bluuebunny/crossref_metadata_2025 huggingface dataset:
Design, Build and Testing of a Portable Methane Measurement Platform
Authors: Riddick Stuart N , Riddick John C. , Kiplimo Elijah , Rainwater Bryan , Mbua Mercy , Cheptonui Fancy , Laughery Kate , Levin Ezra , Zimmerle Daniel J
Published: February 2025

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emission and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large scale deployment of methane analyzers across the millions of emission sites is prohibitively expensive and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH4ERS) has been developed and comprises four sensing technologies: Metal Oxide (MOx,); Non-dispersion Infrared (NDIR); Integrated Infrared (INIR); and Tunable Diode Laser Absorption Spectrometer (TDLAS). This study describes the integration of these sensors to a single unit and test instrument response through calibration and downwind tests. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensor cannot respond fast enough to report concentrations that change on a sub-minute time frame. The NDIR sensor reported linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor re-ported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions and the WATCH4ERS units will be deployed in real-world environments to investigate the utility of each in the future.
The Effectiveness of Debriefing on the Mental Health of Rescue Teams: A Systematic Review
Authors: Ancarani Francesca , Garijo Añaños Pedro , Gutierrez Bain , Pérez-Nievas Juan , Vicente-Rodríguez Germán , Gimeno Marco Fernando
Published: February 2025

Rescue teams and emergency services face high levels of mental health problems due to their frequent exposure to traumatic situations. Although post-incident psychosocial support interventions, such as debriefing, are widely implemented, their effectiveness in preventing disorders such as PTSD or depression remains uncertain. This systematic review, conducted within the framework of the working group “Debriefing and Psychological Support to Rescue Teams” of the University of Zaragoza, analyzes the effectiveness of debriefing in military, emergency, and health personnel. Following PRISMA guidelines and Cochrane methodology, 371 studies were identified, of which 6 met the inclusion criteria and were incorporated into the review. The methodological assessment with the PEDro scale showed that one study was of high quality, four were of acceptable quality, and two had deficiencies. Although some interventions, such as the “512 PIM”, offered promising results in reducing PTSD and anxiety symptoms, overall, the evidence on the effectiveness of debriefing was limited and inconsistent. In addition, adverse effects such as increased alcohol consumption were observed in certain groups. No specific effects have been studied in mountain rescue teams. We found limited evidence on the effectiveness of post-incident psychosocial interventions in rescue and emergency teams. More high-quality studies are urgently needed to understand better how to prevent the development of trauma-related disorders after exposure to traumatic events.
Examining Personal Lifestyle Factors and Lipid Profile on Cardiovascular Disease Risk Prediction
Authors: Badhan Pawan Kumar
Published: February 2025

Cardiovascular disease (CVD) is a major global health challenge, with various factors such as lifestyle choices, lipid profiles, and genetic predispositions contributing to its development. Early and accurate prediction of CVD risk is critical for effective prevention and management strategies. This study aims to develop robust predictive models for CVD risk by integrating demographic, health, genetic, and imaging data, utilizing machine learning (ML) and deep learning (DL). The primary objective is to leverage X-ray image data analyzed through convolutional neural networks (CNNs) to improve the accuracy of risk prediction models. A secondary objective is to explore the potential of combining structured patient data, including demographic information, lipid profiles, and genetic markers, with advanced imaging features to provide a comprehensive and precise CVD risk assessment. By integrating ML and DL algorithms, the study also aims to identify key features and patterns that could contribute to early detection and personalized healthcare interventions for individuals at risk of CVD. The dataset used in this study consists of 264 patients, incorporating demographic details, health indicators, lipid profiles, genetic markers, lifestyle factors, and X-ray images to detect heart-related abnormalities. Data preprocessing techniques such as normalization, resizing, and augmentation were applied to ensure consistency across the dataset. Feature selection was performed using techniques like LASSO and correlation analysis to identify the most predictive variables. Various ML models, including Support Vector Machines (SVM), Random Forest (RF), and Decision Trees (DT), were trained on the curated data. CNNs were employed for image data analysis, and Grad-CAM was used to visualize and interpret the model's decision-making process. The results showed promising outcomes, with ML models achieving up to 95% accuracy, and CNNs using X-ray images achieving 100% accuracy. This research highlights the potential of integrating ML and DL algorithms for early detection and personalized healthcare strategies in CVD.
The Impact of Nanoparticles on the Previtreous Behavior: Glass-Forming Nematogenic E7 Mixture-Based Nanocolloids
Authors: Drozd-Rzoska Aleksandra , Łoś Joanna , Rzoska Sylwester Janusz
Published: February 2025

