bluuebunny/crossref_metadata_2025 huggingface dataset:
Nutritional and Food Safety Characteristics of Jameed—A Traditional Dairy Product of Drylands
Authors: Hilali Muhi El-Dine , Rekik Mourad , Hijazi Jomana , Ababenah Hotheifa
Published: January 2024

Jameed is a traditional dried dairy product in Jordan that is known under different names in the Middle East, Turkey, Central Asia, China, and Mongolia. It has been produced in the region for centuries and makes a significant contribution (up to 20%) to the income of small-scale traditional dairy processors who are based in sheep-producing districts. This study aims to assess the nutritional value of Jameed as a model for traditional dried fermented dairy products and to highlight the safety of the product quality and some of the health risks that may arise. For this purpose, 80 samples of Jameed were collected from the market covering all regions of the Kingdom of Jordan. The samples were analyzed for nutritional value and health risks by standard and approved methods. Results show that the total solids were 84.57%, with a large variation from 73 to 92%. Producers use a lot of salt to control elevated acidity during the drying of Jameed. The salt concentrations in collected samples were 15.68%. The average acidity was 6.79%. Moreover, farmers heavily use antibiotics to control mastitis without observing milk withdrawal. The residues of antibiotics were detected in 50.65% of the analyzed samples. The samples show large variations in measured values, reflecting differences in processing methods, homogeneity, and standardization.
Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
Authors: Quispe-Quispe Arturo , Pérez-Falcón Luis F. , Quispe-Marcatoma Justiniano , Landauro Carlos V. , Peña Rodriguez Victor A.
Published: January 2024

The extensive utilization of dyes across diverse industries has resulted in environmental pollution, leading to the degradation of water bodies. To prevent environmental contamination, the use of eco-friendly dyes and innovative processes for dye degradation is crucial. This study aimed to investigate the color removal process of cochineal dye (Dactylopius coccus Costa) using the atmospheric pressure plasma jet (APPJ: Atmospheric Pressure Plasma Jet) technique. The dye extracted from the cochineal insect was treated with APPJ and the resulting color removal process was analyzed. Optical emission spectroscopy (OES) was used to investigate the plasma emission lines, and UV-Vis spectroscopy was used to monitor the color removal process. The results revealed that the decolorization of cochineal dye was a result of an oxidative degradation process caused by the interaction of the reactive species (NO3− and NO2−) generated by the APPJ plasma discharge with the dye molecules. This color removal process occurs in an acidic medium, leading to a pH change from 5.4 to 2.7. These pH changes can be attributed to fluctuations in the concentrations of reactive species such as nitrates and nitrites in the liquid phase. UV-Vis spectroscopy measurements showed that 90% of the cochineal color was removed within the first 10 min of treatment. This study enhances our understanding of natural color removal and provides insights into its mechanism, opening up possibilities for controlled modification and applications in various fields.
Estimation of Power System Inertia with the Integration of Converter-Interfaced Generation via MEMD during a Large Disturbance
Authors: Muftić Dedović Maja , Mujezinović Adnan , Dautbašić Nedis , Alihodžić Ajdin , Memić Adin , Avdaković Samir
Published: January 2024

The decrease in overall inertia in power systems due to the shift from synchronous generator production to renewable energy sources (RESs) presents a significant challenge. This transition affects the system’s stable frequency response, making it highly sensitive to imbalances between production and consumption, particularly during large disturbances. To address this issue, this paper introduces a novel approach using Multivariate Empirical Mode Decomposition (MEMD) for the accurate estimation of power system inertia. This approach involves applying MEMD, a complex signal processing technique, to power system frequency signals. The study utilizes PMU (Phasor Measurement Unit) data and simulated disturbances in the IEEE 39 bus test system to conduct this analysis. MEMD offers substantial advantages in analyzing multivariate data and frequency signals during disturbances, providing accurate estimations of system inertia. This approach enhances the understanding of power system dynamics in the context of renewable energy integration. However, the complexity of this methodology and the requirement for precise data collection are challenges that need to be addressed. The results from this approach show high accuracy in estimating the rate of change of frequency (RoCoF) and system inertia, with minimal deviation from actual values. The findings highlight the significant impact of renewable energy integration on system inertia and emphasize the necessity of accurate inertia estimation in modern power systems.
Zinc Plating on Inkjet-Printed Ti3C2Tx MXene: Effect of Electrolyte and PEG Additive
Authors: Viviani Prisca , Gibertini Eugenio , Montanelli Vittorio , Magagnin Luca
Published: January 2024

