bluuebunny/crossref_metadata_2025 huggingface dataset:
Community-Engaged Approach to Improve Food Access and Consumption of Fruits and Vegetables in a Rural Appalachian Community
Authors: Luecking Courtney T. , Barr-Porter Makenzie , Brewer Dawn , Cardarelli Kathryn M.
Published: January 2025

Background/Objectives: Community–academic partnerships offer unique opportunities to leverage expertise and resources to modify structural factors that address community priorities. However, few in-depth examples of successful partnerships in rural communities to improve food security are available. This manuscript describes the process of building a partnership to reduce food insecurity in a rural Kentucky community. Methods: The research team worked with Cooperative Extension to form a community advisory board (CAB) representative of diverse sectors of the community who had interest in food security, agriculture, and/or nutrition. The CAB convened regularly, in-person or virtually, to review community assessment information and identify, select, and adapt relevant multi-level interventions. CAB members were invited to complete two surveys and participate in a listening session to provide feedback on working with academic partners. Results: Over the first two years of the project, 17 people served on the CAB. Early in the project, the CAB prioritized interventions for working families, relatives raising children, and lower income households. Some ideas were able to come to fruition (e.g., family cooking social classes, raised garden beds), while others were explored but were unable to gain momentum (e.g., mobile market) due to challenges with feasibility, organizational capacity, and/or interest. CAB members reported high engagement and strong communication between community and academic partners. Conclusions: Full exploration of potential solutions suggested by CABs may build trust between community and academic partners and could provide a strategic selection process for multi-level, evidence-based interventions deemed feasible for addressing complex issues such as food insecurity and nutritional health outcomes.
Influence of Psychological Safety and Safety Climate Perceptions on Nurses’ Infection Prevention and Occupational Safety Practices and Environment
Authors: Thurman Johnson Cara , Owen Nancy Spear , Hessels Amanda J.
Published: January 2025

Background/Objectives: The aim of this study was to describe and examine the relationships among elements of infection prevention practices, the care environment, psychological safety, and safety climate in adult medical surgical units in the wake of the COVID-19 pandemic. Methods: Nurses in adult inpatient medical surgical units in the northeast were surveyed electronically. Each self-rated their infection prevention practices and elements of the care environment in their primary work unit. They were also asked to rate a series of questions regarding how psychologically safe they felt on their units as well as the overall patient safety climate. Results: A total of 259 nurses responded (52% response rate) to the survey. Overall psychological safety was rated neutrally among respondents, with a rating of 3.5 (1 being the lowest and 5 being the highest). Respondents reported better ratings of the safety climate on their unit (4.0) but also identified areas for improvement. Eight of twelve infection prevention practices were correlated with higher safety climate scores and ten were correlated with higher psychological safety scores. Nine of ten environmental factors were correlated with higher safety climate and higher psychological safety scores. Conclusions: Both psychological safety and patient safety climate are related to nurse self-ratings of performance of infection prevention practices. Similarly, the care environment nurses work in has important implications for psychological safety and patient safety. It is essential for nursing leadership to act as a steward in these areas to build a higher quality care environment for nurses and patients alike.
Quiet Quitting in the Hospital Context: Investigating Conflicts, Organizational Support, and Professional Engagement in Greece
Authors: Toska Aikaterini , Dimitriadou Ioanna , Togas Constantinos , Nikolopoulou Eleni , Fradelos Evangelos C. , Papathanasiou Ioanna V. , Sarafis Pavlos , Malliarou Maria , Saridi Maria
Published: January 2025

Background/Objectives: Quiet quitting, defined as employees fulfilling only the minimal requirements of their roles without extra effort or engagement, poses unique challenges in high-stress environments like hospitals where commitment directly impacts patient care. This study investigates the phenomenon of “quiet quitting” within the healthcare sector, with a specific focus on hospital staff in Greece. Methods: A cross-sectional design was employed, surveying 186 healthcare professionals from the General Hospital of Argos using the Questionnaire for Conflicts in Healthcare Organizations and the Quiet Quitting Scale (QQS). Results: Descriptive and inferential statistical analyses revealed that 62% of participants exhibited characteristics of quiet quitting, with “lack of motivation” scoring highest (M = 2.80, SD = 0.987) among QQS subscales. Significant correlations were observed between perceived reward fairness and motivation levels (r = −0.194, p < 0.01) and between management awareness of contributions and both motivation (r = −0.313, p < 0.01) and initiative (r = −0.192, p < 0.01). Logistic regression identified perceptions of management awareness as a key predictor of quiet quitting (p < 0.05). Conclusions: The findings emphasize the critical role of equitable reward systems and managerial recognition in reducing disengagement. Strategies to enhance employee engagement and resolve workplace conflicts are essential for fostering a resilient healthcare workforce.
Precision Medicine for Peritoneal Carcinomatosis—Current Advances in Organoid Drug Testing and Clinical Applicability
Authors: Kaur Harleen , Wright Josephine A. , Worthley Daniel L. , Murphy Elizabeth , Woods Susan L.
Published: January 2025