The report discusses the impact of nanoparticles on the previtreous behavior of glass-forming systems. The presented studies were carried out via broadband dielectric spectroscopy (BDS) in nanocolloids composed of nematogenic liquid crystalline (LC) mixture E7 and paraelectric BaTiO3 nanoparticles. Tests started in the isotropic liquid phase, showing critical-like dielectric constant changes associated with prenematic fluctuations and weakly discontinuous isotropic–nematic phase transition. Subsequently, evolutions of the dielectric constant, two primary relaxation times (related to  τ and α τ' processes), and DC electric conductivity are considered in the nematic phase, down to the glass temperature Tg. The prevalence of the portrayal via the ‘double exponential’ MYEGA equation is shown for dynamic properties. Monitoring of the dielectric constant revealed the exogenic impact of nanoparticles, leading to the permanent arrangement of rod-like LC molecules. For the primary loss curve, critical-like changes of its maximum (peak) are evidenced: peak''∝1T-Tg* for Tg<T<Tg+25K, where Tg*<Tg is the extrapolated singular temperature. Such behavior is shown for both pure E7 and nanocolloids. The heuristic model commenting on this unique behavior is presented. It recalls a hypothetical link between the glass transition and a hidden near-critical phase transition, avoiding a symmetry change.
Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical Data: A Scopus-Based Bibliometric Analysis
Authors: Tsiara Aikaterini A. , Plakias Spyridon , Kokkotis Christos , Veneri Aikaterini , Mina Minas , Tsiakiri Anna , Kitmeridou Sofia , Christidi Foteini , Gourgoulis Evangelos , Ntoskas Triantafylos , Kaltsatou Antonia , Tsamakis Konstantinos , Kazis Dimitrios , Tsiptsios Dimitrios
Published: February 2025

Neurological diseases are increasingly diverse and prevalent, presenting significant challenges for timely and accurate diagnosis. The aim of the present study is to conduct a bibliometric analysis and literature review in the field of neurology to explore advancements in the application of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL). Using VOSviewer software (version 1.6.20.0) and documents retrieved from the Scopus database, the analysis included 113 articles published between January 1, 2018, and December 31, 2024. Key journals, authors, and research collaborations were identified, highlighting major contributions to the field. Science mapping revealed research focus areas such as, biomechanical data, gait analysis, and AI methodologies for neurological disease diagnosis. The co-occurrence analysis of author keywords allowed the identification of four major themes: (a) machine learning and gait analysis; (b) sensors and wearable health technologies; (c) cognitive disorders; and (d) neurological disorders and motion recognition technologies. The bibliometric insights demonstrate a growing and collaborative interest in this domain, while the literature review highlights current methodologies and advancements. This study offers a foundation for future research and provides researchers, clinicians, and occupational therapists with an in-depth understanding of AI's transformative role in neurology.
CacheFormer: High Attention-Based Segment Caching
Authors: Singh Sushant , Mahmood Ausif
Published: February 2025

Efficiently handling long contexts in transformer-based language models with low perplexity is an active area of research. Numerous recent approaches like Linformer, Longformer, Performer, Structured state space models (SSMs)., could not fully resolve this problem. All these models strive to reduce the quadratic time complexity of the attention mechanism while minimizing the loss in quality due to the effective compression of the long context. Inspired by the cache memory mechanism in computer architecture, we improve the work presented in Long-Short Transformer (Transformer LS). Our enhancements include augmenting the architecture with attention on dynamically retrieved uncompressed segments that indicate high attention at the compressed level. Like the cache memory principle, during a cache miss, not only the needed data is retrieved from the memory, but the nearby following data is also obtained. On a similar note, we too retrieve the nearby segments in uncompressed form when a high attention occurs at the compressed level. We further enhance the Transformer LS by augmenting the long attention with compressed overlapping segments to reduce the loss in quality due to segment fragmentation that occurs in sequences with long context. Our perplexity results indicate significant improvements over Transformer LS and other SOTA language models.
The Diversity of Crystals, Microstructures and Texture That Form Ostreoidea Shells
Authors: Sancho Vaquer Anna , Griesshaber Erika , Salas Carmen , Harper Elizabeth M. , Checa Antonio G. , Schmahl Wolfgang W.
Published: February 2025

The shell of Ostreoidea is specific among that of bivalves, as it comprises many crystal microstructures and crystal textures. This stems from the fact that into the basic shell structure, consisting of columnar, foliated, granular calcite and myostracal aragonite, voids are incorporated: the pores of the vesicular segments and the blades/laths of the chalk lenses. Furthermore, cementation to uneven-surfaced substrate requires the formation of corrugated shell morphologies and adjustments of crystal organization for the generation of convex/concave shell surfaces.
We investigated the shell of Magallana gigas, Ostrea stentina, Ostrea edulis (Ostreidae) and Hyotissa hyotis, Hyotissa mcgintyi, Neopycnodonte cochlear (Gryphaeidae) with high-resolution, low-kV, electron backscatter diffraction (EBSD) measurements and scanning electron microscopy (FE-SEM) imaging. From a crystallographic perspective we, (i) characterized the sub-micrometer crystal assembly of ostreoid microstructures and textures, (ii) investigated crystal organization at the changeover from one microstructure into the other, and (iii) examined how curved crystal surfaces are generated at inner shell surface and within the shell, in and at aggregations of folia. We show that Ostreoidea are capable to secrete, within the same shell, single crystalline, graded and polycrystalline calcite, the latter with almost random orientation. We demonstrate that Ostreoidea myostracal aragonite is twinned, while Ostreoidea calcite is not twinned, neither the calcite within the different microstructures, nor the calcite at the changeover from one microstructure into the other. We highlight the very specific microstructure of the foliated shell and demonstrate the strongly regulated gradedness of both, the c- and a*-axes orientation of the foliated calcite crystallites.
Modelling Metrological Traceability
Authors: Hall Blair D.
Published: February 2025