Zinc-ion batteries (ZIBs) are currently being studied as an alternative to lithium-ion batteries (LIBs). The nucleation and growth of the zinc deposition mechanism is a critical field of research in ZIBs, as it directly affects the battery efficiency and lifespan. It is of paramount importance in mitigating the formation of porous, dendritic Zn structures that may cause cell inefficiency and, eventually, short-circuiting failures. Interfacial engineering plays a key role in providing reversible plating and stripping of metallic Zn in ZIBs through the proper regulation of the electrode–electrolyte interface. In this work, we investigated the behavior and characteristics of Zn plating on inkjet-printed Ti3C2Tx MXene-coated substrates according to the different electrolyte compositions. Specifically, ZnCl2 and ZnSO4 solutions were employed, evaluating the effect of a relatively low-molecular-weight polyethylene glycol (PEG400) addition to the electrolyte as additive. Electrochemical analyses demonstrated higher deposition kinetics in chloride-based electrolytes rather than sulfate ones, resulting in lower nucleation overpotentials. However, the morphology and microstructure of the plated Zn, investigated via scanning electron microscopy (SEM) and X-ray diffraction (XRD), revealed that the electrolytic solution played a predominant role in the Zn crystallite formation rather than the Ti3C2Tx MXene coating. Specifically, the preferential Zn [002] orientation could be favored when using additive-free ZnSO4 solution, and a PEG addition was found to be an efficient texturing agent only in ZnCl2 solution.
An Innovative Tubular Thermoelectric Generator (TTEG) for Enhanced Waste Heat Recovery in Industrial and Automotive Applications
Authors: Du Kung-Wen , Wu Chun-I
Published: January 2024

In response to the growing need for efficient energy conversion technologies, this paper introduces an innovative Tubular Thermoelectric Generator (TTEG), specifically designed for the high-efficiency conversion of waste heat into electrical energy. Unlike conventional flat-plate thermoelectric generators (FTEGs), the TTEG incorporates full-ring thermoelectric elements within a tubular layout, offering a novel approach to thermal energy recovery from fluid-based systems. This design significantly enhances the heat transfer capabilities, thereby improving the efficiency of energy conversion. Moreover, it is optimally tailored for integration into any cylindrical pipe to recapture and repurpose waste heat. Comprehensive simulations and analyses form the core of this study, where the performance of the TTEG is rigorously compared with traditional FTEGs. Under identical conditions of heat exposure, volume, and properties of thermoelectric materials, the TTEG demonstrates a 62.5% increase in output power compared to the TGM1-127-1.0-0.8 thermoelectric module (FTEG). These comparisons highlight the advantages of the tubular design in terms of energy conversion efficiency and practical applicability in various scenarios. The findings reveal that the TTEG not only outperforms its flat-plate counterparts but also presents a scalable and adaptable solution for waste heat recovery in a wide range of industrial and automotive applications. This research contributes to the field of energy science and technology by presenting a detailed experimental setup, complete with reproducible procedures and results. It opens new pathways for the development of more sustainable and efficient energy conversion systems, aligning with the goal of harnessing renewable and sustainable energy sources. The potential applications of this technology in enhancing energy efficiency and reducing environmental impact are vast, making it a significant step forward in the quest for clean and sustainable energy solutions.
Recent Advances in Digital Twins for Agriculture 5.0: Applications and Open Issues in Livestock Production Systems
Authors: Symeonaki Eleni , Maraveas Chrysanthos , Arvanitis Konstantinos G.
Published: January 2024

The potential application of the digital twins concept and its principles to all industries, including agriculture, has recently been the subject of considerable research interest. Since livestock farming is one of the mainstays of the agricultural industry, contributing to food security as well as employment and income generation, digital twins appear to be a promising technology to address issues related to the intensive nature of livestock production systems, which are typically characterized by high levels of input and output. The objective of this study is to identify and analyze the latest scientific and technological contributions in the field of digital twins technology, with a view to its adoption and assimilation in livestock production systems in the context of Agriculture 5.0. In particular, the aim of this study is to synthesize the existing research and identify the opportunities and open issues related to the implementation of digital twins in livestock production systems. The results derived in the literature review indicate that digital twins is still in the early stages of development in livestock farming, but it has the potential to transform the industry. As Agriculture 5.0 evolves and the digital twins technology matures, it is expected to play an increasingly important role in the livestock farming industry.
Natural Frequency Response of FG-CNT Coupled Curved Beams in Thermal Conditions
Authors: Masoodi Amir R. , Ghandehari Moein Alreza , Tornabene Francesco , Dimitri Rossana
Published: January 2024

