bluuebunny/crossref_metadata_2025 huggingface dataset:
Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking
Authors: Sánchez Pedroche David , Amigo Daniel , García Jesús , Molina José M. , Zubasti Pablo
Published: November 2024

This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. Consequently, faster one-stage models, such as the tested YOLOv8 architectures, appear to be a more viable option for real-time operations. Notably, the swarm-based approach enables these faster algorithms to achieve an accuracy level comparable to that of slower models. Overall, the experimentation analysis demonstrates how larger YOLO architectures exhibit longer processing times in exchange for superior tracking success rates. However, the inclusion of additional UAVs introduced in the system outweighed the choice of the tracking algorithm if the mission is correctly configured, thus demonstrating that the swarm-based approach facilitates the use of faster algorithms while maintaining performance levels comparable to slower alternatives. However, the perspectives provided by the included UAVs hold additional significance, as they are essential for achieving enhanced results.
Life Satisfaction After Burn Injury—A Comprehensive Review
Authors: Hutter Maria Fernanda , Smolle Christian , Kleinhapl Julia , Kamolz Lars-Peter
Published: November 2024

Burn injuries can have long-lasting effects not only on a person’s bodily integrity but also on their psychosocial well-being. Since medical advancements have increased survival from burn injuries, improving psychosocial health has become a pivotal goal for burn rehabilitation. Besides health-related quality of life, life satisfaction has become an important parameter for evaluating long-term outcomes after burns. We reviewed life satisfaction after burns among adult burn patients to evaluate the current assessment methods and gain insight into recovery patterns. PubMed, EMBASE, Medline, and Cochrane Library were searched systematically for studies in the English language covering life satisfaction after burns, resulting in the inclusion of 18 studies. The Satisfaction With Life Scale (SWLS) was the most commonly used assessment tool. Others included the Life Satisfaction Index-A (LSI-A) and a non-standardized tool. Most studies’ recovery patterns showed a decreased life satisfaction post-burn injury. There was strong agreement that inhalation injury, body dysfunction, an extended hospital stay, and psychological illness before the injury are possible determinants of post-burn life satisfaction and have shown a negative correlation. There seems to be a consistent use of assessment tools, which opens up the possibility of a further comparative investigation to better understand factors that influence life satisfaction after a burn so that this knowledge can be used to improve patients’ recovery.
Characterization of Synanthropic Habitats on Shallow Seabeds Using Map Clustering Techniques: A Case Study in Taranto, Apulia, Italy
Authors: Massarelli Carmine , Campanale Claudia , Uricchio Vito Felice
Published: November 2024

The Mar Piccolo is a transitional water system located in Taranto city (Southern Italy); it is a semi-enclosed basin affected by severe pollution issues due to the presence of various industrial, agricultural and other anthropic activities that require careful monitoring and management. The pollution levels reached over time have harmed marine biodiversity and human health, repeatedly requiring timely actions for its mitigation. Characterization methodologies and techniques today play a fundamental role in supporting the decision-making phase, processing large quantities of data and identifying complex patterns and correlations. An approach focused on gaining detailed knowledge of complex environmental contexts through clustering map techniques enables highly precise results, capturing even the smallest variations in the features of the study object and strongly correlating them with possible sources of pollution. The use of these techniques improves the precision of the analyses and can significantly contribute to improving the understanding of the environmental state in the Mar Piccolo area. This study addresses the issue of pollution in Mar Piccolo due to marine litter, which has led to the formation of synanthropic habitats on the seabed. It also highlights the value of clustering maps and other characterization techniques for achieving detailed insights at various levels of analysis. Data processing through the proposed methodology can generate very detailed mapping useful for planning precision reclamation interventions that also include species conservation actions, as well as a better understanding of how synanthropic habitats are distributed and evolve. In summary, this study demonstrates how it is possible to improve the precision of data processing, providing crucial details for the management and conservation of highly threatened marine ecosystems.
Dysfunctional K+ Homeostasis as a Driver for Brain Inflammation
Authors: Ozsoy Nagihan , Dallas Mark L.
Published: November 2024

