bluuebunny/crossref_metadata_2025 huggingface dataset:
The Complex Role of Gut Microbiota in Systemic Lupus Erythematosus and Lupus Nephritis: From Pathogenetic Factor to Therapeutic Target
Authors: Parodi Emanuele , Novi Marialuisa , Bottino Paolo , La Porta Edoardo , Merlotti Guido , Castello Luigi Mario , Gotta Franca , Rocchetti Andrea , Quaglia Marco
Published: February 2025

The role of gut microbiota (GM) and intestinal dysbiosis in triggering the onset and/or modulating the severity and progression of lupus nephritis (LN) has been the object of intense research over the last few years. Some alterations at the phyla level, such as the abundance of Proteobacteria and reduction in Firmicutes/Bacteroidetes (F/B) ratio and in α-diversity have been consistently reported in systemic lupus erythematosus (SLE), whereas a more specific role has been ascribed to some species (Bacteroides thetaiotaomicron and Ruminococcus gnavus) in LN. Underlying mechanisms include microbial translocation through a “leaky gut” and subsequent molecular mimicry, immune dysregulation (alteration of IFNγ levels and of balance between Treg and Th17 subsets), and epigenetic interactions. Levels of bacterial metabolites, such as butyrate and other short-chain fatty acids (SCFAs), appear to play a key role in modulating LN. Beyond bacterial components of GM, virome and mycobiome are also increasingly recognized as important players in the modulation of an immune response. On the other hand, microbiota-based therapy appears promising and includes diet, prebiotics, probiotics, symbiotics, and fecal microbiota transplantation (FMT). The modulation of microbiota could correct critical alterations, such as F/B ratio and Treg/Th17 imbalance, and blunt production of autoantibodies and renal damage. Despite current limits, GM is emerging as a powerful environmental factor that could be harnessed to interfere with key mechanisms leading to SLE, preventing flares and organ damage, including LN. The aim of this review is to provide a state-of-the-art analysis of the role of GM in triggering and modulating SLE and LN on the one hand, while exploring possible therapeutic manipulation of GM to control the disease on the other hand.
The Effects of Milk and Posterior Intestinal Microorganisms on the Lactation Performance of Dual-Purpose Cattle (Bos taurus) Revealed by 16S rRNA Sequencing
Authors: Wang Weitao , Ma Shengchao , Wang Dan , Xu Lei , Zhang Menghua , Yan Mengjie , Ma Kailun , Hu Zexu , Shang Yanyan , Wei Jiangjiang , Huang Xixia
Published: February 2025

The aim of this research was to employ 16S rRNA high-throughput sequencing to thoroughly explore the interplay between milk and hindgut microbial communities and the effects of microorganisms in milk and the hindgut on the dairy quality of XJBC and CSC. In this study, 96 XJBC milk samples, 94 XJBC hindgut samples, 100 CSC milk samples, and 93 CSC hindgut samples were collected for microbial community analysis. The 16S rRNA sequencing data revealed that the microbial species richness in the milk of CSC exceeded that of XJBC, whereas the opposite was true for the hindgut microbial communities. A chi-square test was conducted using SPSS 19.0. The milk and posterior intestinal microbiota between individuals were analyzed with the Pearson chi-square test, maximum likelihood ratio, and Fisher’s exact test. Nongenetic factors substantially influenced microbial community dynamics in both milk and the hindgut. In the milk of dairy cows, a significant negative correlation was observed between one genus and milk protein production. Nine genera were significantly negatively correlated with milk fat production, whereas one genus was positively correlated. Additionally, six genera were negatively correlated with lactose production, and two genera exhibited positive correlations. Notably, Phascolarctobacterium and Turicibacter were identified as genera originating from the hindgut, which led to reduced milk quality. In the hindgut microbial community of dairy cows, seven genera were significantly negatively associated with milk fat production, whereas one genus was positively associated with milk fat production. These findings indicate that certain mammary microorganisms may migrate from the hindgut, either endogenously or exogenously, disrupting the equilibrium of the mammary microbial community in dairy cows and potentially leading to inflammation. By enhancing feeding conditions and standardizing production practices, the invasion of harmful flora into mammary tissues can be minimized, reducing the risk of inflammation and thereby preserving the health of dairy cows and enhancing milk quality.
Pandemic Events Caused by Bacteria Throughout Human History and the Risks of Antimicrobial Resistance Today
Authors: Souza Pedro Filho Noronha , Filho Nicholas Silva dos Santos , Mororó João Lucas Timbó , Brito Daiane Maria da Silva , da Lima Ana Beatriz , Mesquita Felipe Pantoja , Montenegro Raquel Carvalho
Published: February 2025