This study investigates the sensitivity of dynamic properties in coupled curved beams reinforced with carbon nanotubes (CNTs) to thermal variations. Temperature-dependent (TD) mechanical properties are considered for poly methyl methacrylate (PMMA) to be strengthened with single-walled CNTs (SWCNTs), employing the basic rule of mixture to define the equivalent mechanical properties of nanocomposites. The governing equations of motion are derived using a first-order shear deformation theory (FSDT) and Hamilton’s principle, accounting for elastic interfaces modeled using elastic springs. A meshfree solution method based on a generalized differential quadrature (GDQ) approach is employed to discretize the eigenvalue problem and to obtain the frequency response of the structure. The proposed numerical procedure’s accuracy is verified against predictions in the literature for homogeneous structural cases under a fixed environmental temperature. The systematic investigation assesses the impact of various geometric and material properties, including curvature, boundary conditions, interfacial stiffness, and CNT distribution patterns, on the vibrational behavior.
SCALE-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations
Authors: Glenis Apostolos , Vouros George A.
Published: January 2024

Time-Series-Classification (TSC) is an important machine learning task for many branches of science. Symbolic representations of time series, especially Symbolic Fourier Approximation (SFA), have been proven very effective for this task, given their abilities to reduce noise. In this paper, we improve upon SCALE-BOSS using multiple symbolic representations of time series. More specifically, the proposed SCALE-BOSS-MR incorporates into the process a variety of window sizes combined with multiple dilation parameters applied to the original and to first-order differences’ time series, with the latter modeling trend information. SCALE-BOSS-MR has been evaluated using the eight datasets with the largest training size of the UCR time series repository. The results indicate that SCALE-BOSS-MR can be instantiated to classifiers that are able to achieve state-of-the-art accuracy and can be tuned for scalability.
Impact of Different Solvents and Temperatures on the Extraction of Bioactive Compounds from Rose Fruits (Rosa rugosa) Pomace
Authors: Cendrowski Andrzej , Studnicki Marcin , Kalisz Stanisław
Published: January 2024

The use of waste brings many environmental and economic benefits to the country. One of the by-products of the fruit industry in Poland is rose fruits pomace. Rose fruit has great nutritional value and is a rich source of beneficial bioactive compounds. The aim of this study was to investigate the effects of temperature (25, 45, and 65 °C), time extraction (15, 30, 45, and 60 min), and different solvents on the recovery of total phenolic compounds (measured by Folin–Ciocalteu) and L-ascorbic acid (measured by the HPLC method) of rose fruits (Rosa rugosa) pomace. Higher temperatures (45 °C and 65 °C) showed a higher content of L-ascorbic acid but also faster degradation of this acid after 30 min of extraction. The highest content of polyphenolic compounds was obtained using 50% acetone at 65 °C (average 37.28 mg gallic acid equivalents/g of freeze-dried pomace) and the lowest using 100% acetone at 25 °C (average 12.46 mg gallic acid equivalents/g of freeze-dried pomace). The highest yield of L-ascorbic acid from pomace was obtained using water as a solvent (average 33.64 mg L-ascorbic acid/g of freeze-dried pomace). Rose fruits pomace extracts could therefore be used as natural bioactive molecules for many industrial applications.
Use of Linear Extrapolation to Estimate Critical State Void Ratio from Drained Triaxial Shear Tests on Dense Cohesionless Soil
Authors: Zhang Haifeng , Lei Guohui
Published: January 2024

Within the strain level attainable in drained triaxial tests, it is not uncommon for dense cohesionless soil to be sheared insufficiently to reach the critical state. Linear fitting of the correlative data from the maximum stress ratio or minimum dilatancy to the end of the test, and then extrapolating these fitted lines to the critical stress ratio or zero dilatancy has been frequently used to estimate the critical state void ratio. However, the linear extrapolation method is empirical and involves different choices of correlative test data, which leads to different estimates. Therefore, a series of simulations of drained tests on dense Toyoura sand are performed using a state-dependent model. Multiple data sets are generated, including void ratio e, volumetric strain εv, stress ratio η, and dilatancy D. The linear extrapolation accuracy of the e–η, e–D, and εv–D data sets is examined. It turns out that the e–η data set is best suited. The goodness of the e–η data set is examined against 18 sets of experimental data on dense sand. In addition, the selection of the start point for extrapolation is shown to influence the estimates. The latter 50% of the post-peak data is found to be reliable.
Enhancing Seismic Response in Frame Structures through Rigid Connections to Structures with Vibro-Impacting Mass
Authors: Di Egidio Angelo , Contento Alessandro
Published: January 2024

