical approaches for their optimization, aligning with broader sustainability goals.
Two-Way Perspectives and Attitudes about Environmentally Friendly Agro-Food Production and Consumption: An Empirical Survey for Greece
Authors: Ketelni Aggeliki , Karelakis Christos , Zafeiriou Eleni , Pappas Maria , Papadopoulou Kalliope
Published: August 2024

Relying on pesticides to safeguard crops has led to pest resistance and detrimental environmental and health consequences. Many integrated pest management programs focus on producers’ decision-making processes when selecting and implementing pest management interventions. Alternative approaches such as integrated pest management and biological control have been implemented to address these challenges, with Greece as the research area. A quantitative study was oriented to consumers and producers to investigate their views on the effectiveness of pesticides, aiming to assess whether these perspectives influence their attitudes toward adopting pest management practices. Primary data were gathered through structured questionnaires to consumers and producer associations (focusing on tomatoes, olives, and citrus fruits) and analyzed through multivariate methods. The findings unveiled varying preferences and concerns regarding plant protection tools, with considerations such as product effectiveness, method awareness, and sustainability. Specifically, the study revealed aspects like the popularity and demand for specific plant protection tools, the knowledge of these experts about products and pest control methods, and their stance on sustainable agriculture and environmental aspects of pest control.
Understanding Dual Effects of Social Network Services on Digital Well-Being and Sustainability: A Case Study of Xiaohongshu (RED)
Authors: Wu Qingyue , Gu Lei , Zhang Mingxiao , Liu Huimei
Published: August 2024

Social networking services (SNSs) address estrangement in cyberspace through their communication, recognition, and resonance features, yet they simultaneously generate new forms of alienation. This dual effect can both advance and hinder sustainability. This phenomenon is particularly evident in Xiaohongshu (RED), a platform widely used in China and beyond, which has evolved from providing consumption guidance to facilitating lifestyle sharing. This paper utilizes Hartmut Rosa’s Resonance Theory to explore resonance and alienation within RED, examining the multifaceted impact of SNSs on digital well-being and sustainability through qualitative methodologies, including netnography and thematic analysis. The study reveals that RED fosters material, spiritual, and social resonance, thereby enhancing digital well-being and sustainability. However, RED also generates alienation in these dimensions, which can impede sustainability. Moreover, the resonance and alienation generated by the RED platform affect not only its own sustainability but also broader environmental, social, economic, and cultural sustainability. Theoretically, this study constructs a sociological framework integrating resonance and alienation within SNSs to interpret digital well-being and sustainability. Practically, it offers recommendations for promoting sustainable practices among SNSs, addressing material, spiritual, and social dimensions.
Simulink Modeling and Analysis of a Three-Dimensional Discrete Memristor Map
Authors: Peng Shuangshuang , Shi Honghui , Li Renwang , Xiang Qian , Dai Shaoxuan , Li Yilin
Published: August 2024

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos.
Maximum and Minimum Results for the Green’s Functions in Delta Fractional Difference Settings
Authors: Mohammed Pshtiwan Othman , Lizama Carlos , Lupas Alina Alb , Al-Sarairah Eman , Abdelwahed Mohamed
Published: August 2024

The present paper is dedicated to the examination of maximum and minimum results based on Green’s functions via delta fractional differences for a class of fractional boundary problems. For such a purpose, we built the corresponding Green’s functions based on the falling factorial functions. In addition, using the constructed Green’s function, the positivity of the function and its corresponding delta function are presented. We also verified the occurrence of two distinct functions with the same Green’s function. The maximality and minimality of the Green’s function show a good qualitative agreement. Finally, we considered some special examples to explain the obtained results.
Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China
Authors: Song Zhenang , Cai Jun , Yang Qiyao
Published: August 2024

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.
GAT-Based Bi-CARU with Adaptive Feature-Based Transformation for Video Summarisation
Authors: Chan Ka-Hou , Im Sio-Kei
Published: August 2024

