bluuebunny/crossref_metadata_2025_split huggingface dataset:
Beyond Conventional Enhancements: Self‐Organization of a Buffer Material on Tin Oxide as a Game‐Changer for Improving the Performance of Inverted Organic Solar Cells
Authors: Wu Jifa , Li Yumeng , Tang Feng , Guo Yinchun , Liu Guoqiang , Wu Shaoguang , Hu Bin , Fu Yuang , Lu Xinhui , Lu Guanghao , He Zhicai , Zhu Xuhui , Peng Xiaobin 
Published: June 2024

AbstractInverted organic solar cells (OSCs) have garnered significant interest due to their remarkable stability. In this study, the efficiency and stability of inverted OSCs are enhanced via the in situ self‐organization (SO) of an interfacial modification material Phen‐NaDPO onto tin oxide (SnO2). During the device fabrication, Phen‐NaDPO is spin‐coated with the active materials all together on SnO2. Driven by the interactions with SnO2 and the thermodynamic forces due to its high surface energy and the convection flow, Phen‐NaDPO spontaneously migrates to the SnO2 interface, resulting in the formation of an in situ modification layer on SnO2. This self‐organization of Phen‐NaDPO not only effectively reduces the work function of SnO2, but also enhances the ordered molecular stacking and manipulates the vertical morphology of the active layer, which suppress the surface trap‐assisted recombination and minimize the charge extraction. As a result, the SO devices based on PM6:Y6 exhibit significantly improved photovoltaic performance with an enhanced power conversion efficiency of 17.62%. Moreover, the stability of the SO device is also improved. Furthermore, the SO ternary devices based on PM6:D18:L8‐BO achieved an impressive PCE of 18.87%, standing as one of the highest values for single‐junction inverted organic solar cells to date.
LLM Adoption Trends and Associated Risks
Authors: Schillaci Zachary 
Published: June 2024

AbstractThe emergence of Large Language Models (LLMs) is expected to impact the job market significantly, accelerating automation trends and posing a risk to traditionally creative-oriented jobs. LLMs can automate tasks in various fields, including design, journalism, and creative writing. Companies and public institutions can leverage generative models to enhance productivity and reduce workforce requirements through machine-assisted workflows and natural language interactions. While technical skills like programming may become less important in certain roles, generative models are unlikely to fully replace programmers due to the need for expertise in code validation and niche development. The enterprise landscape of LLMs comprises providers (organizations training proprietary models), integrators (technology companies fine-tuning LLMs for specific applications), and users (companies and individuals adopting LLM-powered solutions). The applications of the models include conversational search, customer service chatbots, content creation, personalized marketing, data analysis, and basic workflow automation. The regulatory landscape is rapidly evolving, with key considerations including copyright, data security, and liability. Government involvement and informed expertise are recommended to guide governance and decision-making processes in this domain.
Copyright-Related Risks in the Creation and Use of ML/AI Systems
Authors: German Daniel M. 
Published: June 2024

AbstractThe potential impact of LLMs on cybersecurity is moderated not only by their adoption, applications, technical capabilities, and safety technology supporting them, but also by the legal framework surrounding their usage. As countries around the world realize the profound impact ML and AI have on their society and economy, they need to ensure that the development and deployment of ML/AI solutions are aligned with the foundational principles of those countries. This chapter attempts to identify recent developments in the legal framework surrounding LLMs that will most likely impact their development and become suitable levers for ensuring their safety and guarding against their malicious usage, focusing on the EU and North America.
LLM Detectors
Authors: Da Silva Gameiro Henrique 
Published: June 2024

AbstractLLM detectors aim at detecting text generated by an LLM. They can be categorized into two main types: specific detectors and general detectors. Specific detectors target a particular type of language or context, such as hate speech or spam. In contrast, general detectors aim to identify a broad range of problematic languages, such as misinformation or propaganda. They typically rely on supervised learning, using large labeled datasets to train the models to recognize patterns in the language. General-purpose detectors have shown bad results, but specific-purpose detectors have shown more promising results. This has to be nuanced due to the broad range of effective attacks, especially the paraphrasing attacks, to which all defense techniques are somewhat vulnerable. There are also many other challenges for developing detectors such as the growing numbers of different LLMs (open source or not) being developed and an effective detector that works with many human languages besides English. Mitigation techniques include storing user conversations with an LLM and watermarking (especially cryptographic).
On-Site Deployment of LLMs
Authors: Schillaci Zachary 
Published: June 2024

