bluuebunny/crossref_metadata_2025 huggingface dataset:
Methodology for 3D Management of University Faculties Using Integrated GIS and BIM Models: A Case Study
Authors: Carrasco César A. , Lombillo Ignacio , Sánchez-Espeso Javier M. , Blanco Haydee , Boffill Yosbel
Published: November 2024

Three-dimensional virtual modeling is one of the tools being rapidly implemented in the construction industry, leading to the need for strategies based on intelligent 3D models of cities and/or digital twins, which allow simulation by interacting with their real physical counterparts, anticipating the outcomes of decision making. In practice, problems arise when creating and managing these twins, as different data, models, technology, and tools must be used, and they cannot all be combined as desired due to certain incompatibilities. On the other hand, today’s traditional building management demands a more optimized process to prevent errors and enable timely reactions to failures and defects. Managing and using a large amount of complex and disparate data are required, which is why the use of CMMS-type software is common (Computerized Maintenance Management System). However, such software is rarely designed for management in a 3D format, often due to the absence of three-dimensional models of the assets. This research aims to contribute to the technological development of the digitalization of the built environment, providing a simple methodology for generating and managing 3D models of cities. To achieve this, the tools and information useful for generating an integrated GIS 3D and BIM model, and for Computer-Aided Maintenance Management in a three-dimensional format (CMMS-3D), are identified. The final model obtained is used to optimize the three-dimensional management of a classroom building on the “Campus de Las Llamas” at the University of Cantabria in Spain. The results demonstrate that it is possible to integrate digital models with simple linking mechanisms between the existing tools, thus achieving an optimal three-dimensional management model.
The Effect of Clarification Protocols on the Vinification of White Wines with Papain and Bromelain Immobilized on Glutaraldehyde Activated Chitosan
Authors: Papamichael Emmanuel M. , Hatziloukas Efstathios , Afendra Amalia-Sofia , Stergiou Panagiota-Yiolanda , Maltabe Violeta
Published: November 2024

The aim of this study is the implementation of reliable, inexpensive, and practical clarification methods of white wines, without affecting their traditional organoleptic characteristics, through treatment of musts and/or white wines with immobilized papain and bromelain. In all vinifications which focused on the production of white wines with improved organoleptic characteristics, the yeast strain Saccharomyces cerevisiae Z622 was used. Both purified papain and bromelain were immobilized on high-molecular-weight chitosan microparticles using three different protocols. The immobilized proteases with the most effective protocol were applied to both unfermented musts and fermented wines. Control vinifications were also performed using bentonite as a clarifying agent. Quantitative analyses of the white wines were carried out at the end of the alcoholic fermentation and after six months of bottling and storage in the refrigerator. The organoleptic characteristics of the clarified white wines treated with the immobilized proteases were compared with those treated with bentonite, a blank untreated white wine sample, and a commercial sample of Debina white wine through sensory evaluation. The results showed that only the musts treated with immobilized papain before fermentation resulted in clarified white wines with stable and improved sensory characteristics, similar to those of the Debina white wine sample.
Short-Term Culture of Human Hyalocytes Retains Their Initial Phenotype and Displays Their Contraction Abilities
Authors: Micera Alessandra , Balzamino Bijorn Omar , Cosimi Pamela , Esposito Graziana , Ripandelli Guido , Rossi Tommaso
Published: November 2024

