bluuebunny/crossref_metadata_2025 huggingface dataset:
Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin
Authors: Sayed Amged , Alshathri Samah , Hemdan Ezz El-Din
Published: October 2024

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions.
Numerical Modeling and Experimental Validation of Icing Phenomena on the External Surface of a U-Bend Tube
Authors: Ishaque Shehryar , Rehman Sana ur , Kim Man-Hoe
Published: October 2024

The regasification of liquefied natural gas (LNG) is a crucial process that involves certain challenges created by the low temperature of LNG and the risk of ice formation on the external surfaces of the tubes of heat exchangers, which can hinder heat transfer and increase flow resistance. This study presents a numerical model for ice formation on the external surface of the U-bend tube of shell-and-tube heat exchangers. The numerical model has been further enhanced by applying a custom user-defined function. The numerical results were validated using experimental data and demonstrated excellent predictive capability, particularly for the surface temperature of the tubes and the thickness of the ice layer. Hence, this model can reliably capture the overall behavior of the ice formation on the external surfaces of the tubes of shell-and-tube heat exchangers. By highlighting the importance of maintaining stable heat transfer conditions to prevent freezing, this study offers valuable insights that can guide the optimization of heat exchanger designs for LNG regasification.
AI in Prosthodontics: A Narrative Review Bridging Established Knowledge and Innovation Gaps Across Regions and Emerging Frontiers
Authors: Iosif Laura , Țâncu Ana Maria Cristina , Amza Oana Elena , Gheorghe Georgiana Florentina , Dimitriu Bogdan , Imre Marina
Published: October 2024

As the discipline of prosthodontics evolves, it encounters a dynamic landscape characterized by innovation and improvement. This comprehensive analysis underscores future developments and transformative solutions across its various subspecialties: fixed, removable, implant, and maxillofacial prosthodontics. The narrative review examines the latest advancements in prosthetic technology, focusing on several critical areas. The integration of artificial intelligence and machine learning into prosthetic design and fitting processes is revolutionizing the field, serving as a common thread that links these innovative technologies across all subspecialties. This includes advancements in automated diagnostics, predictive analysis, and treatment planning. Furthermore, the review offers a forward-looking perspective on how these innovations are influencing each prosthetic dentistry domain, patient outcomes, and current clinical practices. By thoroughly analyzing contemporary research and emerging technologies, the study illustrates how these advancements represent a growing focal point of interest in developing countries, such as Romania, with the potential to redefine the trajectory of prosthetic rehabilitation and enhance patient care not only within this country but also beyond.
A Paleoenvironmental Reconstruction of Lake Vrana on the Island of Cres (Croatia) Based on the Geochemistry and Mineralogy of the Late Pleistocene and Holocene Sediments
Authors: Ilijanić Nikolina , Miko Slobodan , Hasan Ozren , Brunović Dea , Šparica Miko Martina , Mesić Saša
Published: October 2024

A 7.4 m long sediment core has been retrieved from the central part of Lake Vrana on the island of Cres to reconstruct the paleoenvironmental conditions. Lake Vrana is the deepest freshwater lake in Croatia, located in the karst region of the eastern Adriatic coast. A dated sediment sequence in Lake Vrana of 4.4 m has spanned the past 16.4 kyr, and it featured a dynamic sediment deposition until the beginning of the Holocene, including strong sediment input and supply to the lake by runoff sediments of dolomitic origin from the catchment in the period 16.4–14.4 cal kyr BP. High organic carbon content, which originates from mixed terrestrial and aquatic origins in the periods 14.4–13.3 cal kyr BP and 12.7–11.7 cal kyr BP, indicates fluctuating lake levels in shallow water environments during the Late Glacial to Holocene transition. The Holocene sequence indicates the development of more stable conditions and continuous sediment deposition, characterized by an increasing trend of siliciclastic sediments delivered into the lake during the early Holocene (11.7–10 cal kyr BP) and dominantly from 8 to 4.4 cal kyr BP, indicating enhanced input and erosion, which coincides with the humid and pluvial period recorded in the central Mediterranean region. It is followed by sediments with high organic carbon content between 4.4 and 1.6 cal kyr BP, which points to higher lake productivity. Calcite sedimentation prevailed between 1.6 to 0.4 cal kyr BP, indicating stable deeper-lake conditions. Predominantly, siliciclastic sediments from 0.4 to 0.1 cal kyr BP pointed to erosion during the Little Ice Age (LIA), with enhanced precipitation and sediment discharge from the catchment. The re-establishment of calcite sedimentation has been observed over the last 100 years.
Transcending Individual Traditions: Zhang Taiyan’s Interpretation of Zhuangzi’s Notion of Chengxin
Authors: Wang Cheng
Published: October 2024