AbstractAs consumer electronics and tensor computation for machine learning (ML) continue to advance, model execution and training become more accessible. NVIDIA introduced the RTX 4090 graphics cards, marketed initially as gamer-oriented products, in late 2022. Though relatively expensive for consumer use, their manufacturer’s suggested retail price (MSRP) of 1600 USD makes them affordable as a professional tool. These cards’ extensive video random access memory (vRAM), computational power comparable to last-generation flagship professional cards, and ability to use single-byte floats enable a pair of them to train, fine-tune, and run on-premises Large Language Models (LLMs) with up to 7 billion parameters per card. Until this release, such a feat would have required data center-level equipment. Although the RTX 4090 and H100 GPU represent a qualitative step forward, iterative improvements combined with the speculated lowering of computational precision to half-byte floats could make larger models even more accessible for on-premises use. This development might, in one aspect, lower the entry barrier for cyberattackers, simplifying the process for advanced persistent threats (APTs) to camouflage their activities amidst unsophisticated attackers or those employing generative LLMs for non-malicious purposes. Conversely, as an alternative to cloud-hosted models, on-site LLMs may limit the possibility of private information leakage or model poisoning while offering specialized capabilities for legitimate users.
Standards for LLM Security
Authors: Majumdar Subhabrata 
Published: June 2024

AbstractThe National Institute of Standards and Technology (NIST) is a recognized authority on computer security that publishes guidelines and standards for a broad range of technologies, including artificial intelligence (AI). The guidelines include the requirement for LLM decision-making transparency, explainability, testing, and validation to guarantee model reliability and security. Moreover, the NIST has also created standards for cryptography, a critical element of many LLM-based applications, such as secure communication and data encryption. The cryptography standards help ensure that LLM-based applications are secure and resilient against attacks by malicious entities. NIST standards can provide a practical framework for secure and ethical LLM-based application development and deployment. By adhering to these standards, developers and organizations can increase the confidence that their LLM-based applications are dependable, trustworthy, and resistant to attacks.
Towards Safe LLMs Integration
Authors: Majumdar Subhabrata , Vogelsang Terry 
Published: June 2024

AbstractLLMs face a critical vulnerability known as sandbox breakout, where attackers bypass the system designers’ limitations to prevent malicious access to the resources for which the LLM agent is a user interface. Thus, they can access the system and potentially steal data, change the interaction with other users, or inject malicious code or contents into underlying databases. Therefore, it is essential to identify and address vulnerabilities that could be exploited to break out of the sandbox. These vulnerabilities could exist in the sandbox, the operating system, or the LLM’s software dependencies. To mitigate the risk of LLM sandbox breakout, robust security measures, such as regular model updates, automated model red-teaming, testing, and access control policies, must be implemented. In addition, sandboxing should be enforced at multiple levels to reduce the attack surface and prevent attackers from accessing critical systems. By implementing these measures, the risk of LLM sandbox breakout can be significantly reduced, and the security and reliability of LLM-based applications can be improved.
Overview of Existing LLM Families
Authors: Kucharavy Andrei 
Published: June 2024

AbstractWhile the general public discovered Large Language Models (LLMs) with ChatGPT—a generative autoregressive model, they are far from the only models in the LLM family. Various architectures and training regiments optimized for specific usages were designed throughout their development, which were then classified as different LLM families.
Phishing and Social Engineering in the Age of LLMs
Authors: Gallagher Sean , Gelman Ben , Taoufiq Salma , Vörös Tamás , Lee Younghoo , Kyadige Adarsh , Bergeron Sean 
Published: June 2024