Background: Hyalocytes are the main vitreal cell types with critical functions in health and vitreoretinal diseases. Our aim was to develop cultures of human hyalocytes and verify the retention of their initial cellular features after 3 and 6 days of culturing (3 d and 6 d) by analyzing and comparing a few morphological and functional parameters. Methods: Vitreous samples (n = 22) were collected and vitreous cells and bead-enriched hyalocytes were developed and compared (3 d vs. 6 d cultures). Vitreous and conditioned media were tested for collagen, vascular endothelial growth factor (VEGF), transforming growth factor β1 (TGFβ1), nerve growth factor (NGF), matrix metalloproteinases (MMPs)/tissue inhibitors of metalloproteinases (TIMPs) and alpha-smooth muscle actin (αSMA) expression (ELISA, array/IP/WB, RT-PCR). Cells were observed at light and fluorescent microscopy (phenotypical properties) and tested for their 3D collagen gel contraction abilities. Results: An increased expression of collagens, vimentin, fibronectin, and the MMP9/TIMP1 ratio were observed in vitreous tissues. In 3 d cultures, collagens and MMP9 were upregulated while the related tissue-enzymes were deregulated. Vitreous samples also showed high levels of TGFβ1, VEGF, and NGF, and this protein signature was retained at 3 d while decreased at 6 d. The original phenotype (low αSMA) was retained at 3 d from seeding while an increased αSMA expression was observed at 6 d; NGF/trkANGFR was expressed in cultured hyalocytes and partially drives the collagen retraction. Conclusions: The vitreous print comparison between untouched and cultured hyalocytes allowed us, on one side, to select 3 d cultures and, on the other, to highlight the neuroprotective/contractile NGF in vitro hyalocytes effects. The possibility of scoring reactive hyalocytes would represent an interesting aspect of screening the vitreoretinal interface severity.
Study on Low-Temperature Deposition of Diamond-like Carbon Film on the Surface of Bionic Joint Thread and Its Properties
Authors: Wang Chuanliu , Ma Shaoming
Published: November 2024

The double-connection structure of bionic joints of mining drill pipes has solved the problem of drill drop caused by fatigue cracks. However, with low-melting-point elastic–ductile alloy filling in the bionic joint, the thread on the joint cannot be hardened by high-temperature surface hardening treatments such as quenching and nitriding, making it prone to thread gluing or excessive wear. In this paper, the feasibility of diamond-like film deposition on the surface of a bionic drill pipe thread was studied. A tungsten transition film was used to improve the thickness of the film and the interfacial bond strength between the film and the substrate. The test results show that the total thickness of the DLC film is about 3~5 μm, the roughness is less than 2 μm, the hardness of the film reaches 24.4 GPa, the friction coefficient is 0.04, and the critical load is 56 N. SEM and EDS analyses show that the tungsten film and the bionic joint thread form a metallurgical structure. The morphology of the diamond-like carbon film is uniform and dense, and there is no obvious stratification between the substrate material. The joint with a diamond-like coating treatment has a longer service life than joints receiving conventional high-temperature nitriding treatment.
Pork as a Source of Diverse Viral Foodborne Infections: An Escalating Issue
Authors: Szczotka-Bochniarz Anna , Kochanowski Maciej
Published: November 2024

This review synthesizes current knowledge on the risks posed by viral foodborne infections associated with pork, emphasizing their global prevalence and the complexity of managing such pathogens. It covers a range of significant viruses, including hepatitis A and E, norovirus, rotavirus, sapovirus, enterovirus, astrovirus, and enteric adenovirus. The role of pigs as reservoirs for diverse pathogens with zoonotic potential further complicates safety challenges, extending risks to individuals involved in pork production and processing. Various factors influencing viral contamination throughout the meat production chain are explored, from farm-level practices to processing and handling procedures. Emphasis is placed on the critical importance of implementing effective control measures at each stage, including enhanced biosecurity, rigorous hygiene practices, and appropriate thermal processing techniques. Additionally, the need for improved surveillance and detection methods to effectively identify and monitor viral presence in meat products is highlighted. In conclusion, the necessity of adopting a One Health approach that integrates efforts in animal health, food safety, and public health to mitigate the risks of viral foodborne infections associated with meat consumption is underscored. This holistic strategy is essential for safeguarding consumer health and ensuring the safety of the global food supply.
How to Keep Lactose Avoiders Healthy
Authors: Chengolova Zlatina , Shentov Petar , Ivanova Radina , Syarova Reni
Published: November 2024