During human history, many pandemic events have threatened and taken many human lives over the years. The deadliest outbreaks were caused by bacteria such as Yersinia pestis. Nowadays, antimicrobial resistance (AMR) in bacteria is a huge problem for the public worldwide, threatening and taking many lives each year. The present work aimed to gather current evidence published in scientific literature that addresses AMR risks. A literature review was conducted using the following descriptors: antimicrobial resistance, AMR, bacteria, and Boolean operators. The results showed that antimicrobial-resistant genes and antibiotic-resistant bacteria in organisms cause critical infectious diseases and are responsible for the infections caused by antibiotic-resistant bacteria (ARB). This review emphasizes the importance of this topic. It sheds light on the risk of reemerging infections and their relationship with AMR. In addition, it discusses the mechanisms and actions of antibiotics and the mechanisms behind the development of resistance by bacteria, focusing on demonstrating the importance of the search for new drugs, for which research involving peptides is fundamental.
Xanthone Dimers in Angiosperms, Fungi, Lichens: Comprehensive Review of Their Sources, Structures, and Pharmacological Properties
Authors: Shi Fengzhi , Fan Min , Li Haifeng , Li Shiwei , Wang Shuang
Published: February 2025

Xanthone dimers, a distinctive class of natural metabolites renowned for their unique structures, are abundantly present in a diverse array of angiosperms, fungi, and lichens. These compounds not only exhibit remarkable diversity but also possess a broad spectrum of biological activities. In this comprehensive review spanning from 1966 to 2024, we synthesized the relevant literature to delve into the natural occurrence, biological potency, molecular structure and chemical diversity of xanthone dimers. The aim of this review is to serve as an insightful reference point for future scientific inquiries into xanthone dimers and their potential applications.
A Molecular Modeling Study on the Propagation in Free Radical Chain Oxidation of (B)PEI
Authors: Buijs Wim
Published: February 2025

Air oxidation of PEI is a Free Radical Chain Autoxidation process, described as a process following the Basic Autoxidation Scheme with Initiation, Propagation and Termination as discriminating steps. Molecular Modeling was able to identify the most important propagation reactions. HO2(d) is the most likely candidate as the main oxidation chain carrying radical. α-H-abstraction from PEI α-amino hydroperoxides by HO2(d), leading to amide PEI repeat units and eventually to HO2(d) again, is the first step in Propagation. Apart from well-know propagation reactions, the reaction of PEI α-amino CH(d) radicals with H2O2 is of major importance, too, with an estimated contribution of ~50% to Propagation. Furthermore, it provides an explanation for the formation of NH3 and various imine PEI repeat units. PEI α-amino alkoxy radicals might contribute to some extent to Propagation and can lead to chain breaks in PEI and the formation of CO2. Amide and imine PEI repeat units contribute to ~90% of the fully oxidized PEI.
Low-Temperature Solution Combustion-Synthesized CuSNanoparticulated Functional Thin Films: Structural and Optoelectronic Characterization Studies
Authors: Papadas Ioannis T. , Pozov Sergey M. , Hamilton Iain , Sims Marc , Vamvasakis Ioannis , Ioakeimidis Apostolos , Armatas Gerasimos S. , Bradley Donal D. C. , Choulis Stelios A.
Published: February 2025

In this paper, we present a one-step low-temperature solution combustion synthesis (SCS) of CuS nanoparticulated functional films processed via a simple blade-coating technique. This SCS route uses thiourea as a fuel and sulfur source, combined with copper(II) nitrate as an oxidant and a cupric ion source in an aprotic solvent such as non-toxic DMSO. It is hereby shown that the proposed SCS process formed a stable and completely dissolved molecular ink of thiourea and copper ion complexes, crucial for obtaining the pure crystalline phase of CuS nanoparticles (NPs). The CuS was formed by calcination at a low temperature of 200 °C during a brief annealing time of 20 min, to promote the synthesis of ~10 nm CuS NPs. The obtained CuS NPs were thoroughly analyzed in terms of structure and optoelectronic properties using various analytic and spectroscopic techniques, including TGA, XRD, FE-SEM, EDS, AFM, and four-point probe electrical resistivity measurements. The functionality of the prepared CuS nanoparticulated interlayers was evaluated by incorporating them as a hole injection layer (HIL) in Super Yellow (SY) organic light-emitting diodes (OLEDs).
Characterization of Fungal Pathogens Causing Blueberry Fruit Rot Disease in China
Authors: Zhou Yueyan , Zhang Wei , Wu Linna , Chen Pengzhao , Li Xinghong , Wen Guangqin , Tangtrakulwanich Khanobporn , Chethana Kandawatte Wedaralalage Thilini , Al-Otibi Fatimah , Hyde Kevin D. , Yan Jiye
Published: February 2025

Blueberry has been a burgeoning fruit in China in recent years, but its perishable nature places a constant strain on industrial development. To determine the pathogens infecting blueberry fruits, diseased samples were collected from Guizhou and Fujian Provinces. Isolates from the samples were identified by morphological characterization and phylogenetic analyses. Pathogenicity assays were conducted on fresh blueberry fruits using spore suspensions. Sixteen isolates were identified as seven species, namely, Botryosphaeria dothidea, Botrytis cinerea, Cladosporium guizhouense, Colletotrichum fioriniae, Diaporthe anacardii, Fusarium annulatum, and Neopestalotiopsis surinamensis, and their pathogenicity on blueberry fruits were confirmed following Koch’s postulates. The current study reported Cladosporium guizhouense, Fusarium annulatum, and Neopestalotiopsis surinamensis for the first time on blueberry. The study (1) demonstrated that fruit rot disease results from a mixed infection of multiple pathogens; and (2) expanded the understanding of causal agents of blueberry fruit rot during the growth stage, highlighting their potential as latent pathogens that contribute to post-harvest losses. Relevant results provide a reference for the etiological research and disease management in blueberry fruit diseases.
Inhibitory Effect of Nano-Formulated Extract of Passiflora incarnata on Dalton’s Lymphoma Ascites-Bearing Swiss albino Mice
Authors: Deepika Balasubramanian , Janani Gopalarethinam , Jessy Mercy Devadass , Udayakumar Saranya , Girigoswami Agnishwar , Girigoswami Koyeli
Published: February 2025

