bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Holographic Microscopy in Veterinary Medicine—A Feasibility Study to Analyze Label-Free Leukocytes in Blood and Milk of Dairy Cows
Authors: Farschtschi Sabine , Lengl Manuel , Röhrl Stefan , Klenk Christian , Hayden Oliver , Diepold Klaus , Pfaffl Michael W.
Published: November 2024

For several years, the determination of a differential cell count of a raw milk sample has been proposed as a more accurate tool for monitoring the udder health of dairy cows compared with using the absolute somatic cell count. However, the required sample preparation and staining process can be labor- and cost-intensive. Therefore, the aim of our study was to demonstrate the feasibility of analyzing unlabeled blood and milk leukocytes from dairy cows by means of digital holographic microscopy (DHM). For this, we trained three different machine learning methods, i.e., k-Nearest Neighbor, Random Forests, and Support Vector Machine, on sorted leukocyte populations (granulocytes, lymphocytes, and monocytes/macrophages) isolated from blood and milk samples of three dairy cows by using fluorescence-activated cell sorting. Afterward, those classifiers were applied to differentiate unlabeled blood and milk samples analyzed by DHM. A total of 70 blood and 70 milk samples were used. Those samples were collected from five clinically healthy cows at 14-time points within a study period of 26 days. The outcome was compared with the results of the same samples analyzed by flow cytometry and (in the case of blood samples) also to routine analysis in an external laboratory. Moreover, a standard vaccination was used as an immune stimulus during the study to check for changes in cell morphology or cell counts. When applied to isolated leukocytes, Random Forests performed best, with a specificity of 0.93 for blood and 0.84 for milk cells and a sensitivity of 0.90 and 0.81, respectively. Although the results of the three analytical methods differed, it could be demonstrated that a DHM analysis is applicable for blood and milk leukocyte samples with high reliability. Compared with the flow cytometric results, Random Forests showed an MAE of 0.11 (SD = 0.04), an RMSE of 0.13 (SD = 0.14), and an MRE of 1.00 (SD = 1.11) for all blood leukocyte counts and an MAE of 0.20 (SD = 0.11), an RMSE of 0.21 (SD = 0.11) and an MRE of 1.95 (SD = 2.17) for all milk cell populations. Further studies with larger sample sizes and varying immune cell compositions are required to establish method-specific reference ranges.
A Chromosome-Level Genome Assembly of Chiton Acanthochiton rubrolineatus (Chitonida, Polyplacophora, Mollusca)
Authors: Qu Jiangyong , Lu Xiaofei , Tu Chenen , He Fuyang , Li Sutao , Gu Dongyue , Wang Shuang , Xing Zhikai , Zheng Li , Wang Xumin , Wang Lijun
Published: November 2024

(1) Background: Chitons (Mollusca, Polyplacophora) are relatively primitive species in Mollusca that allow the study of biomineralization. Although mitochondrial genomes have been isolated from Polyplacophora, there is no genomic information at the chromosomal level; (2) Methods: Here we report a chromosome-level genome assembly for Acanthochiton rubrolineatus using PacBio (Pacific Biosciences, United States) reads and high-throughput chromosome conformation capture (Hi-C) data; (3) Results: The assembly spans 1.08 Gb with a contig N50 of 3.63 Mb and 99.97% of the genome assigned to eight chromosomes. Among the 32,291 predicted genes, 76.32% had functional predictions. The divergence time of Brachiopoda and Mollusca was ~550.8 Mya (million years ago), and that of A. rubrolineatus and other mollusks was ~548.5 Mya; (4) Conclusions: This study not only offers high-quality reference sequences for the Acanthochiton rubrolineatus genome, but also establishes groundwork for investigating the mechanisms of Polyplacophora biomineralization and its evolutionary history. This research will aid in uncovering the genetic foundations of molluscan adaptations across diverse environments.
Effectiveness of a Combination of Nasturtium Herb and Horseradish Root (Angocin® Anti-Infekt N) Compared to Antibiotics in Managing Acute and Recurrent Urinary Tract Infections: A Retrospective Real-world Cohort Study
Authors: Kassner Nina , Wonnemann Meinolf , Ziegler Yvonne , Vahlensieck Winfried , Kranz Jennifer , Kostev Karel
Published: November 2024