This article examines the nuanced interpretations of chengxin 成心 in the Zhuangzi 莊子, a foundational Daoist text, across different philosophical traditions. Historically, Daoist thinkers like Guo Xiang 郭象 and Cheng Xuanying 成玄英, along with Neo-Confucian scholars such as Lü Huiqing 吕惠卿 and Lin Xiyi 林希逸, have provided differing perspectives on the notion of chengxin. They regard it as either a source of subjective bias or an expression of heavenly principle. The core focus of this study is Zhang Taiyan’s 章太炎 innovative interpretation of chengxin as seeds (bijas) within ālaya-vijñāna (storehouse consciousness), integrating Yogācāra Buddhist concepts with traditional Daoist philosophy. The study conducts a textual analysis of Zhang’s Qiwulun Shi 齊物論釋, comparing his interpretation with earlier commentaries on the Zhuangzi. It is argued that Zhang’s approach is profoundly influenced by the Buddhist doctrine of non-duality, which underscores the interconnectedness of all phenomena. While Zhang’s interpretation has faced criticism from contemporaries and later philosophers for misunderstandings of Yogācāra theory and Kantian philosophy, it employs the method of geyi 格義 (matching concepts), not to impose one tradition over another, but to foster a dialogical process where the insights from each tradition mutually inform and transform one another. This article highlights the interpretative openness of the Zhuangzi, which invites diverse interpretations, and demonstrates how Zhang’s approach enriches this tradition by synthesizing various philosophical and religious frameworks. Zhang’s methodology remains relevant, fostering cross-cultural and inclusive philosophical dialog.
A Gladdening Vision of a Dancing Christ: Findings of a Ritual Ethnography of Intercultural Icons
Authors: Jacob Sebeesh
Published: October 2024

The cultural renaissance in 20th-century India has fostered an aesthetic integration of contemplative mysticism with popular religious practices, influencing various artistic and theological movements. This paper examines Christian artist Joy Elamkunnapuzha’s use of Indian classical and mythical elements in his religious artworks, particularly in two North Indian churches. These intercultural icons, which incorporate symbols from Hindu traditions like mandalas and mudras, have been central to the worship practices of local Catholic communities for over three decades. Through ritual ethnography, the study reveals how these visual representations mediate ritual affectivity and communal imagination, impacting identity formation and spiritual engagement in a multi-religious context. Respondents—including worshippers, ministers, and religious students—attest to the transformative impact of these images, as they negotiate between Christian metaphors and Hindu aesthetic traditions. The research is grounded in practical theology, liturgical theology, and ritual studies, contributing to the works of Indian Christian cultural activists like Jyoti Sahi. By exploring the creative dynamics of visual approach, visual appeal, and visual affinity within worship spaces, the study elucidates the complex processes of meaning making through symbolic mediation in interreligious environments.
Christian Communities During Russia’s War Against Ukraine: Perception of the Churches’ Aid Involvement Through the Eyes of Poles from Lviv—A Pilot Study
Authors: Smuniewski Cezary , Bado Błażej , Składanowski Marcin
Published: October 2024

