bluuebunny/crossref_metadata_2025 huggingface dataset:
Gastroprotective Effect of Hexanic Extract of Heliotropium indicum Against Ethanol-Induced Gastric Lesions in a CD1 Mouse Model
Authors: Sánchez-Mendoza María Elena , López-Lorenzo Yaraset , Torres-Morales Ximena del Rocío , Cruz-Antonio Leticia , Arrieta-Baez Daniel , García-Machorro Jazmín , Arrieta Jesús
Published: December 2024

Peptic ulcers result from an imbalance between protective factors (e.g., prostaglandins, nitric oxide, and sulfhydryl groups) and aggressive risk factors (e.g., consumption of non-steroidal anti-inflammatory drugs, alcohol, or tobacco) regarding the gastric mucosa. While various existing treatments aim to relieve pain, repair the ulcer, and prevent its recurrence, they often produce undesirable side effects. The Heliotropium indicum (H. indicum) plant has been utilized as a traditional medicine due to its gastroprotective activity. In this study, we identified the compounds responsible for the gastroprotective activity of the hexanic extract of H. indicum in an ethanol-induced damage model, in addition to determination of the activities of prostaglandins, nitric oxide, and non-protein sulfhydryl groups, along with the antisecretory and antioxidant activities (i.e., concentration of malondialdehyde and activities of the enzymes superoxide dismutase, catalase, and glutathione peroxidase). We found at least two groups of compounds that are responsible for this activity, namely 1-acyl-glycerol components and retinyl β-glucuronide derivatives. In conclusion, a mixture of compounds responsible for the gastroprotective activity of H. indicum was isolated from its hexanic extract, and non-protein sulfhydryl groups were implicated in its mechanism of action.
Stage-Specific Effects of Silver Nanoparticles on Physiology During the Early Growth Stages of Rice
Authors: Pan Ruxue , Zhang Zailin , Li Ya , Zhu Sihong , Anwar Sumera , Huang Jiaquan , Zhang Chuanling , Yin Liyan
Published: December 2024

Silver nanoparticles (AgNPs), widely utilized nanomaterials, can negatively affect crop growth and development. However, it remains unclear whether crops exhibit similar responses to AgNPs stress at seed germination and seedling stages. In this study, rice seeds and seedlings were exposed to AgNPs, and their growth, photosynthetic efficiency, and antioxidant systems were recorded. demonstrated significant AgNPs accumulation in rice tissues, with notable higher accumulation in seedlings exposed to AgNPs after germination compared to AgNPs exposure during germination. The roots exhibited greater AgNPs accumulation than shoots across both stages. Exposure to AgNPs during the seed germination stage, even at concentrations up to 2 mg/L, did not significantly affect growth, physiological indices, or oxidative stress. In contrast, seedlings exposed to 1 and 2 mg/L AgNPs showed significant reductions in shoot length, biomass, nutrient content, and photosynthetic efficiency. At low AgNPs concentrations, the maximum relative electron transport rate (rETRmax) was significantly reduced, while the higher concentrations caused pronounced declines in the chlorophyll a fluorescence transient curves (OJIP) compared to the control group. Antioxidant enzyme activities increased in both leaves and roots in a dose-dependent manner, with roots exhibiting significantly higher activity, suggesting that roots are the primary site of AgNPs stress responses. In conclusion, rice responds differently to AgNPs exposure at distinct developmental stages, with the seedling stage being more susceptible to AgNPs-induced stress than the seed germination stage. These findings underscore the importance of considering growth stages when assessing the food safety and environmental risks associated with AgNPs exposure.
Is Kraft Pulping the Future of Biorefineries? A Perspective on the Sustainability of Lignocellulosic Product Development
Authors: Rajan Kalavathy , Berton Paula , Rogers Robin D. , Shamshina Julia L.
Published: December 2024

By reflecting on the history and environmental impact of conventional biorefining, such as kraft pulping, we aim to explore important questions about how natural polymers can be more sustainably sourced to develop bio-products and reduce reliance on plastics. Since the Industrial Revolution, chemical pulping processes have enabled the mass production of cellulosic products from woody biomass. Kraft pulping, which dominates within modern pulp and paper mills, has significantly contributed to environmental pollution and carbon emissions due to sulfurous byproducts and its high water and energy consumption. While chemical pulping technologies have advanced over time, with improvements aimed at enhancing sustainability and economic feasibility, conventional biorefineries still face challenges related to biomass conversion efficiency and environmental impact. For example, efforts to fully utilize wood resources, such as isolating lignin from black liquor, have made limited progress. This perspective provides a thoughtful examination of the growth of chemical pulping, particularly the kraft process, in the production of consumer goods and its environmental consequences. It also presents key insights into the bottlenecks in developing truly sustainable biomass conversion technologies and explores potential alternatives to traditional chemical pulping.
Compatibilizer Efficiency in Enhancing Marine Plastic Waste Valorization Through Simulated Recycled Plastic Blends
Authors: Piedade Cestari Sibele , Rodrigues Pedro Veiga , Ribeiro Ana Cristina , Castro Maria Cidália Rodrigues , Cruz Vasco , Torres Ana Rita , Ramos Nuno , Machado Ana Vera
Published: December 2024