Metrological traceability is essential for ensuring the accuracy of measurement results and enabling comparison of results to support critical decision-making in society. This paper explores a structured approach to modelling traceability chains, focusing on the role of residual measurement errors and their impact on measurement accuracy. This work emphasises a scientific description of these errors as physical quantities. By adopting a simple, static modelling framework grounded in physical principles, the paper offers a formal way to track the effects of errors through an entire traceability chain, from primary reference standards to end users. Real-world examples from microwave and optical metrology highlight the effectiveness of this rigorous modelling approach. Additionally, to further advance digital systems development in metrology, the paper advocates a formal semantic structure for modelling, based on principles of Model-Driven Architecture. This architectural approach will enhance the clarity of metrological practices and support ongoing efforts toward the digital transformation of international metrology infrastructure.
Fragmentation-Based Linear-Scaling Method for Strongly Correlated Systems: Divide-and-Conquer Hartree–Fock–Bogoliubov Method, Its Energy Gradient, and Applications to Graphene Nano-Ribbon Systems
Authors: Kobayashi Masato , Kodama Ryosuke , Akama Tomoko , Taketsugu Tetsuya
Published: February 2025

This study introduces a fragmentation-based linear-scaling method for strongly correlated systems, specifically the divide-and-conquer Hartree-–Fock-–Bogoliubov (DC-HFB) approach. Two energy gradient formulations of the DC-HFB method are derived and implemented, enabling efficient optimization of molecular geometries in large systems. This method is applied to graphene nanoribbons (GNRs) to explore their geometries and polyradical characters. Numerical results demonstrate that the present DC-HFB method has a potential to treat the static electron correlation and predict diradical character in GNRs, offering new avenues for studying large-scale strongly correlated systems.
Impact Of Climate Risk On Financial Sector Stability Of The Selected Sadc Countries
Authors: Masunda Shylet Mufandaedza
Published: February 2025

The study investigates the impact of climate risks on the financial sector stability of the selected SADC countries in the context of Angola, Malawi, Mozambique, Madagascar, Tanzania, Eswatini, Democratic Republic of Congo, Tanzania, South Africa, Madagascar, and Zambia. Countries chosen for this study face climate-related shocks such as rising annual carbon dioxide emissions, affecting their agro-based economies and negatively impacting financial stability. The study aprovides insights into the risks and challenges associated with climate risk for the selected SADC countries. The study employed Panel-Corrected Standard Errors (PCSE) and Feasible Generalized Least Squares (FGLS) models to estimate the long-run parameters of climate risks' impact on the region's financial sector stability. The research confirms a negative and statistically significant long-run relationship between climate risk and financial sector stability in the SADC region. The research found carbon dioxide emissions to be statistically significant and have a negative impact on financial stability. The study recommends integrating climate-related risk into financial, supervision, and prudential regulations. It also recommends critical interventions in creating climate risk insurance products and availing incentives towards green investments to enhance financial sector resilience.
Market Efficiency and Stability Under Short Sales Constraints: Evidence from a Natural Experiment with High-Frequency Resolution
Authors: Chan Lin Kun , Lin Chin Yang , Yeh Jin-Huei
Published: February 2025

We examine how short-sales policy regimes affect the efficiency of price adjustments and market stability. Using a ‘threshold error correction model’ (TECM), we find a more rapid convergence rate to new equilibria for upward adjustments than for downward adjustments. Moreover, relaxing the short sales constraints essentially improves price efficiency. Our findings refute the claim that tighter constraints can help stabilize the market since the tightening of the short-sales restriction leads to increases in both market volatility and downside risk. These results hold even when market conditions and liquidity are controlled. Even though prices may fall more sharply without short-sales bans, rather interestingly, the evidence from our counterfactual policy analysis suggests that tighter constraints help restore market confidence. As a result, policymakers may practically optimize to strike a balance between the benefits of restored market order and the cost of elevated market volatility.
Robust Client Selection Strategy Using an Improved Federated Random High Local Performance Algorithm to Address High Non-IID Challenges
Authors: Sittijuk Pramote , Petrot Narin , Tamee Kreangsak
Published: February 2025

