bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comparative Study of Methyl-BEAMing and Droplet Digital PCR for MGMT Gene Promoter Hypermethylation Detection
Authors: Macagno Marco , Pessei Valeria , Congiusta Noemi , Lazzari Luca , Bellomo Sara Erika , Idrees Fariha , Cavaliere Alessandro , Pietrantonio Filippo , Raimondi Alessandra , Gusmaroli Eleonora , Zampino Maria Giulia , Gervaso Lorenzo , Ciardiello Davide , Mondello Giuseppe , Santoro Armando , Personeni Nicola , Bonoldi Emanuela , Aquilano Maria Costanza , Valtorta Emanuele , Siena Salvatore , Sartore-Bianchi Andrea , Amatu Alessio , Bonazzina Erica Francesca , Bencardino Katia Bruna , Serini Guido , Marsoni Silvia , Barault Ludovic , Di Nicolantonio Federica , Maione Federica
Published: November 2024

Background: O-6-methylguanine-DNA methyltransferase is responsible for the direct repair of O6-methylguanine lesions induced by alkylating agents, including temozolomide. O-6-methylguanine-DNA methyltransferase promoter hypermethylation is a well-established biomarker for temozolomide response in glioblastoma patients, also correlated with therapeutic response in colorectal cancer. Objectives: The ARETHUSA clinical trial aims to stratify colorectal cancer patients based on their mismatch repair status. Mismatch repair-deficient patients are eligible for treatment with immune checkpoint inhibitors (anti-PDL-1), whereas mismatch repair-proficient samples are screened for O-6-methylguanine-DNA methyltransferase promoter methylation to identify those suitable for temozolomide treatment. Methods: In this context, a subset of ARETHUSA metastatic colorectal cancer samples was used to compare two different techniques for assessing O-6-methylguanine-DNA methyltransferase hypermethylation: Methyl-BEAMing, a highly sensitive digital PCR approach that combines emulsion PCR and flow cytometry, and droplet digital PCR, a more automated procedure that enables the rapid, operator-independent analysis of a large number of samples. Results: Our study clearly demonstrates that the results obtained using Methyl-BEAMing and droplet digital PCR are comparable, with both techniques showing similar accuracy, sensitivity, and reproducibility. Conclusions: Digital droplet PCR proved to be an efficient method for detecting gene promoter methylation. However, the Methyl-BEAMing method has proved more sensitive for detecting low quantities of DNA.
Anterior and Mid-Penile Hypospadias Repair with TIP Technique—Is It Possible with 20-Hour Catheterization?
Authors: Tatanis Vasileios , Katsakiori Paraskevi , Spinos Theodoros , Peteinaris Angelis , Polyzonis Spyridon , Vrettos Theofanis , Kallidonis Panagiotis , Liatsikos Evangelos , Zoupanos George
Published: November 2024

Background/Objectives: Several surgical procedures have been proposed for the treatment of hypospadias, a common and heterogeneous congenital abnormality in males. Most surgeons utilize either a urethral stent or bladder catheter for 2–14 days as a postoperative urine diversion method depending on the severity of each case and the surgeon’s preferences. The aim of the present study was to evaluate the feasibility of anterior and mid-penile hypospadias repair while placing the urethral catheter for only 20 h. Methods: A single-centre retrospective study was conducted, including all patients who underwent anterior or penile hypospadias repair from January 2012 to January 2023. All operations were performed based on the Tubularized Incised Plate (TIP) Urethroplasty. At the end of the operation, a 6 Fr 100% silicon catheter was stabilized. The catheter was removed 20 h postoperatively, and all patients were discharged on the first postoperative day. The primary endpoint of the study was the complication rate. Results: In total, 104 patients were enrolled. Eighty (76.9%) patients presented with distal penile hypospadias, while 24 patients (23.1%) had mid-penile hypospadias. The mean age at the operation was 20.8 ± 12.4 months, while the mean operative time was 114.2 ± 28.7 min. All the operations were successfully completed. During the follow-up period (mean duration 101.1 ± 44.8 months), complications were recorded in 16.3% of the patients. Conclusions: The application of 20-h catheterization seems to be an effective alternative with outcomes comparable to other conventional drainage approaches, as it reduces the discomfort of the patients without increased risk of complications.
Primary Stability of Zirconia Dental Implants with Cylindrical and Tapered Designs Across Varying Bone Densities: An In Vitro Evaluation
Authors: Fernandes Diogo , Cavaco Francisco , Freitas Filipe , Marques Duarte , Caramês João , Moreira André
Published: November 2024