This study investigated the optimal combination of compatibilizers and stabilizers to enhance the value of marine environment plastic (MEP). The composition of the plastics was analysed, and a simulated recycled plastic blend (sMEP) was prepared based on a simplified composition of actual MEP. Different concentrations of three commercial compatibilizers (C1, C2 and C3) were tested to improve tensile strength. The tensile tests indicated that the blend compatibilized with 10 wt.% C3 (polypropylene grafted with maleic anhydride) exhibited the highest increase in tensile strength. This optimal compatibilization was then combined with two commercial stabilizers and applied to a simulated MEP blend. Scanning electron microscopy images showed that all blends had a continuous polyethylene phase with dispersed poly(ethylene terephthalate) (PET) and polypropylene (PP) droplets. The simulated blend with 10 wt.% C3 exhibited a reduced PET droplet size in the dispersed phase. Differential scanning calorimetry results revealed a decrease in polyethylene crystallinity and an increase in PP crystallinity. The improved properties of the blend were attributed to the effectiveness of the C3 compatibilizer in enhancing the interface between the PP and PET phases. An effective formulation was developed to valorise marine-sourced plastics by leveraging existing scientific knowledge and accessible commercial additives. Applying this enhanced formulation to real MEP not only demonstrated its effectiveness, but also highlighted a practical approach for reducing plastic pollution and supporting circular economy principles, contributing to environmental conservation efforts.
Correction: Habib et al. Thiolated Chitosan Microneedle Patch of Levosulpiride from Fabrication, Characterization to Bioavailability Enhancement Approach. Polymers 2022, 14, 415
Authors: Habib Rukhshanda , Azad Abul Kalam , Akhlaq Muhammad , Al-Joufi Fakhria A. , Shahnaz Gul , Mohamed Hanan R. H. , Naeem Muhammad , Almalki Abdulraheem S. A. , Asghar Junaid , Jalil Aamir , Abdel-Daim Mohamed M.
Published: December 2024

In the original publication [...]
Efficient Prediction of Fatigue Damage Analysis of Carbon Fiber Composites Using Multi-Timescale Analysis and Machine Learning
Authors: Yoshimori Satoru , Koyanagi Jun , Matsuzaki Ryosuke
Published: December 2024

Carbon fiber reinforced plastic (CFRP) possesses numerous advantages, such as a light weight and high strength; however, its complex damage mechanisms make the evaluation of fatigue damage particularly challenging. Therefore, this study proposed and demonstrated an entropy-based damage evaluation model for CFRP that leverages the entropy derived from heat capacity measurements and does not require knowledge of the loading history. This entropy-based fatigue degradation model, though accurate, is computationally intensive and impractical for high-cycle analysis. To address this, we reduce computational cost through multi-timescale analysis, replacing cyclic loading with constant displacement loading. Characteristic variables are optimized using the machine learning model LightGBM and the response surface method (RSM), with LightGBM achieving a 75% lower root mean squared error than RSM by increasing features from 3 to 21. This approach cuts analysis time by over 90% while retaining predictive accuracy, showing that LightGBM outperforms RSM and that multi-timescale analysis effectively reduces computational demands.
In the Clergy’s Sights: Making Anabaptists Visible in Reformed Zurich
Authors: Neufeld David Y.
Published: December 2024

This article examines how Reformed pastors’ understanding and exercise of their office shaped their response to Anabaptists living in rural parishes of the Swiss Confederation in the seventeenth century. In the wake of Swiss reformations, illicit Anabaptist communities continued to represent a threat to territorial religious unity and the Reformed clergy’s spiritual leadership, but the precise contours of their activity and social influence at a village level remained obscure. In the absence of a clear picture of dissent, Reformed churchmen endeavored to make Anabaptism visible, employing tools of information management, into which their training had initiated them. A series of cases from rural jurisdictions (the counties (Landvogteien) of Kyburg and Grüningen) and a seat of ecclesiastical power (Zurich) illustrate how documentary production, organization, and activation consistently drove this project forward. These means rendered Anabaptist life perceptible, facilitating and justifying its elimination by Reformed governments.
Islamic Classical Literature (A.D. 950–1450) on Institutionalisation of Ethics for Regulating Markets and Society
Authors: Khaleel Fawad , Avdukic Alija
Published: December 2024