The central nervous system (CNS) relies on precise regulation of potassium ion (K+) concentrations to maintain physiology. This regulation involves complex cellular and molecular mechanisms that work in concert to regulate both intracellular and extracellular K+ levels. Inflammation, a key physiological response, encompasses a series of cell-specific events leading to inflammasome activation. Perturbations in K+-sensitive processes can result in either chronic or uncontrolled inflammation, highlighting the intricate relationship between K+ homeostasis and inflammatory signalling. This review explores molecular targets that influence K+ homeostasis and have been implicated in inflammatory cascades, offering potential therapeutic avenues for managing inflammation. We examine both cell-specific and common molecular targets across different cell types, providing a comprehensive overview of the interplay between K+ regulation and inflammation in the CNS. By elucidating these mechanisms, we identify leads for drug discovery programmes aimed at modulating inflammatory responses. Additionally, we highlight potential consequences of targeting individual molecular entities for therapeutic purposes, emphasizing the need for a nuanced approach in developing anti-inflammatory strategies. This review considers current knowledge on K+-sensitive inflammatory processes within the CNS, offering critical insights into the molecular underpinnings of inflammation and potential therapeutic interventions. Our findings underscore the importance of considering K+ homeostasis in the development of targeted therapies for inflammatory conditions within the CNS.
Negative Influence of Social Media on Children’s Diets: A Systematic Review
Authors: Prybutok Victor , Prybutok Gayle , Yogarajah Jesudhas
Published: November 2024

The widespread use of social media among children has raised concerns about its impact on their dietary habits and health. This systematic review investigates the negative effects of social media on children’s diets to inform evidence-based interventions and policies. A search of peer-reviewed studies from 2020 to 2024 was conducted using PubMed, Web of Science, and Scopus. Studies involving children aged 5–18 and examining social media’s influence on diet were included. Two independent reviewers screened the studies, and data extraction and quality assessment were done using standardized methods. Of 945 identified studies, 25 met the inclusion criteria. The key themes included (1) exposure to unhealthy food advertisements, (2) peer influence promoting energy-dense, nutrient-poor foods, (3) distorted body image perceptions leading to unhealthy eating, and (4) reduced mealtime quality due to social media distractions. Stronger associations were observed for marketing exposure and peer influence on food choices. The review highlights social media’s negative effects on children’s diets, emphasizing the need for interventions, stricter food marketing regulations, and educational programs to enhance media literacy. Future research should explore the long-term impacts and protective factors to guide policies for creating healthier digital environments for children.
Numerical Analysis of Fire Resistance in Cross-Laminated Timber (CLT) Constructions Using CFD: Implications for Structural Integrity and Fire Protection
Authors: Perković Nikola , Skejić Davor , Rajčić Vlatka
Published: November 2024

Fire represents a serious challenge to the safety and integrity of buildings, especially timber structures exposed to high temperatures and intense heat radiation. The combustibility of timber is one of the main reasons why regulations strictly limit timber as a building material, especially in multi-storey structures. This investigation seeks to assess the fire behaviour of cross-laminated timber (CLT) edifices and examine the ramifications for structural integrity and fire protection. Utilising computational fluid dynamics (CFD) simulations, critical variables including charring rate, heat emission, and smoke generation were analysed across two scenarios: one featuring exposed CLT and another incorporating protected CLT. The outcomes indicated that protective layers markedly diminish charring rates and heat emission, thereby augmenting fire resistance and constraining smoke dissemination. These revelations imply that CFD-based methodologies can proficiently inform fire protection design paradigms for CLT structures, presenting potential cost efficiencies by optimising material utilisation and minimising structural impairment.
Mangrove Extraction from Compact Polarimetric Synthetic Aperture Radar Images Based on Optimal Feature Combinations
Authors: Shu Sijing , Yang Ji , Jing Wenlong , Yang Chuanxun , Wu Jianping
Published: November 2024

As a polarimetric synthetic aperture radar (SAR) mode capable of simultaneously acquiring abundant surface information and conducting large-width observations, compact polarimetric synthetic aperture radar (CP SAR) holds great promise for mangrove dynamics monitoring. Nevertheless, there have been no studies on mangrove identification using CP SAR. This study aims to explore the potential of C-band CP SAR for mangrove monitoring applications, with the objective of identifying the most effective CP SAR descriptors for mangrove discrimination. A systematic comparison of 52 well-known CP features is provided, utilizing CP SAR data derived from the reconstruction of C-band Gaofen-3 quad-polarimetric data. Among all the features, Shannon entropy (SE), a random polarimetric constituent (VB), Shannon entropy (SEI), and the Bragg backscattering constituent (VG) exhibited the best performance. By combining these four features, we designed three supervised classifiers—support vector machine (SVM), maximum likelihood (ML), and artificial neural network (ANN)—for comparative analysis experiments. The results demonstrated that the optimal polarimetric feature combination not only reduced the redundancy of polarimetric feature data but also enhanced overall accuracy. The highest accuracy of mangrove extraction reached 98.04%. Among the three classifiers, SVM outperformed the other classifiers in mangrove extraction, while ML achieved the highest overall classification accuracy.
Resilience in the Internet of Medical Things: A Review and Case Study
Authors: Tomer Vikas , Sharma Sachin , Davis Mark
Published: November 2024