Background: While titanium implants are widely recognized for their clinical success, zirconia implants have emerged as a metal-free alternative. This study aimed to evaluate the influence of zirconia implant macrogeometry and bone density on primary implant stability. Methods: Two types of zirconia implants were tested—the Neodent® Zi Ceramic Implant and the Straumann® PURE Ceramic Implant, that were placed into polyurethane foam blocks mimicking different bone densities (10 PCF, 15 PCF, 20 PCF, 30 PCF, and 40 PCF). Each implant type was inserted and removed multiple times, with primary stability measured using resonance frequency analysis via the Osstell® Beacon device. Statistical tests, including the Shapiro–Wilk test, t-tests, the Mann–Whitney U test, and the Kruskal–Wallis test, were applied, with significance set at 5% (p < 0.05). Results: The tapered Neodent® Zi Ceramic Implant consistently showed higher ISQ values across all foam densities compared to the Straumann® PURE Ceramic Implant (p = 0.035). Additionally, lower-density foams exhibited lower stability scores (p < 0.05). Conclusion: The study concludes that both the macrogeometry of zirconia implants and bone density significantly affect primary implant stability. Specifically, tapered implants demonstrated higher stability than cylindrical designs, suggesting that implant macrogeometry and bone density should be considered for optimal primary stability in clinical settings.
Mission Design and Validation of a Fixed-Wing Unmanned Aerial Vehicle for Environmental Monitoring
Authors: Rufino Giancarlo , Conte Claudia , Basso Pasquale , Tirri Anna Elena , Donato Vincenzo
Published: November 2024

Climate change is becoming a worldwide emergency. In order to prevent catastrophic levels of climate change, three broad categories of action are ongoing: cutting emissions, adapting to climate impacts, and financing required adjustments. Cutting emissions requires stopping the use of fossil fuels in favor of renewable energy sources. Adapting to climate change and financing required adjustments need instruments for the understanding of the source causes and how effective the potential measures are. In this context, the use of Unmanned Aerial Vehicles for environmental monitoring is continuously increasing thanks to their ability to collect a wide range of environmental data, from the quality of air to the health status of vegetation, waters, and lands. This paper describes the research activities that are being performed for the design and development of a 100 kg Max Take Off Mass prototype zero-emission Unmanned Aerial Vehicle, named Daphne, destined for environmental monitoring, surveillance, and inspection missions. The developed prototype will drive the next industrialization of the vehicle. A particular focus is given to the design of the power system, based on the use of Proton Exchange Membrane fuel cells fueled with green hydrogen, the integration of the sensors allowing for multipurpose observations and measurements, and the design and validation of the relative multi-purpose missions via an innovative approach based on Model-Based System Engineering.
A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning
Authors: Liu Bei , Cai Yuefeng , Li Duantengchuan , Lin Ke , Xu Guanghui
Published: November 2024

Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage for various tasks. However, existing path planning algorithms often encounter problems such as high computational costs and a tendency to become trapped in local optima in complex 3D environments with multiple constraints. To tackle these problems, this paper introduces a hybrid multi-strategy artificial rabbits optimization (HARO) for efficient and stable UAV path planning in complex environments. To realistically simulate complex scenarios, we introduce spherical and cylindrical obstacle models. The HARO algorithm balances exploration and exploitation phases using a dual exploration switching strategy and a population migration memory mechanism, enhancing search performance and avoiding local optima. Additionally, a key point retention trajectory optimization strategy is proposed to reduce redundant path points, thus lowering flight costs. Experimental results confirm the HARO algorithm’s superior search performance, planning more efficient and stable paths in complex environments. The key point retention strategy effectively reduces flight costs during trajectory optimization, thereby enhancing adaptability.
Two New Families of Local Asymptotically Minimax Lower Bounds in Parameter Estimation
Authors: Merhav Neri
Published: November 2024

We propose two families of asymptotically local minimax lower bounds on parameter estimation performance. The first family of bounds applies to any convex, symmetric loss function that depends solely on the difference between the estimate and the true underlying parameter value (i.e., the estimation error), whereas the second is more specifically oriented to the moments of the estimation error. The proposed bounds are relatively easy to calculate numerically (in the sense that their optimization is over relatively few auxiliary parameters), yet they turn out to be tighter (sometimes significantly so) than previously reported bounds that are associated with similar calculation efforts, across many application examples. In addition to their relative simplicity, they also have the following advantages: (i) Essentially no regularity conditions are required regarding the parametric family of distributions. (ii) The bounds are local (in a sense to be specified). (iii) The bounds provide the correct order of decay as functions of the number of observations, at least in all the examples examined. (iv) At least the first family of bounds extends straightforwardly to vector parameters.
Advances in Uncertain Information Fusion
Authors: Jiao Lianmeng
Published: November 2024

Information fusion is the combination of information from multiple sources, which aims to draw more comprehensive, specific, and accurate inferences about the world than are achievable from the individual sources in isolation [...]
New Families of Frequency-Hopping Sequence Sets with a Low-Hit-Zone
Authors: Zhou Limengnan , Wu Hanzhou
Published: November 2024

As a means of spread spectrum communication, frequency-hopping technology has good performance in anti-jamming, multiple-access, security, covert communications, and so on. In order to meet the needs of different frequency-hopping multiple-access (FHMA) communication scenarios, the research on frequency-hopping sequence (FHS) sets with a low-hit-zone (LHZ) is now becoming more and more crucial. In this paper, a general construction to obtain new families of LHZ-FHS sets is achieved via interleaving technique. Subsequently, based on two different shift sequences, two classes of LHZ-FHS sets with new flexible parameters not covered in the related literature are presented. The requirements for our new LHZ-FHS sets to obtain optimality or near-optimality with respect to the Peng–Fan–Lee bound are also introduced. Furthermore, as long as the base FHS set is fixed, the performances of new LHZ-FHS sets can be analyzed, such that the parameters of all appropriate shift sequences to obtain desired LHZ-FHS sets are also fixed.
Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation
Authors: Kurowicka Dorota , Aspinall Willy , Cooke Roger
Published: November 2024