Background/Objectives: This study explored the antitumor effect of Passiflora incarnata leaves’ nanoformulation (N-EEP) in fibroblasts, A375 cell lines, and in vivo using Dalton’s lymphoma ascites (DLA)-bearing mice. Methods: N-EEP treatment could significantly slow scratch closing in A375 cells compared to in the extract itself (EEP). Results: The hemolytic assay showed that N-EEP had less than 2% hemolysis, making the formulation highly biocompatible. In vivo N-EEP administration delayed the tumor growth rate, reduced weight gain, and increased the tumor-bearing mice’s life span. Furthermore, the ascitic cells were aspirated from the tumor and investigated for various gene expressions. The tumor suppressor gene p53, which plays a significant role in the mitochondrial-mediated apoptosis pathway, was found to be elevated in animals treated with N-EEP. We assessed the cytotoxicity of isolated DLA cells from induced mice using both the trypan blue and MTT assays, while apoptotic studies were conducted using Hoechst staining. Results from the trypan blue and MTT assays indicated that nearly 80% of the cells were killed by N-EEP treatment (200 μg/mL). Additionally, apoptosis, characterized by condensed nuclei, was observed after N-EEP treatment, confirming that one of the modes of cell death was caspase-dependent apoptosis. Conclusions: Our study suggests that N-EEP delayed the growth of DLA by upregulating p53 gene expression and inducing apoptosis.
The Potential for Trypsin Inhibitor Expression in Leaves to Convey Herbivory Deterrence in Soybean
Authors: Birdwell Audrey E. , Brown Sebe A. , D’Angelo Gino J. , Mazarei Mitra , Stewart Charles Neal
Published: February 2025

Soybean (Glycine max) is the most widely grown legume crop in the world, providing important economic value. Pest herbivory damage by insects and mammalian wildlife, in particular the white-tailed deer (Odocoileus virginianus), limits yields in soybean. Incorporating trypsin inhibitors (TIs) as plant protectant against herbivory pests has been of interest. We previously showed that the overexpression of soybean TIs in soybean conferred insect deterrence under greenhouse experiments. In this study, we examined the potential of transgenic TI-overexpressing lines in deterring insects under field conditions at Knoxville, Tennessee. Our results indicate that the overexpression of TI could lead to a significant reduction in leaf defoliation of the transgenic compared to non-transgenic lines without negatively impacting plant growth and yield under field conditions. Furthermore, we extended our study by comprehensive evaluation of these transgenic plants against the white-tailed deer herbivory in a separate field setting at Jackson, Tennessee, and with controlled deer feeding experiments. No significant differences in growth characteristics were found between transgenic and non-transgenic lines under field conditions. There were also no significant differences in deer deterrence between transgenic and non-transgenic lines in ambient deer herbivory field or controlled deer feeding trials. Our study provides further insights into more exploration of the role of TI genes in pest control in this economically important crop.
Identification of Ethylene Response Factors in Wheat Reveals That TaERF16-B Contributes to Salt Tolerance
Authors: Zhang Lei , Wei Aili , Chen Jiating , Wu Lijuan , Li Tian , Qiao Linyi
Published: February 2025

Soil salinization is a major abiotic stressor that significantly reduces wheat yield. Identifying novel salt-tolerance genes and integrating them into wheat breeding programs can enhance wheat productivity in saline soils. Ethylene response factor (ERF) plays an important role in plant response to salt stress, and thus far, four wheat ERF genes have been identified to be involved in salt stress response. To systematically identify salt tolerance-related ERF genes in wheat, in this study, 213 ERF sequences were isolated from the whole genome of common wheat and classified into 54 members based on subgenome homology, named TaERF1 to TaERF54. Transcriptome sequencing results showed different expression patterns of TaERF members in leaves after 1, 6, 24, and 48 h of NaCl treatment. Based on association analysis, nine TaERF genes were correlated with the leaf salt injury index. Among them, five SNPs of TaERF16-B formed two haplotypes: Hap1 and Hap2. RT-qPCR results showed that the expression level of TaERF16-B was significantly higher in Hap2-typed germplasms than that in Hap1-typed germplasms after 1 and 6 h of NaCl treatment. A Kompetitive Allele-Specific PCR marker K52 was developed for genotyping TaERF16-B haplotypes, which further confirmed the significant correlation between TaERF16-B and salt tolerance-related phenotypes in mapping population and wheat germplasms. This study provides new genes and molecular markers for improving salt tolerance in wheat.
The Structural Design of a New Graftable Antioxidant and the Theoretical Study of Its Role in the Cross-Linking Reaction Process of Polyethylene
Authors: Du Yang , Zhang Hui , Deng Chi , Du Xia , Shang Yan , Wang Xuan , Chen Qingguo , Li Zesheng
Published: February 2025