The purpose of this study is to present the perceptions of Poles from Lviv regarding the aid involvement of Christian churches during Russia’s war against Ukraine. Christian churches appear privileged and effective sites of aid delivery in the narratives of respondents. However, this does not mean that they have a monopoly on aid activities. The foundation that underpins these activities is particularly evident in the categories highlighted in this article: the role of clergy in local communities (leaders), personal knowledge of society, church infrastructure, and the churches’ experience gained during the USSR’s persecution of religion. The assistance identified by respondents boils down to material and emotional support, experiencing religious practices, and involvement in direct activity on the war front. The research was based on individual in-depth interviews (IDIs), conducted in December 2023.
A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network
Authors: Sun Xiwen , Lu Tieding , Hu Shunqiang , Wang Haicheng , Wang Ziyu , He Xiaoxing , Ding Hongqiang , Zhang Yuntao
Published: October 2024

To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.
A Deep Unfolding Network for Multispectral and Hyperspectral Image Fusion
Authors: Zhang Bihui , Cao Xiangyong , Meng Deyu
Published: October 2024

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/HS fusion method is a representative deep learning paradigm due to its excellent performance and sufficient interpretability. However, existing deep unfolding-based MS/HS fusion methods only rely on a fixed linear degradation model, which focuses on modeling the relationships between HRHS and HRMS, as well as HRHS and LRHS. In this paper, we break free from this observation model framework and propose a new observation model. Firstly, the proposed observation model is built based on the convolutional sparse coding (CSC) technique, and then a proximal gradient algorithm is designed to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as MHF-CSCNet, where the proximal operators are learned using convolutional neural networks. Finally, all trainable parameters can be automatically learned end-to-end from the training pairs. Experimental evaluations conducted on various benchmark datasets demonstrate the superiority of our method both quantitatively and qualitatively compared to other state-of-the-art methods.
Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar
Authors: Liu Jialin , Tang Xiaosong , Yang Feng , Qiao Xu , Li Fanruo , Peng Suping , Huang Xinxin , Fang Yuanjin , Xu Maoxuan
Published: October 2024

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations.
Three-Dimensional Reconstruction of Retaining Structure Defects from Crosshole Ground Penetrating Radar Data Using a Generative Adversarial Network
Authors: Zhang Donghao , Wang Zhengzheng , Tang Yu , Pan Shengshan , Pan Tianming
Published: October 2024

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction method based on a generative adversarial network (GAN) to recover 3D permittivity distributions from crosshole GPR images. The established framework, named CGPR2VOX, integrates a fully connected layer, a residual network, and a specialized 3D decoder in the generator to effectively translate crosshole GPR data into 3D permittivity voxels. The discriminator was designed to enhance the generator’s performance by ensuring the physical plausibility and accuracy of the reconstructed models. This adversarial training mechanism enables the network to learn non-linear relationships between crosshole GPR data and subsurface permittivity distributions. CGPR2VOX was trained using a dataset generated through finite-difference time-domain (FDTD) simulations, achieving precision, recall and F1-score of 91.43%, 96.97% and 94.12%, respectively. Model experiments validate that the relative errors of the estimated positions of the defects were 1.67%, 1.65%, and 1.30% in the X-, Y-, and Z-direction, respectively. Meanwhile, the method exhibits noteworthy generalization capabilities under complex conditions, including condition variations, heterogeneous materials and electromagnetic noise, highlighting its reliability and effectiveness for practical quality assurance of retaining structures.
Refined Coseismic Slip and Afterslip Distributions of the 2021 Mw 6.1 Yangbi Earthquake Based on GNSS and InSAR Observations
Authors: Liu Zheng , Zhang Keliang , Gan Weijun , Liang Shiming
Published: October 2024