Peritoneal carcinomatosis from gastrointestinal tumours is considered a poor prognostic factor, with a median overall survival of six to nine months in the absence of intervention. The advent of patient-derived organoid cultures (PDOs) has provided a breakthrough in personalised medicine, allowing researchers and clinicians to model the complexity and heterogeneity of individual tumours in vitro. PDOs hold great promise in this field, as variations in the management of peritoneal carcinomatosis due to differences in the method of delivery of chemotherapeutics, drug selection, exposure duration, and tumour pathology make it impractical to use a single, standardised treatment regimen. We aim to summarise the methodologies and limitations of studies encapsulating organoids derived from peritoneal metastases to encourage design considerations that may improve future clinical relevance, standardise protocols, and address translational challenges in personalising treatment strategies.
Yeast Strains as Probiotic and Postbiotic Agents for the Agglutination of Enteric Pathogens: A Preventive Approach
Authors: Cerdán-Alduán Michelle , Salvador-Erro Josune , Villegas-Remírez Ana , García-Yoldi David , Ceniceros Ana , Pastor Yadira , Gamazo Carlos
Published: January 2025

This study evaluates the potential of various yeast strains as probiotic and postbiotic agents for agglutinating enteric pathogens, offering a preventive approach to gastrointestinal infections. Different yeast species were tested in vitro against a range of pathogenic bacteria, including enterotoxigenic Escherichia coli ETEC, Shigella flexneri, Salmonella enterica serovar Typhimurium, and Salmonella enterica serovar Enteritidis, to assess their capacity for pathogen agglutination. Additionally, inactivated yeasts were obtained using a novel chemical treatment and employed to explore their efficacy as postbiotic agents. The results suggest that both live and inactivated yeasts are able to agglutinate the different pathogens, potentially limiting bacterial colonization. Notably, we also demonstrated that Wickerhamomyces anomalus, Saccharomyces cerevisiae, and Pichia fermentans, exhibiting agglutination activity, were capable of reducing bacterial adhesion to HeLa cells in vitro. This research highlights yeast’s probiotic and postbiotic potential and supports the development of novel yeast-based products for preventing enteric infections.
Understanding the Molecular Interactions Between Influenza A Virus and Streptococcus Proteins in Co-Infection: A Scoping Review
Authors: Alshammari Askar K. , Maina Meshach , Blanchard Adam M. , Daly Janet M. , Dunham Stephen P.
Published: January 2025

Influenza A virus infections are known to predispose infected individuals to bacterial infections of the respiratory tract that result in co-infection with severe disease outcomes. Co-infections involving influenza A viruses and streptococcus bacteria result in protein–protein interactions that can alter disease outcomes, promoting bacterial colonisation, immune evasion, and tissue damage. Focusing on the synergistic effects of proteins from different pathogens during co-infection, this scoping review evaluated evidence for protein–protein interactions between influenza A virus proteins and streptococcus bacterial proteins. Of the 2366 studies initially identified, only 32 satisfied all the inclusion criteria. Analysis of the 32 studies showed that viral and bacterial neuraminidases (including NanA, NanB and NanC) are key players in desialylating host cell receptors, promoting bacterial adherence and colonisation of the respiratory tract. Virus hemagglutinin modulates bacterial virulence factors, hence aiding bacterial internalisation. Pneumococcal surface proteins (PspA and PspK), bacterial M protein, and pneumolysin (PLY) enhance immune evasion during influenza co-infections thus altering disease severity. This review highlights the importance of understanding the interaction of viral and bacterial proteins during influenza virus infection, which could provide opportunities to mitigate the severity of secondary bacterial infections through synergistic mechanisms.
Correction: Maksymowicz et al. The Use of Endo-Cellulase and Endo-Xylanase for the Extraction of Apple Pectins as Factors Modifying Their Anticancer Properties and Affecting Their Synergy with the Active Form of Irinotecan. Pharmaceuticals 2022, 15, 732
Authors: Maksymowicz Jerzy , Palko-Łabuz Anna , Sobieszczańska Beata , Chmielarz Mateusz , Ferens-Sieczkowska Mirosława , Skonieczna Magdalena , Wikiera Agnieszka , Wesołowska Olga , Środa-Pomianek Kamila
Published: January 2025

In the original publication [...]
Pesticides’ Cornea Permeability—How Serious Is This Problem?
Authors: Sobańska Anna W. , Sobański Andrzej M. , Wanat Karolina
Published: January 2025