A large portion of the world’s population has lactose intolerance. Fundamentally, this condition occurs when the small intestine does not produce enough of the lactase enzyme, which digests the disaccharide lactose in milk. Lactose avoiders might unconsciously decide to limit or exclude milk and dairy products from their diets. This group includes people with lactose intolerance, people with an allergy to milk protein, vegans, and those expressing personal preferences. Lactose avoiders are often self-reported as being milk intolerant. In this review, specific amounts of lactose in different types of milk and milk products are presented. The amounts of micro- and macronutrients in them are compared with the daily requirements established by accepted sources. Foods are suggested that can play vital roles in permanently avoiding lactose-containing dairy products, for example, brussels sprouts, as a good source of vitamin B1; kale, as a source of vitamin K; and cereals at breakfast for vitamin B6. Attention is paid to mature cheeses as they are extremely beneficial for health due to their rich vitamin and elemental compositions, and they are also suitable for people with lactose intolerance due to their low lactose content. This information is rarely provided on packaging. In addition, the current state of labeling for the presence of lactose in food and pharmaceutical products is discussed. The term “hidden lactose” is introduced to include added lactose in unexpected foods, drinks, and even medicines.
An Assessment of the Feasibility, Patient Acceptance, and Performance of Point-of-Care Transient Elastography for Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD): A Systematic Review and Meta-Analysis
Authors: Sarkar Das Taranika , Meng Xucong , Abdallah Mohamed , Bilal Mohammad , Sarwar Raiya , Shaukat Aasma
Published: November 2024

Background: Vibration-Controlled Transient Elastography (VCTE) with FibroScan is a non-invasive, reliable diagnostic tool for Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD), enabling early detection and management to prevent severe liver diseases. VCTE’s ease and portability suit primary care, streamlining referrals, promoting lifestyle changes, reducing costs, and benefiting underserved communities. Methods: Studies on point-of-care VCTE were systematically reviewed, followed by meta-analysis using a random-effects model. Pooled proportions with 95% confidence intervals were reported, and heterogeneity was assessed using I2%. Results: A total of twenty studies from 14 countries, including 6159 patients, were analyzed, with three studies from France, two from the U.S., and four from China. The population had a slight male preponderance, with a mean age range of 35–73 years and a BMI range of 24.4–41.1%. The diagnostic accuracy for detecting any fibrosis (≥F1) was reported in four studies (n = 210) with an AUC of 0.74, sensitivity of 69.5%, and specificity of 70.6%. For significant fibrosis (≥F2), eight studies (n = 650) reported an AUC of 0.69, sensitivity of 81.7%, and specificity of 64.6%. Advanced fibrosis (≥F3) was evaluated in 10 studies (n = 619), with an AUC of 0.84, sensitivity of 88.1%, and specificity of 63.8%. Cirrhosis (F4) was assessed in nine studies (n = 533), with an AUC of 0.65, sensitivity of 87.5%, and specificity of 62.6%. Steatosis diagnoses across stages S1 to S3 showed increasing diagnostic accuracies, with AUCs of 0.85, 0.76, and 0.80, respectively. Probe type and BMI were significant covariates influencing diagnostic performance for both fibrosis and steatosis, while the percentage of male participants also showed significant associations. Conclusions: VCTE shows high diagnostic accuracy for fibrosis and steatosis in MASLD patients at the point of care. Future research should assess its implementation in fibroscan settings.
Occupational Physical Activity and Regular Exercise Are Inversely Correlated with Thyroid Function in Patients with Hashimoto’s Thyroiditis
Authors: Vuletić Marko , Kaličanin Dean , Barić Žižić Ana , Cvek Maja , Sladić Sanda , Škrabić Veselin , Punda Ante , Boraska Perica Vesna
Published: November 2024

