bluuebunny/crossref_metadata_2025 huggingface dataset:
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

The 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

In 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.
PMSM Speed Active Disturbance Rejection Control Based on Modified Extended State Observer
Authors: Zhang Wenting , Bai Hongfen
Published: June 2024

In order to address the issues of significant input variations and complex disturbances in speed control of permanent magnet synchronous motors (PMSM), a Modified Active Disturbance Rejection Controller (ADRC) based on an improved Modified Extended State Observer (ESO) is proposed in this paper. In this new method, a nonlinear cascaded structure of observers is adopted. By utilizing the TD filter, high‐frequency noise can be attenuated while preserving the dynamic characteristics of the signal. Moreover, system oscillations caused by minor errors are effectively suppressed. The introduction of propeller and wave models enhances the realism of simulated marine environmental disturbances. Research results indicate that, compared to a single Extended State Observer, the cascaded MESO exhibits better disturbance rejection and noise resistance in complex marine environments, and the proposed MADRC outperforms both PI controllers and ADRC in dealing with marine environmental disturbances. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
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

Aggressive 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

This 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

Modified 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

The 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

This 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”.
Electrical Power Systems
Authors: Lauria Davide , Villacci Domenico
Published: June 2024

The chapter describes the characteristics of the research unit of Electrical Energy Systems, affiliated to the DII, which has been progressively consolidated since the end of 2014. Both the research activities in the thematic areas of the disciplinary scientific sector and those integrated with multiple areas of study and research of the DII Department are summarized.
From Deep Neural Language Models to LLMs
Authors: Kucharavy Andrei
Published: June 2024

Large 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

Social 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

The “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

During 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

Established 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

By 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

Large 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

Conversational 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

Since 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

Large 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.
Sensitize and Qualify University Teachers for Digital Accessibility
Authors: Kuhlmann Judith , Althoff Kim Helen , Kamin Anna-Maria , Johannfunke Michael
Published: June 2024

University teachers face new challenges, particularly in the context of digital teaching and learning. However, teachers often encounter obstacles in implementing digital accessibility in their teaching. One of the challenges is disproportionate additional time effort as well as technical and didactic compatibility issues. The SHUFFLE project aims to support teachers with their needs in designing accessible digital teaching concepts through a material package based on the concept of Universal Design for Learning. The package includes checklists, guides, and a knowledge database, which covers various aspects related to digital teaching – accessible to everyone. The checklists offer advice and assistance on technical and didactic requirements, and the guidelines provide detailed instructions for implementation The knowledge database provides additional information on other aspects of making digital teaching accessible. The material is available in German and English under the Creative Commons license CCBY.
The bubble of nothing under T-duality
Authors: Delgado Matilda
Published: June 2024

Abstract

The bubble of nothing is a solution to Einstein’s equations where a circle shrinks and pinches off smoothly. As such, it is one of the simplest examples of a dynamical cobordism to nothing. We take a first step in studying how this solution transforms under T-duality in bosonic string theory. Applying Buscher’s rules reveals that the dual solution features a singular, strongly coupled core, with a circle blowing-up rather than pinching off. This naive approach to T-duality solely accounts for the zero-modes of the fields after dimensional reduction on the circle. For this reason, we argue that this is not the full picture that the T-dual solution should depend non-trivially on the dual circle. We point out evidence to this effect both in the gravity description and on the worldsheet. A more complete description of the T-dual object would require a full-fledged sigma model for the bubble of nothing. Nevertheless, inspired by similar examples in the literature, we detail one possible scenario where the stringy bubble of nothing is mediated by closed string tachyon condensation and we discuss its T-duality.
Duality between Seiberg-Witten theory and black hole superradiance
Authors: Ge Xian-Hui , Matsumoto Masataka , Zhang Kilar
Published: June 2024

Abstract

The newly established Seiberg-Witten (SW)/Quasinormal Modes (QNM) correspondence offers an efficient analytical approach to calculate the QNM frequencies, which was only available numerically before. This is based on the fact that both sides are characterized by Heun-type equations. We find that a similar duality exists between Seiberg-Witten theory and black hole superradiance, since the latter can also be linked to confluent Heun equation after proper transformation. Then a dictionary is constructed, with the superradiance frequencies written in terms of gauge parameters. Further by instanton counting, and taking care of the boundary conditions through connection formula, the relating frequencies are obtained analytically, which show consistency with known numerical results.
Holographic description of Narain CFTs and their code-based ensembles
Authors: Aharony Ofer , Dymarsky Anatoly , Shapere Alfred D.
Published: June 2024