The third expansion of the Islamic Caliphate (AD950–1450) produced the need for formalising an ethical framework to create an institutionalised approach to market regulations. During these times significant contribution to the literature was made regarding the modelling of the ethical premise of the duty to subscribe good and prohibit wrongs. It ranged from the formation of vigilante-styled civil duties to the institutionalisation of ethics in the form of the institution of hisbah, which is broadly understood as a quasi-autonomous non-governmental organisation (quango) designed to establish the ethical mandate within the medieval Muslim world. Our investigation maps the development of thoughts on embedding ethical rules in markets and within society between AD950–1450. This study explores and conceptualises the models for market regulations proposed by Al-Mawardi (d.AD1058), Al-Ghazali (d.AD1111), Ibn Taymiyya (d.AD1328), and Ibn Khaldun (d.AD1406). We formulate and compare the ethical models of these scholars in the context of their political–social positionings. The rationale for choosing these four scholars is the wide articulation and recognition of their logical ideas throughout Islamic history. This research examines the historic–ethical patterns within the corpus of Islamic thoughts that provide a discourse deixis for constructing regulatory models as conceptualised by these scholars for the institutionalised governance of markets and society in general.
The Dance of Musa: The Life and Afterlife of a Medieval Holy Girl
Authors: Dickason Kathryn Emily
Published: December 2024

This article examines a single figure from Christian history, the reformed sinner known as Musa of Rome (d.c. 593). Tracing the evolution of Musa from Gregory the Great’s Dialogues to early modern pastoral texts, this study explores processes of condemnation, recalibration, and negotiation regarding dance in premodern Christianity. The first section analyzes medieval portrayals of Musa as expressions of “choreophobia,” a term borrowed from dance studies scholar Anthony Shay that denotes cultural anxiety surrounding dance. Here, I argue that choreophobic renditions of Musa sedimented medieval misogyny and conceptualized sin. The second section turns to late medieval sources that assess dance differently vis-à-vis dance studies scholar André Lepecki’s concept of “choreopolice” or “choreopolicing”. For this study, choreopolicing highlights how ecclesiastical authorities refashioned Musa as a moralizing vehicle to articulate and implement clerical agendas. The third and final section explores Musa’s inspiring aura as a sacred muse. In this vein, her kinesthetic afterlives helped Christian laity apprehend Marian piety, visualize the resurrected body, and communicate hope for redemption. Methodologically, this study embraces the frameworks of religious studies, medieval studies, and dance studies. However fictional and embellished retellings of the Musa story were, this article—the first in-depth scholarly study dedicated to Musa of Rome—demonstrates how the medieval dancing body manifested a site of political contestation, ecclesiastical control, and individual redemption.
Religious Pluralism and a Study on Daisaku Ikeda’s Thoughts on Interreligious Dialogue
Authors: Kim Jongman , Kim Andrew Eungi
Published: December 2024

Sōka-gakkai (also known as Soka Gakkai International or SGI) is a new religious movement that originated in Japan in 1930. For more than a half century, the religion was led by its president Daisaku Ikeda, who played a significant role in its growth from 1960 until his death in 2023. Although SGI is one of the largest new religions in the world—it claims to have over 12 million members in 192 countries—there has to date been very limited scholarly attention paid to the religion and Ikeda’s religious thoughts. The latter is particularly important given the fact that Ikeda has written extensively on the applicability and implications of the doctrines of the SGI in many global issues of the day, including peace, education, environment, and interreligious dialogue. Among a wide variety of issues which can be examined, the paper focuses on the following question: What are Ikeda’s religious thoughts, particularly his stance on religious pluralism and interreligious dialogue? In view of this question, this paper examines how his religious ideas are not immersed in any specific religion, but emphasizes the importance of pursuing interreligious dialogue while acknowledging other religions and cultural traditions. This paper also critically assesses whether Ikeda’s call for interfaith dialogue and collaborations with various religious groups is indeed taking place in the SGI’s commitment to fostering a culture of peace and understanding. It is hoped that the examination of Ikeda’s stance on religious pluralism and interreligious dialogue indirectly allows for the exploration of a religion that is largely unknown and misunderstood.
Agnostics’ Well-Being Compared to Believers and Atheists: A Study in Europe’s Religious–Cultural Zones of Christian Heritage
Authors: Karim Moise , Saroglou Vassilis
Published: December 2024

Past research suggests three distinct major trends regarding the links of religion with well-being. First, religious faith contributes to well-being, with believers showing higher well-being than nonbelievers. Alternatively, it is certainty about one’s own worldviews, be they religious or irreligious, that predicts well-being. Finally, secularization moderates the above, making nonbelief normative. We investigated these trends by focusing on agnostics, who, compared to believers and atheists, combine a lack of faith and uncertainty about worldviews and should, thus, be the lowest in well-being. By analyzing European Values Study 2017 data from 29 countries and controlling for personal variables, we found that in countries of Western Christian heritage, be they religious or secularized, agnostics were the least happy compared to believers and atheists. Religionists, compared to atheists, were happier (countries of Protestant heritage) or equally happy (countries of Catholic heritage). In countries of Eastern Orthodox tradition, believers were happier than nonbelievers, agnostics, and atheists alike—but again, agnostics were the lowest in the less religious countries. In sum, uncertainty makes agnostics, consistently across religious cultures, to be the lowest in well-being, whereas the effect of religious faith on well-being varies across cultures.
Analogy Between Theory and Praxis: Hans Urs von Balthasar’s Trinitarian Obedience
Authors: Martínez Endika
Published: December 2024