Cross-linked polyethylene (XLPE) insulation is used in most advanced power cable technology. However, in traditional cross-linking, the conductivity of the cross-linking system sharply increases due to the presence of additives (antioxidants and cross-linked agents). Therefore, reducing the number of antioxidants to further reduce conductivity is a very promising method. The structural design of a new dual-functional antioxidant 5-allyloxy-2-hydroxyl-3-tert-butylbenzophenone (5ATB) has been established. The antioxidant behavior and grafting reaction of 5ATB after photocatalysis under ultraviolet (UV) conditions were further studied using density functional theory (DFT). The reaction potential energy information of the six reaction channels at the B3LYP/6-311+G(d,p) level were obtained. The calculation results indicated that the reaction Gibbs energy barrier of 5ATB with O2 is approximately 0.48 eV lower than that of the polyethylene chain with O2 to achieve an anti-oxidative effect. Furthermore, the reaction-active site of 5ATB accepting H is located on the C of CH2 in a C=C double bond, as demonstrated by an analysis of NBO charge populations. The proposed mechanism has the potential to further expand the design concept of insulation materials for advanced future power cables.
Drilling Overflow Diagnosis Based on the Fusion of Physical and Intelligent Algorithms
Authors: Shi Yadong , Hao Hongda , Liu Rentong , Deng Song , Li Chaowei , Li Qiu , Liu Chengguo
Published: February 2025

The diagnosis of overflow risk has always been an important area of research in drilling operations in the field of oil and gas engineering. In the face of the limitations and lag of traditional overflow diagnosis methods, the practical application effect of existing models and methods is not obvious, and there is no integration of the physical model and the intelligent algorithm model for overflow diagnosis, this paper proposes a method of adaptive weight fusion of physical model and intelligent algorithm model diagnosis results. Based on the fusion of the physical model and the intelligent algorithm model, an overflow diagnosis model of managed pressure drilling is established. The research results show that the fusion model in this paper can combine the accuracy weight of the physical model and the intelligent algorithm model for the intelligent diagnosis of overflow risk, which improves the mechanization and interpretability of the model and diagnosis results while ensuring accuracy and efficiency. And the intelligent algorithm model used in the fusion model is superior to other algorithm models. The overflow diagnosis accuracy of the fusion model on the test set samples reaches more than 98%, and the accuracy of the validation set is 94.25%. The content of this study provides guidance for drilling overflow diagnosis and model fusion methods.
The Return of the Soul—The Role of Religion in Regulating Social Life
Authors: Birher Nándor
Published: February 2025

The article explores the role of religion in shaping societal norms, arguing that religion, alongside law and ethics, plays a critical role in regulating social life. It emphasizes the interdependence of various regulatory systems—religious, ethical, legal, and modern standards—and advocates for reintegrating spirituality into scientific discourse on societal regulation. The study highlights the limitations of purely legalistic approaches and calls for a renewed focus on ethical and religious principles, particularly in light of global crises such as environmental degradation and social inequality. It also discusses the unique position of Christianity in balancing these regulatory frameworks and promoting a harmonious coexistence through values such as love and solidarity.
What Are We Talking About When We Say ‘Hope’? Theological Contributions and Challenges for Christianity Today
Authors: Vilas Boas Susana
Published: February 2025

The concept of hope has varied in meaning from ancient Greece to the present day. Often understood as an expectation or an illusion, hope seems to greatly determine the way human beings live and think about their existence. It is precisely in this context that the questioning of the Christian faith and particularly eschatology arises. The aim of this article is to analyse the concept of Hope, distinguishing it from concepts such as expectations and hopes, and placing it in the sphere of Christian life and theology. In this context, realities such as immortality, eternity, and death will be explored in order to understand if there is room for a Hope that goes beyond empirical certainty or capricious and illusory desire.
The Sciamachy of Modern Sanity: The Interface Between Classical Philosophy and Mental Health Theory and Practice in Twenty-First-Century China
Authors: Zandbergen Robbert
Published: February 2025

Recent years have seen a surge of interest in the interface between mental health and areas formerly viewed as unrelated (the arts, philosophy and religion). In this article, I zoom in on the ways in which central texts and insights from the Daoist corpus can open a window into modern mental health issues. In its critique of the Confucian insistence on ‘mental hygiene’, the Daoist position vouches for an important level of flexibility conducive to what we now refer to as mental health. Importantly, this removes the stigma on so-called neurodiversity by placing it on a spectrum, away from the strict Confucian emphasis on order and harmony. As such, texts like the Zhuangzi discussed below have an uncanny relevance for any serious investigation of mental health and illness in twenty-first-century China.
Deep Learning for Automatic Detection of Volcanic and Earthquake-Related InSAR Deformation
Authors: Liu Xu , Zhang Yingfeng , Shan Xinjian , Wang Zhenjie , Gong Wenyu , Zhang Guohong
Published: February 2025