The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies.
Flexible Hyper-Distributed IoT–Edge–Cloud Platform for Real-Time Digital Twin Applications on 6G-Intended Testbeds for Logistics and Industry
Authors: Crespo-Aguado Maria , Lozano Raul , Hernandez-Gobertti Fernando , Molner Nuria , Gomez-Barquero David
Published: November 2024

This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT–Edge–Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform’s capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform’s support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load.
Comparative Analysis of Individual Carotenoid Profiles in Yellow- and White-Fleshed Potatoes (Solanum tuberosum L.) During Tuber Development
Authors: Suo Haicui , Liu Jitao , Wang Li , Li Chengchen , Shan Jianwei , An Kang , Yang Kun , Li Xiaobo
Published: November 2024

Individual carotenoids provide significant health benefits to humans, and potatoes are recognized as some of the most stable crops, distinguished by their substantial carotenoid content. To elucidate the accumulation patterns of individual carotenoids in potatoes, we quantified the carotenoid content in yellow- and white-fleshed genotypes across five developmental stages using LC-MS/MS. A total of 22 carotenoids were identified in yellow potatoes, whereas 18 were detected in white potatoes. The yellow-fleshed genotype was characterized by high levels of individual carotenoids and xanthophyll esters, with violaxanthin as the dominant component. The white-fleshed genotype exhibited low concentrations of individual carotenoids and xanthophyll esters, with lutein as the predominant compound. Notably, lutein, violaxanthin, zeaxanthin, antheraxanthin, neoxanthin, violaxanthin myristate, and lutein myristate were consistently detected during the developmental period in both genotypes. Violaxanthin myristate was identified as the dominant xanthophyll ester in both genotypes, showing an increasing trend throughout the tuber development stages. In contrast, xanthophyll esters maintained stable low levels in white-fleshed potatoes while exhibiting increasing types and contents in yellow-fleshed potatoes as the tubers continued to grow. Additionally, violaxanthin exhibited a significant correlation with b*, H°, and C*, suggesting that it may play an important role in forming yellow flesh.
Antioxidant, Physicochemical and Rheological Properties of White and Milk Chocolate Compounds Supplemented with Plant-Based Functional Ingredients
Authors: Okstaviyani Elinda , Lestari Puput Dwi , Kawiji Kawiji , Anandito Raden Baskara Katri , Yulviatun Anastriyani , Sefrienda Ardiba Rakhmi , Muhammad Dimas Rahadian Aji
Published: November 2024

Product development must be continuously done by the chocolate industry to face a high level of competitiveness in the market industry. This study investigates the effect of powdered sappan wood and butterfly pea flower incorporation in milk and white chocolate compounds. Four concentrations of each additional ingredient were used (0, 5, 10 and 15%). The results show that incorporating powdered sappan wood and butterfly pea flower significantly improved the total phenolic and flavonoid content and antioxidant activity of milk and white compounds. This study clearly shows that the selected plant could be an alternative to improve the health-promoting properties of milk and white chocolate compounds. However, supplementation also has some drawbacks, particularly in increasing the moisture content and the degree of colour difference between the milk and white compounds containing additional ingredients and the control. Also, powdered sappan wood and butterfly pea flower caused a higher viscosity of milk and white chocolate compounds. The results obtained in this study create a new strategy for using sappan wood and butterfly pea flower in various food products.
Complexation with Alginate in Pumpkin Leaf Protein Solutions for the Encapsulation of Folic Acid: The Effect of Extraction Protocols
Authors: Petrović Predrag , Balanč Bojana , Mijalković Jelena , Đukić Tamara , Bošković Stefan , Đorđević Verica , Bugarski Branko , Nedović Viktor , Knežević-Jugović Zorica
Published: November 2024