This essay presents four different steps that we can follow to establish an analogical connection between the Christian praxis and the Trinitarian immanent life. To illustrate these steps, we have provided a case study of Hans Urs von Balthasar’s treatment of obedience. Obedience is employed analogically to speak of the Trinitarian life of God and of the Christian life, by means of grounding this concept in the personhood of Christ, to be more precise, in his mission. In other words, the Christian obedience becomes a participation in the eternal Trinitarian love between the persons. Balthasar’s treatment of this subject illustrates an example whereby a theory–practice split is overcome with the employment of analogy in systematic theology.
CaLiJD: Camera and LiDAR Joint Contender for 3D Object Detection
Authors: Lyu Jiahang , Qi Yongze , You Suilian , Meng Jin , Meng Xin , Kodagoda Sarath , Wang Shifeng
Published: December 2024

Three-dimensional object detection has been a key area of research in recent years because of its rich spatial information and superior performance in addressing occlusion issues. However, the performance of 3D object detection still lags significantly behind that of 2D object detection, owing to challenges such as difficulties in feature extraction and a lack of texture information. To address this issue, this study proposes a 3D object detection network, CaLiJD (Camera and Lidar Joint Contender for 3D object Detection), guided by two-dimensional detection results. CaLiJD creatively integrates advanced channel attention mechanisms with a novel bounding-box filtering method to improve detection accuracy, especially for small and occluded objects. Bounding boxes are detected by the 2D and 3D networks for the same object in the same scene as an associated pair. The detection results that satisfy the criteria are then fed into the fusion layer for training. In this study, a novel fusion network is proposed. It consists of numerous convolutions arranged in both sequential and parallel forms and includes a Grouped Channel Attention Module for extracting interactions among multi-channel information. Moreover, a novel bounding-box filtering mechanism was introduced, incorporating the normalized distance from the object to the radar as a filtering criterion within the process. Experiments were conducted using the KITTI 3D object detection benchmark. The results showed that a substantial improvement in mean Average Precision (mAP) was achieved by CaLiJD compared with the baseline single-modal 3D detection model, with an enhancement of 7.54%. Moreover, the improvement achieved by our method surpasses that of other classical fusion networks by an additional 0.82%. In particular, CaLiJD achieved mAP values of 73.04% and 59.86%, respectively, thus demonstrating state-of-the-art performance for challenging small-object detection tasks such as those involving cyclists and pedestrians.
Re-Estimating GEDI Ground Elevation Using Deep Learning: Impacts on Canopy Height and Aboveground Biomass
Authors: Mitsuhashi Rei , Sawada Yoshito , Tsutsui Ken , Hirayama Hidetake , Imai Tadashi , Sumita Taishi , Kajiwara Koji , Honda Yoshiaki
Published: December 2024

This paper presents a method to improve ground elevation estimates through waveform analysis from the Global Ecosystem Dynamics Investigation (GEDI) and examines its impact on canopy height and aboveground biomass (AGB) estimation. The method uses a deep learning model to estimate ground elevation from the GEDI waveform. Geographic transferability was demonstrated by recalculating canopy height and AGB estimation accuracy using the improved ground elevation without changing established GEDI formulas for relative height (RH) and AGB. The study covers four regions in Japan and South America, from subarctic to tropical zones, integrating GEDI waveform data with airborne laser scan (ALS) data. Transfer learning was explored to enhance accuracy in regions not used for training. Ground elevation estimates using deep learning showed an RMSE improvement of over 3 m compared to the conventional GEDI L2A product, with generalization performance. Applying transfer learning and retraining with additional data further improved the estimation accuracy, even with limited datasets. The findings suggest that improving ground elevation estimates enhances canopy height and AGB accuracy, maximizing GEDI’s global AGB estimation algorithms. Optimizing models for each region could further enhance accuracy. The broader application of this method may improve global carbon cycle understanding and climate models.
Local Pyramid Vision Transformer: Millimeter-Wave Radar Gesture Recognition Based on Transformer with Integrated Local and Global Awareness
Authors: Wang Zhaocheng , Hu Guangxuan , Zhao Shuo , Wang Ruonan , Kang Hailong , Luo Feng
Published: December 2024

A millimeter-wave radar is widely accepted by the public due to its low susceptibility to interference, such as changes in light, and the protection of personal privacy. With the development of the deep learning theory, the deep learning method has been dominant in the millimeter-wave radar field, which usually uses convolutional neural networks for feature extraction. In recent years, transformer networks have also been highly valued by researchers due to their parallel processing capabilities and long-distance dependency modeling capabilities. However, traditional convolutional neural networks (CNNs) and vision transformers each have their limitations: CNNs usually overlook the global features of images and vision transformers may neglect local image continuity, and both of them may impede gesture recognition performance. In addition, whether CNN or transformer, their implementation is hindered by the scarcity of public radar gesture datasets. To address these limitations, this paper proposes a new recognition method using a local pyramid visual transformer (LPVT) based on millimeter-wave radar. LPVT can capture global and local features in dynamic gesture spectrograms, ultimately improving the recognition ability of gestures. In this paper, we mainly carried out the following two tasks: building the corresponding datasets and executing gesture recognition. First, we constructed a gesture dataset for training. In this stage, we use a 77 GHz radar to collect the echo signals of gestures and preprocess them to build a dataset. Second, we propose the LPVT network specifically designed for gesture recognition tasks. By integrating local sensing into the globally focused transformer, we improve its capacity to capture both global and local features in dynamic gesture spectrograms. The experimental results using the dataset we constructed show that the proposed LPVT network achieved a gesture recognition accuracy of 92.2%, which exceeds the performance of other networks.
Mapping Lithology with Hybrid Attention Mechanism–Long Short-Term Memory: A Hybrid Neural Network Approach Using Remote Sensing and Geophysical Data
Authors: Appiah-Twum Michael , Xu Wenbo , Sunkari Emmanuel Daanoba
Published: December 2024