AbstractThe human factor remains a major vulnerability in cybersecurity. This chapter explores the escalating threats that Large Language Models (LLMs) pose in the field of cybercrime, particularly in phishing and social engineering. Due to their ability to generate highly convincing and individualized content, LLMs enhance the effectiveness and scale of phishing attacks, making them increasingly difficult to detect. The integration of multimodal generative models allows malicious actors to leverage AI-generated text, images, and audio, increasing attack avenues and making attacks more convincing. Two case studies provide a comprehensive look, examining how AI technology orchestrates a phishing attack posing as a typical e-commerce transaction and how an LLM was used in a romance-themed cryptocurrency scam. Both scenarios underline the need for increased awareness and improved defenses against these novel and sophisticated cyber threats.
Development and application of a colloidal-gold immunochromatographic strip for detecting Getah virus antibodies
Authors: Jiang Zhiwen , Qin Ying , Zhang Letian , Xing Gang , Shi Zhiyu , Song Wanjie , Dobrikov Georgi M. , Chen Jie , Su Shuo 
Published: June 2024

Abstract
Getah virus (GETV) is a re-emerging mosquito-borne alphavirus that is highly pathogenic, mainly to pigs and horses. There are no vaccines or treatments available for GETV in swine in China. Therefore, the development of a simple, rapid, specific, and sensitive serological assay for GETV antibodies is essential for the prevention and control of GETV. Current antibody monitoring methods are time-consuming, expensive, and dependent on specialized instrumentation, and these features are not conducive to rapid detection in clinical samples. To address these problem, we developed immunochromatographic test strips (ICTS) using eukaryotically expressed soluble recombinant p62-E1 protein of GETV as a labelled antigen, which has good detection sensitivity and no cross-reactivity with other common porcine virus-positive sera. The ICTS is highly compatible with IFA and ELISA and can be stored for 1 month at 37 °C and for at least 3 months at room temperature. Hence, p62-E1-based ICTS is a rapid, accurate, and convenient method for rapid on-site detection of GETV antibodies.

Key points
• We established a rapid antibody detection method that can monitor GETV infection
• We developed colloidal gold test strips with high sensitivity and specificity
• The development of colloidal gold test strips will aid in the field serologic detection of GETV
Spatial profiles of the bacterial microbiota throughout the gastrointestinal tract of dairy goats
Authors: Hu Qingyong , Luo Jun , Cheng Fei , Wang Ping , Gong Ping , Lv Xuefeng , Wang Xinpei , Yang Min , Wei Pengbo 
Published: June 2024

Abstract
The gastrointestinal tract (GIT) is stationed by a dynamic and complex microbial community with functions in digestion, metabolism, immunomodulation, and reproduction. However, there is relatively little research on the composition and function of microorganisms in different GIT segments in dairy goats. Herein, 80 chyme samples were taken from ten GIT sites of eight Xinong Saanen dairy goats and then analyzed and identified the microbial composition via 16S rRNA V1-V9 amplicon sequencing. A total of 6669 different operational taxonomic units (OTUs) were clustered, and 187 OTUs were shared by ten GIT segments. We observed 264 species belonging to 23 different phyla scattered across ten GITs, with Firmicutes (52.42%) and Bacteroidetes (22.88%) predominating. The results revealed obvious location differences in the composition, diversity, and function of the GIT microbiota. In LEfSe analysis, unidentified_Lachnospiraceae and unidentified_Succinniclassicum were significantly enriched in the four chambers of stomach, with functions in carbohydrate fermentation to compose short-chain fatty acids. Aeriscardovia, Candidatus_Saccharimonas, and Romboutsia were significantly higher in the foregut, playing an important role in synthesizing enzymes, amino acids, and vitamins and immunomodulation. Akkermansia, Bacteroides, and Alistipes were significantly abundant in the hindgut to degrade polysaccharides and oligosaccharides, etc. From rumen to rectum, α-diversity decreased first and then increased, while β-diversity showed the opposite trend. Metabolism was the major function of the GIT microbiome predicted by PICRUSt2, but with variation in target substrates along the regions. In summary, GIT segments play a decisive role in the composition and functions of microorganisms.

