bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Fully Privacy-Preserving Solution for Anomaly Detection in IoT using Federated Learning and Homomorphic Encryption
Authors: Arazzi Marco , Nicolazzo Serena , Nocera Antonino 
Published: November 2023

AbstractAnomaly detection for the Internet of Things (IoT) is a very important topic in the context of cyber-security. Indeed, as the pervasiveness of this technology is increasing, so is the number of threats and attacks targeting smart objects and their interactions. Behavioral fingerprinting has gained attention from researchers in this domain as it represents a novel strategy to model object interactions and assess their correctness and honesty. Still, there exist challenges in terms of the performance of such AI-based solutions. The main reasons can be alleged to scalability, privacy, and limitations on adopted Machine Learning algorithms. Indeed, in classical distributed fingerprinting approaches, an object models the behavior of a target contact by exploiting only the information coming from the direct interaction with it, which represents a very limited view of the target because it does not consider services and messages exchanged with other neighbors. On the other hand, building a global model of a target node behavior leveraging the information coming from the interactions with its neighbors, may lead to critical privacy concerns. To face this issue, the strategy proposed in this paper exploits Federated Learning to compute a global behavioral fingerprinting model for a target object, by analyzing its interactions with different peers in the network. Our solution allows the training of such models in a distributed way by relying also on a secure delegation strategy to involve less capable nodes in IoT. Moreover, through homomorphic encryption and Blockchain technology, our approach guarantees the privacy of both the target object and the different workers, as well as the robustness of the strategy in the presence of attacks. All these features lead to a secure fully privacy-preserving solution whose robustness, correctness, and performance are evaluated in this paper using a detailed security analysis and an extensive experimental campaign. Finally, the performance of our model is very satisfactory, as it consistently discriminates between normal and anomalous behaviors across all evaluated test sets, achieving an average accuracy value of 0.85.
Augmented ant colony algorithm for virtual drug discovery
Authors: Donati Luca , Fackeldey Konstantin , Weber Marcus 
Published: November 2023

AbstractDocking is a fundamental problem in computational biology and drug discovery that seeks to predict a ligand’s binding mode and affinity to a target protein. However, the large search space size and the complexity of the underlying physical interactions make docking a challenging task. Here, we review a docking method, based on the ant colony optimization algorithm, that ranks a set of candidate ligands by solving a minimization problem for each ligand individually. In addition, we propose an augmented version that takes into account all energy functions collectively, allowing only one minimization problem to be solved. The results show that our modification outperforms in accuracy and efficiency.
ISO/TS 21564:2019- based Evaluation of a Semantic Map between Variables in the ISARIC Freestanding Follow Up Survey and ORCHESTRA Studies
Authors: Rinaldi Eugenia , Thun Sylvia , Stellmach Caroline 
Published: November 2023

AbstractThe COVID-19 pandemic has led to tremendous investment in clinical studies to generate much-needed knowledge on the prevention, diagnosis, treatment and long-term effects of the disease. Case report forms, comprised of questions and answers (variables), are commonly used to collect data in clinical trials. Maximizing the value of study data depends on data quality and on the ability to easily pool and share data from several sources. ISARIC, in collaboration with the WHO, has created a case report form that is available for use by the scientific community to collect COVID-19 trial data. One of such research initiatives collecting and analyzing multi-country and multi-cohort COVID-19 study data is the Horizon 2020 project ORCHESTRA. Following the ISO/TS 21564:2019 standard, a mapping between five ORCHESTRA studies’ variables and the ISARIC Freestanding Follow-Up Survey elements was created. Measures of correspondence of shared semantic domain of 0 (perfect match), 1 (fully inclusive match), 2 (partial match), 4 (transformation required) or 4* (not present in ORCHESTRA) as compared to the target code system, ORCHESTRA study variables, were assigned to each of the elements in the ISARIC FUP case report form (CRF) which was considered the source code system. Of the ISARIC FUP CRF’s variables, around 34% were found to show an exact match with corresponding variables in ORCHESTRA studies and about 33% showed a non-inclusive overlap. Matching variables provided information on patient demographics, COVID-19 testing, hospital admission and symptoms. More in-depth details are covered in ORCHESTRA variables with regards to treatment and comorbidities. ORCHESTRA’s Long-Term Sequelae and Fragile population studies’ CRFs include 32 and 27 variables respectively which were evaluated as a perfect match to variables in the ISARIC FUP CRF. Our study serves as an example of the kind of maps between case report form variables from different research projects needed to link ongoing COVID-19 research efforts and facilitate collaboration and data sharing. To enable data aggregation across two data systems, the information they contain needs to be connected through a map to determine compatibility and transformation needs. Combining data from various clinical studies can increase the power of analytical insights.
Evaluating Standard of Care and Obstetrical Outcomes in a Reduced Contact Prenatal Care Model in the COVID-19 Pandemic
Authors: Mei Jenny Y. , Bernstein Megan E. , Patton Eden , Duong Hai-Lang , Negi Masaru 
Published: November 2023