The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes.
Assessment of Potentially Toxic Elements and Their Risks in Water and Sediments of Kitengure Stream, Buhweju Plateau, Uganda
Authors: Kaggwa Andrew , Byamugisha Denis , Omara Timothy , Ntambi Emmanuel
Published: November 2024

Artisanal and small-scale gold mining (ASCGM) provides a livelihood for many communities worldwide, but it has profound environmental impacts, especially on the quality of nearby water resources. This study assessed the impacts of ASCGM on the physicochemical quality of water and sediments from Kitengure stream, Buhweju Plateau, Western Uganda. Surface water (n = 94) and superficial sediments (n = 36) were sampled between October 2021 and January 2022 from three different sections of Kitengure stream (upstream, midstream around the ASCGM area, and downstream). The samples were analyzed for various physicochemical parameters and selected potentially toxic elements (PTXEs), namely: zinc (Zn), cadmium (Cd), lead (Pb), copper (Cu), and arsenic (As). A health risk assessment was performed using the hazard index and incremental life cancer risk methods. Pearson’s bivariate correlation, geoaccumulation, and pollution indices were used to establish the sources and potential risks that PTXEs in sediments could pose to aquatic organisms. The results indicated that water in Kitengure stream draining the ASCGM site was highly colored (1230.00 ± 134.09 Pt-co units; range = 924.00–1576.00 Pt-co units) and turbid (194.75 ± 23.51 NTU; range = 148–257 NTU). Among the five analyzed PTXEs, only Cd (0.082 ± 0.200–0.092 ± 0.001 mg/L) and Cu (0.022 ± 0.004–0.058 ± 0.005 mg/L) were detected in water, and Cd was above the permissible limit of 0.003 mg/L for potable water. Upon calculating the water quality index (WQI), the water samples were categorized as very poor for upstream samples (WQI = 227) and unfit for use (WQI = 965 and 432) for midstream and downstream samples, respectively. In sediments, the mean concentration ranges of Zn, Cd, Pb, Cu, and As were 0.991 ± 0.038–1.161 ± 0.051, 0.121 ± 0.014–0.145 ± 0.025, 0.260 ± 0.027–0.770 ± 0.037, 0.107 ± 0.017–0.422 ± 0.056, and 0.022 ± 0.002–0.073 ± 0.003 mg/kg, respectively, and they were all below their average shale, toxicity reference, and consensus-based sediment quality guidelines. Geoaccumulation indices suggested that there was no enrichment of the elements in the sedimentary phase and the associated ecological risks were low. However, there were potential non-carcinogenic health risks that maybe experienced by children who drink water from Kitengure stream. No discernable health risks were likely due to dermal contact with water and sediments during dredging or panning activities. It is recommended that further studies should determine the total mercury content of water, sediments, and crops grown along the stream as well as the associated ecological and human health risks.
Natural Hazards and Climate Change Impacts on Food Security and Rural–Urban Livelihoods in Mozambique—A Bibliometric Analysis and Framework
Authors: Fekete Alexander
Published: November 2024

Mozambique is confronted with numerous risks related to food security and natural disasters. The study conducted a literature review on natural hazards and food security. This can help to identify gaps and further areas of research. A bibliometric analysis was conducted using standardized text search terms, and the VOSviewer tool was used to analyze over 7000 scientific articles and cluster over 60,000 keyword co-occurrences. The results show that research on natural hazards for food security needs to be integrated. The priority topic of disasters focuses on specific hazards such as climate change, floods, and hurricanes, which are also linked to demographic and other social variables. More studies on food security, such as droughts, sustainable development, and other human and social conditions, are being conducted. Resilience as an emerging research paradigm needs to be addressed in comparison. One result is an analytical framework on impacts on food security in the context of disaster risk, based on the empirical findings of the literature review. It shows how everyday risks such as disease or food security can be conceptually better linked to natural hazards and resilience. It shows that further research is needed on the interlinkages of multiple risks, of which Mozambique is an outstanding example. The methodology presented is also applied to provide a framework for linking multiple risks to food security and natural hazards. The innovative dimension of the research is that this inquiry constitutes one of the pioneering attempts to conduct a bibliometric analysis of the linkages between natural hazards, food security, and resilience in Mozambique. Another noteworthy contribution is introducing a novel analytical framework that integrates food security and disaster risks.
Teaching Science Outdoors: Supporting Pre-Service Teachers’ Skill Development with the Help of Available Mobile Applications
Authors: Kesler Merike , Kaasinen Arja , Kervinen Anttoni
Published: November 2024