Background: A total of 348 pesticides from different chemical families (carbamates, organochlorines organophosphorus compounds, pyrethroids, triazines and miscellaneous) were investigated in the context of their cornea permeability and potential to cause eye corrosion. Methods: Multivariate models of cornea permeability based on compounds whose cornea permeability has been determined experimentally were proposed. The models, applicable to compounds across a relatively broad lipophilicity range (e.g., pesticides with octanol–water partition coefficient log P up to ca. 8), assume a reverse-parabolic relationship between cornea permeability and lipophilicity, expressed as XLOGP3; other main descriptors present in the models are log D at pH 7.4 and polar surface area (PSA). Results: It appears that the trans-corneal transport of all studied pesticides is possible to some degree; however, it is more difficult for the majority of highly lipophilic pesticides from the organochlorine and pyrethroid families. The same set of 348 pesticides was also evaluated for their eye-corrosive potential using novel artificial neural network models involving simple physico-chemical properties of the compounds (lipophilicity, aqueous solubility, polar surface area, H-bond donor and acceptor count and the count of atoms such as N, NH, O, P, S and halogens). Conclusions: It was concluded that eye corrosion is an issue, especially among the pesticides from organochlorine and organophosphorus families.
A Critical Analysis of Dreyfus’s Background Knowledge
Authors: Turanli Aydan
Published: January 2025

The role of background knowledge in human intelligence, knowledge, and consciousness has been a topic of discussion among several philosophers, including Ludwig Wittgenstein, John Searle, Martin Heidegger, and Hubert Dreyfus. Hubert Dreyfus criticizes what he calls the mediational approach and offers the contact theory to clarify the concept within his theoretical framework. In alignment with Heidegger’s existential phenomenological perspective, he posits that our contact and our embodied coping with the world constitute a background by which we become acquainted with preunderstanding that encompasses both prelinguistic and pre-propositional understandings. In this article, Dreyfus’s analysis of background knowledge is criticized by focusing on his latest writings. It is argued that, although Dreyfus claims to be defending horizontal foundationalism rather than vertical foundationalism, he primarily emphasizes the foundational nonlinguistic role of motor intentionality in absorbed coping. Furthermore, it is asserted that nonlinguistic embodied coping alone cannot provide the basis for linguistic communication and a humanly way of understanding. Rather than serving as a foundation, embodied coping is more appropriately situated within a linguistic context, because we perform deeds with words.
Antioxidant, Enzyme-Inhibitory and Antimicrobial Activity of Underutilized Wheat and Maize Crop Residues
Authors: Samardžić Stevan , Veličković Ivona , Milenković Marina T. , Arsenijević Jelena , Medarević Djordje , Maksimović Zoran
Published: January 2025

Global wheat and maize production, which reached two billion tonnes in 2021, generates significant agricultural waste with largely untapped potential. This study investigates the bioactive properties of ethanol extracts from wheat and maize harvest residues, their ethyl acetate fractions, and their principal compounds. In vitro assays (DPPH, ABTS, FRAP, and TRC) revealed variable antioxidant capacities among the samples, with ferulic acid demonstrating the strongest free-radical scavenging and reducing effects, often surpassing those of standard antioxidant controls. Enzyme inhibition assays identified the flavonoid tricin as the most effective inhibitor of α-glucosidase, acetylcholinesterase, and butyrylcholinesterase, while the flavonolignan mixture of salcolins A and B showed the highest inhibitory activity against α-amylase and tyrosinase. Antimicrobial testing using the broth microdilution method resulted in minimum inhibitory concentrations (MICs) ranging from 31.25 µg/mL to >1000 µg/mL. Gram-positive bacteria showed the highest susceptibility, Candida albicans exhibited variable sensitivity, and Gram-negative bacteria were resistant in the tested concentration range. Bioactivity increased in the order of extracts, fractions, and then individual compounds. These findings suggest that wheat and maize residues possess notable bioactive properties, highlighting their potential as sources of valuable and pharmacologically active compounds.
Evaluation of Rice Quality Storage Stability: From Variety Screening to Trait Identification
Authors: Tian Jinyu , Ji Guangmei , Zhang Jiafeng , Luo Danqiu , Zhang Fang , Li Lijiang , Jiang Mingjin , Zhu Dawei , Li Min
Published: January 2025

Rice, a staple global food crop, requires the maintenance of its quality stability during storage. This study aimed to screen rice varieties with high storage stability and elucidate their traits. Thirty-four widely cultivated varieties were selected to examine the changes in grain quality after one-year natural storage. The normalization method, hierarchical analysis, and cluster analysis were used to identify rice varieties maintaining their grain quality during storage. Meanwhile, the yield and its components, panicle traits, grain size, grain major component content, physiological indicators (such as antioxidant enzyme activity), and key growth stages were analyzed at rice maturity. The results showed that the processing, appearance, and eating quality of rice declined after storage. Specifically, the chalkiness degree increased significantly by 32.4%, while the cooked rice appearance, texture, and taste quality decreased significantly by 18.7%, 19.1%, and 14.2%, respectively. The grain quality storage stability was evaluated using a hierarchical analysis method based on the storage stability scores of the brown rice rate, milled rice rate, head milled rice rate, chalkiness degree, cooked rice appearance, cooked rice texture, and cooked rice taste. A judgment matrix was established, determining their corresponding weights of 0.0149, 0.0369, 0.0910, 0.286, 0.060, 0.148, and 0.364, respectively. Based on cluster analysis and the normalization method, these varieties were classified into three categories: high storage stability, intermediate storage stability, and low storage stability, accounting for 26.5%, 52.9%, and 20.6%, respectively. Finally, nine rice varieties with high storage stability were screened. The validation results of the principal component analysis also indicated the reliability of this result. Correlation analysis revealed that the storage stability of the rice grain quality was significantly and negatively correlated with the amylose starch content and malondialdehyde content. The average value and median value of the amylose starch content in high-storage-stability varieties are 10.7% and 6.99% lower than those in sensitive varieties, respectively. Therefore, the major feature of rice varieties with high storage stability is a low amylose starch content. This study provides valuable theoretical insights into the safe storage of rice grains and the selection and breeding of rice varieties with high storage stability.
Phytochemical Analysis and Biological Activities of Flavonoids and Anthraquinones from Cassia alata (Linnaeus) Roxburgh and Their Implications for Atopic Dermatitis Management
Authors: Lee Sue-Kei , Keng Jing-Wen , Yon Jessica-Ai-Lyn , Mai Chun-Wai , Lim Heng-Chee , Chow Sek-Chuen , Akowuah Gabriel Akyirem , Liew Kai Bin , Lee Siew-Keah , Marriott Philip J. , Chew Yik-Ling
Published: January 2025