This paper introduces an improved version of the Federated Random High Local Performance (Fed-RHLP) algorithm, specifically aimed at addressing the difficulties posed by Non-IID (Non-Independent and Identically Distributed) data within the context of federated learning. The refined Fed-RHLP algorithm implements a more targeted client selection approach, emphasizing clients based on the size of their datasets, the diversity of labels, and the performance of their local models. It employs a biased roulette wheel mechanism for selecting clients, which improves the aggregation of the global model. This approach ensures that the global model is primarily influenced by high-performing clients while still permitting contributions from those with lower performance during the model training process. Experimental findings indicate that the improved Fed-RHLP algorithm significantly surpasses existing methodologies, including FedAvg, Power of Choice (PoC), and Fed-Choice, by achieving superior global model accuracy, accelerated conver-gence rates, and decreased execution times, especially under conditions of high Non-IID data. Furthermore, the improved Fed-RHLP algorithm exhibits resilience even when the number of clients participating in local model updates and aggregation is diminished in each communication round. This characteristic positively influences the conservation of limited communication and computational resources.
Empirical Study on the Relationship Between Board Size and Corporate Performance
Authors: Jeddah Chamber , Toshania Mehmetlu
Published: February 2025

Board size is an important component of corporate governance. This study selects data from 55 listed companies in the automotive industry with beta values close to 1 from 2012 to 2024 for empirical research, analyzing the relationship between board size and corporate performance, as well as the main factors influencing board size. The results show that board size is positively correlated with corporate performance, and board size is mainly influenced by environmental characteristics, firm size, development stage, and internal board structure. Based on empirical data, the maximum board size is calculated to be 11 members.
Investigating the Impact of Semi-Supervised Learning Methods to Improve the Quality of Diagnosis of Retinal Diseases from OCT Images
Authors: Alizadeh Armin , Alenezi Ahmad , Khakestari Nastaran , Amizadeh Yashar , Jodeiri Ata
Published: February 2025

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss, especially in elderly, that can be diagnosed from OCT images. OCT, a noninvasive imaging modality, is not only widely applied in detecting retinal diseases but meaningful correlations have also been found between OCT images and neurological disorders like Alzheimer disease. The insufficient labeled dataset is the key challenge in using OCT images for disease detection, highlighting the importance of employing semi-supervised methods to address this issue. In this paper, the first step is to investigate the optimal structure of a supervised model for detecting AMD with the model being based on EfficientNet. In subsequent steps, the dataset size is reduced to 70%, 50%, 20%, and 5% of the total dataset to identify the best model based on an iterative teacher-student approach for detecting AMD disorders. In this study, the available OCT dataset gathered at Noor Eye Hospital consisting of 16,822 retinal OCT images are utilized. The optimized supervised model achieved 87.14% accuracy in distinguishing different AMD stages. As the dataset size is reduced to the most severe conditions (i.e., 20% and 5%), an expected decrease in accuracy to 77.05% and 54.78% has observed. Introduced semi-supervised learning based on iterative teacher-student model improved the accuracy to 88.56% at 20% and 64.15% at 5% volume of dataset, achieving high confidence levels, thereby enhancing the performance of the supervised model. This study introduces a framework that can be used in future studies to detect diseases with an insufficient OCT dataset, aiming to improve model accuracy.
Addressing the Needle in a Haystack Problem in Time Behavioural Study of 3rd Order Gene Combinations in WNT3A Stimulated HEK 293 Cells
Authors: Sinha Shriprakash
Published: February 2025

Gujral and MacBeath [1] provides a quantitative, and dynamic study of WNT3A-mediated stimulation of HEK 293 cells, where they record time based expression profiles of several response genes which correlated significantly with proliferation and migration. By monitoring the dynamics of gene expression using self-organizing maps, they identified clusters of genes that exhibit similar expression dynamics and uncovered previously unrecognized positive and negative feedback loops. However, their study depicts/uses singular measurements of individual gene expression at different time snapshots/points to infer the system wide analysis of the WNT pathway. At any particular time point, it is often the case that genes are working synergistically in combinations, even though their expression measurements are singular in nature. Sinha [2] recently demonstrated the use of machine learning based search engine to rank/reveal gene combinations at 2nd order for the time series data by Gujral and MacBeath [1] and showed how it is possible to locate combinations of priority that might be working synergistically. However, the problem explodes combinatorially with even a small set of 71 recorded genes in the above study, when one steps to explore 3rd order combinations. With the total number of 71C3 (= 57155) combinations, it becomes nearly impossible for any biologist to study the system wide dynamics of any pathway. Here, I • enumerate and rank all 71C3 combinations using four different sensitivity methods; • show the conserved rankings for PORCN-WNT-X combinations, which point to existence of biological synergy of some of these combinations across the different sensitivity methods; and • study the behaviour of some of the combinations related to WNT3A response genes that are ranked by the search engine in time. This study demonstrates how biologists can use the machine learning based search engine to address the needle in a haystack problem of discovering meaningful combinations of higher order in a vast search forest, which on further wet lab test might assist in intervening the pathway at a combinatorial level, in time.
What Is the Best Solution for Smart Buildings? A Case Study of Fog, Edge Computing and Smart IoT Devices
Authors: Chiozzotto Mauro , Ramírez Miguel Arjona
Published: February 2025