Abstract
Introduction
We aimed to investigate the impact of reduced contact prenatal care necessitated by the COVID-19 pandemic on meeting standards of care and perinatal outcomes.

Methods
This was a retrospective case-control study of patients in low-risk obstetrics clinic at a tertiary care county facility serving solely publicly insured patients comparing reduced in-person prenatal care (R) over 12 weeks with a control group (C) receiving traditional prenatal care who delivered prior.

Results
Total 90 patients in reduced contact (R) cohort were matched with controls (C). There were similar rates of standard prenatal care metrics between groups. Gestational age (GA) of anatomy ultrasound was later in R (p = 0.017). Triage visits and missed appointments were similar, though total number of visits (in-person and telehealth) was higher in R (p = 0.043). R group had higher GA at delivery (p = 0.001). Composite neonatal morbidity and length of stay were lower in R (p = 0.017, p = 0.048). Maternal and neonatal outcomes did not otherwise differ between groups. Using Kotelchuck Adequacy of Prenatal Care Utilization index, R had higher rates of adequate prenatal care (45.6% R vs. 24.4% C, p = 0.005).

Discussion
Our study demonstrates the non-inferiority of a hybrid, reduced schedule prenatal schedule to traditional prenatal scheduling. In a reduced contact prenatal care model, more patients met criteria for adequate prenatal care, likely due to higher attendance of telehealth visits. These findings raise the question of revising the prenatal care model to mitigate disparities in disadvantaged populations.
Time-Based and Event-Based Prospective Memory in Mild Cognitive Impairment and Alzheimer’s Disease Patients: A Systematic Review and Meta-analysis
Authors: Román-Caballero Rafael , Mioni Giovanna 
Published: November 2023

AbstractProspective memory (PM) is the ability to remember to perform planned actions in a future moment and it is of fundamental importance for an independent and autonomous lifestyle from development to late adulthood. Deficits in episodic memory and executive functions, which are involved in PM are characteristic features of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Considering that the number of older adults is drastically increasing over the next decades, it is of great interest to understand how PM decline in healthy older adults and patients with different degree of cognitive decline. The present meta-analysis included 46 studies investigating PM performance in AD patients (17 studies) and people with MCI (24 studies); 5 studies included both clinical conditions in the same article. The 46 studies contributed a total of 63 independent samples and 129 effect sizes from 4668 participants (2115 patients and 2553 controls). Unlike previous reviews of the literature, our results with a larger and updated sample of studies confirmed lower PM abilities in AD compared to MCI and controls, although we did not observe conclusive differences between event-based and time-based PM in patients. Surprisingly, PM deficits shown by MCI and AD patients have decreased across years, in parallel to a reduction of the evidence of publication bias and an increase in the number of observations per task. We propose the use of more reliable research designs as one plausible explanation for the reduction of PM impairments.
Tribo-Electric Analysis of Multi-Cycle Wear in Asymmetric Laser-Structured Cu-Sn Connectors
Authors: Schütz Silas , Suarez Sebastian , Mücklich Frank 
Published: November 2023