Outdoor environments provide excellent teaching and learning experiences in science education. However, many teachers find outdoor teaching challenging. In this study, we investigated factors supporting skill development and learning among pre-service teacher during a blended science didactics course that includes mobile interaction in outdoor environments. Available WhatsApp mobile application was used as an interaction platform between the pre-service teachers’ and the teacher educator. Based on the findings, the pre-service teachers learned easy ways of using outdoor environments with pupils. They also identified challenges that may arise in outdoor teaching and upskilled on how to overcome them. From the perspective of interaction, submitting learning tasks, especially visual observations, through mobile messaging and reviewing tasks of other students in the application were perceived as important. However, the most crucial benefit of mobile interaction was considered to be the teacher’s real-time feedback.
Health and Healthy Lifestyle Habits in Primary Education: An Analysis of Spanish Autonomous Curricular Decrees Under the Current Education Law (LOMLOE)
Authors: Arce-Larrory Olatz , Velasco Erlantz , Sáez Iker
Published: November 2024

The Spanish basic education curriculum, specifically the section on primary education, addresses global challenges and recognizes schools as essential in health education. Healthy lifestyle habits are fundamental due to their impact on health and well-being, especially in children. This study analyzes the integration of health and healthy lifestyle habits in Spanish autonomous curricular decrees using a qualitative documentary analysis and a comparative approach with a statistical/descriptive basis. The corpus includes the 17 autonomous curricular decrees derived from the Organic Law 3/2020 (LOMLOE). The data, derived from the 17 autonomous curricular decrees, were examined using lexical and grammatical analyses, then processed with Atlas.ti 23 software. The results reveal that the dimensions and categories related to health and healthy lifestyle habits focus mainly on physical aspects. In common areas or fields, physical education and knowledge of the natural, social and cultural environment are the most relevant areas for integrating health and healthy lifestyle habits into the framework in this study. However, the current curriculum proposal lacks continuity between curricular elements. In summary, this study represents a step forward in the recognition of the extent to which health and healthy lifestyle habits are established in the curriculum.
Increasing Renewable Energy Penetration on Low-Voltage Networks: An Expert Knowledge Approach
Authors: Jansen Lohan A. , Botha Mel G. , van Schoor George , Uren Kenneth R.
Published: November 2024

While South Africa is deemed one of the countries with the highest irradiation levels, it still utilises coal as its primary energy source due to its abundance. Due to the world-wide drive towards carbon neutrality, residential, commercial, agricultural, and industrial consumers are considering small-scale embedded generation systems. The National Rationalised Specifications 097-2-3 document specifies the scale of the embedded generation capacity a consumer is allowed to install. However, specifications do not yet make the required provisions for the addition of energy storage. The effective collective management of the grouped small-scale embedded generation systems could provide a high level of energy security and increase the percentage of renewable energy generation in the total energy mix. Potential challenges come into play when considering the stochastic nature of photovoltaic generation and its effect on the storage capacity and the dispersion in load profiles of the residential units typically present on a low-voltage network. This paper contributes by investigating the utilisation of photovoltaic generation in conjunction with storage as the basis for virtual power plant control, with the aim to safely increase renewable energy penetration and improve energy security, all while remaining within the South African low-voltage regulatory limits. A two-level virtual power plant controller is proposed with the dispersed energy storage units as the primary controllable resources and the dispersed photovoltaic generation as the secondary controllable resources. The objective of the controller is to achieve nodal energy management, energy sharing, and ancillary service provision and finally to increase renewable energy penetration. A representative single-feeder low-voltage network is simulated, and test cases of 50% and 75% renewable energy penetration are investigated as the basis for evaluation. The proposed controller architecture proved to maintain network integrity for both test cases. The adaptability of the controller architecture was also confirmed for a changed feeder topology; in this case, it was a multi-feeder topology. Future work is warranted to inform policy on the allowed levels of renewable energy penetration to be based not only on demand but also on the level of energy storage present in a network.
An Insurtech Platform to Support Claim Management Through the Automatic Detection and Estimation of Car Damage from Pictures
Authors: Atanasious Mohab Mahdy Helmy , Becchetti Valentina , Giuseppi Alessandro , Pietrabissa Antonio , Arconzo Vito , Gorga Gerardo , Gutierrez Gonzalo , Omar Ahmed , Pietrini Marco , Rangisetty Meher Anvesh , Ricciardi Celsi Lorenzo , Santini Federico , Scianaro Enrico
Published: November 2024

Claims management is a complex process through which an insurance company or responsible entity addresses and handles compensation requests from policyholders who have suffered damage or losses. This process entails several stages, including the notification of the claim, damage assessment, settlement of compensation, and, if necessary, dispute resolution. Fair, transparent and timely claims management is crucial for maintaining policyholders’ trust while also limiting the financial impact on the insurer. Technological innovations, such as the use of artificial intelligence and automation, are positively influencing this sector, enabling faster and more effective claims management. This study reports on Insoore AI, an insurtech solution that aims to automate a portion of claims management by integrating a computer vision solution based on some latest developments in deep learning to automatically recognize and localize car damage from user-provided pictures.
Using DL Models in the Service Layer to Enhance the Fault Tolerance of IoT Networks
Authors: Jammalamadaka Sastry Kodanda Rama , Chokara Bhupati , Jammalamadaka Sasi Bhanu , Duvvuri Balakrishna Kamesh
Published: November 2024

