bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Continuous Return of Bt Corn Straw on Soil Nutrients, Enzyme Activities, and Microbial Communities
Authors: Zhang Chenning , Lv Xiao , Liang Xiaomin , Peng Peng , Feng Yuanjiao
Published: November 2024

The impact of Bacillus thuringiensis (Bt) corn straw returning on the soil ecosystem has attracted significant attention. In this study, taking the homologous conventional corn 5422 as a control, we explored the effects of Bt corn (5422Bt1 and 5422CBCL) straw return after five consecutive cycles on soil nutrients, enzyme activities, and microbial communities. The results showed that in the 5422Bt1 treatment, the levels of available phosphorus (AP), total nitrogen (TN), and sucrose enzyme (SUC) activities significantly increased. In the 5422CBCL treatment, organic matter (OM), alkaline nitrogen (AN), and AP contents, as well as SUC and acid phosphatase (ACP) activities, significantly decreased, while available potassium (AK) and TN contents significantly increased. Through Illumina high-throughput sequencing, it was found that the OTU abundance of soil fungi and bacteria changed after straw returning, and there were no significant differences in alpha diversity (α-diversity) among the three treatments. Redundancy analysis (RDA) indicated that soil nutrients and enzyme activities also affect the soil microbial communities. In summary, Bt corn straw returning affects soil nutrients, enzyme activities, and the structure of microbial communities. Overall, this study revealed the impact of continuous Bt corn straw returning on the soil ecosystem, providing a theoretical basis for subsequent studies.
An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
Authors: Tene Talia , Arias Arias Fabian , Paredes-Páliz Karina I. , Haro-Barroso Camilo , Vacacela Gomez Cristian
Published: November 2024

Graphene-enhanced surface plasmon resonance (SPR) biosensors offer promising advancements in viral detection, particularly for SARS-CoV-2. This study presents the design and optimization of a multilayer SPR biosensor incorporating silver, silicon nitride, single-layer graphene, and thiol-tethered ssDNA to achieve high sensitivity and specificity. Key metrics, including SPR angle shift (Δθ), sensitivity (S), detection accuracy (DA), and figure of merit (FoM), were assessed across SARS-CoV-2 concentrations from 150 to 525 mM. The optimized biosensor achieved a sensitivity of 315.91°/RIU at 275 mM and a maximum Δθ of 4.2° at 400 mM, demonstrating strong responsiveness to virus binding. The sensor maintained optimal accuracy and figure of merit at lower concentrations, with a linear sensitivity response up to 400 mM, after which surface saturation limited further responsiveness. These results highlight the suitability of the optimized biosensor for real-time, point-of-care SARS-CoV-2 detection, particularly at low viral loads, supporting its potential in early diagnostics and epidemiological monitoring.
The Molecular Mechanisms of the Antibacterial Activity of Sumac (Rhus typhina L.) Tannin Against Pseudomonas aeruginosa
Authors: Łomanowska Magdalena , Olchowik-Grabarek Ewa , Czerkas Krzysztof , Abdulladjanova Nodira , Sękowski Szymon
Published: November 2024

Treatment of infections caused by Pseudomonas aeruginosa presents a challenge due to its ability to adapt and acquire drug resistance rapidly. Therefore, a key challenge is identifying and investigating new compounds with antibacterial and anti-virulence activity. Tannins, a group of plant polyphenolic compounds, can interact with bacterial cells and their virulence factors. The purpose of this study was to assess the antibacterial potential of using 3,6-bis-O-di-O-galloyl-1,2,4-tri-O-galloyl-β-D-glucose (C55H40O34) isolated from Rhus typhina against P. aeruginosa ATCC BAA-1744. The investigation involved viability analyses using the INT assay, fluorescence analyses of the tannins’ interaction with the cell membrane and membrane proteins of P. aeruginosa, and analysis of changes in the Zeta potential. The results obtained allowed us to conclude that C55H40O34 exhibits antimicrobial activity by inducing changes in the biophysical properties of P. aeruginosa’s cell membrane. The thermodynamic parameters indicated that C55H40O34 binds to bacterial membrane proteins through hydrophobic interactions. These interactions with proteins may impact their structure and disrupt their functions, such as disturbing or inhibiting the efflux pumps, which are part of P. aeruginosa’s resistance mechanisms. Therefore, C55H40O34 may be a new, natural agent and could potentially be used against P. aeruginosa.
Integrating Fuzzy FMEA and RAM Analysis for Evaluating Modernization Strategies in an LNG Plant Pumping and Vaporization Facility
Authors: Durán Orlando , Orellana Fabián , Lobos Gabriel , Ibacache Alexis
Published: November 2024