Remote sensing provides an efficient roadmap in geological analysis and interpretation. However, some challenges arise when remote sensing techniques are integrated with machine learning in geological surveys. Factors including irregular spatial distribution, sample imbalance, interclass resemblances, regolith, and geochemical similarities impede geological feature diagnosis, interpretation, and identification across varied remote sensing datasets. To address these limitations, a hybrid-attention-integrated long short-term memory (LSTM) network is employed to diagnose, interpret, and identify lithological feature representations in a remote sensing-based geological analysis using multisource data fusion. The experimental design integrates varied datasets including Sentinel-2A, Landsat-9, ASTER, ALOS PALSAR DEM, and Bouguer anomaly gravity data. The proposed model incorporates a hybrid attention mechanism (HAM) comprising channel and spatial attention submodules. HAM utilizes an adaptive technique that merges global-average-pooled features with max-pooled features, enhancing the model’s accuracy in identifying lithological units. Additionally, a channel separation operation is employed to allot refined channel features into clusters based on channel attention maps along the channel dimension. The comprehensive analysis of results from comparative extensive experiments demonstrates HAM-LSTM’s state-of-the-art performance, outperforming existing attention modules and attention-based models (ViT, SE-LSTM, and CBAM-LSTM). Comparing HAM-LSTM to baseline LSTM, the HAM module’s integrated configurations equip the proposed model to better diagnose and identify lithological units, thereby increasing the accuracy by 3.69%.
Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge
Authors: Abdrabo Amro
Published: December 2024

The usage of anomaly detection is of critical importance to numerous domains, including structural health monitoring (SHM). In this study, we examine an online setting for damage detection in the Z24 bridge. We evaluate and compare the performance of the elliptic envelope, incremental one-class support vector classification, local outlier factor, half-space trees, and entropy-guided envelopes. Our findings demonstrate that XGBoost exhibits enhanced performance in identifying a limited set of significant features. Additionally, we present a novel approach to manage drift through the application of entropy measures to structural state instances. The study is the first to assess the applicability of one-class classification for anomaly detection on the short-term structural health data of the Z24 bridge.
A Sovereign and Interoperable Data Ecosystem for an Eco-Efficient Nonwovens Industry
Authors: Pohlmeyer Florian , Möbitz Christian , Gries Thomas
Published: December 2024

This study addresses the need for enhanced sustainability in the nonwovens industry by developing a data ecosystem that improves data transparency, interoperability, and decision-making across the value chain. The research focuses on two conceptual models, including the Digital Product Passport (DPP) for tracking sustainability information and a holistic data management system for production environments. The research involved identifying key stakeholders, their tasks, and challenges related to sustainability and applying digital tools to meet these needs. The results demonstrate that integrating these data-space use cases can significantly enhance the availability and verifiability of sustainability data, aligning with European Union objectives such as those in the Gaia-X initiative. However, the proposed concepts have not yet been validated in real-world settings, highlighting the need for further research to assess their effectiveness and scalability. These findings suggest that digital ecosystems have the potential to drive sustainable transformation and foster collaboration in the nonwovens sector, offering a pathway towards more circular and resource-efficient practices.
Does Board Internationalization Affect Corporate ESG Performance? Evidence from Chinese A-Share-Listed Companies
Authors: Wu Haotian , Sun Qiqi , Zhang Zhihong , Wang Dongmei
Published: December 2024

This study empirically investigates whether board internationalization enhances corporate ESG performance, using data from Chinese listed companies from 2012 to 2022. The findings indicate that the introduction of foreign directors significantly improves corporate ESG performance, a conclusion that holds robust after various checks and addressing endogeneity concerns. Further analysis reveals that the positive impact of board internationalization on ESG performance is stronger in samples with older average management age and in high-pollution enterprises. By using Chinese companies as an example, this research uncovers the relationship between board internationalization and ESG performance, offering empirical evidence on how emerging market companies can enhance ESG performance through global human resource management.
Can Low-Carbon Transition Impact the High-Quality Development of the Manufacturing Industry?—Experience Evidence from a Low-Carbon Pilot Policy
Authors: Wang Keqiang , Lin Chunxin
Published: December 2024