AbstractOne of the key objectives of the connector industry for the past years has been to reduce the insertion friction forces of detachable electrical connections, where the majority of approaches combine a particular connector design with the usage of lubricants. The necessity for a solution that uses less contact force to maintain a lasting electrically conductive connection without sacrificing the mechanical robustness of the connection during use is a significant challenge in this situation. The approach in this work is to create surfaces supporting asymmetric friction, meaning lower insertion than removal forces. By Direct Laser Interference Patterning (DLIP), asymmetric saw-tooth structures with different structural inclinations and periodicities were generated on Sn-coated Cu plates. It is determined that topographical interlocking is the primary operating mechanism providing the required anisotropy. Multiple insertion/removal cycles were simulated on the tailored surfaces to tribologically characterize them and to determine the evolution of the electrical contact resistance at the end of each insertion motion. The vast majority of the generated structures showed an improvement compared to the reference state, with lower insertion and higher removal forces while the electrical behavior is not significantly impacted.
Rock typing and uncertainty assessment in geological and petrophysical properties by integrating electrofacies, hydraulic flow units, and geostatistical techniques in the Kangan gas field, Zagros basin
Authors: Bakhtiyari Mohammad , Qajar Jafar , Karimian Torghabeh Amir , Dehghan Abnavi Ali 
Published: November 2023

AbstractThe structural complexities of hydrocarbon reservoirs make it difficult to correlate geological and petrophysical properties. A successful field development depends on accurately mapping the spatial distributions of reservoir key parameters. In this study, we present results on rock type analysis, estimation, and uncertainty evaluation of geological and petrophysical data of 33 wells in one of the south Iranian gas fields. This paper is divided into two parts. In the first part, we used a reservoir rock classification theme based on identifying electrofacies (EFs) and hydraulic flow units by analyzing both log and core data. In the second part of the paper, we performed estimation, uncertainty evaluation, and assessment of the porosity–thickness relationship of the high-quality EFs using geostatistical techniques. We used sequential simulation schemes to map the spatial distributions of porosity and thickness of the high-quality EFs across the field. Using probabilistic approaches, the generated multiple realizations were used to quantify the local and spatial uncertainties. Variogram analysis showed that property distributions had a higher continuity and minimum variance in the NW–SE direction. Based on spatial uncertainty analysis, we found that the indicator-based sequential simulated maps exhibited less spatial uncertainty. Furthermore, the obtained probability maps indicated that the SE part of the study area is more suitable for drilling and production scenarios.
Trust-based health decision-making recruits the neural interoceptive saliency network which relates to temporal trajectories of Hemoglobin A1C in Diabetes Type 1
Authors: Jorge Helena , Duarte Isabel C. , Melo Miguel , Relvas Ana Paula , Castelo-Branco Miguel 
Published: November 2023

AbstractExperimental approaches in neuroeconomics generally involve monetary utility. Utility in the health domain is relevant in diabetes because constant daily life decisions are critical for self-consequential long-term outcomes. We used fMRI to investigate self-consequent decision-making in the health and economic domains in Type 1 Diabetes Mellitus and controls (N = 50). We focused on two critical phases of decision-making: Investment and Feedback (Positive or Negative). Patients showed larger BOLD activation of limbic, and reward/dopaminergic regions in particular in the health trust game. Importantly, the worse the trajectory of metabolic control (increasing HbA1C), the higher the BOLD activity in regions of the interoceptive saliency network. This was manifested by positive correlations between brain activity during investment in anterior cingulate cortex and insula and HbA1c blood level progression. We conclude that the neural correlates of health-consequent decision-making domain involve limbic and reward related dopaminergic regions in Type 1 Diabetes Mellitus. Furthermore, the temporal trajectory of HbA1C blood levels is correlated with neural risk processing in the saliency network. Evidence for differential risk processing in the health versus the neuroeconomic context, and the discovery of a role for the saliency interoceptive network in metabolic control trajectories suggests a new perspective on the development of personalized interventions.
Determine the Optimal Parameters for Biogas Production from Common Reed (Phragmites australis)
Authors: Al-Iraqi Ahmed R. , Gandhi Bhushan P. , Folkard Andrew M. , Barker Philip A. , Semple Kirk T. 
Published: November 2023