In this paper, a promising approach is studied that can efficiently mitigate seismic effects on a frame structure by coupling it with an protection system. Various devices are employed to achieve this objective, including tuned mass dampers, dynamic mass absorbers, elastoplastic dampers, and rocking rigid walls. This paper delves into the efficacy of a vibro-impacting nonlinear energy sink in reducing seismic effects on a frame structure. More precisely, a supplementary apparatus, consisting of an auxiliary structure equipped with a vibro-impacting nonlinear energy sink, is rigidly linked to the first story of the targeted frame structure. The seismic response of this coupled system is derived through a dynamically equivalent, low-dimensional model. As a result of the rigid connection between the frame structure and the protection system, the low-dimensional model includes only three degrees of freedom: two displacements that represent the motion of the frame structure, which is rigidly connected to the external structure, while the third characterizes the motion of the vibro-impacting mass. For the vibro-impacting nonlinear energy sink, an ideal model, which assumes instantaneous impacts, is used for the vibro-impacting mass. The proposed model is used for an in-depth parametric analysis, and the outcomes are presented in gain maps that illustrate the effectiveness of the coupling within a designated parameter plane. The findings demonstrate that the coupling with the external structure, which is equipped with a vibro-impacting mass, effectively mitigates displacements and drifts in the frame structure across a broad range of parameter values that define the protection system.
Depth Image Completion through Iterative Low-Pass Filtering
Authors: Wang Tzu-Kai , Yu Yeh-Wei , Yang Tsung-Hsun , Huang Pin-Duan , Zhu Guan-Yu , Lau Chi-Chung , Sun Ching-Cherng
Published: January 2024

This study introduces a spatial-modulated approach designed to recover missing data in in-depth images. Typically, commercial-grade RGB-D cameras utilize structured light or time-of-flight techniques for capturing scene depth. However, these conventional methods encounter difficulties in acquiring depth data from glossy, transparent, or low-reflective surfaces. Additionally, they are prone to interference from broad-spectrum light sources, resulting in defective areas in the captured data. The generation of dense data is further compromised by the influence of noise. In response to these challenges, we implemented an iterative low-pass filter in the frequency domain, effectively mitigating noise and restoring high-quality depth data across all surfaces. To assess the efficacy of our method, deliberate introduction of significant noise and induced defects in the generated depth images was performed. The experimental results unequivocally demonstrate the promising accuracy, precision, and noise-resilient capabilities of our approach. Our implementation is publicly available on the project’s webpage.
Green’s Function, Earthquakes, and a Fast Ambient Noise Tomography Methodology
Authors: Varotsos Panayiotis K. , Sarlis Nicholas V.
Published: January 2024

Green’s function plays an important role in the relationship of a future strong earthquake epicenter to the average earthquake potential score. In the frame of the latter, the fractal dimension of the unified scaling law for earthquakes naturally arises. Here it is also shown to be a cornerstone for the development of a new ambient noise tomography methodology, which is applied for example to the west coast of Central Greece. In particular, we show that a fast and reliable 3D shear velocity model extraction is possible without the need for a large amount of data, great-circle propagation assumptions, or the intermediate step of inverting for group velocity maps. The tomography results are consistent with previous studies conducted in the neighboring region.
Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences
Authors: Gu Borui , Liu Zhen
Published: January 2024

With the wide utilization of lithium-ion batteries in the fields of electronic devices, electric vehicles, aviation, and aerospace, the prediction of remaining useful life (RUL) for lithium batteries is important. Considering the influence of the environment and manufacturing process, the degradation features differ between the historical batteries and the target ones, and such differences are called individual differences. Currently, lithium battery RUL prediction methods generally use the characteristics of a large group of historical samples to represent the target battery. However, these methods may be vulnerable to individual differences between historical batteries and target ones, which leads to poor accuracy. In order to solve the issue, this paper proposes a prediction method based on transfer learning that fully takes individual differences into consideration. It utilizes an extreme learning machine (ELM) twice. In the first stage, the relationship between the capacity degradation rate and the remaining capacity is constructed by an ELM to obtain the adjusting factor. Then, an ELM-based transfer learning method is used to establish the connection between the remaining capacity and the RUL. Finally, the prediction result is adjusted by the adjusting factor obtained in the first stage. Compared with existing typical data-driven models, the proposed method has better accuracy and efficiency.
Influence of Heat Treatment Parameters on the Microstructure of 17-4 PH Single Tracks Fabricated by Direct Energy Deposition
Authors: Merlin Mattia , Morales Cindy , Ferroni Matteo , Fortini Annalisa , Soffritti Chiara
Published: January 2024