Low-carbon transition is a wide-ranging and deep economic and social transformation. Can we enhance high-quality development of the manufacturing industry (HQDM) during the low-carbon transition process? The enactment of the low-carbon city pilot policy (LCCP) is considered a “quasinatural experiment”. This study initially constructs a theoretical model of the impact of the policy on the high-quality development of a company. A staggered DID approach is then employed to evaluate this impact, using microdata from listed manufacturing enterprises from 2003 to 2021, and a variety of robustness tests, such as PSM-DID and the double machine learning method, are also employed. The main results show that the LCCP led to a significant 9.8% increase in the TFP of the manufacturing industry in the pilot regions. Therefore, the LCCP promoted the HQDM. The impact of the policy has demonstrated a year-by-year upward trend. The influence mechanism test demonstrates that the LCCP resulted in a 14.3% improvement in resource allocation within the manufacturing of the pilot regions but led to a 0.3% decrease in technological innovation. In addition, the LCCP has been more effective in promoting the HQDM in western regions, along with in state-owned enterprises and low-technology manufacturing industries. This paper offers valuable insights into the implementation of differentiated low-carbon policies for manufacturing industries in different regions with different characteristics.
Challenges to Accident Prevention for High-Speed Vessels Used in the Brazilian Amazon
Authors: Fontes Jassiel V. H. , Hernández Irving D. , Mendoza Edgar , Silva Rodolfo , Santander Elvis J. O. , Sanches Ricardo Almeida
Published: December 2024

The Brazilian Amazon has one of the largest river networks in the world, connecting riverine communities, many of which have no roadways. The use of high-speed vessels, including those that are locally made, is necessary for the transportation of people to perform their day-to-day activities. However, regional news and social media data have shown that these vessels are involved in accidents. This work describes some of the key challenges to improving the sustainability and safety of regional high-speed vessels. Field research was performed in the state of Amazonas to document regional vessels and provide graphic evidence of the possible risk factors that could cause accidents as the path to prevent them. Some risk mitigation alternatives were discussed. Cameras and a drone were used to capture information either from the shore or from small boats. The main challenges are related to the adequate management of seasonal changes in river depths; wave and wind behavior; wave wash effects; the interaction of vessels with rocks, sand banks, mud, and vegetation; embarkation and disembarkation activities; the monitoring and regulation of safety requirements and dangerous operations; and the slowness in implementing technological advances that would improve vessels’ safety. It is hoped that the work in this paper will contribute to improving the sustainability of marine activities in the Amazon and similar regions worldwide.
A Potential Technology for Improving the Quality of Lake Water Environments in Cold Zones: A Case Study from Lake Hulun, China
Authors: Gao Hengshuai , Xue Dongyu , Li Wenbao , Zhang Sheng , Shi Yujiao , Zhang Boyao , Tao Yulong
Published: December 2024

Lakes in cold zones have common characteristics of long frozen periods and fragile water ecosystems. More and more lakes in cold zones have been experiencing water quality deterioration due to eutrophication with climate change and human activities. Lake Hulun is located in the cold zone of northern China, in which Cyanobacterial blooms frequently occur. The excessive nutrient input with inflowing river water and the change in lake hydrodynamic condition might be the main factors affecting this. To obtain a better understanding of the effects, the input loads of nutrients from the inflowing rivers were analyzed. A field test of freezing concentration combined with microbial activity regulation was carried out at a river–lake confluence. The results showed the following: (1) Lake Hulun receives a large amount of nitrogen and phosphorus pollutants from river runoff every year, and the water quality of these river is inferior Grade V, which greatly increases the difficulty in ecological purification of cold zone lakes. (2) The microbial activity control technology has a high purification rate for water during the unfrozen period. The order of purification rates for each hydrochemical index was TP > TN > COD > NH4+-N, and the purification rates of TN and COD were between 35% and 36%. Compared with the water before purification, the water quality grade improved from Grade V to Grade III. (3) The composite technology of freezing concentration–microbial activity regulation has a general water purification rate during the frozen period. Under the low-temperature condition, the TN and COD nutrient fluxes in the water were reduced by 9.38% and 9.36%, respectively. After purification, the water quality grade of the ice body was above Grade II, and the water under the ice layer was above Grade IV, which was one grade higher than the water quality grade of the original lake water. This water treatment and purification process is a green, low-energy consumption, and low-cost technology. This study can provide reference for the integration and demonstration of lake water ecological governance and water resources security technology in cold and arid regions. It is beneficial to the sustainable development of the lake.
Adaptive Neural Architecture Search Using Meta-Heuristics: Discovering Fine-Tuned Predictive Models for Photocatalytic CO2 Reduction
Authors: Işıkdağ Ümit , Bekdaş Gebrail , Aydın Yaren , Apak Sudi , Hong Junhee , Geem Zong Woo
Published: December 2024