Interferometric synthetic aperture radar (InSAR) technology plays a crucial role in monitoring surface deformation and has become widely used in volcanic and earthquake research. With the rapid advancement of satellite technology, InSAR now generates vast volumes of deformation data. Deep learning has revolutionized data analysis, offering exceptional capabilities for processing large datasets. Leveraging these advancements, automatic detection of volcanic and earthquake deformation from extensive InSAR datasets has emerged as a major research focus. In this paper, we first introduce several representative deep learning architectures commonly used in InSAR data analysis, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and Transformer networks. Each architecture offers unique advantages for addressing the challenges of InSAR data. We then systematically review recent progress in the automatic detection and identification of volcanic and earthquake deformation signals from InSAR images using deep learning techniques. This review highlights two key aspects: the design of network architectures and the methodologies for constructing datasets. Finally, we discuss the challenges in automatic detection and propose potential solutions. This study aims to provide a comprehensive overview of the current applications of deep learning for extracting InSAR deformation features, with a particular focus on earthquake and volcanic monitoring.
Robust Sidelobe Control for Adaptive Beamformers Against Array Imperfections via Subspace Approximation-Based Optimization
Authors: Zou Yang , Ding Zhoupeng , Li Hongtao , Chen Shengyao , Tian Sirui , He Jin
Published: February 2025

Conventional adaptive beamformers usually suffer from serious performance degradation when the receive array is imperfect and unknown sporadic interferences appear. To enhance robustness against array imperfections and simultaneously suppress sporadic interferences, this paper studies robust adaptive beamforming (RAB) with accurate sidelobe level (SLL) control, where the imperfect array steering vector (SV) is expressed as a spherical uncertainty set. Under the maximum signal-to-interference-plus-noise ratio (SINR) criterion and robust SLL constraints, we formulate the resultant RAB into a second-order cone programming problem, which is computationally prohibitive due to numerous robust quadratic SLL constraints. To tackle this issue, we provide a subspace approximation-based method to approximate the whole sidelobe space, thus replacing all robust SLL constraints with a single subspace constraint. Moreover, we leverage the Gauss–Legendre quadrature-based scheme to generate the sidelobe space in a computationally efficient manner. Additionally, we give an explicit approach for determining the norm upper bound of SV uncertainty sets under various imperfection scenarios, addressing the challenge of obtaining this upper bound in practice.Simulation results showed that the proposed subspace approximation-based RAB beamformer had a better SINR performance than typical counterparts and was much more computationally efficient.
Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM
Authors: Chen Tianxiao , Xu Datao , Zhou Zhifeng , Zhou Huiyu , Shao Shirui , Gu Yaodong
Published: February 2025

Traditional methods for collecting ground reaction forces (GRFs) mainly use lab force plates. Previous research broke this pattern by predicting GRFs with deep learning and data from IMUs like joint acceleration. Joint angle, as a geometric, is easier to collect than acceleration outdoors with cameras. LSTM is one of the deep learning models that have shown good performance in biomechanical studies. xLSTM, as an optimized version of LSTM, has not been used in biomechanical studies and no research has predicted GRFs during running solely using lower limb joint angles. This study collected lower-limb joint angle and vertical ground reaction force data at five speeds from 12 healthy male runners with Xsens sensors. Datasets including three joints and three planes were set as the inputs of four deep learning models for vertical-GRF prediction. CNN-xLSTM consistently performed best in the four deep learning models when different datasets were input (R2 = 0.909 ± 0.064, MAPE = 2.18 ± 0.09, rMSE = 0.061 ± 0.008), and the performance was at a relatively high level at the five speeds. The current findings may contribute to a new GRF measurement and provide a reference for future real-time motion detection and sport injury prediction.
Federated Learning Framework for Real-Time Activity and Context Monitoring Using Edge Devices
Authors: Alharbey Rania A. , Jamil Faisal
Published: February 2025

With the increasing need for effective elderly care solutions, this paper presents a novel federated learning-based system that uses smartphones as edge devices to monitor and enhance elderly care in real-time. In this system, elderly individuals carry smartphones equipped with Inertial Measurement Unit (IMU) sensors, including an accelerometer for activity recognition, a barometer for altitude detection, and a combination of the accelerometer, gyrometer, and magnetometer for location tracking. The smartphones continuously collect real-time data as the elderly individuals go about their daily routines. These data are processed locally on each device to train personalized models for activity recognition and contextual monitoring. The locally trained models are then sent to a federated server, where the FedAvg algorithm is used to aggregate model parameters, creating an improved global model. This aggregated model is subsequently distributed back to the smartphones, enhancing their activity recognition capabilities. In addition to model updates, information on the users’ location, altitude, and context is sent to the server to enable the continuous monitoring and tracking of the elderly. By integrating activity recognition with location and altitude data, the system provides a comprehensive framework for tracking and supporting the well-being of elderly individuals across diverse environments. This approach offers a scalable and efficient solution for elderly care, contributing to enhanced safety and overall quality of life.
Moving on to Greener Pastures? A Review of South Africa’s Housing Megaproject Literature
Authors: Lategan Louis , Fisher-Holloway Brian , Cilliers Juanee , Cilliers Sarel
Published: February 2025