In today’s competitive industrial landscape, Reliability Engineering plays a vital role in minimizing costs and expenses in energy projects. The main focus of this paper is to propose the integration of a fuzzy-based FMECA process into a RAM analysis to assess modernization and reconfiguration strategies for LNG facilities. This approach estimates, through a systematic procedure, the system’s failure probabilities and gauges the impact of various maintenance and topological modification initiatives on the asset and the system’s availability as a driver of profitability. A methodology based on fuzzy-FMEA is proposed to collect and process imprecise data about reliability and maintainability of the components of the facility. Furthermore, Monte Carlo-based RAM experiments are performed. The selection of parameters for conducting Monte Carlo experiments is done after the defuzzification of MTBF and MTTR values defined in the FMEA stage. The proposed procedure allows for the prediction of the system’s reliability across hypothetical scenarios, incorporating design tweaks and potential improvements. As a case study, the proposed was applied to a Pumping and Vaporization facility in a Chilean LNG plant. Sensitivity analysis was performed on critical elements, leading to an optimization strategy for key components like Open Rack Vaporizers (ORV) and Submerged Combustion Vaporizers (SCV). The anticipated availability rate was found to be 99.95% over an 8760 h operating period. Final conclusions and managerial insights are discussed.
Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
Authors: Georgiou Nektarios , Kolias Pavlos , Chouvarda Ioanna
Published: November 2024

The aim of this study was to explore the application of computational models for the analysis of histopathological images in the context of colon cancer. A comprehensive dataset of colon cancer images annotated into eight distinct categories based on their representation of cancerous cell portions was used. The primary objective was to employ various image classification algorithms to assess their efficacy in the context of cancer classification. Additionally, this study investigated the use of feature extraction techniques to derive meaningful data from the images, contributing to a more nuanced understanding of cancerous tissues, comparing the performance of different image classification algorithms in the context of colon cancer image analysis. The findings of this research suggested that XGboost provides the highest accuracy (89.79%) and could contribute to the growing body of knowledge in computational pathology. Other algorithms, such as the random forest, SVM, and CNN, also provided satisfactory results, offering insights into the effectiveness of image classification algorithms in distinguishing between different categories of cancerous cells. This work holds implications for the development of more accurate and efficient tools, underscoring the potential of computational models in enhancing the analysis of histopathological images and improving diagnostic capabilities in cancer research.
RSNC-YOLO: A Deep-Learning-Based Method for Automatic Fine-Grained Tuna Recognition in Complex Environments
Authors: Xu Wenjie , Fang Hui , Yu Shengchi , Yang Shenglong , Yang Haodong , Xie Yujia , Dai Yang
Published: November 2024

Tuna accounts for 20% of the output value of global marine capture fisheries, and it plays a crucial role in maintaining ecosystem stability, ensuring global food security, and supporting economic stability. However, improper management has led to significant overfishing, resulting in a sharp decline in tuna populations. For sustainable tuna fishing, it is essential to accurately identify the species of tuna caught and to count their numbers, as these data are the foundation for setting scientific catch quotas. The traditional manual identification method suffers from several limitations and is prone to errors during prolonged operations, especially due to factors like fatigue, high-intensity workloads, or adverse weather conditions, which ultimately compromise its accuracy. Furthermore, the lack of transparency in the manual process may lead to intentional underreporting, which undermines the integrity of fisheries’ data. In contrast, an intelligent, real-time identification system can reduce the need for human labor, assist in more accurate identification, and enhance transparency in fisheries’ management. This system not only provides reliable data for refined management but also enables fisheries’ authorities to dynamically adjust fishing strategies in real time, issue timely warnings when catch limits are approached or exceeded, and prevent overfishing, thus ultimately contributing to sustainable tuna management. In light of this need, this article proposes the RSNC-YOLO algorithm, an intelligent model designed for recognizing tuna in complex scenarios on fishing vessels. Based on YOLOv8s-seg, RSNC-YOLO integrates Reparameterized C3 (RepC3), Selective Channel Down-sampling (SCDown), a Normalization-based Attention Module (NAM), and C2f-DCNv3-DLKA modules. By utilizing a subset of images selected from the Fishnet Open Image Database, the model achieves a 2.7% improvement in mAP@0.5 and a 0.7% improvement in mAP@0.5:0.95. Additionally, the number of parameters is reduced by approximately 30%, and the model’s weight size is reduced by 9.6 MB, while maintaining an inference speed comparable to that of YOLOv8s-seg.
Enhanced Decision Support for Multi-Objective Factory Layout Optimization: Integrating Human Well-Being and System Performance Analysis
Authors: Lind Andreas , Elango Veeresh , Bandaru Sunith , Hanson Lars , Högberg Dan
Published: November 2024

This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms.
Laboratory Assessment of Manual Wheelchair Propulsion
Authors: Wieczorek Bartosz , Sydor Maciej
Published: November 2024