On 21 May 2021, an Mw 6.1 earthquake occurred in Yangbi County, Dali Bai Autonomous Prefecture, Yunnan Province, with the epicenter located in an unmapped blind fault approximately 7 km west of the Weixi-Qiaohou fault (WQF) on the southeastern margin of the Qinghai–Tibetan Plateau. While numerous studies have been conducted to map the coseismic slip distribution by using the Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and seismic data as well as their combinations, the understanding of deformation characteristics during the postseismic stage remains limited, mostly due to the long revisiting time interval and large uncertainty of most SAR satellites. In this study, we refined coseismic slip and afterslip distributions with nonlinear inversions for both fault geometry and relaxation time. First, we determined the fault geometry and coseismic slip distribution of this earthquake by joint inversion for coseismic offsets in the line-of-sight (LOS) direction of both Sentinel-1A/B ascending and descending track images and GNSS data. Then, the descending track time series of Sentinel-1 were further fitted using nonlinear least squares to extract the coseismic and postseismic deformations. Finally, we obtained the refined coseismic slip and afterslip distributions and investigated the spatiotemporal evolution of fault slip by comparing the afterslip with aftershocks. The refined coseismic moment magnitude, which was of Mw 6.05, was smaller than Mw 6.1 or larger, which was inferred from our joint inversion and previous studies, indicating a significant reduction in early postseismic deformation. In contrast, the afterslip following the mainshock lasted for about six months and was equivalent to a moment release of an Mw 5.8 earthquake. These findings not only offer a novel approach to extracting postseismic deformation from noisy InSAR time series but also provide valuable insights into fault slip mechanisms associated with the Yangbi earthquake, enhancing our understanding of seismic processes.
CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher
Authors: Pratama Derry , Suryanto Naufal , Adiputra Andro Aprila , Le Thi-Thu-Huong , Kadiptya Ahmada Yusril , Iqbal Muhammad , Kim Howon
Published: October 2024

Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a specialized training approach for AI language models. CIPHER was trained using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools augmented in an expert response structure. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q&A benchmarks and provides a realistic and rigorous standard for evaluating LLM’s technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general large language models (LLMs) are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results.
Establishing the Reliability of the GaitON® Motion Analysis System: A Foundational Study for Gait and Posture Analysis in a Healthy Population
Authors: Alam Md Farhan , Zaki Saima , Sharma Saurabh , Nuhmani Shibili
Published: October 2024

Background: Gait and posture analysis plays a crucial role in understanding human movement, with significant applications in rehabilitation, sports science, and clinical settings. The GaitON® system, a 2D motion analysis tool, provides an accessible and cost-effective method for assessing gait and posture. However, its reliability in clinical practice, particularly for intra-rater consistency, remains to be evaluated. This study aims to assess the intra-rater reliability of the GaitON® system in a healthy population, focusing on gait and posture parameters. Methods: A total of 20 healthy participants (10 males and 10 females) aged 18 to 50 years were recruited for the study. Each participant underwent gait and posture assessments using the GaitON® system on two separate occasions, spaced one week apart. Video recordings from anterior and posterior views were used to analyze gait, while images from anterior, posterior, and lateral views were captured to assess posture with markers placed on key anatomical landmarks. The reliability of the measurements was analyzed using intraclass correlation coefficients (ICC), a standard error of measurement (SEM), and the smallest detectable difference (SDD) method. Results: The GaitON® system demonstrated excellent intra-rater reliability across a wide range of gait and posture parameters. ICC values for gait parameters, including hip, knee, and ankle joint angles, ranged from 0.90 to 0.979, indicating strong consistency in repeated measurements. Similarly, ICC values for posture parameters, such as the head alignment, shoulder position, and ASIS alignment, were above 0.90, reflecting excellent reliability. SEM values were low across all parameters, with the smallest SEM recorded for the hip joint angle (0.37°), and SDD values further confirmed the precision of the system. Conclusion: The GaitON® system provides reliable and consistent measurements for both gait and posture analysis in healthy individuals. Its high intra-rater reliability and low measurement error make it a promising tool for clinical and sports applications. Further research is needed to validate its use in clinical populations and compare its performance to more complex 3D motion analysis systems.
Multiple Sclerosis and Subcutaneous Panniculitis-like T Cell Lymphoma with Hemophagocytic Syndrome: The Role of Treatment Sequencing in the Pathogenetic Mechanism
Authors: Trinchillo Assunta , Carotenuto Antonio , Spiezia Antonio Luca , Caliendo Daniele , Severino Alessandro , Di Monaco Cristina , Iacovazzo Carmine , Servillo Giuseppe , Brescia Morra Vincenzo , Lanzillo Roberta
Published: October 2024