South Africa is a leader in the scholarship on green urbanism in the Global South, but academic progress has not translated to broad implementation. Notably, government-subsidized housing projects have produced peripheral developments featuring low build quality, conventional gray infrastructure, and deficient socio-economic and environmental amenities. Declining delivery and increasing informal settlement spawned a 2014 shift to housing megaprojects to increase output and improve living conditions, socio-economic integration, and sustainability. The shift offered opportunities for a normative focus on greener development mirrored in the discourse surrounding project descriptions. Yet, the level of enactment has remained unclear. In reflecting on these points, this paper employs environmental justice as a theoretical framework and completes a comprehensive review of the academic literature on housing megaprojects and the depth of their greener development commitments. A three-phase, seven-stage review protocol retrieves the relevant literature, and bibliometric and qualitative content analyses identify publication trends and themes. Results indicate limited scholarship on new megaprojects with sporadic and superficial references to greener development, mostly reserved for higher-income segments and private developments. In response, this paper calls for more determined action to launch context-aware and just greener megaprojects and offers corresponding guidance for research and practice of value to South Africa and beyond.
A Hybrid Intuitionistic Fuzzy Entropy–BWM–WASPAS Approach for Supplier Selection in Shipbuilding Enterprises
Authors: Jiang Qiankun , Wang Haiyan
Published: February 2025

Supplier selection in the shipbuilding industry is a typical multicriteria group decision-making (MCGDM) problem, often characterized by significant uncertainty and fuzziness. To address this issue effectively, this paper proposes a novel integrated approach for supplier selection in shipbuilding enterprises by combining intuitionistic fuzzy sets (IFSs) with the weighted aggregated sum product assessment (WASPAS) method. The proposed method utilizes IFS operators alongside an innovative process for evaluating indicator weights. Initially, an intuitionistic fuzzy number approach is employed to obtain indicator data, which effectively captures the uncertainty of linguistic variables and ensures accurate reflection of real-world conditions. Subsequently, the indicator weights are evaluated by integrating subjective weights, derived through the best–worst method, with objective weights, calculated using an entropy-based approach, resulting in more balanced and realistic weight assignments. Subsequently, the WASPAS method is used to prioritize alternative suppliers, and a shipbuilding enterprise in Shanghai is taken as an example to verify the effectiveness of the model. In addition, to evaluate the stability of the proposed method, sensitivity analyses were performed for varying attribute values. The results demonstrate that the combination of subjective and objective weights enhances the stability of the method under varying attribute weights. Finally, a comparison with various existing methods based on intuitionistic fuzzy information proves that the proposed method exhibits certain advantages in solving the MCGDM problem under uncertain environments.
Adaptive Grey-Box Modelling for Energy-Efficient Building Retrofits: Case Studies in Denmark
Authors: Yang Yujie , Jradi Muhyiddine
Published: February 2025

Optimizing energy efficiency in existing buildings can yield substantial savings, though collecting the necessary data for energy modelling often poses challenges. This study developed a flexible, room-level framework for evaluating retrofit strategies using simplified energy models. The approach, based on the RC model, estimated parameters from readily available data such as solar radiation, indoor and outdoor temperatures, and heating system characteristics. The model was validated through case studies of an office and a daycare room in Denmark, guiding energy retrofit decisions. Results showed that adding roof insulation provided greater energy savings compared to wall insulation. A multi-objective optimization was employed to balance energy efficiency and thermal comfort, achieving a 6.58% reduction in energy demand during January while maintaining occupant comfort for 744 h. This framework not only facilitates building–energy retrofitting but also supports the development of digital twins and operational optimization, improving both energy performance and indoor environmental quality.
Analysis of Water Source Conservation Driving Factors Based on Machine Learning
Authors: Jia Yixuan , Zhang Zhe , Huang Chunhua , Xie Shuibo
Published: February 2025

This study focuses on the spatiotemporal dynamic changes in water retention capacity and the nonlinear research of its influencing factors. By using the InVEST model, the changing trends of water retention capacity in different regions and at various time scales were analyzed. Based on this, the results were further examined using the CatBoost model with SHAP (SHapley Additive exPlanations) analysis and PDP (Partial Dependence Plot) analysis. The results show the following: (1) From 2003 to 2023, the water conservation capacity first increased and then decreased, and spatially, the water conservation capacity of the mountainous area in the west of the Yiluo River Basin and Xionger Mountain in the middle part of the basin increased as a whole. At the same time, the forest land in the basin contributed more than 60% of the water conservation capacity. (2) Precipitation is the most significant driving factor for water conservation in the basin, and plant water content, soil type, and temperature are also the main driving factors for water conservation in the Yiluo River Basin. (3) The interaction between temperature and other influencing factors can significantly improve water conservation. This research not only provides scientific evidence for understanding the driving mechanisms of water conservation but also offers references for water resource management and ecological protection planning.
Evaluating Perceived Cultural Ecosystem Services in Urban Green Spaces Using Big Data and Machine Learning: Insights from Fragrance Hill Park in Beijing, China
Authors: Fu Lingbo , Fu Hongpeng , Xiong Chengyu
Published: February 2025