This study aimed to assess pumpkin leaves as a protein source and determine the feasibility of these proteins to form complexes with alginate for the encapsulation of folic acid. Different isolation protocols, two based on isoelectric precipitation (one with thermal pretreatment and the other with alkali pre-extraction) and one based on stepwise precipitation with ammonium sulfate, were compared regarding the yield and structural properties of the obtained leaf protein concentrates (LPC). The highest purity of protein was achieved using the thermal-acid protocol and the salting-out protocol at 40% saturation. RuBisCO protein was detected by SDS-PAGE in all LPCs, except for the fractions obtained through salting-out at saturation level ≥ 60%. Complexation of the LPC solutions (1 mg/mL) and sodium alginate solution (10 mg/mL) was monitored as a function of LPC:alginate ratio (2:1, 5:1, and 10:1) and pH (2–8) by zeta-potential measurements and confirmed by FT-IR analysis. Based on the results, the strongest interaction between LPCs and alginate occurred at a pH between 2.20 and 2.80 and an LPC:alginate ratio of 10:1. Complexation resulted in particle yields of 42–71% and folic acid entrapment of 46–92%. The LPC-folic acid interactions elucidated by computational protein–ligand docking demonstrated the high potential of RuBisCO as a biocarrier material for folic acid. The in vitro release study in the simulated gastrointestinal fluids indicated that complexes would be stable in gastric conditions, while folic acid would be gradually released in the intestinal fluids.
Cooking Alters the Metabolites of Onions and Their Ability to Protect Nerve Cells from Lead Damage
Authors: Zhao Li , Wang Liping , Wang Nan , Gao Xinchang , Zhang Bin , Zhao Yufen , Wang Ning
Published: November 2024

Onions (Allium cepa L.) are nutritious vegetables; however, variations in processing methods can influence their chemical composition and functional properties. Raw processing and cooking are the two main food-processing methods for onions, but it is not clear what kind of changes these two methods cause. In the present study, ultrahigh-resolution liquid chromatography–mass spectrometry (UHPLC–MS) was utilized to observe the changes in onion composition during cooking and to investigate the protective effects of raw and cooked onion extracts against lead damage in vitro and at the cellular level. Many compounds were identified, including amino acids, nucleosides, flavonoids, and organosulfur compounds. Cooking causes changes in the content of numerous amino acids (e.g., DL-glutamine) in onions and increases nucleoside content (e.g., 5’-S-methyl-5’-thioadenosine, adenine). Both raw and cooked onion extracts can reduce neuronal cell damage caused by lead exposure, but cooking increased the free radical scavenging (e.g., DPPH, ABTS, hydroxyl radicals) and chelating of lead ions (up to about 25%) of the onion extracts. In conclusion, cooking can cause changes in the chemical composition of onions and increase their antioxidant and lead chelating capacity.
Roles of Response Regulators in the Two-Component System in the Formation of Stress Tolerance, Motility and Biofilm in Salmonella Enteritidis
Authors: Hu Mengjun , Zhou Zhuoan , Liu Chenqi , Zhan Zeqiang , Cui Yan , He Shoukui , Shi Xianming
Published: November 2024

Two-component systems (TCS) of Salmonella enterica serovar Enteritidis are composed of a histidine kinase and a response regulator (RR) and represent a critical mechanism by which bacteria develop resistance to environmental stress. Here, we characterized the functions of RRs in TCS in the formation of stress tolerance, motility and biofilm using twenty-six S. Enteritidis RR-encoding gene deletion mutants. The viability results unraveled their essential roles in resistance to elevated temperature (GlrR), pH alterations (GlrR, TctD, YedW, ArcA and YehT), high salt (PhoB, BaeR, CpxR, PhoP, UvrY and TctD), oxidative stress (PhoB, YedW, BaeR, ArcA, PhoP, UvrY, PgtA and QseB) and motility (ArcA, GlnG, PgtA, PhoB, UhpA, OmpR, UvrY and QseB) of S. Enteritidis. The results of the crystal violet staining, microscopy observation and Congo red binding assays demonstrated that the absence of ArcA, GlnG, PhoP, OmpR, ZraR or SsrB in S. Enteritidis led to a reduction in biofilms and an impairment in red/dry/rough macrocolony formation, whereas the absence of UvrY exhibited an increase in biofilms and formed a brown/smooth/sticky macrocolony. The results indicated the regulatory effects of these RRs on the production of biofilm matrix, curli fimbriae and cellulose. Our findings yielded insights into the role of TCSs, making them a promising target for combating S. Enteritidis.
Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach
Authors: Baierle Ismael Cristofer , Haupt Leandro , Furtado João Carlos , Pinheiro Eluza Toledo , Sellitto Miguel Afonso
Published: November 2024