To study Cassia alata (CA) (Linnaeus) Roxburgh’s effectiveness towards atopic dermatitis (AD), CA leaf extracts were prepared using three methanol-based extraction solvent systems. Bioactive constituents were characterized and quantified via high-performance liquid chromatography with diode array detection. Antioxidant properties and antimicrobial activities against Staphylococcus aureus, a major AD exacerbation factor, were assessed. Four polyphenols (two flavonoids, two anthraquinones) beneficial in AD control were detected (rhein > aloe-emodin > astragalin > kaempferol). The 75% v/v MeOH/water extract had the most polyphenols and the best antioxidant profile (total phenolic content, total flavonoid content, 2,2-diphenyl-1-picrylhydrazyl-hydrate radical scavenging activity, ascorbic acid equivalent antioxidant capacity), with excellent S. aureus inhibition (minimum inhibitory concentration = 0.625 mg/mL; minimum bactericidal concentration = 1.25 mg/mL). Hence, it was selected for the in vitro examination of cytotoxicity and wound healing activity towards human epidermal keratinocyte cells using a 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2h-tetrazolium bromide (MTT) assay and wound scratch assay. The extract showed no cytotoxicity (IC50 > 100 µg/mL) without significant reduction in cell viability up to 200 µg/mL compared to the vehicle control. An amount of 50 μg/mL extract concentration showed the best wound-healing activity (p < 0.05), with a cell migration rate of 5.89 ± 0.80 µm/h over 96 h post-treatment. Such antioxidant, antimicrobial, and wound-healing activities suggest CA and its polyphenols to be promising natural, long-term AD remedies for skin health.
Ultrasound and Microwave-Assisted Extraction of Blackberry (Rubus fruticosus L.) Pomace: Analysis of Chemical Properties and Anticancer Activity
Authors: Čechovičienė Indrė , Tarasevičienė Živilė , Hallman Ewelina , Jabłońska-Trypuć Agata , Česonienė Laima , Šileikienė Daiva
Published: January 2025

Blackberries are seasonal berries that are processed into various products leaving a large amount of residues after processing, and therefore the most effective ways of utilising the residues need to be evaluated. The aim of this study was to determine the effect of different extraction methods on the chemical content of blackberry pomace extracts from different cultivars and their effect on the viability of the Caco-2 colorectal adenocarcinoma cell line and CCD-18Co normal colon fibroblast cancer cells. Blackberry pomace from berries of the cultivars ‘Polar’, ‘Orkan’, and ‘Brzezina’ was extracted by ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE) and a combination of these two extraction methods (MAE+UAE). The phenolic profile and the amount of organic acids and sugars were detected by HPLC. The spectrophotometric method was used to determine the amount of total phenolics, total flavonoids, and total anthocyanins. The cytotoxicity of the extracts was measured by the MTT assay. The chemical content of the extracts depends on the blackberry cultivar, the extraction method, and its interaction. Only the DPPH antioxidant activity did not depend on these factors and had no statistically significant differences between the different extracts. The extracts at a concentration of 5.0% increased the growth of both cancer cells, while the extracts at 1% and 2.5%, depending on the cultivar, reduced the growth of these cells. The MAE and UAE extracts of the ‘Orkan’ cultivar at concentrations of 1%, 1.5%, 2%, and 2.5% best inhibited the viability of Caco-2 cells. The extracts inhibited the growth of the Caco-2 cell line better than CCD-18Co normal colon fibroblasts.
Crashworthy Performance of Sustainable Filled Structures Using Recycled Beverage Cans and Eco-Friendly Multi-Cell Fillers
Authors: Gao Huijing , Xiang Jiangyang , Lu Junyu , Tan Qianbing , Addiego Frédéric , Peng Yong , Wang Kui
Published: January 2025