Cultural ecosystem services (CESs) are essential for the sustainable development and management of urban green spaces. However, there remains a gap in leveraging big data and unsupervised machine learning to comprehensively evaluate perceived CESs. This study introduces a hybrid research methodology integrating latent dirichlet allocation (LDA) and importance–performance analysis (IPA) to analyze 20,087 user-generated reviews of Fragrance Hill Park in Beijing from Meituan. The key findings are the following: (1) ten types of CESs were identified, including five related to personal well-being, four to public well-being, and one bridging both categories; (2) the most significant dimensions were “recreational activities”, “aesthetic appreciation”, “physical well-being”, and “mental well-being”; (3) users expressed positive sentiments toward “history and culture”, “mental well-being”, and “religious engagement”, while “social relations” received the most negative feedback; (4) IPA results highlight “recreational activities” and “aesthetic appreciation” as priority areas for improvement. This study provides a scalable, data-driven framework for evaluating CESs in urban green spaces. The insights gained can inform urban green space management and policy decisions to enhance user experiences and promote sustainable urban development.
Coal Clinker Ash Influence in Mineral Nitrogen Sorption and Leaching in Sandy Soil
Authors: Dube Agnes Aron , Abebe Meseret Gutema , Manneh Elizabeth , Tsubo Mitsuru , Sato Kuniaki , Nishihara Eiji
Published: February 2025

Global energy demands continue to make coal-fired power plants important; however, this has resulted in massive production of clinker ash (CA). The utilisation of CA to improve nutrient retention in agrarian soils is unclear. Nitrogen (N) leaching is one of the greatest limitations in crop production. This study assessed CA’s potential to improve N retention in sandy soil. CA was characterised, and an N sorption study and two-cycle column leaching experiments were conducted. Two CAs were used, with particle sizes of <3 mm (CA1) and <5 mm (CA2). The treatments included control (sandy soil only), CA110, CA120, CA210, and CA220, at 10 and 20% (w/w), respectively, and rice husk biochar (positive control) at 5% (w/w), replicated three times. The results showed that both CAs adsorbed NH4+-N and NO3−-N, with NH4+-N adsorption higher than NO3−-N. NO3−-N leaching was significantly inhibited by applying CA10 of both CAs from day 1 to day 20. NH4+-N retention by CA2 did not enhance the growth of maize seedlings; the same was observed in CA120, which had retained the highest N for the two cycles. CA successfully reduced N leaching and increased N retention in sandy soil.
Business Case for a Regional AI-Based Marketplace for Renewable Energies
Authors: Holzinger Jonas , Nagl Anna , Bozem Karlheinz , Lecon Carsten , Ensinger Andreas , Roessler Jannik , Neufeld Christina
Published: February 2025

The global energy sector is rapidly changing due to decentralization, renewable energy integration, and digitalization, challenging traditional energy business models. This paper explores a startup concept for an AI-assisted regional marketplace for renewable energy, specifically suited for small- and medium-sized enterprises (SMEs). Driven by advancements in artificial intelligence (AI), big data, and Internet of Things (IoT) technology, this marketplace enables efficient energy trading through real-time supply–demand matching with dynamic pricing. Decentralized energy systems, such as solar and wind power, offer benefits like enhanced energy security but also present challenges in balancing supply and demand due to volatility. This research develops and validates an AI-based pricing model to optimize regional energy consumption and incentivize efficient usage to support grid stability. Through a SWOT analysis, this study highlights the strengths, weaknesses, opportunities, and threats of such a platform. Findings indicate that, with scalability, the AI-driven marketplace could significantly support the energy transition by increasing renewable energy use and therefore reducing carbon emissions. This paper presents a viable, scalable solution for SMEs aiming to participate in a resilient, sustainable, and localized energy market.
The Role of Pro-Ecological Packaging in Shaping Purchase Intentions and Brand Image in the Food Sector: An Experimental Study
Authors: Siuda Dagna , Grębosz-Krawczyk Magdalena
Published: February 2025

The main objective of this paper is to assess the influence of claims suggesting the eco-friendly nature of products present on food product packaging, consumers’ purchase intentions, and brand image. To achieve this, an experimental study was conducted in which participants were presented with packaging prototypes to explore their purchase preferences and attitudes toward the brand. The study involved two types of food products—apple juice and a box of chocolates. Some packaging prototypes featured pro-environmental attributes, while others did not. The results confirm the positive impact of pro-environmental packaging elements on consumers’ purchase intentions and brand perception. “Eco-friendly” brands were perceived as high-quality and more appealing. In both product categories, the majority of respondents preferred brands incorporating pro-environmental elements on packaging. These findings highlight the potential benefits for brand owners of integrating sustainability-focused content into packaging, which can serve as a competitive advantage in the food market.
A Lightweight PCB Defect Detection Algorithm Based on Improved YOLOv8-PCB
Authors: Wang Jianan , Xie Xin , Liu Guoying , Wu Liang
Published: February 2025