Background: The goal of this study was to evaluate whether the medical recommendation of Angocin® Anti-Infekt N, compared to standard antibiotic treatment shortly after the diagnosis of a urinary tract infection (UTI) or cystitis, is negatively associated with an early, sporadic, or recurrent UTI, subsequent antibiotic prescriptions, pyelonephritis as a renal complication, or UTI-associated sick leave. Methods: This retrospective cohort study was based on data from the IQVIATM Disease Analyzer database and included patients diagnosed with acute UTI or cystitis by physicians in Germany between 2005 and 2021, who were prescribed either Angocin® or standard antibiotics within 4 days after diagnosis. Patients prescribed antibiotics were matched to those prescribed Angocin® (5:1) using propensity scores. Univariable logistic and Cox regression models were used to investigate the association between Angocin® prescription and the defined study outcomes. The effects of Angocin® were adjusted for age, sex, insurance status, index diagnosis, and physician specialty. Results: A total of 2277 Angocin® patients and 11,385 antibiotic patients were available for analysis. Compared to antibiotic prescriptions, Angocin® prescription was associated with significantly lower odds of an early relapse within 1–30 days after the index date (odds ratio (OR): 0.74; 95% confidence interval (CI): 0.62–0.87; p < 0.001), further sporadic UTI within 31–365 days after the index date (OR: 0.68; 95% CI: 0.58–0.78; p < 0.001), and recurrent UTI (OR: 0.63; 95% CI: 0.48–0.82; p < 0.001). This was also accompanied by reduced antibiotic prescriptions (1–30 days: OR: 0.63; 95% CI: 0.53–0.74, p < 0.001; 31–365 days: OR: 0.56; 95% CI: 0.49–0.64, p < 0.001). A strong, but due to the low incidence, not significant, negative association was observed between Angocin® prescription and the occurrence of pyelonephritis (hazard ratio (HR): 0.67; 95% CI: 0.43–1.06; p = 0.073). Conclusions: The results of this real-world data study demonstrate that Angocin® can be an effective therapeutic option for managing acute and recurrent UTIs and serves as an alternative therapy to antibiotics.
Listeria monocytogenes in Fruits and Vegetables: Antimicrobial Resistance, Biofilm, and Genomic Insights
Authors: Avila-Novoa María Guadalupe , Solis-Velazquez Oscar Alberto , Guerrero-Medina Pedro Javier , Martínez-Chávez Liliana , Martínez-Gonzáles Nanci Edid , Gutiérrez-Lomelí Melesio
Published: November 2024

Background/Objectives: Listeria monocytogenes is a foodborne pathogen that can infect both humans and animals and cause noninvasive gastrointestinal listeriosis or invasive listeriosis. The objectives of this study were to determine the genetic diversity of L. monocytogenes; the genes associated with its resistance to antibiotics, benzalkonium chloride (BC), and cadmium chloride (CdCl2); and its biofilm formation. Methods: A total of 132 fresh fruits (44 samples) and vegetables (88 samples) were selected for this study. The genetic diversity of the isolates and the genes associated with their antibiotic resistance were determined using PCR amplification; meanwhile, their levels of susceptibility to antibiotics were determined using the agar diffusion method. Their levels of resistance to BC and CdCl2 were determined using the minimum inhibitory concentration method, and their capacity for biofilm formation was evaluated using the crystal violet staining method. Results: A total of 17 L. monocytogenes strains were collected: 12.8% (17/132) from fresh fruits and vegetables in this study. The isolates of L. monocytogenes belonged to phylogenetic groups I.1 (29.4% (5/17); serotype 1/2a) and II.2 (70.5% (12/17); serotype 1/2b); strains containing Listeria pathogenicity islands (LIPIs) were also identified at prevalence rates of 100% for LIPI-1 and LIPI-2 (17/17), 29.4% for LIPI-3 (5/17), and 11.7% for LIPI-4 (2/17). The antibiotic susceptibility tests showed that the L. monocytogenes isolates exhibited six different multiresistant patterns, with multiple antibiotic resistance (MAR) index of ≥0.46 (70.5%; 12/17); additionally, the genes Ide, tetM, and msrA, associated with efflux pump Lde, tetracycline, and ciprofloxacin resistance, were detected at 52.9% (9/17), 29.4% (5/17), and 17.6% (3/17), respectively. The phenotypic tests showed that 58.8% (10/17) of cadmium-resistant L. monocytogenes isolates had a co-resistance of 23.5% (4/17) to BC. Finally, all strains of L. monocytogenes exhibited moderate biofilm production. Conclusions: The results of this study contribute to our understanding of the persistence and genetic diversity of L. monocytogenes strains isolated from fresh fruits and vegetables; in addition, their resistance to CdCl2, which is correlated with co-resistance to BC disinfectant, is helpful for the food industry.
Antimicrobial Peptide Delivery Systems as Promising Tools Against Resistant Bacterial Infections
Authors: de Oliveira Kamila Botelho Sampaio , Leite Michel Lopes , Melo Nadielle Tamires Moreira , Lima Letícia Ferreira , Barbosa Talita Cristina Queiroz , Carmo Nathalia Lira , Melo Douglas Afonso Bittencourt , Paes Hugo Costa , Franco Octávio Luiz
Published: November 2024