Objective: We evaluated correlations of occupational physical activity (OPA) and recreational exercise (RE), respectively, with thyroid function in patients with Hashimoto’s thyroiditis (HT). Methods: We included 438 individuals with clinically diagnosed HT. Information on OPA and RE were collected through a self-report questionnaire. We assessed correlations between clinical phenotypes (TSH, T3, T4, fT4, TgAb, TPOAb, thyroid volume, vitamin D) and physical activities (OPA and RE) in all HT patients (ALL) and in two severity-based subgroups of patients (MILD and OVERT). Results: The main novel findings are significant correlations between increase in OPA and (i) a decrease in fT4 (OVERT, r = −0.265, p = 0.0002 and ALL, r = −0.138, p = 0.006); (ii) an increase in TSH (ALL, r = 0.124, p = 0.014 and OVERT, r = 0.183, p = 0.013) and (iii) an increase in TPOAb antibodies (ALL, r = 0.101, p = 0.045). In contrast, we observed correlations between increase in RE and: (i) a decrease in TSH (OVERT, r = −0.238, p = 0.001); (ii) a decrease in TgAb antibodies (OVERT, r = −0.194, p = 0.01) and (iii) an increase in vitamin D levels (ALL, r = 0.146, p = 0.005 and OVERT, r = 0.173, p = 0.023). Conclusions: Our results suggest that, unlike RE, OPA correlates with decreased thyroid function and increased thyroid autoimmunity. Our study proposes that the PA health paradox also applies for the thyroid health.
Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification
Authors: Tariku Girma , Ghiglieno Isabella , Simonetto Anna , Gentilin Fulvio , Armiraglio Stefano , Gilioli Gianni , Serina Ivan
Published: November 2024

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.
Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting
Authors: Pilartes-Congo José A. , Simpson Chase , Starek Michael J. , Berryhill Jacob , Parrish Christopher E. , Slocum Richard K.
Published: November 2024

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.
L1 Adaptive Control for Small-Scale Unmanned Helicopters: Enhancing Speed Regulation
Authors: Bertolani Giulia , Ryals Andrea Dan , de Angelis Emanuele Luigi , Pollini Lorenzo , Giulietti Fabrizio
Published: November 2024

This paper focuses on the application of L1 adaptive control to the speed autopilot loop of small-scale unmanned helicopters. The L1 adaptive control technique is investigated for its potential to provide reliable speed control, particularly in handling external disturbances and model uncertainties. Although L1 adaptive control has been widely studied, its application to small-scale unmanned helicopters remains relatively unexplored. Through numerical simulations and a preliminary experimental test campaign conducted on a small rotary-wing platform, this paper contributes to validating L1 adaptive control as a promising solution for speed regulation in unmanned helicopter platforms.
Harnessing Nth Root Gates for Energy Storage
Authors: Fox Elliot John , Herrera Marcela , Schmidt-Kaler Ferdinand , D’Amico Irene
Published: November 2024

We explore the use of fractional controlled-not gates in quantum thermodynamics. The Nth-root gate allows for a paced application of two-qubit operations. We apply it in quantum thermodynamic protocols for charging a quantum battery. Circuits for three (and two) qubits are analysed by considering the generated ergotropy and other measures of performance. We also perform an optimisation of initial system parameters, e.g.,the initial quantum coherence of one of the qubits strongly affects the efficiency of protocols and the system’s performance as a battery. Finally, we briefly discuss the feasibility for an experimental realization.
Enhancing Efficiency: Halton Draws in the Generalized True Random Effects Model
Authors: Bernstein David H.
Published: November 2024

This paper measures the impact of the number of Halton draws in excess of ⌈n⌉ on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates that increasing the number of Halton draws to ⌈n3/4⌉ (⌈n2/3⌉) decreases the mean squared error of the total technical efficiency estimates by 6.1 (4.9) percent. Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. An energy sector application is included, to demonstrate how these issues can arise in practice, and how increasing Halton draws can improve parameter and efficiency estimates in empirical work.
Likert Scale Variables in Personal Finance Research: The Neutral Category Problem
Authors: Pearson Blain , Lacombe Donald , Khatun Nasima
Published: November 2024

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the “neutral” category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical models, this paper demonstrates a methodology that accounts for the econometric issues of the neutral category. We then analyze the technique through an empirical exercise relevant to personal finance research using data from the National Financial Capability Study. We demonstrate that ignoring the “neutral” category bias can lead to incorrect inferences, hindering the progression of personal finance research. Our findings underscore the importance of refining statistical modeling techniques when dealing with Likert-type data. By accounting for the neutral category, we can enhance the reliability of personal finance research outcomes, fostering improved decision-relevant insights.
Enhancing Fault Location Accuracy in Transmission Lines Using Transient Frequency Spectrum Analysis: An Investigation into Key Factors and Improvement Strategies
Authors: Akdağ Mustafa , Mamiş Mehmet Salih , Akmaz Düzgün
Published: November 2024