The recycling of resources is an important measure to achieve circular economy and sustainable development. In this paper, a sustainable filled structure was proposed and realized by combining recycled empty beverage cans with eco-friendly multi-cell fillers. Quasi-static axial compressions were carried out to characterize the energy absorption performance and synergistic effect of the filled tubes. Experimental results showed that the crashworthiness of sustainable filled structures varied with both filling densities and materials. With the increase in filling density, the specific energy absorption of the filled tubes presented an upward trend. With the variation in filling materials, the filled tubes exhibited different crashworthiness performances. The PLA multi-cell filled tube could withstand larger external force and exhibited higher SEA values, with a maximum value of 9.64 J/g. The PLAS multi-cell filled tube showed excellent loading stability and lower ULC value, with a minimum value of 10%. These findings provided valuable insights for designing novel sustainable energy absorption structures.
Optimal Scheduling of the Microgrid Based on the Dynamic Characteristics of the Natural Gas Pipeline Network and the Thermal Network Along with P2G-CCS
Authors: Wang Fangzong , Tu Zhenghong
Published: January 2025

In the power system, the integration of power-to-gas and carbon capture systems (P2G-CCS) within the microgrid enables the conversion of electrical energy into hydrogen or methane while simultaneously capturing CO2 emissions from power generation units. This approach significantly mitigates carbon emissions and supports the transition to a low-carbon energy system. Concurrently, the dynamic properties of the gas–thermal network exert a critical influence on the flexibility of system scheduling and the regulation of multi-energy coupling. Hence, this paper puts forward an optimal configuration strategy for microgrids with consideration of the dynamic characteristics of the gas–thermal network. Firstly, mathematical models for the dynamic characteristics of the gas network and the heat network were established and incorporated into the microgrid system. Secondly, in conjunction with the P2G-CCS coupling system, an optimization scheduling strategy was formulated with the aim of minimizing the total operational costs of the power grid, the natural gas network, and the heat network. An enhanced African vultures optimization algorithm (AVOA) was put forward. In the end, by setting different scheduling scenarios for conducting a comparative analysis, an appropriate optimization configuration scheme was selected, and the validity of the proposed method was verified through simulation with the improved case study.
Development of an Optimized YOLO-PP-Based Cherry Tomato Detection System for Autonomous Precision Harvesting
Authors: Qin Xiayang , Cao Jingxing , Zhang Yonghong , Dong Tiantian , Cao Haixiao
Published: January 2025

An accurate and efficient detection method for harvesting is crucial for the development of automated harvesting robots in short-cycle, high-yield facility tomato cultivation environments. This study focuses on cherry tomatoes, which grow in clusters, and addresses the complexity and reduced detection speed associated with the current multi-step processes that combine target detection with segmentation and traditional image processing for clustered fruits. We propose YOLO-Picking Point (YOLO-PP), an improved cherry tomato picking point detection network designed to efficiently and accurately identify stem keypoints on embedded devices. YOLO-PP employs a C2FET module with an EfficientViT branch, utilizing parallel dual-path feature extraction to enhance detection performance in dense scenes. Additionally, we designed and implemented a Spatial Pyramid Squeeze Pooling (SPSP) module to extract fine features and capture multi-scale spatial information. Furthermore, a new loss function based on Inner-CIoU was developed specifically for keypoint tasks to further improve detection efficiency.The model was tested on a real greenhouse cherry tomato dataset, achieving an accuracy of 95.81%, a recall rate of 98.86%, and mean Average Precision (mAP) scores of 99.18% and 98.87% for mAP50 and mAP50-95, respectively. Compared to the DEKR, YOLO-Pose, and YOLOv8-Pose models, the mAP value of the YOLO-PP model improved by 16.94%, 10.83%, and 0.81%, respectively. The proposed algorithm has been implemented on NVIDIA Jetson edge computing devices, equipped with a human–computer interaction interface. The results demonstrate that the proposed Improved Picking Point Detection Network exhibits excellent performance and achieves real-time accurate detection of cherry tomato harvesting tasks in facility agriculture.
Social Sins, Structural Virtues, and the Educational Challenge: Reflections on Caritas in Veritate and Laudato Si’
Authors: Máté-Tóth András , Vellankal George Joseph
Published: January 2025

The aim of this article is to reflect on the nature of the structural sins in the present times and to offer light on the structural virtues that are in urgent demand for sustainable development of persons and peoples. Our analysis begins in Benedict XVI’s Caritas in Veritate. In the encyclical, the pope analyses oversimplification of the human reality by ideologies. The simultaneity of moral underdevelopment and a consumeristic super-development, epistemological gulf between faith and reason, erosion of social capital with the shifting of religion to the private sphere, and the collapse of the human ecology beneath the deterioration of environmental ecology are some of the social sins that Benedict XVI points out in the encyclical. Towards the end of the first section, we attempt to show how Benedict XVI understands that these social sins are also the sins of persons and how the personal is derived into the social. In the second section, we try to develop on the proposals for the structural virtues in Laudato Si’. The starting point is Pope Francis’ vision of integral ecology, which is in continuity with Benedict XVI’s finding that human ecology and environmental ecology are interconnected. After a brief analysis of Pope Francis’ thoughts about the current situation of epistemology, we try to understand the dimensions of the common good, law, and personalism in Laudato Si’, from which we can derive threads for the structural virtues.
The Politics of Vulnerability Concerning Sexual and Spiritual Abuse in the Catholic Church
Authors: Hürten Magdalena , Leimgruber Ute , McEwan Tracy , McPhillips Kathleen
Published: January 2025