The extensive use of antibiotics during recent years has led to antimicrobial resistance development, a significant threat to global public health. It is estimated that around 1.27 million people died worldwide in 2019 due to infectious diseases caused by antibiotic-resistant microorganisms, according to the WHO. It is estimated that 700,000 people die each year worldwide, which is expected to rise to 10 million by 2050. Therefore, new and efficient antimicrobials against resistant pathogenic bacteria are urgently needed. Antimicrobial peptides (AMPs) present a broad spectrum of antibacterial effects and are considered potential tools for developing novel therapies to combat resistant infections. However, their clinical application is currently limited due to instability, low selectivity, toxicity, and limited bioavailability, resulting in a narrow therapeutic window. Here we describe an overview of the clinical application of AMPs against resistant bacterial infections through nanoformulation. It evaluates metal, polymeric, and lipid AMP delivery systems as promising for the treatment of resistant bacterial infections, offering a potential solution to the aforementioned limitations.
Ethyl Acetate Fraction of Chestnut Honey Attenuates Scopolamine-Induced Cognitive Impairment in Mice and Glutamate-Induced Neurotoxicity in HT22 Cells
Authors: Jeong Yun Hee , Li Wei , Yang Hye Jin , Kim Se-Gun , Choi Hong Min , Choi Jang-Gi , Oh You-Chang
Published: November 2024

Chestnut honey has various benefits, such as antioxidative, anti-inflammatory, immunomodulatory, antibacterial, and antiviral effects. However, the effects of chestnut honey or the ethyl acetate fraction of chestnut honey (EACH) on neurodegenerative diseases and their related cognitive impairment and neurotoxicity have not yet been established. Therefore, in this study, we investigated the mitigating effect of the EACH on scopolamine (SCO)-injected cognitive decline in mice and glutamate-exposed neurotoxicity in HT22 cells. EACH administration significantly reversed SCO-induced cognitive decline in mice, as demonstrated through the Morris water maze and passive avoidance tests. The EACH treatment showed a significant alleviation effect by recovering more than 80% of the cell viability decrease induced by glutamate exposure in the HT22 neuronal cell model. Furthermore, the EACH significantly reduced reactive oxygen species accumulation, lactate dehydrogenase release, mitochondrial depolarization, and neuronal apoptosis. The EACH regulated the level of apoptosis-related proteins, induced the nuclear translocation of nuclear factor-E2-related factor 2 (Nrf-2) and the expression of related antioxidant proteins, and induced the phosphorylation of tropomyosin-related kinase receptor B (TrkB)/cAMP-calcium response element-binding protein (CREB) and the expression of brain-derived neurotrophic factor. These data indicate that the EACH can prevent neurons from oxidative damage and improve cognitive dysfunction by activating Nrf-2 and TrkB/CREB signaling pathways. Therefore, the EACH demonstrates potential therapeutic value in mitigating oxidative stress-induced neurotoxicity, cognitive decline, and related neurodegenerative diseases.
20-Hydroxyecdysone Boosts Energy Production and Biosynthetic Processes in Non-Transformed Mouse Cells
Authors: Shuvalov Oleg , Kirdeeva Yulia , Fefilova Elizaveta , Daks Alexandra , Fedorova Olga , Parfenyev Sergey , Nazarov Alexander , Vlasova Yulia , Krasnov George S. , Barlev Nick A.
Published: November 2024

20-Hydroxyecdysone (20E) is an arthropod steroid hormone that possesses a number of beneficial pharmacological activities in humans, including anabolic, antioxidant, hypoglycemic, cardioprotective, hepatoprotective, neuroprotective, and antineoplastic properties, etc. While several studies have explored the anabolic activity of 20E in muscle cells, they have concentrated on its effects on myofibril size, protein biosynthesis intensity, and myostatin expression, without assessing energy metabolism. In this research, we have demonstrated that 20E boosts both catabolism and anabolism, coupling energy-producing and biosynthetic metabolic processes in mouse myoblasts and fibroblasts in the same way. Using a transcriptomic approach, we identified the 20E-mediated up-regulation of genes involved in different metabolic processes. Further experiments revealed that 20E increased the levels of enzymes involved in glycolysis and one-carbon metabolism. It also increased the uptake of glucose, glycolysis, respiration, the production of ATP, and global protein biosynthesis in mouse myoblasts and fibroblasts. This phenomenon involves the PI3K/AKT/mTOR signaling pathway. Taken together, the observed 20E-dependent upregulation of energy metabolism may be the main reason for 20E’s well-known anabolic activity.
Validating the Threat of a Virtual Reality Clinical Environment: A Mixed Methods Study
Authors: O’Neill David , Titmus Morgan , Lamont Wesley , Teh Wan Hui , Perimal Enoch , Di Pietro Flavia
Published: November 2024

