bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unified Crystal Phase Control with MACl for Inducing Single‐Crystal‐Like Perovskite Thin Films in High‐Pressure Fusion Toward High Efficiency Perovskite Solar Cell Modules
Authors: Zhang Hanhong , Hou Wenjing , Hao Yuying , Song Jun , Zhang Fan 
Published: June 2024

AbstractPerovskite solar cells, recognized for their high photovoltaic conversion efficiency (PCE), cost‐effectiveness, and simple fabrication, face challenges in PCE improvement due to structural defects in polycrystalline films. This study introduces a novel fabrication method for perovskite films using methylammonium chloride (MACl) to align grain orientation uniformly, followed by a high‐pressure process to merge these grains into a texture resembling single‐crystal perovskite. Employing advanced visual fluorescence microscopy, charge dynamics in these films are analyzed, uncovering the significant impact of grain boundaries on photo‐generated charge transport within perovskite crystals. A key discovery is that optimal charge transport efficiency and speed occur in grain centers when the grain size exceeds 10 µm, challenging the traditional view that efficiency peaks when grain size surpasses film thickness to form a monolayer. Additionally, the presence of large‐sized grains enhances ion activation energy, reducing ion migration under light and improving resistance to photo‐induced degradation. In application, a perovskite solar cell module with large grains achieve a PCE of 22.45%, maintaining performance with no significant degradation under continuous white LED light at 100 mA cm−2 for over 1000 h. This study offers a new approach to perovskite film fabrication and insights into optimizing perovskite solar cell modules.
Stabilizing and Activating Active Sites: 1T‐MoS2 Supported Pd Single Atoms for Efficient Hydrogen Evolution Reaction
Authors: Zhao Lu , Liang Shaojie , Zhang Li , Huang Haoliang , Zhang Qing‐Hua , Ge Weiyi , Wang Shuqi , Tan Ting , Huang Linbo , An Qi 
Published: June 2024

AbstractMetallic 1T‐MoS2 with high intrinsic electronic conductivity performs Pt‐like catalytic activity for hydrogen evolution reaction (HER). However, obtaining pure 1T‐MoS2 is challenging due to its high formation energy and metastable properties. Herein, an in situ SO42−‐anchoring strategy is reported to synthesize a thin layer of 1T‐MoS2 loaded on commercial carbon. Single Pd atoms, constituting a substantial loading of 7.2 wt%, are then immobilized on the 1T‐phase MoS2 via Pd─S bonds to modulate the electronic structure and ensure a stable active phase. The resulting Pd1/1T‐MoS2/C catalyst exhibits superior HER performance, featuring a low overpotential of 53 mV at the current density of 10 mA cm−2, a small Tafel slope of 37 mV dec−1, and minimal charge transfer resistance in alkaline electrolyte. Moreover, the catalyst also demonstrates efficacy in acid and neutral electrolytes. Atomic structural characterization and theoretical calculations reveal that the high activity of Pd1/1T‐MoS2/C is attributed to the near‐zero hydrogen adsorption energy of the activated sulfur sites on the two adjacent shells of atomic Pd.
Spherical Assembly of Halloysite Clay Nanotubes as a General Reservoir of Hydrophobic Pesticides for pH‐Responsive Management of Pests and Weeds
Authors: Teng Guopeng , Chen Chaowen , Ma Xueqi , Mao Hengjian , Yuan Xue , Xu Huan , Wu Zhengyan , Zhang Jia 
Published: June 2024