In an IoT network, the networked servers form a service layer, providing services to the users and the devices. The request to the service servers is routed through the gateway on one side of the services layer and the networked controllers on the other side. Data are transported from the sensors/devices through cluster heads en route to base stations and the controllers to the service servers, where the data are processed and sent for storage in the cloud through gateways. When any device is broken down or becomes non-operational, the inputs are not sensed, creating a gap in the data. The data transmitted from the devices would then become an incomplete flow; such data are not suitable for undertaking data analytics or predictions. The missing data must be first identified as the data flow and estimated or predicated to complete the data before they are transmitted through the cloud for storage and subsequent retrievals. This paper proposes a recurrent (RNN) neural network to predict the missing data. Two models are tested to predict the missing data: the multi-layer perceptron (MLP) model and a long short-term memory (LSTM)-based RNN model. The RNN-based model provides 99.66% accurate data prediction compared to other models.
User Sentiment Analysis of the Shared Charging Service for China’s G318 Route
Authors: Wang Mei , Ramasamy Siva Shankar , Yu Xi , Liu Mutong , Dawod Ahmad Yahya , Chen Huayue
Published: November 2024

Shared charging services have gained popularity for their contribution to green travel. Accurately identifying the core factors that influence user experience (UX) not only enhances service quality and optimizes user satisfaction, but also promotes the dissemination of green travel concepts. However, the influencing factors and their mechanisms vary significantly across regions, particularly along the Chengdu–Lhasa (G318) route, which features large elevation changes, diverse climatic conditions, rugged terrain, and frequent geological disasters, making the influencing factors particularly complex. This study analyzes comment texts from 38 shared charging stations along the G318 route in the e-Charging APP, totaling 15,214 comments. A comprehensive approach is employed, including high-frequency word analysis, term frequency–inverse document frequency (TF-IDF) comparison, co-occurrence semantic network and co-word matrix feature correlation analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis. This multifaceted analysis explores core themes, user viewpoints, and sentiments in the comments, focusing on users’ perspectives on service quality, usage experience, and environmental impact of the charging stations. The findings indicate that charging speed, service attitude, environment, operational status of hardware and software, and pricing are key factors influencing user sentiment. Users have a high demand for the perfection of supporting facilities of shared charging stations, directly affecting user satisfaction and indirectly influencing the brand image and market competitiveness of enterprises.
FPGA Realization of an Image Encryption System Using a 16-CPSK Modulation Technique
Authors: Nuñez-Perez Jose-Cruz , Estudillo-Valdez Miguel-Angel , Sandoval-Ibarra Yuma , Adeyemi Vincent-Ademola
Published: November 2024

Nowadays, M-Quadrature Amplitude Modulation (M-QAM) techniques are widely used to modulate information by bit packets due to their ability to increase transfer rates. These techniques require more power when increasing the modulation index M to avoid interference between symbols. This article proposes a technique that does not suffer from interference between symbols, but instead uses memory elements to store the modulation symbols. In addition, the aim of this paper is to implement a four-dimensional reconfigurable chaotic oscillator that generates 16-Chaotic Phase Shift Keying (16-CPSK) modulation–demodulation carriers. An encryption and modulation transmitter module, a reception module, and a master–slave Hamiltonian synchronization module make up the system. A 16-CPSK modulation scheme implemented in Field Programmable Gate Array (FPGA) and applied to a red-green-blue (RGB) and grayscale image encryption system are the main contributions of this work. Matlab and Vivado were used to verify the modulation–demodulation scheme and synchronization. This proposal achieved excellent correlation coefficients according to various investigations, the lowest being −15.9×10−6 and 0.13×10−3 for RGB and grayscale format images, respectively. The FPGA implementation of the 16-CPSK modulation–demodulation system was carried out using a manufacturer’s card, Xilinx’s Artix-7 AC701 (XC7A200TFBG676-2).
Interpretability as Approximation: Understanding Black-Box Models by Decision Boundary
Authors: Dong Hangcheng , Liu Bingguo , Ye Dong , Liu  Guodong
Published: November 2024

Currently, interpretability methods focus more on less objective human-understandable semantics. To objectify and standardize interpretability research, in this study, we provide notions of interpretability based on approximation theory. We first define explainable models in terms of explicitness and then use completeness to define interpretability, thereby turning interpretability into the process of approximating black-box models with interpretable models. In particular, we think that the decision boundary of a classification model is equivalent to its interpretability. Next, we implement this approximation interpretation on multilayer perceptrons (MLPs) and then propose to use the MLP as a universal interpreter to explain other complex black-box models. Compared to the LIME method, which can only extract local linear features, our method is global and therefore termed as GIME. Extensive experiments demonstrate the effectiveness of our approaches.
Whale Optimization Algorithm with Machine Learning for Microwave Imaging
Authors: Chiu Chien-Ching , Li Ching-Lieh , Chen Po-Hsiang , Cheng Hung-Ming , Jiang Hao
Published: November 2024