There is an increasing need for virtual reality (VR) health applications. In the field of pain, VR has been used mainly as a distraction, with minimal use of VR to answer basic clinical questions. Pain is multifactorial and inherently threatening. Our lab recently designed two VR clinical environments with varying threat values; the present study sought to validate these environments. Subjects were randomly allocated into either the threatening or non-threatening VR consultation room and both subjective (threat questionnaire) and physiological (salivary cortisol) measurements were taken. As hypothesised, subjects in the threat condition recorded a higher threat score (p < 0.001; effect size = 0.76). There was a cortisol change across time in the threat condition (χ2(2) = 13.83, p < 0.001), but there were unexpected decreases at both 20 (p = 0.001) and 26 min (p = 0.03) following VR. While the physiological findings need further clarification, this study provides some validation of the threat value of our VR clinical tools. As such, these VR environments can potentially be used in pain experiments to help better our understanding of basic pain mechanisms. It is only with such understanding that we might offer new avenues for pain management.
The Trail Making Test in Virtual Reality (TMT-VR): The Effects of Interaction Modes and Gaming Skills on Cognitive Performance of Young Adults
Authors: Giatzoglou Evgenia , Vorias Panagiotis , Kemm Ryan , Karayianni Irene , Nega Chrysanthi , Kourtesis Panagiotis
Published: November 2024

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18–35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.
RC Bridge Concrete Surface Cracks and Bug-Holes Detection Using Smartphone Images Based on Flood-Filling Noise Reduction Algorithm
Authors: Qian Haimin , Sun Honglei , Cai Ziyang , Gao Fangshi , Ni Tongyuan , Yuan Ye
Published: November 2024

Noise reduction is a key process in digital image detection technology for concrete cracks and bug-holes. In this study, the threshold range of the flood-filling noise reduction algorithm was investigated experimentally. Surface cracks and bug-holes in RC bridge concrete were detected using mobile terminal images based on the flood-filling noise reduction algorithm. The results showed that the error range was within 10% when threshold range Θ was confined in [60, 80] as the crack width was from 0.1 mm to 2 mm. It is suitable that the threshold range Θ was selected as 70 while the measured crack width range was 0.2 mm to 2 mm. However, by reducing the values of the threshold range Θ to 50, the miscalculation was obviously eliminated. The influences of reducing values of the threshold range on bug-holes of the equivalent diameter and area were not significant. It is suitable that the threshold range Θ was elected on 50 to detect bug-holes in the concrete surface. The threshold range can be selected as a suitable value for the detection of cracks and bug-holes in order to reduce noise.
Signal-to-Noise Ratio Analysis of Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy
Authors: Chen Xinwen , Tan Zheng , Wang Jianwei , Zhao Na , Tang Yinhui , Liu Yangyang , Si Jia , Zhang Yu , Sun Jianying , Li Weiyan , Lv Qunbo
Published: November 2024

In Bandpass Sampling Fourier Transform Spectroscopy, a comprehensive method for evaluating signal-to-noise ratio (SNR) has not yet been established. This paper employs an energy conservation approach to analyze the relationship of SNR between interferogram and spectra in Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy (BPS-FTS). It systematically presents models for the average SNR of the system interferogram and the average SNR of reconstructed spectra under different parameters. These models are compared with SNR models in traditional Fourier transform spectroscopy (FTS) and are mutually validated through theoretical modeling and simulation analysis. It provides a theoretical basis and simulation verification for the design and implementation of the system.
Regenerating Alveolar Bone for Implant Placement: The Efficacy of Autogenous Mineralized Dentin Matrix—A Systematic Review and Meta-Analysis
Authors: Meném Madalena , Santos Alexandre , Mascarenhas Paulo
Published: November 2024

The preservation of the alveolar ridge has gained increasing importance for various types of rehabilitation, including dental implant placement. Consequently, researchers have explored different bone grafts, such as mineralized dentin matrix grafts. However, a comprehensive review of the efficacy of autogenous mineralized dentin (AMD) for alveolar ridge preservation remains lacking. In this review, we evaluated the efficacy of AMD as a method for alveolar ridge preservation in cases of delayed implant placement. A comprehensive search through PubMed, Google Scholar, Cochrane Library, and B-on repositories was conducted without time constraints up to July 2024 to identify peer-reviewed human studies. These studies assessed the percentage of newly formed bone and residual graft following bone regeneration with AMD grafts after tooth extraction, specifically in the context of delayed implant placement. Our analysis included four selected studies involving 55 patients and 67 sockets. The findings suggest that AMD grafts resulted in an average (and 95% confidence interval) of 43.8% [36.6%, 50.8%] newly formed bone, and delayed implant placement was a feasible surgical option for all patients. Although the available literature is scarce, AMD grafting has yielded promising outcomes as a method for bone reconstruction. Nevertheless, additional randomized controlled trials with larger sample sizes and longer follow-ups are required to substantiate these findings.
The Utilization of Vegetable Powders for Bread Enrichment—The Effect on the Content of Selected Minerals, Total Phenolic and Flavonoid Content, and the Coverage of Daily Requirements in the Human Diet
Authors: Purkiewicz Aleksandra , Gul Fatma Hazan , Pietrzak-Fiećko Renata
Published: November 2024