This study presents an innovative machine learning (ML) approach to predicting raw material yield in the leather tanning industry, addressing a critical challenge in production efficiency. Conducted at a tannery in southern Brazil, the research leverages historical production data to develop a predictive model. The methodology encompasses four key stages: data collection, processing, prediction, and evaluation. After rigorous analysis and refinement, the dataset was reduced from 16,046 to 555 high-quality records. Eight ML models were implemented and evaluated using Orange Data Mining software, version 3.38.0, including advanced algorithms such as Random Forest, Gradient Boosting, and neural networks. Model performance was assessed through cross-validation and comprehensive metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Coefficient of Determination (R2). The AdaBoost algorithm emerged as the most accurate predictor, achieving impressive results with an MAE of 0.042, MSE of 0.003, RMSE of 0.057, and R2 of 0.331. This research demonstrates the significant potential of ML techniques in enhancing raw material yield forecasting within the tanning industry. The findings contribute to more efficient forecasting processes, aligning with Industry 4.0 principles and paving the way for data-driven decision-making in manufacturing.
Retrograde Condensation in Gas Reservoirs from Microporous to Field-Scale Simulation
Authors: Canova Manoela Dutra , Barbosa Machado Marcos Vitor , Carvalho Marcio da Silveira
Published: November 2024

Hydrocarbon fields that contain non-associated gas, such as gas condensate, are highly valuable in terms of production. They yield significant amounts of condensate alongside the gas, but their unique behavior presents challenges. These reservoirs experience constant changes in composition and phases during production, which can lead to condensate blockage near wells. This blockage forms condensate bridges that hinder flow and potentially decrease gas production. To address these challenges, engineers rely on numerical simulation as a crucial tool to determine the most effective project management strategy for producing these reservoirs. In particular, relative permeability curves are used in these simulations to represent the physical phenomenon of interest. However, the representativeness of these curves in industry laboratory tests has limitations. To obtain more accurate inputs, simulations at the pore network level are performed. These simulations incorporate models that consider alterations in interfacial tension and flow velocity throughout the reservoir. The validation process involves reproducing a pore network flow simulation as close as possible to a commercial finite difference simulation. A scale-up methodology is then proposed, utilizing an optimization process to ensure fidelity to the original relative permeability curve at a microporous scale. This curve is obtained by simulating the condensation process in the reservoir phenomenologically, using a model that captures the dependence on velocity. To evaluate the effectiveness of the proposed methodology, three relative permeability curves are compared based on field-scale productivities and the evolution of condensate saturation near the wells. The results demonstrate that the methodology accurately captures the influence of condensation on well productivity compared to the relative permeability curve generated from laboratory tests, which assumes greater condensate mobility. This highlights the importance of incorporating more realistic inputs into numerical simulations to improve decision-making in project management strategies for reservoir development.
The Esophageal Microbiota in Esophageal Health and Disease
Authors: Bonazzi Erica , Lorenzon Greta , Maniero Daria , De Barba Caterina , Bertin Luisa , Barberio Brigida , Salvador Renato , Valmasoni Michele , Zingone Fabiana , Ghisa Matteo , Savarino Edoardo Vincenzo
Published: November 2024

The esophagus, traditionally viewed as a sterile conduit, is now recognized as a dynamic habitat for diverse microbial communities. The emerging evidence suggests that the esophageal microbiota plays an important role in maintaining esophageal health and contributing to disease. The aim of this systematic review was to synthesize the current knowledge on the esophageal microbiota composition, its variation between healthy individuals and those with esophageal diseases, and the potential mechanisms through which these microorganisms influence esophageal pathology. A systematic literature search was conducted using multiple databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to July 2024. The inclusion criteria encompassed original research articles that used molecular techniques to characterize the esophageal microbiota in human subjects, comparing healthy individuals with patients affected by esophageal conditions such as gastroesophageal reflux disease (GERD), Barrett’s esophagus, eosinophilic esophagitis, and esophageal cancer. The primary outcomes were the composition and diversity of the esophageal microbiota, and the secondary outcomes included the correlations between microbial profiles and disease states. The esophageal microbiota of healthy individuals was dominated by Gram-positive bacteria, particularly Streptococcus. Conversely, the esophageal microbiota is considerably altered in disease states, with decreased microbial diversity and specific microbial signatures associated with these conditions, which may serve as biomarkers for disease progression and as targets for therapeutic intervention. However, the heterogeneous study designs, populations, and analytical methods underscore the need for standardized approaches in future research. Understanding the esophageal microbiota’s role in health and disease could guide microbiota-based diagnostics and treatments, offering novel avenues for managing esophageal conditions.
An Updated Analysis of Exon-Skipping Applicability for Duchenne Muscular Dystrophy Using the UMD-DMD Database
Authors: Leckie Jamie , Zia Abdullah , Yokota Toshifumi
Published: November 2024