Self-propelled manual wheelchairs offer several advantages over electric wheelchairs, including promoting physical activity and requiring less maintenance due to their simple design. While theoretical analyses provide valuable insights, laboratory testing remains the most reliable method for evaluating and improving the efficiency of manual wheelchair drives. This article reviews and analyzes the laboratory methods for assessing the efficiency of wheelchair propulsion documented in the scientific literature: (1) A wheelchair dynamometer that replicates real-world driving scenarios, quantifies the wheelchair’s motion characteristics, and evaluates the physical exertion required for propulsion. (2) Simultaneous measurements of body position, motion, and upper limb EMG data to analyze biomechanics. (3) A method for determining the wheelchair’s trajectory based on data from the dynamometer. (4) Measurements of the dynamic center of mass (COM) of the human–wheelchair system to assess stability and efficiency; and (5) data analysis techniques for parameterizing large datasets and determining the COM. The key takeaways include the following: (1) manual wheelchairs offer benefits over electric ones but require customization to suit individual user biomechanics; (2) the necessity of laboratory-based ergometer testing for optimizing propulsion efficiency and safety; (3) the feasibility of replicating real-world driving scenarios in laboratory settings; and (4) the importance of efficient data analysis techniques for interpreting biomechanical studies.
Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
Authors: Park Woncheol , Oh Seungmin , Park Seonghyun
Published: November 2024

This study proposes a system for analyzing non-face-to-face counseling data using text-mining techniques to assess psychological states and automatically classify them into predefined categories. The system addresses the challenge of understanding internal issues that may be difficult to express in traditional face-to-face counseling. To solve this problem, a counseling management system based on text mining was developed. In the experiment, we combined TF-IDF and Word Embedding techniques to process and classify client counseling data into five major categories: school, friends, personality, appearance, and family. The classification performance achieved high accuracy and F1-Score, demonstrating the system’s effectiveness in understanding and categorizing clients’ emotions and psychological states. This system offers a structured approach to analyzing counseling data, providing counselors with a foundation for recommending personalized counseling treatments. The findings of this study suggest that in-depth analysis and classification of counseling data can enhance the quality of counseling, even in non-face-to-face environments, offering more efficient and tailored solutions.
Omics-Integrated Approach (Metabolomics, Proteomics and Lipidomics) to Assess the Quality Control of Aquatic and Seafood Products
Authors: Sidira Marianthi , Agriopoulou Sofia , Smaoui Slim , Varzakas Theodoros
Published: November 2024

Since the demand for seafood products is growing and aquaculture provides more than fifty percent of the aquatic food as reported by FAO, the development of more accurate and sensitive analytical techniques in order to screen and evaluate the safety and quality of seafood products is needed. At this point, several omic techniques like proteomics, lipidomics, and metabolomics, or combinations of them, are used for integration into seafood processing and quality control. Moreover, according to the literature, using the respective techniques can prevent, control, and treat diseases in fish as well as address several issues in aquaculture. Proteomic techniques are used for the expression of proteins and their modifications. Metabolomic techniques are used for accurate identification of species, while lipidomics techniques are used for the identification of different or specific lipid molecules in fish species, as well as fatty acid composition and location distribution. This review is to cover the recent proteomics, metabolomics, and lipidomics studies on aquatic and seafood products in the areas of quality, safety, processing, and breeding of fish.
Assessment of Hoisting Conveyance Guiding Forces Based on Field Acceleration Measurements and Numerical Simulation
Authors: Fiołek Przemysław , Jakubowski Jacek
Published: November 2024

Shafts play a key role in the operation of mining plants. They connect underground excavations with the surface and provide the ability to transport people, equipment, and raw materials. The nature of the dynamic interaction of a conveyance moving at a significant speed along deformed guide rails is complex, and the method of assessing the interaction of hoisting conveyances with shaft steelwork, despite ongoing research, still requires further understanding and improvement. Misalignments of the guide rails and conveyance movements transverse to the shaft axis induce impact (guiding) forces, which are the key design parameters of shaft steelwork. The reliable assessment of guiding forces allows the design of safe and economical steelworks and the assessment of their structural safety during operation under deformations and corrosive deterioration. Determining the value of guiding forces requires their field measurements or the use of approximate empirical formulas. Both methods have their limitations—measurement is expensive and interferes with normal shaft operation, while empirical formulas are subject to high error due to the lack of consideration of many structural details specific to each shaft that significantly affect the behavior of the system. This study presents a new method for using a relatively simple-to-implement measurement of hoisting conveyance acceleration to assess guiding forces. A finite element model of the skip and steelwork was built, and simulations of the conveyance interaction with the structure were carried out. A strong relationship between the sliding plate’s impact point location and the guiding force was found. Extreme values of the guiding force were observed in the vicinity of the bunton connection. The study showed that reducing the skip load mass does not affect the force value. Simplified methods of calculating the moments of inertia of the hoisting conveyance significantly overestimate the code-based values of the guiding forces. The presented method considers the actual stiffness and mass distribution of hoisting conveyance and, therefore, allows for a more accurate estimation of the guiding forces and the transport of larger loads. This data-driven approach allows for the continuous monitoring of the guiding forces, the adjustments of the hoisting parameters, the rational planning of repairs, and a reduction in the replacement of corroded shaft steelwork.
Quantum Private Comparison Based on Four-Particle Cluster State
Authors: Hou Min , Wu Yue
Published: November 2024