The aim of this study was to evaluate the content of selected minerals and total phenolic and flavonoid content of wheat bread and bread enriched with varying amounts of carrot powder (CP) and pumpkin powder (PP). In addition, the coverage of daily requirements of selected minerals was evaluated after the consumption of 100 g of each type of bread. The research included seven types of bread: wheat bread (WB) and bread enriched with 10%, 20%, and 30% CP and PP. The vegetable powders were obtained by freeze-drying. The concentrations of minerals—sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn)—were determined by atomic absorption spectroscopy (AAS) method. Coverage of daily requirements after consumption of a serving of bread was determined based on Recommended Daily Allowance (RDA) and Adequate Intake (AI) ratios for minerals, according to the nutritional standards for the Polish population. The addition of 10% PP led to a greater increase in Na, K, Mg, and Cu in the bread compared to CP, while for Ca, Fe, Zn, and Mn a greater increase was observed with the addition of 10% CP than PP. Among macronutrients, the greatest changes in content were recorded for Ca—the addition of 10 and 20% CP and PP resulted in increases of 66, 113, 51 and 59%. Among micronutrients, the addition of CP and PP to wheat bread caused the largest changes in Cu (46–150% increase) and Mn (25–99% increase) content. Additionally, there was a tenfold increase in total phenolic content (TPC) when 30% CP was added to the bread. Consuming 100 g of bread with CP and PP provided the greatest coverage of K (41–60%), Cu (8–17.5%), and Mn (6–17%). These findings suggest that fortification of bread with vegetable powders has the potential to be a useful method of enriching the mineral composition and increasing the proportion of selected minerals in the human diet.
Simulation-Based Design for Inlet Nozzle of Vortex Tube to Enhance Energy Separation Effect
Authors: Lyu Bo-Wei , Jeong Se-Min , Park Jong-Chun
Published: November 2024

The vortex tube, also known as the Ranque–Hilsch vortex tube, is a mechanical device that separates compressed gas into hot and cold streams. It offers a reliable and cost-effective solution to a wide range of cooling applications, as it operates without moving parts, electricity, or refrigerants. Research on vortex tubes has primarily focused on understanding the mechanisms of energy separation and optimizing cooling performance by altering geometric operational parameters. In this study, a Computation Fluid Dynamics (CFD) analysis was conducted to enhance the prediction of energy separation performance and improve the overall energy efficiency of the vortex tube. First, the geometry of the experimental device was modeled to closely match its actual shape, unlike the simplified geometries commonly used in previous CFD studies. Simulations were then carried out with variation in grid systems and turbulence models, and the results demonstrated improved agreement with experimental data compared to those reported in previous studies. Finally, simulations with a modified shape of the inlet nozzle shape were performed, revealing that the energy separation effect of the vortex tube could be enhanced by approximately 15% with an increased inlet expansion ratio (ϵ) while maintaining a constant nozzle length.
Systematic Analysis of Low-Precision Training in Deep Neural Networks: Factors Influencing Matrix Computations
Authors: Shen Ao , Lai Zhiquan , Zhang Lizhi
Published: November 2024

As Deep Neural Networks (DNNs) continue to increase in complexity, the computational demands of their training have become a significant bottleneck. Low-precision training has emerged as a crucial strategy, wherein full-precision values are quantized to lower precisions, reducing computational overhead while aiming to maintain model accuracy. While prior research has primarily focused on minimizing quantization noise and optimizing performance for specific models and tasks, a comprehensive understanding of the general principles governing low-precision computations across diverse DNN architectures has been lacking. In this paper, we address this gap by systematically analyzing the factors that influence low-precision matrix computations, which are fundamental to DNN training. We investigate three critical factors—accumulation in matrix calculations, the frequency of element usage, and the depth of matrices within the model—and their impact on low-precision training. Through controlled experiments on standard models, as well as customized experiments designed to isolate individual factors, we derive several key insights: layers with higher accumulation and matrices with lower usage frequencies demonstrate greater tolerance to low-precision noise, without significantly compromising the stability of model training. Additionally, while the depth of matrices influences the stability of matrix operations to some extent, it does not have a noticeable effect on the overall training outcomes. Our findings contribute to the development of generalizable principles for low-precision training, offering a systematic framework applicable across various DNN architectures. We provide empirical evidence supporting the strategic allocation of training bit-widths based on the analyzed factors, thereby enhancing the efficiency and effectiveness of DNN training in resource-constrained environments.
Enhancing the SRAM PUF with an XOR Gate
Authors: Garrard Jack , Aguilar Rios Manuel , Cambou Bertrand
Published: November 2024