AbstractThe development of smart systems for pesticidal delivery presents a significant advancement in enhancing the utilization efficiency of pesticides and mitigating environmental risks. Here an acid‐responsive pesticidal delivery system using microspheres formed by the self‐assembly of halloysite clay nanotubes (HNTs) is proposed. Insecticide avermectin (AVM) and herbicide prometryn (PMT) are used as two models of hydrophobic pesticide and encapsulated within the porous microspheres, followed by a coating of tannic acid/iron (TA/FeIII) complex films to generate two controlled‐release pesticides, named as HCEAT and HCEPT, resulting in the loading capacity of AVM and PMT being 113.3 and 120.3 mg g−1, respectively. Both HCEAT and HCEPT exhibit responsiveness to weak acid, achieving 24 h‐release ratios of 85.8% and 80.5% at a pH of 5.5. The experiment and simulation results indicate that the coordination interaction between EDTA2− and Ca2+ facilitates the spherical aggregation of HNTs. Furthermore, these novel pesticide formulations demonstrate better resistance against ultraviolet (UV) irradiation, higher foliar affinity, and less leaching effect, with negligible impact of the carrier material on plants and terrestrial organisms. This work presents a promising approach toward the development of efficient and eco‐friendly pesticide formulations, greatly contributing to the sustainable advancement of agriculture.
Towards medical imaging of drug photoactivation: Development of light responsive magnetic resonance imaging and chemical exchange saturation transfer contrast agents
Authors: Welleman Ilse M. , van Beek Carlijn L. F. , Belcin Ioana , Schulte Albert M. , Dierckx Rudi A. J. O. , Feringa Ben L. , Boersma Hendrikus H. , Szymański Wiktor 
Published: June 2024

AbstractIn recent years, the use of light to selectively and precisely activate drugs has been developed along the fundamental concepts of photopharmacology. One of the key methods in this field relies on transiently silencing the drug activity with photocleavable protecting groups (PPGs). To effectively utilize light‐activated drugs in future medical applications, physicians will require a reliable method to assess whether light penetrates deep enough into the tissues to activate the photoresponsive theragnostic agents. Here, we describe the development and evaluation of magnetic resonance (MR) imaging agents that allow for the detection of light penetration and drug activation in the tissues using non‐invasive whole‐body magnetic resonance imaging (MRI) and chemical exchange saturation transfer (CEST)‐MRI modalities. The approach relies on the use of PPG‐protected MR contrast agents, which upon irradiation with light change their imaging signal. A Gadolinium(III)‐based MRI contrast agent is presented that undergoes a significant change in relaxivity (25%) upon uncaging, providing a reliable indicator of light‐induced cargo release. Additionally, we introduce the first light‐responsive CEST‐MRI imaging agent, enabling positive signal enhancement (off‐to‐on) upon light activation, offering a novel approach to visualize the activation of photoactive agents in living tissues. This research provides a proof‐of‐principle for the non‐invasive, whole‐body imaging of light penetration and drug activation with high temporal resolution characteristic of MR methods.
Exploring the genetic basis of resistance to Neopestalotiopsis species in strawberry
Authors: Alam Elissar , Moyer Catalina , Verma Sujeet , Peres Natalia A. , Whitaker Vance M. 
Published: June 2024

AbstractAggressive strains of Neopestalotiopsis sp. have recently emerged as devastating pathogens of strawberry (Fragaria × ananassa Duchesne ex Rozier), infecting nearly all plant parts and causing severe outbreaks of leaf spot and fruit rot in Florida and globally. The development of host resistance is imperative due to the absence of fungicides that effectively inhibit Neopestalotiopsis sp. growth on an infected strawberry crop. Here, we analyzed 1578 individuals from the University of Florida's (UF) strawberry breeding program to identify and dissect genetic variation for resistance to Neopestalotiopsis sp. and to explore the feasibility of genomic selection. We found that less than 12% of elite UF germplasm exhibited resistance, with narrow‐sense heritability estimates ranging from 0.28 to 0.69. Through genome‐wide association studies (GWAS), we identified two loci accounting for 7%–16% of phenotypic variance across four trials and 3 years. Several candidate genes encoding pattern recognition receptors, intra‐cellular nucleotide‐binding leucine‐rich repeats, and downstream components of plant defense pathways co‐localized with the Neopestalotiopsis sp. resistance loci. Interestingly, favorable alleles at the largest‐effect locus were rare in elite UF material and had previously been unintentionally introduced from an exotic cultivar. The array‐based markers and candidate genes described herein provide the foundation for targeting this locus through marker‐assisted selection. The predictive abilities of genomic selection models, with and without explicitly modeling peak GWAS markers as fixed effects, ranged between 0.25 and 0.59, suggesting that genomic selection holds promise for enhancing resistance to Neopestalotiopsis sp. in strawberry.
Assessment of ecological risk and metal contamination caused by the Lihoud River emissary along the Bay of Tangier Littoral in Morocco (Southwestern Mediterranean Sea)
Authors: Simou Ayoub , Mansouri Fouad El , Abdelfattah Bahia , Mrabet Amena , Khaddor Mohamed , Allali Nabil 
Published: June 2024