This article addresses methodological approaches to the study of vulnerable cohorts with specific attention to definitions of the term vulnerability. In particular, it investigates the issue of adults, particularly women, who have experienced abuse in the Catholic Church. Relevant analysis in this area is primarily interdisciplinary-based research and includes sociological and psychological perspectives and theological and historical analysis. This article critically analyzes the current definitions of vulnerability in Church and research contexts, and the methodological approaches utilized in defining vulnerable cohorts. It argues that specific ethical principles and methodological strategies are required to safeguard participants and researchers guided by the principles of feminist ethics. These include (1) attention to the uses and definitions of key terms by participants and researchers; (2) identifying the positionality of researchers as central to an ethical standpoint including their exposure to secondary trauma; and (3) establishing inclusive methods such as participatory research and sensitive recruitment. Paying attention to ongoing issues experienced by cohorts identified as vulnerable in research, including the stigmatization of their status, marginalization of their cases, and trauma of disclosure alongside ongoing risk assessment, is central to the research design process.
Turning Points in the Core–Periphery Displacement of Systemic Risk in the Eurozone: Constrained Weighted Compositional Clustering
Authors: Fiori Anna Maria , Coenders Germà
Published: January 2025

Investigating how systemic risk originates and spreads across the financial system poses an inherently compositional question, i.e., a question concerning the joint distribution of relative risk share across several interdependent contributors. To address this question, we propose a weighted compositional clustering approach aimed at tackling the trajectories and turning points of systemic risk in the Eurozone, from both a chronological and a geographical perspective. The cluster profiles emerging from our analysis indicate a progressive shift from Northern Europe towards the Euro-Mediterranean region in the coordinate center of systemic risk compositions. This shift matures as the outcome of complex interactions between core and peripheral EU countries that compositional methods have the merit of capturing and unifying in a self-contained multivariate framework.
geeSSEBI: Evaluating Actual Evapotranspiration Estimated with a Google Earth Engine Implementation of S-SEBI
Authors: Kabala Jerzy Piotr , Sobrino Jose Antonio , Crisafulli Virginia , Skoković Dražen , Battipaglia Giovanna
Published: January 2025

Quantifying and mapping evapotranspiration (ET) from land surfaces is crucial in the context of climate change. For decades, remote sensing data have been utilized to estimate ET, leading to the development of numerous algorithms. However, their application is still non-trivial, mainly due to practical constraints. This paper introduces geeSSEBI, a Google Earth Engine implementation of the S-SEBI (Simplified Surface Energy Balance Index) model, for deriving ET from Landsat data and ERA5-land radiation. The source code and a graphical user interface implemented as a Google Earth Engine application are provided. The model ran on 871 images, and the estimates were evaluated against multiyear data of four eddy covariance towers belonging to the ICOS network, representative of both forests and agricultural landscapes. The model showed an RMSE of approximately 1 mm/day, and a significant correlation with the observed values, at all the sites. A procedure to upscale the data to monthly is proposed and tested as well, and its accuracy evaluated. Overall, the model showed acceptable accuracy, while performing better on forest ecosystems than on agricultural ones, especially at daily and monthly timescales. This implementation is particularly valuable for mapping evapotranspiration in data-scarce environments by utilizing Landsat archives and ERA5-land radiation estimates.
Research on 3D Reconstruction Methods for Incomplete Building Point Clouds Using Deep Learning and Geometric Primitives
Authors: Ding Ziqi , Lu Yuefeng , Shao Shiwei , Qin Yong , Lu Miao , Song Zhenqi , Sun Dengkuo
Published: January 2025

Point cloud data, known for their accuracy and ease of acquisition, are commonly used for reconstructing level of detail 2 (LoD-2) building models. However, factors like object occlusion can cause incompleteness, negatively impacting the reconstruction process. To address this challenge, this paper proposes a method for reconstructing LoD-2 building models from incomplete point clouds. We design a generative adversarial network model that incorporates geometric constraints. The generator utilizes a multilayer perceptron with a curvature attention mechanism to extract multi-resolution features from the input data and then generates the missing portions of the point cloud through fully connected layers. The discriminator iteratively refines the generator’s predictions using a loss function that is combined with plane-aware Chamfer distance. For model reconstruction, the proposed method extracts a set of candidate polygons from the point cloud and computes weights for each candidate polygon based on a weighted energy term tailored to building characteristics. The most suitable planes are retained to construct the LoD-2 building model. The performance of this method is validated through extensive comparisons with existing state-of-the-art methods, showing a 10.9% reduction in the fitting error of the reconstructed models, and real-world data are tested to evaluate the effectiveness of the method.
Assessing Spatial Correlations Between Land Cover Types and Land Surface Temperature Trends Using Vegetation Index Techniques in Google Earth Engine: A Case Study of Thessaloniki, Greece
Authors: Stamou Aikaterini , Dosiou Anna , Bakousi Aikaterini , Karachaliou Eleni , Tavantzis Ioannis , Stylianidis Efstratios
Published: January 2025