AbstractA series of batch assays have been conducted to investigate the optimal factors that can be adopted to improve the anaerobic digestion (AD) performance of Phragmites australis and increase biogas production. The assays were carried out using 125 mL microcosm reactors with a working volume of 80 mL and incubated at mesophilic conditions (37 ± 1ºC). The effect of particle size (10, 5, 2, and < 1 mm) and alkaline pre-treatment of P. australis using various concentrations of sodium hydroxide (0.5, 1, 2, and 4%) on biogas production was examined. Furthermore, the best pre-treatment incubation time (12, 24, 48, 72, 96, and 120 h) and the optimal inoculum to substrate ratio (ISR: 4:1, 2:1, 1:1, 1:2 and 1:4) were also assessed. The results revealed that the highest biogas production from P. australis was achieved at particle size < 1 mm (27.97 ± 0.07 and 16.67 ± 0.09 mL/g VS added, for pre-treated and untreated P. australis respectively); 2% and 4% NaOH concentration for pre-treatment (70.01 ± 3.75 and 76.14 ± 2.62 mL/g VS added, respectively); pre-treatment incubation time of 72, 96, and 120 h (71.18 ± 1.79, 72.46 ± 1.08, and 73.78 ± 1.87 mL/g VS added, respectively); and ISR of 1:2 for pre-treated P. australis (78.21 ± 0.36 mL/g VS added) and ISR 1:4 for untreated P. australis (28.93 ± 1.55 mL/g VS added). Determining optimal parameters in this work would guide further development of process configurations, such as continuous AD systems.
Extended review of multi-agent solutions to Advanced Public Transportation Systems challenges
Authors: Balbo Flavien , Mandiau René , Zargayouna Mahdi 
Published: November 2023

AbstractOver the past few decades, intelligent transportation systems (ITS) have emerged as an effective way to improve the performance of transportation systems. ITS provide innovative services, enhance travel safety, provide travellers with more choices, and make transportation systems more efficient. Multi-agent systems (MAS), which define autonomous interacting entities, are suitable for modelling distributed and intelligent systems in general and ITS in particular. This paper provides an in-depth review of multi-agent systems applied to Advanced Public Transportation Systems (APTS), a subclass of ITS dedicated to managing public transportation networks. We carefully analysed 38 papers in this study, published in 19 journals during 31 years (1990–2020). We perform a synthetic analysis of the trends in this domain and a qualitative analysis focused on multi-agent systems’ dimensions and properties. We show that the MAS approach is well suited to the real-time management of disturbances thanks to their delegation process, and their pro-activeness and autonomy properties.
Efficacy and Implementation of Stress-Reduction Interventions for Underserved Families of Autistic Preschoolers Across In-Person and Virtual Modalities
Authors: Fenning Rachel M. , Neece Cameron L. , Sanner Catherine M. , Morrell Holly E. R. 
Published: November 2023

Abstract
Objectives
Parents of autistic children experience elevated stress relative to parents of children with neurotypical development and children with other intellectual and developmental disabilities. Adverse effects of parenting stress on parent, child, and family functioning may be especially heightened for marginalized families. We conducted a randomized controlled trial that demonstrated the efficacy of Mindfulness-Based Stress Reduction (MBSR) relative to psychoeducational support (PE) for reducing stress in diverse and underserved parents of autistic preschoolers. This paper presents implementation data, and examines efficacy across in-person and virtual intervention modalities.

Method
Primary caregivers (n = 117; 91% female, 51% Latinx, 44% income < US $50,000) of 3- to 5-year-old autistic children (80% male, 68% with intellectual disability) were randomly assigned to MBSR (n = 59, 46% virtual) or PE (n = 58, 41% virtual). Assessments were conducted at baseline, immediately post-intervention, and at 6 and 12 months post-intervention.