Nowadays, video is a common social media in our lives. Video summarisation has become an interesting task for information extraction, where the challenge of high redundancy of key scenes leads to difficulties in retrieving important messages. To address this challenge, this work presents a novel approach called the Graph Attention (GAT)-based bi-directional content-adaptive recurrent unit model for video summarisation. The model makes use of the graph attention approach to transform the visual features of interesting scene(s) from a video. This transformation is achieved by a mechanism called Adaptive Feature-based Transformation (AFT), which extracts the visual features and elevates them to a higher-level representation. We also introduce a new GAT-based attention model that extracts major features from weight features for information extraction, taking into account the tendency of humans to pay attention to transformations and moving objects. Additionally, we integrate the higher-level visual features obtained from the attention layer with the semantic features processed by Bi-CARU. By combining both visual and semantic information, the proposed work enhances the accuracy of key-scene determination. By addressing the issue of high redundancy among major information and using advanced techniques, our method provides a competitive and efficient way to summarise videos. Experimental results show that our approach outperforms existing state-of-the-art methods in video summarisation.
Artificial Intelligence Applied to Microwave Heating Systems: Prediction of Temperature Profile through Convolutional Neural Networks
Authors: Rosario Núñez Victor , Hernández Alfonso , Rodríguez Iván , Fernández-Pacheco Ruiz Ignacio , Acevedo Luis
Published: August 2024

Microwave heating, which is caused by the interaction of electromagnetic radiation and materials, has become an important component in industrial operations across numerous industries. Despite their importance, conventional numerical simulations of microwave heating are computationally intensive. Concurrently, advances in artificial intelligence (AI), particularly machine learning algorithms, have transformed data processing by increasing accuracy while decreasing computational time. This study tackles the difficulty of efficient and accurate modelling in microwave heating by combining convolutional neural networks (CNNs) with traditional simulation techniques. The major goal of this research is to use CNNs to forecast temperature profiles in a variety of industrial materials, including susceptors, semi-transparent, and microwave-transparent materials, under varying power settings and heating periods. This unique strategy greatly reduces prediction times, with up to 60-fold speed increases over standard methods. Our research is based on examining the electromagnetic and thermal responses of these materials under microwave heating. This study’s findings emphasise the need for extensive datasets and show the transformational potential of CNNs in optimising material processing. It uses artificial intelligence to pave the way for more effective and exact simulations, supporting breakthroughs in industrial microwave heating applications.
Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT
Authors: Alshemaimri Bader Khalid
Published: August 2024

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework’s effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.
Photocatalytic Degradation of Carbofuran in Water Using Laser-Treated TiO2: Parameters Influence Study, Cyto- and Phytotoxicity Assessment
Authors: Tošić Miloš , Savić Jasmina , Valenta Šobot Ana , Živković Sanja , Dimitrijević Aleksandra , Ilić Nevena , Dimitrijević-Branković Suzana , Momčilović Miloš
Published: August 2024

This study investigates the impact of changing parameters on the photocatalytic degradation of carbofuran (CBF) using laser-treated TiO2 nanotube arrays on a Ti mesh under simulated sunlight irradiation and assessing toxicity during photocatalytic degradation. Various parameters, including the stirring effect, light intensity, initial CBF concentration, and variation in the active surface area of laser-treated TiO2 photocatalysts, were examined to determine their impact on degradation efficiency. The photodegradation kinetics were monitored using ultra-performance liquid chromatography with a PDA detector (UPLC-PDA) and UV-Vis spectrophotometry, while mineralization was assessed by a total organic carbon (TOC) analyzer. The photocatalytic degradation of CBF is enhanced by an increase in the active surface area of the TiO2 photocatalyst, light intensity, and the introduction of stirring, but it decreases with an increase in the initial concentration of CBF. The toxicity assessments revealed that the cytotoxicity of CBF initially increased during the degradation process but decreased after further treatment, indicating the formation and subsequent breakdown of toxic intermediates. The phytotoxicity test showed that longer degradation times resulted in higher toxicity to plant growth. This study provides new insights into the photocatalytic degradation of CBF with TiO2, the importance of parameter optimization for more efficient treatment, and the use of toxicity tests to confirm the success of the photocatalytic process.
Plastic Pollution in Paradise: Analyzing Plastic Litter on Malta’s Beaches and Assessing the Release of Potentially Toxic Elements
Authors: Jachimowicz Piotr , Klik Barbara , Osińska Adriana Dorota
Published: August 2024