AbstractThis study aimed to evaluate the environmental risks and metal contamination at Marqala Beach, a popular destination. Wet and dry beach sediments were collected from along this coastline for a full comprehensive analysis of the area. The assessment involved both physicochemical characterization of the sediments and determination of heavy metal contamination levels. Various pollution indices, including the geo‐accumulation index (Igeo), enrichment factor (EF), contamination factor (CF), modified contamination degree (mCd), pollution load index (PLI), potential ecological risk factor (Ef), and potential ecological risk index (RI) were used to evaluate the ecological and health risk. These indices highlight the impact of human activities and indicate that contamination is primarily due to elevated levels of As, Cr, Zn, and Pb. Furthermore, X‐ray fluorescence (XRF) analysis showed that SiO2 was the dominant element in the sediment samples, followed by CaO and Al2O3. This suggests a mineralogical and compositional combination. X‐ray diffraction (XRD) characterization confirmed quartz dominance and the presence of aluminosilicate and calcium silicate minerals, aligning with XRF findings. Additionally, the scanning electron microscope (SEM) analysis revealed a non‐crystalline structure with a sandy texture, as evidenced by the macrostructure and granulometric analyses, while the polluted sediments showed smaller particle sizes, consisting of tiny particles (oxides) and organic matter on quartz crystals.
Entropy scrutinization of magnetized‐hyperbolic tangent nanofluid in the microchannel stuffed by porous media
Authors: Felicita A. , Venkatesh P. , Gireesha B. J. , Kumar Pradeep , Nagaraja B. 
Published: June 2024

AbstractModified starch, derivatives of cellulose and sodium alginate are shear thinning fluids which can be analyzed using hyperbolic tangent model as these fluids can be used as natural thickeners in the ink. Thus, the present article's intent is to study the flow conduct of hyperbolic tangent nanofluid in microchannel situated horizontally. The impact of viscous dissipation and magnetic field is recorded. The suction‐ injection is promoted at the walls of the microchannel. Two imperative slip mechanisms like Brownian motion and thermophoresis are accounted for the study. Entropy scrutiny is carried out for system effectiveness. To simplify the non‐linear equations certain non‐dimensional variables are used. The obtained mathematical formulations are solved using an efficient problem‐solving operation namely Runge–Kutta–Fehlberg 4–5th order method. The parameters attained are studied using graphical illustrations. The findings of this article comprehend that on enlarging Weissenberg number, flow field declines at the bottom wall and levitates at the top wall and the material power law parameter magnifies the velocity distribution. Entropy generated is maximum at top wall and minimum at the bottom wall for the Brownian motion parameter but the reverse manner is attained for thermophoresis parameter. Incorporating porous media to the microchannel for the flow of shear thinning fluid is useful in cell engineering, spotting particles and filtering.
Historical Background and Evolution in the Decade
Authors: Bianco Nicola , Mastrullo Rita M. A. , Moccia Antonio 
Published: June 2024

AbstractThe Department of Industrial Engineering was established on 1 January 2013 in application of Law 240/2010 which has reframed the organization of the Italian university system. This chapter summarizes the efforts and the main achievements of the Department in the research, management of research and technology transfer activities in the decade 2013–2023.
Naval Architecture and Marine Engineering
Authors: Acanfora Maria , Boccadamo Guido 
Published: June 2024

AbstractThis chapter summarizes the main research activities and outcomes of the groups engaged in Naval Architecture and Marine Engineering, in the decade 2013–2023. The research topics are typical of the following sectors: “Architettura Navale” and “Costruzioni e Impianti Navali e Marini”.
From Deep Neural Language Models to LLMs
Authors: Kucharavy Andrei 
Published: June 2024