Abstract

We provide a precise relation between an ensemble of Narain conformal field theories (CFTs) with central charge c = n, and a sum of (U(1) × U(1))n Chern-Simons theories on different handlebody topologies. We begin by reviewing the general relation of additive codes to Narain CFTs. Then we describe a holographic duality between any given Narain theory and a pure Chern-Simons theory on a handlebody manifold. We proceed to consider an ensemble of Narain theories, defined in terms of an ensemble of codes of length n over ℤk × ℤk for prime k. We show that averaging over this ensemble is holographically dual to a level-k (U(1) × U(1))n Chern-Simons theory, summed over a finite number of inequivalent classes of handlebody topologies. In the limit of large k the ensemble approaches the ensemble of all Narain theories, and its bulk dual becomes equivalent to “U(1)-gravity” — the sum of the pertubative part of the Chern-Simons wavefunction over all possible handlebodies — providing a bulk microscopic definition for this theory. Finally, we reformulate the sum over handlebodies in terms of Hecke operators, paving the way for generalizations.
Minimally invasive palliative treatment of malignant tracheoesophageal fistula using cardiac septal occluder
Authors: Teng Lin , Zhou Fei , Xiong Xiaoqi , Zhang Haoyu , Qiao Linchen , Zhang Zaiqiang , Qin Qin , Song Xinyu
Published: June 2024

Introduction

Tracheoesophageal fistula (TEF) especially malignant TEF (mTEF) is an uncommon yet critical medical condition necessitating immediate intervention. This life-threatening condition frequently manifests in critically ill patients who are dependent on prolonged mechanical ventilation and are unsuitable candidates for thoracotomy due to their compromised health status. The Management of these mTEF patients remain a significant challenge.This study aimed to evaluate the safety and efficacy of using a cardiac septal occluder for the closure of mTEF.

Methods

8 patients with mTEF underwent closure surgery using atrial/ventricular septal defect (ASD/VSD) septal occluders at the Respiratory Department of HuBei Yichang Central People’s Hospital from 2021 to 2023. The procedure involved percutaneous placement of the occluder through the fistula to achieve closure.

Results

The placement of the cardiac septal occluder was successfully achieved with ease and efficiency in all patients. The study demonstrated that the use of cardiac septal occluder therapy in patients with mTEF can alleviate symptoms, improve quality of life, and enhance survival rates, with no significant complications observed. Furthermore, the study provided comprehensive details on surgical indications, preoperative evaluation and diagnosis, selection of occluder, methods of occlusion, and postoperative care.

Conclusions

The application of cardiac septal occluder in the treatment of mTEF is a safe and effective palliative treatment. This approach may be particularly beneficial for patients with a high risk of complications and mortality associated with traditional surgical interventions.
Laser-induced 2D/0D graphene-nanoceria freestanding paper-based films for on-site hydrogen peroxide monitoring in no-touch disinfection treatments
Authors: Pidal José M. Gordón , Fiori Selene , Scroccarello Annalisa , Della Pelle Flavio , Maggio Francesca , Serio Annalisa , Ferraro Giovanni , Escarpa Alberto , Compagnone Dario
Published: June 2024

A one-shot CO2 laser-based strategy to generate conductive reduced graphene oxide (rGO) decorated with nanoceria (nCe) is proposed. The 2D/0D rGO-nCe films, integrated as catalytic sensing layers in paper-based sensors, were employed for on-site monitoring of indoor fogging treatments against Listeria monocytogenes (Lm), a ubiquitous pathogenic bacterium. The rGO-nCe laser-assisted synthesis was optimized to preserve the rGO film morphological and electron-transfer features and simultaneously integrate catalytic nCe. The films were characterized by microscopical (SEM), spectroscopical (EDX, Raman, and FTIR), and electrochemical techniques. The most performing film was integrated into a nitrocellulose substrate, and the complete sensor was assembled via a combination of xurography and stencil printing. The rGO-nCe sensor's catalytic activity was proved toward the detection of H2O2, obtaining sensitive determination (LOD = 0.3 µM) and an extended linear range (0.5–1500 µM). Eventually, the rGO-nCe sensor was challenged for the real-time continuous monitoring of hydrogen peroxide aerosol during no-touch fogging treatment conducted following the EU’s recommendation for biocidal product use. Treatment effectiveness was proved toward three Lm strains characterized by different origins, i.e., type strain ATCC 7644, clinical strain 338, and food strain 641/6II. The sensor allows for discrimination and quantification treatments at different environmental biocidal amounts and fogging times, and correlates with the microbiological inhibition, promoting the proposed sensor as a useful tool to modulate and monitor no-touch treatments.