The Urban Heat Island (UHI) phenomenon, combined with reduced vegetation and heat generated by human activities, presents a major environmental challenge for many European urban areas. The UHI effect is especially concerning in hot and temperate climates, like the Mediterranean region, during the summer months as it intensifies the discomfort and raises the risk of heat-related health issues. As a result, assessing urban heat dynamics and steering sustainable land management practices is becoming increasingly crucial. Analyzing the relationship between land cover and Land Surface Temperature (LST) can significantly contribute to achieving this objective. This study evaluates the spatial correlations between various land cover types and LST trends in Thessaloniki, Greece, using data from the Coordination of Information on the Environment (CORINE) program and advanced vegetation index techniques within Google Earth Engine (GEE). Our analysis revealed that there has been a gradual increase in average surface temperature over the past five years, with a more pronounced increase observed in the last two years (2022 and 2023) with mean annual LST values reaching 26.07 °C and 27.09 °C, respectively. By employing indices such as the Normalized Difference Vegetation Index (NDVI) and performing correlation analysis, we further analyzed the influence of diverse urban landscapes on LST distribution across different land use categories over the study area, contributing to a deeper understanding of UHI effects.
Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection
Authors: Wang Yue , Wang Xinhong , Qiu Shi , Chen Xianghui , Liu Zhaoyan , Zhou Chuncheng , Yao Weiyuan , Cheng Hongjia , Zhang Yu , Wang Feihong , Shu Zhan
Published: January 2025

Detecting small targets in infrared images presents significant challenges due to their tiny size and complex backgrounds, making this task a hotspot for research. Traditional methods rely on assumption-based modeling and manual design, struggling to handle the variability of real-world scenarios. Although convolutional neural networks (CNNs) increase robustness to diverse scenes with a data-driven paradigm, many CNN-based methods are insufficient in capturing fine-grained details necessary for small targets and are less effective during multi-scale feature fusion. To overcome these challenges, we propose the novel Wide-scale Gated Fully Fusion Network (WGFFNet) in this article, which contributes to infrared small-target detection (IRSTD). WGFFNet uses a classic encoder–decoder structure, where the designed stepped fusion block (SFB) embedded in the feature extraction stage captures finer local context across multiple scales during encoding, and along the decoding path, the multi-level features are progressively integrated by a Fully Gated Interaction (FGI) Module to enhance feature representation. The inclusion of a boundary difference loss further optimizes the edge details of targets. We conducted comprehensive experiments on two public infrared small-target datasets: SIRST-V2 and IRSTD-1k. Quantitative and qualitative results demonstrate that our WGFFNet outperforms representative methods when considering various evaluation metrics together, achieving an improved detection performance and computational efficiency for detecting small targets in infrared images.
A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
Authors: Armannsson Sveinn E. , Ulfarsson Magnus O. , Sigurdsson Jakob
Published: January 2025

This paper implements an unsupervised single-image sharpening method for multispectral images, focusing on Sentinel-2 and Landsat 8 imagery. Our method combines traditional model-based methods with neural network optimization techniques. Our method solves the same optimization problem as traditional model-based methods while leveraging neural network optimization techniques through a customized U-Net architecture and specialized loss function. The key innovation lies in simultaneously optimizing a low-rank approximation of the target image and a linear transformation from the subspace to the sharpened image within an unsupervised training framework. Our method offers several distinct advantages: it requires no external training data beyond the image being processed, it provides fast training speeds through a compact, interpretable network model, and most importantly, it adapts to different input images without requiring extensive parameter tuning—a common limitation of traditional methods. The method was developed with a focus on sharpening Sentinel-2 imagery. The Copernicus Sentinel-2 satellite constellation captures images at three different spatial resolutions, 10, 20, and 60 m, and many applications benefit from a unified 10 m resolution. Still, the method’s effectiveness extends to other remote sensing tasks, achieving competitive results in both sharpening and multisensor fusion scenarios. It is evaluated using both real and simulated data, and its versatility is shown through successful applications to Sentinel-2 sharpening and Sentinel-2/Landsat 8 fusion. In comparison with leading methods, it is shown to give excellent results.
One-Point Calibration of Low-Cost Sensors for Particulate Air Matter (PM) Concentration Measurement
Authors: Russi Luigi , Guidorzi Paolo , Semprini Giovanni , Trentini Arianna , Pulvirenti Beatrice
Published: January 2025