This study aims to contribute to the reduction of carbon dioxide and the production of hydrogen through an investigation of the photocatalytic reaction process. Machine learning algorithms can be used to predict the hydrogen yield in the photocatalytic carbon dioxide reduction process. Although regression-based approaches provide good results, the accuracy achieved with classification algorithms is not very high. In this context, this study presents a new method, Adaptive Neural Architecture Search (NAS) using metaheuristics, to improve the capacity of ANNs in estimating the hydrogen yield in the photocatalytic carbon dioxide reduction process through classification. The NAS process was carried out with a tool named HyperNetExplorer, which was developed with the aim of finding the ANN architecture providing the best prediction accuracy through changing ANN hyperparameters, such as the number of layers, number of neurons in each layer, and the activation functions of each layer. The nature of the NAS process in this study was adaptive, since the process was accomplished through optimization algorithms. The ANNs discovered with HyperNetExplorer demonstrated significantly higher prediction performance than the classical ML algorithms. The results indicated that the NAS helped to achieve better performance in the estimation of the hydrogen yield in the photocatalytic carbon dioxide reduction process.
The Evolution of the Spatial–Temporal Pattern of Tourism Development and Its Influencing Factors: Evidence from China (2010–2022)
Authors: Zheng Yaomin , Wu Minghan , Shi Jinlian , Yang Huize , Wang Jiaxin , Zhang Xiaoyuan , Zhang Xin
Published: December 2024

The Chinese economy has shifted towards a stage of high-quality development, and promoting high-quality development of tourism is of great significance for its sustainable development. Based on the new development concept, using Geographic Information System (GIS) spatial analysis, Moran’s I Index, and the Geographically Weighted Regression (GWR) model, this paper analyzes the spatial–temporal evolution of tourism development in 31 provinces in China during 2010–2022. The findings are as follows: (1) the overall tourism development level of China from 2010 to 2022 was at a medium-low level, showing a spatial distribution pattern of “higher in southeast and lower in northwest”; (2) from the perspective of the Quality–Quantity relationship, the quality of tourism development in China has gradually improved, the regional gap between the east and the west and the internal gap among western regions/provinces have gradually expanded; and (3) tourism innovation and internationalization level are the main influencing factors of high-quality tourism development in China’s provinces, providing practical decision-making references for advancing the high-quality development of tourism.
Does It Pay Off to Integrate ESG Performance into Bank Investment Portfolio Selection? Empirical Evidence in the European Energy Sector
Authors: Baldissarro Giovanni , Bruni Maria Elena , Iazzolino Gianpaolo , Morea Donato , Veltri Stefania
Published: December 2024

There is a growing awareness of the need to integrate non-financial information arising from environmental, social, and governance (ESG) factors into corporate strategies, processes, and credit risk assessment to generate long-term value. Our paper aims to develop, through a Data Envelopment Analysis (DEA)-based approach, a credit risk assessment tool that could be used by banks in constructing an efficient and sustainable investment portfolio, able to maximize banks’ probability contemporaneously minimizing corporate inefficiency. This study was carried out on a sample of publicly traded energy companies in Europe, with the energy sector being highly environmentally sensitive. Our portfolio selection model proves to be a valuable tool for building an efficient and sustainable investment portfolio because it leads, within a budget constraint, to selecting both the most efficient companies in absolute terms and those for which ESG scores significantly improve corporate financial efficiency. Additionally, our results show that ESG ratings at high or low levels do not affect overall company efficiency, but at a middle level, they increase it. Findings contribute (and provide suggestions) to policymakers, credit risk managers, and academics.
Guest Editor’s Introduction to “Sustainable Urbanism: Definition, Assessment, and Agenda for Future Research”
Authors: Mehaffy Michael W.
Published: December 2024

This Special Issue is based on the premise that urban sustainability (or the lack of it) is a prerequisite for all other efforts toward sustainable development [...]
Research on the Spatiotemporal Evolution Characteristics and Capital Driving Factors of Sustainable Economic Development in Northwest China
Authors: Liu Junfeng , Wang Shiwen , Ji Jianwen , Chen Zizhen , Li Shengyu
Published: December 2024

Frontier research has focused more on the sustainable economic development (SED) of developing countries, with slightly less attention paid to the SED of underdeveloped regions in developing countries, especially without analyzing their driving factors from the dual perspectives of domestic and international capital. In fact, as an important factor in economic growth, capital is also the core driving force for achieving SED. This paper takes the five provinces (autonomous regions) of northwest China (NC) as a case study based on unbalanced panel data from 26 cities from 2000 to 2020 and employs the dynamic degree and MSAR model to analyze the spatiotemporal evolution characteristics of SED; the results indicate that the SED of NC exhibits an irregular pattern overall, the differences between provinces at the same time point are relatively small, and the same applies to prefecture-level cities. The spatial agglomeration characteristic of SED in NC is evident, with significant room for improvement. Additionally, this paper evaluates the impact of capital on the SED of NC through the two-way fixed effects model and the mediation effect model and finds that foreign direct investment (FDI) inflows have a “first-rising-then-declining” impact on the SED of NC, while financing constraints (FC) play a promoting role in the SED of NC. Furthermore, FDI inflows subsequently impact the SED of NC by affecting regional FC, with FC acting as a mediating variable in the influence of FDI on the SED of NC. In terms of policy implications, NC should make rational use of FDI, control the pace of easing FC, and precisely promote the SED of NC from a capital-driven perspective. Other underdeveloped areas in developing countries can take this as a reference to promote SED.
Integrated Simulation of Groundwater Flow and Nitrate Transport in an Alluvial Aquifer Using MODFLOW and MT3D: Insights into Pollution Dynamics and Management Strategies
Authors: Laoufi Abdessalam , Boudjema Abderezzak , Guettaia Sabrine , Derdour Abdessamed , Almaliki Abdulrazak H.
Published: December 2024