This study focuses on designing enhanced Physically Unclonable Functions (PUFs) based on SRAM devices and improving the security of cryptographic systems. Most SRAM PUFs are limited in their number of CRPs, which makes them vulnerable to enrollment attacks. In this research, we present an SRAM-based PUF design that greatly increases the number of CRPs and the entropy of the generated bits by performing exclusive-or (XOR) on the responses of two SRAM devices. This was implemented using a readily available development board, SRAM devices, and a user-friendly custom circuit board for cryptographic key generation. The cryptographic protocol was implemented using both C++ and python3. The proposed SRAM PUF design was experimentally demonstrated and showed substantial improvements in the security of various cryptographic applications as a hardware authentication device. It also addresses the specific vulnerabilities of legacy designs.
A Comprehensive Exploration of Hellwig’s Taxonomic Measure of Development and Its Modifications—A Systematic Review of Algorithms and Applications
Authors: Roszkowska Ewa
Published: November 2024

This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and applications, while also exploring recent trends across multiple research domains. Primarily developed as a method for multidimensional analysis, TMD has evolved into a pivotal tool in multi-criteria decision-making. It is widely used for evaluating and ranking alternatives, particularly in the analysis of complex socio-economic phenomena and decision-making scenarios involving multiple criteria. This study systematically reviews the original algorithm and its subsequent extensions and modifications, including adaptations for fuzzy sets, intuitionistic fuzzy sets, and interval-valued fuzzy sets. Furthermore, it explores an integrated multi-criteria approach based on Hellwig’s method and its practical applications across various domains. This paper introduces an original approach by conducting a detailed, step-by-step analysis of the TMD framework. This process-oriented analysis is a novel contribution that sets this study apart from typical reviews based on statistical or bibliometric data. By examining key steps in the TMD framework—such as data collection, criterion weighting, data normalization, ideal value determination, distance calculation, and normalization factor—this paper highlights the method’s versatility in addressing complex, real-world decision-making problems. Although similar to the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in its reliance on distance to evaluate alternatives, Hellwig’s approach is unique in focusing exclusively on proximity to an ideal solution, without considering distance from a negative ideal. This distinctive emphasis has led to numerous adaptations and extensions that address specific issues such as criterion dependencies, uncertainty, and rank reversal. The findings underscore the continued relevance of the Hellwig method, its recent extensions, and its growing international recognition.
A New Artificial Intelligence-Based System for Optimal Electricity Arbitrage of a Second-Life Battery Station in Day-Ahead Markets
Authors: Kırat Oğuz , Çiçek Alper , Yerlikaya Tarık
Published: November 2024

Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets.
The Recent Progress China Has Made in Mining Method Transformation, Part I: Shrinkage Method Transformed into Backfilling Method
Authors: Li Shuai , Yu Lifeng , Dan Zhenyu , Yin Tubing , Chen Junyu
Published: November 2024

The shrinkage method is one of the earliest and most widely used mining methods in China’s underground mines, but shrinkage mining is often accompanied by a number of problems and safety hazards. With the continuous improvement of the filling process and filling material preparation and transportation technology, the application of trackless equipment such as drill jumbo and LHD (Load-Haul-Dump), and the continuous promulgation of mine safety and environmental protection policies, a large number of mines have transformed from the shrinkage method to the filling mining method. Suichang Gold Mine has improved its technical and economic indexes after transformation from the mine shrinkage method to the filling method. Its daily production capacity has increased from 30 t/d to 110 t/d, the dilution rate has decreased from 40% to 10%, the comprehensive recovery rate has increased from 95% to 98%, and the cost saving and revenue increase in the middle section of the test area of +180 m~+240 m has totaled 18,151,000 RMB.
Characteristics of the Medallists of the World Masters Orienteering Championships
Authors: Cych Piotr , Machowska-Krupa Weronika
Published: November 2024

This research aimed to investigate morphological parameters (body mass, height, BMI) of medallists at the World Masters Orienteering Championships (WMOC), alongside their sports achievements and results. Fifty medallists from the 2022 WMOC, aged 35 to 80, were surveyed using a questionnaire, with non-parametric statistics (Mann–Whitney U test and ρ-Spearman test) applied for analysis. The average height was 179 cm for men and 165 cm for women, with average weights of 70 kg for men and 54 kg for women. BMI was 21.5 (SD = 1.43) for men and 20.4 (SD = 1.74) for women, with significantly lower BMI values in women (Z = 2.054, p = 0.04). Men’s BMI decreased with age (R = −0.534, p < 0.01). The BMI value of masters foot orienteers is typical for middle-distance runners. Women are closer to the somatotype of 1500 m athletes and men to 800 m athletes. Lack of earlier elite success does not preclude being on the podium at the WMOC (only 3 of the 50 medallists surveyed had won a medal in elite competition in the past).
Effect of Stimulus Regularities on Eye Movement Characteristics
Authors: Genova Bilyana , Bocheva Nadejda , Hristov Ivan
Published: November 2024