Introduction: Although panniculitis-like T cell lymphoma (SPTCL) and hemophagocytic syndrome (HSP) have been described as complications following immunosuppressive treatments, there are no reported cases of concomitant SPTCL/HSP and multiple sclerosis (MS). Materials and Methods: We describe the case of a patient affected by an aggressive phenotype of relapsing remitting MS, characterized by consecutive severe relapses with no complete remission. He developed panniculitis-like T cell lymphoma (SPTCL) and hemophagocytic syndrome (HSP) after receiving multiple immunosuppressive treatments in sequence. Despite the aggressive nature of these complications, the patient responded well to a combination of Gemcitabine and Cisplatin. Discussion and Conclusions: With this case, we suggest that physicians always consider blood diseases as possible MS therapy complications, especially in the sequencing setting, and also consider uncommon treatments in those with autoimmune predispositions.
Eco-Friendly Green Approach to the Biosorption of Hazardous Dyes from Aqueous Solution on Ragweed (Ambrosia artemisiifolia) Biomass
Authors: Nedić Natalija , Tadić Tamara , Marković Bojana , Nastasović Aleksandra , Popović Aleksandar , Bulatović Sandra
Published: October 2024

The presented research includes the preparation, characterization, and implementation of magnetic biosorbent (Fe3O4/RWB), obtained from ragweed (Ambrosia artemisiifolia) biomass. Fe3O4/RWB was examined for the removal of a hazardous dye, malachite green (MG), from an aqueous solution in a batch system. The effects of the experimental parameters—initial dye concentration (10–300 mg/L), contact time (0–120 min), biosorbent dose (1–5 g/L), initial pH (2–10), ionic strength (0–1 mol/L), and temperature (298–318 K) on dye biosorption—were studied. The results showed that increases in biosorbent dose, contact time, and initial pH led to an increase in biosorption efficiency, while the increase in initial dye concentration, the ionic strength, and temperature had the opposite effect. The biosorption kinetics for MG on Fe3O4/RWB were analyzed with pseudo-first-order, pseudo-second-order, and Elovich kinetic models, while the Langmuir, Freundlich and Temkin isotherm models were used for equilibrium data analysis. It was observed that the MG biosorption followed the pseudo-second-order kinetic model, whereas the Langmuir model was the best fit for the equilibrium biosorption data of MG, with a Qmax of 34.1 mg/g. the desorption of MG from Fe3O4/RWB indicated reusability in five adsorption/desorption cycles, good performance, and potential in practical applications.
Insights from Smart City Initiatives for Urban Sustainability and Contemporary Urbanism
Authors: Veloso Águeda , Fonseca Fernando , Ramos Rui
Published: October 2024

Urbanization growth poses various challenges, such as congestion, pollution, and resource consumption, prompting city planners and governments to adopt smart systems to manage these issues more efficiently. Despite widespread adoption, there is no consensus on the defining attributes of smart cities, particularly regarding their role in urban sustainability and contemporary urbanism. This paper provides a literature review to understand the implications of smart city initiatives for sustainable urban planning, focusing on practices in Singapore, Helsinki, Barcelona, and Medellin. Based on 71 publications surveyed from Scopus and Web of Science, this paper evaluates smart, sustainable initiatives undertaken in these four cities across six smart domains: mobility, governance, environment, people, living, and economy. This review shows that most studies focus on Barcelona and Singapore, particularly in the domains of smart environment and governance. Despite differing urban contexts, the notion of “smart” is closely tied to using information and communication technologies to drive urban operations. This analysis identifies a lack of assessment studies on the benefits of smart cities in terms of urban sustainability and a lack of holistic approaches to address the complex challenges cities face in achieving sustainable development.
Unveiling the Ethical Dilemmas of Digital Piracy: A Comprehensive Exploration of Motivations, Attitudes, and Behaviors
Authors: Belchior-Rocha Helena , Arslan Aykut , Yener Serdar
Published: October 2024