This paper introduces a novel approach for reconstructing microwave imaging by combining the Whale Optimization Algorithm (WOA) with deep learning techniques. In it, electromagnetic waves are used to illuminate inhomogeneous dielectric objects in free space, and the scattered field is recorded. Due to the highly nonlinear nature of microwave imaging, the WOA is first employed to calculate an initial guess from the measured scattered field of dielectric objects. This step significantly reduces the training complexity for machine learning. Subsequently, the initial guess provided by the WOA is fed into a U-Net to accurately reconstruct the microwave image. Numerical simulation results indicate that the combination of the WOA and machine learning outperforms traditional methods under varying noise levels, enhancing the precision and effectiveness of the reconstruction process. In detail, the RMSE can be reduced 4–10% for dielectric constant distribution from 1 to 2.5 and SSIM can be increased about 30% for most cases.
Endomelanconiopsis endophytica Lipase Immobilized in Calcium Alginate for Production of Biodiesel from Waste Cooking Oil
Authors: Rodrigues Juliana Gisele Corrêa , Cardoso Fernanda Veras , Duvoisin Junior Sergio , Machado Nélio Teixeira , Albuquerque Patrícia Melchionna
Published: November 2024

The increasing global demand for biodiesel is due to the urgent need to replace fossil diesel with a fuel based on renewable energy sources. Although chemical catalysis is widely used to produce biodiesel, it uses harsh operating conditions, has high energy consumption, and generates unwanted byproducts. In this scenario, biocatalysis stands out as an efficient and environmentally friendly alternative to chemical catalysis. In biocatalysis, the use of immobilized enzymes plays an important role in the reduction in costs. In this sense, we investigated the use of the lipase produced by an Amazonian endophytic fungus in an immobilized form in the transesterification of waste cooking oil for biodiesel production. The fungus Endomelanconiopsis endophytica QAT_7AC demonstrated a high production of lipase. The lipolytic extract was precipitated in ethanol, which increased the specific enzyme activity. The lipolytic extract and the precipitated lipolytic extract were immobilized in calcium alginate beads. Immobilization efficiency was over 89%. The immobilized biocatalysts showed thermal stability and were used in the production of biodiesel using waste cooking oil and ethanol. It was possible to reuse them for up to four reaction cycles, with yields greater than 70%. These results prove the efficiency of immobilized biocatalysts in the production of biodiesel from waste oils.
Summer Energy Use and Comfort Analysis in Rural Chinese Dwellings: A Case Study of Low-Income Older Populations in Shandong
Authors: Yang Di , Hamza Neveen , Gilroy Rose
Published: November 2024

This paper aims to investigate the indoor environmental conditions and energy use behaviours of older individuals in rural cold climates of China, with a specific focus on cooling practices during the summer months in the Shandong region. This study employs a mixed-method approach, combining quantitative indoor environmental monitoring with qualitative interviews and observations, to explore the relationship between environmental factors, household living conditions, and energy use patterns across five types of elderly households: three generations living together, older people living with grandchildren, older people living with children, older couples living together, and older people living alone. Data collection was conducted over five weeks during the summer of 2023 using HOBO UX100-003 data loggers, while external weather conditions were monitored by the China Meteorological Administration. Face-to-face interviews were conducted to gain deeper insights into daily cooling behaviours and energy use. The results reveal that cooling practices and indoor environmental conditions vary significantly among the different household types. Multigenerational households showed more complex energy use dynamics, with younger family members frequently operating high-energy appliances like air conditioners, while older individuals tended to rely on natural ventilation and electric fans to reduce energy costs. In contrast, older couples and solitary older individuals demonstrated more conservative cooling behaviours, often enduring higher indoor temperatures due to limited financial resources and a desire to minimize energy expenditures. Despite the high energy use intensity in some households, many homes failed to achieve comfortable indoor environments, particularly in dwellings with minimal insulation and older building materials. This study concludes that economic status, household structure, and building characteristics play crucial roles in shaping cooling behaviours and indoor comfort during the summer.
Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models
Authors: Thwin Su Myat , Park Hyun-Seok
Published: November 2024

This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. The research employs deep learning ensemble models for both segmentation (using U-Net, SegNet, and DeepLabV3) and classification (using VGG16, ResNet-50, and Inception-V3). The ISIC dataset is balanced through oversampling in classification, and preprocessing techniques such as data augmentation and post-processing are applied in segmentation to increase robustness. The ensemble model outperformed individual models, achieving a Dice Coefficient of 0.93, an IoU of 0.90, and an accuracy of 0.95 for segmentation, with 90% accuracy on the original dataset and 99% on the balanced dataset for classification. The use of ensemble models and balanced datasets proved highly effective in improving the accuracy and reliability of automated skin lesion analysis, supporting dermatologists in early detection efforts.
Advanced Cotton Boll Segmentation, Detection, and Counting Using Multi-Level Thresholding Optimized with an Anchor-Free Compact Central Attention Network Model
Authors: Bairi Arathi , Dulhare Uma N.
Published: November 2024