Post-fabrication heat treatment (PFHT) is one of the most applied strategies for achieving the desired microstructure and mechanical resistance on additive manufactured components because of the non-equilibrium microstructural state of the material in the as-built condition. In particular, during PFHT, 17-4 PH martensitic stainless steel is mainly strengthened by the precipitation of Cu-rich nanometric particles and Nb carbides into the metal matrix. In this work, the influence of specifically designed PFHTs on the microstructural and mechanical properties of 17-4 PH single tracks fabricated via direct energy deposition was studied. Different solubilization and aging times, as well as a direct aging strategy, were considered. Optical microscopy, X-ray diffractometry, and transmission electron microscopy were used to investigate the microstructure evolution induced by the PFHTs. Moreover, Vickers microhardness measurements were performed to evaluate the increase in mechanical strength. In all cases, the heat-treated single tracks showed a mean microhardness higher than that of the depositions in the as-built condition. In the single tracks subjected to solution treatment, followed by aging for about 100 h, the presence of both Cu-rich precipitates and Nb carbides was assessed; conversely, when directly aged from the as-built condition, only Nb carbides were detected. In the latter case, the carbides were finer and closer to each other than those in the single tracks aged after the solution treatment.
Multi-Region and Multi-Band Electroencephalogram Emotion Recognition Based on Self-Attention and Capsule Network
Authors: Ke Sheng , Ma Chaoran , Li Wenjie , Lv Jidong , Zou Ling
Published: January 2024

Research on emotion recognition based on electroencephalogram (EEG) signals is important for human emotion detection and improvements in mental health. However, the importance of EEG signals from different brain regions and frequency bands for emotion recognition is different. For this problem, this paper proposes the Capsule–Transformer method for multi-region and multi-band EEG emotion recognition. First, the EEG features are extracted from different brain regions and frequency bands and combined into feature vectors which are input into the fully connected network for feature dimension alignment. Then, the feature vectors are inputted into the Transformer for calculating the self-attention of EEG features among different brain regions and frequency bands to obtain contextual information. Finally, utilizing capsule networks captures the intrinsic relationship between local and global features. It merges features from different brain regions and frequency bands, adaptively computing weights for each brain region and frequency band. Based on the DEAP dataset, experiments show that the Capsule–Transformer method achieves average classification accuracies of 96.75%, 96.88%, and 96.25% on the valence, arousal, and dominance dimensions, respectively. Furthermore, in emotion recognition experiments conducted on individual brain regions or frequency bands, it was observed that the frontal lobe exhibits the highest average classification accuracy, followed by the parietal, temporal, and occipital lobes. Additionally, emotion recognition performance is superior for high-frequency band EEG signals compared to low-frequency band signals.
Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
Authors: Gao Guitang , Zhang Liuya , Wu Ling , Yuan Debao
Published: January 2024

Chlorophyll content in wheat leaves reflects its growth and nutritional status, which can be used as a health index for field management. In order to evaluate the potential of hyperspectral data to estimate the chlorophyll content in wheat leaves, this study focused on the leaves of wheat at the flag-picking stage, flowering stage, grain-filling stage, and maturity stage. Based on the framework of five vegetation indexes, the spectral index was constructed by using the combination of 400–1000 nm bands. The correlation between the constructed spectral index and the measured chlorophyll value was analyzed, and the optimal spectral index was screened using the correlation coefficient. Based on the optimal spectral index, polynomial regression, random forest, decision tree, and artificial neural network were used to establish the estimation model for chlorophyll value, and the optimal model for estimating the chlorophyll value of wheat leaves was selected through model evaluation. The results showed that the five optimal spectral indices at the four growth stages were primarily composed of the red band, red edge band, and near-infrared band. The five optimal spectral indices during the grain-filling stage had the highest correlation with the chlorophyll value, and the absolute value of the correlation coefficient was greater than 0.73. The accuracy of the estimation model established in the four growth stages was different, with the estimation accuracy of the flag stage being the best, showing an R2 and RMSE of 0.79 and 2.63, respectively. These results indicate that hyperspectral data are suitable for estimating the chlorophyll value of wheat leaves, and the polynomial regression model of the flag-picking period can be used as the optimal model for estimating the chlorophyll value of wheat leaves.
An Open-Source Software Reliability Model Considering Learning Factors and Stochastically Introduced Faults
Authors: Wang Jinyong , Zhang Ce
Published: January 2024

In recent years, software development models have undergone changes. In order to meet user needs and functional changes, open-source software continuously improves its software quality through successive releases. Due to the iterative development process of open-source software, open-source software testing also requires continuous learning to understand the changes in the software. Therefore, the fault detection process of open-source software involves a learning process. Additionally, the complexity and uncertainty of the open-source software development process also lead to stochastically introduced faults when troubleshooting in the open-source software debugging process. Considering the phenomenon of learning factors and the random introduction of faults during the testing process of open-source software, this paper proposes a reliability modeling method for open-source software that considers learning factors and the random introduction of faults. Least square estimation and maximal likelihood estimation are used to determine the model parameters. Four fault data sets from Apache open-source software projects are used to compare the model performances. Experimental results indicate that the proposed model is superior to other models. The proposed model can accurately predict the number of remaining faults in the open-source software and be used for actual open-source software reliability evaluation.
Abstractive Summarizers Become Emotional on News Summarization
Authors: Ahuir Vicent , González José-Ángel , Hurtado Lluís-F. , Segarra Encarna
Published: January 2024