AbstractLarge Language Models(LLMs) are scaled-up instances of Deep Neural Language Models—a type ofNatural Language Processing(NLP) tools trained withMachine Learning(ML). To best understand how LLMs work, we must dive into what technologies they build on top of and what makes them different. To achieve this, an overview of the history of LLMs development, starting from the 1990s, is provided before covering the counterintuitive purely probabilistic nature of the Deep Neural Language Models, continuous token embedding spaces, recurrent neural networks-based models, what self-attention brought to the table, and finally, why scaling Deep Neural Language Models led to a qualitative change, warranting a new name for the technology.
LLM-Aided Social Media Influence Operations
Authors: Meier Raphael 
Published: June 2024

AbstractSocial media platforms enable largely unrestricted many-to-many communication. In times of crisis, they offer a space for collective sense-making and give rise to new social phenomena (e.g., open-source investigations). However, they also serve as a tool for threat actors to conduct Cyber-enabled Social Influence Operations (CeSIOs) to shape public opinion and interfere in decision-making processes. CeSIOs employ sock puppet accounts to engage authentic users in online communication, exert influence, and subvert online discourse. Large Language Models (LLMs) may further enhance the deceptive properties of sock puppet accounts. Recent LLMs can generate targeted and persuasive text, which is, for the most part, indistinguishable from human-written content—ideal features for covert influence. This article reviews recent developments at the intersection of LLMs and influence operations, summarizes LLMs’ salience, and explores the potential impact of LLM-instrumented sock puppet accounts for CeSIOs. Finally, mitigation measures for the near future are highlighted.
Deep(er) Web Indexing with LLMs
Authors: Holland Aidan 
Published: June 2024

AbstractThe “Deep Web” contains, among other data, sensitive information that is left unsecured and publicly available but not indexed and thus impossible to locate by search engines. Using search-augmented language models can potentially make the deep web shallower and more searchable, posing a concern for cyber defense, particularly in countries with linguistic specifics. Mitigation strategies include red-teaming of LLM-based search engines, end-to-end encryption, or modifying terms used in critical cyber-physical systems to make resources harder to find. However, these approaches may have limitations and cause potential disruptions to user workflows.
Insurance Outlook for LLM-Induced Risk
Authors: Maréchal Loïc , Celeny Daniel 
Published: June 2024

AbstractDuring the development of information systems, security, and safety considerations often take a back seat to market pressures, demanding shorter development cycles, faster releases, and new product features. Unfortunately, right until a cyber-incident, the price of the trade-off between security and safety and other market imperatives is unclear and, given the general rarity of cyber-incidents, often under-estimated. Fortunately, calculating the security and safety side of the trade-off is the domain of expertise of actuaries in insurance companies offering cyber insurances. It used to be an after-thought for most companies since the 2013 Target data breach, which cost nearly 300 million but was covered at 30% by insurance payout. Since then, insurance for risks of information systems malfunctions has become standard for most companies, and premium reduction has become a primary driver for improving cybersecurity costs for companies. The role of this chapter is to transpose what we have learned about the insurance of cyber-incidents over the last couple of decades and use it as a basis to produce a qualitative forecast of the insurance outlook for a security and safety landscape involving LLMs.
Monitoring Emerging Trends in LLM Research
Authors: Würsch Maxime , David Dimitri Percia , Mermoud Alain 
Published: June 2024

AbstractEstablished methodologies for monitoring and forecasting trends in technological development fall short of capturing advancements inLarge Language Models(LLMs). This chapter suggests a complementary and alternative approach to mitigate this concern. Traditional indicators, such as search volumes and citation frequencies, are demonstrated to inadequately reflect the rapid evolution of LLM-related technologies due to biases, semantic drifts, and inherent lags in data documentation. Our presented methodology analyzes the proximity of technological terms related to LLMs, leveraging the OpenAlex and arXiv databases, and focuses on extracting nouns from scientific papers to provide a nuanced portrayal of advancements in LLM technologies. The approach aims to counteract the inherent lags in data, accommodate semantic drift, and distinctly differentiate between various topics, offering both retrospective and prospective insights in their analytical purview. The insights derived underline the need for refined, robust, adaptable, and precise forecasting models as LLMs intersect with domains like cyber defense. At the same time, they are considering the limitations of singular ontologies and integrating advanced anticipatory measures for a nuanced understanding of evolving LLM technologies.
Adapting LLMs to Downstream Applications
Authors: Kucharavy Andrei 
Published: June 2024