This study investigates plastic litter on two beaches in Malta, Golden Bay and Rivera Beach, with a focus on plastic abundance, characteristics, sources, and the influence of human activity on pollution levels. Conducted in March 2023 during the low-tourist season, 13 sediment samples were collected from a depth of 5 cm using a systematic square sampling method. Plastic litter was quantified and sorted by size, shape, color, and polymer type, and concentrations of potentially toxic elements (PTEs) were measured (Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn, and Fe via ICP-OES). Golden Bay exhibited significantly higher plastic quantities (53.9 ± 4.3 n/m2) compared to Rivera Beach (29.7 ± 4.0 n/m2). Microplastics were dominant on both beaches, with Golden Bay showing a higher proportion (57.0%) than Rivera Beach (50.6%). The plastic litter predominantly consisted of PE (59.6–68.0%) and PP (29.6–38.8%). Golden Bay plastics had PTE concentrations up to 4.9 times higher than those in Rivera Beach, notably for Mn (309.0 μg/g vs. 63.1 μg/g). This research contributes valuable insights into the dynamics of plastic pollution in coastal environments, particularly in areas influenced by tourism.
Effects of Insecticide and Herbicides on Thyroid Disturbances in Zebrafish
Authors: Ma Tingting , An Xiangji , Wu Peng , He Xiaoli , Luo Yongming
Published: August 2024

Thyroid cancer usually begins with thyroid dysfunction and nodules and has become the most common cancer globally, especially in women. Although the causes of thyroid dysfunction are complex, the presence of environmental pollutants, especially certain pesticides as established mutagens, has been widely accepted. Zebrafish (Danio rerio) have similar toxic reactions and signal transduction pathways to humans and are very similar to humans in physiology, development, and metabolic function. Here, the direct toxicity effects and mechanisms of different insecticides and herbicides on zebrafish thyroid functions and indirect toxicity effects originating from thyroid dysfunction were summarized and compared. The overall toxicity of insecticides on the zebrafish thyroid was greater than that of herbicides based on effective concentrations. Penpropathrin and atrazine were more typical thyroid disruptors than other pesticides. Meanwhile, chiral pesticides showed more sophisticated single/combined toxicity effects on both parental and offspring zebrafish. Besides thyroid hormone levels and HPT axis-related gene expression alteration, developmental toxicity, immunotoxicity, and oxidative damage effects were all observed. These data are necessary for understanding the thyroid interference effect of pesticides on humans and for screening for thyroid disruptors in surface water with zebrafish models for the pre-assessment of human health risks and ecological risk control in the future.
Microplastics as a Threat to Aquatic Ecosystems and Human Health
Authors: Witczak Agata , Przedpełska Laura , Pokorska-Niewiada Kamila , Cybulski Jacek
Published: August 2024

The threat posed by microplastics has become one of the world’s most serious problems. Recent reports indicate that the presence of microplastics has been documented not only in coastal areas and beaches, but also in water reservoirs, from which they enter the bodies of aquatic animals and humans. Microplastics can also bioaccumulate contaminants that lead to serious damage to aquatic ecosystems. The lack of comprehensive data makes it challenging to ascertain the potential consequences of acute and chronic exposure, particularly for future generations. It is crucial to acknowledge that there is still a substantial need for rapid and effective techniques to identify microplastic particles for precise evaluation. Additionally, implementing legal regulations, limiting plastic production, and developing biodegradation methods are promising solutions, the implementation of which could limit the spread of toxic microplastics.
Toxicological Analysis of Acetamiprid Degradation by the Dominant Strain Md2 and Its Effect on the Soil Microbial Community
Authors: Zhang Jiale , Wang Xin , Yue Wanlei , Bao Jia , Yao Mengqin , Ge Ling
Published: August 2024

Microbial degradation is acknowledged as a viable and eco-friendly approach for diminishing residues of neonicotinoid insecticides. This study reports the dominant strain of Md2 that degrades acetamiprid was screened from soil and identified as Aspergillus heterochromaticus, and the optimal degradation conditions were determined. Research indicated that the degradation of Md2 to 100 mg/L acetamiprid was 55.30%. Toxicological analyses of acetamiprid and its metabolites subsequently revealed that acetamiprid and its metabolites inhibited the germination of cabbage seed, inhibited the growth of Escherichia coli, and induced the production of micronuclei in the root tip cells of faba beans. Based on the analysis of metabolic pathways, it has been determined that the primary metabolic routes of acetamiprid include N-demethylation to form IM-2-1 and oxidative cleavage of the cyanoimino group to produce IM-1-3. Using 16S rRNA high-throughput sequencing, the results showed that acetamiprid and Md2 elevated the relative abundance of Acidithiobacillus, Ascomycetes, and Stramenobacteria, with increases of 10~12%, 6%, and 9%, respectively, while reducing the relative abundance of Acidobacteria, Chlorobacteria, Ascomycetes, and Sporobacteria, with decreases of 15%, 8%, 32%, and 6%, respectively. The findings will facilitate the safety evaluation of the toxicological properties of neonicotinoid insecticides, their biodegradable metabolites, and associated research on their degradation capabilities.
Lifetime of Long-Lived Sunspot Groups
Authors: Muraközy Judit
Published: August 2024