Driven by several elements, including the unwillingness to pay for material, the spread of subscription services, and the profitability of illegal activity, digital piracy has grown to be a common problem in the contemporary world. Along with financial damages for businesses and content creators, this phenomenon has legal repercussions for individuals involved in piracy and cybersecurity issues. The effective fight against digital piracy depends on an awareness of the drivers and hazards connected with this illegal activity. With an eye towards a thorough knowledge of this intricate problem, in this study, the focus centers on the reasons, attitudes, and actions behind digital piracy. Through examining how people defend their behavior in particular situations, in this study, the aim is to clarify the ethical issues and decision-making procedures related to piracy—even among otherwise law-abiding people. Combining a variety of the relevant literature and ideas, including situation ethics theory, in this study, we seek to provide insights for legislators, business players, and academics eager to meet the difficulties presented by digital piracy. This study adds to the continuous discussion on lessening the detrimental effects of digital piracy on content creators, businesses, and cybersecurity by looking at the underlying reasons for piracy and supporting legal access to material. Along with a dearth of research on the origins and moral questions of digital piracy, there is a lot of contradictory material. By examining the factors behind this unlawful activity, clarifying the complex dynamics involved, and offering understanding of the causes of digital piracy, this study seeks to close a significant void in the knowledge. The findings of this study will enable individuals to develop strategies for handling this prevalent issue in the digital era. Developing successful interventions that can reduce this illegal conduct and safeguard the interests of content producers, businesses, and cybersecurity overall depends on an awareness of the reasons behind digital piracy. This study advances the knowledge of the difficulties presented by this global phenomenon by investigating the ethical issues and decision-making procedures related to digital piracy.
Assessing Sustainable Development Goal Alignment in Local Food Systems: Insights from an Automated Text Analysis of the Organizational Literature
Authors: Gaudreau Coralie , Chouikh Arbi , Guillaumie Laurence , Forget Daniel , Roche Stéphane
Published: October 2024

There is growing interest in assessing local food systems to guide efforts toward sustainability and aligning these assessments with the United Nations’ 17 Sustainable Development Goals (SDGs). However, the complexity of portraying local food systems poses numerous challenges for local communities, and automated text analysis and artificial intelligence (AI) offer promising solutions. This study tested the use of an automated textual analysis to assess the alignment of the Mauricie region’s food system in Quebec, Canada, with the SDGs. The analysis examined 35 organizational documents from the region using an automated text analysis based on a list of keywords for each SDG. Initially, the analysis revealed that several initiatives in the Mauricie region covered specific SDGs quite well, such as eliminating hunger (SDG 2). Areas such as health and well-being (SDG 3) received moderate attention, while SDGs such as life below water and on land (SDGs 14 and 15) were less emphasized. When these results were presented to regional stakeholders, these stakeholders reported that the findings did not closely reflect their perceptions of the food system. This study confirms the potential of automated textual analysis and AI in assessing local food systems and underscores the parameters and challenges of accurately portraying sustainability in local food systems.
Maize Morphophysiological Changes Modulated by Cover Crops Rotation in Northeast Brazil
Authors: Souza José Wilker Germano de , Luz João Henrique Silva da , Silva Dayane Mércia Ribeiro , Silva Ricardo Barros , Costa Bruno Richardson dos Santos , Melo Alan Fontes , Santos Hugo Rodrigues dos , Marques Isabelly Cristina da Silva , Sousa Jadielson Inácio de , Vanderley Mariana Bernardino , Barbosa Luana do Nascimento Silva , Farias Maria Raquel da Silva , Farias Emerson dos Santos Ferreira , Paulino Sivaldo Soares , Santos Neto Antônio Lucrécio dos , Pavinato Paulo Sergio , Silva José Vieira , Santos Valdevan Rosendo dos
Published: October 2024