Key points
• The jejunum and ileum were harsh for microorganisms to colonize due to the presence of bile acids, enzymes, faster chyme circulation, etc., exhibiting the lowest α-diversity and the highest β-diversity.
• Variability in microbial profiles between the three foregut segments was greater than four chambers of stomach and hindgut, with a higher abundance of Firmicutes dominating than others.
• Dairy goats dominated a higher abundance of Kiritimatiellaeota than cows, which was reported to be associated with fatty acid synthesis.
Mucor thermorhizoides—A New Species from Post-mining Site in Sudety Mountains (Poland)
Authors: Abramczyk Beniamin M. , Wiktorowicz Dorota G. , Okrasińska Alicja , Pawłowska Julia Z. 
Published: June 2024

AbstractMucor representatives are mostly rapidly growing cosmopolitan soil saprotrophs of early diverged Mucoromycotina subphylum. Although this is the most speciose genus within the group, some lineages are still understudied. In this study, new species of Mucor was isolated from the post-mining area in southwestern Poland, where soil chemical composition analysis revealed high concentration of hydrocarbons and heavy metals. Phylogenetic analysis based on multigene phylogeny showed that the new isolate clusters distinctly from other Mucor species as a sister group to Mucor microsporus. New species Mucor thermorhizoides Abramczyk (Mucorales, Mucoromycota) is characterized by the extensive rhizoid production in elevated temperatures and formation of two layers of sporangiophores. It also significantly differs from M. microsporus in the shape of spores and the size of sporangia. M. thermorhizoides was shown to be able to grow in oligotrophic conditions at low temperatures. Together with M. microsporus they represent understudied and highly variable lineage of the Mucor genus.
Skin health of community-living older people: a scoping review
Authors: Kottner Jan , Fastner Alexandra , Lintzeri Dimitra-Aikaterini , Blume-Peytavi Ulrike , Griffiths Christopher E. M. 
Published: June 2024

AbstractThe population of older people is steadily increasing and the majority live at home. Although the home and community are the largest care settings worldwide, most of the evidence on dermatological care relates to secondary and tertiary care. The overall aims were to map the available evidence regarding the epidemiology and burden of the most frequent skin conditions and regarding effects of screening, risk assessment, diagnosis, prevention and treatment of the most frequent skin conditions in older people living in the community. A scoping review was conducted. MEDLINE, Embase and Epistemonikos were systematically searched for clinical practice guidelines, reviews and primary studies, as well as Grey Matters and EASY for grey literature published between January 2010 and March 2023. Records were screened and data of included studies extracted by two reviewers, independently. Results were summarised descriptively. In total, 97 publications were included. The vast majority described prevalence or incidence estimates. Ranges of age groups varied widely and unclear reporting was frequent. Sun-exposure and age-related skin conditions such as actinic keratoses, xerosis cutis, neoplasms and inflammatory diseases were the most frequent dermatoses identified, although melanoma and/or non-melanoma skin cancer were the skin conditions investigated most frequently. Evidence regarding the burden of skin conditions included self-reported skin symptoms and concerns, mortality, burden on the health system, and impact on quality of life. A minority of articles reported effects of screening, risk assessment, diagnosis, prevention and treatment, mainly regarding skin cancer. A high number of skin conditions and diseases affect older people living at home and in the community but evidence about the burden and effective prevention and treatment strategies is weak. Best practices of how to improve dermatological care in older people remain to be determined and there is a particular need for interventional studies to support and to improve skin health at home.
A miRNA-based epigenetic molecular clock for biological skin-age prediction
Authors: Roig-Genoves Jose Vicente , García-Giménez José Luis , Mena-Molla Salvador 
Published: June 2024