The use of low-cost sensors has dramatically increased in recent years in all engineering sectors. In the buildings and automotive field, low-cost sensors open very interesting perspectives, because they allow one to monitor temperature and humidity distributions together with air quality in a widespread and punctual way and allow for the control of all energy parameters. The main issue remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems were set to measure Particulate Air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity, and GPS position. These systems were calibrated for PM concentration measurement by comparison with standard and certified sensors used by the regional authority of the Emilia-Romagna region (ARPAE, Italy) for characterizing air quality. The duration of the analysis, three days, is not representative of the diverse environmental conditions that occur across different seasons. However, the innovation of this approach lies in both the in-field comparison of low-cost and high-quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors’ performance is given, with a focus on the effects of time granularity, relative humidity, mass conversion from particle counts, and size detection response. The results show that the low-cost sensors’ measurements of air temperature, relative humidity, and particle number concentration are in good agreement with high-quality sensors’ measurements, with a strong impact of relative humidity on performance indicators. Overall, good quality and consistency of the data among the sensors were achieved.
Multilayered Manufacturing Method for Microfluidic Systems Using Low-Cost, Resin-Based Three-Dimensional Printing
Authors: Manqueros-Avilés Victor Edi , Coto-Fuentes Hesner , Guevara-Amatón Karla Victoria , Valdés-Perezgasga Francisco , Alonso-Chamarro Julian
Published: January 2025

This work presents a multilamination method for fabricating microfluidic devices or analytical microsystems using commercial 3D printers and photocurable resins as primary components. The developed method was validated by fabricating devices for the colorimetric measurement of copper ions in aqueous solutions, achieving results comparable to traditional cyclic olefin copolymer (COC) systems. The microfluidic platforms demonstrated stability and functionality over a twelve-week testing period. Channels with minimum dimensions of 0.4 mm × 0.4 mm were fabricated, and the feasibility of using resin modules for optical applications was demonstrated. This study highlights the potential of combining 3D printing with multilamination procedures as a versatile alternative, offering flexibility through the selection of a variety of available resins and commercial printers, as well as the ease of design development. This method offers significant reductions in cost, time, and manufacturing complexity by eliminating the need for equipment such as CNC machines, presses, and ovens, which are typically required in other multilamination technologies like LTCC and COC.
A Review on Secure Authentication Mechanisms for Mobile Security
Authors: Hasan Syed Shabih Ul , Ghani Anwar , Daud Ali , Akbar Habib , Khan Muhammad Faizan
Published: January 2025

Cybersecurity, complimenting authentication, has become the backbone of the Internet of Things. In the authentication process, the word authentication is of the utmost importance, as it is the door through which both Mr. Right Guy and Mr. Wrong Guy can pass. It is the key to opening the most important and secure accounts worldwide. When authentication is complete, surely there will be passwords. Passwords are a brain-confusing option for the user to choose when making an account during the registration/sign-up process. Providing reliable, effective, and privacy-preserving authentication for individuals in mobile networks is challenging due to user mobility, many attack vectors, and resource-constrained devices. This review paper explores the transformation and modern mobile authentication schemes, categorizing them into password, graphical, behavioral, keystroke, biometric, touchscreen, color, and gaze-based methodologies. It aims to examine the strengths and limitations focused on challenges like security and usability. Standard datasets and performance evaluation measures are also discussed. Finally, research gaps and future directions in this essential and emerging area of research are discussed.
Spatio-Temporal Transformer with Kolmogorov–Arnold Network for Skeleton-Based Hand Gesture Recognition
Authors: Han Pengcheng , He Xin , Matsumaru Takafumi , Dutta Vibekananda
Published: January 2025

Manually crafted features often suffer from being subjective, having an inadequate accuracy, or lacking in robustness in recognition. Meanwhile, existing deep learning methods often overlook the structural and dynamic characteristics of the human hand, failing to fully explore the contextual information of joints in both the spatial and temporal domains. To effectively capture dependencies between the hand joints that are not adjacent but may have potential connections, it is essential to learn long-term relationships. This study proposes a skeleton-based hand gesture recognition framework, the ST-KT, a spatio-temporal graph convolution network, and a transformer with the Kolmogorov–Arnold Network (KAN) model. It incorporates spatio-temporal graph convolution network (ST-GCN) modules and a spatio-temporal transformer module with KAN (KAN–Transformer). ST-GCN modules, which include a spatial graph convolution network (SGCN) and a temporal convolution network (TCN), extract primary features from skeleton sequences by leveraging the strength of graph convolutional networks in the spatio-temporal domain. A spatio-temporal position embedding method integrates node features, enriching representations by including node identities and temporal information. The transformer layer includes a spatial KAN–Transformer (S-KT) and a temporal KAN–Transformer (T-KT), which further extract joint features by learning edge weights and node embeddings, providing richer feature representations and the capability for nonlinear modeling. We evaluated the performance of our method on two challenging skeleton-based dynamic gesture datasets: our method achieved an accuracy of 97.5% on the SHREC’17 track dataset and 94.3% on the DHG-14/28 dataset. These results demonstrate that our proposed method, ST-KT, effectively captures dynamic skeleton changes and complex joint relationships.