Cover crops have gained attention due to their potential benefits for the soil and physiological performance of subsequent crops. This study aimed to evaluate the physiological and productive aspects of maize grown in succession to cover crops in northeastern Brazil. A randomized complete block design with four repetitions was employed, in which the treatments consisted of the following cover crops: sunn hemp, spectabilis, pigeon pea, Brachiaria sp., jack bean, millet, and fallow. Physiological aspects and production components of maize were evaluated at the tasseling (VT) and smooth grain (R3) phenological stages. Millet cover increased carotenoid content in maize leaves by up to 78% at R3. Maize grown after pigeon pea, millet, and Brachiaria sp. showed up to 42% greater CO2 assimilation efficiency compared to jack bean. Carboxylation efficiency increased by up to 34% in maize grown after millet and Brachiaria sp., while water use efficiency improved by up to 76% in maize after sunn hemp and pigeon pea at R3. Sunn hemp, spectabilis, and jack bean reduced soil temperature by 2 °C compared to fallow. The highest maize yield was observed after jack bean, with an 8% increase over fallow. These findings demonstrate the benefits of incorporating cover crops into maize cultivation systems in the semi-arid region of Brazil.
A Study on the Impact of Digital Transformation on Enterprise Performance: The Mediating Role of Dual Innovation and the Moderating Role of Management Power
Authors: Wang Xiyu , Yan Ying
Published: October 2024

Digital transformation and technological innovation have an essential impact on enterprise performance, and clarifying the relationship between the two has become a real problem that enterprise management needs to solve. This study utilizes data from China’s listed enterprises between 2012 and 2022 to comprehensively analyze the influence and mechanism of digital transformation on enterprise performance. The findings indicate the following: (1) Digital transformation can effectively promote improving enterprise performance and dual innovation levels. (2) Digital transformation positively impacts enterprise performance primarily through enhanced exploitative innovation, exploratory innovation, dual innovation balance, and complementarity. (3) Managerial power positively moderates the promotion effect of digital transformation on enterprise performance. (4) The impact of digital transformation on enterprise performance is more significant in the eastern and central regions, high-tech industries, and enterprises with good profitability. Based on these results, enterprises are suggested to accelerate digital transformation, fully tap the synergistic driving effect of dual innovation, strengthen the management’s cognition and leadership of digital transformation, and implement dynamic and differentiated digital transformation strategy based on region, industry, and their own characteristics.
Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques
Authors: El-Sayed Ehab Issa , ElSayed Salah K. , Alsharef Mohammad
Published: October 2024

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.
Circular Economy Practices in Biomass-Fired Power Plants in Brazil: An Assessment Using the ReSOLVE Framework
Authors: Pereira Juliana Araújo , Simioni Flávio José , Soares Juliana Ferreira , Rosário Jeane de Almeida do , Bertol Eduardo , Padilha Souza Fabio Murilo , Coelho Junior Luiz Moreira
Published: October 2024

This study aimed to identify the adoption of circular economy (CE) practices at thermoelectric power plants (TPPs) fueled by forest biomass in Brazil and determine the degree of implementation; social, environmental, and economic impacts; motivations; challenges; and facilitating factors for the adoption of such practices. Data were collected through a questionnaire applied to a sample of 32 TPPs in Brazil employing the ReSOLVE framework from October 2023 to January 2024. The data were analyzed using descriptive statistics, with the assignment of scores, and principal component analysis. The results indicate that optimization practices are the most widespread (6.7) and sharing practices were the least adopted (4.0). The greatest motivators for adopting CE practices are promoting sustainability (9.5) and enhancing corporate image (9.5). One of the most relevant positive impacts of such practices is the improvement in social relations between companies and the community (9.1). A major challenge to CE adoption is government neglect, whereas certification systems constitute one of the major facilitators (with 20 indications). This study provides indicators for decision-makers in the private sector and public managers interested in promoting sustainable practices in the renewable energy industry.
Evolving National Identity in Korea (2012–2021) and Implications for Social Sustainability
Authors: Kang Woonsun
Published: October 2024