Graphical Abstract
Factors affecting the extent of resection and neurological outcomes following transopercular resection of insular gliomas
Authors: Biswas Chandrima , Shetty Prakash M. , Sahu Arpita , Velayutham Parthiban , Singh Vikas , Shah Kanchi , Moiyadi Aliasgar V.
Published: June 2024

Background

Surgical resection of insular gliomas is a challenge. TO resection is considered more versatile and has lower risk of vascular damage. In this study, we aimed to understand the factors that affect resection rates, ischemic changes and neurological outcomes and studied the utility of IONM in patients who underwent TO resection for IGs.

Methods

Retrospective analysis of 66 patients with IG who underwent TO resection was performed.

Results

Radical resection was possible in 39% patients. Involvement of zone II and the absence of contrast enhancement predicted lower resection rate. Persistent deficit rate was 10.9%. Although dominant lobe tumors increased immediate deficit and fronto-orbital operculum involvement reduced prolonged deficit rate, no tumor related factor showed significant association with persistent deficits. 45% of patients developed a postoperative infarct, 53% of whom developed deficits. Most affected vascular territory was lenticulostriate (39%). MEP changes were observed in 9/57 patients. 67% of stable TcMEPs and 74.5% of stable strip MEPs did not develop any postoperative motor deficits. Long-term deficits were seen in 3 and 6% patients with stable TcMEP and strip MEPs respectively. In contrast, 25% and 50% of patients with reversible strip MEP and Tc MEP changes respectively had persistent motor deficits. DWI changes were clinically more relevant when accompanied by MEP changes intraoperatively, with persistent deficit rates three times greater when MEP changes occurred than when MEPs were stable.

Conclusion

Radical resection can be achieved in large, multizone IGs, with reasonable outcomes using TO approach and multimodal intraoperative strategy with IONM.
Very old age impacts masticatory performance: a study among sexagenarians to centenarians
Authors: Sekundo Caroline , Frese Cornelia , Alich Niklas , Langowski Eva , Awounvo Sinclair , Wolff Diana , Zenthöfer Andreas
Published: June 2024

Objective

This cross-sectional pilot study evaluated the impact of age on masticatory performance among individuals aged 65 to 106 years, as part of the Heidelberg Dental Centenarian Study (HD-100Z) conducted in South-Western Germany.

Materials and methods

A total of 31 centenarians were recruited, alongside 31 individuals each from the age groups 75–99 and 65–74, matched based on sex, prosthetic status, and number of teeth. Masticatory performance was assessed using a two-colored chewing gum test and digital image processing. Multiple linear regression analysis was performed to assess the effect of age, sex, number of teeth, type of prosthesis on the masticatory performance.

Results

Masticatory performance, as measured by the standard deviation of hue in the chewing gum test, decreased significantly in centenarians compared to individuals aged 75–99 years (-0.112, p = 0.037) and those aged 65–74 years (-0.274, p < 0.001). The effects of sex, number of teeth, and type of prosthesis on masticatory performance were not significant associations (p ≥ 0.135).

Conclusion

The findings suggest that age may have a significant influence on masticatory performance in the studied age groups, challenging previous notions that aging itself has little impact on masticatory ability. The inclusion of centenarians in the study highlights the need for further investigation into masticatory function in age groups reaching up to 100 years or more.

Clinical relevance

This study contributes to the understanding of how ageing affects oral function, which may guide dental treatment approaches for older individuals, and set the stage for more in-depth investigations in this field in the future.
Chronoamperometric investigations on electrochemical synthesis of iron nitrides in molten salt system
Authors: Thiebes Yannick M. , Engel Katja , Niewa Rainer
Published: June 2024

A systematic investigation of the electrochemical iron nitride synthesis in a LiCl/KCl salt melt at 723 K shows an optimum of $$\epsilon$$
ϵ
-Fe$$_3$$

3

N$$_{1+x}$$

1
+
x

formation in the range of 2.2 to 2.3 V and for $$\gamma '$$

γ
′

-Fe$$_4$$

4

N between 2.4 and 2.5 V enabling the control of the desired iron nitride phase by setting the supplied terminal voltage. The product formation of iron nitrides starts when the electrochemical window is reached, which could be verified by linear sweep voltammetry. Hence, a maximum of nitrogen content in the formed iron nitride phases is observed for 2.27 V. During elongated synthesis periods, convection emerges as the predominant transport mechanism hindering an accelerated reaction rate with higher overpotential applied. Real-time analysis of the background current allows conclusions about the remaining nitride concentration. Additionally, there is concurrent iron nitride formation at the electrode surface through nitride adsorption and autonucleation-induced precipitation of iron and nitride ions. The analysis of the amount of sediment in comparison to the layer thickness of the nitrided working electrode suggests that the autonucleation mechanism dominates over the adsorption mechanism with increasing overpotential and can be further enhanced by this feature.