Emotions are central to understanding contemporary journalism; however, they are overlooked in automatic news summarization. Actually, summaries are an entry point to the source article that could favor some emotions to captivate the reader. Nevertheless, the emotional content of summarization corpora and the emotional behavior of summarization models are still unexplored. In this work, we explore the usage of established methodologies to study the emotional content of summarization corpora and the emotional behavior of summarization models. Using these methodologies, we study the emotional content of two widely used summarization corpora: Cnn/Dailymail and Xsum, and the capabilities of three state-of-the-art transformer-based abstractive systems for eliciting emotions in the generated summaries: Bart, Pegasus, and T5. The main significant findings are as follows: (i) emotions are persistent in the two summarization corpora, (ii) summarizers approach moderately well the emotions of the reference summaries, and (iii) more than 75% of the emotions introduced by novel words in generated summaries are present in the reference ones. The combined use of these methodologies has allowed us to conduct a satisfactory study of the emotional content in news summarization.
Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
Authors: Lu Caiwu , Cao Yunxiang , Wang Zebin
Published: January 2024

To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.
Wintering Conditions and Heat Loss during Hibernation in the Brown Long-Eared Bat
Authors: Kłys Grzegorz , Makuchowska-Fryc Joanna
Published: January 2024

The aim of this study was to estimate heat loss in the brown long-eared bat during hibernation depending on the refugioclimate conditions. The measured values of refugioclimate parameters were: ambient temperature (Ta) 3–10 °C, relative humidity (Rh) 74–98% and air velocity (v) 0.06–0.95 m/s. Heat loss was calculated using convective heat transfer equations. Mean heat loss amounted to 4 W/m2. The results were compared to the heat loss calculated based on the fat burned during hibernation. Bats flying into underground systems during the hibernation period were captured and their body mass was measured. A loss of body mass of 2.6 g over the 126 days of hibernation was observed. Heat loss equalled 3.115 W/m2K.The presented method of calculating energy expenditure allows for non-invasive monitoring of the heat and fat losses of bats during hibernation. Such research may find application in designing artificial wintering sites.
Research on Rejoining Bone Stick Fragment Images: A Method Based on Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour
Authors: He Jingjing , Wang Huiqin , Liu Rui , Mao Li , Wang Ke , Wang Zhan , Wang Ting
Published: January 2024

The rejoining of bone sticks holds significant importance in studying the historical and cultural aspects of the Han Dynasty. Currently, the rejoining work of bone inscriptions heavily relies on manual efforts by experts, demanding a considerable amount of time and energy. This paper introduces a multi-scale feature fusion Siamese network guided by edge contour (MFS-GC) model. Constructing a Siamese network framework, it first uses a residual network to extract features of bone sticks, which is followed by computing the L2 distance for similarity measurement. During the extraction of feature vectors using the residual network, the BN layer tends to lose contour detail information, resulting in less conspicuous feature extraction, especially along fractured edges. To address this issue, the Spatially Adaptive DEnormalization (SPADE) model is employed to guide the normalization of contour images of bone sticks. This ensures that the network can learn multi-scale boundary contour features at each layer. Finally, the extracted multi-scale fused features undergo similarity measurement for local matching of bone stick fragment images. Additionally, a Conjugable Bone Stick Dataset (CBSD) is constructed. In the experimental validation phase, the MFS-GC algorithm is compared with classical similarity calculation methods in terms of precision, recall, and miss detection rate. The experiments demonstrate that the MFS-GC algorithm achieves an average accuracy of 95.5% in the Top-15 on the CBSD. The findings of this research can contribute to solving the rejoining issues of bone sticks.
Subsidence of Artificial Reefs with Bamboo Foundations on a Soft Seabed
Authors: Dini Enrico , Nuzzolese Mario , Cafaro Francesco , di Lernia Annamaria , Gadaleta Alessandra , Sollecito Francesca
Published: January 2024