Fault location estimation in transmission lines is critical for power system reliability. Various methods have been developed for this purpose, among which transient frequency spectrum analysis (TFSA) stands out as a recent method based on travelling wave (TW) theory. TFSA determines the fault location by analyzing the frequency spectrum of transient currents and/or voltages at the instant of the fault, offering advantages such as independence from fault impedance and the ability to locate faults with one-side measurements. Despite its success in fault location, TFSA has several considerations that warrant detailed investigation. This study explores the effects of source inductance, series compensation, fault arc, and current transformer (CT) characteristics on transient frequencies. Additionally, the impact of noise on TFSA results is examined. The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. Strategies to enhance accuracy are discussed and compared to previous studies, including a proposed detection approach providing appropriate data size and precise wave propagation speed calculations. These findings contribute to a deeper understanding of TFSA’s limitations and inform practical improvements for fault location accuracy in power transmission systems.
TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces
Authors: Zhang Yuexia , Liu Changbao , Gang Yuanshuo , Wang Yu
Published: November 2024

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.
Optimal Sliding Speed and Contact Pressure Design of On-Load Tap Changer Based on Multivariate Nonlinear Regression
Authors: Xu Zhiqi , Zhang Sijiang , Zhang Jintao , Wang Xiaobing , Xu Yanwen , Li Zongying , Ma Minghan , Li Shuaibing
Published: November 2024

During the voltage regulation of on-load tap changers (OLTCs), the movement of the contacts can easily cause arcing, which may lead to erosion or malfunction. To reduce the energy and probability of arcing, we focus on designing an optimal range for the sliding speed and contact pressure of the contacts to minimize arc energy. Initially, our research introduces a novel OLTC arc testing platform to simulate the motion of static and dynamic contacts, exploring the relationship between different sliding speeds, contact pressures, and factors like arc voltage waveform, arcing rate, arc resistance, and arc energy. Subsequently, by employing multiple nonlinear regression methods, we establish functional relationships between sliding speed and arc energy, as well as contact pressure and arc energy, evaluating the fit using correlation coefficients. Finally, through analyzing their nonlinear behaviors, we determine the ideal sliding speed and contact pressure. The results indicate that when the OLTC contacts slide at an optimal speed between 89 and 103 mm/s and optimal contact pressure between 1.5 and 1.7 N, the arc energy can be minimized, thereby enhancing the performance and lifespan of the on-load tap changer. This study offers feasible insights for the design and operation of OLTCs, aiding in the improvement of power system regulation.
Enhancing Structured Query Language Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques
Authors: Le Thi-Thu-Huong , Hwang Yeonjeong , Choi Changwoo , Wardhani Rini Wisnu , Putranto Dedy Septono Catur , Kim  Howon
Published: November 2024

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.
IFGAN: Pre- to Post-Contrast Medical Image Synthesis Based on Interactive Frequency GAN
Authors: Lei Yanrong , Xu Liming , Wang Xian , Fan Xueying , Zheng Bochuan
Published: November 2024

Medical images provide a visual representation of the internal structure of the human body. Injecting a contrast agent can increase the contrast of diseased tissues and assist in the accurate identification and assessment of conditions. Considering the adverse reactions and side effects caused by contrast agents, previous methods synthesized post-contrast images with pre-contrast images to bypass the administration process. However, existing methods pay inadequate attention to reasonable mapping of the lesion area and ignore gaps between post-contrast and real images in the frequency domain. Thus, in this paper, we propose an interactive frequency generative adversarial network (IFGAN) to solve the above problems and synthesize post-contrast images from pre-contrast images. We first designed an enhanced interaction module that is embedded in the generator to focus on the contrast enhancement region. Within it, target and reconstruction branch features interact to control the local contrast enhancement region feature and maintain the anatomical structure. We propose focal frequency loss to ensure the consistency of post-contrast and real images in the frequency domain. The experimental results demonstrated that IFGAN outperforms other sophisticated approaches in terms of preserving the accurate contrast enhancement of lesion regions and anatomical structures. Specifically, our method produces substantial improvements of 7.9% in structural similarity (SSIM), 36.3% in the peak signal-to-noise ratio (PSNR), and 8.5% in multiscale structural similarity (MSIM) compared with recent state-of-the-art methods.
A Multi-Scale Approach to Early Fire Detection in Smart Homes
Authors: Abdusalomov Akmalbek , Umirzakova Sabina , Safarov Furkat , Mirzakhalilov Sanjar , Egamberdiev Nodir , Cho Young-Im
Published: November 2024