AbstractBy themselves, pretrained Large Language Models (LLMs) are interesting objects of study. However, they need to undergo a subsequent transfer learning phase to make them useful for downstream applications. While historically referred to as “fine-tuning,” the range of the tools available to LLMs users to better adapt base models to their applications is now significantly wider than the traditional fine-tuning. In order to provide the reader with an idea of the strengths and weaknesses of each method and allow them to pick one that would suit their needs best, an overview and classification of the most notable methods is provided, specifically the prompt optimization, pre-prompting and implicit prompting (system prompting), model coordination through actor agents, integration with auxiliary tools, parameter-efficient fine-tuning, further model pre-training, from-scratch retraining, and finally domain-specific distillation.
Robust and Private Federated Learning on LLMs
Authors: Guerraoui Rachid , Gupta Nirupam 
Published: June 2024

AbstractLarge Language Models(LLMs) have gained significant attention in recent years due to their potential to revolutionize various industries and sectors. However, scaling LLMs further requires access to substantial linguistic resources that are being rapidly depleted. Moreover, the available text sources such as emails, social media interactions, or internal documents may contain private information, making them susceptible to misuse. On-premisesFederated Learning(FL) with privacy-preserving model updates is an alternative avenue for LLMs’ development that ensures data sovereignty and enables peers to collaborate while ensuring that the sensitive parts of their private data cannot be reconstructed. However, in the case of large-scale FL, there is also a risk of malicious users attempting to poison LLMs for their benefit. The problem of protecting the learning procedure against such users is known as Byzantine-robustness, and it is crucial to develop models that perform accurately despite faulty machines and poisonous data. Designing FL methods that are simultaneously privacy-preserving and Byzantine-robust is challenging. However, ongoing research suggests ways to incorporate the differentially-private Gaussian mechanism for privacy preservation and spectral robust-averaging for robustness. However, whether this approach applies to LLMs or whether a major player in the domain would emerge and capture all private information sources through network effects remains to be seen.
Conversational Agents
Authors: Dolamic Ljiljana 
Published: June 2024

AbstractConversational agents (CA) are engaged in interactive conversations with users, providing responses and assistance while combining Natural Language Processing (NLP), Understanding (NLU), and Generating (NLG) techniques. Two tiers of conversational agent derivation from Large Language Models (LLMs) exist. The first tier involves conversational fine-tuning from datasets, representing expected user questions and desired conversational agent responses. The second tier requires manual prompting by human operators and evaluation of model output, which is then used for further fine-tuning. Fine-tuning with Reinforcement Learning from Human Feedback (RLHF) models perform better but are resource-intensive and specific for each model. Another critical difference in the performance of various CA is their ability to access auxiliary services for task delegation.
Tasks for LLMs and Their Evaluation
Authors: Ostapuk Natalia , Audiffren Julien 
Published: June 2024

AbstractSince their inception, LLMs have been evaluated on a wide range of natural language tasks. These tasks include Reading Comprehension, Question Answering, Reasoning, and Text Generation. While LLMs have shown promising results, in particular as general models, their capabilities vary depending on their architecture, training dataset, and the nature of the task. We will briefly define the natural language tasks and give an overview of LLMs’ current state of the art.
Private Information Leakage in LLMs
Authors: Buesser Beat 
Published: June 2024

AbstractLarge Language Models(LLMs) can memorize training data and, if specifically prompted, reproduce or leak information on their training data. Information leakage has been observed for all types of machine-learning models. However, this threat exists at a much larger scale for LLMs because of their various applications as generative AI. This chapter relates the threat of information leakage with other adversarial threats, provides an overview of the current state of research on the mechanisms involved in memorization in LLMs, and discusses adversarial attacks aiming to extract memorized information from LLMs.