Graphical abstract
Optimal investment by large consumers in an electricity market with generator market power
Authors: Verma Pranjal Pragya , Hesamzadeh Mohammad Reza , Rebennack Steffen , Bunn Derek , Swarup K. Shanti , Srinivasan Dipti
Published: June 2024

The investment decisions of energy-intensive consumers can alter the balance of supply and demand in an electricity market. In particular, they can increase the market power of incumbent generators such that prices may increase as a consequence of their investments. Whilst it is therefore intuitive that such investors will wish to consider their effects on the market, it is a challenging problem analytically and one that has been under-researched. In general, the problem can be manifest in any supply chain where demand-side investments influence endogenous price formation in the intermediate product markets. Theoretically, we show how the presence of producer market power decreases demand-side investments and then, computationally we formulate a quad-level program to model the operational implications for a demand-side investor in more detail. With an innovative reduction in complexity to a bilevel model, an efficient solution algorithm for the optimal investment by a demand-side investor is facilitated. We demonstrate computability on a small scale electricity system and the results confirm the theory.
Quantitative 23Na magnetic resonance imaging in the abdomen at 3 T
Authors: Birchall Jonathan Richard , Horvat-Menih Ines , Kaggie Joshua Daniel , Riemer Frank , Benjamin Arnold Julian Vinoj , Graves Martin John , Wilkinson Ian , Gallagher Ferdia Aidan , McLean Mary Anne
Published: June 2024

Objectives

To assess the feasibility of sodium-23 MRI for performing quantitative and non-invasive measurements of total sodium concentration (TSC) and relaxation in a variety of abdominal organs.

Materials and methods

Proton and sodium imaging of the abdomen was performed in 19 healthy volunteers using a 3D cones sequence and a sodium-tuned 4-rung transmit/receive body coil on a clinical 3 T system. The effects of B1 non-uniformity on TSC measurements were corrected using the double-angle method. The long-component of 23Na T2* relaxation time was measured using a series of variable echo-times.

Results

The mean and standard deviation of TSC and long-component 23Na T2* values were calculated across the healthy volunteer group in the kidneys, cerebrospinal fluid (CSF), liver, gallbladder, spleen, aorta, and inferior vena cava.

Discussion

Mean TSC values in the kidneys, liver, and spleen were similar to those reported using 23Na-MRI previously in the literature. Measurements in the CSF and gallbladder were lower, potentially due to the reduced spatial resolution achievable in a clinically acceptable scan time. Mean long-component 23Na T2* values were consistent with previous reports from the kidneys and CSF. Intra-population standard error was larger in smaller, fluid-filled structures due to fluid motion and partial volume effects.
Active convolutional neural networks sign language (ActiveCNN-SL) framework: a paradigm shift in deaf-mute communication
Authors: ZainEldin Hanaa , Baghdadi Nadiah A. , Gamel Samah A. , Aljohani Mansourah , Talaat Fatma M. , Malki Amer , Badawy Mahmoud , Elhosseini Mostafa
Published: June 2024

Real-time speech-to-text and text-to-speech technologies have significantly influenced the accessibility of communication for individuals who are deaf or mute. This research aims to assess the efficacy of these technologies in facilitating communication between deaf or mute individuals and those who are neither deaf nor mute. A mixed-method approach will incorporate qualitative and quantitative data collection and analysis techniques. The study will involve participants from deaf or mute and non-deaf or non-mute communities. The research will scrutinize the precision and efficiency of communication using these technologies and evaluate user experience and satisfaction. Furthermore, the study intends to pinpoint potential obstacles and limitations of these technologies and offer suggestions for enhancing their effectiveness in fostering inclusivity. The study proposes an active learning framework for sign language gesture recognition, termed Active Convolutional Neural Networks—Sign Language (ActiveCNN-SL). ActiveCNN-SL aims to minimize the labeled data required for training and augment the accuracy of sign language gesture recognition through iterative human feedback. This proposed framework holds the potential to enhance communication accessibility for deaf and mute individuals and encourage inclusivity across various environments. The proposed framework is trained using two primary datasets: (i) the Sign Language Gesture Images Dataset and (ii) the American Sign Language Letters (ASL)—v1. The framework employs Resnet50 and YoloV.8 to train the datasets. It has demonstrated high performance in terms of precision and accuracy. The ResNet model achieved a remarkable accuracy rate of 99.98% during training, and it also exhibited a validation accuracy of 100%, surpassing the baseline CNN and RNN models. The YOLOv8 model outperformed previous methods on the ASL alphabet dataset, achieving an overall mean average accuracy for all classes of 97.8%.
Patterns of Psychotropic Prescribing Practices in Autistic Children and Adolescents: An Australian Perspective of Two Cohorts Five Years Apart
Authors: Baldes Anna , May Tamara , Brignell Amanda , Williams Katrina
Published: June 2024