In recent years, advancements in smart home technologies have underscored the need for the development of early fire and smoke detection systems to enhance safety and security. Traditional fire detection methods relying on thermal or smoke sensors exhibit limitations in terms of response time and environmental adaptability. To address these issues, this paper introduces the multi-scale information transformer–DETR (MITI-DETR) model, which incorporates multi-scale feature extraction and transformer-based attention mechanisms, tailored specifically for fire detection in smart homes. MITI-DETR achieves a precision of 99.00%, a recall of 99.50%, and a mean average precision (mAP) of 99.00% on a custom dataset designed to reflect diverse lighting and spatial conditions in smart homes. Extensive experiments demonstrate that MITI-DETR outperforms state-of-the-art models in terms of these metrics, especially under challenging environmental conditions. This work provides a robust solution for early fire detection in smart homes, combining high accuracy with real-time deployment feasibility.
Data-Driven Day-Ahead Dispatch Method for Grid-Tied Distributed Batteries Considering Conflict Between Service Interests
Authors: Zhang Yajun , Yang Xingang , Fang Lurui , Lyu Yanxi , Xiong Xuejun , Zhang Yufan
Published: November 2024

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies.
Response Surface Methodology-Aided Optimization of Bioactive Compound Extraction from Apple Peels Through Pulsed Electric Field Pretreatment and Ultrasonication
Authors: Mantiniotou Martha , Bujor Bogdan-Cristian , Athanasiadis Vassilis , Chatzimitakos Theodoros , Kalompatsios Dimitrios , Kotsou Konstantina , Bozinou Eleni , Lalas Stavros I.
Published: November 2024

Apple by-products (i.e., peels) are often thrown away, yet they are highly nutritious and provide numerous advantages as they contain a variety of nutrients such as vitamins, minerals, and antioxidants. Apple peels also comprise a high level of antioxidants, particularly polyphenols and flavonoids. This research aimed to determine the most efficacious extraction techniques and parameters to accomplish maximum bioactive compounds recovery from apple peels. Several extractions were conducted, including stirring, ultrasonication, and pulsed electric field-assisted extractions. Response surface methodology and several factors such as temperature, extraction duration, and solvent composition were considered to have a major impact on the isolation of bioactive compounds. The findings indicated that the most practical and efficient approach was to combine the pulsed electric field process with ultrasonication and stirring at 80 °C for 30 min, while 75% aqueous ethanol comprised the optimal solvent concentration, demonstrating the critical role of the solvent in optimizing extraction efficiency. The optimal conditions were obtained through response surface methodology with a statistical significance of p < 0.05. The extract exhibited a total polyphenolic content (TPC) of 17.23 mg gallic acid equivalents (GAE) per g of dry weight (dw), an ascorbic acid content (AAC) of 3.99 mg/g dw, and antioxidant activity of 130.87 μmol ascorbic acid equivalents (AAE)/g dw, as determined by FRAP and 95.38 μmol AAE/g dw from the DPPH assay. The measured antioxidant activity highlighted the significant potential of apple peels as a cost-effective source of exceptionally potent extracts.
Scrutinizing the Statistical Distribution of a Composite Index of Soil Degradation as a Measure of Early Desertification Risk in Advanced Economies
Authors: Imbrenda Vito , Maialetti Marco , Sateriano Adele , Scarpitta Donato , Quaranta Giovanni , Chelli Francesco , Salvati Luca
Published: November 2024