Humans have the unique ability to discern spatial and temporal regularities in their surroundings. However, the effect of learning these regularities on eye movement characteristics has not been studied enough. In the present study, we investigated the effect of the frequency of occurrence and the presence of common chunks in visual images on eye movement characteristics like the fixation duration, saccade amplitude and number, and gaze number across sequential experimental epochs. The participants had to discriminate the patterns presented in pairs as the same or different. The order of pairs was repeated six times. Our results show an increase in fixation duration and a decrease in saccade amplitude in the sequential epochs, suggesting a transition from ambient to focal information processing as participants acquire knowledge. This transition indicates deeper cognitive engagement and extended analysis of the stimulus information. Interestingly, contrary to our expectations, the saccade number increased, and the gaze number decreased. These unexpected results might imply a reduction in the memory load and a narrowing of attentional focus when the relevant stimulus characteristics are already determined.
A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
Authors: Lavrinovica Ingrida , Judvaitis Janis , Laksis Dans , Skromule Marija , Ozols Kaspars
Published: November 2024

In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring.
DC-SoC: Optimizing a Blockchain Data Dissemination Model Based on Density Clustering and Social Mechanisms
Authors: Dong Xinhua , Dang Xiaohong , Xu Zhigang , Ye Kangze , Han Hongmu , Zheng Enda
Published: November 2024

Due to its partially decentralized and highly scalable features, the consortium blockchain has currently overtaken other blockchain technologies as the one most frequently used and studied across various industries. However, performance issues such as low transaction efficiency and redundant communication processes continue to hinder the development of consortium blockchains. In the Hyperledger Fabric consortium blockchain system, transaction efficiency is largely influenced by the consensus protocol and broadcast protocol. This paper introduces a novel consortium blockchain network model, DC-SoC, focused on optimizing broadcast protocols. By incorporating the concept of density clustering, a stable propagation structure is established for the blockchain network, thus optimizing data dissemination in the Gossip protocol. Additionally, the concept of social networks is integrated, using trustworthiness scores and economic incentives to evaluate node security. The experimental results demonstrate that when DC-SoC is applied in a large-scale consortium blockchain environment, it significantly improves communication performance between nodes and ensures transmission reliability.
H2 Optimization of a New Type of Tuned Lever Inerter-like Mass Damper (TLIMD) for Attenuating Structure Vibrations
Authors: Xu Kai , Wang Weiwei , Liang Hui , Liu Aifeng , Yang Jianmin , Gao Jingzhou , Chen Bei
Published: November 2024

The lever or the lever-type mechanism can achieve an inertia amplification effect by appropriately calibrating its structural configuration, and it is also proven to be one of the most cost-effective solution for the inerter realization compared with other mechanical devices. Benefitting from this property, the present paper adopted a new type of tuned lever inerter-like mass damper (TLIMD) for attenuating stochastic load-induced structure dynamic responses. A set of closed-form formulae for the TLIMD optimal parameters are developed by the use of H2 norm optimization criterion, wherein the structure’s inherent damping is explicitly accounted for. It is theoretically demonstrated that the TLIMD optimal parameters are mainly dominated by three critical parameters, i.e., the damper mass ratio, the lever length ratio (known as the inertia amplification ratio) and also the host structural damping. The proposed formulae for the TLIMD optimization are validated through the seismic analysis, where two classic inerter-based dampers (i.e., the tuned mass damper inerter (TMDI) and the tuned inerter damper (TID)) optimized by the numerical technique are included in the discussion. It is found that the TLIMD has a superior advantage in reducing the structure responses and also exhibits stronger robustness for the detuning condition than the classic inerter-based dampers. Furthermore, the increase in the damper mass ratio and the lever length ratio can be beneficial for enhancing its performance.
Motor Imagery Classification Improvement of Two-Class Data with Covariance Decentering Eigenface Analysis for Brain–Computer Interface Systems
Authors: Choi Hojong , Park Junghun , Yang Yeon-Mo
Published: November 2024