Tackling the widespread problems of inaccuracies, slow detection speed, and poor adaptability in small object defect detection on PCB circuits, this study suggests a lightweight printed circuit board surface defect identification algorithm, building upon an improved YOLOv8-PCB. This algorithm first introduces the C2f_SHSA attention mechanism in the backbone network, which unites the merits of channel attention and spatial attention, facilitating an efficient fusion of local and global features in a lightweight manner, thereby enhancing the model’s identification preciseness for small defects. Subsequently, in the neck network, the C2f_IdentityFormer structure, which combines the C2f structure with the IdentityFormer structure, supplants the initial C2f structure. This enhancement improves the model’s sensitivity to subtle features and further optimizes the effect of feature fusion. Eventually, the PIoU is presented to enhance the model’s adaptability to small, complex PCB defects with varying sizes and shapes, while also accelerating the mode’s convergence speed. Experimental outcomes reveal that the improved YOLOv8-PCB algorithm displays remarkable performance in the PCB dataset, with a Recall rate of 94.0%, a mean Average Precision (mAP) of 96.1%, and an F1 score of 94.35%. Moreover, the model’s weight size is only 5.2 MB. Compared to the YOLOv8n baseline model, the Recall rate has a 3.6% improvement, the mAP is raised by 1.8%, and the F1 score is enhanced by 1.9%, while the model’s weight is reduced by 17.46%. The enhancements in performance metrics confirm that the improved algorithm not only fulfills the requirements for efficient and real-time detection in PCB surface defect identification tasks but is also better suited for deployment and operation on edge devices.
Disinfection of Secondary Urban Wastewater Using Hydrogen Peroxide Combined with UV/Visible Radiation: Effect of Operating Conditions and Assessment of Microorganism Competition
Authors: Gomes Ana L. R. , Ribeirinho-Soares Sara , Madeira Luis M. , Nunes Olga C. , Rodrigues Carmen S. D.
Published: February 2025

The growing and unprecedented water crisis leads to the need to find alternative water resources, and the reuse of treated urban wastewater is an excellent approach. Accordingly, in this work, the disinfection of a secondary effluent (W) discharged from a wastewater treatment plant (WWTP) by hydrogen peroxide combined with radiation (H2O2+UV/visible) was studied with the aim of obtaining treated water that can be reused. Firstly, the effect of hydrogen peroxide alone, radiation per se and the combined H2O2+UV/Visible process in the inactivation of enterobacteria were assessed. It was found that the oxidant alone is not efficient; the maximum inactivation is achieved when the oxidant and radiation are used simultaneously. For the first time, the effect of some operational parameters, namely the hydrogen peroxide concentration (between 50 and 125 mg/L), initial pH (from 5.0 to 7.0), temperature (between 15 and 25 °C), and radiation intensity (100 to 500 W/m2), on the efficiency of the disinfection process was assessed. When the process was carried out under the best operating conditions found ([H2O2] = 75 mg/L, pH = 5.0, T = 25 °C, and UV/visible light with I = 500 W/m2), total enterobacteria and total heterotrophs were inactivated and the abundance of the 16S rRNA, blaTEM, qnrS, and intl1 genes was reduced. The cultivable microorganisms grew again after 3 days of storing the treated wastewater (TW), making it impossible to reuse such effluent after storage. Therefore, the potential capacity of a diverse bacterial community present in river water to inhibit the regrowth of potentially harmful bacteria present in the urban secondary wastewater after the application of the treatment process was also evaluated. To the authors’ knowledge, this has never been studied before. For this purpose, the TW was diluted with river water (R) at a volumetric percentage of 50/50—sample R+TW. It was found that, after storage, only the total heterotrophs grew, while the abundance of the targeted genes remained practically constant. The R+TW sample after storage met the legal limits for reuse in urban and agricultural applications. The results of this study suggest that the combination of the H2O2+UV/visible radiation treatment with dilution of the final treated effluent with natural surface water can contribute to reducing the burden of water scarcity.
Exploring Factors Influencing E-Bike Adoption Among Filipino Commuters: An Integrated Diffusion of Innovation and Technology Acceptance Model
Authors: Gumasing Ma. Janice J.
Published: February 2025

This study examines the factors influencing the behavioral intention and usage behavior of e-bikes in Metro Manila and the National Capital Region (NCR) using constructs from the Diffusion of Innovation (DOI) theory, the Technology Acceptance Model (TAM), and external variables such as government policies, infrastructure, social influence, and public awareness. A cross-sectional survey of 340 respondents was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to explore the relationships among these constructs. The results indicate that perceived usefulness (PU), perceived ease of use (PEOU), social influence, and public awareness significantly affect behavioral intention, which strongly predicts e-bike usage behavior. Infrastructure also positively impacts ease of use, while trialability enhances perceived usefulness. However, the relationships between government policies and perceived usefulness, as well as between trialability and ease of use, were insignificant, reflecting contextual limitations in the Philippine setting. The findings highlight the importance of behavioral intention as a primary driver of e-bike adoption. To address barriers, practical recommendations include expanding bike lanes, implementing e-bike sharing systems, strengthening government incentives, and launching public awareness campaigns that highlight the benefits of e-bikes, such as cost savings and environmental sustainability. This study also contributes to theory by integrating DOI and TAM frameworks, offering a comprehensive understanding of e-bike adoption in developing urban settings. These insights provide actionable strategies for policymakers, manufacturers, and stakeholders to promote sustainable transportation in the Philippines and similar contexts.