We present a case study of Fog Computing, Edge Computing (EC) and Intelligent EC applied to Smart Cities or Smart buildings, where there is the deployment of innovative services and smart IoT devices, discussing new architecture as Software Defined Network (SDN) and Network Functions Virtualization (NFV). Specifically, a comprehensive solution of a Smart Building case is proposed to validate main statements and conclusions are drawn, providing a general guideline to address the problem of choosing between Edge or Fog Computing and motivating new innovative, cost-benefit solutions that should be applied on it in order to make benefits available to citizens in Smart Cities.
The Art of PEGylation: "From Simple Polymer to Sophisticated Drug Delivery System"
Authors: Makharadze Davit , del Valle Luis J. , Katsarava Ramaz , Puiggalí Jordi
Published: February 2025

The development of effective drug delivery systems is a major challenge in cancer therapy, gene therapy, and infectious disease treatment because of its low bioavailability, rapid clearance, and toxicity towards non-targeted healthy tissues. This review discusses how PEGylation, the covalent attachment of poly(ethylene glycol) (PEG), enhances the pharmacokinetic profiles of the drug-containing nanosystems through the "stealth effect" that avoids immune system detection and improves circulation times in different nano-delivery systems. The review provides an overview of the synthetic methods of PEG derivatives, their conjugation with nanoparticles, proteins, and drugs, and their characterization using modern analytical tools. The paper explores various PEGylation strategies, including covalent conjugation and self-assembly, and discusses the influence of PEG chain length, density, and conformation on drug delivery efficiency. Despite its advantages, there are several challenges associated with PEGylation such as the immunogenicity of anti-PEG responses, the potential for accelerated clearance of PEGylated drugs, reduced therapeutic efficacy, and the possibility of allergic reactions. Consequently, the balance between the benefits of PEGylation and its immunogenic risks remains a critical area of investigation.
The Role and Challenges of AIGC-Driven Personalised Learning in Promoting Educational Sustainability: A Case Study Involving University Students in China
Authors: Yixin Li , Zhileng Xiong , Qing Huang , Ying Cheng
Published: February 2025

With the rapid development of Artificial Intelligence Generated Content (AIGC) technology, education is undergoing profound changes. AIGC significantly promotes inclusiveness, equity and quality of education by providing personalised learning content and intelligent instructional support to tailor the learning experience for students, which aligns with the United Nations 2030 Sustainable Development Goals (SDG4) requirements on the sustainability of education. However, despite the great potential of AIGC in education, its practical application still faces many challenges, such as unstable technology, untimely content updates, and privacy protection. This study takes Chinese college students as an example to explore the role and challenges of AIGC-based personalised learning in promoting educational sustainability. Through the analysis of 928 valid questionnaires and the SWOT-AHP method, this paper evaluates students' attitudes towards AIGC personalised learning and its impact on improving the quality of education, access to resources and fairness. The study also identified the main challenges in using AIGC, including technical problems, insufficient teacher-student interaction, and privacy security. The results show that AIGC positively impacts education's sustainability, but relevant technical and ethical challenges must be overcome to achieve its complete application. This study provides theoretical support for AIGC-based personalised learning and practical guidance for the future application and development of educational technology, aiming to provide a valuable reference for innovation and sustainable development in education.
Influence of the Layout of Cells in a Traction Battery on the Evolution of a Fire in the Event of a Failure
Authors: Olona Ana , Castejon Luis
Published: February 2025

Research on the safety and impact of lithium-ion battery failure has focused on individual cells as lithium-ion batteries began to be used in small devices. However, large and complex battery packs need to be considered and how the failure of a single cell can affect the system needs to be analyzed. As this initial failure at the level of a single cell can lead to thermal runaway of other cells within the pack, resulting in increased risk. This article focuses on tests of mechanical abuse (perforation of cylindrical cells), overcharge (pouch cells), and heating (cylindrical cells with different arrangements and types of connection) to analyse how various parameters influence the mechanism of thermal runaway (TR) propagation. Parameters such as SoC (State of Charge), environment, arrangement, and type of connection are thoroughly evaluated. The tests also analyse the final state of the post-mortem cells and measure the internal resistance of the cells before and after testing. It is concluded that temperature is a crucial parameter, as overheating of the battery can cause an exothermic reaction and destroy the battery completely. Also, overcharging the cell can compromise its internal structure, which underlines the importance of a well-functioning battery management system (BMS).
Fully Conjugated Heteroatomic Non- and Quasi-Alternant Polyradicals
Authors: Betkhoshvili Sergi , Poater Jordi , Moreira Ibério de P. R. , Bofill Josep Maria
Published: February 2025

In this work, we present fully π-conjugated diradical(oid)s and tetraradical(oid)s with five-membered non-alternant cyclopentadienyl and quasi-alternant thiophene rings, latter of which is used as a source of aromatic stabilization. By controlling the topology of the π-systems, we can restrict the lower-bound number of unpaired electrons. Aromaticity and/or anti-aromaticity in the different configurations of the compounds can be used to design conjugated compounds with high open-shell characters. We also designed the diradical(oid) based only on the five-membered rings, without any terminal radical groups. This work exemplifies the application of our theory of rational design of polyradicals with the heteroatomic and non/quasi-alternant organic systems. The ability to create polyradicals with different classes of organic compounds offers possibilities to create multifunctional organic materials with tunable magnetic properties.
Automated Mapping of the Freshwater Ecosystem Functional Groups of the International Union for Conservation of Nature Global Ecosystem Typology in a Large Region of Arid Australia
Authors: Francis Roxane J , Grantham Hedley S. , Keith David A , Ferrer-Paris Jose R , Kingsford Richard T
Published: February 2025