AbstractSkin aging is one of the visible characteristics of the aging process in humans. In recent years, different biological clocks have been generated based on protein or epigenetic markers, but few have focused on biological age in the skin. Arrest the aging process or even being able to restore an organism from an older to a younger stage is one of the main challenges in the last 20 years in biomedical research. We have implemented several machine learning models, including regression and classification algorithms, in order to create an epigenetic molecular clock based on miRNA expression profiles of healthy subjects to predict biological age-related to skin. Our best models are capable of classifying skin samples according to age groups (18–28; 29–39; 40–50; 51–60 or 61–83 years old) with an accuracy of 80% or predict age with a mean absolute error of 10.89 years using the expression levels of 1856 unique miRNAs. Our results suggest that this kind of epigenetic clocks arises as a promising tool with several applications in the pharmaco-cosmetic industry.
Surgeon assessment of significant rectal polyps using white light endoscopy alone and in comparison to fluorescence-augmented AI lesion classification
Authors: Hardy Niall P. , Moynihan Alice , Dalli Jeffrey , Epperlein Jonathan P. , McEntee Philip D. , Boland Patrick A. , Neary Peter M. , Cahill Ronan A. 
Published: June 2024

Abstract
Purpose
Perioperative decision making for large (> 2 cm) rectal polyps with ambiguous features is complex. The most common intraprocedural assessment is clinician judgement alone while radiological and endoscopic biopsy can provide periprocedural detail. Fluorescence-augmented machine learning (FA-ML) methods may optimise local treatment strategy.

Methods
Surgeons of varying grades, all performing colonoscopies independently, were asked to visually judge endoscopic videos of large benign and early-stage malignant (potentially suitable for local excision) rectal lesions on an interactive video platform (Mindstamp) with results compared with and between final pathology, radiology and a novel FA-ML classifier. Statistical analyses of data used Fleiss Multi-rater Kappa scoring, Spearman Coefficient and Frequency tables.

Results
Thirty-two surgeons judged 14 ambiguous polyp videos (7 benign, 7 malignant). In all cancers, initial endoscopic biopsy had yielded false-negative results. Five of each lesion type had had a pre-excision MRI with a 60% false-positive malignancy prediction in benign lesions and a 60% over-staging and 40% equivocal rate in cancers. Average clinical visual cancer judgement accuracy was 49% (with only ‘fair’ inter-rater agreement), many reporting uncertainty and higher reported decision confidence did not correspond to higher accuracy. This compared to 86% ML accuracy. Size was misjudged visually by a mean of 20% with polyp size underestimated in 4/6 and overestimated in 2/6. Subjective narratives regarding decision-making requested for 7/14 lesions revealed wide rationale variation between participants.

Conclusion
Current available clinical means of ambiguous rectal lesion assessment is suboptimal with wide inter-observer variation. Fluorescence based AI augmentation may advance this field via objective, explainable ML methods.
Giant root-rat engineering and livestock grazing activities regulate plant functional trait diversity of an Afroalpine vegetation community in the Bale Mountains, Ethiopia
Authors: Asefa Addisu , Reuber Victoria M. , Miehe Georg , Wraase Luise , Wube Tilaye , Farwig Nina , Schabo Dana G. 
Published: June 2024

AbstractDisturbances from rodent engineering and human activities profoundly impact ecosystem structure and functioning. Whilst we know that disturbances modulate plant communities, comprehending the mechanisms through which rodent and human disturbances influence the functional trait diversity and trait composition of plant communities is important to allow projecting future changes and to enable informed decisions in response to changing intensity of the disturbances. Here, we evaluated the changes in functional trait diversity and composition of Afroalpine plant communities in the Bale Mountains of Ethiopia along gradients of engineering disturbances of a subterranean endemic rodent, the giant root-rat (Tachyoryctes macrocephalus Rüppell 1842) and human activities (settlement establishment and livestock grazing). We conducted RLQ (co-inertia analysis) and fourth-corner analyses to test for trait-disturbance (rodent engineering/human activities) covariation. Overall, our results show an increase in plant functional trait diversity with increasing root-rat engineering and increasing human activities. We found disturbance specific association with traits. Specifically, we found strong positive association of larger seed mass with increasing root-rat fresh burrow density, rhizomatous vegetative propagation negatively associated with increasing root-rat old burrow, and stolonifereous vegetative propagation positively associated with presence of root-rat mima mound. Moreover, both leaf size and leaf nitrogen content were positively associated with livestock dung abundance but negatively with distance from settlement. Overall, our results suggest that disturbances by rodents filter plant traits related to survival and reproduction strategies, whereas human activities such as livestock grazing act as filters for traits related to leaf economics spectrum along acquisitive resource-use strategy.