The design of artificial reefs must be based on the criteria of both durability and serviceability. In this respect, the seabed subsidence rate related to the weight of the reefs and of their foundation system should be limited. The horizontal drift of the reefs during their life should be small too. In this study, pertaining to the behavior of artificial reefs made of microsilica concrete and realized with foundations in bamboo near the international airport of Hong Kong, 1-year monitoring data and calculations of reef foundation settlements are shown and discussed to assess the performance of the adopted mixed foundation and to deduce the nature of the long-term subsidence. The subsidence related to the settlements of the foundations was monitored using a Multibeam Echo Sounder System. It was found that the observed subsidence rate cannot be justified by the primary consolidation of the soils forming the seabed. Indeed, secondary consolidation seems to play an important role. Overall, after one year of service, the artificial reefs are considered to be effective in terms of limitation of both subsidence and drift.
RoseCliff Algorithm: Making Passwords Dynamic
Authors: Umejiaku Afamefuna P. , Sheng Victor S.
Published: January 2024

Authentication in the digital landscape faces persistent challenges due to evolving cyber threats. Traditional text-based passwords, which are vulnerable to various attacks, necessitate innovative solutions to fortify user systems. This paper introduces the RoseCliff Algorithm, which is a dual authentication mechanism designed to enhance resilience against sophisticated hacking attempts and to continuously evolve stored passwords. The study explores encryption techniques, including symmetric, asymmetric, and hybrid encryption, thereby addressing the emerging threats posed by quantum computers. The RoseCliff Algorithm introduces introduces dynamism into passwords that allows for more secured communication across multiple platforms. To assess the algorithm’s robustness, potential attacks such as brute force, dictionary attacks, man-in-the-middle attacks, and machine learning-based attacks are examined. The RoseCliff Algorithm, through its dynamic password generation and encryption methodology, proves effective against these threats. Usability evaluation encompasses the implementation and management phase, focusing on seamless integration, and the user experience, emphasizing clarity and satisfaction. Limitations are acknowledged, thus urging further research into encryption technique resilience, robustness against breaches, and the integration of emerging technologies. In conclusion, the RoseCliff Algorithm emerges as a promising solution, thereby effectively addressing the complexities of modern authentication challenges and providing a foundation for future research and enhancements in digital security.
Effects of Photobiomodulation Using Near-Infrared Light on the Dentin and Periodontal Ligament in a Beagle Model
Authors: Kim Hong Bae , Baik Ku Youn , Kang Moon Ho , Chung Jong Hoon
Published: January 2024

In this study, we investigated the effect of photobiomodulation (PBM) using near-infrared light on the dentin and periodontal ligament in a beagle model. We utilized a specific PBM device to irradiate NIR light with a wavelength of 810 nm and an energy density of 80.22 mJ/cm2. The device’s settings were optimized for a frequency of 300 Hz and a 30% duty cycle, allowing precise and controlled light exposure. Through a comprehensive analysis involving micro-computed tomography, scanning electron microscopy, and hematoxylin and eosin staining, we demonstrated increased odontoblast activity at the pulp–dentin interface in PBM-treated samples. This increased activity may be postulated to potentially contribute to alleviating dental hypersensitivity through the differentiation of dental pulp stem cells and the promotion of vascular development within the odontoblast layer. Moreover, our observations also indicated an improvement in the strength and integrity of fibrous connective tissue within the periodontal ligament. These findings highlight the potential of PBM with specific parameters applied using NIR as a valuable treatment method for tooth tissue regeneration. It shows particular promise in the treatment of dental diseases associated with dentin and periodontal ligament damage and offers a new perspective in the management of tooth hypersensitivity and other related dental diseases.
Prediction of Traffic Incident Locations with a Geohash-Based Model Using Machine Learning Algorithms
Authors: Ulu Mesut , Kilic Erdal , Türkan Yusuf Sait
Published: January 2024

This paper presents a novel geohash-based approach for predicting traffic incident locations using machine learning algorithms. The study utilized a three-stage model for predicting the locations of traffic incidents, which encompassed accidents, breakdowns, and other incidents. In the model, firstly, ArcGIS was used to convert the coordinates of traffic incidents into geohash areas, leading to the definition of incident locations. Secondly, variables affecting traffic incidents were extracted, and a dataset was created by utilizing the values of these variables in geohash fields. Finally, machine learning algorithms such as decision tree (DT), k-nearest neighbor (k-NN), random forest (RF), and support vector machine (SVM) algorithms were used to predict the geohash region of traffic incidents. After conducting hyperparameter optimization, we evaluated the efficacy of various machine learning algorithms in predicting the location of traffic incidents using different evaluation metrics. Our findings indicate that the RF, SVM, and DT models performed the best, with accuracy percentages of 91%, 88%, and 87%, respectively. The findings of the research revealed that traffic incident locations can be successfully predicted with the geohash-based forecasting model. The results offer traffic managers and emergency responders new perspectives on how to manage traffic incidents more effectively and improve drivers’ safety.
A Relevant Characterization and Compatibility for Reuse the Sediments from Reservoirs in Southern Italy
Authors: Martellotta Audrey Maria Noemi , Levacher Daniel , Gentile Francesco , Ranieri Gennaro , Trabace Teresa , Piccinni Alberto Ferruccio
Published: January 2024