The classification of freshwater ecosystems is essential for effective biodiversity conser-vation and ecosystem management, particularly with increasing threats. We developed an automated approach to mapping and classifying freshwater ecosystem functional groups based on the IUCN Global Ecosystem Typology (GET), offering a scalable, dy-namic, and efficient alternative to current manual methods. Our method leveraged remote sensing data and thresholding algorithms to classify ecosystems into distinct ecosystem functional groups, accounting for challenges such as temporal and spatial variability of freshwater ecosystems, inconsistencies in manual classification, and the complexities of dynamic ecosystems. Unlike traditional approaches, which rely on manual cross-referencing to adapt existing maps and subjective biases, our system is repeatable, transparent, and adaptable to new incoming satellite data. We demonstrate the applicability of this method in the Paroo-Warrego region of Australia (~14,000,000 ha), highlighting the automated classification’s capacity to process large areas with diverse ecosystems. Although some functional groups require static datasets due to current lim-itations in satellite data resolution, the overall approach had high accuracy (84%) and potential for global scalability. This work provides a foundation for future applications to other freshwater ecosystems around the world underpinning biodiversity management, monitoring and reporting worldwide.
A Regional Approach to Strengthening Implementation of Sustainable Antimicrobial Stewardship Programs in Five countries in East, Central, and Southern Africa
Authors: Wesangula Evelyn , Chizimu Joseph Y , Mapunjo Siana , Mudenda Steward , Seni Jeremiah , Mitambo Collins , Yamba Kaunda , Gashegu Misbah , Nhantumbo Aquino , Francis Emiliana , Moremi Nyambura , Athiany Henry , Matu Martin
Published: February 2025

Background: Antimicrobial stewardship (AMS) programs optimize use of antimicrobi-als and reduce antimicrobial resistance (AMR). This study evaluated implementation of AMS programs in Africa using a harmonized regional approach. Methods: This was an exploratory cross-sectional study across five countries involving 32 hospitals using adapted Periodic National and Hospitals Assessment Tool in WHO policy guidance on integrated AMS activities in human health. Results: This study found baseline scores for AMS core elements ranging from 34% to 79% and improved to 58% to 92% at the end line. At baseline, DTC functionality in updating facility-specific medicines and medical devices ranged from 58% to 100% and ranged from 79 to 100% at endline. Classifying antibiotics by WHO AWaRe classification ranged from 33% to 83% at baseline and 64% to 100% at endline. Leadership commitment scores were 47% at baseline and 66% at endline. Education and training scores were 42% and 63% at baseline and end-line respectively. Reporting and feedback was 34% at baseline and 58% at endline. Conclusions: Our study showed that understanding context and standardizing regional stewardship approaches enhanced cross-country learning and improved AMS implementation. Although challenges in LMICs are similar, they vary by country and can be addressed by strengthening AMS regulatory frameworks and surveillance systems.
Merkel Cell Carcinoma of the Skin: Deducing the Pattern of Spread from an International Aggregated Database of 303 Patients
Authors: Tai Patricia , Joseph Kurian , Lian Jidong , Thachuthara Aoife , Prajapati Vimal , Assouline Avi , Yu Edward , Veness Michael
Published: February 2025

Background: There is controversy if Merkel cell carcinomas (MCCs) spread to lymph nodes or distant metastases (DM) first. Methods: Data from six institutions (March 1982 to Feb 2015) formed an aggregated database of 303 patients. The primary outcome was recurrence patterns. Results: (a) More patients presented with lymph node metastases (LNM) than DM, 19.5% (59/303) versus 2.6% (8/303). (b) 26.1% (79/303) had lifetime DM, of whom 47/79 also developed LNM: 31/47 (66%) prior to DM. (c) A shorter median time interval of 1.5 (range: 0-47.0) months from initial diagnosis to LNM; and 8 (0-107.8) months from diagnosis to DM. Another additional observation was 7/79 patients with initial primaries <1 cm in maximum dimension developed DM in their lifetime, the smallest being 0.2 cm. Conclusions: Three observations favor prior LNM giving rise to subsequent DM as the main pathway of dissemination in MCC. These observations are especially important in developing countries with inadequate staging resources for patient management. Even small MCCs <1 cm in maximum dimension, including a 0.2 cm primary, can metastasize. Therefore, we believe this report might be practice-changing since some thought these small primaries do not require any adjuvant therapy.
Feasibility of Modified Mindfulness Training Program on Antenatal Depression and Perceived Stress Among Expected Mothers with Male Child Preference
Authors: - Badil , Naz Najma , Muhammad Dildar , - Khalid Rehman
Published: February 2025