Background/Objectives: Antisense oligonucleotide (ASO)-mediated exon-skipping is an effective approach to restore the disrupted reading frame of the dystrophin gene for the treatment of Duchenne muscular dystrophy (DMD). Currently, four FDA-approved ASOs can target three different exons, but these therapies are mutation-specific and only benefit a subset of patients. Understanding the broad applicability of exon-skipping approaches is essential for prioritizing the development of additional therapies with the greatest potential impact on the DMD population. This review offers an updated analysis of all theoretical exon-skipping strategies and their applicability across the patient population, with a specific focus on DMD-associated mutations documented in the UMD-DMD database. Unlike previous studies, this approach leverages the inclusion of phenotypic data for each mutation, providing a more comprehensive and clinically relevant perspective. Methods: The theoretical applicability of all single and double exon-skipping strategies, along with multi exon-skipping strategies targeting exons 3–9 and 45–55, was evaluated for all DMD mutations reported in the UMD-DMD database. Results: Single and double exon-skipping approaches were applicable for 92.8% of large deletions, 93.7% of small lesions, 72.4% of duplications, and 90.3% of all mutations analyzed. Exon 51 was the most relevant target and was applicable for 10.6% of all mutations and 17.2% of large deletions. Additionally, two multi-exon-skipping approaches, targeting exons 45–55 and 3–9, were relevant for 70.6% of large deletions and 19.2% of small lesions. Conclusions: Current FDA-approved ASOs were applicable to 27% of the UMD-DMD population analyzed, leaving a significant portion of patients without access to exon-skipping therapies. The clinical translation of alternative approaches is critical to expanding the accessibility of these therapies for the DMD population.
Patient Navigation in Mothers at Risk for and Surviving with Breast/Ovarian Cancer: The Role of Children’s Ages in Program Utilization and Health Outcomes
Authors: Zamir Talia , Statman Muriel R. , Sleiman Marcelo M. , Fleischmann Adina , Silber Elana , Tercyak Kenneth P.
Published: November 2024

Background/Objectives: Many women at risk for and surviving with breast/ovarian cancer are simultaneously raising children. These women often experience unique challenges due to concurrent demands as both parents and patients with cancer. Community-based cancer control organizations offer vital patient navigation (PN), including psychoeducational services. Yet, little is known about how PN addresses these mothers’ comprehensive care needs. Methods: We examined PN program data from N = 1758 women served by a national cancer organization. Results: Out of the 69% of navigated women who were mothers, most were raising adult children only (age ≥ 18; 56%); however, 31% were mothers with young children only (age < 18), and 13% were mothers with both adult and young children (χ2 = 341.46, p < 0.001). While mothers with adult children reported poorer quality of life (QoL) than mothers with young children (physically unhealthy days, t = −2.2, df = 526, p < 0.05; total unhealthy days, t = −1.2, df = 533, p < 0.05), there were no significant differences in their PN experiences. For mothers with young children, a better QoL was associated with a lower genetic risk for cancer (r = −0.12) and a stronger sense of psychosocial empowerment (r = 0.10) (all p’s < 0.05). In an adjusted multivariate regression model of QoL, as empowerment increased, the influence of PN quality decreased (ß = −0.007, SE of ß = 0.00, p = 0.02), suggesting that strengthening mothers of young children’s sense of agency over their breast/ovarian cancer is critical to achieving overall well-being. Conclusions: CBO-led cancer control programming that supportively cares for mothers across their cancer journey can be essential to their QoL, especially for those who are raising minors.
Putting the Car Before the Horse: The Diffusion of the Automobile and the Rise of Technocratic Primacy
Authors: Gordon Cameron Elliott
Published: November 2024

A key question in the literature of technology in society is the degree to which technology is a product of society and vice versa. A related question is how the differing cross-causal lines between the two change both technological and social forms. This paper considers these questions by conducting a review of the literature on technology and society, and then conducting a structured historical examination of the social, economic, and technical dimensions of the rolling-out and utilisation of the automobile in the United States since the early 20th century in light of that literature. This examination is broken into analytical “phases”, starting with the early days of the auto steeped largely (but not exclusively) in technological idealism, moving through an age of car-ascendancy that peaked in the 1950s and 1960s (with the birth of a literal “car culture”), and into the present day of automobile dependency and changes in auto technology that have varying societal implications that are still largely unexplored. This history is assessed according to three different broad approaches offered by the literature, including (1) an instrumental approach viewing technical change as largely socially “neutral” and technology as primarily driven by the need to solve specific material problems; (2) a values approach in which technology is viewed as having technical components but with technical change driven largely as an expression of social values, both of particular interest groups and societal idea-systems more broadly; and (3) a relational approach in which social and technical change arises from an interplay of different entities, human and non-human, relating to each other in dynamic ways. The American historical experience with the automobile suggests that all three models apply at different times, but that instrumental technological approaches have dominated there, often subordinating the human to the machine, rather than the other way around.
Integrating Soil, Leaf, Fruitlet, and Fruit Nutrients, Along with Fruit Quality, to Predict Post-Storage Quality of Staccato Sweet Cherries
Authors: Sharifi Mehdi , Wolk William , Asefpour Vakilian Keyvan , Xu Hao , Slamka Stephanie , Fong Karen
Published: November 2024