Using descriptive and inferential techniques together with simplified metrics derived from the ecological discipline, we offer a long-term investigation of the Environmental Sensitive Area Index (ESAI) as a proxy of land degradation vulnerability in Italy. This assessment was specifically carried out on a decadal scale from 1960 to 2020 at the province (NUTS-3 sensu Eurostat) level and benefited from a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic (‘what … if’) approach. The spatial distribution of the ESAI was investigated at each observation year (1960, 1970, 1980, 1990, 2000, 2010, 2020, 2030) calculating descriptive statistics (central tendency, variability, and distribution shape), deviation from normality, and the increase (or decrease) in diversification in the index scores. Based on nearly 300 thousand observations all over Italy, provinces were considered representative spatial units because they include a relatively broad number of ESAI measures. Assuming a large sample size as a pre-requisite for the stable distribution of the most relevant moments of any statistical distribution—because of the convergence law underlying the central limit theorem—we found that the ESAI scores have increased significantly over time in both central values (i.e., means or medians) and variability across the central tendency (i.e., coefficient of variation). Additionally, ecological metrics reflecting diversification trends in the vulnerability scores delineated a latent shift toward a less diversified (statistical) distribution with a concentration of the observed values toward the highest ESAI scores—possibly reflecting a net increase in the level of soil degradation, at least in some areas. Multiple exploratory techniques (namely, a Principal Component Analysis and a two-way hierarchical clustering) were run on the two-way (data) matrix including distributional metrics (by columns) and temporal observations (by rows). The empirical findings of these techniques delineate the consolidation of worse predisposing conditions to soil degradation in recent times, as reflected in a sudden increase in the ESAI scores—both average and maximum values. These trends underline latent environmental dynamics leading to an early desertification risk, thus representing a valid predictive tool both in the present conditions and in future scenarios. A comprehensive scrutiny of past, present, and future trends in the ESAI scores using mixed (parametric and non-parametric) statistical tools proved to be an original contribution to the study of soil degradation in advanced economies.
Big Epidemiology: The Birth, Life, Death, and Resurgence of Diseases on a Global Timescale
Authors: Bragazzi Nicola Luigi , Lehr Thorsten
Published: November 2024

Big Epidemiology represents an innovative framework that extends the interdisciplinary approach of Big History to understand disease patterns, causes, and effects across human history on a global scale. This comprehensive methodology integrates epidemiology, genetics, environmental science, sociology, history, and data science to address contemporary and future public health challenges through a broad historical and societal lens. The foundational research agenda involves mapping the historical occurrence of diseases and their impact on societies over time, utilizing archeological findings, biological data, and historical records. By analyzing skeletal remains, ancient DNA, and artifacts, researchers can trace the origins and spread of diseases, such as Yersinia pestis in the Black Death. Historical documents, including chronicles and medical treatises, provide contextual narratives and quantitative data on past disease outbreaks, societal responses, and disruptions. Modern genetic studies reveal the evolution and migration patterns of pathogens and human adaptations to diseases, offering insights into co-evolutionary dynamics. This integrative approach allows for temporal and spatial mapping of disease patterns, linking them to social upheavals, population changes, and economic transformations. Big Epidemiology also examines the roles of environmental changes and socioeconomic factors in disease emergence and re-emergence, incorporating climate science, urban development, and economic history to inform public health strategies. The framework reviews historical and contemporary policy responses to pandemics, aiming to enhance future global health governance. By addressing ethical, legal, and societal implications, Big Epidemiology seeks to ensure responsible and effective epidemiological research and interventions. This approach aims to profoundly impact how we understand, prevent, and respond to diseases, leveraging historical perspectives to enrich modern scientific inquiry and global public health strategies.
Development and Large-Scale Production of High-Oleic Acid Oil by Fermentation of Microalgae
Authors: Parker Leon , Ward Kevin , Pilarski Thomas , Price James , Derkach Paul , Correa Mona , Miller Roberta , Benites Veronica , Athanasiadis Dino , Doherty Bryce , Edy Lucy , Alnozaili Gawharah , Reyes Nina , Wittenberg Jon , Eliares Gener , Destaillats Frédéric , Rakitsky Walter , Franklin Scott
Published: November 2024