This study aims to describe the utilisation of psychotropic medications in Australian autistic children and adolescents. All children and adolescents with available Pharmaceutical Benefits Scheme data who endorsed an autism diagnosis in The Longitudinal Study of Australian Children, including both B (n = 233, age 0–1 years in wave 1) and K cohorts (n = 157, age 4–5 years in wave 1), were included to describe psychotropic prescribing patterns. 212 (54.4%) autistic children and adolescents received at least one psychotropic prescription and 99 (25.4%) had polypharmacy. The most common psychotropic class prescribed was antidepressants (31.3%). Children in the B cohort were more likely to have a parent-reported diagnosis of anxiety or depression (χ2 = 12.18, p < 0.001) and tended to be more likely to have received a psychotropic prescription (χ2 = 3.54, p = 0.06). Psychotropic prescribing in Australian autistic children is common despite limited evidence for efficacy and tolerability of psychotropics in this group.
Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution
Authors: Nadizar Giorgia , Sakallioglu Berfin , Garrow Fraser , Silva Sara , Vanneschi Leonardo
Published: June 2024

Geometric Semantic Genetic Programming (GSGP) has shown notable success in symbolic regression with the introduction of Linear Scaling (LS). This achievement stems from the synergy of the geometric semantic genetic operators of GSGP with the scaling of the individuals for computing their fitness, which favours programs with a promising behaviour. However, the initial combination of GSGP and LS (GSGP-LS) underutilised the potential of LS, scaling individuals only for fitness evaluation, neglecting to incorporate improvements into their genetic material. In this paper we propose an advancement, GSGP with Lamarckian LS (GSGP-LLS), wherein we update the individuals in the population with their scaling coefficients in a Lamarckian fashion, i.e., by inheritance of acquired traits. We assess GSGP-LS and GSGP-LLS against standard GSGP for the task of symbolic regression on five hand-tailored benchmarks and six real-life problems. On the former ones, GSGP-LS and GSGP-LLS both consistently improve GSGP, though with no clear global superiority between them. On the real-world problems, instead, GSGP-LLS steadily outperforms GSGP-LS, achieving faster convergence and superior final performance. Notably, even in cases where LS induces overfitting on challenging problems, GSGP-LLS surpasses GSGP-LS, due to its slower and more localised optimisation steps.
Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning
Authors: Shin Jongkyung , Lee Donggi Augustine , Kim Juram , Lim Chiehyeon , Choi Byung-Kwan
Published: June 2024

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.
Adaptation of COMPASS for use in Autism-Specific Schools in Australia: A Cluster Randomized Trial
Authors: Love Abigail M. A. , Cai Ru Ying , Stephenson Jennifer , Gallagher Emma , Toland Michael D. , Gibbs Vicki
Published: June 2024

Autistic students are educated in Australia across a variety of contexts and almost all educators use individualized goal-setting as a way of ensuring appropriate accommodations and curriculum modifications. Educators experience similar challenges when developing individualized goals for students, including lack of a standardized process for goal setting, inconsistent support networks, and challenges with data-driven goal-setting. The purpose of our research was to adapt and trial the Collaborative Model for Competence and Success (COMPASS), a research-based intervention aimed at improving the individualized goal-setting process for autistic students. Our primary hypothesis was that autistic students who participate in COMPASS would demonstrate more growth on their individualized outcomes relative to autistic students who receive services as usual (SAU). To answer our primary research question, we applied a single-blind cluster randomized trial. Participants were randomized into one of two groups: (a) a COMPASS intervention group and (b) a SAU group. Results indicate that students whose teachers received the intervention made more progress on their final individualized goals than those who received SAU, replicating previous findings regarding COMPASS in the US. Also, the adaptation of COMPASS for an Australian context showed high rates of satisfaction and fidelity. The success of this intervention in improving the progress that students make on their individualized goals demonstrates the benefits of a standardized intervention that supports teachers and families in this critical practice.