Predicting the post-storage quality of cherry fruits is crucial for determining their suitability for long-distance shipping or domestic distribution. This study aimed to forecast key quality attributes of Staccato sweet cherries after storage, simulating shipping conditions, by analyzing spring soil, leaf, fruitlet, and at-harvest data from thirty orchards in the Okanagan Valley, British Columbia, Canada, over two years. A support vector machine (SVM) was used to predict post-storage variables, with pre-harvest and at-harvest data selected by a genetic algorithm. The SVM accurately predicted soluble solids (R2 = 0.88), firmness (R2 = 0.83), and acidity (R2 = 0.79) after four weeks of storage, as well as visual disorders like slip skin and stem browning. Spring soil properties (Ca, Mg), leaf (N, Ca, Mg, Fe, Zn, B), and fruitlet data (N, Ca, Mg, B) were key predictors. Leaf Ca was vital for firmness and total soluble solids (TSS) prediction, while N in leaves and fruitlets influenced firmness, acidity, and disorders. Leaf Zn helped predict weight and acidity/TSS ratio, and Mg impacted fruit color. Pre-harvest leaf nutrition measured 3–4 weeks before harvest, proved most effective in predicting post-storage quality.
Hydrogen-Rich Syngas Production Based on a Co-Gasification Process Coupled to a Water–Gas Shift Reactor Without Steam Injection
Authors: Monteiro Eliseu , Bourguig Ouissal , Rouboa Abel
Published: November 2024

Future decarbonized applications that rely on renewable and carbon-dioxide-neutral hydrogen production could benefit from the gasification of waste to produce hydrogen. In the current study, an Aspen Plus® model was developed by coupling a co-gasification model to a water–gas shift (WGS) model. The feedstock employed in the simulations was a blend of municipal solid waste (MSW) and biomass from Morocco. A parametric assessment was conducted to analyze the effect of the steam-to-feedstock ratio (SFR) on the syngas composition and the WGS reactor temperature. This study also presents a comparison between the results of the gasification process before and after the WGS reactor, using air and steam as the gasifying agent. The results show an increase in hydrogen volumetric percentage for higher steam-to-feedstock ratios in the gasifier. Moreover, the inclusion of a WGS reactor enhances hydrogen and carbon dioxide while reducing the amount of carbon monoxide in the syngas for both air and steam as the gasifying agents. It can be concluded that a co-gasification process can be intensified by coupling it to a WGS reactor without steam injection to produce hydrogen-rich syngas with reduced operational expenditures.
Neglected Issues in T Lymphocyte Metabolism: Purine Metabolism and Control of Nuclear Envelope Regulatory Processes. New Insights into Triggering Potential Metabolic Fragilities
Authors: Torchia Naomi , Brescia Carolina , Chiarella Emanuela , Audia Salvatore , Trapasso Francesco , Amato Rosario
Published: November 2024

The metabolism of T-lymphocytes has recently emerged as a pivotal area of investigation, offering insights into the supra-genic modulations that can influence the genetic mechanisms underlying lymphocyte clustering processes. Furthermore, it has become a crucial aspect in understanding lymphocyte plasticity within the immune microenvironment, both in physiological and pathological contexts. T-lymphocyte metabolism has recently emerged as a pivotal factor in both targeted therapy and the genetic signature of the T-lymphocyte, as a result of its influence on gatekeeper processes. From this perspective, the interconnections between the metabolic processes traditionally associated with energy production and the capacity to influence the genetic fate of the T lymphocyte have identified purine metabolism and nuclear/cytoplasmic signaling as pivotal elements in comprehending the intricacies of these molecular phenomena. The two aspects of purine metabolism and metabolic/molecular control of the nuclear envelope have been the subject of a number of significant studies published in recent years. However, from a certain perspective, the existing evidence remains sparse and inconclusive, hindering a comprehensive understanding of the subject matter. In this review, we endeavor to establish a connection between these aspects for the first time and to present a review of the molecular, immunological and genetic events that determine how these aspects, which have hitherto received insufficient attention, may represent a new avenue for lymphocyte reprogramming in the therapeutic field. This will be achieved by understanding the connections between nuclear control and purine flux within and outside the cell.
Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation
Authors: Amato Flora , Cirillo Egidia , Fonisto Mattia , Moccardi Alberto
Published: November 2024