A quantum private comparison (QPC) protocol enables two parties to securely compare their private data without disclosing the actual values to one another, utilizing quantum mechanics to maintain confidentiality. Many current QPC protocols mainly concentrate on comparing the equality of private information between two users during a single execution, which restricts their scalability. To overcome this limitation, we present an efficient QPC protocol aimed at evaluating the equality of private information between two groups of users in one execution. This is achieved by leveraging the entanglement correlations present in each particle of a four-particle cluster state. In our approach, users encode their private data using bit flip or phase shift operators on the quantum sequence they receive, which is then sent back to a semi-trusted party which then determines whether the secrets of the two groups are equal and communicates the results to the users. By employing this method and facilitating the distributed transmission of the quantum sequence, our protocol achieves a qubit efficiency of 50%. Security analyses reveal that neither external attacks nor insider threats can successfully compromise the confidentiality of private data.
Experimental Investigation of the Seismic Response of Small Freestanding Replicas of Ancient Vessels
Authors: Papalou Angeliki
Published: November 2024

Monumental artifacts belong to our cultural heritage, and there is a great need to protect them from earthquake damage. This study experimentally investigates the behavior of replicas of ancient vessels under seismic excitations. Each vessel was placed on a wooden base, which was attached to a shake table and was excited by earthquake signals. The effect of the amplitude of the excitation and the friction coefficient between the object and the base of support was examined. The dynamic response of the vessels included sliding and rocking, which, at high excitation levels, could involve rotation about their vertical axis and translation motion. High levels of excitation could cause the vessels to overturn but this did not always occur at the same level of excitation. The coefficient of friction is a key parameter of their behavior. If it is high, sliding motion is reduced while rocking parallel to the direction of excitation increases, starting at low excitation levels. This could lead to an early overturning of the object. The geometric characteristics and irregularities of the vessel can play an important role in its dynamic response.
Biodiversity Patterns and DNA Barcode Gap Analysis of COI in Coastal Lagoons of Albania
Authors: Ismailaj Mariola , Zangaro Francesco , Specchia Valeria , Sangiorgio Franca , Marcucci Francesca , Kiçaj Hajdar , Basset Alberto , Pinna Maurizio
Published: November 2024

Aquatic biodiversity includes a variety of unique species, their habitats, and their interactions with each other. Albania has a large hydrographic network including rivers, lakes, wetlands and coastal marine areas, contributing to a high level of aquatic biodiversity. Currently, evaluating aquatic biodiversity relies on morphological species identification methods, but DNA-based taxonomic identification could improve the monitoring and assessment of aquatic ecosystems. This study aims to evaluate the coverage of COI DNA barcodes in the reference libraries for the known aquatic animal species present in the coastal lagoons of Albania. In this study, the six most studied coastal lagoons of Albania were selected. Species data were gathered from the scientific literature and publicly available sites and studies. The collected species lists were taxonomically standardised using global public taxonomic databases like WORMS. The standardised lists were used to analyse the barcode gap of COI based on two public DNA barcode libraries: Barcode of Life Data Systems (BOLD) and NCBI GenBank. The results show that the COI DNA barcode gap in the coastal lagoons of Albania ranges from 7% (Lagoon of Patok) to 33% (Karavasta Lagoon). Fishes and Amphibia represent the groups with the lowest barcode gap (8% each), while Annelida shows the highest (47%). In conclusion, the COI gene marker for DNA-based biodiversity assessments is reliable for the coastal lagoons of Albania.
Polygenic Risk Score Improves Melanoma Risk Assessment in a Patient Cohort from the Veneto Region of Italy
Authors: Pellegrini Stefania , Potjer Thomas P. , Del Bianco Paola , Vecchiato Antonella , Fabozzi Alessio , Piccin Luisa , Tonello Debora , van der Stoep Nienke , Tinsley Emily , Landi Maria Teresa , Iles Mark M. , Menin Chiara
Published: November 2024

Recent genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs) that alone weakly affect melanoma risk, but their combined effect on a polygenic risk score (PRS) can have a far bigger impact on estimating risk. However, the PRS is not yet at the stage of being utilized in clinical practice, and further evidence is needed. In this study, 270 melanoma patients fulfilling the criteria for a suspected genetic predisposition but with a negative genetic test for high/medium-penetrance genes were genotyped for 57 SNPs selected in previous GWASs to construct a PRS model. We found a significantly higher mean PRS57 in all melanoma cases than in controls (0.58 vs. 0.00, p < 0.001), and the mean PRS57 in multiple primary melanoma cases was twice that in single melanoma cases (0.689 vs. 0.362, p = 0.025). Interestingly, our results confirm the association of the PRS57 not only with other melanoma risk factors but also with a younger age at diagnosis. This evidence supports the potentially powerful discriminative role of PRS in the selection of high-risk patients who should undergo stricter surveillance protocols.
Hybrid Solar PV–Agro-Waste-Driven Combined Heat and Power Energy System as Feasible Energy Source for Schools in Sub-Saharan Africa
Authors: Diemuodeke Ogheneruona Endurance , Vera David , Ojapah Mohammed Moore , Nwachukwu Chinedum Oscar , Nwosu Harold U. , Aikhuele Daniel O. , Ofodu Joseph C. , Seidu Nuhu Banasco
Published: November 2024