Background and Objectives: Antenatal depression and perceived stress are prevalent mental health challenges in pregnant women, associated with male-child preference. The study aimed to assess the feasibility of a modified mindfulness training program on antenatal depression and perceived stress levels among expected mothers with male child preference at a public sector tertiary care hospital in Karachi, Pakistan. Materials and Methods: The present feasibility trial was conducted among expected mothers having antenatal depression and perceived stress with a male child's preference by using the ADAPT-ITT framework. Need assessment and experiences of the new target population were carried out through an exploratory, descriptive qualitative study. In-depth interviews were conducted using a semi-structured interview guide and analysed using a thematic analysis process. A repeated measures MANOVA was employed to investigate the effect of time on antenatal depression and perceived stress scores in the feasibility of the intervention. Results: Five major themes emerged from qualitative data. Significant influence of time was established on antenatal depression scores and a perceived score of F (2, 326) = 21.244, p < .001 and F (2, 326) = 310.748, p < 0.001 respectively. Antenatal depression scores significantly decreased from T1 to T2, mean difference = 4.00, p < .001, and slightly significant decline from T2 to T3, mean difference = 1.167, p = 0.001. Perceived stress scores significantly reduced from T1 to T2, mean difference = 10.214, p < .001, and a minor but significant decline from T2 to T3, mean difference = 0.333, p = 0.043.
Conclusion: The study concluded the modified mindful training program is a culturally suitable, contextually relevant intervention in Pakistan's context and it significantly reduced antenatal depression and perceived stress in expected mothers.
Phosphite Compounds Suppress Anthracnose in Soybean Seeds Infected by Colletotrichum truncatum, Stimulate Growth and Defense Mechanisms
Authors: Silva Júnior Manoel Batista da , Resende Mário Lúcio Vilela , Pozza Edson Ampelio , Resende Alexandre Ribeiro Maia de , Silveria Gustavo César Dias , da Veiga Jayne Deboni , Oliveira Júlia Marques , Silva André Costa da
Published: February 2025

Soybean is one of the main agricultural commodities, and its productivity is limited by several diseases, such as anthracnose, which is caused by a complex of fungal species, with Colletotrichum truncatum being the most prevalent. Management is mainly carried out through chemical seed treatment. However, a reduction in the sensitivity of C. truncatum to fungicides was observed. Therefore, it is extremely important to search for products that are effective in controlling the disease. The objectives of this study were to evaluate the efficacy of commercial formulations of Cu, K, Mn and Zn phosphites in the treatment of soybean seeds infected by C. truncatum and their ability to induce defense mechanisms against the fungus. For this purpose, seeds inoculated with C. truncatum were treated with phosphites and a fungicide (carbendazim + thiram). The seeds were subjected to germination, health and vigor tests. Fungal toxicity and the ability of phosphites to induce defense through the activities of catalase, peroxidase and superoxide dismutase enzymes, as well as the levels of lignin and total soluble phenols, were also evaluated. Mn and Zn phosphites were toxic to C. truncatum and effective in controlling anthracnose in infected soybean seeds, showing no difference in relation to the fungicide. Mn phosphite induced the production of CAT, POX and lignin, while Zn phosphite increased the production of CAT and POX. These results demonstrate the effectiveness of Mn and Zn phosphites in controlling anthracnose in infected soybean seeds, their direct toxic action and their ability to induce defense mechanisms.
Endodontic Regeneration Therapy: Current Strategies and Tissue Engineering Solutions
Authors: Kyaw Moe Sandar , Kamano Yuya , Yahata Yoshio , Tanaka Toshinori , Sato Nobuya , Toyama Fusami , Noguchi Tomose , Saito Marina , Nakano Masato , Harada Futaba , Saito Masahiro
Published: February 2025

With increasing life expectancy and an aging population, the demand for dental treatments that preserve natural teeth has grown significantly. Among these treatments, endodontic therapies for pulpitis and apical periodontitis play a vital role not only in keeping occlusal function, but also in preventing the exacerbation of systemic diseases. Both pulpitis and apical periodontitis are primarily caused by infections of the oral pathobiont within the root canal, leading to inflammation and destruction of the pulp, apical periodontal tissue and bone. Standard root canal therapy aims to remove the infection source and facilitate natural tissue healing through the body’s regenerative capacity. However, challenges remain, including limited tooth functionality after complete pulp removal in pulpitis and insufficient recovery of the large bone defect in apical periodontitis. To address these limitations, endodontic regenerative therapies have emerged as promising alternatives. Pulp regeneration therapy seeks to restore the functionality of dental pulp, while bone regeneration therapy aims to repair and regenerate large bone defects affected by apical periodontal tissue.
Are There Gender-Based Differences in COPD Patients with Type 2 Respiratory Failure? Impact on Clinical Practice
Authors: Ozdemir Tarkan , Yıldız Murat , Arı Maşide , Arı Emrah , Eraslan Doğanay Güler , Cırık Mustafa Özgür , Doğancı Melek , Özdilekcan Çiğdem , Kızılgöz Derya , Şipit Yusuf Tuğrul
Published: February 2025