Studies of active region (AR) lifetimes are mostly restricted to short-lived ARs. The aim of this paper is to include recurrent ARs, which should be identified unambiguously. The first step is the algorithmic listing of possible returns; then, the candidates are visually checked using the unique HTML-feature of the Debrecen sunspot database. The final step is application of an asymmetric Gaussian function, introduced in previous articles, for short-lived ARs. This function has a surprisingly good fit to the data on correctly identified recurrent sunspot groups over several rotations enabling the reconstruction of the development on the far side of the sun. The Gnevyshev–Waldmeier rule for the area–lifetime relationship is not applicable for recurrent ARs; however, as a novel approach, a linear regression analysis extended to long lifetimes made it possible to recognize two populations of sizes for which two different area–lifetime relationships can be obtained. The lifetimes exhibit weak dependencies on the heliographic latitude and solar cycle phase. If an asymmetric Gaussian cannot be fit to the data, then they presumably belong to consecutive members of an active nest.
Integrating Hyperspectral Reflectance and Physiological Parameters to Detect Urban Tree Stress: A Study of Drought and Simulated Acid Rain
Authors: Jeong Ukhan , Yun Young Jo , Cheong Eun Ju
Published: August 2024

With urbanization and climate change worsening, urban trees are constantly exposed to environmental stress. To enhance the functionality and health of trees, it is crucial to rapidly and non-destructively detect and respond to tree stress. Research utilizing hyperspectral characteristics for detecting various stresses has recently been actively pursued. This study conducted comparative analysis using various leaf physiological parameters (chlorophyll content, chlorophyll fluorescence, leaf water, and gas exchange status) and hyperspectral data (VIS: visible ray; SWIR: short-wave infrared) to diagnose stress in Prunus yedoensis, commonly grown urban trees, by subjecting them simultaneously to different stresses (drought and simulated acid rain). The findings suggest that hyperspectral reflectance proved more responsive in identifying stress compared to the physiological parameters. Initially, VIS was more effective in detecting two stress responses than SWIR through a classification model (PLS-DA: partial least squares-discriminant analysis). Although SWIR initially faced challenges in simulated acid rain stress detection, spectral preprocessing (SNV: standard normal variate, + S.G 2nd: Savitzky–Golay 2nd derivative) enhanced its stress classification accuracy. Over time, the SWIR bands (1437 nm, 1667 nm, and 1949 nm) exhibited characteristics (such as moisture detection) more closely aligned with stress responses compared to VIS, as determined through PCA (principal component analysis). Hyperspectral reflectance also revealed the potential to measure chlorophyll fluorescence (Fo: minimum fluorescence). Building upon the foundational data of this study, the future potential of diagnosing urban tree stress using portable spectrometers is strong.
Molecular Bases and Specificity behind the Activation of the Immune System OAS/RNAse L Pathway by Viral RNA
Authors: Jung-Rodriguez Emma , Barbault Florent , Bignon Emmanuelle , Monari Antonio
Published: August 2024

The first line of defense against invading pathogens usually relies on innate immune systems. In this context, the recognition of exogenous RNA structures is primordial to fight, notably, against RNA viruses. One of the most efficient immune response pathways is based on the sensing of RNA double helical motifs by the oligoadenylate synthase (OAS) proteins, which in turn triggers the activity of RNase L and, thus, cleaves cellular and viral RNA. In this contribution, by using long-range molecular dynamics simulations, complemented with enhanced sampling techniques, we elucidate the structural features leading to the activation of OAS by interaction with a model double-strand RNA oligomer mimicking a viral RNA. We characterize the allosteric regulation induced by the nucleic acid leading to the population of the active form of the protein. Furthermore, we also identify the free energy profile connected to the active vs. inactive conformational transitions in the presence and absence of RNA. Finally, the role of two RNA mutations, identified as able to downregulate OAS activation, in shaping the protein/nucleic acid interface and the conformational landscape of OAS is also analyzed.