Poor access to electricity in rural communities has been linked to a poor educational system, as electricity is essential for supporting laboratories, technical practice, and long study hours for students. Therefore, this work presents the techno-economic analysis of a hybrid solar PV–agro-wastes (syngas) energy system for electricity, heat, and cooling generation to improve energy access in rural schools. The system is located in Ghana at Tuna (lat. 9°29′18.28″ N and long. 2°25′51.02″ W) and serves a secondary school for enhanced quality education. The system relies on agro-waste (gasifier-generator) and sunlight (solar PV), with a battery energy storage system, to meet the school’s energy demand. The study employs HOMER Pro Version 3.16.2 software to comprehensively analyze technical, economic, and environmental aspects. The system can generate 221,621 kWh of electricity (at a unit cost of electricity of 0.295 EUR/kWh) and 110,896 kWh of thermal energy yearly. The cost of electricity from the proposed system is cheaper than the cost of electricity from an equivalent diesel generator at 0.380 EUR/kWh. The thermal energy can meet the heating demand of the school in addition to powering a vapor absorption chiller. The system is environmentally friendly, with the capacity to sink 0.526 kg of CO2 yearly. Government policies that moderate interest rates for bioenergy/solar PV systems and social solution on feedstock pricing will favor the economic sustainability of the proposed system. The system will address the energy access challenge (SDG 7), enhance the quality of education (SDG 4), and contribute to climate mitigation through carbon sequestration (SDG 13).
Application of Real-Time Palm Imaging with Nelder–Mead Particle Swarm Optimization/Regression Algorithms for Non-Contact Blood Pressure Detection
Authors: Su Te-Jen , Hung Ya-Chung , Lin Wei-Hong , Yang Wen-Rong , Zhuang Qian-Yi , Fei Yan-Xiang , Wang Shih-Ming
Published: November 2024

In response to the rising prevalence of hypertension due to lifestyle changes, this study introduces a novel approach for non-contact blood pressure (BP) monitoring. Recognizing the “silent killer” nature of hypertension, this research focuses on developing accessible, non-invasive BP measurement methods. This study compares two distinct non-contact BP measurement approaches: one combining the Nelder–Mead simplex method with particle swarm optimization (NM-PSO) and the other using machine learning regression analysis. In the NM-PSO method, a standard webcam captures continuous images of the palm, extracting physiological data through light wave reflection and employing independent component analysis (ICA) to remove noise artifacts. The NM-PSO achieves a verified root mean square error (RMSE) of 2.71 mmHg for systolic blood pressure (SBP) and 3.42 mmHg for diastolic blood pressure (DBP). Alternatively, the regression method derives BP values through machine learning-based regression formulas, resulting in an RMSE of 2.88 mmHg for SBP and 2.60 mmHg for DBP. Both methods enable fast, accurate, and convenient BP measurement within 10 s, suitable for home use. This study demonstrates a cost-effective solution for non-contact BP monitoring and highlights each method’s advantages. The NM-PSO approach emphasizes optimization in noise handling, while the regression method leverages formulaic efficiency in BP estimation. These results offer a biomimetic approach that could replace traditional contact-based BP measurement devices, contributing to enhanced accessibility in hypertension management.
3D Printing and Property of Biomimetic Hydroxyapatite Scaffold
Authors: Zhao Xueni , Li Lingna , Zhang Yu , Liu Zhaoyang , Xing Haotian , Gu Zexin
Published: November 2024

The 3D printing of a biomimetic scaffold with a high hydroxyapatite (HA) content (>80%) and excellent mechanical property is a serious challenge because of the difficulty of forming and printing, insufficient cohesion, and low mechanical property of the scaffold. In this study, hydroxyapatite whiskers (HAWs), with their superior mechanical property, biodegradability, and biocompatibility, were used to reinforce spherical HA scaffolds by 3D printing. The compressive strength and energy absorption capacity of HAW-reinforced spherical HA (HAW/HA) scaffolds increased when the HAW/HA ratio increased from 0:10 to 4:6 and then dropped with any further increases in the HAW/HA ratio. Bioceramic content (HAWs and spherical HA) in the scaffolds reached 83%, and the scaffold with a HAW/HA ratio of 4:6 (4-HAW/HA) exhibited an optimum compressive strength and energy absorption capacity. The scaffold using polyvinyl alcohol (PVA) as an additive possessed a good bonding between HA and PVA as well as a higher strength, which allowed the scaffold to bear a higher stress at the same strain. The compressive strength and toughness of the 4-HAW/HA-PVA scaffold were 1.96 and 1.63 times that of the 4-HAW/HA scaffold with hydroxypropyl methyl cellulose (HPMC), respectively. The mechanical property and inorganic components of the biomimetic HAW/HA scaffold were similar to those of human bone, which would make it ideal for repairing bone defects.
Sodium Alginate–Starch Capsules for Enhanced Stability of Metformin in Simulated Gastrointestinal Fluids
Authors: Gheorghita Roxana , Sirbu Ioan-Ovidiu , Lobiuc Andrei , Covasa Mihai
Published: November 2024