Our classical strain improvement began with an isolate showing 28% palmitic and 60% oleic acids. UV and chemical mutagenesis enhanced our strain’s productivity, carbon yield, and oleic acid content. The iterative methodology we used involved the creation of mutant libraries followed by clonal isolation, assessments of feedstock utilization and growth, oil titer, and the validation of oil composition. Screening these libraries facilitated the identification of isolates with the ability to produce elevated levels of oleic acid, aligning with the targets for high-oleic acid substitutes. Utilizing a classical strain improvement approach, we successfully isolated a high-oleic acid strain wherein the level of oleic acid was increased from 60 to >86% of total FA. The performance of the classically improved high oleic acid-producing strain was assessed at fermentation scales ranging from 1 L to 4000 L, demonstrating the utility of our strain and process at an industrial scale. These oils offer promise in various applications across both the food and industrial sectors, with the added potential of furthering sustainability and health-conscious initiatives.
Fourier Neural Operator Networks for Solving Reaction–Diffusion Equations
Authors: Hao Yaobin , Song Fangying
Published: November 2024

In this paper, we used Fourier Neural Operator (FNO) networks to solve reaction–diffusion equations. The FNO is a novel framework designed to solve partial differential equations by learning mappings between infinite-dimensional functional spaces. We applied the FNO to the Surface Quasi-Geostrophic (SQG) equation, and we tested the model with two significantly different initial conditions: Vortex Initial Conditions and Sinusoidal Initial Conditions. Furthermore, we explored the generalization ability of the model by evaluating its performance when trained on Vortex Initial Conditions and applied to Sinusoidal Initial Conditions. Additionally, we investigated the modes (frequency parameters) used during training, analyzing their impact on the experimental results, and we determined the most suitable modes for this study. Next, we conducted experiments on the number of convolutional layers. The results showed that the performance of the models did not differ significantly when using two, three, or four layers, with the performance of two or three layers even slightly surpassing that of four layers. However, as the number of layers increased to five, the performance improved significantly. Beyond 10 layers, overfitting became evident. Based on these observations, we selected the optimal number of layers to ensure the best model performance. Given the autoregressive nature of the FNO, we also applied it to solve the Gray–Scott (GS) model, analyzing the impact of different input time steps on the performance of the model during recursive solving. The results indicated that the FNO requires sufficient information to capture the long-term evolution of the equations. However, compared to traditional methods, the FNO offers a significant advantage by requiring almost no additional computation time when predicting with new initial conditions.
Can Front-of-Pack Labeling Encourage Food Reformulation? A Cross-Sectional Study on Packaged Bread
Authors: Martini Daniela , Angelino Donato , Tucci Massimiliano , La Bruna Edoardo , Pellegrini Nicoletta , Del Bo’ Cristian , Riso Patrizia
Published: November 2024

Front-of-pack labeling (FOPL) may represent an important instrument for the food industry in the promotion of food product reformulation. The present cross-sectional study used salt reduction in packaged breads as a case study, aiming to investigate whether two different types of FOPL (i.e., Nutri-Score (NS) and NutrInform battery (NIB)) can capture food reformulation and thus be effective tools for encouraging reformulation. The Nutri-Score and NIB were calculated by consulting the nutritional declarations and ingredient lists of 527 packaged breads currently sold in Italy before and after applying three different theoretical reformulation strategies: (i) a 25% salt decrease from the current median salt content in bread; (ii) a reduction of up to 0.825 g/100 g of salt, corresponding to the sodium benchmark of 330 mg/100 g set by the World Health Organization (WHO); and (iii) the minimum salt reduction needed to improve the NS by one grade. The results show that only ~44% of breads had improved NSs when the sodium was lowered to reach the WHO benchmark or when salt was reduced by 25%, whereas large variability was observed in the minimum salt reduction needed to improve the NS. Regarding the NIB, the battery for salt improved when both strategies of reformulation were applied. FOPL is not always effective in capturing food reformulation in terms of salt reduction, possibly discouraging the efforts of food companies to improve the nutritional quality of foods.