Results
Both MBSR and PE demonstrated strong feasibility, acceptability, and utility for our diverse families. Comparable efficacy was observed across modalities. However, attendance was significantly better for virtual groups than for in-person groups. Parents participating in virtual MBSR also reported less difficulty completing homework and utilizing learned skills in everyday life than did in-person MBSR participants.

Conclusions
MBSR and PE appear feasible, acceptable, and efficacious for diverse and underserved parents of young autistic children. Preliminary evidence of comparable efficacy across virtual and in-person modalities indicates the potential to expand access to vital stress-reduction interventions through use of telehealth technology.

Preregistration
ClinicalTrials.gov Identifier: NCT03459625.
Impact of climate change-induced warming on groundwater temperatures and quality
Authors: Neidhardt Harald , Shao Wen 
Published: November 2023

AbstractThe impacts of climate change-induced warming on our ecosystems can no longer be neglected, but our understanding of consequences for groundwater ecosystems in general and groundwater quality in particular is alarmingly incomplete. In this review, we therefore provide an overview of the current state of knowledge related to the impact of global warming on our precious groundwater resources. Groundwater warming in shallow aquifers is closely associated with increasing average land surface temperatures and has already reached + 1 K compared to pe-industrial times. Until the end of the twenty-first century, temperature increases in local groundwater of up to + 10 K are possible. Monitoring data, laboratory and field experiments all provide evidence that such temperature increases are sufficient to substantially modify groundwater quality through numerous and interlinked biogeochemical processes, which we have summarized in a conceptual overview. Warming impacts on groundwater are highly site-specific and spatially heterogeneous, which complicates their assessment and prediction. Locally, shallow unconfined and nutrient-rich floodplain aquifers are most susceptible to warming-induced changes. Importantly, processes affecting water quality are not only modified by a long-term rise in groundwater temperatures, but also in the short-term during weather extremes, which is of great relevance for riverbank filtration. At the regional scale, aquifers in cold regions impacted by permafrost thawing are especially vulnerable to warming. As the majority of temperature-sensitive processes affecting groundwater quality are not or only very slowly reversable, we pressingly require comprehensive mechanistic understanding before it is too late to develop suitable countermeasures and management strategies.
Data Augmentation for Mask-Based Leaf Segmentation of UAV-Images as a Basis to Extract Leaf-Based Phenotyping Parameters
Authors: Barreto Abel , Reifenrath Lasse , Vogg Richard , Sinz Fabian , Mahlein Anne-Katrin 
Published: November 2023

AbstractIn crop protection, disease quantification parameters such as disease incidence (DI) and disease severity (DS) are the principal indicators for decision making, aimed at ensuring the safety and productivity of crop yield. The quantification is standardized with leaf organs, defined as individual scoring units. This study focuses on identifying and segmenting individual leaves in agricultural fields using unmanned aerial vehicle (UAV), multispectral imagery of sugar beet fields, and deep instance segmentation networks (Mask R-CNN). Five strategies for achieving network robustness with limited labeled images are tested and compared, employing simple and copy-paste image augmentation techniques. The study also evaluates the impact of environmental conditions on network performance. Metrics of performance show that multispectral UAV images recorded under sunny conditions lead to a performance drop. Focusing on the practical application, we employ Mask R-CNN models in an image-processing pipeline to calculate leaf-based parameters including DS and DI. The pipeline was applied in time-series in an experimental trial with five varieties and two fungicide strategies to illustrate epidemiological development. Disease severity calculated with the model with highest Average Precision (AP) shows the strongest correlation with the same parameter assessed by experts. The time-series development of disease severity and disease incidence demonstrates the advantages of multispectral UAV-imagery in contrasting varieties for resistance, as well as the limits for disease control measurements. This study identifies key components for automatic leaf segmentation of diseased plants using UAV imagery, such as illumination and disease condition. It also provides a tool for delivering leaf-based parameters relevant to optimize crop production through automated disease quantification by imaging tools.