The damming of watercourses results in sediment accumulation and, therefore, in the reduction of useful storage capacity. The storage capacity can be recovered through dredging, but this process generates large volumes of sediments that require proper management. To avoid landfilling and promote recovery operations, sediment characterization is the preliminary step to any assessment and decision. This paper presents the results of tests on sediments sampled at two reservoirs in southern Italy, the Camastra and the San Giuliano, in Basilicata. These investigations include testing of organic matter, heavy metals grain size distribution, and the assessment of the pollution degree. A lack of correlation between the sampling point and the heavy metal content was observed in sediments, except Be, Cr and Ni for the San Giuliano reservoir. This may be attributed to the presence of agricultural activities and fertilizer use in its watershed. Similarly, there is no dependence between the organic carbon and the grain size distribution, the former being scarcely found in both reservoirs (on average 0.91% for the Camastra sediments and 0.38% for the San Giuliano sediments), the latter being predominantly characterized by sandy matrices downstream of the reservoirs (on average 64.3% ± 32.9%) and by silty-clayey matrices in the upstream areas (on average 65% ± 14.3%). Finally, the determination of the single pollution index Pi and the Nemerow integrated pollution index PN highlights that sediments are not contaminated with heavy metals. Most of them show values of the indices above between 0 and 1 (“unpolluted”) and, in a few cases, values between 1 and 2 (“poorly polluted”). The findings suggest that these sediments can be reused for environmental and material recovery, using them as secondary raw materials for sub-bases and embankments, for filling in disused quarries, for reprofiling and reconstructing the morphology of coastlines or riverbeds, for beach nourishment and in the agronomic and construction industry fields.
Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques
Authors: Rodriguez-Guillen Reinier , Kern John , Urrea Claudio
Published: January 2024

Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP50, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations.
A Study on the Fabrication of Pressure Measurement Sensors and Intention Verification in a Personalized Socket of Intelligent Above-Knee Prostheses: A Guideline for Fabricating Flexible Sensors Using Velostat Film
Authors: Park Na-Yeon , Eom Su-Hong , Lee Eung-Hyuk
Published: January 2024

Intelligent transfemoral prostheses, which have recently been studied, are equipped with a microcontroller, providing appropriate motion functions for their walking environments. Thus, studies have been conducted to estimate user intentions in locomotion movements by applying biomechanical sensors inside the socket. Among them, a pressure sensor is used to determine the intentions of locomotion movements through changes in the internal pressure of the prosthetic socket. However, existing studies have a problem in that the reproducibility of pressure change data is degraded due to the non-detection and saturation of the pressure measurement value. Accordingly, this study proposes a fabrication method for a wide and flexible pressure sensor that can solve this problem and a method for the identification of user intentions in locomotion movements using it. The proposed system was fabricated with Velostat film, which has a smaller noise impact and can be fabricated in various sizes and shapes. The fabricated sensor was attached to four points inside the socket, confirming the possibility of detecting the intention of six movements according to the multi-critical detection method. The proposed pressure-sensor-based intention detection system can be applied individually by prosthetic users through simple tasks. Moreover, it will be universally applicable for commercialization.
Investigation of the Influence of Contact Patterns of Worm-Gear Sets on Friction Heat Generation during Meshing
Authors: Miltenović Aleksandar , Banić Milan , Vitković Nikola , Simonović Miloš , Perić Marko , Rangelov Damjan
Published: January 2024

Friction losses and scuffing failures are interesting research topics for worm gears. One of the factors leading to scuffing is the heat generated in the contact of gear teeth. The contact geometry of worm gears is complex, leading to high friction between contact surfaces. High friction between contact surfaces during operation generates heat friction that causes the occurrence of scuffing, which in turn determines the scuffing load capacity. To analyse the thermal characteristics of a worm-gear pair and the thermal behaviour of contact teeth, a direct-coupled thermal–structural 3D finite element model was applied. The heat flux due to friction-generated heat was determined on the gear tooth to investigate thermal characteristics and predict transient temperature fields. This study permits an in-depth understanding of the temperature fields and the friction heat generation process. Also, better control of the contact pattern between worm-gear teeth would decrease friction heat and increase scuffing load capacity. This paper investigates the transient thermal behaviour among different pinion machine setting parameters that can result in an optimal tooth-contact pattern that produces a lower temperature field, thus achieving higher transmission efficiency.