This study is intended to improve the motor imagery classification performance of two-class data points using newly developed covariance decentering eigenface analysis (CDC-EFA). When extracting the classification for the given data points, it is necessary to precisely distinguish the classes because the left and right features are difficult to differentiate. However, when centering is performed, the unique average data of each feature are lost, making them difficult to distinguish. CDC-EFA reverses the centering method to enhance data characteristics, making it possible to assign weights to data with a high correlation with other data. In experiments with the BCI dataset, the proposed CDC-EFA method was used after preprocessing by filtering and selecting the electroencephalogram data. The decentering process was then performed on the covariance matrix calculated when acquiring the unique face. Subsequently, we verified the classification improvement performance via simulations using several BCI competition datasets. Several signal processing methods were applied to compare the accuracy results of the motor imagery classification. The proposed CDC-EFA method yielded an average accuracy result of 98.89%. Thus, it showed improved accuracy compared with the other methods and stable performance with a low standard deviation.
Influence of Printing Angulation on the Flexural Strength of 3D Printed Resins: An In Vitro Study
Authors: Casucci Alessio , Verniani Giulia , Sami Haichal Wissam , Manfredini Daniele , Ferrari Marco , Ferrari Cagidiaco Edoardo
Published: November 2024

This study compared the flexural strength of various 3D printed resins fabricated at different building angles (0°, 45°, and 90°). Four groups of resins were tested: Varseo Smile Teeth (Bego GmbH & Co., Bremen, Germany), V-print C&B Temp (Voco GmbH, Cuxhaven, Germany), Bego Triniq (Bego GmbH & Co. KG, Bremen, Germany), and Sprintray Crown (SprintRay, Los Angeles, CA, USA). A digital light processing 3D printer (Asiga MAX UV, NSW, Sydney, Australia) was used to fabricate the samples at the specified build angles (0°, 45°, and 90°) in accordance with the ISO 4049:2019 standard. Flexural strength was measured using a universal testing machine (Instron 5567; Instron Ltd., Norwood, MA, USA), and fracture analysis was performed using a scanning electron microscope (Jeol JSM-6060LV, Tokyo, Japan). Statistical analysis was carried out using the Statistical Package for the Social Sciences (SPSS, version 26; IBM Corp., Chicago, IL, USA). Means and standard deviations were calculated for each group, and statistical differences were assessed using one-way ANOVA followed by the Bonferroni post hoc test (p < 0.05). All tested resins exhibited high flexural strength values. The maximum flexural strength was observed in the 0° printed samples (137.18 ± 18.92 MPa), while the lowest values were recorded for the 90° printed samples (116.75 ± 24.74 MPa). For V-print C&B Temp, the flexural strength at 90° (116.97 ± 34.87 MPa) was significantly lower compared to the 0° (156.56 ± 25.58 MPa) and 45° (130.46 ± 12.33 MPa) orientations. In contrast, Bego Triniq samples printed at 45° (148.91 ± 21.23 MPa) demonstrated significantly higher flexural strength than those printed at 0° (113.37 ± 31.93 MPa) or 90° (100.96 ± 16.66 MPa). Overall, the results indicate that the printing angle has a significant impact on the flexural strength of the materials, with some resins showing lower strength values at the 90° build angle.
A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
Authors: Davoodi Laleh , Mezei József
Published: November 2024

The primary goal of this study is to predict and analyze customer trust in e-commerce by leveraging neural computation within large language models (LLMs) alongside configurational approaches. We employ LLMs to predict trust levels based on customer reviews, applying artificial intelligence to analyze key aspects of the e-commerce experience, such as customer service, refund processes, item quality, and shipping. To extend beyond predictive performance, we integrate Qualitative Comparative Analysis (QCA) to identify the causal relationships between trust and various stages of the customer journey, including selection, delivery, and post-purchase support (recovery). This dual approach not only showcases the power of neural computation in predicting trust outcomes but also provides a deeper understanding of how specific configurations of customer experience elements contribute to either positive or negative trust. By combining machine learning techniques and QCA, this study contributes to the application of LLMs and configurational approaches, offering novel insights into the drivers of trust in e-commerce.
A Novel Breast-Volume Self-Measurement Method with Improved Convenient and Accuracy
Authors: Zhao Yulin , Wu Chihua , Luh Dingbang , Zhang Xinyu
Published: November 2024

Breast volume is crucial for ensuring proper bra fit and comfort, significantly influencing women’s physiological and psychological well-being. This study aims to develop a novel method for breast-volume self-measurement, allowing women to accurately assess their breast volume without specialized equipment. We employed a geometric approximation of the breast as a combination of a partial elliptical cone and an irregular partial ellipsoid, leading to the formulation of a new volume equation. The method was validated against established standards, including the specimen drainage method and 3D scanning techniques. The findings revealed that our self-measurement approach achieved a relative error of only 3.8%, outperforming the 4.8% of 3D scanning and the 86.3% associated with traditional breast-volume equations. This innovative self-measurement technique enhances accuracy and serves as a practical solution for health and nutritional assessments, alongside body image evaluations. Its user-friendly nature positions it as a valuable tool for women’s health, particularly in personal fitness and ergonomic design.