This study employs an integrated numerical modeling approach using MODFLOW and MT3D to simulate groundwater flow and nitrate transport in the alluvial aquifer of Hennaya plain. The groundwater flow model was calibrated and validated against observed hydraulic heads, showing excellent agreement in both steady-state and transient conditions, with a correlation coefficients (R2) of 0.99 and 0.987, respectively. Meticulous calibration yielded adjusted hydraulic conductivity values between 10−1 and 10−11 m/s, with effective porosity ranging from 0.03 to 0.34 and total porosity values varying from 0.29 to 0.38 across the aquifer. Water budget analysis revealed that the aquifer’s primary recharge occurs from the southern side. Nitrate transport modeling indicated that advection is the dominant process, with contaminants migration predominantly occurring from south to north, following the groundwater flow direction. Pollution levels were found to decrease gradually with distance from sources, confirming agricultural activities and sewage disposal as primary contributors to nitrate contamination. Predictive scenarios over a 40-year period explored various management strategies, which suggest that maintaining current nitrogen input rates will lead to continued increases in nitrate pollution, while a 50% reduction in agricultural inputs could significantly improve groundwater quality. However, even with substantial reductions, nitrate concentrations are not expected to reach levels safe for drinking within the simulation timeframe. This study underscores the need for immediate and sustained action to address nitrate pollution in the Hennaya Plain aquifer, emphasizing the importance of stringent nitrogen management practices, particularly in the agricultural sector.
Sources, Contamination and Risk Assessment of Heavy Metals in Riparian Soils of the Weihe River Based on a Receptor Model and Monte Carlo Simulation
Authors: Dong Wen , Niu Bohan , Li Huaien , Yan Dandan , Li Jiake , Xu Zhiqiang , Wang Dongqi , Yang Xiuying , Zhang Yakun , Chen Yutong , Wang Hui
Published: December 2024

The riparian ecosystem is highly susceptible to pollution, particularly heavy metals (HMs), due to its unique spatial position and landscape characteristics. Therefore, assessing the risks of HM pollution and identifying potential sources are crucial for formulating effective prevention and control measures. This study investigates the characteristics of HMs (Ni, Cr, Zn, Cd, Cu, Pb) pollution in the Weihe River riparian zone, identifies their sources, and assesses the associated ecological and human health risks. The results indicate that Ni, Zn, and Cd are the primary pollutants in riparian soil, with the average Cd concentration being 5.64 times higher than the background value, indicating a high potential ecological risk. Spatially, the average HM concentrations in the middle and upper reaches are higher than in the lower reaches. Vertically, as the distance from the riparian increases, the HM content exhibits a “U”-shaped pattern (increase-decrease-increase). The Absolute principal components multiple regression (APCS-MLR) receptor model identified four potential pollution sources: traffic sources; agricultural sources; industrial sources; and natural sources. Additionally, the Monte Carlo simulation-based human health risk assessment indicates that the non-carcinogenic health risk indices for all HMs are within acceptable ranges. For carcinogenic health risk indices, there is a 1.14% probability for children. However, the vast majority of the risks fall within acceptable or no-risk categories.
Can Multimodal Large Language Models Enhance Performance Benefits Among Higher Education Students? An Investigation Based on the Task–Technology Fit Theory and the Artificial Intelligence Device Use Acceptance Model
Authors: Al-Dokhny Amany , Alismaiel Omar , Youssif Samia , Nasr Nermeen , Drwish Amr , Samir Amira
Published: December 2024

The current study highlights the potential of multimodal large language models (MLLMs) to transform higher education by identifying key factors influencing their acceptance and effectiveness. Aligning technology features with educational needs can enhance student engagement and learning outcomes. The study examined the role of MLLMs in enhancing performance benefits among higher education students, using the task–technology fit (T-TF) theory and the artificial intelligence device use acceptance (AIDUA) model. A structured questionnaire was used to assess the perceptions of 550 Saudi university students from various academic disciplines. The data were analyzed via structural equation modeling (SEM) using SmartPLS 3.0. The findings revealed that social influence negatively affected effort expectancy regarding MLLMs and that hedonic motivation was also negatively related to effort expectancy. The findings revealed that social influence and hedonic motivation negatively affected effort expectancy for MLLMs. Effort expectancy was also negatively associated with T-TF in the learning context. In contrast, task and technology characteristics significantly influenced T-TF, which positively impacted both performance benefits and the willingness to accept the use of MLLMs. A strong relationship was found between adoption willingness and improved performance benefits. The findings empower educators to strategically enhance MLLMs adoption strategically, driving transformative learning outcomes.