Nowadays, cotton boll detection techniques are becoming essential for weaving and textile industries based on the production of cotton. There are limited techniques developed to segment, detect, and count cotton bolls precisely. This analysis identified several limitations and issues with these techniques, including their complex structure, low performance, time complexity, poor quality data, and so on. A proposed technique was developed to overcome these issues and enhance the performance of the detection and counting of cotton bolls. Initially, data were gathered from the dataset, and a pre-processing stage was performed to enhance image quality. An adaptive Gaussian–Wiener filter (AGWF) was utilized to remove noise from the acquired images. Then, an improved Harris Hawks arithmetic optimization algorithm (IH2AOA) was used for segmentation. Finally, an anchor-free compact central attention cotton boll detection network (A-frC2AcbdN) was utilized for cotton boll detection and counting. The proposed technique utilized an annotated dataset extracted from weakly supervised cotton boll detection and counting, aiming to enhance the accuracy and efficiency in identifying and quantifying cotton bolls in the agricultural domain. The accuracy of the proposed technique was 94%, which is higher than that of other related techniques. Similarly, the precision, recall, F1-score, and specificity of the proposed technique were 93.8%, 92.99%, 93.48%, and 92.99%, respectively.
Technical and Economic Analyses for the Implementation of a Biohydrogen Production System Using Bioelectricity from Vinasse Biogas of the Sugarcane and Alcohol Industry
Authors: Picón Lescano Germán Darío , Silva Paulino Regina Franciélle , de Assis Wendling Júnior José Marcelo , Tuna Celso Eduardo , Silveira José Luz
Published: November 2024

This paper studies the implementation of a system consisting of an electrolyzer connected to a bioelectricity generator set that operates using vinasse biogas. Data on total ethanol production across all regions of Brazil were collected, and two ethanol plants in São Paulo state were selected for analysis (São Jose da Estiva and São Manoel). The feasibility of implementing this system at these facilities was evaluated using energy analysis for biohydrogen production. The process’s energy efficiency was assessed, allowing for the construction of a Sankey Diagram for both plants. Additionally, the costs of producing bioelectricity and biohydrogen via electrolysis using vinasse biogas from Brazil’s sugar/alcohol industry were determined, factoring in the payback period, equivalent utilization period, and annual interest rate. The study demonstrates the efficiency and cost-effectiveness of producing biohydrogen using vinasse biogas. The cost of producing bioelectricity at the two plants decreases significantly over the 25-year system implementation period, starting at around 0.09–0.08 USD/kWh in the first year and dropping to a minimum of 0.027–0.039 USD/kWh by the end of the period. The initial cost of producing biohydrogen ranges from 0.24 to 0.25 USD/kWh at São Jose da Estiva and from 0.30 to 0.32 USD/kWh at São Manoel in the first year. However, prices drop by nearly half in the second year, reaching 0.05 to 0.06 USD/kWh at São Jose da Estiva and 0.06 to 0.07 USD/kWh at São Manoel. The payback period for the biohydrogen and bioelectricity production system increases, with São Jose da Estiva generating income by 1st year for a 4% to 12% interest rate. For São Manoel, income is expected by the 1.5 year for a 4% to 12% interest rate. Both plants recover their investment within the first 2 years of operation. By the 10th year, São Jose da Estiva’s income is projected to be between 3,888,501.96 USD (4%) and 3,678,407.29 USD (12%), while São Manoel’s income is expected to range from 3,123,824.69 USD (4%) to 2,932,050.63 USD (12%), demonstrating that the system is viable for sugar and ethanol plants.
Privacy-Preserving Data Analytics in Internet of Medical Things
Authors: Mudassar Bakhtawar , Tahir Shahzaib , Khan Fawad , Shah Syed Aziz , Shah Syed Ikram , Abbasi Qammer Hussain
Published: November 2024

The healthcare sector has changed dramatically in recent years due to depending more and more on big data to improve patient care, enhance or improve operational effectiveness, and forward medical research. Protecting patient privacy in the era of digital health records is a major challenge, as there could be a chance of privacy leakage during the process of collecting patient data. To overcome this issue, we propose a secure, privacy-preserving scheme for healthcare data to ensure maximum privacy of an individual while also maintaining their utility and allowing for the performance of queries based on sensitive attributes under differential privacy. We implemented differential privacy on two publicly available healthcare datasets, the Breast Cancer Prediction Dataset and the Nursing Home COVID-19 Dataset. Moreover, we examined the impact of varying privacy parameter (ε) values on both the privacy and utility of the data. A significant part of this study involved the selection of ε, which determines the degree of privacy protection. We also conducted a computational time comparison by performing multiple complex queries on these datasets to analyse the computational overhead introduced by differential privacy. The outcomes demonstrate that, despite a slight increase in query processing time, it remains within reasonable bounds, ensuring the practicality of differential privacy for real-time applications.
Self-Healing and Mechanical Behaviour of Fibre-Reinforced Ultra-High-Performance Concrete Incorporating Superabsorbent Polymer Under Repeated and Sustained Loadings
Authors: Alameri Mohammad , Mohamed Ali M.S. , Elchalakani Mohamed , Sheikh Abdul , Fan Rong
Published: November 2024