National identity in Korea has undergone significant changes in recent years, reflecting broader shifts in social and political landscapes. Traditionally rooted in ethnocultural and ethnic frameworks, the concept of national identity is increasingly influenced by civic and pluralistic values, reflecting Korea’s growing diversity and globalization. This study aims to explore the transformation of national identity in Korea between 2012 and 2021 and its implications for social sustainability, with particular attention paid to Sustainable Development Goal 16. Using latent class analysis, this research examines survey data collected from Korean adults over the specified period. The analysis reveals a decline in ethnocultural and ethnic identities, with a corresponding rise in civic-oriented and flexible identity groups, such as Non-Territorial Ethnocultural Civic, Pluralistic, and Territorial-Cultural groups. These new categories illustrate a more complex understanding of national belonging, shaped by multiple dimensions. The results suggest that this evolving identity landscape promotes greater inclusivity and adaptability, crucial for fostering long-term social cohesion in a diverse society. The study highlights the need for policies, educational reforms, and media narratives that embrace this dynamic understanding of national identity. The findings offer important insights for policymakers and educators as Korea continues to navigate the challenges of diversity and globalization, ensuring a sustainable social future.
Exploring the Association Between Artificial Intelligence Management and Green Innovation: Expanding the Research Field for Sustainable Outcomes
Authors: Du Jiaxing , Cai Han , Jin Xiu
Published: October 2024

Green innovation is essential for achieving sustainable development goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production), as it fosters environmental and social benefits while also creating new economic opportunities. Despite previous studies actively conducting empirical analyses on green innovation, research on guiding the green innovation process through artificial intelligence remains scarce. This study aims to explore key variables that affect green innovation, thereby promoting the sustainable development of organizations, and to investigate the incentive mechanisms behind it. By uncovering the internal and external factors that drive green innovation and their interactions, we can better understand and optimize the process of fostering green innovation. Unlike previous studies, this research not only explores variables and verifies main effects but also provides and validates a research model related to the occurrence of green innovation. Additionally, this study determines the extent to which artificial intelligence management influences green innovation through knowledge sharing and examines whether an innovative culture moderates the impact of artificial intelligence management and knowledge sharing on green innovation, as well as whether it moderates the mediating effect of knowledge sharing within the model. Therefore, this study collected data from 331 adult employees of SMEs across 23 provinces, cities, and districts in China and conducted empirical analyses, including confirmatory factor analysis (CFA) and reliability analysis. The results indicate that artificial intelligence management directly affects green innovation and indirectly affects it through the partial mediating role of knowledge sharing. Furthermore, an innovative culture significantly moderates the mediating role of knowledge sharing between artificial intelligence management and green innovation. In exploring the variables of green innovation, this study established an adjusted mediating model and verified its significance. In summary, the causal relationship between artificial intelligence management and green innovation, as demonstrated through this process, contributes to the expansion of the research field and the advancement of SDGs, specifically Goals 9 and 12. The study’s findings highlight the importance of integrating artificial intelligence management to enhance green innovation, which is vital for the sustainable development and economic growth outlined in the SDGs.
Sovereign ESG and Foreign Direct Investment in the GCC: The Amplifying Role of Trade Openness in Economic Diversification
Authors: Bannour Souad , Abdelkawy Nagwa Amin
Published: October 2024

This study examines the relationship between Sovereign Environmental, Social, and Governance (SESG) initiatives and foreign direct investment (FDI) within the context of the Gulf Cooperation Council (GCC) countries from 2000 to 2022. The research explores how national ESG performance influences the attractiveness of these countries to foreign investors, with a particular focus on the moderating role of trade openness. Using a comprehensive panel data analysis, the study finds that strong SESG frameworks are positively correlated with higher FDI inflows, with trade openness amplifying the positive impact of SESG. Additionally, the use of Instrumental Variables (IV) estimation addresses endogeneity concerns, further confirming the robustness of the results. Moreover, the analysis reveals that trade openness amplifies the positive impact of SESG on FDI, suggesting that GCC countries with liberal trade policies and robust ESG practices are more successful in attracting sustainable and responsible investments. These findings contribute to the growing body of literature on ESG and FDI, offering valuable insights for policymakers seeking to enhance economic growth through strategic investments in sustainability and governance. The findings offer valuable insights for policymakers in aligning SESG frameworks with global ESG standards and leveraging global liquidity conditions to enhance FDI inflows, particularly in light of ongoing economic diversification efforts in the GCC.