The use of biopolymers in pharmaceuticals is well established, particularly for encapsulating biologically active compounds due to their beneficial properties. Alginate, widely recognized for its excellent encapsulation abilities, is the most commonly used biopolymer, while starch, typically known as insoluble dietary fiber, also serves as an effective agent for trapping and protecting compounds during processing, storage, and gastrointestinal transit. Sodium alginate–starch capsules with varying compositions were analyzed to develop metformin hydrochloride (MET) containing capsules with adequate physicochemical properties. In vitro testing with simulated gastrointestinal fluids showed that after 1 h, capsules with equal amounts of alginate and starch had a higher swelling ratio and better drug release behavior, despite lower MET entrapment efficiency compared to other formulations. Microstructural analysis revealed stability in simulated gastric fluids and solubility in simulated intestinal fluids, key factors in drug development. The results suggest that these biopolymeric compositions are highly resistant to gastric fluids and minimally soluble in the intestines, making them suitable for extended drug release. This research evaluates key technological parameters of a cost-effective encapsulation method for the controlled release of active substances, providing a versatile solution for pharmaceutical and biomedical applications.
Academic Social Entrepreneurship: A Contemporary Reflection from Schumpeter’s Economic Sociology
Authors: Pinto Hugo , Sampaio Fábio , Ferreira Sílvia , Elston Jennifer
Published: November 2024

Entrepreneurship has gained significant relevance in contemporary societies due to its role in generating economic and social value, including job creation, new businesses, and technological and social innovations. Scientific interest in entrepreneurship, which dates back to the 17th century, has increased since the 1990s. This field of study has evolved to encompass not only strict business creation but also impactful social initiatives. This article explores the intersection of academic and social entrepreneurship, examining factors to understand impactful initiatives through the seminal ideas presented by Joseph Schumpeter. The text offers insights and recommendations for advancing the transdisciplinary study of academic social entrepreneurship starting from an Economic Sociology perspective.
Thirty-Fifth Annual Meeting of the Society for Light Treatment and Biological Rhythms (SLTBR), 20 June–22 June, Prague, Czech Republic
Authors: Cajochen Christian
Published: November 2024

I am delighted to introduce this collection of abstracts from our recent 35th Annual SLTBR Meeting in Prague [...]
Data Analysis of the Situation of the Residential Sector in Extremadura and Its Energy Classification
Authors: Pavón-Tapia Marina A. , Carmona-Fernández Diego , Dimitrova-Angelova Dorotea , González-González Juan Félix
Published: November 2024

This study explores sustainable practices in energy resource management, with the aim of optimizing energy consumption while maintaining quality of life. It focuses on energy efficiency in the residential sector of the autonomous community of Extremadura. The methodology involved collecting data from 50 households, using a computer tool to assess and classify energy efficiency levels. The results showed that homes built before 1994 had low energy ratings, whereas those built afterward demonstrated improvement, reflecting a growing environmental awareness. It was found that small investments, such as the installation of control devices and the adoption of conscious consumption habits, can significantly improve energy efficiency. The study highlights the importance of raising community awareness about energy efficiency and notes that, despite limitations in the data, the findings provide valuable insights for future interventions. In conclusion, energy efficiency is key to a sustainable future, and we all share the responsibility of improving it.
Precision and Efficiency in Dam Crack Inspection: A Lightweight Object Detection Method Based on Joint Distillation for Unmanned Aerial Vehicles (UAVs)
Authors: Dong Hangcheng , Wang Nan , Fu Dongge , Wei Fupeng , Liu Guodong , Liu Bingguo
Published: November 2024

Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very important to ensure their normal operation. Traditional detection methods rely on manual inspection, which consumes a lot of time and labor, while deep learning methods can greatly alleviate this problem. However, previous studies have often focused on how to better detect crack defects, with the corresponding image resolution not being particularly high. In this study, targeting the scenario of real-time detection by drones, we propose an automatic detection method for dam crack targets directly on high-resolution remote sensing images. First, for high-resolution remote sensing images, we designed a sliding window processing method and proposed corresponding methods to eliminate redundant detection frames. Then, we introduced a Gaussian distribution in the loss function to calculate the similarity of predicted frames and incorporated a self-attention mechanism in the spatial pooling module to further enhance the detection performance of crack targets at various scales. Finally, we proposed a pruning-after-distillation scheme, using the compressed model as the student and the pre-compression model as the teacher and proposed a joint distillation method that allows more efficient distillation under this compression relationship between teacher and student models. Ultimately, a high-performance target detection model can be deployed in a more lightweight form for field operations such as UAV patrols. Experimental results show that our method achieves an mAP of 80.4%, with a parameter count of only 0.725 M, providing strong support for future tasks such as UAV field inspections.
Joint Optimization Strategy of Task Migration and Power Allocation Based on Soft Actor-Critic in Unmanned Aerial Vehicle-Assisted Internet of Vehicles Environment
Authors: Bai Jingpan , Zhao Yifan , Yang Bozhong , Ji Houling , Liu Botao , Chen Yunhao
Published: November 2024

In recent years, the unmanned aerial vehicle-assisted internet of vehicles has been extensively studied to enhance communication and computation services in vehicular environments where ground infrastructures are limited or absent. However, due to the limited-service range and battery life of unmanned aerial vehicles, along with the high mobility of vehicles, an unmanned aerial vehicle cannot continuously cover and serve the same vehicle, leading to interruptions in vehicular application services. Therefore, this paper proposes a joint optimization strategy for task migration and power allocation based on soft actor-critic (JOTMAP-SAC). First, communication models, computational resource allocation models, and computation models are established sequentially based on the computational resource and dynamic coordinate of each node. The joint optimization problem of task migration and power allocation is then formulated. Considering the dynamic nature of the unmanned aerial vehicle-assisted internet of vehicles environment and the continuity of the action space, a soft actor-critic based algorithm for task migration and power allocation is designed. This algorithm iteratively finds the optimal solution to the joint optimization problem, thereby reducing the processing delay in unmanned aerial vehicle-assisted internet of vehicles and ensuring the continuity of internet of vehicles task processing.
A Novel Information Complexity Approach to Score Receiver Operating Characteristic (ROC) Curve Modeling
Authors: Gocoglu Aylin , Demirel Neslihan , Bozdogan Hamparsum
Published: November 2024

Performance metrics are measures of success or performance that can be used to evaluate how well a model makes accurate predictions or classifications. However, there is no single measure since each performance metric emphasizes a different classification aspect. Model selection procedures based on information criteria offer a quantitative measure that balances model complexity with goodness of fit, providing a better alternative to classical approaches. In this paper, we introduce and develop a novel Information Complexity–Receiver Operating Characteristic, abbreviated as ICOMP-ROC, criterion approach to fit and study the performance of ROC curve models. We construct and derive the Universal ROC (UROC) for a combination of sixteen Bi-distributional ROC models to choose the best Bi-distributional ROC by minimizing the ICOMP-ROC criterion. We conduct large-scale Monte Carlo simulations using the sixteen Bi-distributional ROC models with the Normal–Normal and Weibull–Gamma pairs as the pseudo-true ROC models. We report the frequency of hits of the ICOMP-ROC criterion, showing its remarkable recovery rate. In addition to Bi-distributional fitting, we consider a high-dimensional real Magnetic Resonance Imaging (MRI) of the Brain dataset and Wisconsin Breast Cancer (WBC) dataset to study the performance of the common performance metrics and the ICOMP-ROC criterion using several machine learning (ML) classification algorithms. We use the genetic algorithm (GA) to reduce the dimensions of these two datasets to choose the best subset of the features to study and compare the performance of the newly proposed ICOMP-ROC criterion along with the traditional performance metrics. The choice of a suitable metric is not just contingent upon the ML model used, but it also depends upon the complexity and high dimensionality of the input datasets, since the traditional performance metrics give different results and have inherent limitations. Our numerical results show the consistency and reliability of the ICOMP-ROC criterion over the traditional performance metrics as a clever model selection criterion to choose the best fitting Bi-distributional ROC model and the best classification algorithm among the ones considered. This shows the utility and the versatility of our newly proposed approach in ROC curve modeling that integrates and robustifies currently used procedures.
Multi-Label Feature Selection with Feature–Label Subgraph Association and Graph Representation Learning
Authors: Ruan Jinghou , Wang Mingwei , Liu Deqing , Chen Maolin , Gao Xianjun
Published: November 2024

In multi-label data, a sample is associated with multiple labels at the same time, and the computational complexity is manifested in the high-dimensional feature space as well as the interdependence and unbalanced distribution of labels, which leads to challenges regarding feature selection. As a result, a multi-label feature selection method based on feature–label subgraph association with graph representation learning (SAGRL) is proposed to represent the complex correlations of features and labels, especially the relationships between features and labels. Specifically, features and labels are mapped to nodes in the graph structure, and the connections between nodes are established to form feature and label sets, respectively, which increase intra-class correlation and decrease inter-class correlation. Further, feature–label subgraphs are constructed by feature and label sets to provide abundant feature combinations. The relationship between each subgraph is adjusted by graph representation learning, the crucial features in different label sets are selected, and the optimal feature subset is obtained by ranking. Experimental studies on 11 datasets show the superior performance of the proposed method with six evaluation metrics over some state-of-the-art multi-label feature selection methods.
Information-Theoretic Generalization Bounds for Batch Reinforcement Learning
Authors: Liu Xingtu
Published: November 2024