This study investigated the mechanical responses and self-healing capability of incorporating superabsorbent polymer (SAP) particles in Fibre-Reinforced Ultra-High-Performance Concrete (UHPC) mixes under repetitive flexural and sustained tensile loadings. UHPC with SAP addition of 0.3% and 0.4% of the binder ratio were studied along with a control UHPC mix. The methodology included investigating the mechanical properties of these mixes under ambient, water, and 100% of relative humidity (RH) curing conditions. In addition, the mechanical performance of ambient-, water-, and 100% RH-cured prismatic specimens (100 mm × 100 mm × 500 mm) under repeated load was studied under the same curing conditions. Prismatic specimens (75 mm × 75 mm × 500 mm) were kept under cure conditions of wet and dry cycles with applied tensile load for 28 days for the sustained tensile load. The results showed that incorporating SAP into UHPC enhances the elastic modulus, flexural strength, and tensile strength. Also, mixes with SAP have exhibited compressive strength above 120 MPa after 90 days. Furthermore, the load recovery of the prisms under repetitive flexural load and prisms under sustained tensile loading demonstrated the self-healing efficiency of SAP incorporated into the UHPC mixes higher than the control mix specimens.
Unraveling the Impact of Aspergillus sojae—A Food-Grade Fungus—On Phytoalexins, Phenolic Acids, and the Antioxidant and Antidiabetic Activity of Different Legumes
Authors: Rana Shalika , Broussard William , Elliott Steven , Burow Matthew E. , Boue Stephen M.
Published: November 2024

Legumes are a rich source of polyphenolic compounds known for their ability to promote health. Under stress conditions, legumes have been shown to produce higher levels of secondary metabolites, as a defensive mechanism. Hence, the present study aimed to induce legume seeds (e.g., soybean, chickpea, green pea, and red kidney bean) by inoculating them with Aspergillus sojae (A. sojae) and to evaluate the extracts for phytoalexins, phenolics, and antioxidant, antiobesity, and antidiabetic potentials. The UPLC-DAD findings of A. sojae-induced legumes showed medicarpin and maackiain in chickpea, pisatin in green pea, glyceollin I-III in soybean, and kievitone and phaseollin in red kidney bean. All induced legumes exhibited a higher total polyphenol content compared to the non-induced ones. Among induced legumes, soybean exhibited a higher (4.85 mg GAE/g) polyphenol content. The UPLC-ESI-QTOF-MS/MS findings established that legumes contained substantial levels of protocatechuic acid, vanillic acid, ferulic acid, chlorogenic acid, coumaric acid, 4-hydroxybenzoic acid, and caffeic acid. The results of antioxidant assays revealed a significantly higher level of activity in induced red kidney bean and soybean, whereas the level of activity in non-induced legumes was markedly lower. Moreover, induced red kidney bean effectively inhibited α-glucosidase (87.2%) and α-amylase (63.90%) at 5 mg/mL. Additionally, the maximum lipase inhibitory effects were displayed by induced soybean (72.54%) at 20 mg/mL.
Review of Some Modified Generalized Korteweg–De Vries–Kuramoto–Sivashinsky (mgKdV-KS) Equations
Authors: Aimar Marie-Thérèse , Intissar Abdelkader
Published: November 2024

This paper reviews the results of existence and uniqueness of the solutions of these equations: the Korteweg–De Vries equation, the Kuramoto–Sivashinsky equation, the generalized Korteweg–De Vries–Kuramoto–Sivashinsky equation and the nonhomogeneous boundary value problem for the KdV-KS equation in quarter plane.
Review of Some Modified Generalized Korteweg–de Vries–Kuramoto–Sivashinsky Equations (Part II)
Authors: Aimar Marie-Thérèse , Intissar Abdelkader
Published: November 2024

In part I of this work to appear in Foudations-MDPI 2024, some existence and uniqueness results for the solutions of some equations were reviewed, such as the Korteweg–de Vries equation (KdV), the Kuramoto–Sivashinsky equation (KS), the generalized Korteweg–de Vries–Kuramoto–Sivashinsky equation (gKdV-KS), and the nonhomogeneous boundary value problem for the KdV-KS equation in quarter plane. The main objective of this paper is to review some results of the existence of global attractors for the evolution equations with nonlinearity of the form N(ux), where ux denotes the derivative of u with respect to x, focusing in particular on the Kuramoto–Sivashinsky equation in one and two dimensions. In order to illustrate the general abstract results, we have chosen to discuss in detail the existence of global attractors for the Kuramoto–Sivashinsky (KS) equation in 1D and 2D. Once a global attractor is obtained, the question arises whether it has special regularity properties. Then we give an integrated version of the homogeneous steady state Kuramoto–Sivashinsky equation in Rn. This work ends with a change from rectangular to polar coordinates in the three-dimensional KS equation to give an energy estimate in this case.