Purpose: To contribute to clinical practice by identifying gender-based differences in patients diagnosed with COPD who are monitored in the intensive care unit due to type 2 respiratory failure.
Materials and Methods: The study was planned as a prospective, observational, and cross-sectional investigation. A total of 258 patients, 91 females and 167 males, were included in the study between 2023 and 2024. Demographic data and clinical parameters of COPD patients admitted to intensive care due to hypercapnic respiratory failure and treated with NIMV were compared between genders.
Results: The number of male patients was higher than female patients, while the mean age of female patients was higher than that of males. Body mass index (BMI), morbid obesity, atrial fibrillation, renal disease, heart failure, hypertension, hypothyroidism, Charlson Comorbidity Index (CCI), and cardiothoracic ratio were found to be significantly higher in female patients. Emphysema and steroid use in treatment were more common in male patients. In laboratory analyses conducted at the time of admission, the average D-dimer and BNP levels were higher in female patients. The mean pCO2 level assessed prior to discharge was also higher in female patients.
Conclusion: Heart failure and risk factors that may lead to heart failure are more prominent in female COPD patients with type 2 respiratory failure. Despite the lower number of female patients compared to males, the significantly higher comorbidity burden in females as per CCI scores suggests that medical processes may be more challenging to manage in females. We believe that these findings will contribute to clinical practice and provide clinicians with insights for patient management.
Enhancement in Performance and Reliability of Fully Trans-Parent A-IGZO Top Gate Thin-Film Transistors by a Two-Step Annealing TreatmentEnhancement in Performance and Reliability of Fully Trans-Parent A-IGZO Top Gate Thin-Film Transistors by a Two-Step Annealing Treatment
Authors: Zheng Shuaiying , Wang Chengyuan , Lv Shaocong , Dong Liwei , Li Zhijun , Xin Qian , Song Aimin , Zhang Jiawei , Li Yuxiang
Published: February 2025

A two-step annealing treatment was applied on a fully transparent amorphous InGaZnO4 (a-IGZO) top gate thin-film transistor (TG-TFT) to improve the device perfor-mance. The electrical properties and stabilities of a-IGZO TG TFTs were significantly im-proved as the 1st-annealing temperature increased from 150°C to 350°C with a 300°C 2nd-annealing treatment. The a-IGZO TG-TFT with the 300°C 1st-annealing treatment demonstrated the overall best performance, which has a mobility of 13.05 cm2/(V·s), a threshold voltage (Vth) of 0.33 V, a subthreshold swing of 130 mV/dec, and a Ion/Ioff of 1.73×108. The Vth deviation (ΔVth) was -0.032 V and -0.044 V, respectively, after a 7200 s positive and negative bias stress under the gate bias voltage VG = ± 3 V and VD = 0.1 V. The PL spectra results revealed that the distribution and the density of defects in a-IGZO films were changed after the 1st-annealing treatment, whereas, XPS results displayed that the contents of oxygen vacancy and Ga-O bond varied in annealed a-IGZO films. In addition, a-IGZO TG-TFT had achieved a transmittance of over 90%. Researches on effects of the 1st-annealing treatment will contribute the fabrication of highly stable top-gate TFTs in the fields of transparent flexible electronics.
Relevancy Prediction of the Emerging Pathogens with Porcine Diarrhea by Logistic Regression Model
Authors: Li Benqiang , Tao Jie , Li Xin , Cheng Jinghua , Shi Ying , Tang Pan , Liu Huili
Published: February 2025

Porcine viral diarrhea has been one of the three most important diseases in China, with increasing kinds of porcine diarrheal pathogens and severe mixed-infection. Analysis of the dominant mixed-infection model is a fundamental step in boosting the prevention and control of porcine diarrhea disease. In this study, 3256 procine fecal samples were collected in Shanghai, China, from 2015 to 2023 to identify novel pathogenic infection patterns. The results confirmed that porcine astrovirus (PAstV), porcine sapelovirus (PSV), and porcine epidemic diarrhea virus (PEDV) were the top three agents with positive rates of 28.47%, 20.71%, and 20.23%, respectively. Porcine rotavirus (PoRV) and transmissible gastroenteritis virus (TGEV) accounted for only 8.12% and 1.12%, respectively. Importantly, mixed infection rates were high and complicated. The double infection rate was higher than that of a single infection. Next, the mixed-infection model of PEDV and emerging diarrheal pathogens was explored. The predominant dual-infection models were PEDV/PKoV (Porcine kobuvirus) (14.18%), PEDV/PAstV (10.02%), and PEDV/PSV (9.29%). The predominant triple infection models were PEDV/PKoV/PAstV (18.93%), PEDV/PSV/PAstV (10.65%), and PEDV/PKoV/PSV (7.10%). The dominant quadruple-infection model was PEDV/PAstV/PSV/PKoV (46.82%). In conclusion, PEDV is mainly mix-infected with PAstV, PSV, and PKoV in clinical settings. Furthermore, multiple-factor logistic regression analysis confirmed that PAstV, PKoV, BVDV, and PEDV were closely related to porcine diarrhea. PEDV/PKoV, PEDV/PoSaV, PKoV/BVDV, PoSaV/BVDV, and PDCoV/PoSaV had great co-infection dominance, which will be helpful for porcine co-infection research.