Despite considerable advancements in integrating the Internet of Things (IoT) and artificial intelligence (AI) within the industrial maintenance framework, the increasing reliance on these innovative technologies introduces significant vulnerabilities due to cybersecurity risks, potentially compromising the integrity of decision-making processes. Accordingly, this study aims to offer comprehensive insights into the cybersecurity challenges associated with predictive maintenance, proposing a novel methodology that leverages generative AI for data augmentation, enhancing threat detection capabilities. Experimental evaluations conducted using the NASA Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset affirm the viability of this approach leveraging the state-of-the-art TimeGAN model for temporal-aware data generation and building a recurrent classifier for attack discrimination in a balanced dataset. The classifier’s results demonstrate the satisfactory and robust performance achieved in terms of accuracy (between 80% and 90%) and how the strategic generation of data can effectively bolster the resilience of intelligent maintenance systems against cyber threats.
Machine Learning-Based Methodologies for Cyber-Attacks and Network Traffic Monitoring: A Review and Insights
Authors: Genuario Filippo , Santoro Giuseppe , Giliberti Michele , Bello Stefania , Zazzera Elvira , Impedovo Donato
Published: November 2024

The number of connected IoT devices is increasing significantly due to their many benefits, including automation, improved efficiency and quality of life, and reducing waste. However, these devices have several vulnerabilities that have led to the rapid growth in the number of attacks. Therefore, several machine learning-based intrusion detection system (IDS) tools have been developed to detect intrusions and suspicious activity to and from a host (HIDS—Host IDS) or, in general, within the traffic of a network (NIDS—Network IDS). The proposed work performs a comparative analysis and an ablative study among recent machine learning-based NIDSs to develop a benchmark of the different proposed strategies. The proposed work compares both shallow learning algorithms, such as decision trees, random forests, Naïve Bayes, logistic regression, XGBoost, and support vector machines, and deep learning algorithms, such as DNNs, CNNs, and LSTM, whose approach is relatively new in the literature. Also, the ensembles are tested. The algorithms are evaluated on the KDD-99, NSL-KDD, UNSW-NB15, IoT-23, and UNB-CIC IoT 2023 datasets. The results show that the NIDS tools based on deep learning approaches achieve better performance in detecting network anomalies than shallow learning approaches, and ensembles outperform all the other models.
Effects of Differential Displacements Between the Ground Anchors in Suspension Bridges
Authors: Clemente Paolo
Published: November 2024

A simple model to evaluate the effects of relative displacements between the ground anchors of a suspension bridge is proposed. An equation system is defined, which allows for the evaluation of the structural response under a general displacement set of the ground anchor points. Then, the most interesting and likely cases are analyzed in detail with reference to a suspension bridge having geometrical and mechanical characteristics typical of a long-span bridge. A simple procedure for the assessment of variation in cable stress is also given, which can be used to choose the optimum values for stress in cables under dead loads, as a percentage of their strength. The results obtained showed that expected movements do not significantly impact the structure in its lifetime and that the effects become negligible for very long-span bridges. Finally, the results obtained can be easily used for the condition monitoring of suspension bridges.
Blue Vane and Pan Traps Are More Effective for Profiling Multiple Facets of Bee Diversity in Subtropical Forests
Authors: Xie Ting-Ting , Wang Ming-Qiang , Li Yi , Su Cheng-Yong , Zhang Dan , Zhou Qing-Song , Niu Ze-Qing , Yuan Feng , Liu Xiu-Wei , Ma Ke-Ping , Zhu Chao-Dong , Hao Jia-Sheng , Chesters Douglas
Published: November 2024

The choice of trap in entomological surveys affects the composition of captured insects, though previous comparative studies have been limited in the types of composition measured, and the effects of environmental context. We assessed the sampling bias of several traps commonly used in pollinator monitoring: blue, yellow, and white pan traps, and blue vane traps, towards different taxonomic and functional groups and their efficiency in measuring taxonomic, phylogenetic, and functional diversity. Analyses were performed in monoculture and mixed forests to understand the environmental context of trap efficiency. We found that blue pan traps generally outperformed other types in bee capture and exhibited a preference for Halictidae bees. Blue pan traps yielded the highest species richness and phylogenetic diversity, while blue vane traps captured the highest functional richness. Bias differences were frequently detected in mixed forests compared with monoculture forests. We also found the combination of blue vane and pan traps consistently correlated highest with a complete survey among two-method combinations. Based on our findings, we recommend a combination of blue vane and pan traps to obtain a more comprehensive bee collection in an efficient manner. Additionally, it is crucial to consider habitat type when designing bee trapping protocols to ensure an accurate representation of bee communities.