We analyze the generalization properties of batch reinforcement learning (batch RL) with value function approximation from an information-theoretic perspective. We derive generalization bounds for batch RL using (conditional) mutual information. In addition, we demonstrate how to establish a connection between certain structural assumptions on the value function space and conditional mutual information. As a by-product, we derive a high-probability generalization bound via conditional mutual information, which was left open and may be of independent interest.
How Do Transformers Model Physics? Investigating the Simple Harmonic Oscillator
Authors: Kantamneni Subhash , Liu Ziming , Tegmark Max
Published: November 2024

How do transformers model physics? Do transformers model systems with interpretable analytical solutions or do they create an “alien physics” that is difficult for humans to decipher? We have taken a step towards demystifying this larger puzzle by investigating the simple harmonic oscillator (SHO), x¨+2γx˙+ω02x=0, one of the most fundamental systems in physics. Our goal was to identify the methods transformers use to model the SHO, and to do so we hypothesized and evaluated possible methods by analyzing the encoding of these methods’ intermediates. We developed four criteria for the use of a method within the simple test bed of linear regression, where our method was y=wx and our intermediate was w: (1) Can the intermediate be predicted from hidden states? (2) Is the intermediate’s encoding quality correlated with the model performance? (3) Can the majority of variance in hidden states be explained by the intermediate? (4) Can we intervene on hidden states to produce predictable outcomes? Armed with these two correlational (1,2), weak causal (3), and strong causal (4) criteria, we determined that transformers use known numerical methods to model the trajectories of the simple harmonic oscillator, specifically, the matrix exponential method. Our analysis framework can conveniently extend to high-dimensional linear systems and nonlinear systems, which we hope will help reveal the “world model” hidden in transformers.
Predicting Mortality in Severe Burns: A Comparison of Four Mortality Prediction Scores and the Role of Organizational Changes in the Croatian Burn Center
Authors: Skunca Agata , Mesic Ana , Zagorac Dorotea , Dobric Mirela , Lokosek Vedran , Banic Morana , Munjiza Aleksandra , Muratovic Aisa
Published: November 2024

Background: The primary aim of this study was to evaluate the performance of four burn prognostic scores—Abbreviated Burn Severity Index (ABSI), Ryan, Belgium Outcome Burn Injury (BOBI), and revised Baux score (rBaux) in a Croatian burn center. A secondary aim was to compare patient outcomes before and after the organizational and protocol changes. Methods: A retrospective study and comparison of four prediction scores was conducted over a nine-year period in burn patients with ≥20% total body surface area (TBSA) burned. Additionally, outcomes before and after organizational changes were compared. Results: A total of 149 patients were included, with the mean patient age of 54.62 ± 19.38 years, the mean of TBSA of 42.98 ± 19.90, and an overall mortality rate of 48.99%. The area under the ROC curve (AUROC) was 0.79 for the rBaux and ABSI score, 0.77 for the BOBI score, and 0.76 for the Ryan score. The duration of mechanical ventilation and length of stay (LOS) in burn intensive care units (BICU) decreased after the organizational changes, though survival rates remained similar. Conclusions: Prognostic scores are good predictors of mortality but with moderate predictive accuracy. Continuity of care in intensive care could be important for better outcomes.
Survivors’ Narratives of the Oklahoma City Bombing Retold Seven Years Post-Disaster
Authors: Pollio Elizabeth W. , Abu-Hamad Samir , Wang Jennifer , North Carol S. , Pollio David E.
Published: November 2024

Introduction: A large proportion of the existing voluminous disaster mental health research literature represents the quantitative study of psychopathology, especially posttraumatic stress disorder. Subjective disaster experience is relatively unexplored. Qualitative narratives of surviving a disaster may provide insight into individual experiences of it and efforts to derive meaning from it. Methods: From an initial random sample of 182 survivors of the Oklahoma City bombing, narrative descriptions of this experience were collected 7 years after the bomb blast from 116 of the original sample, for the purpose of examining persistent as well as newly evolving content through qualitative analysis. The narrative content was analyzed for the evolution of thematic content in narrative data also collected at 6 months post-disaster and 1 year later. Results: The thematic content of the bombing experience was structured in a chronological fashion from the bomb blast (sensory, cognitive, and emotional), its immediate aftermath (e.g., escaping danger), and later experiences, (e.g., leaving the bomb site and receiving hospital treatment). During the time between interviews, the focus and general content of the narratives changed minimally, despite considerable compression of detail. Conclusions: The consistency of the material in these narratives over 7 years may reflect the persistence and salience of disaster memories, with the potential for its continuation for the rest of their lives.